[
  {
    "id": 1,
    "name": "GPES Data for Pandemic Planning and Research (COVID-19)",
    "description": "Coronavirus (COVID-19) has led to increased demand on general practices, including an increasing number of requests to provide patient data to inform planning and support vital research on the cause, effects, treatments and outcomes for patients of the virus. To support the response to the coronavirus outbreak, NHS Digital has been legally directed to collect and analyse healthcare information about patients, including from their GP record, for the duration of the coronavirus emergency period, under the COVID-19 Public Health Directions 2020 (COVID-19 Direction). All GP practices in England are legally required to share data with NHS Digital for this purpose under the Health and Social Care Act 2012. More information about this requirement is contained in the Data Provision Notice issued by NHS Digital to GP practices.\n\nThis collection will reduce burden on general practices, allowing them to focus on patient care and support the coronavirus response.\n\nTimescales for dissemination of agreed data can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process)",
    "url": "https://healthdatagateway.org/en/dataset/874",
    "uid": "c7eed372-9f1e-467e-8a37-948f4de5abc4",
    "datasource_id": 874,
    "source": "HDRUK"
  },
  {
    "id": 2,
    "name": "Hospital Episode Statistics Admitted Patient Care",
    "description": "Hospital Episode Statistics (HES) is a database containing details of all admissions, A and E attendances and outpatient appointments at NHS hospitals in England.\n\nInitially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the [Secondary Uses Service (SUS)](https://digital.nhs.uk/services/secondary-uses-service-sus) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver.\n\nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the HES data set.\n\nHES data covers all NHS Clinical Commissioning Groups (CCGs) in England, including:\n\n*   private patients treated in NHS hospitals\n*   patients resident outside of England\n*   care delivered by treatment centres (including those in the independent sector) funded by the NHS\n\nEach HES record contains a wide range of information about an individual patient admitted to an NHS hospital, including:\n\n*   clinical information about diagnoses and operations\n*   patient information, such as age group, gender and ethnicity\n*   administrative information, such as dates and methods of admission and discharge\n*   geographical information such as where patients are treated and the area where they live\n\nWe apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published HES data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\n\nTimescales for dissemination can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process)",
    "url": "https://healthdatagateway.org/en/dataset/875",
    "uid": "6599230a-df54-4615-937c-d724d239491f",
    "datasource_id": 875,
    "source": "HDRUK"
  },
  {
    "id": 3,
    "name": "Civil Registration - Deaths",
    "description": "Deaths registration data (all deaths in England and Wales) collected from The Registrar General for England and Wales.\n\nRecord-level patient data set, where a record represents one death registration.\n\nThe data are collected at source from Local Registry offices at the point of death registration.\n\nThe data are collated nationally by the Register General before being passed to the Office for National Statistics (ONS), for use in statistics and coding.\n\nCoding includes such things cause of death coding using the International Classifications of Diseases (ICD) coding system, attributing socio-economic code classifications based on occupation.\n\nONS maintains its own version of the data set for its use in statistics.\n\nIn addition to the description, please add \"Timescales for dissemination can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process) \". Supports HDR UK Delivery Lead Time below which has a standard answer of OTHER: If the typical timeframe does not fit into the broad ranges i.e. lightweight application vs linked data application, please choose “Other” and indicate the typical timeframe within the description for the dataset\n\nThe Civil Registration of Deaths data contains 14 million unique registrations since 1993 onwards, increasing yearly. Average 500,000 - 600,000 new death registrations yearly.\n\nRecords cover all deaths registered in England & Wales. ICD coding is 9th and 10th.\n\n[https://digital.nhs.uk/services/data-access-request-service-dars](https://digital.nhs.uk/services/data-access-request-service-dars)\n\nTimescales for dissemination can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process)",
    "url": "https://healthdatagateway.org/en/dataset/877",
    "uid": "b9009bc5-b7a6-4036-9d4b-a5133c0b14a3",
    "datasource_id": 877,
    "source": "HDRUK"
  },
  {
    "id": 4,
    "name": "Covid-19 Second Generation Surveillance System",
    "description": "Data forming the Covid-19 Second Generation Surveillance Systems data set relate to demographic and diagnostic information from Pillar 1 swab testing in PHE labs and NHS hospitals for those with a clinical need, and health and care workers and Pillar 2 Swab testing in the community at drive through test centres, walk in centres, home kits returned by posts, care homes, prisons etc).\n\nTimescales for dissemination can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process)",
    "url": "https://healthdatagateway.org/en/dataset/854",
    "uid": "4e289a0f-50e7-4413-9376-ffbd13fc1a59",
    "datasource_id": 854,
    "source": "HDRUK"
  },
  {
    "id": 5,
    "name": "CPRD GOLD",
    "description": "The CPRD Gold database contains longitudinal routinely-collected electronic health records (EHR) from UK primary care practices using Vision® general practice patient management software. The database captures information on demographic characteristics; diagnoses and symptoms; drug exposures; vaccination history; laboratory tests; and referrals to hospital and specialist care.",
    "url": "https://healthdatagateway.org/en/dataset/694",
    "uid": "a29feafa-7bdd-44e9-b977-c9d26425e67f",
    "datasource_id": 694,
    "source": "HDRUK"
  },
  {
    "id": 6,
    "name": "CPRD Aurum",
    "description": "The CPRD Aurum database contains longitudinal routinely-collected electronic health records (EHR) from UK primary care practices using EMIS Web® general practice patient management software. The database captures information on demographic characteristics; diagnoses and symptoms; drug exposures; vaccination history; laboratory tests; and referrals to hospital and specialist care.",
    "url": "https://healthdatagateway.org/en/dataset/692",
    "uid": "1d574c14-1af0-490d-9c4e-be88cfd0e345",
    "datasource_id": 692,
    "source": "HDRUK"
  },
  {
    "id": 9,
    "name": "HES Admitted Patient Care data for CPRD GOLD",
    "description": "CPRD GOLD linked Hospital Episode Statistics Admitted Patient Care (HES APC) data contain details of all admissions to, or attendances at English NHS healthcare providers. This includes private patients treated in NHS hospitals, patients resident outside of England and care delivered by treatment centres (including those in the independent sector) funded by the NHS. All NHS healthcare providers in England, including acute hospital trusts, primary care trusts and mental health trusts, provide data.\nHES APC data include the complete set of hospital episode information (admission and discharge dates, diagnoses (identifying primary diagnosis), specialists seen under and procedures undertaken) for each linked patient with a hospitalisation record. In addition, Augmented care data (intensive and/or high dependency levels of care) and Maternity data are available.",
    "url": "https://healthdatagateway.org/en/dataset/674",
    "uid": "4bcf64a6-f404-4ef4-ae6e-172512ab5f43",
    "datasource_id": 674,
    "source": "HDRUK"
  },
  {
    "id": 10,
    "name": "HES Admitted Patient Care data for CPRD Aurum",
    "description": "HES Admitted Patient Care (HES APC) data contains details of all admissions to, or attendances at English NHS healthcare providers. It includes private patients treated in NHS hospitals, patients resident outside of England and care delivered by treatment centres (including those in the independent sector) funded by the NHS. All NHS healthcare providers in England, including acute hospital trusts, primary care trusts and mental health trusts provide data.\n\nHES APC data includes the complete set of hospital episode information (admission and discharge dates, diagnoses (identifying primary diagnosis), specialists seen under and procedures undertaken) for each linked patient with a hospitalisation record. In addition, Augmented care data (intensive and/or high dependency levels of care) and Maternity data are available.\n\nDiagnostic data recorded in HES are coded using the International Classification of Diseases version 10 (ICD10) coding frame; procedure information is coded using the UK Office of Population, Census and Surveys classification (OPCS) 4.6.Requests for HES APC data access are subject to prior protocol approval. Further information is available at https://www.cprd.com/data/linked-data.",
    "url": "https://healthdatagateway.org/en/dataset/675",
    "uid": "ed2de8fe-a204-4a4e-bcfa-9e3ad85e2fee",
    "datasource_id": 675,
    "source": "HDRUK"
  },
  {
    "id": 11,
    "name": "Death Registration data for CPRD GOLD",
    "description": "CPRD GOLD linked Death Registration data from the Office for National Statistics (ONS) include information on the official date and causes of death.",
    "url": "https://healthdatagateway.org/en/dataset/653",
    "uid": "1d35e229-13eb-4be1-9dae-4752b6c27c4a",
    "datasource_id": 653,
    "source": "HDRUK"
  },
  {
    "id": 12,
    "name": "CPRD Aurum Civil Registrations of Death",
    "description": "The Office for National Statistics (ONS) collects and publishes official statistics related to the economy, population, and society in the UK. Civil Registrations of Death relates to information recorded on UK death certificates.\n\nCivil Registrations of Death data from the ONS includes information on the official date and causes of death, coded using ICD codes. Please note that late registration for some deaths means that the proportion of deaths captured is lower for the last year of the coverage period, and this proportion is likely to differ by age at death and cause of death. This is especially pronounced for the last 1-2 weeks of available death data which shows an under count of the total number of deaths as these data do not capture those where the registration of a death has been delayed (e.g. deaths referred to coroners in England, Wales and Northern Ireland, which cannot be registered until investigations have been concluded, and can result in delays of months or years).",
    "url": "https://healthdatagateway.org/en/dataset/661",
    "uid": "ad8f07ef-a984-449b-8a60-d1dd564cc36d",
    "datasource_id": 661,
    "source": "HDRUK"
  },
  {
    "id": 13,
    "name": "UK Biobank",
    "description": "UK Biobank is a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants. The database, which is regularly augmented with additional data, is globally accessible to approved researchers and scientists undertaking vital research into the most common and life-threatening diseases. UK Biobank’s research resource is a major contributor to the advancement of modern medicine and treatment and has enabled several scientific discoveries that improve human health.\n\nSince 2006, UK Biobank has collected an unprecedented amount of biological and medical data on half a million people, aged between 40 and 69 years old and living in the UK, as part of a large-scale prospective study. With their consent they regularly provide blood, urine and saliva samples, as well as detailed information about their lifestyle which is then linked to their health-related records to provide a deeper understanding of how individuals experience diseases. Genotyping, whole exome sequencing and whole genome sequencing is available for the whole cohort. Blood and urine biomarkers, telomere data, metabolomic and proteomic data and infectious disease markers have been assayed from the samples provided.\n\nSince 2014 we have been undertaking the largest imaging study to date. We aim to undertake brain, cardiac and neck to knee MRI, whole body DXA and carotid ultrasound of 100,000 participants. We additionally have retinal images for 100,000 participants from baseline assessment, and accelerometer data for 100,000 participants collected 2013-2014.\n\nQuestionnaires that aim to capture data that is not readily captured by health data linkages are regularly sent to our participants.\n\nThe data – the largest and richest dataset of its kind – is de-identified and made widely accessible by UK Biobank to registered researchers around the world who use it to make new scientific discoveries about common and life-threatening diseases – such as cancer, heart disease and stroke – in order to improve public health.",
    "url": "https://healthdatagateway.org/en/dataset/700",
    "uid": "6a9f93ad-2434-41d9-93bf-6e9d2eee04e5",
    "datasource_id": 700,
    "source": "HDRUK"
  },
  {
    "id": 14,
    "name": "The Health Improvement Network (THIN)",
    "description": "",
    "url": "",
    "uid": "",
    "datasource_id": 14,
    "source": "HDRUK"
  },
  {
    "id": 15,
    "name": "Kings College Hospital NHS Foundation Trust",
    "description": "",
    "url": "",
    "uid": "",
    "datasource_id": 15,
    "source": "HDRUK"
  },
  {
    "id": 17,
    "name": "General Acute Inpatient and Day Case - Scottish Morbidity Record (SMR01)",
    "description": "The dataset contains patient identifiers such as name, date of birth, Community Health Index number, NHS number, postcode and ethnicity and episode management data. Of particular interest to researchers would be variables such as where the episode took place, admission type (includes patient injury classifications such as self-inflicted or home accident), waiting times, patients condition (as classified under ICD-10), operations, and discharge location. A wide variety of geographical data is also included in the dataset including Scottish Index of Multiple Deprivation and Carstairs measures, census output area, NHS Board, Electoral Ward and Parliamentary constituency.",
    "url": "https://healthdatagateway.org/en/dataset/74",
    "uid": "98cda353-0011-45b2-80ca-4ed24cd084bf",
    "datasource_id": 74,
    "source": "HDRUK"
  },
  {
    "id": 18,
    "name": "Scottish Birth Record (SBR)",
    "description": "The Scottish Birth Record is a web-based system developed on the NHSNet. It was introduced in 2002 as a replacement for SMR11. It provides the functionality to record all of a baby's neonatal care in Scotland, from antenatal through to post delivery, including readmissions and transfers in one electronic record. SBR is based on individuals and events rather than episodes and is completed for all births including stillbirths and home births. The system has been implemented to varying degrees (either directly or indirectly via interfaces with existing hospital systems) in all Scottish hospitals providing midwifery and/or neonatal care. A CHI number is generated soon after a baby is born in order to minimise the chances of a baby being lost on the database through a change of name after birth. The SBR collects a wide variety of information on the child from birth and during the baby's first year of life, with up to four hundred data items recorded for any one individual. This includes gestation, weight, congenital anomalies and discharge details. Identifiers such as name, date of birth, Community Health Index number and postcode are also included.",
    "url": "https://healthdatagateway.org/en/dataset/70",
    "uid": "81c53293-ef64-4507-8fcf-2a254dc19936",
    "datasource_id": 70,
    "source": "HDRUK"
  },
  {
    "id": 19,
    "name": "Office of National Statistics (ONS)",
    "description": "",
    "url": "",
    "uid": "",
    "datasource_id": 19,
    "source": "HDRUK"
  },
  {
    "id": 20,
    "name": "Primary care",
    "description": "",
    "url": "",
    "uid": "",
    "datasource_id": 20,
    "source": "HDRUK"
  },
  {
    "id": 21,
    "name": "University Hospitals Birmingham electronic patient administration and communications system",
    "description": "",
    "url": "",
    "uid": "",
    "datasource_id": 21,
    "source": "HDRUK"
  },
  {
    "id": 22,
    "name": "University Hospitals Birmingham HES submission",
    "description": "",
    "url": "",
    "uid": "",
    "datasource_id": 22,
    "source": "HDRUK"
  },
  {
    "id": 23,
    "name": "CPRD GOLD Small Area data (practice)",
    "description": "The general practice postcode linkage includes several well-known area-based measures of deprivation, including the Index of Multiple Deprivation, Townsend Deprivation Index and Carstairs Index, and Rural-Urban Classification, which are available at the LSOA level for linkage to CPRD primary care data through the practice postcode. Additionally, Sub-Integrated Care Board Locations (Sub-ICB Locs) pseudonym (practice level, England-only) is available.",
    "url": "https://healthdatagateway.org/en/dataset/687",
    "uid": "3326d554-ca11-4dc6-aff0-c5b314442d27",
    "datasource_id": 687,
    "source": "HDRUK"
  },
  {
    "id": 24,
    "name": "QResearch®",
    "description": "",
    "url": "https://healthdatagateway.org/en/dataset/794",
    "uid": "777d6ac1-6879-4750-9a85-9e42d28bb8d4",
    "datasource_id": 794,
    "source": "HDRUK"
  },
  {
    "id": 28,
    "name": "Doctors Independent Network",
    "description": "",
    "url": "",
    "uid": "",
    "datasource_id": 28,
    "source": "HDRUK"
  },
  {
    "id": 31,
    "name": "Primary care (CPRD GOLD and Aurum)",
    "description": "",
    "url": "",
    "uid": "",
    "datasource_id": 31,
    "source": "HDRUK"
  },
  {
    "id": 32,
    "name": "mortality data (ONS)",
    "description": "",
    "url": "",
    "uid": "",
    "datasource_id": 32,
    "source": "HDRUK"
  },
  {
    "id": 33,
    "name": "UK Biobank self-reported data",
    "description": "",
    "url": "",
    "uid": "",
    "datasource_id": 33,
    "source": "HDRUK"
  },
  {
    "id": 34,
    "name": "POpulation Level Analysis and Reporting (POLAR) Database Australia",
    "description": null,
    "url": null,
    "uid": null,
    "datasource_id": 34,
    "source": "HDRUK"
  },
  {
    "id": 35,
    "name": "Welsh Longitudinal General Practice Dataset (WLGP) - Welsh Primary Care",
    "description": "This dataset covers 86% of the population of Wales and 83% of GP practices in Wales. It is linkable with anonymised fields for individuals and GPs to other datasets, including bespoke project specific cohorts. Each GP practice uses a clinical information system to maintain an electronic health record for each of their patients; capturing the signs, symptoms, test results, diagnoses, prescribed treatment, referrals for specialist treatment and social aspects relating to the patients home environment.\n\nThe majority of the data is entered by the clinician during the patient consultation. Test results are electronically transferred from secondary care systems.\n\nThere are no standard rules for recording data within primary care clinical information systems. Therefore, each individual clinician can record information in their own way. The majority use Read Code Terminology, however, sometimes this is applied behind the scenes by the clinical system and sometimes local codes are used. Read codes are not as precise as ICD 10 or OPCS codes.\n\nCoding standards have been agreed on for conditions monitored by the QOF (Quality Outcomes Framework) returns. Since the implementation of QOF these conditions have been coded in a more consistent way.\n\nTime coverage varies between each practice.\n\nA link to the number of GP practices per local health board in this dataset can be found in the Associated media.",
    "url": "https://healthdatagateway.org/en/dataset/355",
    "uid": "33fc3ffd-aa4c-4a16-a32f-0c900aaea3d2",
    "datasource_id": 355,
    "source": "HDRUK"
  },
  {
    "id": 36,
    "name": "OpenSAFELY SNOMED CT",
    "description": null,
    "url": null,
    "uid": null,
    "datasource_id": 36,
    "source": "HDRUK"
  },
  {
    "id": 37,
    "name": "Trusted Research Environments for CVD-COVID-UK / COVID-IMPACT",
    "description": "",
    "url": "https://healthdatagateway.org/en/dataset/1379",
    "uid": "7e5f0247-f033-4f98-aed3-3d7422b9dc6d",
    "datasource_id": 1379,
    "source": "HDRUK"
  },
  {
    "id": 38,
    "name": "Secondary Uses Services Payment By Results",
    "description": "The Secondary Uses Service (SUS +) is a collection of healthcare data required by hospitals and used for planning health care, supporting payments, commissioning policy development and research.\n\nThe Secondary Uses Services Payment By Results data set is derived from SUS+ and includes key data in support of the national tariff system which is used to determine the reimbursement of NHS funded care in England.\n\nFollowing the handover of responsibility for the NHS Payment system from DH to NHS England and NHS improvements (formerly Monitor) in April 2013, PbR was effectively replaced by the National Tariff Payment System (NTPS) in April 2014. This new payment system currently retains the vast majority of PbR policy. Due to the embedded terminology, data item and extract naming consistency, SUS continues to refer PbR in SUS and therefore the terms 'Payment by Results', 'PbR', 'National Tariff Payment System' and 'NTPS' should be considered interchangeable when using SUS or any SUS Guidance.\n\nPayment by Results (PbR) provides a transparent, rules-based national tariff system, used to determine the reimbursement of NHS funded care in England. PbR rewards efficiency, supports patient choice and diversity and encourages activity for sustainable waiting time reductions. Payment is linked to activity and adjusted for casemix. This ensures a fair and consistent basis for hospital funding rather than being reliant principally on historic budgets and the negotiating skills of individual managers. PbR is the payment system in England under which commissioners pay providers of NHS-funded healthcare for each patient seen or treated, considering the complexity of the patient’s healthcare needs. The two fundamental features of PbR are nationally determined currencies and tariffs. Currencies are the unit of healthcare for which a payment is made and can take a number of forms covering different time periods from an outpatient attendance or a stay in hospital, to a year SUS+ PbR Reference Manual v4.64 Copyright © 2019 NHS Digital 5 of care for a long-term condition. Tariffs are the set prices paid for each currency.\n\nPbR currently covers most of the acute healthcare in hospitals, with national tariffs for admitted patient care, outpatient attendances and accident and emergency. This activity is submitted using Commissioning Data Sets (CDS). Current policy intends that the scope of PbR and national tariff will expand in future by introducing currencies and tariffs for mental health, community and other services\n\nTimescales for dissemination can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process)",
    "url": "https://healthdatagateway.org/en/dataset/851",
    "uid": "d4c2361e-556f-4812-b17e-7f371b04c0d2",
    "datasource_id": 851,
    "source": "HDRUK"
  },
  {
    "id": 39,
    "name": "COVID-19 Test Results (PATD)",
    "description": "Test results for COVID-19 tests. Details tests, outcomes, and some clinically relevant patient information about COVID-19 Tests in Wales.",
    "url": "https://healthdatagateway.org/en/dataset/345",
    "uid": "f5f6d882-163d-4ef1-a53e-000fba409480",
    "datasource_id": 345,
    "source": "HDRUK"
  },
  {
    "id": 40,
    "name": "Patient Episode Dataset for Wales (PEDW)",
    "description": "NHS Wales hospital admissions (Inpatients and daycases) dataset comprising of attendance and clinical information for all hospital admissions: includes diagnoses and operations performed. Includes spell and episode level data.\n\nThe data are collected and coded at each hospital. Administrative information is collected from the central PAS (Patient Administrative System), such as specialty of care, admission and discharge dates. After the patient is discharged the handwritten patient notes are transcribed by clinical coder into medical coding terminology (ICD10 and OPCS).\n\nThe data held in PEDW is of interest to public health services since it can provide information regarding both health service utilisation and also the incidence and prevalence of disease. However, since PEDW was created to track hospital activity from the point of view of payments for services, rather than epidemiological analysis, the use of PEDW for public health work is not straightforward. For example:\n\nCounts will vary depending on the number of diagnosis fields used e.g. primary only, all fields;\nThere are a number of different things that can be counted in PEDW e.g. individual episodes of care, admissions, discharges, periods of continuous care (group of episodes), patients or procedures.\nWhen looking at diagnosis or procedures the number will vary depending on whether you look at only in the primary diagnosis / procedure field or if the secondary fields are also included.\nCoding practices vary. In particular, coding practices for recording secondary diagnoses is likely to vary for different hospitals. This makes regional variations more difficult to interpret. The validation process led by the Corporate Health Improvement Programme and implemented by Digital Health and Care Wales (DHCW) is aiming to address some of these inconsistencies.\n\nDue to the complexity and pitfalls of PEDW it is recommended that any PEDW requests for public health purposes are discussed with a member of the SAIL team. In turn the SAIL will seek advice from DHCW if required.\n\nThis dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.",
    "url": "https://healthdatagateway.org/en/dataset/318",
    "uid": "4c33a5d2-164c-41d7-9797-dc2b008cc852",
    "datasource_id": 318,
    "source": "HDRUK"
  },
  {
    "id": 41,
    "name": "Welsh Longitudinal General Practice Dataset (WLGP) - Welsh Primary Care",
    "description": "This dataset covers 86% of the population of Wales and 83% of GP practices in Wales. It is linkable with anonymised fields for individuals and GPs to other datasets, including bespoke project specific cohorts. Each GP practice uses a clinical information system to maintain an electronic health record for each of their patients; capturing the signs, symptoms, test results, diagnoses, prescribed treatment, referrals for specialist treatment and social aspects relating to the patients home environment.\n\nThe majority of the data is entered by the clinician during the patient consultation. Test results are electronically transferred from secondary care systems.\n\nThere are no standard rules for recording data within primary care clinical information systems. Therefore, each individual clinician can record information in their own way. The majority use Read Code Terminology, however, sometimes this is applied behind the scenes by the clinical system and sometimes local codes are used. Read codes are not as precise as ICD 10 or OPCS codes.\n\nCoding standards have been agreed on for conditions monitored by the QOF (Quality Outcomes Framework) returns. Since the implementation of QOF these conditions have been coded in a more consistent way.\n\nTime coverage varies between each practice.\n\nA link to the number of GP practices per local health board in this dataset can be found in the Associated media.",
    "url": "https://healthdatagateway.org/en/dataset/355",
    "uid": "33fc3ffd-aa4c-4a16-a32f-0c900aaea3d2",
    "datasource_id": 355,
    "source": "HDRUK"
  },
  {
    "id": 42,
    "name": "COVID-19 SARI-Watch (formerly CHESS)",
    "description": "Data forming the COVID-19 SARI-Watch data set relate to demographic, risk factor, treatment, and outcome information for patients admitted to hospital with a confirmed COVID-19 diagnosis, as recorded in the PHE COVID-19 SARI-Watch Surveillance System.\n\nSARI-Watch data are to be collected for the purposes of direct care, service monitoring, planning and research in response to the spread of COVID-19, including for the following purposes identified in the COVID-19 Directions (see below): •understanding information about patient access to health services and adult social care services as a direct or indirect result of COVID-19 and the availability and capacity of those services •monitoring and managing the response to COVID-19 by health and social care bodies and the Government, including providing information to the public about COVID-19 and its effectiveness, and information about capacity, medicines, equipment, supplies, services and the workforce within the health services and adult social care services •research and planning in relation to COVID-19, such as providing COVID-19 diagnosis.\n\nTimescales for dissemination can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process) [Standard wording](https://web.www.healthdatagateway.org/dataset/f61f116d-f732-450e-9d34-c6ae00e3fd1e)\n\nNHS Digital will only disseminate SARI-Watch data collected from PHE where the information is linked to other information controlled by NHS Digital.",
    "url": "https://healthdatagateway.org/en/dataset/881",
    "uid": "4cddc46b-3dc0-406c-adf1-3a78ebab3cb3",
    "datasource_id": 881,
    "source": "HDRUK"
  },
  {
    "id": 43,
    "name": "ARIA Dataset",
    "description": "Clinical system used on St. Barts site as a chemotherapy prescribing system, sometimes also referred to as Varian.",
    "url": null,
    "uid": "621dd611-adcf-4434-b538-eecdbe5f72cf",
    "datasource_id": 43,
    "source": "HDRUK"
  },
  {
    "id": 44,
    "name": "ARIA Medonc",
    "description": "A complete mirror of the chemotherapy prescribing system. Aria Medonc is a shared resource across the Thames Valley Cancer Network.",
    "url": null,
    "uid": "88eb839c-bc51-45f0-b15e-ea89bba9ffca",
    "datasource_id": 44,
    "source": "HDRUK"
  },
  {
    "id": 45,
    "name": "ARIA Radonc",
    "description": "A complete mirror of the radiotherapy prescribing system.",
    "url": null,
    "uid": "59d72237-6969-4f59-9e87-48138978abe8",
    "datasource_id": 45,
    "source": "HDRUK"
  },
  {
    "id": 46,
    "name": "Active Adult Survey Dataset (AASD)",
    "description": "Provides the basis from which to strategically monitor and track trends in sport in Wales, as well as forming a base from which to shape policy and practice.\n\nThe Active Adults Survey is a large scale survey of the adult population in Wales using CAPI. Adults (defined as aged 15 and above) living in private households in Wales were eligible to take part in the survey. The survey is done face to face, with an interviewer visiting the person at their home. Households are selected at random, and the interviewer randomly selects someone from the household to take part in the survey, when they visit.",
    "url": "https://healthdatagateway.org/en/dataset/322",
    "uid": "1eb67fd5-33f6-49dd-a43d-854fff9f25f5",
    "datasource_id": 322,
    "source": "HDRUK"
  },
  {
    "id": 47,
    "name": "Admitted Patient Care Dataset",
    "description": "Nationally defined dataset which ontaining administrative details for inpatient admissions (elective, emergency and maternity) and good coverage of clinical coding of diagnosis (ICD10) and procedures (OPCS4). Includes home birth and delivery spells.",
    "url": null,
    "uid": "b67f0edd-fed2-4d68-a25f-d225759aa3b0",
    "datasource_id": 47,
    "source": "HDRUK"
  },
  {
    "id": 48,
    "name": "Annual District Birth Extract (ADBE)",
    "description": "Office for National Statistics (ONS) register of all births in Wales. These data are obtained from the Office for National Statistics and are derived from information collected during civil registration, which is a legal requirement.",
    "url": "https://healthdatagateway.org/en/dataset/321",
    "uid": "12a77014-4a77-4eb1-8b26-f49689352d1b",
    "datasource_id": 321,
    "source": "HDRUK"
  },
  {
    "id": 49,
    "name": "Annual District Death Daily (ADDD) - Legacy",
    "description": "ADDD is a project specific dataset which was for specific COVID-19 related projects only. Data relating to deaths are available from the ADDE dataset.\n\nDaily version of Annual District Deaths Datasets. Office for National Statistics (ONS) register of all deaths relating to Welsh residents, including those that died outside of Wales.\n\nThe data are collected from death registrations.\n\nLegacy dataset - no longer available, however the Annual District Death Extract (ADDE) is a separate dataset and is still available.",
    "url": "https://healthdatagateway.org/en/dataset/323",
    "uid": "584bf8c8-d58f-44c6-9b65-e1611144fd54",
    "datasource_id": 323,
    "source": "HDRUK"
  },
  {
    "id": 50,
    "name": "Annual District Death Extract (ADDE)",
    "description": "Office for National Statistics (ONS) register of all deaths relating to Welsh residents, including those that died outside of Wales.\n\nThe data are collected from death registrations.\n\nThis dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.",
    "url": "https://healthdatagateway.org/en/dataset/316",
    "uid": "15cf4241-abad-4dcc-95b0-8cd7c02be999",
    "datasource_id": 316,
    "source": "HDRUK"
  },
  {
    "id": 51,
    "name": "Arden Tissue Bank",
    "description": "Arden Tissue Bank operates under a Human Tissue Authority post mortem licence within a district general hospital. \nThe biobank has generic ethical approval, and is able to collect tissues prospectively across a wide range of tissue types.\nIn addition the biobank has access to the full pathological archive across three hospital sites. The cellular pathology archive is searchable by SNOMED coding for selection of specific conditions of interest. The cellular pathology archive alone totals almost 2 million blocks, with full patient data available from 2005 including pathology reports.",
    "url": "https://healthdatagateway.org/en/dataset/444",
    "uid": "62fdfd1c-f195-49cc-b3a1-7d9fd2cc8e1b",
    "datasource_id": 444,
    "source": "HDRUK"
  },
  {
    "id": 52,
    "name": "Assessing the eDPSEEA model in seasonal pollen induced asthma in Islamabad",
    "description": "The eDPSEEA model (ecosystems-enriched Drivers, Pressures, State, Exposure, Effects, Actions) is a\nconceptual framework for an integrated assessment of human and ecosystem health, which facilitates\nan understanding and prediction of complex human-environment and ecosystem interactions.\n\nThis project aims to assess the feasibility of using the eDPSEEA model in predicting the shedding of\npaper mulberry pollen which may cause acute asthma, by collecting pollen data and correlating it with weather and other parameters; while also studying a cohort of sensitised vs non-sensitised asthma patients, and their response to pollen allergens. A modelling exercise in collaboration with German scientists, will help to devise the prediction model, to predict pollen shedding and dispersal up to 3 days prior to the event. This is important to allow patients and other stakeholders to plan for an impending peak of pollen allergy and it’s subsequent associated complications.\n\nThe outcome of this project will help to use the eDPSEEA model in other countries as well, as it is currently being used in Malaysia too. \n\nFor further information, see: https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/seasonal-pollen-induced-asthma",
    "url": "https://healthdatagateway.org/en/dataset/276",
    "uid": "bfa012ad-e609-4d15-a824-6d143758ca4d",
    "datasource_id": 276,
    "source": "HDRUK"
  },
  {
    "id": 53,
    "name": "Avon Longitudinal Study of Parents and Children",
    "description": "ALSPAC was established to understand how genetic, biological, environmental, social, psychological and psychosocial factors influence the health and development of children and their parents. ALSPAC is a multi-generation prospective cohort based in Bristol in the South West of England. More than 14,000 women (G0) were enrolled in 1991 and 1992. Their partners (also G0), children (G1) and now grandchildren (G2) have been recruited and followed up over multiple timepoints. A wide variety of biological samples have been collected along with a vast array of exposure and outcome data collected via questionnaire, face to face clinics and through linkage to administrative data.\n\nDuring the index pregnancy women were sent 3 questionnaires. Since then, over a period of some 25 years, women have been sent almost annual questionnaires asking about their own health and well-being. From 2008, women were invited to attend four focus clinical assessments. Assessments of the children have been administered frequently, with multiple data collection time points since birth. These include numerous child-completed questionnaires, a number of clinical assessments from the age of 7 years and further questionnaires about the child completed by the mother or other main caregiver. Partners of the mothers have also completed a number of questionnaires and been invited to one focus clinic assessment. The second generation (children of the children) have been and continue to be recruited with data collected via questionnaire and clinical assessment at multiple time points.\n\nThe study has multiple datasets arranged by cohort member, data type (usually questionnaire or clinic) and time point. In addition, the study reacted quickly to the COVID-19 pandemic and has multiple data collections available from March 2020.",
    "url": "https://healthdatagateway.org/en/dataset/23",
    "uid": "255d70c6-9a50-4b62-adc0-44216606a216",
    "datasource_id": 23,
    "source": "HDRUK"
  },
  {
    "id": 54,
    "name": "BRAIN UK",
    "description": "BRAIN UK is a virtual brain bank which provides access to tissue already available in NHS archives as well as the provision of generic ethics. The BRAIN UK network includes 23 of 24 Neuropathology Centres across the UK.\nWe provide access to collection of samples and data across the following diseases: \n•\tAlzheimer's disease (disorder)\n•\tCerebrovascular disease (disorder)\n•\tDegenerative brain disorder (disorder)\n•\tFit and well Glioma (disorder)Meningitis (disorder)\n•\tMultiple sclerosis (disorder)\n•\tProgressive sclerosing poliodystrophy (disorder)\n•\tSubarachnoid intracranial haemorrhage (disorder)",
    "url": "https://healthdatagateway.org/en/dataset/426",
    "uid": "1c455ad3-af51-4b93-aff4-c5d3dacfa9f4",
    "datasource_id": 426,
    "source": "HDRUK"
  },
  {
    "id": 55,
    "name": "Barts CTU",
    "description": "We conduct research into the prevention of cancer with particular focus on preventive therapy and screening. We are involved in clinical trials and epidemiology.",
    "url": "https://healthdatagateway.org/en/dataset/417",
    "uid": "22aa219f-f3c8-4a25-822e-aa2504f6f29a",
    "datasource_id": 417,
    "source": "HDRUK"
  },
  {
    "id": 56,
    "name": "BioDock",
    "description": "Proud to be an industry leader in cryogenic storage. Our state-of-the-art facilities are based in the UK and Switzerland. Storing over 500,000 samples from over 70 different countries.",
    "url": "https://healthdatagateway.org/en/dataset/419",
    "uid": "ecb82441-286f-408d-880c-9239a25fb5cd",
    "datasource_id": 419,
    "source": "HDRUK"
  },
  {
    "id": 57,
    "name": "Biobank for patients with retinal degenerations & dystrophies",
    "description": "Outer retinal disease is the most common cause of blindness in the UK. It can be caused by a variety of single gene defects, including conditions such as retinitis pigmentosa which often result in early onset blindness in childhood, whilst the risk of age related macular degeneration is caused by a variety of complex multigene defects causing visual loss in later life. Treatments are evolving for both diseases but the underlying pathogenesis and treatment of these diseases  remains elusive. We  have formed the biobank as a repository of fibroblasts (from skin and hair samples) from patients with a variety of retinal diseases that could then be used with induced pluripotent stem cell technology  to investigate causes and new treatments for these conditions.",
    "url": "https://healthdatagateway.org/en/dataset/415",
    "uid": "cee932f8-151c-4834-ac6d-61d842f9d170",
    "datasource_id": 415,
    "source": "HDRUK"
  },
  {
    "id": 58,
    "name": "Birmingham Out of Hours Research Database",
    "description": "Electronic health record of all out of hours primary care contacts (500,000 over 4 years). This contains coded and free text data as well as physiological measurements, prescriptions and outcomes of consultations.",
    "url": null,
    "uid": "3edea0f6-42be-4cd4-9141-d3948bd69f02",
    "datasource_id": 58,
    "source": "HDRUK"
  },
  {
    "id": 59,
    "name": "Bloodwise Childhood Leukaemia Cell Bank",
    "description": "The Bloodwise Childhood Leukaemia Cell Bank is a national collection of samples from children and young people with paediatric haematological malignancies. It is very well annotated with demographic, clinical and genetic features. This annotation enables identification and curation of very rare subgroups. There is a range of sample types including viable cells and DNA from bone marrow, plasma and CSF supporting many different types of project. \nThe Bank is open to international as well as UK-based researchers where at least one of the investigators is based at a UK university or NHS institution. Applications are reviewed rapidly by an independent review panel. Our team can give help and advice at all stages of the application from initial enquiry to dispatch of samples. \nThe Bank also holds a collection of HLA typed cord blood which are subject to the same review process.",
    "url": "https://healthdatagateway.org/en/dataset/497",
    "uid": "93baeb13-cc4b-47fd-9dae-5ae5277cba54",
    "datasource_id": 497,
    "source": "HDRUK"
  },
  {
    "id": 60,
    "name": "Bowel Screening Wales (SBSW)",
    "description": "Administrative and clinical information for bowel screening; currently offered to men and women who are resident in Wales aged between 60 and 74 years old.\n\nPlease note: the two tables 'BSW Consultation' and 'BSW Outcomes' are project-specific, and require additional governance permission from the data owner before they can be provisioned.",
    "url": "https://healthdatagateway.org/en/dataset/368",
    "uid": "9f29d47e-4e9a-41b8-b7bc-5ef3570294ef",
    "datasource_id": 368,
    "source": "HDRUK"
  },
  {
    "id": 61,
    "name": "Breast Test Wales (SBTW)",
    "description": "Administrative and clinical information for breast screening; routine screening is currently offered to women who are resident in Wales aged 50 to 70 years. Older women can self-refer.\n\nThis dataset contains all individuals who are eligible for breast screening: routine invitations, self-referrals and family history screening women.",
    "url": "https://healthdatagateway.org/en/dataset/370",
    "uid": "8b9efd45-f56d-4aa3-b2b8-c66ff3813a2b",
    "datasource_id": 370,
    "source": "HDRUK"
  },
  {
    "id": 62,
    "name": "Brecon Dataset (BREC)",
    "description": "A register of children diagnosed with type 1 diabetes in Wales, collected from Paediatric diabetes clinics in Wales.  Maintained by the Brecon Group. Two capture-recapture studies have been done showing >97% completeness for type 1 diabetes diagnoses in Wales. Data has been collected since 1995 and is complete since then, but some people diagnosed earlier are also included.",
    "url": "https://healthdatagateway.org/en/dataset/326",
    "uid": "9c6e891f-6449-48d2-a1c3-8dd236e34bbd",
    "datasource_id": 326,
    "source": "HDRUK"
  },
  {
    "id": 63,
    "name": "Bridge file: Hospital Episode Statistics to Diagnostic Imaging Dataset",
    "description": "Linked Data Set - Hospital Episode Statistics to Diagnostic Imaging Data Set",
    "url": "https://healthdatagateway.org/en/dataset/860",
    "uid": "2f73575d-23c8-4bb8-87db-115cde076127",
    "datasource_id": 860,
    "source": "HDRUK"
  },
  {
    "id": 64,
    "name": "Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set",
    "description": "Linked Data Set - Hospital Episode Statistics to Mental Health Minimum Data Set",
    "url": null,
    "uid": "31bb8022-f8b1-405a-a22f-140f7c4b4fa6",
    "datasource_id": 64,
    "source": "HDRUK"
  },
  {
    "id": 65,
    "name": "Bristol Biobank",
    "description": "The Bristol Biobank (funded by the David Telling Charitable Trust with stakeholders from the University of Bristol and University Hospitals NHS Foundation Trust) stores samples collected from patients and healthy volunteers for use in biomedical research. Researchers in Bristol and beyond can apply to use these samples in their research. The collection of a wide range of samples will provide a platform for research into complex conditions.\nResearchers may request to deposit samples into the Biobank following the end of a NHS Research Ethics Committee approved study. Consent must have been taken using study specific documentation for the storage and use of these samples in research beyond the study.\nThe Bristol Biobank team will also be happy to receive applications to deposit samples for specific projects you wish to set-up using Biobank permissions and documentation.\nThe Bristol Biobank is licensed by the Human Tissue Authority (licence 12512) to store human tissue for research and has ethics approval from Wales Research Ethics Committee 3 as a research tissue bank to collect and issue biomaterials for biomedical research across a range of therapeutic areas.",
    "url": "https://healthdatagateway.org/en/dataset/490",
    "uid": "9d56ae46-5955-4f03-a93c-a4a6d9f641ab",
    "datasource_id": 490,
    "source": "HDRUK"
  },
  {
    "id": 66,
    "name": "Bristol Dental School Saliva Bank",
    "description": "Whole human saliva is collected from donors at the Bristol Dental School at the University of Bristol.",
    "url": "https://healthdatagateway.org/en/dataset/466",
    "uid": "8d77f0ad-f376-4d61-a97d-5e996c2bbe72",
    "datasource_id": 466,
    "source": "HDRUK"
  },
  {
    "id": 67,
    "name": "CASPS: A Phase II trial of Cediranib in ASPS patients",
    "description": "CASPS is a two-arm, randomised, double-blind, placebo-controlled, Phase II trial of cediranib in ASPS patients. The primary objective is to evaluate the efficacy of cediranib by measuring the percentage change in the sum of target marker lesion diameters from randomisation to week 24 compared to placebo. Secondary objectives include: progression-free survival, overall survival and safety and tolerability of cediranib in ASPS patients. Tissue markers of tumour response, circulating markers of angiogenesis, and changes in circulating endothelial cells/precursor cells in response to cediranib will be explored.  Thirty six patients with progressive, metastatic, histologically confirmed ASPS will be recruited. Patients will be randomised to 24 weeks of blinded cediranib or placebo, after which treatment will be unblinded and all patients offered open-label cediranib until objective disease progression, or death if sooner.",
    "url": "https://healthdatagateway.org/en/dataset/512",
    "uid": "7dcd5c0a-1922-4fe2-9b7e-090e4c39861b",
    "datasource_id": 512,
    "source": "HDRUK"
  },
  {
    "id": 68,
    "name": "CDE Patient Demographics",
    "description": "Locally defined dataset containing a full list of patient registrations held within the Trust's EHR system. Details extend to include GP details and patient identifers.",
    "url": null,
    "uid": "209d7fae-0521-4d07-8a4d-e0843a46a107",
    "datasource_id": 68,
    "source": "HDRUK"
  },
  {
    "id": 69,
    "name": "CDE Surginet Documentation",
    "description": "Locally defined dataset containing details of a patients peri-operative documentation recorded within the Trust's EHR system.   Items are coded using local Millennium internal codes.",
    "url": null,
    "uid": "4c4bc482-16f5-462f-8daf-51ec6492074d",
    "datasource_id": 69,
    "source": "HDRUK"
  },
  {
    "id": 70,
    "name": "COG-UK Viral Genome Sequences",
    "description": "The current COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents a major threat to health in the UK and globally. To fully understand the transmission and evolution of the virus requires sequencing and analysing viral genomes at scale and speed. The numbers of samples calls for a rapid increase in the UK’s pathogen genome sequencing capacity rapidly and robustly.\n\nTo provide this increased capacity to collect, sequence and analyse the whole genomes of virus samples in the UK, the COVID-19 Genomics UK (COG-UK) consortium is pooling the world leading knowledge and expertise in genomics of the four UK Public Health Agencies, multiple regional University hubs, and large sequencing centres such as the Wellcome Sanger Institute.",
    "url": "https://healthdatagateway.org/en/dataset/49",
    "uid": "1fe02b65-7b77-43bf-93c8-4f0743fee672",
    "datasource_id": 49,
    "source": "HDRUK"
  },
  {
    "id": 71,
    "name": "COPDMAP Consortium",
    "description": "COPDMAP is building a number of research questions around a group of COPD patients. The aim is to get a holistic view of disease progression — all studies are conducted on the same patient samples and groups with full clinical histories and phenotypes.\n\nData are shared across all partners in real time. Key areas for research include understanding the patients more deeply, investigating the exacerbation of symptoms after infection, identifying new disease mechanisms and better understanding the muscle wasting associated with COPD.",
    "url": "https://healthdatagateway.org/en/dataset/233",
    "uid": "bcbc41cb-99f7-482e-ba3d-561164abbd4e",
    "datasource_id": 233,
    "source": "HDRUK"
  },
  {
    "id": 72,
    "name": "COVID-19 Detection from Chest X-Rays using Deep Learning",
    "description": "COVID-19 is a pandemic having devastating implications on healthcare systems globally. Evidence shows that COVID-19 infected patients with pneumonia may present on chest x-rays with a pattern that is difficult to characterise using only the human eye. Therefore, artificial intelligence (AI) techniques using deep learning, which can consistently identify infected patients from non-infected ones given a radiographic examination of the patient, can be used as a reliable diagnostic tool. Considering chest x-rays are one of the most commonly performed radiological studies (coupled with the near universal availability of testing machines), applying AI techniques on them could prove to be valuable for COVID-19 diagnosis during clinical management. We therefore aim to establish a reliable diagnostic tool based on a deep-learning framework for the screening of patients who present with COVID-19 related abnormalities on chest x-rays. Over the course of 7 months we will build a dataset using open source data which are freely available, as well as with de-identified patient data collected from health institutions in Pakistan. Using this dataset, a deep learning model will be trained, which would be able to accurately screen patients who present with abnormalities relevant to COVID-19 in their radiographic examination. This tool will ultimately aid in expediting the diagnosis and referral of COVID-19 patients, resulting in improved clinical outcomes.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/covid-19/covid-19-detection-chest-x-rays",
    "url": "https://healthdatagateway.org/en/dataset/278",
    "uid": "57526718-0a9a-42ad-8b7a-582d9ef96d13",
    "datasource_id": 278,
    "source": "HDRUK"
  },
  {
    "id": 73,
    "name": "COVID-19 Shielded People List (CVSP) - Static",
    "description": "The Chief Medical Officer (CMO) for England, working with the CMOs of the devolved nations and other senior clinicians, commissioned NHS Digital to produce a list of people at “high risk” of complications from COVID-19, who should be shielded for at least 12 weeks. The CMO for Wales commissioned a collaboration of national bodies in Wales (NWIS, DU, NWSSP, PHW) to identify “high risk” people for the Welsh population, based largely on the NHS Digital methodology. This list is referred to as the Shielded Patient List (SPL). \n\nThe “high risk” list was defined as a subset of a wider group of people who may be “at risk”. Specific advice applies to these groups, currently this advice is:\n • “At Risk” – large group normally at risk from the flu - should practice strict social distancing \n• “At high risk” – a smaller sub-group (circa 70k), defined by CMO – should practice complete social “shielding” NHS Digital have described the methodology that has been used to identify patients who meet the high risk criteria due to their inclusion in one or more of the disease groups. \n\nAs there are differences in some of the systems used across the devolved nations, nuanced differences in application and interpretation of CMO guidance, this document describes the Welsh methodology.\n\nhttps://nwis.nhs.wales/coronavirus/coronavirus-content/coronavirus-documents/covid-19-high-risk-shielded-patient-list-identification-methodology/\n\nThe dataset time coverage ends in August 2022.",
    "url": "https://healthdatagateway.org/en/dataset/334",
    "uid": "dc22db56-8791-4e96-9ce6-6b6a58b1241f",
    "datasource_id": 334,
    "source": "HDRUK"
  },
  {
    "id": 74,
    "name": "COVID-19 Symptom Tracker Dataset (CVST)",
    "description": "The COVID Symptom Tracker (https://covid.joinzoe.com/) mobile application was designed by doctors and scientists at King's College London, Guys and St Thomas’ Hospitals working in partnership with ZOE Global Ltd – a health science company.\n\nThis research is led by Dr Tim Spector, professor of genetic epidemiology at King’s College London and director of TwinsUK a scientific study of 15,000 identical and non-identical twins, which has been running for nearly three decades.\n\nThe dataset schema includes:\n\nDemographic Information (Year of Birth, Gender, Height, Weight, Postcode)\nHealth Screening Questions (Activity, Heart Disease, Diabetes, Lung Disease, Smoking Status, Kidney Disease, Chemotherapy, Immunosuppressants, Corticosteroids, Blood Pressure Medications, Previous COVID, COVID Symptoms, Needs Help, Housebound Problems, Help Availability, Mobility Aid)\nCOVID Testing Conducted\nHow You Feel?\nSymptom Description\nLocation Information (Home, Hospital, Back From Hospital)\nTreatment Received\nThe data is hosted within the SAIL Databank, a trusted research environment facilitating remote access to health, social care, and administrative data for various national organisations.\n\nThe process for requesting access to the data is dependent on your use case. SAIL is currently expediting all requests that feed directly into the response to the COVID-19 national emergency, and therefore requests from NHS or Government institutions, or organisations working alongside such care providers and policymakers to feed intelligence directly back into the national response, are being expedited with a ~48-hour governance turnaround for such applications once made. Please make enquiries using the link at the bottom of the page which will go the SAIL Databank team, or to Chris Orton at c.orton@swansea.ac.uk\n\nSAIL is welcoming requests from other organisations and for longer-term academic study on the dataset, but please note if this is not directly relevant to the emergency research being carried out which directly interfaces with national responding agencies, there may be an access delay whilst priority use cases are serviced.\n\nPlease note: the CVST dataset in SAIL has not been updated since 01/11/2023.\n\nThis dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.",
    "url": "https://healthdatagateway.org/en/dataset/364",
    "uid": "594cfe55-96e3-45ff-874c-2c0006eeb881",
    "datasource_id": 364,
    "source": "HDRUK"
  },
  {
    "id": 75,
    "name": "COVID-19 Test Results (PATD)",
    "description": "Test results for COVID-19 tests. Details tests, outcomes, and some clinically relevant patient information about COVID-19 Tests in Wales.",
    "url": "https://healthdatagateway.org/en/dataset/345",
    "uid": "f5f6d882-163d-4ef1-a53e-000fba409480",
    "datasource_id": 345,
    "source": "HDRUK"
  },
  {
    "id": 76,
    "name": "CRIS",
    "description": "Tayside(1994) and Fife(2008) radiology dataset.   This dataset is provided to HIC as extracts from a system called CRIS, a popular Radiology Information System (RIS). This is deployed in over 700 separate locations and supports over 200,000 users and 25 million imaging events every year.\n\nMore can read about CRIS here https://www.wellbeingsoftware.com/solutions/sector/radiology/",
    "url": "https://healthdatagateway.org/en/dataset/116",
    "uid": "35808620-ea01-410a-8948-cee412f1fe23",
    "datasource_id": 116,
    "source": "HDRUK"
  },
  {
    "id": 77,
    "name": "Cam-UroOnc Biorepository",
    "description": "The primary role of the CamUro-Onc is to consent, collect, log and store frozen tumour/normal tissues, blood and urine for use in approved research projects. The bank has been in operation since 2003 and now has an extensive collection of biosamples from urological diseases.\nType of samples collected;\n-\tFresh frozen and paraffin wax embedded tumour/normal tissue (where possible) from kidney, bladder and prostate.\n-\tMatching blood and urine samples.\n-\tBlood and urine samples collected as part of several research projects.\nThese samples have been collected as part of various ethically approved research studies.\nThe CamUro-Oncology Biobank is part of the Cambridge site of the ProMPT NCRI Prostate Cancer Collaborative. ProMPT provides infrastructure to take forward translational research in prostate cancer including the establishment of biorepositories and tissue collections together with epidemiological and clinical information. \nDIAMOND Study-(Discovery and Analysis of novel biomarkers in Urological diseases)- Collection of initial and sequential samples from subjects with benign and cancerous urological diseases.",
    "url": "https://healthdatagateway.org/en/dataset/429",
    "uid": "21044030-1cd5-46b1-b072-c9959fd330b4",
    "datasource_id": 429,
    "source": "HDRUK"
  },
  {
    "id": 78,
    "name": "Cambridge Blood and Stem Cell Biobank",
    "description": "Cambridge Blood and Stem Cell Biobank collects and curates blood and blood-product derived samples from normal individuals and patients with blood and related malignancies, with particular emphasis on accessibility to purified tumour and stem cell populations from these samples. Set up in 2009, by 2016 the bank already contains over 12,000 samples, 70% from patients enrolled on research studies and clinical trials, and the remainder from cord blood donors. We specialise in bespoke fresh specimen collections for research into clonal blood cell disorders, autoimmune disorders and normal blood cell development.",
    "url": "https://healthdatagateway.org/en/dataset/485",
    "uid": "c51ab59a-33ce-4145-a642-7f3f96bca545",
    "datasource_id": 485,
    "source": "HDRUK"
  },
  {
    "id": 79,
    "name": "Cancer Group CTR Cardiff trials",
    "description": "Centre for Trials Research Cardiff University. Trial sample collections",
    "url": "https://healthdatagateway.org/en/dataset/493",
    "uid": "cd833805-f0a8-4ddb-a50d-b12695c92120",
    "datasource_id": 493,
    "source": "HDRUK"
  },
  {
    "id": 80,
    "name": "Cancer Patient Experience Survey (CPES) for CPRD Aurum",
    "description": "CPRD Aurum linked Cancer Patient Experience Survey (CPES) data include information from patients who have responded to the CPES about their cancer journey from their initial GP visit prior to diagnosis, through diagnosis and treatment and to the ongoing management of their cancer.",
    "url": "https://healthdatagateway.org/en/dataset/688",
    "uid": "fded6e24-8fef-47a4-a65e-9455bed043d3",
    "datasource_id": 688,
    "source": "HDRUK"
  },
  {
    "id": 81,
    "name": "Cancer Patient Experience Survey (CPES) for CPRD GOLD",
    "description": "CPRD GOLD linked Cancer Patient Experience Survey (CPES) data include information from patients who have responded to the CPES about their cancer journey from their initial GP visit prior to diagnosis, through diagnosis and treatment and to the ongoing management of their cancer.",
    "url": "https://healthdatagateway.org/en/dataset/656",
    "uid": "a4ecb8df-c72f-4018-8f23-e180c28dfdb6",
    "datasource_id": 656,
    "source": "HDRUK"
  },
  {
    "id": 82,
    "name": "Cancer Registration Data for CPRD Aurum",
    "description": "The data contains a record for each registrable tumour diagnosed or treated in England, of which the NCRAS has been notified. Cancers are coded using the International Classification of Diseases for Oncology, revision 3, 2011. They are also back mapped to the tenth revision of the International Classification of Diseases version 10.",
    "url": "https://healthdatagateway.org/en/dataset/652",
    "uid": "3f3df3be-05a8-42b2-89d9-f731deb7ea66",
    "datasource_id": 652,
    "source": "HDRUK"
  },
  {
    "id": 83,
    "name": "Cancer registration data for CPRD GOLD",
    "description": "CPRD GOLD linked National Cancer Registration and Analysis Service (NCRAS) cancer registration data contain records for each registrable tumour diagnosed or treated in England, of which the NCRAS has been notified.",
    "url": "https://healthdatagateway.org/en/dataset/655",
    "uid": "e2f5d637-94b3-447b-b7eb-ea957ce449b6",
    "datasource_id": 655,
    "source": "HDRUK"
  },
  {
    "id": 84,
    "name": "Cardamon Clinical Trial Samples",
    "description": "Samples taken as part of the Cardamon clinical trial",
    "url": "https://healthdatagateway.org/en/dataset/487",
    "uid": "c0dcceca-c8cf-4269-a0e0-0c18b6ba2de1",
    "datasource_id": 487,
    "source": "HDRUK"
  },
  {
    "id": 85,
    "name": "Cardiff School of Dentistry Tooth Bank",
    "description": "Collection of both deciduous and permanent teeth.  Collection of fresh teeth for extraction of pulpal cells can be arranged.",
    "url": "https://healthdatagateway.org/en/dataset/439",
    "uid": "0d4f0103-82d8-43f4-8058-4aec80ea562f",
    "datasource_id": 439,
    "source": "HDRUK"
  },
  {
    "id": 86,
    "name": "Cardiff University Biobank",
    "description": "The Cardiff University Biobank is a centralised biobanking facility sited at the University Hospital of Wales.  We offer high quality human biosamples for research undertaken for patient and public benefit to academic and commercial organisations. We have established collections from a number of different disease areas and welcome approaches to initiate new collections not already established within the facility.  The biobank also welcome applications to deposit samples from completed research projects or clinical trails.",
    "url": "https://healthdatagateway.org/en/dataset/421",
    "uid": "097540aa-a0ed-492e-853b-9ebcfd061dea",
    "datasource_id": 421,
    "source": "HDRUK"
  },
  {
    "id": 87,
    "name": "Care Home Dataset (CARE)",
    "description": "This database contains residential and geographical information data about care homes in Wales.\n\nTo link this dataset in SAIL, you must also specifically request the Residential Anonymous Linkage Field (RALF) variable from the Welsh Demographic Service Dataset (WDSD) as part of the governance (IGRP) application, to link this dataset at the residence-level.",
    "url": "https://healthdatagateway.org/en/dataset/327",
    "uid": "aae02f9e-1bad-415d-9730-c46a07a990aa",
    "datasource_id": 327,
    "source": "HDRUK"
  },
  {
    "id": 88,
    "name": "Case report form",
    "description": "Each participant recruited on the basis of their disease (rare or common) has a disease-specific CRF completed by their clinical care team. These are non-exhaustive-we have the chance to ask for more information-but form the basis of analysis and recall.",
    "url": null,
    "uid": "cbdcfebe-0a3d-4e11-95fc-f40642420107",
    "datasource_id": 88,
    "source": "HDRUK"
  },
  {
    "id": 89,
    "name": "CellPath",
    "description": "A complete mirror of authorised pathology reports issued by the histopathology services in the trust.",
    "url": null,
    "uid": "c4538af4-505f-45df-aeae-788ddfd7cb54",
    "datasource_id": 89,
    "source": "HDRUK"
  },
  {
    "id": 90,
    "name": "Central England Haemato-oncology and oncology Research Biobank (CEHRB)",
    "description": "The Central England Haemato-Oncology and oncology Research BioBank (CEHRB) predominantly stores excess material from haemato-oncology and oncology samples referred for diagnostic testing and disease monitoring at the West Midlands Regional Genetics Laboratory (WMRGL). Haemato-oncology samples are stored at presentation and throughout the disease course, including at remission and relapse. In addition CEHRB stores haemato-oncology and oncology samples which are specifically taken for the biobank, usually in response to a specific project.\nCEHRB is housed within the WMRGL which is accredited by United Kingdom Accreditation Services to ISO15189:2012. The WMRGL serves a population of about 6 million and is the largest UK NHS genetics lab. Due to the large patient population CEHRB is able to collate sufficient research material from all classifications of neoplastic haematological disorders including those that are rare.",
    "url": "https://healthdatagateway.org/en/dataset/414",
    "uid": "314953f6-58e7-49c0-adfd-c60e623eba74",
    "datasource_id": 414,
    "source": "HDRUK"
  },
  {
    "id": 91,
    "name": "Cervical Screening Wales (SCSW)",
    "description": "Administrative and clinical information for cervical screening; currently offered to women who are resident in Wales aged between 20 and 64 years old. Data coverage period differs by event type: Invitation and screening, January 1990; Assessment data, April 2011.",
    "url": "https://healthdatagateway.org/en/dataset/367",
    "uid": "b32325d7-7f00-4d20-9935-dd02e1bc6932",
    "datasource_id": 367,
    "source": "HDRUK"
  },
  {
    "id": 92,
    "name": "Child Health Systems Programme - 13-15 Month Review",
    "description": "The 13-15 month review form is completed at around 13-15 months of age and is carried out by a health visitor. This review started in April 2017 and is offered to all children (although implementation of the review across Scotland may vary). Examples of information collected include: development (social, behavioural, communication, gross motor, vision, hearing), physical measurements (height and weight) and diagnoses / issues (Read coded). Identification data such as name, address, GP etc. are also checked and updated.",
    "url": "https://healthdatagateway.org/en/dataset/52",
    "uid": "e77af7a3-32fa-4a08-8455-4fb489e5b64a",
    "datasource_id": 52,
    "source": "HDRUK"
  },
  {
    "id": 93,
    "name": "Child Health Systems Programme - 27-30Months Review",
    "description": "The 27-30 month review form is completed at around 27-30 months of age and is carried out by a health visitor. This review started in April 2013 and is offered to all children (previously only children requiring structured additional or intensive support were invited for a review at this stage). Examples of information collected include: development (social, behavioural, communication, gross motor, vision, hearing), physical measurements (height and weight) and diagnoses / issues (Read coded). Identification data such as name, address, GP etc. are also checked and updated.",
    "url": "https://healthdatagateway.org/en/dataset/50",
    "uid": "d2d57b7f-95d8-425f-b2fd-cc9aff05e659",
    "datasource_id": 50,
    "source": "HDRUK"
  },
  {
    "id": 94,
    "name": "Child Health Systems Programme - 4-5 year review",
    "description": "The 4-5 year review form is completed at around 4-5 years of age and is carried out by a health visitor. This review started in April 2017 and is offered to all children (implementation of this review may vary across Scotland). Examples of information collected include: development (social, behavioural, communication, gross motor, vision, hearing), physical measurements (height and weight) and diagnoses / issues (Read coded). Identification data such as name, address, GP etc. are also checked and updated.",
    "url": "https://healthdatagateway.org/en/dataset/65",
    "uid": "5844bde6-628d-4e76-bb56-3534e2728eb6",
    "datasource_id": 65,
    "source": "HDRUK"
  },
  {
    "id": 95,
    "name": "Child Health Systems Programme - 6-8week review",
    "description": "The 6-8 week review form is generally completed at around 6-8 weeks after the birth of the child and is often a health visitor and GP combined review. Examples of information collected include: feeding of baby (breast, bottle or both); parental concerns (feeding, appearance; behaviour; hearing; eyes; sleeping; movement; illness; crying; weight gain and other); development (gross motor, hearing & communication, vision & social awareness); physical (length, weight, heart, hips, testes, genitalia, femoral pulses and eyes); diagnoses/concerns (Read coded); sleeping position (prone, supine and side). Identification data such as name, address, GP etc. are also checked and updated.",
    "url": "https://healthdatagateway.org/en/dataset/71",
    "uid": "f9cf4be7-fbc9-4601-8beb-5d3c3596bf04",
    "datasource_id": 71,
    "source": "HDRUK"
  },
  {
    "id": 96,
    "name": "Child Health Systems Programme - First Visit",
    "description": "The Health Visitor First Visit form is generally completed at around 10 days after the birth of the child. This begins the child's electronic surveillance record and gathers basic identification, family and social data. Examples of information collected include: mother's age; smokers in household and infant feeding (at birth, hospital discharge and current method). Identification data such as name, address, GP etc are also checked and updated.",
    "url": "https://healthdatagateway.org/en/dataset/61",
    "uid": "dec04153-f595-41ce-9d7e-5ef0c3fe8289",
    "datasource_id": 61,
    "source": "HDRUK"
  },
  {
    "id": 97,
    "name": "Child Health Systems Programme - School",
    "description": "The aim of the school health service is to promote the physical, mental and social well-being of children within a school setting. It also provides remedial action and support for pupils with health problems and services for pupils with special educational needs. The Child Health Systems Programme School System (CHSP School) facilitates the call/recall of both primary and secondary school pupils for screening, review and immunisation. It records referrals and referral updates as well as supporting efficient and effective administrative practice.",
    "url": "https://healthdatagateway.org/en/dataset/55",
    "uid": "2c31cb1c-2b3d-40c5-b8d4-bf0b81f089b4",
    "datasource_id": 55,
    "source": "HDRUK"
  },
  {
    "id": 98,
    "name": "Children Receiving Care and Support Census (CRCS)",
    "description": "Census data relating to children with a care and support plan.\n\nFollowing the commencement of the Social Services and Well-being (Wales) Act in April 2016, the children in need census (CINW) was discontinued and replaced by the children receiving care and support census (CRCS).\n\nFurther information and code breakdown can be found here, under 'Guidance':\nhttps://www.gov.wales/data-collection-local-authority-social-services",
    "url": "https://healthdatagateway.org/en/dataset/330",
    "uid": "fb3fac03-a428-4b3f-8058-5622e1fd57d8",
    "datasource_id": 330,
    "source": "HDRUK"
  },
  {
    "id": 99,
    "name": "Children's Cancer and Leukaemia Group (CCLG) Tissue Bank",
    "description": "National collection of childhood, teenagers and young adults solid tumour samples and lymphomas in the UK.\nSamples are centrally stored at the Central Bank, Newcastle University.\nWe provide access to collection of samples and data across the following diseases: \n•\tAstrocytoma of brain (disorder)\n•\tEpendymoma (disorder)\n•\tGerminoma (morphologic abnormality)\n•\tGlioma (disorder)\n•\tHepatoblastoma (disorder)\n•\tHodgkin's disease (disorder)\n•\tLangerhans cell histiocytosis (disorder)\n•\tMedulloblastoma (disorder)\n•\tNephroblastoma (disorder)\n•\tNeuroblastoma (disorder)\n•\tOsteosarcoma of bone\n•\tRetinoblastoma (disorder\n•\tRhabdomyosarcoma (disorder)",
    "url": "https://healthdatagateway.org/en/dataset/422",
    "uid": "c232fa07-5d28-4bdc-b540-1ad8d933c5f9",
    "datasource_id": 422,
    "source": "HDRUK"
  },
  {
    "id": 100,
    "name": "Children's Health in London and Luton (CHILL)",
    "description": "CHILL is a research study which aims to find out whether reducing air pollution from traffic is good for children’s health. We are particularly interested in whether interventions to reduce air pollution can improve children’s lung growth and respiratory symptoms, activity levels and brain function. We are also interested in whether exposure to air pollution in childhood leaves markers on genes that reflect pollution levels over time. We have recruited over 3,300 primary school pupils across London and Luton to take part from 44 London schools and 41 Luton schools. These children will be assessed once a year for up to four years.",
    "url": "https://healthdatagateway.org/en/dataset/217",
    "uid": "722be723-04bd-48b4-923b-e7d393f0c22b",
    "datasource_id": 217,
    "source": "HDRUK"
  },
  {
    "id": 101,
    "name": "Children’s Kidney Cancers - Great Ormond Street Hospital",
    "description": "GOSH is the custodian of the most recent study dataset (“IMPORT” - 650 patients, 2012-2019). All of the trial/study datasets include: Full patient and tumour\ndemographics, including associated congenital abnormalities, tumour stage, treatment received (surgery, chemotherapy, radiotherapy) with individual drug names and doses and additional pathological details on histological subtype (risk group), follow up for relapse and death. In addition, the most recent study – “IMPORT” (Improving Population Outcomes for Renal Tumours of childhood) contains data on the presenting symptoms that led to diagnosis of kidney cancer,\nan imaging archive of CT and MRI scans sent for central radiology review and biological samples (frozen tumour, blood, normal kidney) stored centrally on ~two\nthirds of cases. There is also tumour genomic copy number data available on about one third of the prior trial – SIOP WT 2001 (that ran in the UK, 2002-2011).\n\nChildhood renal tumour dataset\n\nThis document describes the research dataset constructed through the Improving Population Outcomes for Renal Tumours of childhood (IMPORT) study from 2012 – 2019 (https://www.cancerresearchuk.org/about-cancer/find-a-clinical-trial/study-improving-treatment-children-kidney-cancer. The aim of this prospective clinical observational study is to achieve continuous improvement in short and long term outcomes for children with Wilms tumour and other rarer types of childhood renal tumours. The study collects clinical, molecular and imaging data to underpin the introduction of a more personalised approach to risk stratification – the process by which each child is assigned to one of several ‘standard of care’ treatment arms according to the predicted risk of their tumour recurring. \n\nIt is a live database, currently comprising data on 670 children with kidney tumours who are resident across the UK and Republic of Ireland. This represents >90% of all incident cases diagnosed in the population. Clinical data and patient samples are collected with explicit consent of their parent/guardian for use in research and for sharing with the relevant national cancer registration service according to where the child is resident at diagnosis.\n\nData are collected throughout the patient journey and are presented in tables with each data item as a comma separated value (CSV) corresponding to a data field captured through a case report form (CRF). These span the following timepoints from presentation to completion of treatment and then annual follow up or reporting of any events (relapse/death/second tumour). \n\nF1 Registration information\nF2 Pre-operative chemotherapy (Unilateral tumours only)\nF3 Surgical form – operative findings\nF4 Pathology form – institutional pathologist\nF6 Post-operative chemotherapy\nF11 Cardiotoxicity reporting form\nF12 Bilateral Wilms tumour form – additional registration information (12a) and response assessment (12b)\nF15 Reference Pathology – “Central review”\nF17 Treatment of relapse or progression\nF18 Follow up form – for annual follow up reporting and to report any relapse or death.\nF20A End of treatment summary (Unilateral tumours)\nF20B End of treatment summary (Bilateral tumours)\n\nDetails of the content of each CRF are in the table below.\n\nMost children (~90%) with Wilms tumour survive their cancer. Hence, an important aspect of the dataset is the planned linkage of each child’s data to their national cancer registration record. This will permit measurement of very long term health outcomes in adulthood by linkage to routine health care data utilising the approved information governance systems through which the UK’s national cancer registration services work with the NHS to ensure patient confidentiality. \n\nThe IMPORT study is one of five sequential clinical trials and studies that registered the majority of children diagnosed with kidney cancer in the UK and Republic of Ireland between 1980-2019 (over 3,000 patients). It is intended that these additional four closed trial datasets will be made available in the same way during 2020.\n\nThese trials’ primary endpoints, of relapse-free and overall survival according to clinically defined risk groups, have been published, with full clinical quality assurance completed. Over that 39 year period, risk stratification and treatment schedules have been refined many times, to reduce exposure to therapies that may cause long term side effects such as heart damage or second tumours. These adverse ‘late effects’ may take decades before they are first seen. Hence, these linked datasets are an important proof of principle about how very long term outcomes can be studied through linkage of clinical trials to routine health care data sources.  \n\nThe IMPORT dataset will continue to be enlarged and enriched with tumour molecular information (copy number, genomic sequencing) and imaging review data (CT/MRI scans). These data items are not available for the previous closed trials.\n\n\nOptional material if needed:\n\nPlans for the linked dataset with the national cancer registration and analysis service (NCRAS) of Public Health England. \n\nThis project will create a new permanent data linkage between each patient’s trial identifier and data and their existing national cancer registration record, with all personally identifiable data being handled within the existing nationally approved information governance and data protection frameworks of the clinical trial units and the cancer registration service of each nation. The following technical description applies to Public Health England’s National Cancer Registration and Analysis Service (NCRAS). Access to the linked community prescription data is made available through PHE’s Office for Data Release.\n\nNCRAS routinely collects and quality assures clinical information on all cases of cancer that occur in people living in England to construct a cancer registration record.  The data collected by NCRAS comes from several sources including Multi-Disciplinary Team meetings and pathology reports. The patient identifiers collected facilitate linkage to other routinely collected datasets, that include Hospital Episode Statistics (HES) with details of all admissions, A&E attendances and outpatient appointments at NHS hospitals in England, mortality data, the Systemic Anti-Cancer Therapy and Radiotherapy datasets from all hospital providers and community dispensed prescriptions data. \n\nPatient-level linkage to inpatient HES is routinely processed by NCRAS. PHE receives pseudonymised dispensed prescriptions data from NHS Business Services Authority, allowing patient-level linkage to the National Cancer Registration Dataset without revealing the identities of those without cancer.  Prescription data is available from April 2015 to February 2019, providing a 3-4 year window of prescription drug usage by survivors of childhood renal tumours. \n\nThe project’s primary aim is addressed by calculating cumulative and relative risks of hospitalisation for specific organ dysfunctions from the HES-linked data set and relative risks of prescriptions for specific drugs compared to the general population rates. This newly created linked dataset can be refreshed for future follow up studies and linkage to additional datasets that may become available such as patient-reported outcome measures.\n\nFor HES-based analyses, follow-up will start at whichever is the later of 1 April 1997 and date of renal tumour diagnosis.  For community prescriptions analyses, follow-up will start at whichever is the later of 1 April 2015 and date of renal tumour diagnosis.  For both sets of analyses, follow-up will end at whichever is earliest of death, emigration, latest date for which linked death certificate data are complete, and the latest date for which data are complete in the linked databases for HES or community prescriptions as applicable.\n\nCumulative risk of hospitalisation for specific organ dysfunctions will be estimated from the HES-linked data set with death treated as a competing risk.  Relative risk of hospitalisation for specific organ dysfunctions will be estimated by dividing the observed number in the HES-linked data set by the expected number obtained by multiplying the number of person-years accumulated in each age-sex-calendar year stratum in the study cohort by the general population rate in the corresponding stratum calculated from the entire HES database.  Relative risks of prescriptions for specific drugs will be estimated in a similar way using the linked community prescription data set and the entire community prescription database.\n\nThe IMPORT study is one of five sequential clinical trials and studies that registered the majority of children diagnosed with kidney cancer in the UK and Republic of Ireland between 1980-2019 (over 3,000 patients). It is intended that these additional four closed trial datasets will be made available in the same way during 2020",
    "url": "https://healthdatagateway.org/en/dataset/30",
    "uid": "3f71bef5-c5a2-400c-a482-2ad30b29a240",
    "datasource_id": 30,
    "source": "HDRUK"
  },
  {
    "id": 102,
    "name": "Community Services Dataset",
    "description": "Nationally defined dataset containing both administrative & clinical details for community services activity for defined services.  Items are coded using the national definitions.",
    "url": null,
    "uid": "cae0a9a5-1bf2-49c3-b8d1-c315d3239074",
    "datasource_id": 102,
    "source": "HDRUK"
  },
  {
    "id": 103,
    "name": "Comprehensive Patient Records for Cancer Outcomes",
    "description": "The data is derived from linked primary, secondary and tertiary care electronic health records and participant survey responses. Data is de-identified at source (Leeds Teaching Hospitals NHS Trust (LTHT) and ResearchOne) and linked using matching pseudonymous digests that are re-pseudonymised upon linkage by University of Leeds IT to produce irreversibly pseudonymous data that is processed into a research dataset. The data relates to the medical history of cancer patients prior to cancer, during their cancer diagnosis and treatment, and following their long-term outcomes, and the medical history of matched non-cancer patients that form a comparator cohort.\n\nThe data relates to 431,352 patients in the UK that LTHT have a ‘legitimate patient relationship’ with and that were determined by LTHT to have had a cancer diagnosis between 2004 and 2018 or be a matched non-cancer patient. Where available, data from ResearchOne provides primary care information for these patients. Where the patients were invited to participate in a patient reported outcomes measures survey (PROMS), this status is recorded. Where the patient returned a consented PROMS, the PROMS data will also be available once it has completed the extract, transform and load process. \n\nThe dataset is currently 5.7 GB and further ResearchOne and PROMS data is anticipated. The dataset is arranged as a relational database, with tables linking on the patient level by a pseudonymous digest. Each table is a comma separated values (CSV) file and relates to an event type, such as prescription cost, address history or diagnosis. All patients have an entry (row) in the demographics table; the number of times a patient has an entry in the other tables depends on how many events of that type were recorded for the patient. \n\nThe dataset is split into two files, each with similar table structure; the main dataset contains all patients and the PROMs dataset contains only those in the PROMs cohort (for whom additional PROMs data will be added). Each table has a re-pseudonymised digest field, “Digest2” and an indicator as to whether the patient has data from ResearchOne available, “TPP_Linked” (0 or 1). Additional fields per table are defined in Table 1. No fields contain sensitive information.\n\nContains patients in the UK that LTHT have a ‘legitimate patient relationship’ with and that were determined by LTHT to have had a cancer diagnosis between 2004 and 2018 or be a matched non-cancer patient.",
    "url": "https://healthdatagateway.org/en/dataset/28",
    "uid": "ce4582a8-0985-46c6-b95f-29a5de862d4a",
    "datasource_id": 28,
    "source": "HDRUK"
  },
  {
    "id": 104,
    "name": "Congenital Anomaly Register and Information Service (CARS)",
    "description": "CARIS aims to collect reliable data about congenital anomalies that can then be used to help:\n\n- build up and monitor the picture of congenital anomalies in Wales\n- assess interventions intended to help prevent or detect congenital anomalies\n- plan and co-ordinate provision of health services for affected babies and children\n- assess possible clusters of birth defects and their causes\n\nCARIS collects information about any fetus or baby who has or is suspected of having a congenital anomaly and whose mother is normally resident in Wales at time of birth. It includes babies in whom anomalies are diagnosed at any time from conception to the end of the first year of life. Data collection commenced on 1st January 1998 and includes any baby where pregnancy ended after this date.\n\nCARIS uses a multi-source data collection method using a wide range of sources within the NHS. This ranges from antenatal ultrasound, clinical letters, post-mortems and laboratory results. CARIS also accesses a number of databases including SHIRE (Medical Genetics database), PEDW, NCCHD, Paediatric Cardiology database. Medical records are accessed to confirm, validate and add further details to the information already collected.\n\nDue to the nature of this dataset linkage based on the babies is challenging. This is because many pregnancies (approximately 20% of the total) end in fetal loss or termination. However, linkage based on the mother can be performed.",
    "url": "https://healthdatagateway.org/en/dataset/319",
    "uid": "6fe7e004-eb51-4c7b-9905-1ec9a4827dca",
    "datasource_id": 319,
    "source": "HDRUK"
  },
  {
    "id": 105,
    "name": "Consent records",
    "description": "The NIHR BioResource records consent dates and versions for each and every participant consent event.  We also record use of opt-ins and opt-outs including pre-GDPR. This permits us to manage data releases in line with participants' expectations.",
    "url": null,
    "uid": "371c5c35-8d27-49be-8531-69e51745ac34",
    "datasource_id": 105,
    "source": "HDRUK"
  },
  {
    "id": 106,
    "name": "Contact detail",
    "description": "The NIHR BioResource acquires contact details - name, address, email address, phone/mobile number - from participants at recruitment. This is used to recontact participants to invite them to take part in experimental medicine studies.",
    "url": null,
    "uid": "c12c53cc-d1f5-4b01-b86d-5e04c67ee70a",
    "datasource_id": 106,
    "source": "HDRUK"
  },
  {
    "id": 107,
    "name": "Critical Care Dataset (CCDS)",
    "description": "A good supplementary to hospital inpatient dataset (PEDW), covers the period of critical care patient received, including intensity of care (e.g. bed levels, organ support), treatment specialty, and outcome.\n\nThis dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.",
    "url": "https://healthdatagateway.org/en/dataset/301",
    "uid": "1789286a-deaf-49df-88cd-660e92934af6",
    "datasource_id": 301,
    "source": "HDRUK"
  },
  {
    "id": 108,
    "name": "Critical Care Minimum Dataset",
    "description": "Nationally defined dataset containing administrative details for stays within an adult, pead or neonatal critical care unit. Items are coded using the national definitions.",
    "url": null,
    "uid": "c71fbdc3-0311-4b3f-a9ff-7d2f2c0ba993",
    "datasource_id": 108,
    "source": "HDRUK"
  },
  {
    "id": 109,
    "name": "UK Cystic Fibrosis Patient Annual Review",
    "description": "The Annual Review Dataset contains routinely collected clinical data from annual review assessments of individuals diagnosed with cystic fibrosis. These reviews are part of standard clinical care and include comprehensive evaluations of respiratory health, nutritional status, treatment history, and disease complications. The dataset supports research into disease progression, treatment effectiveness, and clinical outcomes in cystic fibrosis. It can be used for epidemiological studies, healthcare planning, and evaluation of new interventions.\nTo find out more visit our: https://www.cysticfibrosis.org.uk/about-us/uk-cf-registry/reporting-and-resources",
    "url": "https://healthdatagateway.org/en/dataset/3",
    "uid": "11360490-8da3-4a25-bb87-4fcc249b9af5",
    "datasource_id": 3,
    "source": "HDRUK"
  },
  {
    "id": 110,
    "name": "Cystic Fibrosis Patient CFQ-R",
    "description": "The UK CF Registry is a centralised database of all 60 CF centres across the UK. Data are manually entered  in calendar years by CF clinical teams for the 99% of people with a diagnosis of CF who consent to their data being donated to the Registry. Data are entered onto a secure web-portal. For more information please see www.cysticfibrosis.org.uk/registry and 'Data Resource Profile: The UK CF Registry' published in the International Journal of Epidemiology (2018 Feb 1;47(1)9-10e).",
    "url": "https://healthdatagateway.org/en/dataset/7",
    "uid": "9cf5dbd3-a109-4e8d-b1bb-7e4126bd887f",
    "datasource_id": 7,
    "source": "HDRUK"
  },
  {
    "id": 111,
    "name": "Cystic Fibrosis Patient Chronic Medication",
    "description": "The UK CF Registry is a centralised database of all 60 CF centres across the UK. Data are manually enterd in calendar years by CF clinical teams for the 99% of people with a diagnosis of CF who consent to their data being donated to the Registry. Data are entered onto a secure web-portal. For more information please see www.cysticfibrosis.org.uk/registry and 'Data Resource Profile: The UK CF Registry' published in the International Journal of Epidemiology (2018 Feb 1;47(1)9-10e).",
    "url": "https://healthdatagateway.org/en/dataset/10",
    "uid": "0ad31772-0893-4cf7-bab0-683e80302201",
    "datasource_id": 10,
    "source": "HDRUK"
  },
  {
    "id": 112,
    "name": "Cystic Fibrosis Patient Demographics",
    "description": "The UK Cystic Fibrosis Registry Demographic is made up of data items relating key demographic information about CF patients, relating to their diagnosis and genotype.",
    "url": null,
    "uid": "19bc55c2-13d7-4488-a52c-35287483ea4e",
    "datasource_id": 112,
    "source": "HDRUK"
  },
  {
    "id": 113,
    "name": "Cystic Fibrosis Patient NTM culture",
    "description": "The UK CF Registry is a centralised database of all 60 CF centres across the UK. Data are manually entered in calendar years by CF clinical teams for the 99% of people with a diagnosis of CF who consent to their data being donated to the Registry. Data are entered onto a secure web-portal. For more information please see www.cysticfibrosis.org.uk/registry and 'Data Resource Profile: The UK CF Registry' published in the International Journal of Epidemiology (2018 Feb 1;47(1)9-10e).",
    "url": "https://healthdatagateway.org/en/dataset/6",
    "uid": "69bdac5f-bb70-4ca5-a5ce-51a6106ad00b",
    "datasource_id": 6,
    "source": "HDRUK"
  },
  {
    "id": 114,
    "name": "Cystic Fibrosis Patient Sweat Tests",
    "description": "The UK CF Registry is a centralised database of all 60 CF centres across the UK. Data are manually entered in calendar years by CF clinical teams for the 99% of people with a diagnosis of CF who consent to their data being donated to the Registry. Data are entered onto a secure web-portal. Sweat tests refer to sweat chloride tests taken by CF patients in relation to diagnosis of CF, investigations or as part of protocol for initiating onto CFTR modifier drug treatments. For more information please see www.cysticfibrosis.org.uk/registry and 'Data Resource Profile: The UK CF Registry' published in the International Journal of Epidemiology (2018 Feb 1;47(1)9-10e).",
    "url": "https://healthdatagateway.org/en/dataset/5",
    "uid": "34f0c2bd-d42e-4441-a89b-e0e384f12afa",
    "datasource_id": 5,
    "source": "HDRUK"
  },
  {
    "id": 115,
    "name": "Cystic Fibrosis Patient Tendon Rupture",
    "description": "The UK CF Registry is a centralised database of all 60 CF centres across the UK. Data are manually entered in calendar years by CF clinical teams for the 99% of people with a diagnosis of CF who consent to their data being donated to the Registry. Data are entered onto a secure web-portal. For more information please see www.cysticfibrosis.org.uk/registry and 'Data Resource Profile: The UK CF Registry' published in the International Journal of Epidemiology (2018 Feb 1;47(1)9-10e).",
    "url": "https://healthdatagateway.org/en/dataset/8",
    "uid": "4fcc8b11-2b9b-4f68-b51a-978749d89acc",
    "datasource_id": 8,
    "source": "HDRUK"
  },
  {
    "id": 116,
    "name": "Cystic Fibrosis Patient Transplants",
    "description": "The UK CF Registry is a centralised database of all 60 CF centres across the UK. Data are manually entered in calendar years by CF clinical teams for the 99% of people with a diagnosis of CF who consent to their data being donated to the Registry. Data are entered onto a secure web-portal. For more information please see www.cysticfibrosis.org.uk/registry and 'Data Resource Profile: The UK CF Registry' published in the International Journal of Epidemiology (2018 Feb 1;47(1)9-10e).",
    "url": "https://healthdatagateway.org/en/dataset/4",
    "uid": "bea354e8-c552-41ef-80c2-a76ca5767e4b",
    "datasource_id": 4,
    "source": "HDRUK"
  },
  {
    "id": 117,
    "name": "DARWIN1 Trial",
    "description": "Blood and tissue from lung cancer patients",
    "url": "https://healthdatagateway.org/en/dataset/441",
    "uid": "7f9672fa-dad5-479d-a929-52719e8cc2fa",
    "datasource_id": 441,
    "source": "HDRUK"
  },
  {
    "id": 118,
    "name": "Daisy Tumour Bank",
    "description": "The Daisy Tumour Bank (DTB) is creating various collections of human samples to share with researchers, thus providing a resource of fit for purpose biological material to facilitate ethically approved cancer research. The DTB is collecting human tissue and blood samples from cancer patients within Hull University Teaching Hospitals NHS Trust. The DTB is a newly established biobank which is in the accrual process, therefore samples and data are not accessible to researchers at this time.",
    "url": "https://healthdatagateway.org/en/dataset/428",
    "uid": "b3ec5c18-e339-4bd8-8a59-5a3445a81f16",
    "datasource_id": 428,
    "source": "HDRUK"
  },
  {
    "id": 119,
    "name": "Deaths – National Records Scotland",
    "description": "This data can be used to identify causes of death.  As this data does not originate from the health service, it is not originally CHI seeded.  From approx. 2015 onwards, the data was supplied with CHI number, though only covering around 95% of the data.\nPrior to 2015, and for those records supplied with no CHI an automated mechanism which identifies over 90% of the records is used to CHI seed.  This is further augmented by manual data entry.\n\nDue to these procedures, this dataset does not necessarily represent all deceased persons.",
    "url": "https://healthdatagateway.org/en/dataset/113",
    "uid": "5362cfea-d554-43ba-916a-4460da39cc0c",
    "datasource_id": 113,
    "source": "HDRUK"
  },
  {
    "id": 120,
    "name": "Deaths – Date of Death",
    "description": "This dataset combines the death status from various sources into a single dataset with rules applied to decide which date of death is more accurate when the information differs between sources. Tayside Circa. 1980 – Current; Fife: 2009 - Current.",
    "url": "https://healthdatagateway.org/en/dataset/108",
    "uid": "faf2fc49-d09f-4ea2-a2cb-b1d30fffa406",
    "datasource_id": 108,
    "source": "HDRUK"
  },
  {
    "id": 121,
    "name": "Demographic",
    "description": "The NIHR BioResource acquires broad demographics – e.g. age, sex, ethnicity - from participants at recruitment.  This is used to pre-screen or match participants when inviting them to take part in experimental medicine studies",
    "url": null,
    "uid": "df28dcef-1d04-4f7d-b380-c76e07229a14",
    "datasource_id": 121,
    "source": "HDRUK"
  },
  {
    "id": 122,
    "name": "Demography",
    "description": "Demographic data comes from the national CHI dataset, however only Tayside and Fife health boards can be extracted for HIC projects. Therefore the demography dataset contains a single record for each cohort, this record will be details for the most recent time the person was in either Tayside or Fife.  \n\nThis is the current or most recent record for each person, aiming for a single record per person.",
    "url": "https://healthdatagateway.org/en/dataset/123",
    "uid": "b88c2b24-b75d-4d28-9391-2dbac1605a17",
    "datasource_id": 123,
    "source": "HDRUK"
  },
  {
    "id": 123,
    "name": "Diagnostic Imaging Dataset",
    "description": "The DID is a monthly collection of information about diagnostic imaging tests carried out on NHS patients in England which enables analysis of demographic and geographic variation in access to different test types and providers. The dataset, collected at patient level, includes patient identifiers to enable linkage to other datasets, most notably cancer registration data. Information from the DID may inform the accreditation processes for imaging departments through the UK Imaging Services Accreditation Scheme and the assessment of imaging services by the Care Quality Commission. The dataset provides information on the utilisation of high value imaging equipment such as MRI scanners. Information about diagnostic testing is being linked to cancer patient’s records held in Cancer Registries, expanding the understanding we have of their treatment pathway. Further information can be found on the NHS Digital DID website. Release Date - Aggregated data at Trust level is published the last Thursday of the month by NHS England on the DID webpage iView - DID data is available to the DID user community and the DHSC via Community View in iView. This data resource has been designed for professionals working with health and social care data on a regular basis. Access to iView can be requested by contacting NHS Digital on 0300 303 5678 or enquiries@nhsdigital.nhs.uk",
    "url": "https://healthdatagateway.org/en/dataset/868",
    "uid": "106da799-3f39-4173-8848-19c9930d10cb",
    "datasource_id": 868,
    "source": "HDRUK"
  },
  {
    "id": 124,
    "name": "Diagnostic and Therapy Services Waiting Times (DATW)",
    "description": "Capture waiting times for specified diagnostic and therapy services for the NHS in Wales.\n\nMonthly return of counts of waiting times for referrals to specified diagnostic and therapy services submitted by Local Health Boards.\n\nThis dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.\n\nPlease note: this dataset is aggregated and not at an individual person level.",
    "url": "https://healthdatagateway.org/en/dataset/369",
    "uid": "97896866-ab5e-408a-ac5a-072802db9faf",
    "datasource_id": 369,
    "source": "HDRUK"
  },
  {
    "id": 125,
    "name": "Hospital Episode Statistics Accident and Emergency",
    "description": "Hospital Episode Statistics (HES) is a database containing details of all admissions, A and E attendances and outpatient appointments at NHS hospitals in England.\n\nInitially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the [Secondary Uses Service (SUS)](https://digital.nhs.uk/services/secondary-uses-service-sus) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver.\n\nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the HES data set.\n\n*   HES data covers all NHS Clinical Commissioning Groups (CCGs) in England, including:\n*   private patients treated in NHS hospitals\n*   patients resident outside of England care delivered by treatment centres (including those in the independent sector) funded by the NHS\n\nEach HES record contains a wide range of information about an individual patient admitted to an NHS hospital, including:\n\nclinical information about diagnoses and operations\n\npatient information, such as age group, gender and ethnicity\n\nadministrative information, such as dates and methods of admission and discharge\n\ngeographical information such as where patients are treated and the area where they live\n\nWe apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published HES data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained. [https://digital.nhs.uk/data-and-information/publications/statistical/hospital-accident--emergency-activity](https://digital.nhs.uk/data-and-information/publications/statistical/hospital-accident--emergency-activity)",
    "url": "https://healthdatagateway.org/en/dataset/861",
    "uid": "101a7d68-1675-4b47-bfac-dde7b7b57877",
    "datasource_id": 861,
    "source": "HDRUK"
  },
  {
    "id": 126,
    "name": "ESPAC-TPlus",
    "description": "To centralise tissue and other clinical samples from the ESPAC studies, and make them available for future studies. Storage of the samples is designed to facilitate specific studies on response to chemotherapeutics. These separate studies will be subject to individual ethics applications. If successful they will allow subgroups of patients to be identified who will be predicted to benefit from particular chemotherapeutic regimens, improving survival for individual patients and facilitating clinical trials.",
    "url": "https://healthdatagateway.org/en/dataset/442",
    "uid": "47f979bc-da9d-4e9e-9f2b-15e09366db04",
    "datasource_id": 442,
    "source": "HDRUK"
  },
  {
    "id": 127,
    "name": "Emergency Care Data Set (ECDS)",
    "description": "The Emergency Care Data Set (ECDS) is the national data set for urgent and emergency care. It replaced Accident & Emergency Commissioning Data Set (CDS type 010) and was implemented through: ECDS (CDS 6.2.2 Type 011). ECDS allows NHS Digital to provide information to support the care provided in emergency departments by including the data items needed to understand capacity and demand and help improve patient care.\n\nECDS Type 011 is better equipped to keep pace with the increasing complexity of delivering emergency care than its predecessor. This means that the improved quality of data collected in emergency departments provides better support to healthcare planning and better-informed decision making on improvements to services.\n\nThis improved data helps improve understanding of: The complexity and acuity of attending patients The causes of rising demand The value added by emergency departments.\n\nECDS also allows: The capture of better diagnostic data to ensure an enhanced understanding of need, activity and outcomes. Consistent monitoring of data across local and national initiatives support for injury surveillance, such that it will be possible to identify patterns that may be amenable to targeted interventions and improved public health. Which in turn informs more effective and efficient resource deployment.\n\nPlease note, data can be submitted to the ECDS on a daily basis, however data extracts are made available via the DARS process on the second Thursday of each month, comprising provisional data for the full financial year up to and including the penultimate full month prior to the publication date. For example: Data for events that occurred 01/04/2020 to 31/10/2020 is made available via DARS extract on 10/12/2020.\n\nTimescales for dissemination of agreed data can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process)",
    "url": "https://healthdatagateway.org/en/dataset/871",
    "uid": "8883f996-7943-4ac3-a625-7793c19949cc",
    "datasource_id": 871,
    "source": "HDRUK"
  },
  {
    "id": 128,
    "name": "Emergency Care Dataset (ECDS)",
    "description": "Nationally defined dataset which reports on type 1-4 ED departments. The dataset has been revised recently to capture more clinically relevent set of data items including coding using SNOMED.",
    "url": null,
    "uid": "57fa56f2-78d1-49af-ab8d-e8146d711868",
    "datasource_id": 128,
    "source": "HDRUK"
  },
  {
    "id": 129,
    "name": "Emergency Department Dataset (EDDS)",
    "description": "Administrative and clinical information for all NHS Wales Accident and Emergency department attendances. Includes the All Wales Injury Surveillance Systems (AWISS) dataset.\n\nData recording practices may vary, especially in some of the minor A&E and Minor Injury Units which could account for some local differences.\n\nThis dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.",
    "url": "https://healthdatagateway.org/en/dataset/299",
    "uid": "75c4dcb8-33bf-43f4-b2bb-db51b6621b2c",
    "datasource_id": 299,
    "source": "HDRUK"
  },
  {
    "id": 130,
    "name": "Emergency Department Dataset Daily (EDDD)",
    "description": "EDDD is a project specific dataset for specific COVID-19 related projects only. Administrative and clinical information for all NHS Wales Accident and Emergency department attendances are available from the EDDS dataset.\n\nThe data covers administrative and clinical information for all NHS Wales Accident and Emergency department attendances. This is in contrast to EDDS data which are not daily extracts. Includes the All Wales Injury Surveillance Systems (AWISS) dataset. \n\nData recording practices may vary, especially in some of the minor A&E and Minor Injury Units which could account for some local differences.",
    "url": "https://healthdatagateway.org/en/dataset/306",
    "uid": "fa257aa8-51fc-4eb7-a448-e473fa54686d",
    "datasource_id": 306,
    "source": "HDRUK"
  },
  {
    "id": 131,
    "name": "Endobase",
    "description": "A complete capture of the endoscopy information sytem. The dataset comprises Information relating to the endoscopy exam procedure and the resultant freetext reports produced by clinical staff.",
    "url": null,
    "uid": "e830b2a5-14d6-4643-a206-619bf5372dc8",
    "datasource_id": 131,
    "source": "HDRUK"
  },
  {
    "id": 132,
    "name": "Epilepsy 12 - National organisational audit and Trust profile",
    "description": "A dataset comprising a yearly survey of Trusts' paediatric epilepsy services. The dataset covers England and Wales and includes information on 10 areas of the provision and organisation of services: Workforce, Epilepsy clinic configuration, Tertiary provision, Investigations, Service contact, Young people and transition, Mental health provision, Neurodevelopmental support, Care planning, and the use of a Patient database/register.\n\nhttps://www.rcpch.ac.uk/sites/default/files/2018-07/epilepsy12_organisational_audit_dataset_may_2018_0.pdf",
    "url": "https://healthdatagateway.org/en/dataset/583",
    "uid": "2c737d63-69f4-4a02-8df9-54c1e65e845f",
    "datasource_id": 583,
    "source": "HDRUK"
  },
  {
    "id": 133,
    "name": "Epilepsy 12 - Patient Reported Experience Measure",
    "description": "A dataset comprising anonymised record level data from paper based questionnaires being handed out to consecutive families with epilepsy attending clinics and completed in the waiting area before clinic review.",
    "url": null,
    "uid": "cefa4d82-5a22-4c19-82ab-4663a364e49e",
    "datasource_id": 133,
    "source": "HDRUK"
  },
  {
    "id": 134,
    "name": "Epilepsy 12 - clinical audit",
    "description": "A prospective national clinical audit dataset which aims to include information on the investigation, diagnosis, treatment, care planning and outcomes of all children and young people with a new onset of epilepsy.\n\nDetails are recorded about the first assessment children and young people receive following a suspected seizure. Their care is then followed for 12 months. The audit uses 12 key measures drawn from clinical guidelines which include: involvement of paediatricians and nurses with experise in epilepsy, appropriate referrals to tertiary and surgical services,  \ncontent of the first assessment, appropriate seizure classification, appropriate diagnostic investigations, use of epilepsy medications, and evidence of comprehensive care planning.\n\nhttps://www.rcpch.ac.uk/sites/default/files/2019-06/epilepsy12_r3_patient_reg_and_clin_audit_dataset_june_19.pdf",
    "url": "https://healthdatagateway.org/en/dataset/580",
    "uid": "1fcaf429-8762-454c-b999-c5fdd757b880",
    "datasource_id": 580,
    "source": "HDRUK"
  },
  {
    "id": 135,
    "name": "Epilepsy Society Brain and Tissue Bank",
    "description": "The Epilepsy Society Brain and Tissue Bank is a recently established tissue bank at the Institute of Neurology, which has ethical approval and is supported by the Epilepsy Society. It is located within UCL Institute of Neurology in the Department of Neuropathology and is governed by a committee including members of the Department of Clinical and Experimental Epilepsy, the Epilepsy Society as well as SUDEP Action.\nThe collection comprises consented tissue samples from patients who have undergone epilepsy surgery, mainly at the National Hospital for Neurology and Neurosurgery. In addition the tissue bank archives whole brain samples from patients with epilepsy and particularly epilepsy-related deaths, such as sudden and unexpected death in epilepsy (SUDEP).\nWe have a donor registry for people who have epilepsy, seizures or who are healthy, without seizures, who wish to donate brain tissue following death.",
    "url": "https://healthdatagateway.org/en/dataset/423",
    "uid": "f13258c3-f85a-4772-b42e-da8a84c5064a",
    "datasource_id": 423,
    "source": "HDRUK"
  },
  {
    "id": 136,
    "name": "Ethical Tissue - University of Bradford",
    "description": "Ethical Tissue is a research tissue bank, licensed by the Human Tissue Authority (HTA); to collect, store and supply a wide range of human tissue, cells and fluids to biomedical research groups in academia and industry.  Also includes tissue donated after death.\nWe provide access to collection of samples and data across the following diseases: \n•\tCarcinoma in situ of liver\n•\tDementia (disorder)\n•\tFit and well\n•\tMalignant neoplasm of brain\n•\tMalignant neoplasm of liver\n•\tMalignant tumour of oral cavity (disorder)\n•\tMalignant tumour of prostate (disorder)\n•\tMalignant tumour of stomach (disorder)\n•\t,Malignant tumour of colon\n•\tMalignant tumour of lung\n•\tMalignant tumour of oesophagus",
    "url": "https://healthdatagateway.org/en/dataset/427",
    "uid": "fdf9ac68-fc89-4adf-9a73-3a94a9974aef",
    "datasource_id": 427,
    "source": "HDRUK"
  },
  {
    "id": 137,
    "name": "Exploration of Pneumonia Related Policy Formation and Implementation in Pakistan",
    "description": "In Pakistan, numerous programs have been launched for management of acute respiratory infections.However, despite completion, state of pneumonia mortality remains unchanged as no sustainable solutions have been yielded by these programs. The objective of this study is to identify and analyze pneumonia related policies in Pakistan for children under five and identify key stakeholders who influence these policies and program financially and technically.The study is being conducted in two phases. The first was of content analysis of pneumonia related documents and IDI’s of policy stakeholders. The second phase consists of social network research conducted by studying actors linked together by social relations achieved through net mapping exercise. \n\nFor further information, see: https://www.ed.ac.uk/usher/respire/acute-respiratory-disorders/pneumonia-policy",
    "url": "https://healthdatagateway.org/en/dataset/227",
    "uid": "e0d00cb7-62e1-4a38-8df6-cf537b59228d",
    "datasource_id": 227,
    "source": "HDRUK"
  },
  {
    "id": 138,
    "name": "Extended Cohort for E-health, Environment and DNA (EXCEED)",
    "description": "https://exceed.org.uk/\n\nEXCEED has been described in a cohort profile paper accessible here: https://academic.oup.com/ije/article/48/3/678/5485771\n \nEXCEED is a longitudinal population-based cohort which facilitates investigation of genetic, environmental and lifestyle-related determinants of a broad range of diseases and of multiple morbidity through data collected at baseline and via electronic healthcare record linkage. Recruitment has taken place in Leicester, Leicestershire and Rutland since 2013 and is ongoing, with >10,650 participants aged 30-69 to date. The population of Leicester is diverse and additional recruitment from the local South Asian community is ongoing. Participants provided a DNA sample, have consented to follow-up for up to 25 years through electronic health records and additional bespoke data collection is planned. Data available includes baseline demographics, anthropometry, spirometry, lifestyle factors (smoking and alcohol use) and longitudinal health information from primary care records, with additional linkage to other EHR datasets planned. Patients have consented to be contacted for recall-by-genotype and recall-by-phenotype sub-studies. Genome-wide genotype data are available via EGA for 5218 individuals.",
    "url": "https://healthdatagateway.org/en/dataset/283",
    "uid": "e22ddf52-7f9e-4072-ae61-cb722461b37d",
    "datasource_id": 283,
    "source": "HDRUK"
  },
  {
    "id": 139,
    "name": "Falls & Fragility Fracture Audit  Fracture Liaison Service  clinical dataset",
    "description": "The FLS-DB is a continuously ascertained, record-level dataset which commenced in January 2016. It contains data from England and Wales on the patterns of assessment and treatment for osteoporosis and falls, across primary and secondary care, in patients who have sustained a fragility fracture. Full dataset available: https://www.rcplondon.ac.uk/projects/outputs/participating-fls-db",
    "url": "https://healthdatagateway.org/en/dataset/594",
    "uid": "d94a604c-5733-4f07-93bb-e77b6ea2313f",
    "datasource_id": 594,
    "source": "HDRUK"
  },
  {
    "id": 140,
    "name": "Falls and Fragility Fracture Audit  Programme - Inpatient Falls clinical dataset",
    "description": "The FFFAP NAIF is a continuously ascertained, record-level audit which evaluates both falls prevention activity prior to the hip fracture and post-falls care, when inpatients have fallen within acute, community and mental health hospital care in England and Wales. Data collection started in January 2019.",
    "url": "https://healthdatagateway.org/en/dataset/593",
    "uid": "3c19e2c9-96d5-45f3-9c55-fc3cec762cd7",
    "datasource_id": 593,
    "source": "HDRUK"
  },
  {
    "id": 141,
    "name": "FFFAP -  National Hip Fracture Database Facilities Survey 2019",
    "description": "The NHFD FA is an annual survey of all trauma centres in the UK. It includes data on the support for clinical governance, organisation of theatres and care bundles that are used. Full datasets available on request: NHFD@rcplondon.ac.uk",
    "url": "https://healthdatagateway.org/en/dataset/561",
    "uid": "025c9e8a-938b-4886-9e03-fcad90fe7157",
    "datasource_id": 561,
    "source": "HDRUK"
  },
  {
    "id": 142,
    "name": "Falls and Fragility Fracture Audit  National Hip Fracture clinical dataset",
    "description": "This continuously ascertained record level dataset contains information on all patients admitted to hospital with hip fractures in England, Wales and Northern Ireland. The dataset incorporates multiple aspects of patients' care including time to orthogeriatric assessment, time to surgery, assessment of delirium and discharge destination.\n\nFull clinical dataset and theatre sheet available upon request: NHFD@rcplondon.ac.uk",
    "url": "https://healthdatagateway.org/en/dataset/595",
    "uid": "e06b951b-e9d8-4a2c-a51f-1d819f669205",
    "datasource_id": 595,
    "source": "HDRUK"
  },
  {
    "id": 143,
    "name": "Fife Microbiology: Isolations",
    "description": "Antibiotic sensitivities on organisms within microbiology samples. Fife 2006 - Current.",
    "url": null,
    "uid": "79086e92-cbbf-4615-b642-bc65d8a3bc6d",
    "datasource_id": 143,
    "source": "HDRUK"
  },
  {
    "id": 144,
    "name": "Fife Microbiology Lab",
    "description": "Data on specimens for microbiology testing.  If an organism/bacteria is found this is isolated within the sample and results of the organism's growth status under the application of various antibiotics are recorded.\n\nThis dataset also contains free text laboratory comments, adding additional details about the specimens supplied.",
    "url": "https://healthdatagateway.org/en/dataset/117",
    "uid": "0c43c0b6-de3a-478b-8190-4da37fbe5ff5",
    "datasource_id": 117,
    "source": "HDRUK"
  },
  {
    "id": 145,
    "name": "Fife Radiology",
    "description": "NHS Fife laboratory data. Fife 2009 – 2015.",
    "url": null,
    "uid": "d020302b-a691-49fb-9d10-1274af2f3110",
    "datasource_id": 145,
    "source": "HDRUK"
  },
  {
    "id": 146,
    "name": "Freetext Dataset",
    "description": "Locally defined dataset which contains information from unstructured data held against a patient record. These include freetext notes in the patient record as well as radiology reports and discharge letters.",
    "url": null,
    "uid": "0f07f939-1265-4e8b-aaa6-d897d254c2f3",
    "datasource_id": 146,
    "source": "HDRUK"
  },
  {
    "id": 147,
    "name": "Full Blood Counts",
    "description": "Blood donor studies are particularly exercised about the characteristics of blood received from donors, and measures FBCs using Sysmex machines at the NIHR National Biosample Centre, which is where all samples are sent.",
    "url": null,
    "uid": "4de9fdc6-7d92-4f62-847d-22243a9c3ea2",
    "datasource_id": 147,
    "source": "HDRUK"
  },
  {
    "id": 148,
    "name": "GENOMICS ENGLAND 100K BIOINFORMATICS DATA",
    "description": "Contains tables with data related to genomic data and the outputs from the GEL interpretation pipeline data for participants from both cancer and rare disease programmes. These tables do not directly include primary + secondary sources of clinical data.",
    "url": null,
    "uid": "98999c67-0133-4f11-be95-b84ee5727c84",
    "datasource_id": 148,
    "source": "HDRUK"
  },
  {
    "id": 149,
    "name": "GENOMICS ENGLAND 100K CANCER & COMMON",
    "description": "Cancer data are presented for either the patient level cancer diagnosis or “disease type” or the tumour specific sample details of participants in the Cancer arm of the 100,000 Genomes Project.",
    "url": null,
    "uid": "702f0198-bdfe-401e-97f2-35f5d26357ad",
    "datasource_id": 149,
    "source": "HDRUK"
  },
  {
    "id": 150,
    "name": "GENOMICS ENGLAND 100K NHSD LINKED DATA",
    "description": "NHS national data sets collect information from care records, systems and organisations on specific areas of health and care.",
    "url": null,
    "uid": "14c31b39-f189-4417-bd38-635268495593",
    "datasource_id": 150,
    "source": "HDRUK"
  },
  {
    "id": 151,
    "name": "GENOMICS ENGLAND 100K PHE LINKED DATA",
    "description": "This dataset brings together data from more than 500 local and regional datasets to build a picture of an individual’s treatment from diagnosis. Available for patients diagnosed with Cancer (ICD10 C00-97, D00-48) from 1 January 1995 -31 December 2017.",
    "url": null,
    "uid": "ea748742-568a-46e2-b603-2fada49024e1",
    "datasource_id": 151,
    "source": "HDRUK"
  },
  {
    "id": 152,
    "name": "GENOMICS ENGLAND 100K QUICK VIEW",
    "description": "Data views that bring together data from several LabKey tables for convenient access",
    "url": null,
    "uid": "4cc5ed33-3190-4f80-953b-cabd5a2b1c7c",
    "datasource_id": 152,
    "source": "HDRUK"
  },
  {
    "id": 153,
    "name": "GENOMICS ENGLAND 100K RARE DISEASE & COMMON",
    "description": "Rare Disease (RD) data are presented at the level of RD families, RD pedigrees, and participants. Participants are consenting individuals who have had their genome sequenced. Pedigree members are extended members of the proband’s family.",
    "url": null,
    "uid": "3f2b37f1-e8f7-42e5-83c4-fc9e1c58725d",
    "datasource_id": 153,
    "source": "HDRUK"
  },
  {
    "id": 154,
    "name": "GP Out of Hours",
    "description": "NHS Boards provide Primary Care OOH services for patients when their registered GP Practice is closed. Scottish Government commissioned the National Services Scotland now Public Health Scotland to develop and introduce a dataset to collect information on GP Out of Hours patients across Scotland. National data collection began in April 2014. Data on patients seen by GP Out of Hours (OOH) services across Scotland are collected and maintained by PHS in the national data warehouse known as the GP OOH datamart. Data is collected on local IT system (Adastra), then extracted and submitted to the DataMart on a weekly basis.",
    "url": "https://healthdatagateway.org/en/dataset/78",
    "uid": "731dc868-3e79-4722-9b79-6bb8dec9435b",
    "datasource_id": 78,
    "source": "HDRUK"
  },
  {
    "id": 155,
    "name": "Generation Scotland",
    "description": "A collaboration between the Universities of Edinburgh, Glasgow, Dundee and Aberdeen, and NHS Scotland, to provide resources for genetic and medical research.",
    "url": "https://healthdatagateway.org/en/dataset/430",
    "uid": "019fad36-65ea-4d47-8d6d-a47240eeb801",
    "datasource_id": 430,
    "source": "HDRUK"
  },
  {
    "id": 156,
    "name": "Genetics of Asthma Severity & Phenotypes",
    "description": "The Genetics of Asthma Severity & Phenotypes initiative was set up in 2013 as part of an Asthma UK funded programme of work to directly address the need for a large cohort of moderate-severe asthma patients for genetic studies.\n\nThe cohort was established by bringing together all of the major respiratory centres across the UK to provide access to existing samples/clinical data but also to prospectively recruit patients (still ongoing). This cohort has been utilised in our recent GWAS of Moderate-Severe asthma (PMID: 30552067). \n\nThe cohort is currently ~4,000 individuals and to date 2,536 individuals have Affymetrix Axion array data that has completed QC imputed to provide 33M variants. The cohort has extensive clinical and immunological data although absolute numbers of these measures are variable due to the nature of the formation of the cohort.",
    "url": "https://healthdatagateway.org/en/dataset/210",
    "uid": "6277abc6-c039-4886-8489-d4a2cdb8190c",
    "datasource_id": 210,
    "source": "HDRUK"
  },
  {
    "id": 157,
    "name": "Genvasc Primary Care Data",
    "description": "The GENVASC Primary Care Data is made up of data extracted from EMIS and SystmOne primary care systems coded with Read v2, Read CTV3 and SNOMED.  The GENVASC cohort contains healthy volunteers recruited in general practices at the time of their Cardiovascular Risk Health Check.",
    "url": "https://healthdatagateway.org/en/dataset/706",
    "uid": "f397acff-a7af-4062-8e8b-c85889cc41df",
    "datasource_id": 706,
    "source": "HDRUK"
  },
  {
    "id": 158,
    "name": "HCV Research UK",
    "description": "HCV Research UK is a UK-wide consortium established in 2011 to underpin research into hepatitis C (HCV).  This was achieved by establishing a biorepository and clinical research database. The biorepository is housed in the MRC-University of Glasgow Centre for Virus Research, directed by Dr John McLauchlan and managed by Dr Sarah McDonald. Samples have been obtained from around 10,000 patients.\nAccess to samples and data is governed by a Tissue and Data Access Committee (TDAC). These are reviewed for ethical and scientific merit by TDAC and a decision reached. \nSerum and plasma are obtained from all patients in the cohort. Buffy coats are also collected, and DNA can be extracted when required. Additionally, peripheral blood mononuclear cells (PBMCs) and PAX gene tubes are held for smaller cohorts of patients. The extensive clinical data collected complements the biorepository and allows selection of samples from patients with characteristics of interest.\nSub-cohorts:\n-\tSerial samples from patients who were treated as part of the NHS England Early Access Program.\n-\tYearly samples from cirrhotic patients (beginning 2015)\n-\tSpontaneous resolvers\n-\tSmall paediatric cohort\nClinical Data\n-\tBasic demographics including place of birth and ethnicity\n-\tHistory of HCV infection including exposure to risk factors and dates\n-\tDate of diagnosis, date of first attendance at clinic\n-\tCo-morbidities and co-medications at time of enrolment\n-\tLiver disease status and how diagnosed\n-\tTreatment status at enrolment; \n-\tSocial history including alcohol, cigarettes, cannabis; BMI\n-\tHCV RNA status, viral load and genotype/subtype\n-\tHistorical data from the patients' notes regarding previous treatment episodes (dates, regimen, viral loads, outcome) and liver biopsies\n-\tLaboratory data including imaging and fibroscan;\n-\tNew treatment episodes and changes in liver disease status are recorded over time, as are any newly developed co-morbidities.\nAll data generated by researchers who access our biorepository must be returned into the database. Over time this will include host genetics, full length viral sequences, immunophenotyping and biomarker studies.",
    "url": "https://healthdatagateway.org/en/dataset/431",
    "uid": "01492322-9b33-41cc-9f51-484024e45878",
    "datasource_id": 431,
    "source": "HDRUK"
  },
  {
    "id": 159,
    "name": "HDR UK Papers & Preprints",
    "description": "Publications that mention HDR-UK (or any variant thereof) in Acknowledgements or Author Affiliations",
    "url": null,
    "uid": "fd8d0743-344a-4758-bb97-f8ad84a37357",
    "datasource_id": 159,
    "source": "HDRUK"
  },
  {
    "id": 160,
    "name": "HES Accident and Emergency data for CPRD Aurum",
    "description": "CPRD Aurum linked Hospital Episode Statistics Accident and Emergency (HES A&E) data consists of individual records of patient care administered in the accident and emergency setting in England. These data are a subset of national A&E data collected by NHS England to monitor the national standard that 95% of patients attending A&E should wait no longer than 4 hours from arrival to admission, transfer or discharge. A&E data are submitted by A&E providers of all types in England. Data collected includes details about patients’ attendance, outcomes of attendance, waiting times, referral source, A&E diagnosis, A&E treatment (drugs prescribed not recorded), A&E investigations and Health Resource Group. \nHES A&E may be used to clarify the health care pathway, to quantity health resource use and costs in the emergency setting, and to assess variations in the uptake of emergency services over time.\n\nOnly CPRD Aurum has been linked in the March 2026 update. As there were no currently contributing practices in England in CPRD Gold, patients were not eligible for linkage. The latest linked data available for CPRD Gold is the set 21 data. Further information is available at https://www.cprd.com/data/linked-data.",
    "url": "https://healthdatagateway.org/en/dataset/654",
    "uid": "c43501ec-08b4-4d33-a7dd-d19fca821925",
    "datasource_id": 654,
    "source": "HDRUK"
  },
  {
    "id": 161,
    "name": "HES Accident and Emergency data for CPRD GOLD",
    "description": "CPRD GOLD linked Hospital Episode Statistics Accident and Emergency (HES A&E) data contain individual records of patient care administered in the accident and emergency setting in England. These data are a subset of national A&E data collected by NHS England to monitor the national standard that 95% of patients attending A&E should wait no longer than 4 hours from arrival to admission, transfer or discharge. A&E data are submitted by A&E providers of all types in England. Data collected includes details about patients’ attendance, outcomes of attendance, waiting times, referral source, A&E diagnosis, A&E treatment (drugs prescribed not recorded), A&E investigations and Health Resource Group. \nHES A&E may be used to clarify the health care pathway, to quantity health resource use and costs in the emergency setting, and to assess variations in the uptake of emergency services over time.",
    "url": "https://healthdatagateway.org/en/dataset/662",
    "uid": "733b1060-f958-4c3e-ab6a-2b87901d9a9a",
    "datasource_id": 662,
    "source": "HDRUK"
  },
  {
    "id": 162,
    "name": "HES Diagnostic Imaging Dataset for CPRD Aurum",
    "description": "CPRD Aurum linked Hospital Episode Statistics Diagnostic Imaging Dataset (HES DID) is a collection of detailed information about diagnostic imaging tests, such as x-rays and MRI scans, taken from NHS providers' radiological information systems. HES DID includes information on imaging tests carried out from 1 April 2012 on NHS patients in England. It does not include the images that are produced as a result of these tests. HES DID captures information about referral source and patient type, details of the test (type of test and body site), plus items about waiting times for each diagnostic imaging event, from time of test request through to time of reporting. The DID enables analysis of demographic and geographic variation in access to different test types and different providers, enabling users to analyse patient care pathways.",
    "url": "https://healthdatagateway.org/en/dataset/677",
    "uid": "617f9f54-3fb6-40c3-b391-fe51d1fc7c49",
    "datasource_id": 677,
    "source": "HDRUK"
  },
  {
    "id": 163,
    "name": "HES Diagnostic Imaging Dataset for CPRD GOLD",
    "description": "CPRD GOLD linked Hospital Episode Statistics Diagnostic Imaging Dataset (HES DID) is a collection of detailed information about diagnostic imaging tests, such as x-rays and MRI scans, taken from NHS providers' radiological information systems. HES DID includes information on imaging tests carried out from 1 April 2012 on NHS patients in England. It does not include the images that are produced as a result of these tests. The DID captures information about referral source and patient type, details of the test (type of test and body site), plus items about waiting times for each diagnostic imaging event, from time of test request through to time of reporting. \nHES DID enables analysis of demographic and geographic variation in access to different test types and different providers, enabling users to analyse patient care pathways.",
    "url": "https://healthdatagateway.org/en/dataset/691",
    "uid": "a1bef3a6-9700-43a5-8f3e-dad44ce1fe10",
    "datasource_id": 691,
    "source": "HDRUK"
  },
  {
    "id": 164,
    "name": "HES Outpatient data for CPRD Aurum",
    "description": "Outpatient (HES OP) data are a collection of individual records of outpatient appointments occurring in England only. The data includes information on the type of outpatient consultation appointment dates, the main specialty and treatment specialty under which the patient was treated, referral source, waiting times, clinical diagnosis and procedures performed. HES OP data can be used to support health resource utilisation studies, clarify clinical health care pathways and enable variations in the uptake of services to be evaluated, for example by gender and age.Requests for HES OP data access are subject to prior protocol approval. Further information is available at  https://www.cprd.com/linked-data.",
    "url": "https://healthdatagateway.org/en/dataset/668",
    "uid": "8d3f3e58-be79-4450-b2e3-f5ab8a25fb0e",
    "datasource_id": 668,
    "source": "HDRUK"
  },
  {
    "id": 165,
    "name": "HES Outpatient data for CPRD GOLD",
    "description": "CPRD GOLD linked Hospital Episode Statistics Outpatient (HES OP) data are a collection of individual records of outpatient appointments occurring in England. The data includes information on the type of outpatient consultation appointment dates, the main specialty and treatment specialty under which the patient was treated, referral source, waiting times, clinical diagnosis and procedures performed. \nHES OP data can be used to support health resource utilisation studies, clarify clinical health care pathways and enable variations in the uptake of services to be evaluated, for example by gender and age.",
    "url": "https://healthdatagateway.org/en/dataset/665",
    "uid": "21dc2345-3983-4a46-960c-f17974bc7b71",
    "datasource_id": 665,
    "source": "HDRUK"
  },
  {
    "id": 166,
    "name": "HES Patient Reported Outcomes Measures (PROMs) data for CPRD Aurum",
    "description": "CPRD Aurum linked Hospital Episode Statistics Patient Reported Outcomes Measures (PROMs) data covers common elective surgical procedures performed in NHS England including groin hernia operations, hip replacements, knee replacements and varicose vein operations. The programme covers over 300 NHS hospitals and Independent Sector Providers in England that undertake elective operations. The purpose of PROMs is to capture patients’ own assessments of their health and health-related quality of life, shortly before and some months after surgery. Patient questionnaires administered comprise a disease-specific instrument, a generic instrument and a series of additional questions about the patient’s health and symptoms. Note, mandatory varicose vein surgery and groin-hernia surgery national PROMs collections ended on 1 October 2017.",
    "url": "https://healthdatagateway.org/en/dataset/680",
    "uid": "7958450d-bd25-42c4-b79e-0e3d0806ac21",
    "datasource_id": 680,
    "source": "HDRUK"
  },
  {
    "id": 167,
    "name": "HES Patient Reported Outcomes Measures (PROMs) data for CPRD GOLD",
    "description": "CPRD GOLD linked Hospital Episode Statistics Patient Reported Outcomes Measures (PROMs) data covers common elective surgical procedures performed in NHS England including groin hernia operations, hip replacements, knee replacements and varicose vein operations. The programme covers over 300 NHS hospitals and Independent Sector Providers in England that undertake elective operations. \nThe purpose of PROMs is to capture patients’ own assessments of their health and health-related quality of life, shortly before and some months after surgery. Patient questionnaires administered comprise a disease-specific instrument, a generic instrument and a series of additional questions about the patient’s health and symptoms. Note, mandatory varicose vein surgery and groin-hernia surgery national PROMs collections ended on 1 October 2017.",
    "url": "https://healthdatagateway.org/en/dataset/660",
    "uid": "2439a88e-d961-4152-a7b5-042097946209",
    "datasource_id": 660,
    "source": "HDRUK"
  },
  {
    "id": 168,
    "name": "HES:Civil Registration (Deaths) bridge",
    "description": "Linked Data Set - Hospital Episode Statistics to Civil Registration of Deaths",
    "url": "https://healthdatagateway.org/en/dataset/878",
    "uid": "21aec912-444f-447d-b3c4-6791a6c82667",
    "datasource_id": 878,
    "source": "HDRUK"
  },
  {
    "id": 169,
    "name": "HIC Acute Coronary Syndromes",
    "description": "https://hic.nihr.ac.uk/?page_id=43",
    "url": "https://healthdatagateway.org/en/dataset/517",
    "uid": "869a9b4e-3537-449f-9a59-a0eac523c4e4",
    "datasource_id": 517,
    "source": "HDRUK"
  },
  {
    "id": 170,
    "name": "HIC Hepatitis v2",
    "description": "https://hic.nihr.ac.uk/?page_id=57",
    "url": "https://healthdatagateway.org/en/dataset/137",
    "uid": "66be725a-b950-4bf8-9a35-7e33ce765a25",
    "datasource_id": 137,
    "source": "HDRUK"
  },
  {
    "id": 171,
    "name": "HIC ICU 8.3.2",
    "description": "https://hic.nihr.ac.uk/?page_id=55",
    "url": "https://healthdatagateway.org/en/dataset/135",
    "uid": "4f546f1e-433c-4884-a090-2a4520c878ac",
    "datasource_id": 135,
    "source": "HDRUK"
  },
  {
    "id": 172,
    "name": "HIC Transplantation",
    "description": "https://hic.nihr.ac.uk/?page_id=61",
    "url": "https://healthdatagateway.org/en/dataset/87",
    "uid": "359a322e-3781-4796-b6b2-81962fa19b7a",
    "datasource_id": 87,
    "source": "HDRUK"
  },
  {
    "id": 173,
    "name": "HPB Biobank",
    "description": "Prospectively collected fresh tissues from surgery. All patients fully consented. Snap frozen paired cancers and normal tissues. Stored in liquid nitrogen. 1010 samples currently in bank.\n-\tHCC\n-\tCholangiocarcinoma\n-\tLiver meets\n-\tBenign tumours\n-\tGallbladder cancer\n-\tPancreas and ampullary cancers",
    "url": "https://healthdatagateway.org/en/dataset/433",
    "uid": "ebe6c9dd-fb95-4301-a929-c21b8077753a",
    "datasource_id": 433,
    "source": "HDRUK"
  },
  {
    "id": 174,
    "name": "Health and Lifestyle Questionnaire",
    "description": "Most NIHR BioResource participants complete a self-report form on recruitment.  Typically this contains e.g. height, weight, smoking history and alcohol consumption, but also includes questions relating to disease history and current medications",
    "url": null,
    "uid": "504c0612-ad68-45b4-ae9b-b94f1f81e959",
    "datasource_id": 174,
    "source": "HDRUK"
  },
  {
    "id": 175,
    "name": "Hospital Episode Statistics Accident and Emergency",
    "description": "Record-level patient data set of patients attending Accident and Emergency Departments (including minor injury units and walk-in centres) in England. A record represents one attendance.",
    "url": null,
    "uid": "3ddfccc3-ba32-4653-a999-966b8d6c412f",
    "datasource_id": 175,
    "source": "HDRUK"
  },
  {
    "id": 176,
    "name": "Hospital Episode Statistics Critical Care",
    "description": "Hospital Episode Statistics (HES) is a database containing details of all admissions, A and E attendances and outpatient appointments at NHS hospitals in England. Adult Critical Care (ACC) is a subset of APC data. An Intensive Care Unit (ICU) or High Dependency Unit (HDU) ward in a hospital, known as a critical care unit, provides support, monitoring and treatment for critically ill patients requiring constant support and monitoring to maintain function in at least one organ, and often in multiple organs. Medical equipment is used to take the place of patients’ organs during their recovery. Some critical care units are attached to condition-specific treatment units, such as heart, kidney, liver, breathing, circulation or nervous disorders. Others specialise in neonatal care (babies), paediatric care (children) or patients with severe injury or trauma. Initially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver. This same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the HES data set. HES data covers all NHS Clinical Commissioning Groups (CCGs) in England, including: private patients treated in NHS hospitals patients resident outside of England care delivered by treatment centres (including those in the independent sector) funded by the NHS Each HES record contains a wide range of information about an individual patient admitted to an NHS hospital, including: clinical information about diagnoses and operations patient information, such as age group, gender and ethnicity administrative information, such as dates and methods of admission and discharge geographical information such as where patients are treated and the area where they live We apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published HES data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained. https://digital.nhs.uk/data-and-information/publications/statistical/hospital-admitted-patient-care-activity",
    "url": "https://healthdatagateway.org/en/dataset/879",
    "uid": "d0eeba22-bc3f-4c40-be69-a99f916897a2",
    "datasource_id": 879,
    "source": "HDRUK"
  },
  {
    "id": 177,
    "name": "Hospital Episode Statistics Outpatients",
    "description": "Hospital Episode Statistics (HES) is a database containing details of all admissions, A and E attendances and outpatient appointments at NHS hospitals in England. Initially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver. This same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the HES data set. HES data covers all NHS Clinical Commissioning Groups (CCGs) in England, including: private patients treated in NHS hospitals patients resident outside of England care delivered by treatment centres (including those in the independent sector) funded by the NHS Each HES record contains a wide range of information about an individual patient admitted to an NHS hospital, including: clinical information about diagnoses and operations patient information, such as age group, gender and ethnicity administrative information, such as dates and methods of admission and discharge geographical information such as where patients are treated and the area where they live We apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published HES data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained. https://digital.nhs.uk/data-and-information/publications/statistical/hospital-outpatient-activity",
    "url": "https://healthdatagateway.org/en/dataset/856",
    "uid": "2566eebb-417f-4a24-9c5d-7fa1f4d9d5e8",
    "datasource_id": 856,
    "source": "HDRUK"
  },
  {
    "id": 178,
    "name": "Human Biomaterials Resource Centre",
    "description": "The HBRC is an HTA-licensed biorepository dedicated to the collection of appropriately consented high quality human biomaterials, their processing, storage and distribution to biomedical researchers. The HBRC resides within the purpose-built Advanced Therapies Facility (ATF) within the College of Medical and Dental Sciences (CMDS) at the University of Birmingham (UoB). The ATF also houses both cell and gene therapy manufacturing units.\nSamples are collected, processed and stored (or released) from patients in a variety of disease settings in response to local demand and research strategies in order to facilitate existing research and enable future research areas to be developed. Samples may comprise adult or paediatric tissue which is waste, or surplus to diagnosis taken at the time of surgery or treatment, additional samples taken specifically for research purposes, material taken from patients enrolled in clinical trials, and control material. The HBRC works with local researchers and carries out bespoke tissue collections. All samples can be annotated with the appropriate clinical data which makes them scientifically useful. \nAlthough the HBRC is primarily a resource for local researchers within CMDS and local NHS Trusts, applications from other UK research groups and the commercial sector are also considered. The policy and procedures associated with the review of these applications, and the subsequent release of samples and associated data, are the same for both internal and external applicants. All samples and associated data are released for research in a fully anonymised form, and a Tissue Transfer Agreement ensures that they will only be used for the purposes approved at release and will not be transferred without written permission. In order to enhance the value of the HBRC collection, every attempt is made to secure the return of useful research data following the completion of research studies.",
    "url": "https://healthdatagateway.org/en/dataset/434",
    "uid": "2f085ac6-d33d-4b9b-8a99-624cc51c3f17",
    "datasource_id": 434,
    "source": "HDRUK"
  },
  {
    "id": 179,
    "name": "Human Developmental Biology Resource (HDBR)",
    "description": "The Human Developmental Biology Resource (HDBR) is an ongoing collection of human embryonic and fatal material ranging from 3 to 20 weeks of development. Material is available to researchers internationally following registration with the tissue bank.  Jointly funded by the MRC and Wellcome Trust, the biobank has been operating since 1999 and is based at two centres: Institute of Genetic Medicine '“ Newcastle University and the Institute of Child Health '“ University College London.\nTissues can be dissected to meet specific research needs and samples supplied fixed or frozen for histology or nucleic acid extraction, or the tissue can be collected into culture media to be used to establish cell lines. Material pre-sectioned to microscope slides is available for immunohistochemistry (IHC) or tissue in situ hybridisation studies (TISH) and stage, tissue specific RNA/DNA/cDNA can be requested.\nOur In House Gene Expression Service will perform IHC or TISH experiments on behalf of registered users and provide them with high quality annotated images of results ready for publication.",
    "url": "https://healthdatagateway.org/en/dataset/432",
    "uid": "399a6d67-7577-4476-a3b6-4bbceb5a8ff7",
    "datasource_id": 432,
    "source": "HDRUK"
  },
  {
    "id": 180,
    "name": "IBD Registry",
    "description": "The IBD Registry captures longitudinal clinical data on patients with IBD at point of care from NHS Trusts across the UK. All patient records have a recorded diagnosis.  The dataset includes broad demographics; phenotype of the disease; medications, plus greater details on biological treatments; clinical assessment and patient-reported outcomes. The dataset is available for retrospective studies (service evaluation and quality of care), and for research with prospective recruitment of consented patients for data already held. The dataset is currently held by NHS Digital, with explicit permissions for linkage, and as such is linkable to NHS Digital datasets such as HES.",
    "url": "https://healthdatagateway.org/en/dataset/631",
    "uid": "a406d06b-350e-4392-ab4e-b1012e422a34",
    "datasource_id": 631,
    "source": "HDRUK"
  },
  {
    "id": 181,
    "name": "ICON9 Trial",
    "description": "An international phase III randomised study to evaluate the efficacy of maintenance cediranib and olaparib combination therapy or olaparib alone in patients with relapsed platinum-sensitive ovarian cancer following a response to platinum-based chemotherapy",
    "url": "https://healthdatagateway.org/en/dataset/436",
    "uid": "1fdf266c-d5d8-418d-a423-81265da540a2",
    "datasource_id": 436,
    "source": "HDRUK"
  },
  {
    "id": 182,
    "name": "IDRIS Trial",
    "description": "Phase III randomised study of immunomodulatory therapy in high risk solitary bone plasmacytoma.\nIDRIS is a randomised, open label, multicentre phase III study. The aim is to investigate whether the administration of adjuvant lenalidomide and dexamethasone following standard radiotherapy treatment for SBP prevents or prolongs the time to development of further plasmacytomas or progression to myeloma, or death (whichever comes first), in patients with high-risk disease compared with RT only.\nPatients are risk-stratified following registration into the trial. Risk stratification is based on BM immunophenotyping and SFLC ratio. All patients will receive standard local radiotherapy; this is not regarded as trial treatment and may be administered prior to study entry.\n- Patients with high-risk features will be randomised to receive adjuvant therapy in the form of lenalidomide and dexamethasone or no further treatment.\n- Patients without high-risk features will receive no further therapy and will be observed according to local practice.\n140 patients:\n- 98 patients with high risk features to be randomised at a 1:1 ratio\n- 42 low risk patients for registration only",
    "url": "https://healthdatagateway.org/en/dataset/437",
    "uid": "8437b207-c925-4dd1-9478-5af329cd736e",
    "datasource_id": 437,
    "source": "HDRUK"
  },
  {
    "id": 183,
    "name": "IMPORT HIGH trial blood samples",
    "description": "Blood samples from patients with early stage breast cancer who received breast conserving surgery and appropriate systemic therapy and radiotherapy.",
    "url": "https://healthdatagateway.org/en/dataset/438",
    "uid": "edafebac-9479-4d2c-b44e-349091d5b35b",
    "datasource_id": 438,
    "source": "HDRUK"
  },
  {
    "id": 184,
    "name": "INCA Trial",
    "description": "INCA is a multicentre, randomised, phase II trial comparing IO-R-CVP with Gem-R-CVP in the first line treatment of patients with DLBCL who are not fit for anthracycline-containing chemotherapy. \n132 patients will be randomised to receive either IO-R-CVP or Gem-R-CVP.\nSamples collected for trial: 7ml EDTA blood sample taken at baseline; Formalin fixed paraffin embedded tumour block - both sent to HMDS, Leeds.  Blood serum sample (4.9ml)  taken at Baseline (between day -14 pre registration and day 1 pre-treatment), Cycle 1 day 3 (+/- 1 day), Cycle 1 day 8 (+/-1 day) and Cycle 2 day 1 (-1 day) to be sent to Cancer Research UK Manchester Institute, Macclesfield.",
    "url": "https://healthdatagateway.org/en/dataset/435",
    "uid": "edd55990-a616-4a9c-a4a2-38af136a349b",
    "datasource_id": 435,
    "source": "HDRUK"
  },
  {
    "id": 185,
    "name": "Idiopathic Pulmonary Fibrosis (IPF) Genome-wide Association Study",
    "description": "https://doi.org/10.1164/rccm.201905-1017OC \n\nIdiopathic pulmonary fibrosis (IPF) is characterized by the build-up of scar tissue in the lungs. It is believed that the damage to the alveolar epithelium is followed by an aberrant wound healing response leading to the deposition of dense fibrotic tissue, reducing the lungs’ flexibility and inhibiting gas transfer. IPF still has limited therapeutic interventions and a high mortality rate within 3-5 years from diagnosis. To date, genome-wide association studies (GWAS) of IPF susceptibility have associated common variants (minor allele frequency [MAF]>5%) near genes involved in host defence, telomere maintenance, cell-cell adhesion and signalling in disease susceptibility. \nMeta-analysis of GWAS of IPF susceptibility. Building up on published GWAS results (PMID: 24429156, 23583980, 29066090) and novel study samples, we have performed the largest GWAS of IPF susceptibility to date to identify novel genes and further advance in the understanding of IPF pathogenesis and risk (bioRxiv https://doi.org/10.1101/636761 and PMID:31710517). The discovery stage of the study comprised up to 2,668 IPF cases and 8,591 controls from 3 studies (Chicago study: 541 IPF cases and 542 controls, Colorado study: 1515 fibrotic Idiopathic Interstitial pneumonia cases and 4683 controls, UK Study: 612 IPF cases and 3366 controls)  and replication was pursued in an additional 1,456 IPF cases and 11,874 controls. The genome-wide association study summary statistics from the meta-analysis of three studies totalling  2,668 cases and 8,591 controls are available here.",
    "url": "https://healthdatagateway.org/en/dataset/237",
    "uid": "b850e56c-5c5b-413e-b829-743772053978",
    "datasource_id": 237,
    "source": "HDRUK"
  },
  {
    "id": 186,
    "name": "Infectious Diseases BioBank at King's College London",
    "description": "Fractionated blood products from patients with HIV, hepatitis C viral infections and others with bacteraemias.",
    "url": "https://healthdatagateway.org/en/dataset/448",
    "uid": "47b544f4-301c-43e0-b347-fc623ea1a8b3",
    "datasource_id": 448,
    "source": "HDRUK"
  },
  {
    "id": 187,
    "name": "Infoflex Cancer Registry",
    "description": "A complete mirror of the trust's instance of Infoflex 5 which is used for the management of Cancer patients within the trust.",
    "url": null,
    "uid": "5505e91d-9e0a-4969-87d5-00f256d91306",
    "datasource_id": 187,
    "source": "HDRUK"
  },
  {
    "id": 188,
    "name": "International Severe Asthma Registry",
    "description": "ISAR is the first global severe asthma registry; a joint initiative where national registries (both newly created and pre-existing) retain ownership of their own data but open their borders and share data with ISAR for ethically approved research purposes. The ISAR initiative is conducted by Optimum Patient Care Global Limited (OPC), with scientific oversight from the ISAR Core Steering Committee (ISC), academic support from the Respiratory Effectiveness Group (REG), ethical governance from the Anonymized Data Ethics & Protocol Committee (ADEPT), co-funding from OPC and AstraZeneca since May 2017. Prospective patient recruitment by 2022 is 13,150 patients with severe asthma.",
    "url": "https://healthdatagateway.org/en/dataset/215",
    "uid": "9711e6cc-426c-4cef-afb3-64382f8d7ed4",
    "datasource_id": 215,
    "source": "HDRUK"
  },
  {
    "id": 189,
    "name": "IoN Trial",
    "description": "IoN is a multicentre phase II/III asking is ablative radioiodine necessary for low risk differentiated thyroid cancer patients. \n560 patients will be recruited and randomised 1:1 to receive radioiodine ablation or no ablation. \nSamples collected during trial: Stimulated Tg blood samples for patients taken at the treatment visit, 2 months post treatment, 6-9 months post treatment and every 6 months thereafter for 5 years.",
    "url": "https://healthdatagateway.org/en/dataset/449",
    "uid": "e10a660d-b84c-475f-abd4-024dd1af7a5d",
    "datasource_id": 449,
    "source": "HDRUK"
  },
  {
    "id": 190,
    "name": "Its Not JUST Idiopathic pulmonary fibrosis Study (INJUSTIS)",
    "description": "The It's Not JUST Idiopathic pulmonary fibrosis Study (INJUSTIS) is a multicentre, prospective, observational cohort study. The aims of this study are to identify genetic, serum and other biomarkers that may identify specific molecular mechanisms, reflecting disease endotypes that are shared among patients with progressive pulmonary fibrosis regardless of aetiology. Furthermore, it is anticipated that these biomarkers will help predict fibrotic activity that may identify patterns of disease behaviour with greater accuracy than current clinical phenotyping.\n\n200 participants with the multidisciplinary team confirmed fibrotic lung disease (50 each of rheumatoid-interstitial lung disease (ILD), asbestosis, chronic hypersensitivity pneumonitis and unclassifiable ILD) and 50 idiopathic pulmonary fibrosis participants, recruited as positive controls, will be followed up for 2 years. Participants will have blood samples, lung function tests, quality of life questionnaires and a subgroup will be offered bronchoscopy. Participants will also be given the option of undertaking blinded home handheld spirometry for the first 3 months of the study. The primary end point will be identification of a biomarker that predicts disease progression, defined as 10% relative change in forced vital capacity (FVC) or death at 12 months.",
    "url": "https://healthdatagateway.org/en/dataset/232",
    "uid": "cc6f926a-90cf-4083-84e7-131d1e63813b",
    "datasource_id": 232,
    "source": "HDRUK"
  },
  {
    "id": 191,
    "name": "JANUS",
    "description": "A complete mirror of the JANUS Colorectal Surgery database, containing information about the diagnosis, treatment and management of patients with colorectal disese, particularly cancer.",
    "url": null,
    "uid": "6590f79d-cfea-4695-9fe0-dc5f85e6d273",
    "datasource_id": 191,
    "source": "HDRUK"
  },
  {
    "id": 192,
    "name": "King's Health Partners Cancer Biobank",
    "description": "King's Health Partners Cancer Biobank collects blood, tissue and urine samples from patients with a range of cancer types who are referred to Guy's & St Thomas' NHS Trust. The Breast Biobank alone has been providing tumour samples with matching clinico-pathological data for research studies since the 1970's. Other tumour types including lung, head and neck, prostate, UGI, bladder, colorectal, lymphoma and MPD have been added since 2008. The Biobank is accessible to both academic groups and commercial companies.",
    "url": "https://healthdatagateway.org/en/dataset/447",
    "uid": "a218d0b4-0273-4a04-ba17-c01dae6dcf33",
    "datasource_id": 447,
    "source": "HDRUK"
  },
  {
    "id": 193,
    "name": "LBIH Biobank",
    "description": "The LBIH Biobank has been specifically created to provide SMEs and academic researchers with high quality biosamples, data, and analytical services. We house a vast array of biosamples, both cancerous and non-cancerous, and have the ability to collect bespoke samples and data to suit the needs of researchers. We have access to a wide variety of samples types including, but not limited to, frozen tissue, fresh tissue, blood products, urine and FFPE blocks. \n\nThe LBIH Biobank can further process these into a number of products such as DNA, frozen sections, and cryo-aliquots. In order to offer a 'one-stop shop' for research, we offer additional services such as IHC, next generation sequencing, TMA creation, and project management for studies and projects, to include storage of trial samples.\n\nWe provide access to collection of samples and data across the following diseases: \n•\tMalignant melanoma of eye (disorder)\n•\tMalignant neoplasm of endometrium of corpus uteri (disorder)\n•\tMalignant neoplasm of liver\n•\tMalignant tumour of adrenal gland (disorder)\n•\tMalignant tumour of cervix\n•\tMalignant tumour of kidney (disorder)\n•\tMalignant tumour of oral cavity (disorder)\n•\tMalignant tumour of prostate (disorder)\n•\tMalignant tumour of stomach (disorder)\n•\tMalignant tumour of thyroid gland (disorder)\n•\tMalignant tumour of urinary bladder (disorder)\n•\tMalignant tumour of vulva (disorder)\n•\tMalignant tumour of breast\n•\tMalignant tumour of colon\n•\tMalignant tumour of oesophagus\n•\tMalignant tumour of ovary\n•\tMalignant tumour of pancreas",
    "url": "https://healthdatagateway.org/en/dataset/450",
    "uid": "c0318a31-a32c-4d3a-9591-dfe9352bf2b9",
    "datasource_id": 450,
    "source": "HDRUK"
  },
  {
    "id": 194,
    "name": "LIMS",
    "description": "A complete capture of the local laboratory information system that captures the ordering/resulting for biochemistry related tests.",
    "url": null,
    "uid": "df70b490-3c00-458e-a6dd-ea3242a3ebac",
    "datasource_id": 194,
    "source": "HDRUK"
  },
  {
    "id": 195,
    "name": "Leeds Breast Research Tissue Bank",
    "description": "The Leeds Breast Research Tissue Bank (LBRTB) was established in 2010 and collects malignant and normal breast tissue and associated samples from donors consented through the Leeds Teaching Hospitals Trust. The LBRTB is based in the Leeds Institute of Cancer and Pathology on the St. James's University Hospital site and is led by Professor Valerie Speirs. The LBRTB is a founder member of the Breast Cancer Now Tissue Bank (BCNTB), a ground-breaking multi-million pound initiative linking five centres around the country working together as one national resource. The BCNTB represents the UK's single largest unique collection of high quality breast tissue samples.",
    "url": "https://healthdatagateway.org/en/dataset/452",
    "uid": "2dc1218d-df5f-4025-826c-cf5ca7d67882",
    "datasource_id": 452,
    "source": "HDRUK"
  },
  {
    "id": 196,
    "name": "Leeds Dental Institute/School of Dentistry Leeds, Skeletal Tissues Bank",
    "description": "Tissue bank which stores teeth.",
    "url": "https://healthdatagateway.org/en/dataset/451",
    "uid": "f94168a1-cab8-4211-90e0-38a241609697",
    "datasource_id": 451,
    "source": "HDRUK"
  },
  {
    "id": 197,
    "name": "Leeds Multidisciplinary Research Tissue Bank",
    "description": "Background:\nThe Leeds Biobanking and Sample Processing Lab (LBSPL) is based at St James's University Hospital and is an important element in the Leeds Clinical Research Facility (LCRF). LBSPL is dedicated to providing a sample processing service for clinical trials, and for many biobanking activities within the University and Leeds Teaching Hospitals Trust.  These currently include collections of tissue, and fluid samples such as blood and urine obtained from consented patients diagnosed with kidney, bladder, colorectal and ovarian cancer, brain tumours, and patients undergoing renal transplant. These are processed and stored in research tissue banks (Leeds Multidisciplinary RTB and Leeds NIHR Biomarker RTB) and used as a valuable resource for wider research activities.\nThe Leeds Multidisciplinary Research Tissue Bank (RTB) was given favourable ethical opinion by the Leeds (East) Research Ethics Committee on 3rd March 2010 and renewed 5 years later (Current REC ref: 15/YH/0080).  \n\nCurrent situation:\nThe LBSPL provides ongoing sample processing support to a variety of local or commercial clinical trials requiring samples to be collected for PK/PD endpoints or other associated translational studies. The Leeds Multidisciplinary RTB currently holds matched normal and malignant frozen renal tissue samples from ~600 patients with renal cancer, and plasma, serum, buffy coat and urine samples from ~800 renal cancer patients either prior to surgery/treatment or during treatment, for example with sunitinib. In addition, fluid samples have been collected from patients with benign urological conditions, healthy controls, patients prior to and following renal transplant A population-based TMA including tissue from ~300 RCC patients has also been established. From having solely a renal focus, the RTB has expanded to now also include frozen normal tissue samples, and frozen tissue and urine samples from >1,000 patients with bladder cancer, frozen tissue from patients with brain tumours and limited samples from patients with ovarian cancer or colorectal cancer (no new collections ongoing in these areas). The Leeds NIHR BioRTB has closed to recruitment and contains a bank of fluid samples from patients with liver disease, renal cancer and patient undergoing renal transplantation. These were collected as part of a multicentre initiative in the UK, together with associated clinical data, and are intended for use in biomarker validation studies.  \n\nSample processing and clinical data:\nSamples are processed according to the relevant quality controlled Standard Operating Procedures (SOPs) within Good Clinical Practice (GCLP) laboratories. The dedicated sample processing team of laboratory technicians, administrative staff and a Quality Assurance manager works together to ensure that all samples are handled, processed, stored and tracked to the highest standards. The samples are stored accordingly at the correct temperature (e.g. snap frozen tissue in liquid nitrogen, plasma, serum and buffy coats at -80oC). All freezers and liquid nitrogen dewars which hold patient samples are monitored using the Tutela system, a 24/7 web-based alert response temperature monitoring system.\nFull clinical data is being collected using CRFs. Follow-up data and data available later such as pathology results are obtained from the relevant hospital records/databases by trained personnel with appropriate access. We hope to move to more automated data linkage in the future through working with the Leeds Institute of Data Analytics.  \nTo find further information about collaborative access to these RTB samples please e-mail Roz Banks or Jo Brown at rtb@leeds.ac.uk.",
    "url": "https://healthdatagateway.org/en/dataset/453",
    "uid": "7e60a651-b3d6-40ef-93dc-1f87dabee7be",
    "datasource_id": 453,
    "source": "HDRUK"
  },
  {
    "id": 198,
    "name": "Leeds NIHR Biomarker Research Tissue Bank",
    "description": "Background:\nThe Leeds NIHR Biomarker Research Tissue Bank (RTB) is part of a National Institute for Health Research (NIHR) Programme Grants for Applied Research award, focused on biomarker evaluation in selected disease areas.  The RTB was established for the multicentre collection and storage of samples from patients with liver diseases recruited within a randomised controlled trial (ELUCIDATE) of a biomarker panel, renal cancer patients and patients undergoing kidney transplant.\nThe Leeds NIHR Biomarker RTB is jointly managed along with the Multidisciplinary RTB by the Joint RTB Management Committee. The Leeds NIHR Research Tissue Bank (RTB) was given favourable ethical opinion by the Leeds (East) Research Ethics Committee on 15th June 2010 (Current REC ref: 15/YH/0099).  \n\nSample collections:\nOver the duration of the programme, 2,116 participants were recruited in total with 5,976 samples. Sample collection has taken place in multiple centres in the UK. These comprise:\n-\t847 patients with liver disease each with a single serum samples \n-\t514 patients on the kidney transplant waiting list including 312 subsequently transplanted, with 3,806 samples, each sample including multiple aliquots of serum, plasma and urine\n-\t706 patients with suspected renal cancer (200 longitudinal and 506 cross-sectional)  with 1,132 samples, including multiple aliquots of serum, plasma, buffy coat and urine and an FFPE tissue block (frozen available in Leeds patients only)\n-\t149 healthy volunteers with 191 samples, each sample including multiple aliquots of serum, plasma and urine.\nAll samples were collected according to SOPs and have been shipped from the participating sites and stored centrally in Leeds Biobanking and Sample Processing Facility.  Associated clinical data has been collected using standard study-specific case report forms (CRFs), including long-term follow-up in many cases. \nInitially access to samples is prioritised for the needs of the Programme and investigators involved but additional collaborative access will then be possible. To find out further information please e-mail our Research Tissue Bank and Sample Processing Facility Manager, Pirkko-Liisa Muhonen at rtb@leeds.ac.uk.",
    "url": "https://healthdatagateway.org/en/dataset/454",
    "uid": "8648d251-6186-4383-82fc-c036a87c3842",
    "datasource_id": 454,
    "source": "HDRUK"
  },
  {
    "id": 199,
    "name": "Leicester Respiratory Research Database",
    "description": "Leicester Biomedical Research Centre - Respiratory (LBRC) holds this ethically approved database designed for recruitment and research purposes. Adult members of the public and patients are invited to participate in the database by reading and signing an information sheet and consent form. The research section of the database stores anonymised data and information to be used for respiratory research purposes (no identifiable information). Researchers, either internal or external to the LBRC, are invited to formally apply to use the database for research purposes.",
    "url": "https://healthdatagateway.org/en/dataset/266",
    "uid": "01c3d08c-6948-4059-a027-3437e4f5d53b",
    "datasource_id": 266,
    "source": "HDRUK"
  },
  {
    "id": 200,
    "name": "Looked After Children Wales (LACW)",
    "description": "The primary dataset containing information relating to Looked After Children in Wales (LACW). It is an annual census that includes individual demographic and episode level information of care that a child has received that year.\n\nSubsets of this main dataset include: Looked After Children Adoption (LACA); Looked After Children Care Leavers aged 16 and over (LACC); Looked After Children Birthday 19 (LACB: 1999 - 2016); and Looked after Children - Education Qualifications (LACE). LACE was discontinued in 2016 (1999 - 2016) and included within LACW. \n\nDue to the small number of looked after children with an Anonymised Linkage Field (ALF, 37%), a two-stage algorithm was developed. This algorithm utilises other datasets within SAIL to allocate children within the LACW ALF, increasing the overall ALF match rate to 61%. The improved ALFs are available in the LAC ALF DERIVED table.",
    "url": "https://healthdatagateway.org/en/dataset/348",
    "uid": "cfdafacb-48f0-4ad8-9f20-193a5eec2da4",
    "datasource_id": 348,
    "source": "HDRUK"
  },
  {
    "id": 201,
    "name": "MDSBio",
    "description": "Molecular and functional characterisation of bone marrow function in normal subjects, myelodysplastic syndromes (MDS), acute myeloid leukaemia (AML) and secondary disorders of haematopoiesis.",
    "url": "https://healthdatagateway.org/en/dataset/456",
    "uid": "13c71637-144e-4742-8aea-aefb778a360e",
    "datasource_id": 456,
    "source": "HDRUK"
  },
  {
    "id": 202,
    "name": "MFT Biobank",
    "description": "The UK Brain Bank Network is an initiative, led by the MRC, to establish a coordinated national network of UK brain tissue resources (banks) for researchers to use.\nThe banks store post-mortem brain and central nervous system (CNS) tissue donated by the public for diagnosis and research into disorders.  Advances in understanding genetics and many of the molecules that define brain function mean that more and more research questions can be answered from human brain tissue.\nWe provide access to collection of samples and data across the following diseases: \n•\tAlzheimer's disease (disorder),Asthma (disorder)\n•\tAtrial fibrillation and flutter (disorder)\n•\tCerebrovascular disease (disorder)\n•\tComplete trisomy 21 syndrome (disorder)\n•\tCongenital anomaly of brain (disorder)\n•\tDegenerative brain disorder (disorder)\n•\tDementia (disorder),Depressive disorder (disorder)\n•\tDiabetes mellitus type 1\n•\tDiabetes mellitus type 2,Epilepsy (disorder)\n•\tFit and well\n•\tHuntington's chorea (disorder)\n•\tIschemic heart disease (disorder)\n•\tJakob-Creutzfeldt disease (disorder)\n•\tMalignant neoplasm of brain\n•\tMalignant tumour of prostate (disorder)\n•\tMalignant tumour of urinary bladder (disorder)\n•\tMalignant tumour of breast\n•\tMalignant tumour of colon\n•\tMalignant tumour of lung\n•\tMalignant tumour of oesophagus\n•\tMalignant tumour of ovary\n•\tMotor neuron disease (disorder)\n•\tMultiple sclerosis (disorder)\n•\tMultiple system atrophy (disorder)\n•\tParkinson's disease (disorder)\n•\tProgressive supranuclear ophthalmoplegia (disorder)\n•\tPsychotic disorder (disorder)\n•\tSpinal muscular atrophy (disorder)\n•\tVascular dementia (disorder)",
    "url": "https://healthdatagateway.org/en/dataset/455",
    "uid": "d17efe5d-325f-4fcf-b25e-87d1f6269bea",
    "datasource_id": 455,
    "source": "HDRUK"
  },
  {
    "id": 203,
    "name": "UK Brain Banks Network",
    "description": "The UK Brain Bank Network is an initiative, led by the \"Brains for Dementia Research\" cohort study, to establish a coordinated national network of UK brain tissue resources (banks) for researchers to use.\n\nThe banks store post-mortem brain and central nervous system (CNS) tissue donated by the public for diagnosis and research into disorders.  Advances in understanding genetics and many of the molecules that define brain function mean that more and more research questions can be answered from human brain tissue.\n\nCollection of samples and data across the following diseases: Alzheimer's disease (disorder), Asthma (disorder), Atrial fibrillation and flutter (disorder), Cerebrovascular disease (disorder),Complete trisomy 21 syndrome (disorder), Congenital anomaly of brain (disorder), Degenerative brain disorder (disorder), Dementia (disorder), Depressive disorder (disorder), Diabetes mellitus type 1, Diabetes mellitus type 2, Epilepsy (disorder), Fit and well, Huntington's chorea (disorder), Ischemic heart disease (disorder), Jakob-Creutzfeldt disease (disorder), Malignant neoplasm of brain, Malignant tumour of prostate (disorder), Malignant tumour of urinary bladder (disorder), Malignant tumour of breast, Malignant tumour of colon, Malignant tumour of lung, Malignant tumour of oesophagus, Malignant tumour of ovary, Motor neuron disease (disorder), Multiple sclerosis (disorder), Multiple system atrophy (disorder), Parkinson's disease (disorder), Progressive supranuclear ophthalmoplegia (disorder), Psychotic disorder (disorder), Spinal muscular atrophy (disorder), Vascular dementia (disorder).",
    "url": "https://healthdatagateway.org/en/dataset/416",
    "uid": "6f1b9a4e-2367-47f1-a985-3ed9bf3a7211",
    "datasource_id": 416,
    "source": "HDRUK"
  },
  {
    "id": 204,
    "name": "MSDS (Maternity Services Data Set)",
    "description": "The Maternity Services Data Set (MSDS) is a patient-level data set that captures key information generated as part of care within maternity service providers at each stage of the maternity care pathway including the antenatal pathway, the birth episode pathway, and the postnatal pathway. The antenatal pathway starts when the pregnant woman has her first antenatal appointment with her maternity provider, at around 10 weeks’ gestation. The MSDS continues to capture information until the date on which the mother ceased to be cared for in maternity services. Information on care prior to the first antenatal booking appointment or following discharge from maternity services is not contained in the MSDS.",
    "url": "https://healthdatagateway.org/en/dataset/847",
    "uid": "0681a1e9-3778-4f43-88b3-6540515404b6",
    "datasource_id": 847,
    "source": "HDRUK"
  },
  {
    "id": 205,
    "name": "Manchester Cancer Research Centre (MCRC) Biobank",
    "description": "Facilitating high quality cancer research by bringing a flexible and committed approach to ethical sample and data collection",
    "url": "https://healthdatagateway.org/en/dataset/457",
    "uid": "095d805c-4b62-4726-b968-bb2f1c463701",
    "datasource_id": 457,
    "source": "HDRUK"
  },
  {
    "id": 206,
    "name": "Manchester Eye Tissue Repository",
    "description": "Repository of post mortem donor eye tissue collected after removal of the corneas for transplantation. It has been established particularly for research into macular disease (age-related macular degeneration), but other ocular tissues have been stored frozen including peripheral retina, sclera, lens, vitreous and optic nerve.  Macular tissue has been stored frozen for immunohistochemistry (by embedding in OCT) and for biochemical studies.  The donor tissue had been genotyped in relation to AMD risk. The Repository is funded by The Macular Society.",
    "url": "https://healthdatagateway.org/en/dataset/459",
    "uid": "47fc8f03-eba1-4efa-ae3e-148a76b56517",
    "datasource_id": 459,
    "source": "HDRUK"
  },
  {
    "id": 207,
    "name": "Maternal, Newborn and Infant Clinical Outcome Review Programme - perinatal surveillance dataset",
    "description": "Clinical continuous case record level dataset of all late fetal losses, stillbirths and neonatal deaths in the UK and Crown Dependencies. Contains information about the mother's health, lifestyle, pregnancy history, antenatal care, labour and delivery, as well as the baby's outcomes, management and cause of death.",
    "url": "https://healthdatagateway.org/en/dataset/548",
    "uid": "11deee42-5876-4ebe-acca-e996702e1343",
    "datasource_id": 548,
    "source": "HDRUK"
  },
  {
    "id": 208,
    "name": "Maternity Indicators Dataset (MIDS)",
    "description": "The Maternity Indicators Data Set captures data relating to the woman at initial assessment and to mother and baby (or babies) for all births. This relates to initial assessment and birth activity undertaken in Wales only. Each Health Board must make available data in relation to the initial assessments and/or birth events which they managed.\n\nFor example, if they only carried out the initial assessment the Health Board would only be required to provide the initial assessment data.  This is further detailed in the technical specification (see ‘return submission details’). \n\nWhere the initial assessment and birth events take place in different Health Boards, data will be linked nationally by the NHS Wales Informatics Service.\n\nVelindre NHS Trust are excluded from this requirement, as they do not provide any maternity services.\n\nMonthly activity data must include only initial assessment and birth activity that took place in the previous month.\n\nThis dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.",
    "url": "https://healthdatagateway.org/en/dataset/353",
    "uid": "a0c27454-8cbe-418f-bdae-a85d5e92e9d4",
    "datasource_id": 353,
    "source": "HDRUK"
  },
  {
    "id": 209,
    "name": "Maternity Inpatient and Day Case - Scottish Morbidity Record (SMR02)",
    "description": "Scottish Morbidity Record 02 (SMR02) is submitted by maternity hospitals to ISD, who have collected this information since 1975.\n\nA wide range of information is collected on the SMR02 - for example:\n\nMother - age, height, smoking history, previous obstetric history.\nBirth - induction, analgesia, method of delivery, outcome.\nBaby - apgar score, sex, gestation, weight.",
    "url": "https://healthdatagateway.org/en/dataset/81",
    "uid": "d47c809f-3da8-4f8a-b1ee-258d21aad530",
    "datasource_id": 81,
    "source": "HDRUK"
  },
  {
    "id": 210,
    "name": "Maternity Services Dataset",
    "description": "Nationally defined dataset containing both administrative & clinical details for maternity services activity. Items are coded using the national definitions as well as some in SNOMED.",
    "url": null,
    "uid": "173d07ab-5930-4d19-b3de-dcca193e62fd",
    "datasource_id": 210,
    "source": "HDRUK"
  },
  {
    "id": 211,
    "name": "Mental Health & Learning Disabilities Dataset v 1 (Non-Sensitive) Episodes",
    "description": "The Mental Health and Learning Disabilities Data Set version 1 (Episode Level - sensitive data exclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health and Learning Disabilities Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": "https://healthdatagateway.org/en/dataset/849",
    "uid": "334afc50-a05a-43b9-aa1e-a012a22733e0",
    "datasource_id": 849,
    "source": "HDRUK"
  },
  {
    "id": 212,
    "name": "Mental Health & Learning Disabilities Dataset v 1 (Non-Sensitive) Events",
    "description": "The Mental Health and Learning Disabilities Data Set version 1 (Event Level - sensitive data exclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health and Learning Disabilities Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": "https://healthdatagateway.org/en/dataset/865",
    "uid": "9c8bd383-b117-4167-a25c-9e15fa5ec1c6",
    "datasource_id": 865,
    "source": "HDRUK"
  },
  {
    "id": 213,
    "name": "Mental Health & Learning Disabilities Dataset v 1 (Non-Sensitive) Records",
    "description": "The Mental Health and Learning Disabilities Data Set version 1 (Record Level - sensitive data exclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health and Learning Disabilities Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": "https://healthdatagateway.org/en/dataset/859",
    "uid": "1a8fc23c-b67b-4171-b5e1-1b650fce1ef4",
    "datasource_id": 859,
    "source": "HDRUK"
  },
  {
    "id": 214,
    "name": "Mental Health & Learning Disabilities Dataset v 1 (Sensitive) Episodes",
    "description": "The Mental Health and Learning Disabilities Data Set version 1 (Episode Level - sensitive data inclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health and Learning Disabilities Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": "https://healthdatagateway.org/en/dataset/870",
    "uid": "a27400b0-0efb-4af8-a7bc-e2c7e59796e3",
    "datasource_id": 870,
    "source": "HDRUK"
  },
  {
    "id": 215,
    "name": "Mental Health & Learning Disabilities Dataset v 1 (Sensitive) Events",
    "description": "The Mental Health and Learning Disabilities Data Set version 1 (Event Level - sensitive data inclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health and Learning Disabilities Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": "https://healthdatagateway.org/en/dataset/848",
    "uid": "3d348613-2d66-4814-a294-7e0c365f43f8",
    "datasource_id": 848,
    "source": "HDRUK"
  },
  {
    "id": 216,
    "name": "Mental Health & Learning Disabilities Dataset v 1 (Sensitive) Records",
    "description": "The Mental Health and Learning Disabilities Data Set version 1 (Record Level - sensitive data inclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health and Learning Disabilities Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": "https://healthdatagateway.org/en/dataset/853",
    "uid": "7c2b435c-5442-4a05-9b8d-08024ec57c30",
    "datasource_id": 853,
    "source": "HDRUK"
  },
  {
    "id": 217,
    "name": "Mental Health Dataset (MHDS) for CPRD Aurum",
    "description": "CPRD Aurum linked Mental Health Dataset (MHDS) is a collection of patient records of individuals who accessed secondary care adult mental health services and who are thought to be suffering from a mental illness. The data include information about the type and location of care received, different episodes of care received within a spell of illness and the events that occurred such as recording of Health of the Nation Outcome Scales (HoNOS) scores, Patient Health Questionnaire (PHQ-9) scores or diagnoses. MHDS data can be used to support research into resource utilisation and provide information about patient access to secondary mental health care services. This can be useful to understand patient pathways and consider associations between primary care and access to and outcomes recorded in secondary mental health care services.",
    "url": "https://healthdatagateway.org/en/dataset/672",
    "uid": "48627b2d-f247-4921-9ee1-6c37adfff10d",
    "datasource_id": 672,
    "source": "HDRUK"
  },
  {
    "id": 218,
    "name": "Mental Health Dataset (MHDS) for CPRD GOLD",
    "description": "CPRD GOLD linked Mental Health Dataset (MHDS) is a collection of patient records of individuals who accessed secondary care adult mental health services and who are thought to be suffering from a mental illness. The data include information about the type and location of care received, different episodes of care received within a spell of illness and the events that occurred such as recording of Health of the Nation Outcome Scales (HoNOS) scores, Patient Health Questionnaire (PHQ-9) scores or diagnoses. \nMHDS data can be used to support research into resource utilisation and provide information about patient access to secondary mental health care services. This can be useful to understand patient pathways and consider associations between primary care and access to and outcomes recorded in secondary mental health care services.",
    "url": "https://healthdatagateway.org/en/dataset/679",
    "uid": "d5c0cc93-3347-46f0-b8dd-f44e82718e38",
    "datasource_id": 679,
    "source": "HDRUK"
  },
  {
    "id": 219,
    "name": "Mental Health Inpatient and Day Case - Scottish Morbidity Record (SMR04)",
    "description": "The dataset contains a wide variety of information such as patient characteristics, mental health diagnosis, length of stay, destination on discharge, whether they are admitted under Mental Health Legislation and any previous psychiatric care. Patient identifiers such as name, date of birth, Community Health Index number, NHS number, and postcode are included together with a wide variety of geographical measures. This includes the Scottish Index of Multiple Deprivation and Carstairs measures, census output area, NHS Board, Electoral Ward and Parliamentary constituency.",
    "url": "https://healthdatagateway.org/en/dataset/85",
    "uid": "24de9d1a-1f48-4c10-b8fc-a3bd7449e191",
    "datasource_id": 85,
    "source": "HDRUK"
  },
  {
    "id": 220,
    "name": "Mental Health Minimum Data Set",
    "description": "The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "285ae0ae-196b-4a75-b1f1-78b07162acfa",
    "datasource_id": 220,
    "source": "HDRUK"
  },
  {
    "id": 221,
    "name": "Mental Health Minimum Dataset v2.6 (Non-Sensitive)",
    "description": "The Mental Health Minimum Data Set version 2.6 (sensitive data exclusion). The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "702201ff-e729-4e29-a428-32953c9a0332",
    "datasource_id": 221,
    "source": "HDRUK"
  },
  {
    "id": 222,
    "name": "Mental Health Minimum Dataset v2.6 (Sensitive)",
    "description": "The Mental Health Minimum Data Set version 2.6 (sensitive data inclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "dc37fcd6-1e68-46e5-b73d-4896e069746a",
    "datasource_id": 222,
    "source": "HDRUK"
  },
  {
    "id": 223,
    "name": "Mental Health Minimum Dataset v3_3.5 (Non-Sensitive)",
    "description": "The Mental Health Minimum Data Set version 3 and 3.5 (sensitive data exclusion).The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "348b12fb-7718-477e-a572-4fc3c716152d",
    "datasource_id": 223,
    "source": "HDRUK"
  },
  {
    "id": 224,
    "name": "Mental Health Minimum Dataset v3_3.5 (Sensitive)",
    "description": "The Mental Health Minimum Data Set version 3 and 3.5 (sensitive data inclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "c123f74c-0731-428e-b31b-eee2723df54d",
    "datasource_id": 224,
    "source": "HDRUK"
  },
  {
    "id": 225,
    "name": "Mental Health Minimum Dataset v4 (Non-Sensitive) Episodes",
    "description": "The Mental Health Minimum Data Set version 4 (Episode Level - sensitive data exclusion). The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "2964d1b0-2630-468a-82c1-00f5ac901b3d",
    "datasource_id": 225,
    "source": "HDRUK"
  },
  {
    "id": 226,
    "name": "Mental Health Minimum Dataset v4 (Non-Sensitive) Events",
    "description": "The Mental Health Minimum Data Set version 4 (Event Level - sensitive data exclusion). The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "3adeb2f4-a924-49cb-9537-84a9a4242a62",
    "datasource_id": 226,
    "source": "HDRUK"
  },
  {
    "id": 227,
    "name": "Mental Health Minimum Dataset v4 (Non-Sensitive) Records",
    "description": "The Mental Health Minimum Data Set version 4 (Record Level - sensitive data exclusion). The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "b9f24f81-bcf2-4e47-8909-0d5414e2d7a4",
    "datasource_id": 227,
    "source": "HDRUK"
  },
  {
    "id": 228,
    "name": "Mental Health Minimum Dataset v4 (Sensitive) Episodes",
    "description": "The Mental Health Minimum Data Set version 4 (Episode Level - sensitive data inclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "29c61f09-c4a1-425f-9bbc-888c23d99516",
    "datasource_id": 228,
    "source": "HDRUK"
  },
  {
    "id": 229,
    "name": "Mental Health Minimum Dataset v4 (Sensitive) Events",
    "description": "The Mental Health Minimum Data Set version 4 (Event Level - sensitive data inclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "9c31a2bd-ff3a-4379-a4b8-0b90527a4c38",
    "datasource_id": 229,
    "source": "HDRUK"
  },
  {
    "id": 230,
    "name": "Mental Health Minimum Dataset v4 (Sensitive) Records",
    "description": "The Mental Health Minimum Data Set version 4 (Record Level - sensitive data inclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "eab996ed-b268-439a-bff4-19925698336a",
    "datasource_id": 230,
    "source": "HDRUK"
  },
  {
    "id": 231,
    "name": "Mental Health Minimum Dataset v4.1 (Non-Sensitive) Episodes",
    "description": "The Mental Health Minimum Data Set version 4.1 (Episode Level - sensitive data exclusion). The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "ef22c16c-c294-46e1-abf9-5204212e9053",
    "datasource_id": 231,
    "source": "HDRUK"
  },
  {
    "id": 232,
    "name": "Mental Health Minimum Dataset v4.1 (Non-Sensitive) Events",
    "description": "The Mental Health Minimum Data Set version 4.1 (Event Level - sensitive data exclusion). The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "7b31b427-b447-432e-9a6c-ad2099343491",
    "datasource_id": 232,
    "source": "HDRUK"
  },
  {
    "id": 233,
    "name": "Mental Health Minimum Dataset v4.1 (Non-Sensitive) Records",
    "description": "The Mental Health Minimum Data Set version 4.1 (Record Level - sensitive data exclusion). The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "01c88a30-b14d-401e-82bb-165cbde81942",
    "datasource_id": 233,
    "source": "HDRUK"
  },
  {
    "id": 234,
    "name": "Mental Health Minimum Dataset v4.1 (Sensitive) Episodes",
    "description": "The Mental Health Minimum Data Set version 4.1 (Episode Level - sensitive data inclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "efecbe58-4822-4f17-bf5c-92113d143db9",
    "datasource_id": 234,
    "source": "HDRUK"
  },
  {
    "id": 235,
    "name": "Mental Health Minimum Dataset v4.1 (Sensitive) Events",
    "description": "The Mental Health Minimum Data Set version 4.1 (Event Level - sensitive data inclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "a1154967-a80e-41da-a9ae-6183584ecf6b",
    "datasource_id": 235,
    "source": "HDRUK"
  },
  {
    "id": 236,
    "name": "Mental Health Minimum Dataset v4.1 (Sensitive) Records",
    "description": "The Mental Health Minimum Data Set version 4.1 (Record Level - sensitive data inclusion).  The Mental Health Minimum Data Set was superseded by the Mental Health and Learning Disabilities Data Set, which in turn was superseded by the Mental Health Services Data Set.  The Mental Health Minimum Data Set collected data from the health records of individual children, young people and adults who were in contact with mental health services.",
    "url": null,
    "uid": "c00d5ad2-d5d2-4318-8849-4c2fe52aabd0",
    "datasource_id": 236,
    "source": "HDRUK"
  },
  {
    "id": 237,
    "name": "Mental Health Services Data Set",
    "description": "The Mental Health Services Data Set is a patient level, output based, secondary uses data set which aims to deliver robust, comprehensive, nationally consistent and comparable person-based information for children, young people and adults who are in contact with mental health services located in England or located outside England but treating patients commissioned by an English CCG or NHS England specialised commissioner. As a secondary uses data set it re-uses clinical and operational data for purposes other than direct patient care, for example: commissioning, service improvement and service design. It defines the data items, definitions and associated value sets extracted or derived from local Electronic Patient Record systems.\n\nIn Scope: All activity relating to people who receive specialist secondary mental health care services and have, or are thought to have, a mental illness; or who receive specialist secondary learning disabilities or autism spectrum disorder services and have, or are thought to have, a learning disability or autism spectrum disorder is within scope of the MHSDS. The scope of the data set requires record level data submission for each person attending a service located in England: • if the person is wholly funded by the NHS data submission for that person is mandatory • if the person is partially funded by the NHS data submission for that person is mandatory • if the person is wholly funded by any means that is not NHS data submission is optional\n\nFor each person attending a service located outside England, but commissioned by an English CCG or NHS England specialised commissioner: • data submission is optional.\n\nIncluded Organisation Types: Service providers and organisations that provide specialist secondary mental health and/or learning disabilities and/or autism spectrum disorder services including: • NHS Mental Health Trusts • NHS Learning Disabilities Trusts • NHS Acute Trusts • NHS Care Trusts • Independent sector healthcare providers offering a service model that includes NHS funded patients • Any qualified provider offering specialist secondary mental health, learning disabilities or autism spectrum disorder services • Community services offering secondary care to children\n\nOut of Scope: The following areas are currently out of scope and should not be included: • Any patient receiving treatment through an in-scope service but is not thought to have a mental illness, learning disability or autism spectrum disorder e.g. o Smoking cessation services o Addictions and substance misuse services o Some alternative therapy services o Some counselling services. • Mental health, learning disabilities, and autism spectrum disorder services provided only at a primary care level (such as within general practices or adult IAPT).\n\nAdditional information inc. technical specifications and user guidance: https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/mental-health-services-data-set",
    "url": "https://healthdatagateway.org/en/dataset/863",
    "uid": "805f77c9-f269-4f1f-9e22-ab63847e6096",
    "datasource_id": 863,
    "source": "HDRUK"
  },
  {
    "id": 238,
    "name": "Mental Health Services Data Set - Community",
    "description": "https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/mental-health-services-data-set",
    "url": "https://healthdatagateway.org/en/dataset/846",
    "uid": "f3bb6a68-2c32-40a7-9a79-61a718068893",
    "datasource_id": 846,
    "source": "HDRUK"
  },
  {
    "id": 239,
    "name": "Mental Health Services Data Set - Currencies",
    "description": "https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/mental-health-services-data-set",
    "url": "https://healthdatagateway.org/en/dataset/855",
    "uid": "add485fa-4c54-4a3e-8676-abe2ff4725a5",
    "datasource_id": 855,
    "source": "HDRUK"
  },
  {
    "id": 240,
    "name": "Mental Health Services Data Set - Inpatients",
    "description": "https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/mental-health-services-data-set",
    "url": "https://healthdatagateway.org/en/dataset/852",
    "uid": "275ac6a4-798b-4f49-a25e-008245050bbc",
    "datasource_id": 852,
    "source": "HDRUK"
  },
  {
    "id": 241,
    "name": "Mental Health Services Data Set - Service Users",
    "description": "https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/mental-health-services-data-set",
    "url": "https://healthdatagateway.org/en/dataset/857",
    "uid": "f48ad0a1-f90c-4a81-8c04-b23f2d8baf7a",
    "datasource_id": 857,
    "source": "HDRUK"
  },
  {
    "id": 242,
    "name": "Mental Health and Learning Disabilities Data Set",
    "description": "https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/mental-health-services-data-set",
    "url": "https://healthdatagateway.org/en/dataset/869",
    "uid": "e9b43936-e700-40c9-8b32-57ed44583f3d",
    "datasource_id": 869,
    "source": "HDRUK"
  },
  {
    "id": 243,
    "name": "MesobanK",
    "description": "A global solution to a global problem.\nMesobank is a Research Tissue Bank dedicated to the study of mesothelioma. We provide a comprehensive sample and data set from patients with mesothelioma and support biomedical research directly concerned with asbestos related disease undertaken within the UK, EEA, USA, Canada, Australia and New Zealand..",
    "url": "https://healthdatagateway.org/en/dataset/458",
    "uid": "b03835b7-7408-49a0-a9aa-ea405f9855c9",
    "datasource_id": 458,
    "source": "HDRUK"
  },
  {
    "id": 244,
    "name": "Metabolite data",
    "description": "Some of the general population participants have had sera or plasma measured for metabolites, using Metabolon technology.",
    "url": null,
    "uid": "92c80a25-fd93-40a6-b98f-4112ed0c233b",
    "datasource_id": 244,
    "source": "HDRUK"
  },
  {
    "id": 245,
    "name": "Millennium Clinical Events",
    "description": "A list of coded clinical events emitted from the trust's Millennium EPR. A wide range of events are recorded such as information streamed from vital signs monitoring systems, nursing assessments, and orders for imaging procedures.",
    "url": null,
    "uid": "5f45bcd1-970b-44ba-8ae2-0fb49b85ffa9",
    "datasource_id": 245,
    "source": "HDRUK"
  },
  {
    "id": 246,
    "name": "Moorfields Eye Image BioResource 001",
    "description": "The Imaging and Ocular Phenotype BioResource consists of routinely collected imaging data at Moorfields Eye Hospital - a leading provider of eye health services in the UK and a world-class centre of excellence for ophthalmic research and education.\n\nThis includes longitudinal sequential scans and metadata descriptors for approximately 26.5 million eye images from 472,393 patients who attended routine outpatient appointments and ophthalmic accident and emergency at Moorfields sites across Greater London. This is currently supplemented with over 3 million new images each year from an ethnically and socio-economically diverse population (53.8% non white).\n\nThe BioResource data includes eye imaging modalities, such as:\n- Optical coherence tomography (CSO, Heidelberg, Optos, Topcon, Zeiss)\n- Colour fundus photographs (Topcon, Zeiss)\n- Ultra-wide field photographs (Optos, Zeiss)\n- Iris photographs (CSO, Zeiss)\n- Keratoscope topography (CSO)\n- Infrared photographs (Heidelberg, Topcon, Zeiss)\n- Fluorescein angiography (Heidelberg, Optos, Topcon, Zeiss)\n- Indocyanine green angiography (Heidelberg, Optos, Topcon)\n- Fundus autofluorescence (Heidelberg, Optos, Zeiss)\n\nImaging data from CSO is subject to additional approvals.\n\nThis dataset consists of the imaging and its associated metadata (such as laterality, fixation, resolution, device manufacturer and model name). Additional information is provided in the ‘technical details’ tab.",
    "url": "https://healthdatagateway.org/en/dataset/88",
    "uid": "6bfe44ef-9532-4986-b8a2-9c2cda4a89cc",
    "datasource_id": 88,
    "source": "HDRUK"
  },
  {
    "id": 247,
    "name": "Mother-Baby Link for CPRD GOLD",
    "description": "A list of all likely mother-baby pairs in the CPRD GOLD database generated using a probabilistic algorithm applied to the primary care data. Algorithmic linkage is done based on household number plus maternity information from the mother’s primary care record, the infant’s month of birth and care records of newly registered babies.",
    "url": "https://healthdatagateway.org/en/dataset/693",
    "uid": "b734a1b5-37d3-4859-85d4-bf011df2a951",
    "datasource_id": 693,
    "source": "HDRUK"
  },
  {
    "id": 248,
    "name": "Multiple Sclerosis and Parkinson's Tissue Bank",
    "description": "The Multiple Sclerosis and Parkinson's Tissue Bank is a national collection of CNS tissue samples donated by individuals with multiple sclerosis (MS), Parkinson's disease and related neuroinflammatory and neurodegenerative conditions. The Tissue Bank's mission is to facilitate discoveries by making well-characterised human material of the highest quality readily available to the research community engaged in studies aimed at finding the cause and better treatments for MS and Parkinson's. Furthermore, we aim to encourage the greater use of the material in scientific studies. It is by carrying out this work that the Tissue Bank fulfills the last, generous and selfless wishes of all those who have registered on the donor scheme and bequeathed their CNS tissues to research. The work of the Tissue Bank is supported by the Multiple Sclerosis Society and Parkinson's UK in partnership with Imperial College London.",
    "url": "https://healthdatagateway.org/en/dataset/461",
    "uid": "4684d391-a6a7-44f7-bf4b-62b6b693f9fa",
    "datasource_id": 461,
    "source": "HDRUK"
  },
  {
    "id": 249,
    "name": "Myeloma XII clinical trial samples",
    "description": "Samples collected from myeloma patients at first relapse registered into the Myeloma XII trial, and sent to the following central labs: \n1. Central immunology laboratory, University of Birmingham - peripheral clotted blood and urine collected at various timepoints throughout the trial; \n2. Haematology Malignancy Diagnostic Service (HMDS) laboratory, St James's University Hospital, Leeds - bone marrow aspirate, collected at various timepoints throughout the trial; \n3. Leeds Institute of Cancer and Pathology (LICAP), University of Leeds - peripheral blood, serum, and bone marrow aspirate collected at various timepoints throughout the trial.",
    "url": "https://healthdatagateway.org/en/dataset/463",
    "uid": "576fbcf8-ff25-4230-ae69-e72ca02b95f1",
    "datasource_id": 463,
    "source": "HDRUK"
  },
  {
    "id": 250,
    "name": "NHS Digital data",
    "description": "Accessing NHS Digital data both for concrete projects in e.g. mental health (where CRFs are absent) and on a sub-licensing basis for the IBD Hub is currently under discussion.",
    "url": null,
    "uid": "2c5e2f38-b0e1-4d15-a257-5dd19f2f2b64",
    "datasource_id": 250,
    "source": "HDRUK"
  },
  {
    "id": 251,
    "name": "NHS Grampian Biorepository",
    "description": "The Grampian Biorepository was developed to provide core facilities that could undertake research based around the collection, storage, analysis and distribution of human biological samples collected within NHS Grampian. The biorepository supports a range of tissue based projects. The main areas of research activity are translational research programs in colorectal cancer, lung cancer. In addition we have the following collections: orthopaedic tissues and liver samples. We have access to the Pathology Department archive consisting of patient identifiable paper documents, tissue blocks and glass slides containing in excess 1 million patient samples.",
    "url": "https://healthdatagateway.org/en/dataset/464",
    "uid": "10d0def8-dc03-473f-b60b-4c0dc3642a25",
    "datasource_id": 464,
    "source": "HDRUK"
  },
  {
    "id": 252,
    "name": "NHS Greater Glasgow & Clyde Bio-Repository",
    "description": "The NHS Greater Glasgow & Clyde Bio-repository is part of NHS Scotland NHS Research Scotland Infrastructure. There is a network of Bio-repository across Scotland designed to encourage use of tissue in research and boost availability of tissue across Scotland.\nEach Bio-repository node holds the responsibility for tissue governance for local and partner Health Boards with a main focus on facilitating access to surplus diagnostic and surgical tissue for use in research.",
    "url": "https://healthdatagateway.org/en/dataset/462",
    "uid": "762976cc-83d0-4b31-a879-37f87f947406",
    "datasource_id": 462,
    "source": "HDRUK"
  },
  {
    "id": 253,
    "name": "NHS Tayside A&E, Diagnosis During A&E visit",
    "description": "The working diagnosis(es) on discharge from A&E, or where no working diagnosis is made, the main symptom, abnormal finding, or problem. This includes all patients who attend for emergency care in an A&E department, minor injuries unit or medical assessment unit.",
    "url": "https://healthdatagateway.org/en/dataset/115",
    "uid": "14a39e96-8ac3-4df3-b42b-07718121c585",
    "datasource_id": 115,
    "source": "HDRUK"
  },
  {
    "id": 254,
    "name": "NHS Tayside and Fife Accident & Emergency Dataset (PHS/National)",
    "description": "Accident and Emergency Statistics. The A&E datamart was established in June 2007 to monitor the compliance of each NHS Board against the 4 hour wait standard. In July 2010 the A&E data mart was extended further to collect items such as diagnosis, several injury fields and an alcohol involved flag, which will be used to identify whether the patient’s alcohol consumption was a factor in the attendance. The collection of the new fields has been driven by a variety of SG policy decisions and interest from a number of organisations. Although there is now the facility to submit these additional fields, they are still under development and PHS are working with the NHS Boards to support data collection and quality. There are two types of data submitted to the A&E datamart: episode and aggregate level data. All hospitals with Emergency Departments submit episode level data containing a detailed record for each patient attendance. Some smaller sites with minor injury units or community hospitals only submit aggregate files containing monthly summary attendance and compliance figures only. This is because they do not have the information systems and support to enable collection of detailed patient based information. Sites that submit episode level data account for around 94% of all attendances at A&E.",
    "url": "https://healthdatagateway.org/en/dataset/126",
    "uid": "750063ea-6bfd-4492-9748-2a18080cf6ea",
    "datasource_id": 126,
    "source": "HDRUK"
  },
  {
    "id": 255,
    "name": "NHS Tayside and Fife Haematology",
    "description": "This data is provided in a format based upon the SCI-Store system. SCI-Store is a data repository which retains patient laboratory test result information at a health board level. Data prior to 2007 for Tayside has been transformed to fit this structure.  Fife data starts in 2005 and all originates from SCI-Store. There is earlier data available for Fife.",
    "url": "https://healthdatagateway.org/en/dataset/130",
    "uid": "c294bb01-062b-494e-942b-1efe8cbac3dc",
    "datasource_id": 130,
    "source": "HDRUK"
  },
  {
    "id": 256,
    "name": "NHS Tayside and Fife Biochemistry",
    "description": "This data is provided in a format based upon the SCI-Store system. SCI-Store is a data repository which retains patient laboratory test result information at a health board level. Data prior to 2007 for Tayside has been transformed to fit this structure.  Fife data starts in 2005 and all originates from SCI-Store. There is earlier data available for Fife.",
    "url": "https://healthdatagateway.org/en/dataset/109",
    "uid": "10ffac16-d0c2-4d53-ab6a-69a38e283223",
    "datasource_id": 109,
    "source": "HDRUK"
  },
  {
    "id": 257,
    "name": "NHS Tayside and Fife Immunology",
    "description": "This data is provided in a format based upon the SCI-Store system. SCI-Store is a data repository which retains patient laboratory test result information at a health board level. Data prior to 2007 for Tayside has been transformed to fit this structure.  Fife data starts in 2005 and all originates from SCI-Store. There is earlier data available for Fife.",
    "url": "https://healthdatagateway.org/en/dataset/127",
    "uid": "39055449-c074-4b58-adb2-b1c0d2441c4d",
    "datasource_id": 127,
    "source": "HDRUK"
  },
  {
    "id": 258,
    "name": "NHS Tayside Virology",
    "description": "This covers lab tests for a large variety of virus antigen and antibody data, including covid19 from 2010 onwards.",
    "url": "https://healthdatagateway.org/en/dataset/129",
    "uid": "855a84b2-9d58-4b2b-a450-b6c6d534e403",
    "datasource_id": 129,
    "source": "HDRUK"
  },
  {
    "id": 259,
    "name": "NHS Tayside - A&E drugs given during an A&E visit",
    "description": "This dataset provides information on the drugs used during the Accident and Emergency visit in Tayside",
    "url": "https://healthdatagateway.org/en/dataset/114",
    "uid": "acfbef89-344c-49a5-8079-7344f4316e0f",
    "datasource_id": 114,
    "source": "HDRUK"
  },
  {
    "id": 260,
    "name": "NHS Trust data",
    "description": "Data received as part of the HDR UK Sprint Exemplar project, on up to 1600 participants with one of 3 rare diseases (BPD, PAH, PID) at one of the 5 NHS Trusts (Cambridge, Leeds, Liverpool, Newcastle and Papworth).",
    "url": null,
    "uid": "dc524013-8249-4c28-8be4-52f95e71cc92",
    "datasource_id": 260,
    "source": "HDRUK"
  },
  {
    "id": 261,
    "name": "NICAM: Nilotinib treatment for c-KIT mutated advanced AMM",
    "description": "Phase II single arm study evaluating the activity of nilotinib in rare c-KIT mutated acral and mucosal melanoma (AMM). Entry into the trial is a 2 step process.  Patients presenting with AMM are first registered (Step 1) for screening including confirmation of c-KIT mutation status.  Eligible patients who proceed to study entry (step 2) commence treatment with nilotinib 400 mgs twice daily for as long as they continue to benefit from treatment.",
    "url": "https://healthdatagateway.org/en/dataset/465",
    "uid": "ba0edcaf-854d-4907-851a-280b56128b3b",
    "datasource_id": 465,
    "source": "HDRUK"
  },
  {
    "id": 262,
    "name": "NIHR Exeter Clinical Research Facility",
    "description": "NIHR Exeter Clinical Research Facility is a partnership between the University of Exeter Medical School and the Royal Devon and Exeter Foundation Trust. It is dedicated to facilitation of clinical and translational research.\nWe have two main collections: the Peninsula Research Bank (PRB) and the Royal Devon and Exeter Tissue Bank (RDETB). \nThe PRB has ethics to accept gifted samples from completed studies, and the bulk of the collection is from a dedicated biobank collection called EXTEND (the EXeter TEN thousanD), which aims to recruit one in ten of the local Exeter population. The majority (c. 75%) of our studies are local CI led, and we specialise mainly in diabetes, obesity and related pathologies. However, the majority of people within the EXTEND study are fit and well.\nThe RDETB has overarching ethics to collect samples from diagnostic procedures with patients' consent. Each collection within the RDETB uses a standardised consent and collection approach to obtain material that would otherwise be destroyed or not retained. There is scope for prospective studies recruiting through the RDETB.\nBoth collections have a steering committee controlling access, including lay members from within the biobank. There is a standardised approach for requests and the committee sits once a month. In addition, access to gifted samples within the PRB will involve contact with the PI, where possible. Some studies have additional steering committees where they have been carried out in consortia with other centres.\nFirst contact via this directory is encouraged, to establish your needs and our ability to meet them.",
    "url": "https://healthdatagateway.org/en/dataset/468",
    "uid": "df9b7bb1-1b26-4e67-b885-df2dcb8e3450",
    "datasource_id": 468,
    "source": "HDRUK"
  },
  {
    "id": 263,
    "name": "NIHR Nottingham Digestive Diseases Collection",
    "description": "The NIHR Nottingham Digestive Diseases Biomedical Research Unit (NDDBRU) has become the central hub to the gastroenterology and hepatology research within the partnership of University of Nottingham and Nottingham University Trust, bringing together 67 principal investigators bridging basic scientific and clinical fields. Among this large group, we have expertise in a wide range of areas, techniques and methodologies although we focus on early translational studies including pre-clinical and phase I/II studies. We host large deeply phenotyped cohort of patients, linked databases and biological samples. Our academic partnership extends to industry links and we do have experience in developing and evaluating, drugs, devices and health care interventions.\nNIHR NDDBRU has a mission to take the most promising basic biomedical research breakthroughs and translate them into patient benefit. We focus on developing novel tests, techniques as well as new treatments. We perform experimental medicine investigations into mechanisms underlying disease processes using biological samples from people; we evaluate the efficacy of interventions in volunteers and patients.\nOur research focus is on 'The infections, inflammation and consequences in the GI tract and liver'.",
    "url": "https://healthdatagateway.org/en/dataset/469",
    "uid": "f20bf814-ee80-4e67-ab00-afff884ddbb4",
    "datasource_id": 469,
    "source": "HDRUK"
  },
  {
    "id": 264,
    "name": "NIMRAD-TRANS",
    "description": "A retrospective sample collection of formalin fixed paraffin embedded (FFPE) pre-treatment diagnostic biopsies from patients with Head and Neck Squamous Cell carcinoma (HNSCC) enrolled on the NIMRAD trial.",
    "url": "https://healthdatagateway.org/en/dataset/503",
    "uid": "039b55f7-d416-49ab-9bdb-4ff7dbc733df",
    "datasource_id": 503,
    "source": "HDRUK"
  },
  {
    "id": 265,
    "name": "National Asthma and COPD Audit (NACAP): Adult Asthma secondary care clinical",
    "description": "This audit aims to collect information on all people admitted to hospital adult services with asthma attacks. Admission data, obtained from patient case notes, is collected and entered into a secure and bespoke audit web tool.",
    "url": "https://healthdatagateway.org/en/dataset/557",
    "uid": "becd2624-9a01-4f01-bcf8-56e94545a49a",
    "datasource_id": 557,
    "source": "HDRUK"
  },
  {
    "id": 266,
    "name": "National Asthma and COPD Audit (NACAP): COPD secondary care clinical dataset",
    "description": "A continuously ascertained, record level dataset of patients admitted to hospital in England and Wales with COPD since February 2017, with Scotland joining in late 2018. The dataset includes information on patient demographics, acute observations, admission and review, comorbidities and discharge.",
    "url": "https://healthdatagateway.org/en/dataset/582",
    "uid": "85b4dc84-88f4-4740-80fa-fa1eb04a0ae6",
    "datasource_id": 582,
    "source": "HDRUK"
  },
  {
    "id": 267,
    "name": "NACAP Pulmonary rehabilitation - clinical audit",
    "description": "A continuously ascertained, record level dataset of COPD patients attending pulmonary rehabilitation services in England Scotland and Wales. Data collection commenced in March 2019 and includes patient demographics, referral information, assessment, programme enrolment, discharge.",
    "url": "https://healthdatagateway.org/en/dataset/555",
    "uid": "3afbb83d-d47b-43b3-a202-0d3081e5d294",
    "datasource_id": 555,
    "source": "HDRUK"
  },
  {
    "id": 268,
    "name": "National Asthma and COPD Audit Programme - Pulmonary rehab organisational audit",
    "description": "Contains organisational survey data of pulmonary rehabilitation services collected between July and September 2019. The dataset includes information on the organisation and resourcing of pulmonary rehabilitation services.",
    "url": "https://healthdatagateway.org/en/dataset/559",
    "uid": "4b33fc6d-3cac-4404-adac-d693328456fa",
    "datasource_id": 559,
    "source": "HDRUK"
  },
  {
    "id": 269,
    "name": "National Asthma and COPD Audit Programme (NACAP): Wales primary care dataset",
    "description": "Dataset of record level Read and SNOMED coded data from general practice computer systems in Wales extracted in June 2019. Data is included on all patients over the age of 18 recorded to have asthma or COPD and includes patient demographics, investigation and diagnostic information as well as smoking, treatment and the provision of personalised asthma plans.",
    "url": "https://healthdatagateway.org/en/dataset/598",
    "uid": "8a3ec96a-0600-4c6d-8761-9d277f07b9da",
    "datasource_id": 598,
    "source": "HDRUK"
  },
  {
    "id": 270,
    "name": "National Asthma and COPD Audit (NACAP): secondary care  – CYP asthma (clinical)",
    "description": "A continuously ascertained, record level dataset of paediatric patients aged 1-18 years admitted to hospital in England, Wales and Scotland since June 2019 with an asthma attack.",
    "url": "https://healthdatagateway.org/en/dataset/596",
    "uid": "33b36393-3760-40d8-bb98-61806fea5398",
    "datasource_id": 596,
    "source": "HDRUK"
  },
  {
    "id": 271,
    "name": "National Asthma and COPD Audit  (NACAP): secondary care  – CYP asthma (org)",
    "description": "Contains organisational survey data collected from hospitals in England, Wales and Scotland in December 2019.",
    "url": "https://healthdatagateway.org/en/dataset/597",
    "uid": "3efd9b30-1f84-48c0-a3e0-402ecb5c7724",
    "datasource_id": 597,
    "source": "HDRUK"
  },
  {
    "id": 272,
    "name": "National Asthma & COPD Audit:COPD and adult asthma organisational dataset",
    "description": "Contains organisational survey data collected from hospitals in England, Wales and Scotland between 1 April 2019 and 1 July 2019. Includes data on admissions numbers and beds, staffing levels, access to specialist staff/services, 7-day working, management and integration of care, patient and carer engagement, transitional care, cost reimbursement.",
    "url": "https://healthdatagateway.org/en/dataset/581",
    "uid": "1982d9ef-890a-41ff-a86a-de61d65aaa09",
    "datasource_id": 581,
    "source": "HDRUK"
  },
  {
    "id": 273,
    "name": "National Audit of Care at the End of Life - Quality Survey",
    "description": "A survey dataset from an online survey of the experiences of those close to the dying person, of the end of life care provided to both the dying person and themselves during that person's last admission leading to death in acute and community hospitals. Data relates to deaths in April and May 2019 in England and Wales.",
    "url": "https://healthdatagateway.org/en/dataset/552",
    "uid": "51a8dfba-5c94-48bd-9c68-3707f0ebc2a2",
    "datasource_id": 552,
    "source": "HDRUK"
  },
  {
    "id": 274,
    "name": "National Audit of Care at the End of Life - case note review",
    "description": "A Case Note Review completed by acute and community providers only, which  reviewed consecutive deaths in the first two weeks of April 2019 and\nthe first two weeks of May 2019 (acute providers) or deaths in April and May 2019 (community\nproviders).  Data collection period was June to October 2019 in England and Wales. Dataset focuses on patient demographics, recognition of death and the provision of an individualised end of life plan of care. Up to 40 cases were submitted per site in England and Wales.",
    "url": "https://healthdatagateway.org/en/dataset/577",
    "uid": "d8c67899-2d81-48c2-8482-31cd33973a36",
    "datasource_id": 577,
    "source": "HDRUK"
  },
  {
    "id": 275,
    "name": "National Audit of Care at the End of Life - organisational level audit",
    "description": "A hospital-level organisational survey dataset covering the provision of end of life care in acute, community hospitals and mental health inpatient providers. Includes service models and specialise palliative workforce within the hospital /site. Data collected June to October 2019 in England and Wales.",
    "url": "https://healthdatagateway.org/en/dataset/554",
    "uid": "a290592c-a6f5-4874-b283-1d33e923c487",
    "datasource_id": 554,
    "source": "HDRUK"
  },
  {
    "id": 276,
    "name": "National Audit of Dementia - spotlight audit on  psychotropic medication, 2019",
    "description": "One-off data collection looking at reasons and review of prescriptions of any psychotropic medication to people with dementia in hospital. Around 50 hospitals in England provided data. \n\nhttps://www.rcpsych.ac.uk/docs/default-source/improving-care/ccqi/national-clinical-audits/national-audit-of-dementia/r4-resources/spotlight/nad-spotlight-report-feb2020.pdf?sfvrsn=6a86cfbd_6",
    "url": "https://healthdatagateway.org/en/dataset/601",
    "uid": "949d9d7a-a3f1-480a-8563-3fb024300561",
    "datasource_id": 601,
    "source": "HDRUK"
  },
  {
    "id": 277,
    "name": "National Audit of Dementia Round 4 - carer experience survey",
    "description": "The Carer Questionnaire was developed for the third round of the audit in conjunction with the Patient Experience Research Centre at Imperial College London. It was designed to assess carers’ perceptions of care received by the person they care for, in addition to their satisfaction with their own involvement during the patient’s admission. Repeated in fourth round of audit to allow for comparison between the rounds.\nThe questionnaire produces two scores, the carer rating of the overall quality of care, and the carer rating of the quality of information and communication.  Data collected via questionnaires online and paper,  July- September 2018\n\nhttps://www.rcpsych.ac.uk/docs/default-source/improving-care/ccqi/national-clinical-audits/national-audit-of-dementia/r4-resources/tools-guidance/cq-info-sheet-and-tool-combined.pdf?sfvrsn=d82136d0_6",
    "url": "https://healthdatagateway.org/en/dataset/602",
    "uid": "3b5907da-256d-49a3-b24b-00893afe8bdd",
    "datasource_id": 602,
    "source": "HDRUK"
  },
  {
    "id": 278,
    "name": "National Audit of Dementia Round 4 - casenote audit",
    "description": "Includes data on the assessments, discharge planning and personal information used in care, received by people with dementia during their stay in hospital. Standards have been drawn from national and professional guidance. Minimum of 50 cases per hospital. Data collected April-October 2018. Scores produced for assessment and discharge planning  \n\nhttps://www.rcpsych.ac.uk/docs/default-source/improving-care/ccqi/national-clinical-audits/national-audit-of-dementia/r4-resources/tools-guidance/nad4-(2018)-casenote-audit-v1.pdf?sfvrsn=7ed8b6a1_6",
    "url": "https://healthdatagateway.org/en/dataset/600",
    "uid": "587a94e6-48a4-4e5e-8230-4018897bc7c7",
    "datasource_id": 600,
    "source": "HDRUK"
  },
  {
    "id": 279,
    "name": "National Audit of Dementia Round 4 - organisational checklist",
    "description": "Includes information on structures, resources, areas of identified good practice and monitoring\nthat the hospital has put in place to improve the care, treatment and support of people with dementia. Standards have been drawn from national and professional guidance. Data collected April-October 2018.  Scores produced for governance and quality of planned nutrition.\n\nhttps://www.rcpsych.ac.uk/docs/default-source/improving-care/ccqi/national-clinical-audits/national-audit-of-dementia/r4-resources/tools-guidance/nad-oc-for-round-4-(2018)-v1.pdf?sfvrsn=66894e81_8",
    "url": "https://healthdatagateway.org/en/dataset/599",
    "uid": "0a305b29-cf23-4261-9299-5251e41ee9f0",
    "datasource_id": 599,
    "source": "HDRUK"
  },
  {
    "id": 280,
    "name": "National Audit of Dementia Round 4 - staff questionnaire",
    "description": "This was developed for Round 3 of the audit and repeated in Round 4 to  assess how well staff feel they are supported to provide good quality care/support to inpatients with dementia/possible dementia. All clinical staff and ward-based administrative staff working at the hospital in an inpatient-facing role involving contact with people with dementia, were eligible to complete the questionnaire.  Data collected June - October 2018.  Score produced on staff rating of the quality of communication between staff, patients, and carers.\n\nhttps://www.rcpsych.ac.uk/docs/default-source/improving-care/ccqi/national-clinical-audits/national-audit-of-dementia/r4-resources/tools-guidance/nad-r4-staff-questionnaire---paper-version-v1.pdf?sfvrsn=8b323d9e_6",
    "url": "https://healthdatagateway.org/en/dataset/603",
    "uid": "ef6e68d6-dcba-410c-943c-26ca21376a3a",
    "datasource_id": 603,
    "source": "HDRUK"
  },
  {
    "id": 281,
    "name": "National Audit of Psychosis - Audit on Early Intervention in Psychosis services -  patient experience survey",
    "description": "National Audit of Psychosis - Audit on Early Intervention in Psychosis services -  patient experience survey.",
    "url": "https://healthdatagateway.org/en/dataset/545",
    "uid": "c3e63860-bd61-4154-ab5f-ca927fb029c4",
    "datasource_id": 545,
    "source": "HDRUK"
  },
  {
    "id": 282,
    "name": "National Audit of Psychosis - Audit on Early Intervention in Psychosis services - case note audit",
    "description": "This dataset contains information about patients attending Early Intervention Psychosis (EIP) services, sampled over a one year period. It includes whether people received interventions such as Cognitive Behavioural Therapy for Psychosis (CBTp) or physical health screening.",
    "url": "https://healthdatagateway.org/en/dataset/563",
    "uid": "24b88d1a-4363-4619-870e-9ba9fc5d09e1",
    "datasource_id": 563,
    "source": "HDRUK"
  },
  {
    "id": 283,
    "name": "National Audit of Psychosis - Audit on Early Intervention in Psychosis services - contextual data",
    "description": "This dataset contains organisational survey data on the organisation of Early Intervention Psychosis (EIP) services, the data they collect about their service, their staffing and caseload.",
    "url": "https://healthdatagateway.org/en/dataset/550",
    "uid": "4559a14f-563d-4fd2-93ae-27c6c0fd891e",
    "datasource_id": 550,
    "source": "HDRUK"
  },
  {
    "id": 284,
    "name": "National Audit of Psychosis - Early Intervention Psychosis Audit patient experience survey",
    "description": "Survey data of people who have been treated in Early Intervention Psychosis (EIP) services, about the care they have received, and how they feel about it. People who have used EIP services helped develop the survey, and the survey asks about elements of care they felt were important.",
    "url": "https://healthdatagateway.org/en/dataset/546",
    "uid": "a464c126-8591-4af6-a10f-99c020f59527",
    "datasource_id": 546,
    "source": "HDRUK"
  },
  {
    "id": 285,
    "name": "National Bowel Cancer Audit -  Organisational Survey dataset",
    "description": "A survey completed by lead consultant surgeons at each hospital in England and Wales detailing the availability of a wide range of services for patients with bowel cancer including palliative care, oncology, diagnostics, enhanced recovery and specialist surgical services.",
    "url": "https://healthdatagateway.org/en/dataset/564",
    "uid": "4e232aa8-ad56-4d82-adcc-cd06715a5c45",
    "datasource_id": 564,
    "source": "HDRUK"
  },
  {
    "id": 286,
    "name": "National Bowel Cancer Audit - clinical dataset",
    "description": "A continuously ascertained, record-level dataset of the hospital treatment and outcomes of patients with bowel cancer in England and Wales. Data include information on the patient and tumour characteristics, the route to diagnosis, diagnosis, treatment, length of stay, complications and outcomes.",
    "url": "https://healthdatagateway.org/en/dataset/556",
    "uid": "c8a28f0b-a5f8-4dbb-8d71-5b89c7cb3111",
    "datasource_id": 556,
    "source": "HDRUK"
  },
  {
    "id": 287,
    "name": "National Cancer Registration Dataset 2018",
    "description": "Population-based cancer registration data, using event-based registration, for all patients diagnosed with a primary tumour (ICD 10 C00-97x, D00-48x) in England.",
    "url": null,
    "uid": "267e13c1-d857-4702-853a-71abc34e6287",
    "datasource_id": 287,
    "source": "HDRUK"
  },
  {
    "id": 288,
    "name": "National Cardiac Audit Programme - Cardiac Rhythm Management (CRM)",
    "description": "The dataset contains record-level information about all implanted cardiac devices and all patients receiving interventional procedures for management of cardiac rhythm disorders in the UK, including pacemaker, ICD, CRT and cardiac ablation procedures.",
    "url": null,
    "uid": "3cc81e6c-0c17-4ccd-8202-a9aceb82c989",
    "datasource_id": 288,
    "source": "HDRUK"
  },
  {
    "id": 289,
    "name": "National Cardiac Audit Programme - Myocardial Ischaemia National Audit Project (MINAP)",
    "description": "The dataset contains continuously ascertained record-level information on the care provided to patients who are admitted to hospital with acute coronary syndromes (heart attack).",
    "url": null,
    "uid": "3558f8a1-6d52-4b59-b513-51c068095bc2",
    "datasource_id": 289,
    "source": "HDRUK"
  },
  {
    "id": 290,
    "name": "National Cardiac Audit Programme - National Adult Cardiac Surgery Audit (NACSA)",
    "description": "The National Adult Cardiac Surgery Audit (NACSA) collects data from NHS hospitals that carry out adult heart surgery, including coronary artery bypass grafts (CABG), valve surgery and aortic surgery.",
    "url": null,
    "uid": "5f37e3c2-b5ac-43eb-8eba-ac94dbbff103",
    "datasource_id": 290,
    "source": "HDRUK"
  },
  {
    "id": 291,
    "name": "National Cardiac Audit Programme - National Audit of Percutaneous Coronary Interventions (PCI)",
    "description": "The dataset information on the types, timing, processes and outcomes of care provided to patients undergoing percutaneous coronary intervention procedures (including stents and angioplasties).",
    "url": null,
    "uid": "ac7016ce-f7a9-4c71-bebb-6af6efa27844",
    "datasource_id": 291,
    "source": "HDRUK"
  },
  {
    "id": 292,
    "name": "National Cardiac Audit Programme - National Congenital Heart Disease Audit (NCHDA)",
    "description": "The National Congenital Heart Disease Audit (NCHDA) dataset includes data from all centres undertaking paediatric and congenital cardiac surgery and interventional procedures, in the United Kingdom and Republic of Ireland.",
    "url": null,
    "uid": "2b640175-725a-465f-9aec-6794e6d27791",
    "datasource_id": 292,
    "source": "HDRUK"
  },
  {
    "id": 293,
    "name": "National Cardiac Audit Programme - National Heart Failure Audit",
    "description": "The dataset contains continuously ascertained record-level information on patients with an unscheduled admission to hospital in England and Wales where heart failure is the primary factor in the record of their death or discharge.",
    "url": null,
    "uid": "834618f3-0c6d-4ed2-abae-39208c6b3080",
    "datasource_id": 293,
    "source": "HDRUK"
  },
  {
    "id": 294,
    "name": "National Clinical Audit of Anxiety and Depression - Core audit of practice",
    "description": "A clinical audit dataset containing data on care for patients admitted to inpatient mental health services in England with anxiety and depression over a six month period April - September 2017.",
    "url": null,
    "uid": "4a96b9bf-beb0-41b1-9211-e6ce39a21c1a",
    "datasource_id": 294,
    "source": "HDRUK"
  },
  {
    "id": 295,
    "name": "National Clinical Audit of Anxiety and Depression - spotlight audit on psychological therapies (clinical)",
    "description": "A clinical dataset covering the quality of psychological assessment, formulation and therapy delivered by secondary care mental health services to people aged 18 years and over in England with data collected between October 2018 and January 2019.",
    "url": null,
    "uid": "6c81b082-bdc5-4dcc-838a-e6e52e1be1cb",
    "datasource_id": 295,
    "source": "HDRUK"
  },
  {
    "id": 296,
    "name": "National Clinical Audit of Anxiety and Depression - spotlight audit on psychological therapies (patient experience survey)",
    "description": "A patient experience survey dataset covering the experience and satisfaction with psychological therapy delivered by secondary care mental health services to people aged 18 years and over in secondary care mental health services in England.",
    "url": null,
    "uid": "d94eb31c-04c4-41b6-ad38-45f6aef30132",
    "datasource_id": 296,
    "source": "HDRUK"
  },
  {
    "id": 297,
    "name": "National Clinical Audit of Anxiety and Depression - spotlight audit on psychological therapies (therapist survey)",
    "description": "A dataset of survey responses from members of staff who provide psychological therapies including trainees and voluntary staff. Data covers training, supervision, and support the quality of psychological assessment, formulation and therapy delivered.",
    "url": null,
    "uid": "bf2d780d-936b-4f4c-88f9-a280b849b652",
    "datasource_id": 297,
    "source": "HDRUK"
  },
  {
    "id": 298,
    "name": "National Community Child Health Database (NCCH)",
    "description": "The Child Health System in Wales; includes birth registration and monitoring of child health examinations and immunisations.\n\nThe dataset brings together data from local Child Health System databases which are held by NHS Trusts and used by them to administer child immunisation and health surveillance programmes.\n\nThe dataset contains all children born, resident or treated in Wales and born after 1987.\n\nThis dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.\n\nPlease note: the view 'CHILD_MEASUREMENT_PROGRAM' must be explicitly requested in the IGRP (governance) application in Section 5,  where the information required from this view must be listed.",
    "url": "https://healthdatagateway.org/en/dataset/360",
    "uid": "20fe153c-a5e5-4991-900e-8fa9988e771a",
    "datasource_id": 360,
    "source": "HDRUK"
  },
  {
    "id": 299,
    "name": "National Diabetes Audit",
    "description": "Audit collects Information about general diabetes care. Data submitted by health care services, relevant to service they provide i.e. Secondary Care Bodies = Type 1, GP practices = Type 2. Includes demographics and diabetes relevant biometric information.",
    "url": "https://healthdatagateway.org/en/dataset/862",
    "uid": "f04f5e7e-3c1a-4bce-b050-513ebdb3471e",
    "datasource_id": 862,
    "source": "HDRUK"
  },
  {
    "id": 300,
    "name": "National Diabetes Audit - Core Clinical Audit Dataset",
    "description": "Primary care data on patients of all ages with diabetes, secondary care data is adult patients only.  The dataset includes treatment targets, diabetes prevention programme, insulin pump data, patients with learning disabilities and severe mental illness.",
    "url": null,
    "uid": "f4c8900b-5db5-4426-95e9-2f1617c92a76",
    "datasource_id": 300,
    "source": "HDRUK"
  },
  {
    "id": 301,
    "name": "National Diabetes Audit - National Diabetes Foot Care Audit clinical dataset",
    "description": "The National Diabetes Foot Care Audit (NDFA) contains continuously-ascertained, record-level data on diabetic foot disease in England and Wales, collected from diabetes foot care services.",
    "url": null,
    "uid": "dabf34b3-7368-4097-94c5-5b4f4be68fc6",
    "datasource_id": 301,
    "source": "HDRUK"
  },
  {
    "id": 302,
    "name": "National Diabetes Audit - National Diabetes in Pregnancy clinical Audit dataset",
    "description": "The National Pregnancy in Diabetes Audit contains continuously-ascertained, record-level data on the quality of pre-gestational diabetes hospital care against NICE guideline based criteria and the outcomes of pre gestational diabetic pregnancy.",
    "url": null,
    "uid": "7c789df2-46f0-4e0e-b6ff-b04dce801445",
    "datasource_id": 302,
    "source": "HDRUK"
  },
  {
    "id": 303,
    "name": "National Diabetes Inpatient Audit - Harms clinical dataset",
    "description": "NaDIA-Harms dataset is collected for patients who have had one of four potential harms: hypoglycaemic rescue, diabetic ketoacidosis, hyperglycaemic hyperosmolar state and new foot ulcer.",
    "url": null,
    "uid": "f1471a45-a6f4-4ed7-96b0-56a1af21086e",
    "datasource_id": 303,
    "source": "HDRUK"
  },
  {
    "id": 304,
    "name": "National Diabetes Inpatient Audit Hospital Characteristics Survey dataset",
    "description": "The National Diabetes Inpatient Audit (NaDIA) Hospital Characteristics Survey dataset contains data on staffing levels related to diabetes care, the use of health technology and the receipt of transformation funding.",
    "url": null,
    "uid": "a9098b51-f8ec-432e-a52f-f4c1f2c1e4f6",
    "datasource_id": 304,
    "source": "HDRUK"
  },
  {
    "id": 305,
    "name": "National Diet and Nutrition Survey (NDNS)",
    "description": "A National cross sectional population based survey.\nAdditional data available including 4 day food diary, BP, biochemical analytes.",
    "url": "https://healthdatagateway.org/en/dataset/480",
    "uid": "f86400cb-7cab-4e34-aab6-57138b6a96c2",
    "datasource_id": 480,
    "source": "HDRUK"
  },
  {
    "id": 306,
    "name": "National Early Inflammatory Arthritis Audit - clinical dataset",
    "description": "Continuously ascertained clinical dataset of all patients in England and Wales presenting to secondary rheumatology services over the age of 16 years with suspected inflammatory arthritis. The record-level dataset includes patient demographics, patient diagnosis data, and baseline, 3 months and 12 months follow on data.",
    "url": "https://healthdatagateway.org/en/dataset/551",
    "uid": "b0797c05-352f-455f-8d27-150b6f6354cc",
    "datasource_id": 551,
    "source": "HDRUK"
  },
  {
    "id": 307,
    "name": "National Early Inflammatory Arthritis Audit - organisational survey dataset",
    "description": "Contains organisational survey data from specialist rheumatology units in England and Wales, refreshed on an annual basis",
    "url": "https://healthdatagateway.org/en/dataset/571",
    "uid": "a9ac99fe-32be-40ef-9509-03e6b2713ddf",
    "datasource_id": 571,
    "source": "HDRUK"
  },
  {
    "id": 308,
    "name": "National Early Inflammatory Arthritis Audit - patient survey",
    "description": "Continuously ascertained collection of patient reported symptoms, quality of life and employment for all patients in England and Wales presenting to secondary rheumatology services over the age of 16 years with confirmed inflammatory arthritis. Measures are included  at three survey times: at diagnosis, 3 months later and 1 year after diagnosis.",
    "url": "https://healthdatagateway.org/en/dataset/562",
    "uid": "e87410ec-a819-43db-99d1-f887c977e525",
    "datasource_id": 562,
    "source": "HDRUK"
  },
  {
    "id": 309,
    "name": "National Emergency Laparotomy Audit - Patient Audit Dataset",
    "description": "Contains patient level data on the input, processes, and outcomes of care for adults undergoing emergency laparotomy (bowel surgery) in England and Wales. This is a continuous audit. The Year 8 audit questionnaire can be found here: https://data.nela.org.uk/Support/YEAR-8-NELA-Patient-Audit-Questions-Proforma-Activ.aspx. \n\nData is collected on patient demographics and hospital admission information, preoperative information, preoperative risk stratification, intraoperative information, postoperative risk stratification, postoperative information and outcomes, and COVID-specific questions.",
    "url": "https://healthdatagateway.org/en/dataset/575",
    "uid": "25b93812-191f-49bb-bbd0-782fb85b2bd2",
    "datasource_id": 575,
    "source": "HDRUK"
  },
  {
    "id": 310,
    "name": "National Exercise Referral Scheme (NERS)",
    "description": "The National Exercise Referral Scheme (NERS) is a Public Health Wales (PHW) funded scheme which has been in development since 2007. The Scheme targets clients aged 16 and over who have, or are at risk of developing, a chronic disease. The scheme is centrally managed by the Welsh local Government Association.\n\nNERS is an evidence-based health intervention incorporating physical activity and behavioural change techniques to support referred clients to make lifestyle changes to improve their health and wellbeing.\n\nThe principal aims of the scheme:\n\nTo offer a high quality National Exercise Referral Scheme across Wales\nTo increase the long term adherence of clients to physical activity\nTo improve the physical and mental health of clients\nTo determine the effectiveness of the intervention in increasing clients’ activity levels and improving their health.",
    "url": "https://healthdatagateway.org/en/dataset/307",
    "uid": "131784c0-769d-4f7b-b7d0-b5f4b89886d4",
    "datasource_id": 307,
    "source": "HDRUK"
  },
  {
    "id": 311,
    "name": "National Joint Registry - Primary Ankle Replacement dataset",
    "description": "The National Joint Registry for England, Wales, Northern Ireland and the Isle of Man is a database containing details of all primary and revision total hip, knee, shoulder, elbow and ankle replacement procedures carried out in NHS and independent sector hospitals in England, Wales, Northern Ireland and the Isle of Man.  Primary hip replacement has its own specific dataset for procedures.\nInitially this data is collected during a patient's time at hospital as part of bespoke data collection to support the NJR . This is submitted to NEC Software Solutions (contracted to the NJR) for processing and is used to monitor the quality and safety of patient care and outcomes. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the NJR research ready data set.\nNJR data covers all procedures carried out in NHS and independent sector hospitals  in England, Wales (from 2003), Northern Ireland )from 2013), the Isle of Man (from 2015) and the States of Guernsey (from 2019).\nEach NJR record contains a wide range of information about an individual patient treated at an NHS or independent sector hospital, including:\n• clinical information about surgical indications and operations\n• patient information, such as age group and gender\n• component information, such as the brand and size of prosthesis used\n• outcomes, such as whether a revision procedure has been undertaken\nNJR apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published NJR data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\nWho NJR data is for\nNJR can provide data for the purpose of healthcare analysis to the NHS, government and others including:\n• national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n• local Clinical Commissioning Groups (CCGs)\n• provider organisations\n• government departments\n• researchers and commercial healthcare bodies\n• National Institute for Clinical Excellence (NICE)\n• patients, service users and carers\n• the media\nUses of the statistics\nThe statistics are known to be used for:\n• national policy making\n• benchmarking performance against other hospital providers or CCGs  \n• academic research\n• analysing service usage and planning change\n• providing advice to ministers and answering a wide range of parliamentary questions\n• national and local press articles\n• international comparison\nMore information can be found at http://www.njrcentre.org.uk/njrcentre/",
    "url": "https://healthdatagateway.org/en/dataset/589",
    "uid": "b8d009d0-0163-4aaf-8bd6-ad363c859862",
    "datasource_id": 589,
    "source": "HDRUK"
  },
  {
    "id": 312,
    "name": "National Joint Registry - Primary Elbow Replacement dataset",
    "description": "The National Joint Registry for England, Wales, Northern Ireland and the Isle of Man is a database containing details of all primary and revision total hip, knee, shoulder, elbow and ankle replacement procedures carried out in NHS and independent sector hospitals in England, Wales, Northern Ireland and the Isle of Man.  Primary hip replacement has its own specific dataset for procedures.\nInitially this data is collected during a patient's time at hospital as part of bespoke data collection to support the NJR . This is submitted to NEC Software Solutions (contracted to the NJR) for processing and is used to monitor the quality and safety of patient care and outcomes. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the NJR research ready data set.\nNJR data covers all procedures carried out in NHS and independent sector hospitals  in England, Wales (from 2003), Northern Ireland )from 2013), the Isle of Man (from 2015) and the States of Guernsey (from 2019).\nEach NJR record contains a wide range of information about an individual patient treated at an NHS or independent sector hospital, including:\n• clinical information about surgical indications and operations\n• patient information, such as age group and gender\n• component information, such as the brand and size of prosthesis used\n• outcomes, such as whether a revision procedure has been undertaken\nNJR apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published NJR data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\nWho NJR data is for\nNJR can provide data for the purpose of healthcare analysis to the NHS, government and others including:\n• national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n• local Clinical Commissioning Groups (CCGs)\n• provider organisations\n• government departments\n• researchers and commercial healthcare bodies\n• National Institute for Clinical Excellence (NICE)\n• patients, service users and carers\n• the media\nUses of the statistics\nThe statistics are known to be used for:\n• national policy making\n• benchmarking performance against other hospital providers or CCGs  \n• academic research\n• analysing service usage and planning change\n• providing advice to ministers and answering a wide range of parliamentary questions\n• national and local press articles\n• international comparison\nMore information can be found at http://www.njrcentre.org.uk/njrcentre/",
    "url": "https://healthdatagateway.org/en/dataset/585",
    "uid": "b06415af-9151-4ccc-83a1-ab859943fb2b",
    "datasource_id": 585,
    "source": "HDRUK"
  },
  {
    "id": 313,
    "name": "National Joint Registry - Primary Hip Replacement dataset",
    "description": "The National Joint Registry for England, Wales, Northern Ireland and the Isle of Man is a database containing details of all primary and revision total hip, knee, shoulder, elbow and ankle replacement procedures carried out in NHS and independent sector hospitals in England, Wales, Northern Ireland and the Isle of Man.  Primary hip replacement has its own specific dataset for procedures.\nInitially this data is collected during a patient's time at hospital as part of bespoke data collection to support the NJR . This is submitted to NEC Software Solutions (contracted to the NJR) for processing and is used to monitor the quality and safety of patient care and outcomes. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the NJR research ready data set.\nNJR data covers all procedures carried out in NHS and independent sector hospitals  in England, Wales (from 2003), Northern Ireland )from 2013), the Isle of Man (from 2015) and the States of Guernsey (from 2019).\nEach NJR record contains a wide range of information about an individual patient treated at an NHS or independent sector hospital, including:\n• clinical information about surgical indications and operations\n• patient information, such as age group and gender\n• component information, such as the brand and size of prosthesis used\n• outcomes, such as whether a revision procedure has been undertaken\nNJR apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published NJR data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\nWho NJR data is for\nNJR can provide data for the purpose of healthcare analysis to the NHS, government and others including:\n• national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n• local Clinical Commissioning Groups (CCGs)\n• provider organisations\n• government departments\n• researchers and commercial healthcare bodies\n• National Institute for Clinical Excellence (NICE)\n• patients, service users and carers\n• the media\nUses of the statistics\nThe statistics are known to be used for:\n• national policy making\n• benchmarking performance against other hospital providers or CCGs  \n• academic research\n• analysing service usage and planning change\n• providing advice to ministers and answering a wide range of parliamentary questions\n• national and local press articles\n• international comparison\nMore information can be found at http://www.njrcentre.org.uk/njrcentre/",
    "url": "https://healthdatagateway.org/en/dataset/584",
    "uid": "03280e87-6975-4a2a-967d-b332cb788e44",
    "datasource_id": 584,
    "source": "HDRUK"
  },
  {
    "id": 314,
    "name": "National Joint Registry - Primary Knee Replacement dataset",
    "description": "The National Joint Registry for England, Wales, Northern Ireland and the Isle of Man is a database containing details of all primary and revision total hip, knee, shoulder, elbow and ankle replacement procedures carried out in NHS and independent sector hospitals in England, Wales, Northern Ireland and the Isle of Man.  Primary hip replacement has its own specific dataset for procedures.\nInitially this data is collected during a patient's time at hospital as part of bespoke data collection to support the NJR . This is submitted to NEC Software Solutions (contracted to the NJR) for processing and is used to monitor the quality and safety of patient care and outcomes. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the NJR research ready data set.\nNJR data covers all procedures carried out in NHS and independent sector hospitals  in England, Wales (from 2003), Northern Ireland )from 2013), the Isle of Man (from 2015) and the States of Guernsey (from 2019).\nEach NJR record contains a wide range of information about an individual patient treated at an NHS or independent sector hospital, including:\n• clinical information about surgical indications and operations\n• patient information, such as age group and gender\n• component information, such as the brand and size of prosthesis used\n• outcomes, such as whether a revision procedure has been undertaken\nNJR apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published NJR data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\nWho NJR data is for\nNJR can provide data for the purpose of healthcare analysis to the NHS, government and others including:\n• national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n• local Clinical Commissioning Groups (CCGs)\n• provider organisations\n• government departments\n• researchers and commercial healthcare bodies\n• National Institute for Clinical Excellence (NICE)\n• patients, service users and carers\n• the media\nUses of the statistics\nThe statistics are known to be used for:\n• national policy making\n• benchmarking performance against other hospital providers or CCGs  \n• academic research\n• analysing service usage and planning change\n• providing advice to ministers and answering a wide range of parliamentary questions\n• national and local press articles\n• international comparison\nMore information can be found at http://www.njrcentre.org.uk/njrcentre/",
    "url": "https://healthdatagateway.org/en/dataset/592",
    "uid": "d517e6fc-9a88-4647-b787-dcbed3635885",
    "datasource_id": 592,
    "source": "HDRUK"
  },
  {
    "id": 315,
    "name": "National Joint Registry - Primary Shoulder Replacement dataset",
    "description": "The National Joint Registry for England, Wales, Northern Ireland and the Isle of Man is a database containing details of all primary and revision total hip, knee, shoulder, elbow and ankle replacement procedures carried out in NHS and independent sector hospitals in England, Wales, Northern Ireland and the Isle of Man.  Primary hip replacement has its own specific dataset for procedures.\nInitially this data is collected during a patient's time at hospital as part of bespoke data collection to support the NJR . This is submitted to NEC Software Solutions (contracted to the NJR) for processing and is used to monitor the quality and safety of patient care and outcomes. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the NJR research ready data set.\nNJR data covers all procedures carried out in NHS and independent sector hospitals  in England, Wales (from 2003), Northern Ireland )from 2013), the Isle of Man (from 2015) and the States of Guernsey (from 2019).\nEach NJR record contains a wide range of information about an individual patient treated at an NHS or independent sector hospital, including:\n• clinical information about surgical indications and operations\n• patient information, such as age group and gender\n• component information, such as the brand and size of prosthesis used\n• outcomes, such as whether a revision procedure has been undertaken\nNJR apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published NJR data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\nWho NJR data is for\nNJR can provide data for the purpose of healthcare analysis to the NHS, government and others including:\n• national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n• local Clinical Commissioning Groups (CCGs)\n• provider organisations\n• government departments\n• researchers and commercial healthcare bodies\n• National Institute for Clinical Excellence (NICE)\n• patients, service users and carers\n• the media\nUses of the statistics\nThe statistics are known to be used for:\n• national policy making\n• benchmarking performance against other hospital providers or CCGs  \n• academic research\n• analysing service usage and planning change\n• providing advice to ministers and answering a wide range of parliamentary questions\n• national and local press articles\n• international comparison\nMore information can be found at http://www.njrcentre.org.uk/njrcentre/",
    "url": "https://healthdatagateway.org/en/dataset/586",
    "uid": "0e903b9e-a329-478a-937c-480eecee4996",
    "datasource_id": 586,
    "source": "HDRUK"
  },
  {
    "id": 316,
    "name": "National Joint Registry - Revision Ankle Replacement dataset",
    "description": "The National Joint Registry for England, Wales, Northern Ireland and the Isle of Man is a database containing details of all primary and revision total hip, knee, shoulder, elbow and ankle replacement procedures carried out in NHS and independent sector hospitals in England, Wales, Northern Ireland and the Isle of Man.  Primary hip replacement has its own specific dataset for procedures.\nInitially this data is collected during a patient's time at hospital as part of bespoke data collection to support the NJR . This is submitted to NEC Software Solutions (contracted to the NJR) for processing and is used to monitor the quality and safety of patient care and outcomes. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the NJR research ready data set.\nNJR data covers all procedures carried out in NHS and independent sector hospitals  in England, Wales (from 2003), Northern Ireland )from 2013), the Isle of Man (from 2015) and the States of Guernsey (from 2019).\nEach NJR record contains a wide range of information about an individual patient treated at an NHS or independent sector hospital, including:\n• clinical information about surgical indications and operations\n• patient information, such as age group and gender\n• component information, such as the brand and size of prosthesis used\n• outcomes, such as whether a revision procedure has been undertaken\nNJR apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published NJR data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\nWho NJR data is for\nNJR can provide data for the purpose of healthcare analysis to the NHS, government and others including:\n• national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n• local Clinical Commissioning Groups (CCGs)\n• provider organisations\n• government departments\n• researchers and commercial healthcare bodies\n• National Institute for Clinical Excellence (NICE)\n• patients, service users and carers\n• the media\nUses of the statistics\nThe statistics are known to be used for:\n• national policy making\n• benchmarking performance against other hospital providers or CCGs  \n• academic research\n• analysing service usage and planning change\n• providing advice to ministers and answering a wide range of parliamentary questions\n• national and local press articles\n• international comparison\nMore information can be found at http://www.njrcentre.org.uk/njrcentre/",
    "url": "https://healthdatagateway.org/en/dataset/568",
    "uid": "b4f076d2-c9a3-49ba-84f4-94148dc8511a",
    "datasource_id": 568,
    "source": "HDRUK"
  },
  {
    "id": 317,
    "name": "National Joint Registry - Revision Elbow Replacement dataset",
    "description": "The National Joint Registry for England, Wales, Northern Ireland and the Isle of Man is a database containing details of all primary and revision total hip, knee, shoulder, elbow and ankle replacement procedures carried out in NHS and independent sector hospitals in England, Wales, Northern Ireland and the Isle of Man.  Primary hip replacement has its own specific dataset for procedures.\nInitially this data is collected during a patient's time at hospital as part of bespoke data collection to support the NJR . This is submitted to NEC Software Solutions (contracted to the NJR) for processing and is used to monitor the quality and safety of patient care and outcomes. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the NJR research ready data set.\nNJR data covers all procedures carried out in NHS and independent sector hospitals  in England, Wales (from 2003), Northern Ireland )from 2013), the Isle of Man (from 2015) and the States of Guernsey (from 2019).\nEach NJR record contains a wide range of information about an individual patient treated at an NHS or independent sector hospital, including:\n• clinical information about surgical indications and operations\n• patient information, such as age group and gender\n• component information, such as the brand and size of prosthesis used\n• outcomes, such as whether a revision procedure has been undertaken\nNJR apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published NJR data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\nWho NJR data is for\nNJR can provide data for the purpose of healthcare analysis to the NHS, government and others including:\n• national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n• local Clinical Commissioning Groups (CCGs)\n• provider organisations\n• government departments\n• researchers and commercial healthcare bodies\n• National Institute for Clinical Excellence (NICE)\n• patients, service users and carers\n• the media\nUses of the statistics\nThe statistics are known to be used for:\n• national policy making\n• benchmarking performance against other hospital providers or CCGs  \n• academic research\n• analysing service usage and planning change\n• providing advice to ministers and answering a wide range of parliamentary questions\n• national and local press articles\n• international comparison\nMore information can be found at http://www.njrcentre.org.uk/njrcentre/",
    "url": "https://healthdatagateway.org/en/dataset/588",
    "uid": "12babf2b-42e8-4b1e-9143-3475a07bb343",
    "datasource_id": 588,
    "source": "HDRUK"
  },
  {
    "id": 318,
    "name": "National Joint Registry - Revision Hip Replacement dataset",
    "description": "The National Joint Registry for England, Wales, Northern Ireland and the Isle of Man is a database containing details of all primary and revision total hip, knee, shoulder, elbow and ankle replacement procedures carried out in NHS and independent sector hospitals in England, Wales, Northern Ireland and the Isle of Man.  Primary hip replacement has its own specific dataset for procedures.\nInitially this data is collected during a patient's time at hospital as part of bespoke data collection to support the NJR . This is submitted to NEC Software Solutions (contracted to the NJR) for processing and is used to monitor the quality and safety of patient care and outcomes. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the NJR research ready data set.\nNJR data covers all procedures carried out in NHS and independent sector hospitals  in England, Wales (from 2003), Northern Ireland )from 2013), the Isle of Man (from 2015) and the States of Guernsey (from 2019).\nEach NJR record contains a wide range of information about an individual patient treated at an NHS or independent sector hospital, including:\n• clinical information about surgical indications and operations\n• patient information, such as age group and gender\n• component information, such as the brand and size of prosthesis used\n• outcomes, such as whether a revision procedure has been undertaken\nNJR apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published NJR data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\nWho NJR data is for\nNJR can provide data for the purpose of healthcare analysis to the NHS, government and others including:\n• national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n• local Clinical Commissioning Groups (CCGs)\n• provider organisations\n• government departments\n• researchers and commercial healthcare bodies\n• National Institute for Clinical Excellence (NICE)\n• patients, service users and carers\n• the media\nUses of the statistics\nThe statistics are known to be used for:\n• national policy making\n• benchmarking performance against other hospital providers or CCGs  \n• academic research\n• analysing service usage and planning change\n• providing advice to ministers and answering a wide range of parliamentary questions\n• national and local press articles\n• international comparison\nMore information can be found at http://www.njrcentre.org.uk/njrcentre/",
    "url": "https://healthdatagateway.org/en/dataset/590",
    "uid": "4b8286e2-c4e4-4db4-8fd9-5f1c8b4e56de",
    "datasource_id": 590,
    "source": "HDRUK"
  },
  {
    "id": 319,
    "name": "National Joint Registry - Revision Knee Replacement dataset",
    "description": "The National Joint Registry for England, Wales, Northern Ireland and the Isle of Man is a database containing details of all primary and revision total hip, knee, shoulder, elbow and ankle replacement procedures carried out in NHS and independent sector hospitals in England, Wales, Northern Ireland and the Isle of Man.  Primary hip replacement has its own specific dataset for procedures.\nInitially this data is collected during a patient's time at hospital as part of bespoke data collection to support the NJR . This is submitted to NEC Software Solutions (contracted to the NJR) for processing and is used to monitor the quality and safety of patient care and outcomes. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the NJR research ready data set.\nNJR data covers all procedures carried out in NHS and independent sector hospitals  in England, Wales (from 2003), Northern Ireland (from 2013), the Isle of Man (from 2015) and the States of Guernsey (from 2019).\nEach NJR record contains a wide range of information about an individual patient treated at an NHS or independent sector hospital, including:\n• clinical information about surgical indications and operations\n• patient information, such as age group and gender\n• component information, such as the brand and size of prosthesis used\n• outcomes, such as whether a revision procedure has been undertaken\nNJR apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published NJR data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\nWho NJR data is for\nNJR can provide data for the purpose of healthcare analysis to the NHS, government and others including:\n• national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n• local Clinical Commissioning Groups (CCGs)\n• provider organisations\n• government departments\n• researchers and commercial healthcare bodies\n• National Institute for Clinical Excellence (NICE)\n• patients, service users and carers\n• the media\nUses of the statistics\nThe statistics are known to be used for:\n• national policy making\n• benchmarking performance against other hospital providers or CCGs  \n• academic research\n• analysing service usage and planning change\n• providing advice to ministers and answering a wide range of parliamentary questions\n• national and local press articles\n• international comparison\nMore information can be found at http://www.njrcentre.org.uk/njrcentre/",
    "url": "https://healthdatagateway.org/en/dataset/587",
    "uid": "6664317b-39b9-4133-b6c7-ec4bddf78c76",
    "datasource_id": 587,
    "source": "HDRUK"
  },
  {
    "id": 320,
    "name": "National Joint Registry - Revision Shoulder Replacement dataset",
    "description": "The National Joint Registry for England, Wales, Northern Ireland and the Isle of Man is a database containing details of all primary and revision total hip, knee, shoulder, elbow and ankle replacement procedures carried out in NHS and independent sector hospitals in England, Wales, Northern Ireland and the Isle of Man.  Primary hip replacement has its own specific dataset for procedures.\nInitially this data is collected during a patient's time at hospital as part of bespoke data collection to support the NJR . This is submitted to NEC Software Solutions (contracted to the NJR) for processing and is used to monitor the quality and safety of patient care and outcomes. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the NJR research ready data set.\nNJR data covers all procedures carried out in NHS and independent sector hospitals  in England, Wales (from 2003), Northern Ireland )from 2013), the Isle of Man (from 2015) and the States of Guernsey (from 2019).\nEach NJR record contains a wide range of information about an individual patient treated at an NHS or independent sector hospital, including:\n• clinical information about surgical indications and operations\n• patient information, such as age group and gender\n• component information, such as the brand and size of prosthesis used\n• outcomes, such as whether a revision procedure has been undertaken\nNJR apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published NJR data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\nWho NJR data is for\nNJR can provide data for the purpose of healthcare analysis to the NHS, government and others including:\n• national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n• local Clinical Commissioning Groups (CCGs)\n• provider organisations\n• government departments\n• researchers and commercial healthcare bodies\n• National Institute for Clinical Excellence (NICE)\n• patients, service users and carers\n• the media\nUses of the statistics\nThe statistics are known to be used for:\n• national policy making\n• benchmarking performance against other hospital providers or CCGs  \n• academic research\n• analysing service usage and planning change\n• providing advice to ministers and answering a wide range of parliamentary questions\n• national and local press articles\n• international comparison\nMore information can be found at http://www.njrcentre.org.uk/njrcentre/",
    "url": "https://healthdatagateway.org/en/dataset/591",
    "uid": "9623e62b-3c50-44aa-8923-0bbb94e7ad61",
    "datasource_id": 591,
    "source": "HDRUK"
  },
  {
    "id": 321,
    "name": "National Maternity and Perinatal Audit Organisational Survey 2019",
    "description": "The National Maternity and Perinatal Audit (NMPA) organisational survey provides an overview of care provision by NHS maternity services in all settings across England, Scotland and Wales as a snapshot of care in January 2019, and reflect the changes which are being implemented as a result of the maternity and neonatal services reviews and other initiatives.",
    "url": "https://healthdatagateway.org/en/dataset/572",
    "uid": "01ad25ed-8cde-43d7-bc77-d8cc3d165df4",
    "datasource_id": 572,
    "source": "HDRUK"
  },
  {
    "id": 322,
    "name": "National Maternity and Perinatal Audit clinical audit",
    "description": "The NMPA clinical audit measures cover various aspects of maternity and neonatal care provided by NHS maternity services in England, Scotland and Wales. The dataset is continuous and formed from linking around 15 national and local routine datasets.",
    "url": null,
    "uid": "1419533e-3d46-4d8a-992b-b5311e6bd27b",
    "datasource_id": 322,
    "source": "HDRUK"
  },
  {
    "id": 323,
    "name": "National Neonatal Audit Programme - 2 year outcomes audit",
    "description": "The NNAP assesses whether babies admitted to neonatal units receive consistent high quality care. This continuous audit assesses the processes of care for babies admitted for neonatal care in England, Scotland and Wales.",
    "url": null,
    "uid": "3bb02d77-9678-4e6e-832d-c2ed3ede412c",
    "datasource_id": 323,
    "source": "HDRUK"
  },
  {
    "id": 324,
    "name": "National Neonatal Audit Programme - clinical audit",
    "description": "The National Neonatal Audit Programme (NNAP) assesses whether babies admitted to neonatal units receive consistent high quality care. This continuous audit assesses the processes of care for babies admitted for neonatal care in England, Scotland and Wales. Data are downloaded from the BadgerNet patient record system used in neonatal units.",
    "url": "https://healthdatagateway.org/en/dataset/578",
    "uid": "49b67625-4c9a-4101-a982-e733e7db20ca",
    "datasource_id": 578,
    "source": "HDRUK"
  },
  {
    "id": 325,
    "name": "National Neonatal Research Database (NNRD)",
    "description": "The NNRD is a national resource holding real-world clinical data captured in the course of care on all admissions to NHS neonatal units in England, Wales, Scotland and the Isle of Man. Neonatal units submit data through their Electronic Patient Record system supplier. At present, there is information on around 1.6 million babies and  over10 million days of care in the NNRD.\n\nThe NNRD is available to support audit, evaluations, bench-marking, quality improvement and clinical, epidemiological, health services and policy research to improve patient care and outcomes. Data in the NNRD comprise the Neonatal Data Set (ISB1595), an approved NHS Information Standard and include demographic details, daily records of interventions and treatments throughout the neonatal inpatient stay, information on diagnoses and outcomes, and follow-up health status at age two years. \n\nThe Neonatal Data Analysis Unit was founded to support the management and development of the National Neonatal Research Database (NNRD) established in 2007 by Professor Modi, and related research.\n\nMore information can be found at\nhttps://www.imperial.ac.uk/neonatal-data-analysis-unit",
    "url": "https://healthdatagateway.org/en/dataset/619",
    "uid": "67020745-9def-4c6e-b5ac-bb273bd0a20e",
    "datasource_id": 619,
    "source": "HDRUK"
  },
  {
    "id": 326,
    "name": "National Oesophago-Gastric Cancer Audit - clinical dataset",
    "description": "The NOGCA dataset includes continuously ascertained, record-level data on the diagnosis, investigation and management) received in hospitals in England and Wales for patients with invasive epithelial cancer of the oesophagus, gastro-oesophageal junction (GOJ) or stomach cancer.",
    "url": "https://healthdatagateway.org/en/dataset/544",
    "uid": "306274ee-a098-43cc-b113-2df8116a8703",
    "datasource_id": 544,
    "source": "HDRUK"
  },
  {
    "id": 327,
    "name": "National Paediatric Diabetes Audit - Parent and Patient Experience measures",
    "description": "Patient and parent captured experience data on paediatric diabetes unit activity captured February to July 2019 throughout England and Wales. The dataset includes questions about both the health checks received and diabetes outcomes achieved. No personal identifiable information is collected, but the identity of the unit where the patient was treated is included in the dataset.",
    "url": "https://healthdatagateway.org/en/dataset/573",
    "uid": "b0979e07-3331-408f-853b-5eeba80ce8f9",
    "datasource_id": 573,
    "source": "HDRUK"
  },
  {
    "id": 328,
    "name": "National Paediatric Diabetes Audit - survey of diabetes-related technologies",
    "description": "Organisational survey data collection covering the use of diabetes-related technologies in paediatric diabetes units (PDUs), based on the situation at each Paediatric Diabetes Unit on 31 March 2018. contains results from all one hundred and seventy-three  PDUs in England and Wales.",
    "url": "https://healthdatagateway.org/en/dataset/579",
    "uid": "eecbdd8d-92a0-4dfd-a216-1cf7f1f8c98f",
    "datasource_id": 579,
    "source": "HDRUK"
  },
  {
    "id": 329,
    "name": "National Paediatric Diabetes Audit - survey of workforce in PDUs",
    "description": "Organisational survey data collection covering the workforce in paediatric diabetes units (PDUs), based on the situation at each Paediatric Diabetes Unit on 31 March 2018. contains results from all one hundred and seventy-three PDUs in England and Wales.",
    "url": "https://healthdatagateway.org/en/dataset/576",
    "uid": "fe8f8e2c-2f77-464d-9d45-a601cb2f8bf9",
    "datasource_id": 576,
    "source": "HDRUK"
  },
  {
    "id": 330,
    "name": "National Paediatric Diabetes Audit - core clinical dataset",
    "description": "The National Paediatric Diabetes Audit (NPDA) includes record-level clinical data on the health checks (care processes) and outcomes for children and young people with diabetes who have attended paediatric diabetes units (PDUs) in England and Wales.",
    "url": "https://healthdatagateway.org/en/dataset/574",
    "uid": "e355c9b9-44f0-4ba3-a872-645aa88f4f2b",
    "datasource_id": 574,
    "source": "HDRUK"
  },
  {
    "id": 331,
    "name": "National Radiotherapy Dataset (RTDS) for CPRD Aurum",
    "description": "CPRD Aurum linked National Radiotherapy Dataset (RTDS) data contain records of radiotherapy services provided since April 2009, including teletherapy and brachytherapy. All radiotherapy delivered in England to patients in NHS facilities, or in private facilities where delivery was funded by the NHS, is included. Brachytherapy delivered for the treatment of non-malignant disease, radiotherapy delivered using unsealed sources, and non-therapeutic exposures delivered using radiotherapy machines (e.g. imaging) are not included.",
    "url": "https://healthdatagateway.org/en/dataset/681",
    "uid": "9c535c8d-ebac-4212-b602-fc2489df2c92",
    "datasource_id": 681,
    "source": "HDRUK"
  },
  {
    "id": 332,
    "name": "National Radiotherapy Dataset (RTDS) for CPRD GOLD",
    "description": "CPRD GOLD linked National Radiotherapy Dataset (RTDS) data contain records of radiotherapy services provided since April 2009, including teletherapy and brachytherapy. All radiotherapy delivered in England to patients in NHS facilities, or in private facilities where delivery was funded by the NHS, is included. Brachytherapy delivered for the treatment of non-malignant disease, radiotherapy delivered using unsealed sources, and non-therapeutic exposures delivered using radiotherapy machines (e.g. imaging) are not included.",
    "url": "https://healthdatagateway.org/en/dataset/698",
    "uid": "56a66de8-3470-4a36-ac71-4d5471e72654",
    "datasource_id": 698,
    "source": "HDRUK"
  },
  {
    "id": 333,
    "name": "National Records of Scotland (NRS) - Births Data",
    "description": "All Registrations to the National Records of Scotland of live births",
    "url": "https://healthdatagateway.org/en/dataset/86",
    "uid": "3b87eef0-bfb6-4bf4-84b9-29880197fec9",
    "datasource_id": 86,
    "source": "HDRUK"
  },
  {
    "id": 334,
    "name": "National Records of Scotland (NRS) - Deaths Data",
    "description": "All registrations to the National Records of Scotland of deaths",
    "url": "https://healthdatagateway.org/en/dataset/64",
    "uid": "e600dae2-a83c-4b7a-8d23-af4ac31ca374",
    "datasource_id": 64,
    "source": "HDRUK"
  },
  {
    "id": 335,
    "name": "National Records of Scotland (NRS) - Stillbirth Data",
    "description": "Registrations to the National Records of Scotland of births, stillbirths and deaths in the first year of life.",
    "url": "https://healthdatagateway.org/en/dataset/75",
    "uid": "dca0c95b-ae7d-4163-8c3b-a881f779df02",
    "datasource_id": 75,
    "source": "HDRUK"
  },
  {
    "id": 336,
    "name": "National Survey for Wales Dataset (NSWD)",
    "description": "The National Survey for Wales (NSW) is commissioned by the Welsh Government, Sport Wales, Natural Resources Wales, and the Arts Council of Wales. It is used in decision-making by those organisations and other public-sector bodies across Wales.\n\nThe survey covers a broad range of topics including education, exercise, health, social care, use of the internet, community cohesion, wellbeing, employment, and finances. The topics change regularly in order to keep up with changing needs for information. Some topics are only included periodically, where the results are slow-changing; and some topics are only asked of a random subsample of respondents, which allows more topics to be included.\n\nThe survey sample is adults aged 16+ living in private households. The survey does not cover people living in communal establishments (e.g. care homes, residential youth offender homes, hostels, and student halls).  A range of demographic questions is included, to allow for detailed cross-analysis of the results. \n\nFieldwork runs continuously, with topics updated each April. Each year’s data (from April to the following March) is deposited around six months later at the UK Data Archive so that the data is widely accessible for research purposes. The data collected is also linked with other datasets via the SAIL Databank (excluding any respondents who have asked for their data to not be linked). Respondents are able to opt out of having their results linked if they wish.\n\nFrom 2016-17 onwards, the National Survey for Wales replaced the Welsh Health Survey by incorporating questions on health conditions, physical activity, alcohol consumption and smoking.",
    "url": "https://healthdatagateway.org/en/dataset/315",
    "uid": "bcca421a-9077-4c47-9daa-b1bc030d3dee",
    "datasource_id": 315,
    "source": "HDRUK"
  },
  {
    "id": 337,
    "name": "National Unified Renal Translational Research Enterprise (NURTuRE)",
    "description": "The NURTuRE project was devised to create a national kidney biobank as recommended in the UK Renal Research Strategy 2016. \nStrategic Aims: To work towards achieving this NURTuRE will:\n1.\t Create a national Kidney Bio Bank for collection and storage of biological samples from 3,000 CKD patients and up > 800 NS patients, to provide a strategic resource for fundamental and translational research.\n2.\tDevelop and implement proactive UK protocol driven cohort studies in CKD and NS to investigate determinants of and risk factors for clinically important adverse outcomes.\n3.\tEngage patient cohorts, with consent to approach for any future research study.\nNURTuRE Objectives:\n1.\tThe provision of comprehensive clinical and laboratory data from cohort studies.\n2.\tThe provision of high quality bio-samples with centralised storage/retrieval.\n3.\tTo carry out core biomarker analysis of biopsy specimens in biofluids of all patients recruited and parallel assessment.\n4.\tFollow-up specimen collection. \nFirst patient recruitment - By 31 June 2017  \nCKD - baseline and 100 % follow up collections '“ over 2 years\nNS: baseline and 20% follow up - over 3 years.\nHealthy Volunteers - baseline",
    "url": "https://healthdatagateway.org/en/dataset/481",
    "uid": "15f07cef-f4cc-4a50-90bc-9fd40f8b6327",
    "datasource_id": 481,
    "source": "HDRUK"
  },
  {
    "id": 338,
    "name": "National Vascular Registry -  Abdominal Aortic Aneurysm Repair clinical dataset",
    "description": "The dataset contains continuously ascertained patient-level data for patients undergoing repair of an abdominal aortic aneurysm in NHS hospitals in England, Wales, Scotland and Northern Ireland. It includes information on referral, pre-procedural investigations and risk scoring, procedural details, readmission, follow up and in-hospital and 30-day mortality.",
    "url": "https://healthdatagateway.org/en/dataset/560",
    "uid": "fbe1ae63-0c12-48e5-be16-87d6bc7bd179",
    "datasource_id": 560,
    "source": "HDRUK"
  },
  {
    "id": 339,
    "name": "National Vascular Registry - Lower Limb Angioplasty clinical dataset",
    "description": "The dataset contains continuously ascertained patient-level data for patients undergoing lower limb angioplasty in NHS hospitals in England, Wales, Scotland and Northern Ireland. It includes information on referral, pre-procedural investigations and risk scoring, procedural details, post-operative outcome, readmission, complications noted at follow up and in-hospital and 30-day mortality.",
    "url": "https://healthdatagateway.org/en/dataset/558",
    "uid": "c8b6c319-239b-4792-9898-644a63d07ab5",
    "datasource_id": 558,
    "source": "HDRUK"
  },
  {
    "id": 340,
    "name": "National Vascular Registry - Organisational Audit dataset",
    "description": "The NVR organisational audit dataset includes data collected between 9 July and 8 October 2018 on hospital vascular services in England, Wales, Scotland and Northern Ireland. It includes data on the availability of and access to arterial surgical services, personnel and facilities, as well as detailed information about the organisation and systems relating to specific vascular interventions, such as bypass surgery and angioplasty for lower limb ischaemia.",
    "url": "https://healthdatagateway.org/en/dataset/570",
    "uid": "3c661240-e4d2-456c-a809-133c23f9c469",
    "datasource_id": 570,
    "source": "HDRUK"
  },
  {
    "id": 341,
    "name": "National Vascular Registry Audit - Lower Limb Bypass clinical dataset",
    "description": "The dataset contains continuously ascertained patient-level data for patients undergoing lower limb vascular bypass in NHS hospitals in England, Wales, Scotland and Northern Ireland. It includes information on referral, indications, pre-procedural investigations and risk scoring, procedural details, post-operative assessment, postoperative pathway, complications, additional unplanned procedures, readmission, follow up and in-hospital and 30-day mortality.",
    "url": "https://healthdatagateway.org/en/dataset/565",
    "uid": "d357be1e-27fe-4b2f-9174-3a2a03ecc12b",
    "datasource_id": 565,
    "source": "HDRUK"
  },
  {
    "id": 342,
    "name": "National Vascular Registry Audit - Carotid Endarterectomy Clinical Dataset",
    "description": "The dataset contains continuously ascertained patient-level data for patients undergoing carotid endarterectomy in NHS hospitals in England, Wales, Scotland and Northern Ireland. It includes referral, pre-procedural investigations and risk scoring, procedural details, follow up and in-hospital and 30-day mortality.",
    "url": "https://healthdatagateway.org/en/dataset/567",
    "uid": "f857055b-c29e-4c7f-99ce-65f4ed59648e",
    "datasource_id": 567,
    "source": "HDRUK"
  },
  {
    "id": 343,
    "name": "National Vascular Registry Audit  - Lower Limb Amputation clinical dataset",
    "description": "The dataset contains continuously ascertained patient-level data for patients undergoing lower limb amputation in NHS hospitals in England, Wales, Scotland and Northern Ireland. It includes information on referral, pre-procedural investigations, procedural details, post-operative pathway, additional unplanned procedures, readmission, follow up and in-hospital and 30-day mortality.",
    "url": "https://healthdatagateway.org/en/dataset/547",
    "uid": "03e77b9d-57b8-44d5-8d64-9b1325958b22",
    "datasource_id": 547,
    "source": "HDRUK"
  },
  {
    "id": 344,
    "name": "North West London Accident and Emergency Data (NWL A&E)",
    "description": "Initially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the SUS data set.\nSUS data covers all NHS Clinical Commissioning Groups (CCGs) in England, including:\n•\tprivate patients treated in NHS hospitals\n•\tpatients resident outside of England\n•\tcare delivered by treatment centres (including those in the independent sector) funded by the NHS\nEach SUS record contains a wide range of information about an individual patient admitted to an NHS hospital, including:\n•\tclinical information about diagnoses and operations\n•\tpatient information, such as age group, gender and ethnicity\n•\tadministrative information, such as dates and methods of admission and discharge\n•\tgeographical information such as where patients are treated and the area where they live\nNHS Digital apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published SUS data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\n\nWho SUS is for\nSUS provides data for the purpose of healthcare analysis to the NHS, government and others including:\n\nThe Secondary Users Service (SUS) database is made up of many data items relating to A&E care delivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data Set (CDS), and are generated by the patient administration systems within each hospital.\n•\tnational bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n•\tlocal Clinical Commissioning Groups (CCGs)\n•\tprovider organisations\n•\tgovernment departments\n•\tresearchers and commercial healthcare bodies\n•\tNational Institute for Clinical Excellence (NICE)\n•\tpatients, service users and carers\n•\tthe media\n\nUses of the statistics\nThe statistics are known to be used for:\n•\tnational policy making\n•\tbenchmarking performance against other hospital providers or CCGs  \n•\tacademic research\n•\tanalysing service usage and planning change\n•\tproviding advice to ministers and answering a wide range of parliamentary questions\n•\tnational and local press articles\n•\tinternational comparison\nMore information can be found at \nhttps://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/hospital-episode-statistics\nhttps://digital.nhs.uk/data-and-information/publications/statistical/hospital-accident--emergency-activity\"",
    "url": "https://healthdatagateway.org/en/dataset/529",
    "uid": "721db252-8069-433d-bb6d-4b44b243d364",
    "datasource_id": 529,
    "source": "HDRUK"
  },
  {
    "id": 345,
    "name": "North West London Admitted Patient Care Data (NWL APC)",
    "description": "The Secondary Users Service (SUS) database is made up of many data items relating to admitted patient care delivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data Set (CDS), and are generated by the patient administration systems within each hospital.  When a patient or service user is treated or cared for, information is collected which supports their treatment. This information is also useful to commissioners and providers of NHS-funded care for 'secondary' purposes - purposes other than direct or 'primary' clinical care - such as:  Healthcare planning Commissioning of services National Tariff reimbursement Development of national policy SUS is a secure data warehouse that stores this patient-level information in line with national standards and applies complex derivations which support national tariff policy and secondary analysis.   Access to SUS is managed using Role-Based Access Control (RBAC) which grants appropriate access levels to identifiable or de-identified data based on the users job role.",
    "url": "https://healthdatagateway.org/en/dataset/538",
    "uid": "788857b5-6a5b-4ec2-825e-d965e291cf45",
    "datasource_id": 538,
    "source": "HDRUK"
  },
  {
    "id": 346,
    "name": "North West London Adult Social Care Data (NWL ASC)",
    "description": "The Adult Social Care Outcomes Framework (ASCOF) measures how well care and support services achieve the outcomes that matter most to people. The measures are grouped into four domains which are typically reviewed in terms of movement over time. These domains are:\n\n- enhancing quality of life for people with care and support needs\n- delaying and reducing the need for care and support\n- ensuring that people have a positive experience of care and support\n- safeguarding adults whose circumstances make them vulnerable and protecting from avoidable harm\n\nThe ASCOF aims to give an indication of the strengths and weaknesses of social care in delivering better outcomes for people who use services. This report will be of interest to:\n\n- central government - for policy development and monitoring, and for parliamentary questions and Prime Minister's Questions\n- Councils with Adult Social Services Responsibilities (CASSRs) - for measuring local performance and for benchmarking against other CASSRs\n- charities\n- academics\n- the general public.",
    "url": "https://healthdatagateway.org/en/dataset/526",
    "uid": "de9cf0f5-b26a-4363-9df8-3628a4aa6b6e",
    "datasource_id": 526,
    "source": "HDRUK"
  },
  {
    "id": 347,
    "name": "North West London COVID-19 Patient Level Situation Report (NWL COVID19 PLD SITREP)",
    "description": "The NWL COVID19 PLD SITREP linked table is a direct daily feed from NWL providers. The table provides the patient level data related to COVID admissions in hospital since the outbreak of the pandemic, includes bed status/ventilation status etc.",
    "url": null,
    "uid": "1d7a0222-d5ae-41f7-afe9-8d3bd564b00e",
    "datasource_id": 347,
    "source": "HDRUK"
  },
  {
    "id": 348,
    "name": "North West London Community Data (NWL COM)",
    "description": "Providers of publicly-funded community services are legally mandated to collect and submit community health data, as set out by the Health and Social Care Act 2012. \n\nThe Community Services Data Set (CSDS) expands the scope of the  Children and Young People's Health Services Data Set (CYPHS) data set, by removing the 0-18 age restriction. The CSDS supersedes the CYPHS data set, to allow adult community data to be submitted.\n\nThe structure and content of the CSDS remains the same as the CYPHS data set. The Community Information Data Set (CIDS) has been retired, to remove the need for a separate local collection and reduce burden on providers.\n\nReports from the CSDS are available to download from the Community Services Data Set reports webpage.",
    "url": "https://healthdatagateway.org/en/dataset/539",
    "uid": "65dad445-ed81-4005-9f5a-4f8a7164043e",
    "datasource_id": 539,
    "source": "HDRUK"
  },
  {
    "id": 349,
    "name": "North West London Coordinate My Care (NWL CMC)",
    "description": "List of patients with CMC care plan (end of life care plan).",
    "url": "https://healthdatagateway.org/en/dataset/518",
    "uid": "c853b41b-fbd1-4896-b0fb-85303109fec4",
    "datasource_id": 518,
    "source": "HDRUK"
  },
  {
    "id": 350,
    "name": "North West London High Cost Drugs Data (NWL HCD)",
    "description": "The purpose of the Drugs Patient Level Contract Monitoring (DrPLCM) is to enable the interchange, in a uniform format, of monthly patient level drug contract monitoring data between commissioners and providers of healthcare. This will ensure that contract monitoring and reporting is consistent and comparable across all commissioning organisations and their footprints.",
    "url": "https://healthdatagateway.org/en/dataset/537",
    "uid": "75638d2c-556a-46b6-afcc-7ab425b34679",
    "datasource_id": 537,
    "source": "HDRUK"
  },
  {
    "id": 351,
    "name": "North West London Integrated Care Record (NWL ICR)",
    "description": "This dataset is a merged dataset of the existing datasets already published. This dataset contains all the columns made available within the individual datasets but as one large amalgamated dataset. This provides a longitudinal linked dataset of NWL patients to support pathway analysis, population health analysis and research analytics that requires data from various care settings.",
    "url": "https://healthdatagateway.org/en/dataset/541",
    "uid": "602d8093-6a1e-42cf-a89c-8bf0d47ecc8c",
    "datasource_id": 541,
    "source": "HDRUK"
  },
  {
    "id": 352,
    "name": "North West London Mental Health Data (NWL MH)",
    "description": "The MHSDS brings together information captured on clinical systems as part of patient care. It covers:\n\n- adult and children's mental health\n- learning disabilities or autism spectrum disorder\n- Children and Young People Improving Access to Psychological Therapies (CYP-IAPT) services\n- early intervention care pathway\n\nThe MHSDS covers not only services provided in hospitals but also outpatient clinics and in the community, where the majority of people in contact with these services are treated.",
    "url": "https://healthdatagateway.org/en/dataset/528",
    "uid": "2d3ec99a-a087-4355-8539-5a22cef58910",
    "datasource_id": 528,
    "source": "HDRUK"
  },
  {
    "id": 353,
    "name": "North West London Outpatient Care Data (NWL OP)",
    "description": "When a patient or service user is treated or cared for, information is collected which supports their treatment. This information is also useful to commissioners and providers of NHS-funded care for 'secondary' purposes - purposes other than direct or 'primary' clinical care - such as:  Healthcare planning Commissioning of services National Tariff reimbursement Development of national policy SUS is a secure data warehouse that stores this patient-level information in line with national standards and applies complex derivations which support national tariff policy and secondary analysis.   Access to SUS is managed using Role-Based Access Control (RBAC) which grants appropriate access levels to identifiable or de-identified data based on the users job role.  The Secondary Users Service (SUS) database is made up of many data items relating to outpatient care delivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data Set (CDS), and are generated by the patient administration systems within each hospital.",
    "url": "https://healthdatagateway.org/en/dataset/532",
    "uid": "9f1df859-f1d0-4f1e-959e-affd05138a25",
    "datasource_id": 532,
    "source": "HDRUK"
  },
  {
    "id": 354,
    "name": "North West London Pathology (NWL PATH)",
    "description": "Pathology results from NWL Pathology and Doctors Labs in regards to COVID-19 Tests.",
    "url": "https://healthdatagateway.org/en/dataset/519",
    "uid": "2c5f3ef6-53f8-42e6-a077-8718261df026",
    "datasource_id": 519,
    "source": "HDRUK"
  },
  {
    "id": 355,
    "name": "North West London Patient Index (NWL PI)",
    "description": "When a patient or service user is treated or cared for, information is collected which supports their treatment. This information is also useful to commissioners and providers of NHS-funded care for 'secondary' purposes - purposes other than direct or 'primary' clinical care - such as:\n\n- Healthcare planning\n- Commissioning of services\n- National Tariff reimbursement\n- Development of national policy\n\nSUS is a secure data warehouse that stores this patient-level information in line with national standards and applies complex derivations which support national tariff policy and secondary analysis. \n\nAccess to SUS is managed using Role-Based Access Control (RBAC) which grants appropriate access levels to identifiable, anonymised or pseudonymised data based on the users job role.",
    "url": "https://healthdatagateway.org/en/dataset/521",
    "uid": "1fe1e7bb-ef6e-4d89-9824-bb560afc81f0",
    "datasource_id": 521,
    "source": "HDRUK"
  },
  {
    "id": 356,
    "name": "North West London Primary Care Events Data (NWL PCE)",
    "description": "Organisations we collect information for include:\n\nNHS England - information is used to collect GP payments, based on achievements under the Quality and Outcomes Framework (QOF) and the delivery of quality services\nother government departments - for information about certain medical conditions and GP activity\nuniversities and other organisations - for academic research and services such as screening programmes\nThe systems that we use to collect data and information include:\n \n\nGeneral Practice Extraction Service (GPES) - used to collect information and data\nCalculating Quality Reporting Service (CQRS) - to record practice participation and to process and display information\nGP clinical systems - to record information at practice level\nWe are responsible for producing and maintaining the extract specification (business rules) to enable the extraction of these services.\n\nInteraction between a patient and the practice they are registered to\n\nAll Appointments data\n\nAll clinical diagnostics data\n\nAll administrative activity related to patients communication with practice\n\nAll test results related to patients\n\nAll screening information related to patients",
    "url": "https://healthdatagateway.org/en/dataset/536",
    "uid": "0f930154-0a0e-4bef-9464-59e657b904d6",
    "datasource_id": 536,
    "source": "HDRUK"
  },
  {
    "id": 357,
    "name": "North West London Primary Care Prescriptions Data (NWL PCP)",
    "description": "Organisations we collect information for include:\n\nNHS England - information is used to collect GP payments, based on achievements under the Quality and Outcomes Framework (QOF) and the delivery of quality services\nother government departments - for information about certain medical conditions and GP activity\nuniversities and other organisations - for academic research and services such as screening programmes\nThe systems that we use to collect data and information include:\n \n\nGeneral Practice Extraction Service (GPES) - used to collect information and data\nCalculating Quality Reporting Service (CQRS) - to record practice participation and to process and display information\nGP clinical systems - to record information at practice level\nWe are responsible for producing and maintaining the extract specification (business rules) to enable the extraction of these services.",
    "url": "https://healthdatagateway.org/en/dataset/531",
    "uid": "0fb33df6-ab36-4170-9fad-1eaf35a3a812",
    "datasource_id": 531,
    "source": "HDRUK"
  },
  {
    "id": 358,
    "name": "North West London population data (NWL POP)",
    "description": "NHAIS is responsible for providing critical national systems and providing a large range of products and services underpinning vital operations in the NHS.\n\nThese data-intense services include payments to GPs and managing Patient Registration records.",
    "url": "https://healthdatagateway.org/en/dataset/525",
    "uid": "1032850f-7631-4d9e-a760-b0532bffd21a",
    "datasource_id": 525,
    "source": "HDRUK"
  },
  {
    "id": 359,
    "name": "Northern Ireland Biobank (NIB)",
    "description": "Northern Ireland Biobank was established in 2010 to collect, store and distribute human samples for translational research and is primarily funded by the Northern Ireland Health and Social Care Research & Development Division of the Public Health Agency.  Cancer Research UK (CR-UK), the Friends of the Cancer Centre and Prostate Cancer UK have also provided financial support. The NIB complies with approvals from both the Office of Research Ethics in Northern Ireland and HSC R&D governance to host and distribute cohorts of quality assured biological samples linked with well-defined clinical and pathological data sets.",
    "url": "https://healthdatagateway.org/en/dataset/488",
    "uid": "c6e1ea88-f5bf-4a57-953f-688f313fcb04",
    "datasource_id": 488,
    "source": "HDRUK"
  },
  {
    "id": 360,
    "name": "OUH Patient Master Index",
    "description": "Locally defined dataset containing a full list of patient registrations held within the trust's EPR system. Details extend to include GP details, patient identifers and mortality data.",
    "url": null,
    "uid": "5d7465f6-ced8-434b-9907-d357ad125024",
    "datasource_id": 360,
    "source": "HDRUK"
  },
  {
    "id": 361,
    "name": "Obesity Research Biobank Syndicate (ORBiS)",
    "description": "Bariatric surgery produces substantial, long-term weight loss with reduced morbidity and mortality. Genetics can strongly influence this response, as well as the initial propensity to obesity. Several genes have been implicated but more in-depth mechanistic studies are needed to understand how genes affect energy regulation and mediate the beneficial effects of bariatric surgery.\nThe Obesity Research Biobank Syndicate (ORBiS) aims to fulfil this need. It provides a comprehensive collection of high-quality biological samples and patient data to facilitate mechanistic research and help translate it to improved treatments for patients. \nPatients undergoing bariatric surgery are recruited from multiple centres in the UK. With informed consent, blood or saliva samples are collected pre-operatively and tissue collected during surgery. Samples include: adipose tissue (subcutaneous and visceral), muscle, liver, stomach and small intestine. Relevant clinical and demographic data are linked and stored pseudo anonymised in a secure database. Tissue collections are transported to and processed in the ORBiS laboratory at University College London, and stored at UCL-Royal Free Hospital Biobank for future use.\nResearch programmes will be supported within and beyond the contributing sites. External researchers will be required to obtain individual REC approval prior to submitting an application.",
    "url": "https://healthdatagateway.org/en/dataset/482",
    "uid": "264a1987-a8cb-4a83-81e0-70b4c3893b7d",
    "datasource_id": 482,
    "source": "HDRUK"
  },
  {
    "id": 362,
    "name": "Oncology Clinical Trials Office, University of Oxford",
    "description": "OCTO was established in 2002 to run trials concerned with the practical application of high quality research into innovative and effective cancer therapies and prevention strategies.\nOur portfolio of trials includes a range of projects from first in human drug trials through to large Phase III clinical studies. Studies assess interventions including radiotherapy, drug combinations and novel imaging techniques.\nTumour types include: colorectal, oesophageal, melanoma, lung, breast, cervical, haematology, and bone sarcoma, in both adjuvant and advanced disease.",
    "url": "https://healthdatagateway.org/en/dataset/478",
    "uid": "4a65f6d6-f2e4-4d98-a3a4-94a32206836d",
    "datasource_id": 478,
    "source": "HDRUK"
  },
  {
    "id": 363,
    "name": "Optimum Patient Care Research Database",
    "description": "The Optimum Patient Care Research Database (OPCRD) is a primary care dataset from over 700 practices across the UK covering over 7.3 million patients augmented with respiratory questionnaire and clinical review data. OPCRD is established and maintained by Optimum Patient Care (OPC), a UK based social enterprise. OPC is guided by leading clinical and academic experts, it is one of the biggest primary care research networks in the world. The anonymous electronic medical records and patient questionnaires collected within OPCRD provide an essential source of real world data to promote evidence based research and quality improvement. OPC has grown to become a global leader in the provision of technologically enhanced health care data and clinical research services.",
    "url": "https://healthdatagateway.org/en/dataset/211",
    "uid": "e3ca35c1-b2e2-4479-adf1-6a78b1c8c088",
    "datasource_id": 211,
    "source": "HDRUK"
  },
  {
    "id": 364,
    "name": "Outpatient Appointment Dataset",
    "description": "Nationally defined dataset which ontaining administrative details on Outpatient appointments (attended, cancelled, DNA'ed) and some clinical coding of procedures using OPCS4",
    "url": null,
    "uid": "1c27bfbf-c659-42ff-8afe-d58250b49c07",
    "datasource_id": 364,
    "source": "HDRUK"
  },
  {
    "id": 365,
    "name": "Outpatient Appointments and Attendances - Scottish Morbidity Record (SMR00)",
    "description": "An SMR00 is generated for outpatients receiving care in the specialties listed when:\n\n-they attend a medical consultant outpatient clinic;\n-they meet with a consultant or senior member of his/her team outwith an outpatient clinic session (including the patient's home).\n-they attend a clinic run by a nurse or an AHP identified as the Health Care Professional Responsible for Care for that clinic and who has legal and clinical responsibility for that patient.\n\nThe dataset is generally fully complete and ready for analysis three month preceding the current date.  So for example at the end of August, data is available until the end of May.",
    "url": "https://healthdatagateway.org/en/dataset/68",
    "uid": "04cb9964-54fb-4529-b09b-735c3daa1c7b",
    "datasource_id": 68,
    "source": "HDRUK"
  },
  {
    "id": 366,
    "name": "Outpatient Database for Wales (OPDW)",
    "description": "Attendance information for all NHS Wales hospital outpatient appointments.\n\nThe data are collected and coded at each hospital. Administrative information is collected from the central PAS (Patient Administrative System), such as specialty of care, appointment date and attendance status.\n\nThis dataset contains all scheduled outpatient appointments, including those where the patient failed to attend.",
    "url": "https://healthdatagateway.org/en/dataset/346",
    "uid": "d331159b-b286-4ab9-8b36-db39123ec229",
    "datasource_id": 346,
    "source": "HDRUK"
  },
  {
    "id": 367,
    "name": "Outpatient Referral (OPRD)",
    "description": "Monthly return submitted by Local Health Boards.\nA complete referral pathway to secondary care, including all clinical referrals received from General Practitioner, General Dental Practitioners, Community Dental Services, A&E Departments, self referrals, walk-ins or emergency patients accompanied by a GP letter, and Consultant to Consultant Referrals.\n\nPlease be aware that these columns no longer contain data from the provider:\nREF_PARENT_CURR_NAME, REF_PARENT_CURR_CD, REF_ORG_CURR_NAME_WELSH_ONLY, REF_ORG_CURR_CD_WELSH_ONLY, REF_ORG_CURR_NAME, REF_ORG_CURR_CD, REF_ORG_NAME, REF_ORG_CD, PAT_REQ_GP_CLUSTER_NAME, PAT_REG_GP_CLUSTER_CD",
    "url": "https://healthdatagateway.org/en/dataset/308",
    "uid": "7465c65e-c321-42e9-a2f4-6bc664caf1fc",
    "datasource_id": 308,
    "source": "HDRUK"
  },
  {
    "id": 368,
    "name": "Oxford Musculoskeletal Biobank",
    "description": "The Oxford Musculoskeletal Biobank (OMB) is a resource of tissue and blood samples donated by patients for use in medical research (primarily musculoskeletal). The Biobank provides a simple and efficient way to collect and store samples according to regulatory requirements, and it ensures fair access to the samples. Samples will usually be used for research studies which may contribute to increasing the knowledge and understanding of musculoskeletal diseases in order to improve diagnosis and treatment, and ultimately patient care.",
    "url": "https://healthdatagateway.org/en/dataset/492",
    "uid": "ccd6e59d-3582-4f47-a4cd-f5845192d75c",
    "datasource_id": 492,
    "source": "HDRUK"
  },
  {
    "id": 369,
    "name": "PHOENIX DDR-Anti-PD-L1 Trial",
    "description": "PHOENIX DDR/Anti-PD-L1 Trial: A pre-surgical window of opportunity and post-surgical adjuvant biomarker study of DNA damage response inhibition and/or anti-PD-L1 immunotherapy in patients with neoadjuvant chemotherapy resistant residual triple negative breast cancer.",
    "url": "https://healthdatagateway.org/en/dataset/486",
    "uid": "df94730b-374b-4d91-8116-1c10a7c77c4e",
    "datasource_id": 486,
    "source": "HDRUK"
  },
  {
    "id": 370,
    "name": "PHOTO Translational bladder cancer biorepository",
    "description": "Translational study associated with the PHOTO trial - A pragmatic randomised controlled phase III trial, investigating the efficacy transurethral resection of bladder tumour (TURBT) using photo-dynamic diagnosis (PDD) under blue light in intermediate and high risk non-muscle invasive bladder cancer (NMIBC). The translational study is establishing a well-characterised cohort of patients with intermediate and\nhigh-risk NMIBC including clinical data, urine, blood\nand tumour specimens that would be available for\nseparately funded research of genotypic and phenotypic studies.",
    "url": "https://healthdatagateway.org/en/dataset/489",
    "uid": "07d1b768-78f6-41e8-995a-66afd2549b34",
    "datasource_id": 489,
    "source": "HDRUK"
  },
  {
    "id": 371,
    "name": "PIONEER - Genomics Patients and related data",
    "description": "A dataset containing longitudinal data for WM Genomics Patients from the 100K Genomes project",
    "url": null,
    "uid": "915a2105-623d-4829-8414-4e6b8e3c11b9",
    "datasource_id": 371,
    "source": "HDRUK"
  },
  {
    "id": 372,
    "name": "POUT-T",
    "description": "POUT-T: The translational substudy of a phase III randomised trial of peri-operative chemotherapy versus surveillance in upper tract urothelial cancer.\nThe objectives of POUT-T are: to investigate the molecular pathogenesis of Upper Tract Urothelial Carcinoma (UTUC); to identify prognostic and predictive biomarkers of UTUC; and to identify diagnostic biomarkers of UTUC.\nPOUT-T participants are requested to provide the following specimens pre-operatively, post operatively, 6 months following surgery, and at disease recurrence: whole blood for germline DNA analysis; whole blood for cell-free DNA analysis; first morning urine for DNA, proteome and metabolome analyses. In addition, paraffin-embedded tumour tissue from nephro-ureterectomy is requested for immunohistochemistry and DNA/RNA analyses.",
    "url": "https://healthdatagateway.org/en/dataset/420",
    "uid": "c6e0e70e-7f23-4a44-a2cf-1d7d87be7808",
    "datasource_id": 420,
    "source": "HDRUK"
  },
  {
    "id": 373,
    "name": "PTCL Biobank",
    "description": "An Observational Study of Peripheral T cell Lymphoma: Establishment of a Biobank and Database.\nThe outcome of this study will be a biobank of PTCL cases with linked clinical data and serum, saliva and plasma samples to enable assessments of treatment response and prediction of relapse.",
    "url": "https://healthdatagateway.org/en/dataset/418",
    "uid": "19fa25a7-c15d-441c-b635-ad49043e7a96",
    "datasource_id": 418,
    "source": "HDRUK"
  },
  {
    "id": 374,
    "name": "Paediatric Intensive Care Audit Network - core dataset (admission)",
    "description": "Paediatric Intensive Care Audit Network (PICANet) is an audit database recording details of the treatment of all critically ill children in paediatric intensive care units (PICUs).  The admission dataset includes admission details, past medical history, blood tests, diagnoses and procedures, daily interventions, discharge and 30 day survival.",
    "url": "https://healthdatagateway.org/en/dataset/566",
    "uid": "4ca76471-6970-4ad4-87c7-fae77a98cbf5",
    "datasource_id": 566,
    "source": "HDRUK"
  },
  {
    "id": 375,
    "name": "Paediatric Intensive Care Audit Network - core dataset (referral)",
    "description": "Paediatric Intensive Care Audit Network (PICANet) is an audit database recording details of the treatment of all critically ill children in paediatric intensive care units (PICUs).  The referral dataset is completed for every patient where there is a request for transport within the PIC service and/or a PICU admission when clinicians agree that the patient requires PIC transport and/or a PICU bed.",
    "url": "https://healthdatagateway.org/en/dataset/542",
    "uid": "79ae4881-6223-471a-a278-6b2d3b84f971",
    "datasource_id": 542,
    "source": "HDRUK"
  },
  {
    "id": 376,
    "name": "Paediatric Intensive Care Audit Network - core dataset (transport)",
    "description": "Paediatric Intensive Care Audit Network (PICANet) is an audit database recording details of the treatment of all critically ill children in paediatric intensive care units (PICUs). The transport dataset includes information at patient level on the type of transport team, staffing of transport, outcome of transport and critical incidents.",
    "url": "https://healthdatagateway.org/en/dataset/569",
    "uid": "45310176-6331-4bdd-bc53-d9194e212a35",
    "datasource_id": 569,
    "source": "HDRUK"
  },
  {
    "id": 377,
    "name": "Paediatric Intensive Care Audit Network - customised data collection",
    "description": "Paediatric Intensive Care Audit Network (PICANet) is an audit database recording details of the treatment of all critically ill children in paediatric intensive care units (PICUs). Customised Data Collection is the collection of any additional data beyond the PICANet core dataset for the purposes of audit. Collection is voluntary and topics vary.",
    "url": "https://healthdatagateway.org/en/dataset/549",
    "uid": "6c78af2c-3898-435e-b1f4-e6a44682dc71",
    "datasource_id": 549,
    "source": "HDRUK"
  },
  {
    "id": 378,
    "name": "Pathology Data from WRRS (PATH) - Legacy",
    "description": "Please note this is a legacy dataset where the latest available data was updated in 2018. It is advised you refer to the live dataset WRRS (Welsh Results Reports Service) for post-2018 coverage.\n\nPathology Test Results and all Radiology reports for Wales. Data coverage differs by geography:\n\n2012 for Swansea (ABMU)\n\n2017 for Newport (AB)\n\n2012 for North Wales (BC)\n\n2007 for Cwm Taf\n\n2015/16 for Cardiff (CV)\n\n2014 for West Wales (HD)",
    "url": "https://healthdatagateway.org/en/dataset/293",
    "uid": "48ef8f54-9606-4e6f-9256-3cc07ffd10b8",
    "datasource_id": 293,
    "source": "HDRUK"
  },
  {
    "id": 379,
    "name": "Patient Episode Dataset for Wales (PEDW)",
    "description": "NHS Wales hospital admissions (Inpatients and daycases) dataset comprising of attendance and clinical information for all hospital admissions: includes diagnoses and operations performed. Includes spell and episode level data.\n\nThe data are collected and coded at each hospital. Administrative information is collected from the central PAS (Patient Administrative System), such as specialty of care, admission and discharge dates. After the patient is discharged the handwritten patient notes are transcribed by clinical coder into medical coding terminology (ICD10 and OPCS).\n\nThe data held in PEDW is of interest to public health services since it can provide information regarding both health service utilisation and also the incidence and prevalence of disease. However, since PEDW was created to track hospital activity from the point of view of payments for services, rather than epidemiological analysis, the use of PEDW for public health work is not straightforward. For example:\n\nCounts will vary depending on the number of diagnosis fields used e.g. primary only, all fields;\nThere are a number of different things that can be counted in PEDW e.g. individual episodes of care, admissions, discharges, periods of continuous care (group of episodes), patients or procedures.\nWhen looking at diagnosis or procedures the number will vary depending on whether you look at only in the primary diagnosis / procedure field or if the secondary fields are also included.\nCoding practices vary. In particular, coding practices for recording secondary diagnoses is likely to vary for different hospitals. This makes regional variations more difficult to interpret. The validation process led by the Corporate Health Improvement Programme and implemented by Digital Health and Care Wales (DHCW) is aiming to address some of these inconsistencies.\n\nDue to the complexity and pitfalls of PEDW it is recommended that any PEDW requests for public health purposes are discussed with a member of the SAIL team. In turn the SAIL will seek advice from DHCW if required.\n\nThis dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.",
    "url": "https://healthdatagateway.org/en/dataset/318",
    "uid": "4c33a5d2-164c-41d7-9797-dc2b008cc852",
    "datasource_id": 318,
    "source": "HDRUK"
  },
  {
    "id": 380,
    "name": "Patient Reported Outcome Measures",
    "description": "Patient Reported Outcome Measures (PROMs) have been collected nationally since April 2009.The PROMs programme covers four common elective surgical procedures: groin hernia operations, hip replacements, knee replacements and varicose vein operations. PROMs are a means of collecting information on the effectiveness of care delivered to NHS patients as perceived by the patients themselves. The collection of this data will add to the set of information available on the care delivered to NHS-funded patients and will complement, and be used in conjunction with, existing information on the quality of services. Data was released for the first time as an experimental statistic in April 2010 at which time the extract service was also launched. \n\nThe PROMs dataset is made up of many data items relating to information collected through the Patient Reported Outcome Measures questionnaires completed by patients for a number of common elective procedures. Patients submit the questionnaires before and after their operation in order to establish their perceived levels of health and the impact the operation has had on their quality of life. The PROMs dataset includes responses to individual questions as well as overall totals for a number of different scoring systems for both pre-operative and post-operative questionnaires",
    "url": "https://healthdatagateway.org/en/dataset/866",
    "uid": "55614ed3-2484-4408-a4e9-e5fffed9e8e4",
    "datasource_id": 866,
    "source": "HDRUK"
  },
  {
    "id": 381,
    "name": "Patient SIMD Postcode",
    "description": "Longitudinal postcode and Scottish Deprivation data.From 1995 - 2017.",
    "url": null,
    "uid": "798567eb-d790-466e-880b-75a1e94ffdf2",
    "datasource_id": 381,
    "source": "HDRUK"
  },
  {
    "id": 382,
    "name": "CPRD Aurum Small Area data (patient)",
    "description": "Patient postcode linked measures are available for patients in English practices that have consented to participate in the linkage scheme. The latest available patient postcode of residence is mapped to an LSOA boundary. The LSOA of residence then allows linkage to several measures of area level deprivation and a rural-urban classification. These measures can be used as a proxy for socio-demographic and socio-economic data which are generally poorly recorded in the primary care data given they do not directly relate to a patient's care.\nAccess is provided by CPRD subject to protocol approval. Further information is available at https://www.cprd.com/linked-data.",
    "url": "https://healthdatagateway.org/en/dataset/670",
    "uid": "bd85a09c-fe4a-41b8-81d2-77feadcd1a0a",
    "datasource_id": 670,
    "source": "HDRUK"
  },
  {
    "id": 383,
    "name": "CPRD GOLD Small Area data (patient)",
    "description": "Patient postcode linked measures are available for patients in English practices that have consented to participate in the linkage scheme. The latest available patient postcode of residence is mapped to an LSOA boundary. The LSOA of residence then allows linkage to several measures of area level deprivation and a rural-urban classification. These measures can be used as a proxy for socio-demographic and socio-economic data which are generally poorly recorded in the primary care data given they do not directly relate to a patient's care.\nAccess is provided by CPRD subject to protocol approval. Further information is available at https://www.cprd.com/linked-data.",
    "url": "https://healthdatagateway.org/en/dataset/671",
    "uid": "f5c9ba01-a9cb-4a85-9850-d9466b2ff4fb",
    "datasource_id": 671,
    "source": "HDRUK"
  },
  {
    "id": 384,
    "name": "Personal Demographic Service",
    "description": "The PDS helps healthcare professionals to identify patients and match them to their health records. It also allows them to contact and communicate with patients.",
    "url": null,
    "uid": "f8be6d46-61de-4ed3-b127-76cdf92209d0",
    "datasource_id": 384,
    "source": "HDRUK"
  },
  {
    "id": 385,
    "name": "Pneumonia case management practices in selected communities in Pakistan",
    "description": "We carried out an on the ground assessment of pneumonia case management practices at three levels of healthcare – community, first level care facility and practitioner level. Observations were conducted across three provinces of Pakistan (Punjab, Sindh, Khyber Pakhtun Khwah), Azad Jammu Kashmir and the federal capital. Study sites were randomly selected. Upon site selection, observations were made across the following levels of healthcare: \n- community level, \n- first level care facility (FLCF) and \n- practitioner level both in the public and private sector.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/acute-respiratory-disorders/pneumonia-case-management",
    "url": "https://healthdatagateway.org/en/dataset/250",
    "uid": "cf190350-74c6-45f2-8ede-69ace62eb88d",
    "datasource_id": 250,
    "source": "HDRUK"
  },
  {
    "id": 386,
    "name": "Postponed Admitted Procedures (CAPD)",
    "description": "Information on reason for cancelled and postponed admitted procedures. Covers elective inpatient and day case activity. Be aware of several exclusion criteria: LHBs are required to submit data for elective inpatient and day case activity only via the submission of the postponed intended admission. Cancelled Regular Day / Night Attendance procedures are excluded. Maternity activity (i.e. Admission Method ‘31’ and ‘32’) is excluded. Inpatient and day case patients who are admitted and discharged without having their elective procedure undertaken are included. Those procedures that are brought forward are excluded. Procedures that are postponed one or more times during the same admitted stay but are subsequently performed during that admitted stay are excluded. Procedures that are postponed one or more times during the same admitted stay and not performed during that stay should be reported as one postponement. Procedures that are postponed twice for the same intended admission date should be counted as one postponement. Procedures that are postponed but are subsequently rescheduled and performed during the same admitted stay are excluded. Patients whose procedures were scheduled to take place outside Wales are excluded. Local Health Boards are required to submit data on Welsh residents whose procedures are intended to take place in their Local Health Board. For example, if a patient is on a waiting list in Cardiff University Health Board but are sent to Cwm Taf to have their procedure (waiting list initiative). Cwm Taf schedule the intended admission date and subsequently postpone the procedure, the postponement should be submitted by Cwm Taf. Velindre NHS Trust are excluded.\n\nFormerly known as Cancelled Admitted Procedures Dataset.\n\nThis dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.",
    "url": "https://healthdatagateway.org/en/dataset/325",
    "uid": "bff94600-a254-4111-82f7-6167084d81ca",
    "datasource_id": 325,
    "source": "HDRUK"
  },
  {
    "id": 387,
    "name": "CPRD Aurum Small Area data (practice)",
    "description": "The general practice postcode linkages are available for all practices in CPRD GOLD and CPRD Aurum and use the general practice postcode which is linked via LSOA, SOA in Northern Ireland and datazone (DZ) in Scotland. The general practice postcode linkage includes several well-known area-based measures of deprivation, including the Index of Multiple Deprivation, Townsend Deprivation Index and Carstairs Index, and Rural-Urban Classification, which are available at the LSOA level for linkage to CPRD primary care data through the practice postcode. Additionally, Sub-Integrated Care Board Locations (Sub-ICB Locs) pseudonym (practice level, England-only) is available.",
    "url": "https://healthdatagateway.org/en/dataset/678",
    "uid": "9f689ef4-26c6-4f85-80ff-5116974f315a",
    "datasource_id": 678,
    "source": "HDRUK"
  },
  {
    "id": 388,
    "name": "Pregnancy Register for CPRD GOLD",
    "description": "The CPRD GOLD Pregnancy Register contains a list of all pregnancy episodes recorded in CPRD GOLD. The CPRD Pregnancy Registers are derived from the primary care data based on an algorithm. Each version of the Pregnancy Register is built from a corresponding CPRD GOLD database release.\n\nEach record within the Pregnancy Register represents a unique pregnancy episode with a number of variables provided including details of the start and end of the pregnancy, trimester dates and the outcome of the pregnancy. There may be more than one episode per woman. In addition to this, live births in the CPRD GOLD Pregnancy Register are linked to the CPRD GOLD Mother-Baby Link so that researchers may access de-identified information on the resulting infants.",
    "url": "https://healthdatagateway.org/en/dataset/684",
    "uid": "14aaf184-5922-4459-affb-6c9e55c12176",
    "datasource_id": 684,
    "source": "HDRUK"
  },
  {
    "id": 389,
    "name": "Prescribing",
    "description": "Primary care prescriptions for patients in the Tayside and Fife regions in Scotland. The data contains granular information such as drug strength, unit and dose.\nTayside 1989 – Current; Fife 2009 – onwards.\nThe data is coded using the BNF standard but can also be provided with SNOMED/DM+D codes, or in the OMOP format.",
    "url": "https://healthdatagateway.org/en/dataset/104",
    "uid": "6d044471-62d2-4190-b315-503f77d33ef2",
    "datasource_id": 104,
    "source": "HDRUK"
  },
  {
    "id": 390,
    "name": "Prescribing Information System (PIS)",
    "description": "The information is supplied by Practitioner & Counter Fraud Services Division (P&CFS) who is responsible for the processing and pricing of all prescriptions dispensed in Scotland. These data are augmented with information on prescriptions written in Scotland that were dispensed elsewhere in the United Kingdom. GP’s write the vast majority of these prescriptions, with the remainder written by other authorised prescribers such as nurses and dentists. Also included in the dataset are prescriptions written in hospitals that are dispensed in the community. Note that prescriptions dispensed within hospitals are not included. Data includes CHI number, prescriber and dispenser details for community prescribing, costs and drug information. Data on practices (e.g. list size), organisational structures (e.g. practices within Community Health Partnerships (CHPs) and NHS Boards), prescribable items (e.g. manufacturer, formulation code, strength) are also included.\nAround 100 million data items are loaded per annum.",
    "url": "https://healthdatagateway.org/en/dataset/69",
    "uid": "22e3943e-edb5-44a1-9e4e-22b0f7a31767",
    "datasource_id": 69,
    "source": "HDRUK"
  },
  {
    "id": 391,
    "name": "Problems, Diagnosis and Procedures",
    "description": "Locally defined dataset which contains SNOMED recorded terms for patient Problems, Diagnosis and Procedures",
    "url": null,
    "uid": "51b41359-1558-48df-9950-88f75ca6ac4c",
    "datasource_id": 391,
    "source": "HDRUK"
  },
  {
    "id": 392,
    "name": "Prospective Observation of Fibrosis in the Lung Clinical Endpoints",
    "description": "https://clinicaltrials.gov/ct2/show/NCT01134822\n\nThe PROFILE study is a longitudinal observational study of patients with Idiopathic Pulmonary Fibrosis and Non Specific Interstitial Pneumonitis. Patients were recruited to the study within 6 months of diagnosis and followed up for three years with physiological, biological, genetic and quality of life assessments assessments at various intervals over these three years. The aim of this study was to understand the natural history of fibrotic lung disease and identify factors that would be able to predict the heterogenous disease course amongst these patients.",
    "url": "https://healthdatagateway.org/en/dataset/262",
    "uid": "bb8de9d2-cc38-4c40-b104-ffd167f64f76",
    "datasource_id": 262,
    "source": "HDRUK"
  },
  {
    "id": 393,
    "name": "Public Health England Seroepidemiology Unit",
    "description": "The basis of the PHE (formerly the Health Protection Agency) Seroepidemiology Programme is a large collection of sera representative of the general population of England, forming a unique and valuable public health resource. The collection is stored and maintained by the Seroepidemiology Unit (SEU) at the Public Health Laboratory (PHL), Manchester. \nSera submitted to the SEU are residues of specimens submitted for diagnostic testing.  They sample the population range and are anonymised prior to archiving (retaining age, sex, date of collection and source laboratory only). Collection of sera is continuing through collaboration with the PHE Microbiology Services Division (MSD) and some NHS laboratories throughout England, and has occurred annually since 1986.  Over 230,000 sera are now stored and catalogued. The collection can be made available for testing to anyone wishing to use it to address issues related to public health policy.",
    "url": "https://healthdatagateway.org/en/dataset/424",
    "uid": "89497432-17e0-49f4-96a4-65ef57d07701",
    "datasource_id": 424,
    "source": "HDRUK"
  },
  {
    "id": 394,
    "name": "Isolates Cohorts",
    "description": "The QTL (Quantitative Trait Locus) programme was based at the MRC Human Genetics Unit in the University of Edinburgh. The ongoing research programme uses the unique population structures in our Scottish and Croatian cohorts to deliver biological understanding of the causes of variation in complex traits. Together, the Viking Genes studies contain 10,000 samples from volunteers with ancestry from the Northern and Western Isles of Scotland, www.ed.ac.uk/viking. Collectively, the CROATIA cohorts contain 6,000 participants. All of these biobanks were constructed from people with high kinship and extensive pedigree structures. They were collected together with detailed phenotype data, some of which is longitudinal. Plasma, serum, urine and DNA samples, as well as detailed genomic, proteomic and metabolomics data can be made available for collaborative research.",
    "url": "https://healthdatagateway.org/en/dataset/471",
    "uid": "351cc017-caff-4303-8a48-1aa1b90fce2b",
    "datasource_id": 471,
    "source": "HDRUK"
  },
  {
    "id": 395,
    "name": "Quality of Life of Cancer Survivors in England: Pilot Patient Reported Outcomes Measures Survey (2011) for CPRD Aurum",
    "description": "CPRD Aurum linked Quality of Life of Cancer Survivors in England pilot survey (QOLP) data contain quality of life information from samples of survivors with breast, colorectal, prostate cancer or non-Hodgkin’s lymphoma, diagnosed between 2006 and 2010.",
    "url": null,
    "uid": "3a387099-e69e-479d-a937-2cc3795d7170",
    "datasource_id": 395,
    "source": "HDRUK"
  },
  {
    "id": 396,
    "name": "Quality of Life of Cancer Survivors in England: Pilot Patient Reported Outcomes Measures Survey (2011) for CPRD GOLD",
    "description": "CPRD GOLD linked Quality of Life of Cancer Survivors in England pilot survey (QOLP) data contain quality of life information from samples of survivors with breast, colorectal, prostate cancer or non-Hodgkin’s lymphoma, diagnosed between 2006 and 2010.",
    "url": null,
    "uid": "4b1da116-44a5-4952-a09e-deb0d37af2f2",
    "datasource_id": 396,
    "source": "HDRUK"
  },
  {
    "id": 397,
    "name": "Quality of Life of Colorectal Cancer Survivors in England: Patient Reported Outcome Measures Survey for CPRD Aurum",
    "description": "CPRD Aurum linked Quality of Life of Colorectal Cancer Survivors in England survey (QOLC) data contain patient recorded outcomes from samples of cancer survivors with colorectal cancer diagnosed between 2010 and 2011.",
    "url": null,
    "uid": "c84192af-a477-4ed5-a95b-c88e1f98ce1b",
    "datasource_id": 397,
    "source": "HDRUK"
  },
  {
    "id": 398,
    "name": "Quality of Life of Colorectal Cancer Survivors in England: Patient Reported Outcome Measures Survey for CPRD GOLD",
    "description": "CPRD GOLD linked Quality of Life of Colorectal Cancer Survivors in England survey (QOLC) data contain patient recorded outcomes from samples of cancer survivors with colorectal cancer diagnosed between 2010 and 2011.",
    "url": null,
    "uid": "7e9d06a2-efe8-447f-ba3e-d4cbb7df2ef7",
    "datasource_id": 398,
    "source": "HDRUK"
  },
  {
    "id": 399,
    "name": "RAPPER",
    "description": "RAPPER (Radiogenomics: Assessment of Polymorphisms for Predicting the Effects of Radiotherapy) is a national radiogenomics study investigating the association between common genetic variation determined by single nucleotide polymorphisms (SNPs) and radiation toxicity.",
    "url": "https://healthdatagateway.org/en/dataset/445",
    "uid": "76d26f0f-0136-4ae5-8be9-37782a70bac5",
    "datasource_id": 445,
    "source": "HDRUK"
  },
  {
    "id": 400,
    "name": "RATHL Trial",
    "description": "RATHL is a multicentre, randomised, phase III trial comparing treatment outcome for patients with advanced Hodgkin lymphoma, using FDG-PET imaging after 2 cycles of ABVD to determine response and subsequent management.\nRecruitment target: 1200 patients\nPatients received 2 cycles of ABVD and then had a PET-CT scan.    PET negative patients were randomised to either ABVD or AVD for a further 4 cycles.  PET positive patients received either BEACOPP-14, for 4-6 cycles or BEACOPP escalated, for 3-4 cycles.\nSamples collected for trial:  Formalin fixed paraffin embedded tumour block - sent to HMDS, Leeds.  Blood sample to be analysed at site.  Blood sample - sent to Simpson Centre for Reproductive Health, Edinburgh.",
    "url": "https://healthdatagateway.org/en/dataset/446",
    "uid": "e5ec63dd-4da5-4119-99c8-5cc79045618c",
    "datasource_id": 446,
    "source": "HDRUK"
  },
  {
    "id": 401,
    "name": "REQUITE",
    "description": "Validating Predictive Models and Biomarkers of Radiotherapy Toxicity to Reduce Side-Effects and Improve Quality of Life in Cancer Survivors. The purpose of this international study is to try to predict which patients are more likely to have side effects from radiotherapy. Funded by the European Commission FP7 HEALTH scheme.",
    "url": "https://healthdatagateway.org/en/dataset/443",
    "uid": "b5217dcb-582f-4914-ae45-9eb3899add30",
    "datasource_id": 443,
    "source": "HDRUK"
  },
  {
    "id": 402,
    "name": "RIO",
    "description": "Window study of the PARP inhibitor rucaparib in patients with primary triple negative or BRCA1/2 related breast cancer (RIO)",
    "url": "https://healthdatagateway.org/en/dataset/460",
    "uid": "18bc81e3-7d99-4152-bd7a-3c90ee7e489d",
    "datasource_id": 460,
    "source": "HDRUK"
  },
  {
    "id": 403,
    "name": "Referral to Treatment Times (RTTD)",
    "description": "Monitoring the 26 week Referral to Treatment Time target.\n\nMonthly return submitted by Local Health Boards.\n\nTreatment/diagnostic intervention referral pathway, including welsh residents treated (or waiting for treatment) in England.\n\nhttps://data.gov.uk/dataset/169ea19a-21e0-472a-8245-ec2d60c219f9/referral-to-treatment-times-for-wales",
    "url": "https://healthdatagateway.org/en/dataset/349",
    "uid": "0bf54842-c1db-4bdb-9f55-c5be490bf758",
    "datasource_id": 349,
    "source": "HDRUK"
  },
  {
    "id": 404,
    "name": "Renal Register – PHS National Dataset",
    "description": "Extract from PHS Renal register for Tayside and Fife. Patients included are High Dependency, Peritoneal Dependent and transplant. Patients excluded would be any others. Eg Low Clearance, Renal Referrals - basically any patients who are not actually dialysing or transplanted.",
    "url": "https://healthdatagateway.org/en/dataset/120",
    "uid": "32edd77d-533c-423f-b045-937507f8505d",
    "datasource_id": 120,
    "source": "HDRUK"
  },
  {
    "id": 405,
    "name": "Research Donors Ltd",
    "description": "For researchers   \nBlood collection\nWe collect blood from donors aged between 18-60, who have been screened to ensure that it is safe for them to donate, that they are in good health, and not taking medications that might interfere with experiments.\nDonors will have given consent for their samples to be used in a wide range of biomedical research applications, including genetic research and that undertaken by commercial organisations.\nWe are also able to collect samples from specific donors, for example within a certain age range, ethnicity or with a specific blood group. We can collect samples into a wide range of anticoagulant tubes, blood bags and other devices as required.\nProcessing\nWe use advanced technologies in our laboratory to test donated blood samples and to process them into the formats which are most useful to researchers. These include:\nWhole blood\n From a single tube of whole blood in a specific anticoagulant tube, up to a full bag of blood we can collect the exact volume of blood you need from donors best suited to your study and deliver it within precisely defined timeframes at the best temperature for your need.\nBuffy coat\n We routinely process whole blood units into buffy coat preparations which are used as a source of PBMCs by our clients. We can also process smaller volumes of blood from blood collection tubes into buffy coat.\nPlasma\n We can either collect a specific volume of blood in the anticoagulant of your choice and prepare plasma, or you can source plasma samples that we have banked from a wide selection of donors for screening purposes.\nSerum\n We can either collect a specific volume of blood and prepare serum to suit your needs, or you can select from serum samples that we have previously banked from a wide selection of donors.\nPBMCs\n We are preparing PBMCs from whole units of blood within a very short timeframe from venepuncture (typically processing begins within 1 hour). PBMCs are produced using a density centrifugation, characterized and frozen in a proprietary freezing solution designed to maximise cell viability.\nCustom services\n We can develop specific processes to suit your exact requirements, so please get in contact if there is something specific you need for your research",
    "url": "https://healthdatagateway.org/en/dataset/491",
    "uid": "fcb62cb3-c876-47a3-961e-fb27c6f718fa",
    "datasource_id": 491,
    "source": "HDRUK"
  },
  {
    "id": 406,
    "name": "Returned datasets",
    "description": "The NIHR BioResource asks those collecting data on participants as part of sample-only or recall studies to offer data for re-use by others. These offers may be taken up in future.",
    "url": null,
    "uid": "145f008a-e4ff-4b87-bd16-98016b928f46",
    "datasource_id": 406,
    "source": "HDRUK"
  },
  {
    "id": 407,
    "name": "Rural-Urban classification for CPRD Aurum",
    "description": "CPRD Aurum linked Rural-Urban classification data differentiate between rural and urban areas at the small area level. It may be important to distinguish between rural and urban areas when investigating differences in social and economic characteristics of small areas. Populations can vary in their composition between urban and rural areas, as can access to services, employment and educational opportunities, and quality of life. \nThe measures available for patient (England only) and practice postcode are: 2011 England and Wales Rural-Urban classification; 2015 Northern Ireland Rural-Urban classification; 2016 Scottish Rural-Urban classification.",
    "url": "https://healthdatagateway.org/en/dataset/663",
    "uid": "b7d24b82-f8a4-4838-844c-ecd86ddf5db2",
    "datasource_id": 663,
    "source": "HDRUK"
  },
  {
    "id": 408,
    "name": "Rural-Urban classification for CPRD GOLD",
    "description": "Patient postcode linked measures are available for patients in English practices that have consented to participate in the linkage scheme. The latest available patient postcode of residence is mapped to an LSOA boundary. The LSOA of residence then allows linkage to several measures of area level deprivation and a rural-urban classification. These measures can be used as a proxy for socio-demographic and socio-economic data which are generally poorly recorded in the primary care data given they do not directly relate to a patient's care.\nAccess is provided by CPRD subject to protocol approval. Further information is available at https://www.cprd.com/linked-data.",
    "url": "https://healthdatagateway.org/en/dataset/659",
    "uid": "a7d8c6a8-0825-4e73-8f29-3d76194d7114",
    "datasource_id": 659,
    "source": "HDRUK"
  },
  {
    "id": 409,
    "name": "SCOT translational sample collection",
    "description": "The SCOT study  enrolled more than 6000 patients over a 5 year period and is the largest single trial ever conducted in CRC. Tissues collected from patients entered in to the study are physically hosted in two sites; blood samples and blood fractions e.g. DNA, are held at the University of Oxford and FFPE samples are housed at the Glasgow Biobank. There are approximately 3000 blood and 3000 tissue samples in the collection.\nThe associated clinical data is held by the Cancer Research UK Clinical Trials Unit, Glasgow.",
    "url": "https://healthdatagateway.org/en/dataset/499",
    "uid": "b3b790a7-a455-4efb-9c68-60e7131cb836",
    "datasource_id": 499,
    "source": "HDRUK"
  },
  {
    "id": 410,
    "name": "SNP chip data",
    "description": "In order to do recall by genotype, participants have their DNA tested using one the SNP chip arrays from eg. Illumina and Affymetrix (now Thermosfisher).  The current iteration is the UK Biobank v2.1 from Thermofisher, which measures ~820k markers.",
    "url": null,
    "uid": "baa2085e-2e87-4161-abb3-bbea94dbc0e5",
    "datasource_id": 410,
    "source": "HDRUK"
  },
  {
    "id": 411,
    "name": "SNP imputation data",
    "description": "SNP chip data can be used to impute many of the (non-rare) SNPs not included on the chips.  The NIHR BioResource is using a modified version of the UK Biobank protocol to improve the options for recall.",
    "url": null,
    "uid": "0b083692-ca52-44bf-9f00-41bbb71a56d1",
    "datasource_id": 411,
    "source": "HDRUK"
  },
  {
    "id": 412,
    "name": "SOLUS",
    "description": "A complete capture of the SOLUS cardiology information system relating cardiac procedures and ECG reporting.",
    "url": null,
    "uid": "e653b264-3e44-4ed1-a376-86df45db7f72",
    "datasource_id": 412,
    "source": "HDRUK"
  },
  {
    "id": 413,
    "name": "STAMPEDE",
    "description": "In Progress",
    "url": "https://healthdatagateway.org/en/dataset/477",
    "uid": "71eb0192-1124-4b27-86e4-1139e0047a02",
    "datasource_id": 477,
    "source": "HDRUK"
  },
  {
    "id": 414,
    "name": "SWIFT-RTB",
    "description": "Foetal tissue",
    "url": "https://healthdatagateway.org/en/dataset/502",
    "uid": "d387ed7c-14fa-49e2-8ced-df0f9372eb88",
    "datasource_id": 502,
    "source": "HDRUK"
  },
  {
    "id": 415,
    "name": "Sample holding",
    "description": "NIHR BioResource samples are held at the NIHR National Biosample Centre in Milton Keynes. Metadata on what is available should become available through the UK CRC Tissue Directory, as mandated by Research Tissue Bank status.",
    "url": null,
    "uid": "6fbb76b9-6da0-4689-8a48-a331aa0828b1",
    "datasource_id": 415,
    "source": "HDRUK"
  },
  {
    "id": 416,
    "name": "Scotland Accident and Emergency",
    "description": "The A&E datamart was established in June 2007 to monitor the compliance of each NHS Board against the 4 hour wait standard. In July 2010 the A&E data mart was extended further to collect items such as diagnosis, several injury fields and an alcohol involved flag, which will be used to identify whether the patient’s alcohol consumption was a factor in the attendance. The collection of the new fields has been driven by a variety of SG policy decisions and interest from a number of organisations. Although there is now the facility to submit these additional fields, completeness and quality of the data is generally poor and further development is needed to improve it. There are two types of data submitted to the A&E datamart: episode and aggregate level data. All hospitals with Emergency Departments submit episode level data containing a detailed record for each patient attendance. Some smaller sites with minor injury units or community hospitals only submit aggregate files containing monthly summary attendance and compliance figures only. This is because they do not have the information systems and support to enable collection of detailed patient based information. Sites that submit episode level data account for around 97% of all attendances at A&E. This linked version of the dataset includes only A&E data submitted at episode level.  Additional items are included to enable further analysis: chronic conditions flags, based on historic hospital admissions data; a care home flag derived from the CHI database",
    "url": "https://healthdatagateway.org/en/dataset/82",
    "uid": "05e6752e-3a0b-4809-aa14-207b4761ef60",
    "datasource_id": 82,
    "source": "HDRUK"
  },
  {
    "id": 417,
    "name": "Scottish Cancer Registry (SMR06)",
    "description": "The registry began in 1958 collecting personal, demographic and diagnosis information (such as site, histology, behaviour, histological confirmation and hospital of diagnosis) from cancer patients. In 1997, a new electronic cancer recording system was launched and at this point the registry was extended to include extra information on tumour stage (for breast, cervical and colorectal cancer), tumour grade and treatment information. A wide variety of geographical data is also included in the dataset including Scottish Index of Multiple Deprivation and Carstairs measures, census output area, NHS Board, Electoral Ward and Parliamentary constituency.",
    "url": "https://healthdatagateway.org/en/dataset/79",
    "uid": "ad36dc03-1856-44de-99b0-1af6f312d86b",
    "datasource_id": 79,
    "source": "HDRUK"
  },
  {
    "id": 418,
    "name": "Scottish Human Papillomavirus Archive",
    "description": "The Scottish HPV Archive is a biorepository of samples that provides a vital resource for researchers to improve the way we detect and manage HPV associated disease.\nThe archive is housed within the Queen's Medical Research Institute, University of Edinburgh, and follows the governance policies of NHS Lothian for tissue collections and the bio-repository principles of the Lothian NRS Bioresource (SAHSC).\nIt currently holds over 40,000 samples, the majority of which are cervical liquid-based cytology (LBC) samples although derivatives thereof (including nucleic acid) and other anogenital sample types are also available. For most of the samples, aliquots are kept at different temperatures (-80ËšC or -25ËšC) and different volumes (original sample and concentrated aliquot). \nThe key attribute of the Scottish HPV archive is the annotation of samples with HPV and vaccination status as well as pathology information. Such linkage is possible in Scotland with the help of the Community Health Index (CHI), Scottish Cervical Call and Recall System (SCCRS) and Cancer Registry.",
    "url": "https://healthdatagateway.org/en/dataset/509",
    "uid": "4883530f-1c8d-49aa-886e-ea3b958772ea",
    "datasource_id": 509,
    "source": "HDRUK"
  },
  {
    "id": 419,
    "name": "Scottish Immunisation Recall System (SIRS)",
    "description": "The Scottish Immunisation & Recall System began in 1987 and has been used by all NHS boards since 2002 when it incorporated the GIRS (Grampian Immunisation & Recall System). The primary aim of the Scottish Immunisation & Recall System (SIRS) is to ensure that children under the age of six years receive the appropriate immunisation according to the UK childhood immunisation schedule. SIRS calls children when a scheduled vaccination is due and allows recording of immunisation data. The dataset also includes data on HPV (Human papillomavirus) and the teenage booster immunisations and is used conjunction with the data on the Child Health Surveillance Programme School system (CHSP School) to monitor the uptake of teenage immunisations routinely given in schools.",
    "url": "https://healthdatagateway.org/en/dataset/56",
    "uid": "c027d0ca-7c7d-4864-b4dd-8040f4020fbb",
    "datasource_id": 56,
    "source": "HDRUK"
  },
  {
    "id": 420,
    "name": "Scottish Stillbirth and Infant Death Survey",
    "description": "In later years, information on certain congenital anomalies occurring in live births, stillbirths, miscarriages and terminations had also been included. In 2011, a more detailed data collection form was used and a new system for classifying the cause of death was introduced. The quality and completeness of information improved and causes of death reflected modern practice and knowledge.",
    "url": "https://healthdatagateway.org/en/dataset/80",
    "uid": "e54d35a6-2e7c-422d-a4a5-a7da930b08f2",
    "datasource_id": 80,
    "source": "HDRUK"
  },
  {
    "id": 421,
    "name": "Secondary Uses Services",
    "description": "Single, comprehensive repository for healthcare data in England which enables a range of reporting and analyses to support the NHS in the delivery of healthcare services. Used primarily by Providers and Commissioners to drive system of payments.\\nData covers: Finished general episodes admitted patient care; Outpatients; A&E attendances.",
    "url": null,
    "uid": "cd206097-3509-4495-88e6-9f182b34b99f",
    "datasource_id": 421,
    "source": "HDRUK"
  },
  {
    "id": 422,
    "name": "Sectra PACS",
    "description": "Sectra is the Imaging system used by Trusts Radiology department across all sites. Datasets are available for both the PACS data model and the RIS data model",
    "url": null,
    "uid": "10bf4c08-6702-44fe-8faa-1280dea6b0c9",
    "datasource_id": 422,
    "source": "HDRUK"
  },
  {
    "id": 423,
    "name": "Sentinel Stroke National Audit Programme Acute Organisational survey dataset",
    "description": "The acute organisational audit dataset is refreshed biennially and includes the quality of stroke service organisation in acute settings by ascertaining staffing levels, acute care processes, TIA (mini stroke) services, access to specialist support and communication with patients and carers. Surveys were undertaken in England and Wales in 2012,2014, 2016 and 2019.",
    "url": "https://healthdatagateway.org/en/dataset/553",
    "uid": "fe6a4c83-f46f-4f61-8460-e120fdf8d864",
    "datasource_id": 553,
    "source": "HDRUK"
  },
  {
    "id": 424,
    "name": "Sentinel Stroke National Audit Programme Clinical Dataset",
    "description": "This continuously ascertained, record-level dataset audit collects information on stroke patients in England, Wales and Northern Ireland in every acute hospital, and follows the pathway through recovery, rehabilitation, and outcomes at the point of 6-month assessment.",
    "url": "https://healthdatagateway.org/en/dataset/543",
    "uid": "059a6495-a7d5-435b-8e2b-806c6ccccf2e",
    "datasource_id": 543,
    "source": "HDRUK"
  },
  {
    "id": 425,
    "name": "Sheffield Brain Tissue Bank (SBTB)",
    "description": "Sheffield Brain bank comprises of tissue samples mainly of Degenerative diseases (MND, Alzheimer's)",
    "url": "https://healthdatagateway.org/en/dataset/508",
    "uid": "bdda7221-7c02-4ced-b7a8-3806a073d981",
    "datasource_id": 508,
    "source": "HDRUK"
  },
  {
    "id": 426,
    "name": "Society for Acute Medicine Benchmarking Audit",
    "description": "SAMBA is an annual 'day of care' survey which collects data on processes of care, patient demographics and clinical outcomes in a consecutive cohort of all patients seen within a sinble 24 hour period across hospitals in the UK. It is voluntary, registered with the Healthcare Quality Improvement Partnership, and alongisde the patient data, it collects data on organisation and delivery of care.",
    "url": null,
    "uid": "dbb85646-d428-45dd-a1bd-1429658bf8eb",
    "datasource_id": 426,
    "source": "HDRUK"
  },
  {
    "id": 427,
    "name": "Somerset Cancer Registry",
    "description": "SCR is a specialist clinical system used on St Barts site. System developed by the NHS for recording cancer patient pathways and tracking patients’ care across multiple organisations.",
    "url": null,
    "uid": "3ec5f1c4-95c0-4b1d-899b-399cec32ad49",
    "datasource_id": 427,
    "source": "HDRUK"
  },
  {
    "id": 428,
    "name": "South London and Maudsley NHS Foundation Trust (SLaM) Clinical Record Interactive Search (CRIS) platform",
    "description": "CRIS provides researcher access to a live de-identified copy of SLaM's electronic health record, currently representing over 400,000 mental health service users. Structured and text fields are represented, the latter enhanced through a range of natural language processing algorithms. CRIS has also been linked to a range of external data resources, listed on http://www.maudsleybrc.nihr.ac.uk/facilities/clinical-record-interactive-search-cris. CRIS data are held within SLaM's firewall and are used within a patient-led governance framework with Caldicott and Research Ethics approval.",
    "url": "https://healthdatagateway.org/en/dataset/199",
    "uid": "a25a9892-9141-4e78-8415-945ba73da11e",
    "datasource_id": 199,
    "source": "HDRUK"
  },
  {
    "id": 429,
    "name": "SpiroMeta Consortium Dataset",
    "description": "https://www.nature.com/articles/s41588-018-0321-7",
    "url": "https://healthdatagateway.org/en/dataset/267",
    "uid": "fb6d98c5-e41d-4804-bb28-839556e0f657",
    "datasource_id": 267,
    "source": "HDRUK"
  },
  {
    "id": 430,
    "name": "St Thomas' Hospitals Plasma, serum & DNA Bio bank from patients with antiphospholipid antibodies",
    "description": "A frozen biobank collection of plasma, serum, and DNA from APS patients. Each sample is anonymised blood sample collected from patients who consent for its use in future research into Antiphospholipid Syndrome.  APS is defined as the association of antiphospholipid antibodies (aPL) with arterial or venous thrombosis and /or pregnancy morbidly. Antiphospholipid syndrome is an autoimmune disorder in which aPL are involved in the development of venous and/or arterial thrombosis.",
    "url": "https://healthdatagateway.org/en/dataset/511",
    "uid": "425f4534-51c4-4f75-8ca0-d7c731dbd903",
    "datasource_id": 511,
    "source": "HDRUK"
  },
  {
    "id": 431,
    "name": "Substance Misuse Dataset (SMDS)",
    "description": "The Substance Misuse Data Set (SMDS) (also known as Welsh National Database for Substance Misuse - WNDSM) captures information relating to a client journey in a substance misuse agency.  This journey is made up of a number of ‘events’ – one referral, one assessment, one (or more) Treatment Modalities, multiple Treatment Outcomes Profile (TOPs) and one discharge. \n\nWelsh providers delivering substance misuse treatment and who are in receipt of Welsh Government substance misuse revenue funding are required to submit the Dataset.\n\nTreatment Outcomes Profiles are only required to be completed for Adults (age 16 and over) in receipt of structured treatments.\n\nA Data Explained report on SMDS is available here: https://phw.nhs.wales/publications/publications1/data-explained-welsh-national-database-for-substance-misuse-wndsm-data-quality-audit-and-considerations/",
    "url": "https://healthdatagateway.org/en/dataset/347",
    "uid": "291c0dd4-0988-42d2-8e0b-878a892add57",
    "datasource_id": 347,
    "source": "HDRUK"
  },
  {
    "id": 432,
    "name": "Sunquest",
    "description": "A complete capture of the local laboratory information system that captures the ordering/resulting for microbiology related tests.",
    "url": null,
    "uid": "a80b2567-4755-46f1-bd2a-f3817ce4f10c",
    "datasource_id": 432,
    "source": "HDRUK"
  },
  {
    "id": 433,
    "name": "Systemic Anti-Cancer Treatment (SACT) data for CPRD Aurum",
    "description": "CPRD Aurum linked Systemic Anti-Cancer Treatment (SACT) data cover chemotherapy treatment for all solid tumour and haematological malignancies, including those in clinical trials. Information is included about programme and regime of treatment, and the outcome for each treatment.",
    "url": "https://healthdatagateway.org/en/dataset/666",
    "uid": "f8af632e-442d-4931-a370-b7fa2fa4b896",
    "datasource_id": 666,
    "source": "HDRUK"
  },
  {
    "id": 434,
    "name": "Systemic Anti-Cancer Treatment (SACT) data for CPRD GOLD",
    "description": "CPRD GOLD linked Systemic Anti-Cancer Treatment (SACT) data covers chemotherapy treatment for all solid tumour and haematological malignancies, including those in clinical trials. Information is included about programme and regime of treatment, and the outcome for each treatment.",
    "url": "https://healthdatagateway.org/en/dataset/673",
    "uid": "014e7973-b43e-4a4a-91b4-de3f353b71b9",
    "datasource_id": 673,
    "source": "HDRUK"
  },
  {
    "id": 435,
    "name": "TNT: Triple Negative breast cancer Trial",
    "description": "TNT is a phase III, multi centre, randomised trial of carboplatin versus docetaxel in women with ER-, PgR- and HER2- metastatic or recurrent locally advanced breast cancer.  Patients will be randomised (1:1) to carboplatin or docetaxel and will cross over to the alternative treatment (docetaxel (if randomised to carboplatin) or carboplatin (if randomised to docetaxel)) on progression.  Trial Treatment: Group A:  Carboplatin AUC 6, q 3 weeks for 6 cycles (18 weeks) Group B:  Docetaxel 100mg/m2, q 3 weeks for 6 cycles (18 weeks) On evidence of disease progression, patients will cross over to the alternative treatment.",
    "url": "https://healthdatagateway.org/en/dataset/516",
    "uid": "5f4c825d-d6c3-453e-9667-8cdf2329b6e1",
    "datasource_id": 516,
    "source": "HDRUK"
  },
  {
    "id": 436,
    "name": "TRICON8",
    "description": "TRICON8 is the translational research sub-study of the ICON8 trial. Its aim is to establish a large, comprehensive biobank comprising tumour tissue, blood and serial plasma samples with associated clinical data which will be an invaluable resource for high-quality translational research in ovarian cancer. \nICON8 is a phase III randomised controlled trial designed to investigate the safety and efficacy of two dose-dense, dose-fractionated, weekly carboplatin-paclitaxel combination chemotherapy regimens for the treatment of newly diagnosed ovarian cancer compared to standard three-weekly carboplatin-paclitaxel. There is a growing body of evidence that using dose-fractionated paclitaxel in particular may have increased anti-tumour effects, and this is thought to be due to enhanced anti-angiogenic and pro-apoptotic effects.",
    "url": "https://healthdatagateway.org/en/dataset/504",
    "uid": "85113a93-89ed-40e3-a479-33b461b91858",
    "datasource_id": 504,
    "source": "HDRUK"
  },
  {
    "id": 437,
    "name": "TRICON8B",
    "description": "TRICON8B is the translational research sub-study of the ICON8B trial. The aim is to establish a large, comprehensive biobank comprising tumour tissue, blood and serial plasma samples with associated clinical data which will be an invaluable resource for high-quality translational research in ovarian cancer. \nICON8B is a phase III randomised trial investigating the combination of dose-fractionated chemotherapy and bevacizumab compared to either strategy alone for the first-line treatment of women with newly diagnosed high-risk stage III-IV epithelial ovarian, fallopian tube or primary peritoneal cancer.",
    "url": "https://healthdatagateway.org/en/dataset/507",
    "uid": "ff875c08-58e6-4e33-8ffe-6f51c8364333",
    "datasource_id": 507,
    "source": "HDRUK"
  },
  {
    "id": 438,
    "name": "Tayside & Fife Diabetes – Summary and a range of diabetes related datasets.",
    "description": "This dataset contains a wide range of healthcare data relating to diabetes care for all diabetic patients across the Tayside and Fife regions in Scotland. Data subjects contain, but are not limited to:\n- Albumin measurements\n- Amputations\n- Blood Glucose\n- BMI\n- Blood Pressure\n- Clinical Outcomes\n- Ethnicity\n- Eyes\n- Foot ulcers\n- Lower Limbs\n- Smoking & Alcohol\n- Treatment Type(s)\n- Drug Prescriptions",
    "url": "https://healthdatagateway.org/en/dataset/122",
    "uid": "49e63323-e564-48ac-9f11-cf77bf0fe20b",
    "datasource_id": 122,
    "source": "HDRUK"
  },
  {
    "id": 439,
    "name": "Tayside Biorepository",
    "description": "Tayside Biorepository is an established bio-resource responsible for the provision of a wide range of human tissue from consenting patients in addition to providing a large range services including staining and extraction methods.\nWe provide access to collection of samples and data across the following diseases: \n•\tFit and well\n•\tIschemic heart disease (disorder)\n•\tMalignant neoplasm of endometrium of corpus uteri (disorder)\n•\tMalignant neoplasm of liver\n•\tMalignant neoplasm of skin\n•\tMalignant tumour of kidney (disorder)\n•\tMalignant tumour of stomach (disorder)\n•\tMalignant tumour of urinary bladder (disorder)\n•\tMalignant tumour of breast\n•\tMalignant tumour of colon\n•\tMalignant tumour of oesophagus\n•\tMalignant tumour of ovary\n•\tMalignant tumour of pancreas\n•\tOsteoarthritis of knee (disorder))",
    "url": "https://healthdatagateway.org/en/dataset/483",
    "uid": "6ac9b356-00f1-49da-825a-6eb04d18e49d",
    "datasource_id": 483,
    "source": "HDRUK"
  },
  {
    "id": 440,
    "name": "Tayside Bowel Screening Dataset",
    "description": "From NHS Tayside health board bowel sceening program.\nCovers Pilot and Programme period.\nPilot runs from Mar 2000 to mid-2007.\nProgramme runs from 2007 onwards.",
    "url": "https://healthdatagateway.org/en/dataset/110",
    "uid": "00201dde-1faf-41d3-b282-5c24c0ca3697",
    "datasource_id": 110,
    "source": "HDRUK"
  },
  {
    "id": 441,
    "name": "Tayside Microbiology: Isolations",
    "description": "Antibiotic sensitivities on organisms within microbiology samples. Tayside 1999 - Current.",
    "url": null,
    "uid": "eaa4bab0-0501-4511-aa2d-4e4fee0f0bf3",
    "datasource_id": 441,
    "source": "HDRUK"
  },
  {
    "id": 442,
    "name": "Tayside Microbiology Lab",
    "description": "Data on specimens for microbiology testing.  If an organism/bacteria is found this is isolated within the sample and results of the organism's growth status under the application of various antibiotics are recorded.\nThis dataset also contains free text laboratory comments, adding additional details about the specimens supplied.",
    "url": "https://healthdatagateway.org/en/dataset/111",
    "uid": "826be5be-9977-49f9-b6d1-83ed9de08ebe",
    "datasource_id": 111,
    "source": "HDRUK"
  },
  {
    "id": 443,
    "name": "Tayside Pathology",
    "description": "NHS Tayside laboratory data. Tayside 1999 – Current.",
    "url": "https://healthdatagateway.org/en/dataset/105",
    "uid": "419e5a82-2d82-48a7-a8f3-a83e64a54e92",
    "datasource_id": 105,
    "source": "HDRUK"
  },
  {
    "id": 444,
    "name": "Tayside & Fife Radiology",
    "description": "Data for Tayside starts in 1994 and for Fife 2008.",
    "url": "https://healthdatagateway.org/en/dataset/121",
    "uid": "af10ec29-1866-4726-b33f-3948afe182f2",
    "datasource_id": 121,
    "source": "HDRUK"
  },
  {
    "id": 445,
    "name": "The Cleft Collective",
    "description": "The Cleft Collective is a longitudinal cohort study looking to investigate the biological and environmental causes of cleft and the best treatments of cleft on those affected and their families.  The study comprises two separate cohorts, a Birth Cohort and a 5-year-old Cohort.  The birth cohort is further split into two sub-groups, postnatal and antenatal, allocation to these groups is determined by the time of recruitment.  Recruitment to the two cohorts is currently ongoing across the UK.  A large amount of phenotypic and environmental exposure data is being collected via questionnaires and record linkage. A data dictionary, available on the study website, contains details of all the available data.  \nAs a minimum, biological mother and affected child are recruited to the study.  Where possible, biological father or mother's partner is also recruited.  In addition, the study aims to recruit unaffected and affected siblings for a small proportion of the cohort.  \nBiological samples are collected from all participants.  Parents and siblings of both cohorts and affected children of the 5-year-old cohort provide saliva samples.  Residual tissue and blood samples are collected from affected children recruited to the birth cohort.  In addition, cord blood samples are collected from families recruited to the antenatal strand.",
    "url": "https://healthdatagateway.org/en/dataset/515",
    "uid": "12288034-00a4-47a8-a779-1cf7a55270e4",
    "datasource_id": 515,
    "source": "HDRUK"
  },
  {
    "id": 446,
    "name": "The Leicester City and County Chronic Kidney Disease Cohort",
    "description": "LCC is a primary care observational cohort of chronic kidney disease. GP practices are based in the 3 Leicestershire based clinical commissioning groups. Data is linked to local secondary care outcomes for cardiovascular, endstage renal disease (dialysis or kidney transplantation) and mortality. Number of GP practices = 38, Number of anonymised patient records approximately 17,000.",
    "url": "https://healthdatagateway.org/en/dataset/704",
    "uid": "4dfec37d-dd24-465f-85ea-30209aff3a26",
    "datasource_id": 704,
    "source": "HDRUK"
  },
  {
    "id": 447,
    "name": "The National COVID-19 Chest Imaging Database",
    "description": "The National COVID-19 Chest Imaging Database is a joint collaboration between NHSX, BSTI and Royal Surrey NHS Foundation Trust to create a centralised UK database of X-Ray, CT and MRI images from hospital patients to inform the COVID-19 response.",
    "url": null,
    "uid": "31f0148b-f965-4136-ab39-6c5bbbf8c2d9",
    "datasource_id": 447,
    "source": "HDRUK"
  },
  {
    "id": 448,
    "name": "The PEACE Study",
    "description": "The PEACE (Posthumous Evaluation of Advanced Cancer Environment) Study",
    "url": "https://healthdatagateway.org/en/dataset/514",
    "uid": "33b1a989-5f61-412e-bdae-b551c92d1ed6",
    "datasource_id": 514,
    "source": "HDRUK"
  },
  {
    "id": 449,
    "name": "The Primary-Secondary Care Partnership to Improve Outcomes in Chronic Kidney Disease",
    "description": "Cluster randomised controlled trial of chronic kidney disease nurse practitioners in primary care. Number of GP practices for trial = 46, Number of anonymised records for trial approimately 23,500. All practices are based in the Nene clinical commisssioning group. Mortality, cardiovascular and endstage renal outcomes (dialysis or kidney transplantation) outcomes are available from primary care records.  Regional secondary care outcomes for endstage renal disease are linked. Linkage for local secondary care outcomes for cardiovascular outcomes is work in progress. Follow-up 3 to 3.5 years for trial. Extended observational follow-up (upto an additional 2.5 years) available for 38 out of 46 practices. Full trial paper published in https://jasn.asnjournals.org/content/30/7/1261.",
    "url": "https://healthdatagateway.org/en/dataset/705",
    "uid": "d472a895-8587-4503-956e-3bd66d990979",
    "datasource_id": 705,
    "source": "HDRUK"
  },
  {
    "id": 450,
    "name": "The Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) primary care sentinel network and database",
    "description": "The Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) nationally representative general practice sentinel network and database\nNational primary care sentinel network:\nThe RCGP RSC is nationally representative a network of over 500 general practices (GP) providing pseudonymised data for weekly surveillance of infectious diseases; our data are also available for quality improvement, research and education.  Research based on the RCGP RSC data have been published in the best journals including BMJ, Lancet and Diabetes Care.\nObservational research\nThe disease surveillance program is commissioned by Public Health England (PHE) and covers 37 infectious diseases including influenza. The RCGP RSC is a nationally representative population.  The RCGP RSC and PHE, and its predecessor organizations have an over 50-year history of collaboration in influenza and respiratory disease surveillance and vaccine effectiveness studies.  Each practice in the network is reported on by all 37 infectious disease on a weekly basis and are graphically compared with the network as a whole (on their own web based dashboard); this drives both quality improvement in clinical care and also raises data quality, with our practice liaison team providing timely support to practices requiring help with any disparities in their information.  Thus, the observational researcher benefits from a data warehouse of patient level records going back decades with consistently coded data, able to support both longitudinal and cross-sectional study designs.\nInterventional research – ambition to be a hub for trials \nThe RCGP RSC has produced a ‘Weekly Return’ of infections and respiratory disease since 1967, though over time it has expanded in terms of size, scope, sample collection and its capability with linking with other datasets. The databases systems at the RCGP RSC can process large numbers of patients’ data rapidly. The typical duration for generating the weekly report is under 4 hours. The weekly report reached its highest denominator of 3 million patients in last week of October 2019.\nThe RCGP RSC hosts Workload Observatory (funded by NHS England) for GPs to understand trends of patients with multiple and more complex conditions. Using disease specific dashboards, general practices are provided weekly updated data quality indicators and comparison with other practices in the RCGP RSC network. This process allows practices to continuously improve their data quality and increase the quality of the RCGP RSC studies over time.\nWe regularly administer questionnaires in our studies and are experienced in collecting samples from our network of practices.  Practice level support from our dedicated practice liaison team is enhanced by our suite of dashboard technologies delivering rapid feedback to each of our practices. Hence combing our expertise in real world data analysis, high frequency of practice support and our continual development of internet-based practice level interaction, we are well placed for providing a low cost pragmatic trials platform for quality improvement studies or as a medicinal trials platform. \n\nClinical concepts are ontologically defined for clinical concepts as part of developing a study protocol.  We have clinical and programming expertise to assist researchers in developing new ontologies for case finding and have a pool of existing ontologies and associated computer algorithms for extracting the data.  \n\n\nWe have ongoing research across a wide range of medical areas, with international collaborations in child health, diabetes and influenza vaccination and have an extensive set of data extraction, processing and analytic procedures we have developed which are readily available to existing and new research collaborators.",
    "url": "https://healthdatagateway.org/en/dataset/717",
    "uid": "d5049af5-e351-4b22-8f75-6f3dfc96419b",
    "datasource_id": 717,
    "source": "HDRUK"
  },
  {
    "id": 451,
    "name": "TheatreEvents",
    "description": "This dataset comprises data pulled from Cerner SURGINET and a bespoke theatre information system (TIMS) that is used within the trust. Currently the Cerner Surginet module is only used partially within the trust. The data relates to the theatre case procedure, pre and post op timings, and staff involved in the case.",
    "url": null,
    "uid": "2c6adf80-f30b-46c9-b9d4-fe3e68622469",
    "datasource_id": 451,
    "source": "HDRUK"
  },
  {
    "id": 452,
    "name": "Tissue Access for Patient Benefit (TAPb)",
    "description": "We aim to facilitate the pathway for access, storage, use and transfer of human organs, cells and tissue between clinical centres within UCL Partners, academic groups in UCL, other universities, hospitals, medical researcher and biotechnology companies, to enhance the ability for researchers to access the materials they need. Alongside this, researcher will be able to exchange information and access guides on regulatory, ethics and practical issues concerning access, transfer and use of this type of material. These guides will be video and documents format, based on talks at organised events given by experts in the relevant fields.",
    "url": "https://healthdatagateway.org/en/dataset/513",
    "uid": "35985f8e-f01a-41cc-a9e2-30299d3d6eed",
    "datasource_id": 513,
    "source": "HDRUK"
  },
  {
    "id": 453,
    "name": "Tissue Solutions Ltd",
    "description": "Tissue Solutions specializes in sourcing biological material for academics, pharma & biotech companies and CROs and is working with clients worldwide (USA, UK, Europe and Japan). We are a virtual tissue bank, working with multiple sources to acquire samples on behalf of our clients for use mainly during the preclinical research phase. We never retain tissues ourselves for our own use. We provide access to banked human tissues and set up prospective collections in the UK and USA.  We specialise in sourcing \"tough\" tissues, e.g. fresh samples or those with specific inclusion and exclusion criteria. We provide both diseased and non-diseased samples, FFPE and fresh frozen and fresh samples.",
    "url": "https://healthdatagateway.org/en/dataset/498",
    "uid": "bbcdc352-e0f7-4672-93a7-134bcf5001b1",
    "datasource_id": 498,
    "source": "HDRUK"
  },
  {
    "id": 454,
    "name": "Tommy's National Reproductive Health Biobank",
    "description": "The Tommy's Biobank aims to prospectively collect samples from pregnant and non pregnant women to help research in to pregnancy complications. The biobank has been granted permission to ethically approve research projects conducted in the field of reproductive health. The biobank will bring together six biobanks that will all collect samples of high quality according to the standard operating procedures written by experts in the field of reproductive health. \nWe have obtained approval to collect:\n1-Endometrium\n2-Myometrium\n3-Placenta,cord, cord blood\n4-Urine\n5-Maternal blood\n6-Amniotic fluid\n7-Vaginal Swabs\n8-Omentum\n9-Subcutaneous Fat\nSamples from 1 week old baby\n1-Urine\n2-saliva\n3-Buccal swab\n4-meconium (stool)\n5-Stool\nCurrent Collections:\n1- Myometrium from pregnant women who have undergone caesarean section or have had hysterectomies. \n2-Endometrium from recurrent miscarriage patients (Frozen, FFPE)\n3-DNA ( Whole blood) from recurrent miscarriage patients",
    "url": "https://healthdatagateway.org/en/dataset/506",
    "uid": "20639743-9a28-43dd-a8af-7571ece2a7f8",
    "datasource_id": 506,
    "source": "HDRUK"
  },
  {
    "id": 455,
    "name": "TwinsUK",
    "description": "The TwinsUK cohort (https://twinsuk.ac.uk/), set up in 1992, is a major volunteer-based genomic epidemiology resource with longitudinal deep genomic and phenomics data from over 15,000 adult twins (18+) from across the UK who are highly engaged and recallable. The cohort is predominantly female (80%) for historical reasons. It is one of the most deeply characterised adult twin cohort in the world, providing a rich platform for scientists to research health and ageing longitudinally. There are over 700,000 biological samples stored and data collected on twins with repeat measures at multiple timepoints. Extremely large datasets (billions of data points) have been generated for each TwinsUK participant over 30 years, including phenotypes from questionnaires, multiple clinical visits, and record linkage, and genetic and ‘omic data from biological samples. TwinsUK ensures derived datasets from raw data are returned by collaborators to enhance the resource. TwinsUK also holds a wide range of laboratory samples, including plasma, serum, DNA, faecal microbiome and tissue (skin, fat, colonic biopsies) within HTA-regulated facilities at King's College London. \n\nMore recently, postal and at-home collection strategies have allowed sample collections from frail twins, our whole cohort for COVID-19 studies, and for new twin recruits. The cohort is recallable either on a four-year longitudinal sweep visit or, based on diagnosis or genotype. \n\nMore than 1,000 data access collaborations and 250,000 samples have been shared with external researchers, resulting in over 800 publications since 2012. \n\nTwinsUK is now working to link to twins’ official health, education and environmental records for health research purposes, which will further enhance the resource, education and environmental records for health research purposes, which will further enhance the resource.",
    "url": "https://healthdatagateway.org/en/dataset/728",
    "uid": "19b4cd38-3828-4bb1-a980-4f7420658965",
    "datasource_id": 728,
    "source": "HDRUK"
  },
  {
    "id": 456,
    "name": "UCL / UCLH Biobank for Studying Health and Disease",
    "description": "The Biobank stores normal and pathological specimens, surplus to diagnostic requirements, from relevant tissues and bodily fluids. Stored tissues include; snap-frozen or cryopreserved tissue, formalin-fixed tissue, paraffin-embedded tissues, and slides prepared for histological examination. Tissues include resection specimens obtained surgically or by needle core biopsy. Bodily fluids include; whole blood, serum, plasma, urine, cerebrospinal fluid, milk, saliva and buccal smears and cytological specimens such as sputum and cervical smears. Fine needle aspirates obtained from tissues and bodily cavities (e.g. pleura and peritoneum) are also collected. Where appropriate the Biobank also stores separated cells, protein, DNA and RNA isolated from collected tissues and bodily fluids described above. Some of the tissue and aspirated samples are stored as part of the diagnostic archive.",
    "url": "https://healthdatagateway.org/en/dataset/510",
    "uid": "ce533c67-5565-4010-bedf-dca3863a9084",
    "datasource_id": 510,
    "source": "HDRUK"
  },
  {
    "id": 457,
    "name": "UCL Infection DNA Bank",
    "description": "The UCL Infection DNA Bank aims to facilitate research into infectious diseases through the enhanced availability of samples to researchers. This availability currently supports research in the UCL Division of Infection and Immunity but it will also support  researchers nationally and internationally, increasing the potential for collaboration.",
    "url": "https://healthdatagateway.org/en/dataset/425",
    "uid": "3e5fb54a-84ec-4888-9a72-4093c6e90dbc",
    "datasource_id": 425,
    "source": "HDRUK"
  },
  {
    "id": 458,
    "name": "UHB Eye Image Dataset Release 001",
    "description": "There are two data sets of eye scans available. The first of these is a set fundus images of which the are c. 7.0 million. The other is a set of OCT scans of which there are c. 440, 000.\n\nThis dataset contains routine clinical ophthalmology data for every patient who have been seen at Queen Elizabeth Hospital and the Birmingham, Solihull and Black Country Diabetic Retinopathy screening program at University Hospitals Birmingham NHS Foundation Trust, with longitudinal follow-up for 15 years. Key data included are:\n• Total number of patients. \n• Demographic information (including age, sex and ethnicity)\n• Past ocular history\n• Intravitreal injections\n• Length of time since eye diagnosis \n• Visual acuity\n• The national screening diabetic grade category (seven categories from R0M0 to R3M1)\n• Reason for sight and severe sight impairment\n\nGeography\nUniversity Hospitals Birmingham is set within the West Midlands and it has a catchment population of circa 5.9million.  The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData source:  Ophthalmology department at Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic screening scheme in Europe. \n\nPathway: The routine secondary care follow-up in the hospital eye services for all ophthalmic diseases at Queen Elizabeth Hospital. The Birmingham, Solihull and Black Country dataset is representative of the patient pathway for community screening and grading of diabetic eye disease.",
    "url": "https://healthdatagateway.org/en/dataset/96",
    "uid": "21fb56d1-726f-40e3-a998-e4b34ba3e46f",
    "datasource_id": 96,
    "source": "HDRUK"
  },
  {
    "id": 459,
    "name": "UK BiLEVE Consortium Dataset",
    "description": "https://www.nature.com/articles/s41588-018-0321-7       \n\nLung function is an important indicator of respiratory health and mortality. Measures of lung function show irreversible airway obstruction in chronic obstructive pulmonary disease (COPD), a progressive condition affecting 900,000 people in the UK. Smoking is a strong risk factor for COPD but not all smokers are equally susceptible. Genetic approaches to understanding the mechanisms underlying the maintenance of good lungs aim to reveal previously unknown molecular targets for drug development and to facilitate stratified approaches to treatment and care. This project aims to detect rare genetic variants associated with lung function. Once discovered, such variants tend to exert a large effect on disease risk and provide a means to translate findings from genetic studies of lung function to clinical relevant research and development. The proposed study leverages the power of Uk Biobank and respiratory genomics to advance understanding of lung function and COPD.",
    "url": "https://healthdatagateway.org/en/dataset/281",
    "uid": "b8bcda0b-3c90-4382-aeb7-b01fc27e58fa",
    "datasource_id": 281,
    "source": "HDRUK"
  },
  {
    "id": 460,
    "name": "UK CLL Trials Biobank",
    "description": "A collection of Chronic Lymphocytic Leukaemia samples from patients on clinical trials. Includes the trials; AdMIRE, ARCTIC, CHOP-OR, CLL210, CLEAR, COSMIC, RIAltO, FLAIR",
    "url": "https://healthdatagateway.org/en/dataset/440",
    "uid": "636e216a-072b-4c8b-b867-23b7666d2cb2",
    "datasource_id": 440,
    "source": "HDRUK"
  },
  {
    "id": 461,
    "name": "UK Lung Volume Reduction Surgery",
    "description": "Many people with chronic obstructive pulmonary disease (COPD) remain very breathless and limited. In some patients, with the appropriate pattern of emphysema, an operation called lung volume reduction surgery is effective at removing the worst affected area of lung. New techniques have been developed where emphysema can be treated using a fibre-optic camera called a bronchoscope. Trials have shown that using a bronchoscope to place endobronchial valves into the airways can be very effective in carefully selected patients and the technique is now being adopted in hospitals across the UK.\n\nThis study will collect data from people undergoing these procedures at hospitals across the UK to evaluate how well they work in practice and what factors at baseline influence response.\n\nBaseline, three month and 12 month follow up data will be collected. This will include lung function data, measures of exercise capacity, questionnaires about health status and CT scan results.\n\nQuestions addressed will include:\n\n(1) What lung function improvement is seen in clinical practice?\n\n(2) What factors determine who is most likely to respond?\n\n(3) How safe are the procedures and what is the rate of complications?\n\n(4) What proportion of people undergoing bronchoscopic procedures require repeat procedures or surgery subsequently?\n\n(5) Does long term survival differ between people undergoing the different treatments?\n\nThe study is supported by The British Lung Foundation and sponsored by Imperial College, London. By building collaboration, the establishment of the network will also produce a structure that will make evaluation of future bronchoscopic techniques easier bringing innovative treatments into play more quickly.",
    "url": "https://healthdatagateway.org/en/dataset/234",
    "uid": "9f98a674-12f6-42f4-b1a5-322b3b75eef1",
    "datasource_id": 234,
    "source": "HDRUK"
  },
  {
    "id": 462,
    "name": "UK ME/CFS Biobank",
    "description": "Participants:\nOver 650 participants are included in the study, including ME/CFS participants diagnosed by a physician and compliant with CDC '94 (Fukuda) and Canadian Consensus Criteria (CCC), with  case definition compliance available for four other commonly-used criteria. Healthy and MS matched controls are also available.\n-\tMild and Moderate ME/CFS\n-\tSevere (Home-Bound) ME/CFS\n-\tMultiple Sclerosis\n-\tHealthy Controls\nSamples Available:\nOver 32,000 aliquots available.\n-\tWhole Blood\n-\tSerum\n-\tPlasma\n-\tRBCs\n-\tPBMCs (Sodium Heparin and K2 EDTA)\n-\tBlood for RNA Extraction (PaxGENE)\nAssociated Data Available:\n-\tBaseline Standard Laboratory Tests to exclude comorbidity causes of fatigue\n-\tIn-person clinical assessment data \n-\tDetailed Symptoms Assessments\n-\tDemographic information\n-\tStandardised instruments of Fatigue Severity and Functional Impairment",
    "url": "https://healthdatagateway.org/en/dataset/496",
    "uid": "d0694e15-b6ba-4a95-962a-dbf627ca207c",
    "datasource_id": 496,
    "source": "HDRUK"
  },
  {
    "id": 463,
    "name": "UK MND Collections",
    "description": "The UK MND Collections (formerly known as the UK MND DNA Bank) was established to provide the international research community with a resource that would help to identify and understand the causative and disease modifying factors involved with motor neurone disease. These samples are available for research into MND and associated conditions such as fronto-temporal dementia only.\nThe UK MND Collections combines more than 3,000 biological samples and accompanying clinical information; as well as epidemiology data from 400 participants, including people with MND, controls and family members.\nThe DNA Bank was the original Collection of whole genome DNA from over 3,000 blood samples, which are stored at CIGMR (Centre for Integrated Genomic Medical Research) in Manchester, UK. The DNA Bank also has clinical information (divided into a minimum and extended dataset) which is available to researchers.\nThe Cell Lines Collection offers a sub set of the DNA Bank samples as EBV-transformed lymphoblastoid cell lines and peripheral blood lymphocytes. This was originally set up as an everlasting supply of DNA, with the lymphoblastoid cell lines now available to researchers to help them understand how the disease is developing.\nThe Epidemiology Collection has just over 200 patient and matched control blood samples with extensive environmental and lifestyle data (both from self-report questionnaires and telephone interviews) available to researchers.\nMore information on the DNA and cell bank is available in Smith et al BMC Genetics (2015) 16:84.",
    "url": "https://healthdatagateway.org/en/dataset/470",
    "uid": "89e2d798-c488-4eaf-a50f-a93616093304",
    "datasource_id": 470,
    "source": "HDRUK"
  },
  {
    "id": 464,
    "name": "UK primary Sjogren's syndrome Registry",
    "description": "Peripheral blood samples (DNA, RNA, serum, PBMC) from patients with primary Sjogren's syndrome, with detailed contemporaneous clinical data at the time of sample collection.",
    "url": "https://healthdatagateway.org/en/dataset/475",
    "uid": "38fe1a10-ebf9-4dc1-8c35-2e7b33b1f6b5",
    "datasource_id": 475,
    "source": "HDRUK"
  },
  {
    "id": 465,
    "name": "UK real-world lymphoid tissue bank",
    "description": "The UK real-world lymphoid malignancies tissue Bank (UKTB) provides a national and international resource for research on lymphomas and other lymphoproliferative disorders including chronic lymphocytic leukaemia. This Research Tissue Bank was set up in 2014 and given favourable ethical opinion (REC ref: 14/SC/0030). The samples are stored in the HTA licenced Faculty of Medicine Tissue Bank. The RTB currently holds matched normal and malignant frozen material from ~900 patients. The samples collected can be associated with clinical diagnostic details and outcome endpoint, which can be readily informative to patient prognosis.",
    "url": "https://healthdatagateway.org/en/dataset/472",
    "uid": "c9f82f86-79e1-4067-932f-5e6dc939006d",
    "datasource_id": 472,
    "source": "HDRUK"
  },
  {
    "id": 466,
    "name": "UKALL14 Trial",
    "description": "A randomized trial for adults with newly diagnosed acute lymphoblastic leukaemia.\nMultisite, randomised controlled trial, recruiting 811 patients over 7.5 years.\nSamples will be collected throughout the trial and sent to the central lab (Adult ALL MRD Lab @ UCL CI) as follows; \n- bone marrow samples taken at diagnosis, at recover post phase 1 and phase 2, post transplant (for those patients who proceed with a non-myeloablative transplant) and at relapse.\n- peripheral blood sample taken during phase 1 on days 4 & 18 (in patients 40 and under) and on D18 & 32 in patients 41 or over), and during intensification. \nAdditionally for those patients who proceed for a non-myeloablative transplant peripheral blood will be sent to the local chimerism labs and a copy of the report will be sent to UCL CTC.",
    "url": "https://healthdatagateway.org/en/dataset/473",
    "uid": "b5533cb5-0c59-495b-9b11-3c401daeb958",
    "datasource_id": 473,
    "source": "HDRUK"
  },
  {
    "id": 467,
    "name": "UKCTOCS Longitudinal Women's Cohort (UKLWC)",
    "description": "This is the bioresource resulting from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), one of the world's largest randomised controlled trials. Between 2001-2005 1.2 million women aged 50-74 years were invited from the general population and 202,638 post-menopausal were randomised (2:1:1) to routine care, or annual CA125 blood testing (7-11 rounds) or ultrasound to evaluate the impact of ovarian cancer screening on disease mortality. All participants provided a serum sample at recruitment with 50,262 providing further longitudinal annual samples (median of 9 samples). Women were free of any active malignancy at enrolment (2001-2005). During the follow-up period of >15 years to-date a proportion of those have subsequently developed a number of different diseases. Available comprehensive electronic health record linkage of the cohort's participants (including Hospital Episode Statistics (HES); ONS (Cancer registry, Death Certificates); National Cancer Intelligence Network (NCIN); and Myocardial Ischaemia National Audit Project (MINAP) allows exploiting clinical phenotyping and diseases diagnoses made during routine healthcare in the NHS. Women were also sent postal questionnaires. Secondary studies have to date generated additional data on symptoms, menopause and its management and further details of individual cancers. \nThe biobank contains >540,000 high-quality serum samples (10 x 500μL aliquots in straws), composed of baseline (from >189,000 women) and a unique longitudinal set of >350,000 annual serial samples (median 9) (from approximately 50,200 women). Samples have already been validated for multi-omics analysis with academic and commercial collaborators in nested case/controls sets used for genotyping, proteomics (including SWATH technology), methylation, NMR metabolomics, autoantibody profiling, ELISA-based assays, lipidomics and miRNA.\nWe provide access to collection of samples and data across the following diseases: \n•\tCerebrovascular accident (disorder)\n•\tDementia (disorder)\n•\tFit and well\n•\tHeart disease (disorder)\n•\tMalignant neoplasm of endometrium of corpus uteri (disorder)\n•\tMalignant tumour of breast, Malignant tumour of colon, Malignant tumour of lung, Malignant tumour of ovary\n•\tMalignant tumour of pancreas\nAll data is stored in the IG Toolkit-compliant and ISO 27001:2013-certified UCL Data Safe Haven.\nAll samples from the biobank have been collected using a standardised protocol and stored in liquid nitrogen at HTA licensed facilities at UK Biocentre, Oxford, UK.",
    "url": "https://healthdatagateway.org/en/dataset/474",
    "uid": "e0b951ba-0f7d-4743-9374-b965042accdd",
    "datasource_id": 474,
    "source": "HDRUK"
  },
  {
    "id": 468,
    "name": "UNIRAD UK Sample collection",
    "description": "Randomised, double-blind, multicentre phase III trial evaluating the safety and benefit of adding everolimus to adjuvant hormone therapy in women with high risk of relapse, ER+ and HER2- primary breast cancer who remain free of disease (UNIRAD).",
    "url": "https://healthdatagateway.org/en/dataset/476",
    "uid": "a1d16f3f-c7e7-41c5-a071-07889bf49ab1",
    "datasource_id": 476,
    "source": "HDRUK"
  },
  {
    "id": 469,
    "name": "Unified Cohorts Research Network",
    "description": "UNICORN leverages UK cohort studies with a focus on asthma and allergy more productively, and develops a sustainable, scalable scientific infrastructure to facilitate future work in this and other biomedical domains. It includes Study Team for Early Life Asthma Research (STELAR), which includes Avon Longitudinal Study of Parents and Children (ALSPAC), Ashford and Isle of Wight cohorts (IoW), Manchester Asthma and Allergy Study (MAAS), Aberdeen Study of Eczema and Asthma To Observe the Effects of Nutrition (SEATON); Breathing Together; U-BIOPRED; and the Royal Brompton Severe Asthma cohort (paediatric) cohorts. When/where possible these data are to be linked to NHS data.\n\nAsthma and allergic diseases (food allergies, rhinitis, eczema) are the most common chronic diseases in childhood and adolescence. They usually start before school-age and are responsible for a heavy burden of ill health, including premature death. In response, we propose an innovative scientific research program which embraces a truly trans-disciplinary team science approach, and is of fundamental relevance both to biomedical research and to evidence-based health science more broadly. UNICORN builds directly on the substantive prior investments in both the science and infrastructure underpinning research in this domain. The project will assist UK to leverage its cohort studies with a focus on asthma and allergy more productively, and to develop a sustainable, scalable scientific infrastructure to facilitate future work in this and other biomedical domains.",
    "url": "https://healthdatagateway.org/en/dataset/224",
    "uid": "cffcf30d-cedb-421b-a5dc-6726633cb99a",
    "datasource_id": 224,
    "source": "HDRUK"
  },
  {
    "id": 470,
    "name": "University Hospitals Birmingham PICS data",
    "description": "Complete electronic health record for over 1,000,000 acute episodes since 2009 including 24,713,053 prescriptions, 268,880,960 investigations and 25,728,361 procedures.",
    "url": null,
    "uid": "f2aed928-0e3b-4dca-ac92-d35b55d18619",
    "datasource_id": 470,
    "source": "HDRUK"
  },
  {
    "id": 471,
    "name": "University of Liverpool - GCP Laboratory Facility",
    "description": "Combined repository of clinical trial samples for 'future use'.  \nWill include samples from clinical trials co-ordinated by the Liverpool Cancer Trials Unit and from studies with Liverpool Principal Investigators. \nWe provide access to collection of samples and data across the following diseases: \n•\tCarcinoma in situ of breast\n•\tMalignant lymphoma (disorder)\n•\tMalignant melanoma of eye (disorder)\n•\tMalignant tumour of oral cavity (disorder)\n•\tMalignant tumour of urinary bladder (disorder)\n•\tMalignant tumour of breast\n•\tMalignant tumour of pancreas\n•\tOsteoradionecrosis (disorder)",
    "url": "https://healthdatagateway.org/en/dataset/479",
    "uid": "029b7f0f-911e-46ce-837f-031959f067da",
    "datasource_id": 479,
    "source": "HDRUK"
  },
  {
    "id": 472,
    "name": "University of Southampton Faculty of Medicine Tissue Bank",
    "description": "The Tissue Bank is licensed by the Human Tissue Authority (HTA) to source, organise, collect, prepare, store and distribute a diverse collection of human tissues and biological products.\nAll tissue is collected with patient consent and distributed anonymously only to National Research Ethics Service (NRES) approved studies. This valuable resource is available  to aid the study of cancer biology and other associated research. The Tissue Bank allows for rapid access to a plethora of biological materials supported by an inventory management system. Tissues currently available include normal and malignant snap frozen tissue, FFPE blocks, fresh biopsy tissues, blood products and biological fluids. Collections are organized by Tissue Bank staff and include a wide variety of cancer classifications. The Tissue Bank currently holds over 100,000 vials.\nWe provide access to collection of samples and data across the following diseases: \n•\tMalignant lymphoma (disorder)\n•\tMalignant neoplasm of liver\n•\tMalignant neoplasm of skin\n•\tMalignant tumour of kidney (disorder)\n•\tMalignant tumour of prostate (disorder)\n•\tMalignant tumour of thyroid gland (disorder)\n•\tMalignant tumour of breast\n•\tMalignant tumour of colon\n•\tMalignant tumour of lung\n•\tMalignant tumour of ovary\n•\tOsteosarcoma of bone",
    "url": "https://healthdatagateway.org/en/dataset/484",
    "uid": "786563e9-5377-4d18-8d32-65ff14d53987",
    "datasource_id": 484,
    "source": "HDRUK"
  },
  {
    "id": 473,
    "name": "Unscheduled Care Data Mart",
    "description": "The Unscheduled Care Data Mart (UCD) is a collaboration between Public Health Scotland (PHS), NHS 24 and Scottish Ambulance Service (SAS). The data mart links data from (NHS 24, Scottish Ambulance Service, Out of Hours Primary Care, Emergency Department, Acute, Mental Health and Deaths) to show a patient journey for records with a valid CHI number. This data will help understand the full patient journey through emergency and urgent care services e.g. from first contact by telephone with NHS 24, transport by ambulance to an A&E department and then emergency hospital admission.\n\nPROVISIONAL DATA: NHS24/SAS SUBMITTED DAILY. A&E DATA (MONTHLY). INPATIENT ADMISSIONS (MONTHLY)",
    "url": "https://healthdatagateway.org/en/dataset/57",
    "uid": "a265af2e-40fc-4759-af61-eab293cdad5a",
    "datasource_id": 57,
    "source": "HDRUK"
  },
  {
    "id": 474,
    "name": "VinCaP",
    "description": "VinCaP is a phase II, multicentre, non-randomised trial of Vinflunine chemotherapy in locally-advanced and metastatic carcinoma of the Penis. 22 evaluable participants will be recruited from 9 centres and all patients will receive 4 cycles of IV vinflunine 320mg/m2 on day 1, to be repeated at intervals of 21 days. The primary endpoint, determined by RECIST v1.1, is clinical benefit (objective response + stable disease rate) measured after four cycles of vinflunine chemotherapy. Secondary endpoints are objective response rate (CR+PR), toxicity, progression-free survival, overall survival and treatment compliance. Formalin fixed paraffin embedded (FFPE) tumour blocks are held at the Orchid Tissue Laboratory and Barts and The London School of Medicine and Dentistry.",
    "url": "https://healthdatagateway.org/en/dataset/467",
    "uid": "e57dd7a2-3c5c-4285-8001-8e2589c395e9",
    "datasource_id": 467,
    "source": "HDRUK"
  },
  {
    "id": 475,
    "name": "Vitamin D supplementation meta-analyses",
    "description": "Vitamin D supplementation to prevent asthma exacerbations, COPD exacerbations and respiratory tract infections is a meta-analyses database of trials testing vitamin D supplementation to prevent asthma exacerbations, COPD exacerbations and acute respiratory tract infection, incorporating patient level data for which the relevant permissions for data sharing have been obtained. Asthma from seven randomised controlled trials (RCTs); COPD from four RCTs; acute RTI from 25 RCTs.",
    "url": "https://healthdatagateway.org/en/dataset/242",
    "uid": "2454a964-be58-4d1f-8f9a-ffae9db9129c",
    "datasource_id": 242,
    "source": "HDRUK"
  },
  {
    "id": 476,
    "name": "Wales Asthma Observatory",
    "description": "The dataset currently contains (1) a table of time periods (defined with start and end dates) during which a patient had any diagnosis of asthma; (2) and table of time periods for asthma severity level (based on prescriptions), asthma exacerbations, asthma-related hospital episodes, and asthma control; and (3) a table for other asthma-related data represented as events (e.g., lung function, blood tests, and A&E visit).\n\nIncludes an e-cohort of most people with a history of asthma in Wales, derived from the SAIL Databank's core datasets. Individuals are identified from the Welsh Longitudinal General Practice (WLGP) using several case definitions (e.g., having ever GP asthma diagnosis, asthma treatment in the last 12 months, or both). Data for each patient include essential research-ready asthma-related variables derived from primary and secondary care data, such as asthma treatment step, asthma severity, asthma exacerbations, and asthma-related death. The case definitions and some clinical variables are represented as clinical states. Additional case definitions and variables are actively being added. The WAO dataset is intended to support a wide range of cross-sectional and longitudinal epidemiological asthma studies as well as asthma surveillance, service planning, and health policy.",
    "url": "https://healthdatagateway.org/en/dataset/213",
    "uid": "8604bcdc-235a-4c70-b29b-1617396815e6",
    "datasource_id": 213,
    "source": "HDRUK"
  },
  {
    "id": 477,
    "name": "Wales Cancer Bank",
    "description": "The Wales Cancer Bank (WCB) was set up in 2004 and consented the first patient in 2005. The project is hosted by Cardiff University and receives funding from the Welsh Government, Cancer Research Wales and Velindre Charitable funds.\nA Cancer Bank is literally a collection of tissue and blood which has been collected from patients where cancer is a possible diagnosis and is being stored to facilitate future research into cancer.\nStatistics suggest that four out of ten people will be diagnosed with cancer at some point during their life. Cancers are complex diseases and it is an on-going quest to understand how they develop, spread and can be treated. The development of more effective, targeted treatment for cancer depends on increased understanding of the molecular mechanisms involved in the initiation of the tumour, its progression to metastatic disease and response and resistance to treatment. Research studies rely on the availability of high quality biological material from patients with cancer and large studies are needed to correlate biology with clinical outcome.\nThe Welsh population is relatively stable and this makes it an ideal cohort to collect and study. Linkage with the all Wales cancer clinical database (CANISC) enables good correlation of science with clinical follow-up. This will eventually enable the results from hundreds of research projects to be integrated and linked to clinical outcome and this will be an invaluable source of data for bioinformatics specialists to examine. All the data collected is being stored on a database housed in the NHS to ensure security and confidentiality.\nThe WCB currently consents patients in 12 hospitals around Wales with specially trained research nurses and clinical teams.\nWe provide access to collection of samples and data across the following diseases: \n•\tHodgkin's disease (disorder)\n•\tMalignant neoplasm of connective tissue (disorder)\n•\tMalignant neoplasm of endometrium of corpus uteri (disorder)\n•\tMalignant neoplasm of skin\n•\tMalignant neoplasm of upper respiratory tract (disorder)\n•\tMalignant tumour of adrenal gland (disorder)\n•\tMalignant tumour of anal canal (disorder)\n•\tMalignant tumour of biliary tract (disorder)\n•\tMalignant tumour of cervix\n•\tMalignant tumour of kidney (disorder)\n•\tMalignant tumour of nasal sinuses (disorder)\n•\tMalignant tumour of oral cavity (disorder)\n•\tMalignant tumour of penis (disorder)\n•\tMalignant tumour of prostate (disorder)\n•\tMalignant tumour of salivary gland (disorder)\n•\tMalignant tumour of small intestine (disorder)\n•\tMalignant tumour of stomach (disorder)\n•\tMalignant tumour of testis (disorder)\n•\tMalignant tumour of thyroid gland (disorder)\n•\tMalignant tumour of urinary bladder (disorder)\n•\tMalignant tumour of vagina (disorder)\n•\tMalignant tumour of vulva (disorder)\n•\tMalignant tumour of breast\n•\tMalignant tumour of colon\n•\tMalignant tumour of lung\n•\tMalignant tumour of oesophagus\n•\tMalignant tumour of ovary\n•\tMalignant tumour of pancreas",
    "url": "https://healthdatagateway.org/en/dataset/505",
    "uid": "37886fb5-d0ae-400f-89eb-78664897bcf2",
    "datasource_id": 505,
    "source": "HDRUK"
  },
  {
    "id": 478,
    "name": "Walker Study Data",
    "description": "Cohort of over 48,000 birth records (pregnancy, labour, birth and care) in Dundee. Between 1952-1966.",
    "url": "https://healthdatagateway.org/en/dataset/128",
    "uid": "245bffa9-07c3-40b6-916b-161062cdf674",
    "datasource_id": 128,
    "source": "HDRUK"
  },
  {
    "id": 479,
    "name": "Watch and Wait Trial",
    "description": "Watch and Wait is a randomised phase III trial to determine whether initial treatment with rituximab in patients with advanced stage asymptomatic follicular lymphoma  (grades 1, 2 and 3a) results in a significant delay in the initiation of chemotherapy or radiotherapy and the impact of each strategy on patient-related quality of life.\n360 patients randomised to receive either Rituximab treatment and maintenance or to a Watch and Wait strategy.\nSamples collected for trial: Formalin fixed paraffin embedded tumour block or unstained slides (lymph node and bone marrow)- sent to HMDS.  Blood and bone marrow sample taken at baseline and if patient in CR clinically and radiologically at 7, 13 and 25 months - sent to University College London Hospital.",
    "url": "https://healthdatagateway.org/en/dataset/501",
    "uid": "e46dccbf-484d-45d2-a42a-4d308b87c113",
    "datasource_id": 501,
    "source": "HDRUK"
  },
  {
    "id": 480,
    "name": "Welsh Cancer Intelligence and Surveillance Unit (WCSU)",
    "description": "The Welsh Cancer Intelligence & Surveillance Unit (WCISU) is the National Cancer Registry for Wales and its primary role is to record, store and report on all incidence of cancer for the resident population of Wales wherever they are treated. Cancer registration in Wales began almost five decades ago and today’s electronic database which holds records going back to 1972 contains in the region of 686,000 records.\n\nWCISU collects data about occurrences of cancer in Welsh residents via direct or indirect submissions from Welsh Hospitals.\n\nStaging of malignant melanoma (ICD 10 code C43), breast (C50), colorectal (C18-C20) and cervix (C53) started in 2001 since this was when we started receiving pathological information. Staging for all other cancers started in 2010.\n\nTreatment information started in 1995.\n\nThe WCISU team at Public Health Wales provide publicly available official statistics on cancer incidence in Wales from 2002 for all cancers excluding non-melanoma skin cancer. Statistics are available by cancer type, sex, area of residence, stage at diagnosis, area deprivation and UK comparisons, at: https://publichealthwales.shinyapps.io/Cancer_Reporting_Tool_PHW",
    "url": "https://healthdatagateway.org/en/dataset/357",
    "uid": "368ad790-3ad8-4f93-9a7c-0bfb64dc6015",
    "datasource_id": 357,
    "source": "HDRUK"
  },
  {
    "id": 481,
    "name": "Welsh Demographic Service Dataset (WDSD)",
    "description": "Administrative information about individuals in Wales that use NHS services; such as address and practice registration history. It replaced the NHS Wales Administrative Register (NHSAR) in 2009.\n\nData drawn from GP practices via Exeter System.\n\nThis dataset provides linkage from anonymous individual to anonymous residences, thus enable to group households of individuals.\n\nThe single views are now provisioned to new projects and described here, the metadata for the old three-view WDSD version can be found in a separate legacy metadata entry.",
    "url": "https://healthdatagateway.org/en/dataset/359",
    "uid": "8a8a5e90-b0c6-4839-bcd2-c69e6e8dca6d",
    "datasource_id": 359,
    "source": "HDRUK"
  },
  {
    "id": 482,
    "name": "Whole Genome Sequencing",
    "description": "The NIHR BIoResource ran the pilot for GEL's 100,000 Genomes Project. Most of the participants with rare disease were recruited on the basis of having no known diagnosis, and have had extensive work up on WGS data, including reporting to the clinical team.",
    "url": null,
    "uid": "edaa2d9a-b78a-4209-869e-27a2fa67924f",
    "datasource_id": 482,
    "source": "HDRUK"
  },
  {
    "id": 483,
    "name": "York Tissue Bank",
    "description": "In collaboration with York Teaching Hospital NHS Trust, the University of York has established the York Tissue Bank: a tissue bank to help research into human disease. We aim to collect, store and build a repository of human tissue samples, such as urine, blood and tumours. These can then be given to researchers for their studies. These studies will aim to improve our understanding of human health and potentially lead to new methods of diagnosis, better treatments and vaccines for a wide range of diseases. We rely on participants to voluntarily gift their tissue samples for this vital future research.\nOur Bioresource is described in detail here: https://openbioresources.metajnl.com/articles/10.5334/ojb.49/",
    "url": "https://healthdatagateway.org/en/dataset/500",
    "uid": "71e898d1-db8c-4887-a605-dd6336b92a34",
    "datasource_id": 500,
    "source": "HDRUK"
  },
  {
    "id": 484,
    "name": "Yorkshire Specialist Register of Cancer in Children and Young People",
    "description": "A regional asset for Yorkshire and Humber Data, a population-based database of all children and young people (0–29 years) diagnosed with cancer residing in the Yorkshire and Humber region in England (10,000 tumour registrations in children aged 0-14 years since 1974 and Adolescents and Young Adults aged 15-29 years since 1990). The database contains detailed information on clinical prognostic factors including detailed treatment and stage information. A unique quality of the Yorkshire Register is that it has pre-linked data with hospital admissions datasets including inpatient, outpatient, A&E, Mental Health HES which has been used to explore long-term morbidity in the cohort. Primary care linked data have been requested and are pending. Linked educational attainment and unemployment/social benefits data are currently being sought (ethical approval in place). Demand is largely underpinned by ongoing epidemiological and cancer\noutcomes requests from University postgraduate researchers, PGT students, and other children’s tumour registers. However, pharma has also requested some of the registry data. Ethics and Research Governance are all current, the former approved as a Research Database by the Northern & Yorkshire MREC (Ref MREC 0/3/1) from which data can be requested by external researchers, and approval from the Health Research Authority Confidentiality Advisory Group to process identifiable patient data without consent (Ref—CAG 1-07(b)/2014). The Register goes beyond the dataset held by NCRAS in having much more rich and detailed treatment histories of every patient through active follow-up so beyond the traditional 6-month window since diagnosis used by NCRAS, as well as more complete tumour staging information. Register also records any cancer recurrence alongside any subsequent treatment received. \n\nThe Yorkshire Specialist Register of Cancer in Children and Young People exists primarily to underpin epidemiological research examining the patterns and causes of cancer in children and young people and to facilitate health services research describing the patient experience in the context of care received by health professionals. The YSRCCYP is an established information resource for local clinicians and commissioners, and forms the basis for national and international collaborative research.\nThe core aims of the YSRCCYP are to:\n•\tMaintain the accurate and complete collection of clinical and socio-demographic data on children, teenagers and young adults with cancer in Yorkshire, specifically information not otherwise available from other routine NHS sources.\n•\tInvestigate the effectiveness of healthcare delivery for children and young people, specifically the impact on survival and long-term health.\n•\tUndertake epidemiological research, comprising incidence and survival analyses.\n•\tDescribe environmental risk factors for childhood and young adult cancer.\n\nExample of three recent research studies by the Yorkshire Register research team are as follows:\n\n1. Respiratory morbidity in young people surviving cancer: Population based study of hospital admissions, treatment related risk factors and subsequent mortality\n(Smith et al, 2019)\n\nRespiratory diseases are a major cause of late morbidity and mortality amongst childhood cancer survivors. This population-based study provides comprehensive analysis of hospitalisations for respiratory conditions, the associated risks of admission by earlier cancer treatment and trends in readmissions and subsequent mortality in long-term survivors of cancers diagnosed under 30 years. The risk of hospitalisation was significantly higher in cancer survivors compared to the general population. Treatment with chemotherapy with known lung toxicity was associated with an increased risk of admissions for all respiratory disease especially pneumonia. Subsequent mortality was highest in those admitted for pneumonia compared to other respiratory conditions.\n\n2. Long term survival after childhood acute lymphoblastic leukaemia: population-based trends in cure and relapse by clinical characteristics\n(Smith et al, 2018)\n\nStatistical “cure models” provide additional metrics useful to identify and describe trends in survival. Additional measures include the proportion cured which is a summary of the long term survival and the median survival of the uncured which give information on those who are not long-term survivors. In this study we used a statistical cure model to explore trends in long-term survival and relapse for childhood acute lymphoblastic leukaemia (ALL) over time and by clinical characteristics. The proportion of patients cured, defined either by overall survival or relapse free survival, has increased over time while there was slight decrease in the median survival time of the uncured. We also observed a significant reduction in the risk of relapse over time.\n\n3. Comparison of ethnic group classification using naming analysis and routinely collected data\n(Smith et al, 2017)\n\nIn this study we compared cancer incidence trends using different methods for assigning ethnic groups to individuals: 1 – using ethnic group recorded in hospital medical records, 2 – using a naming software program to assign an ethnic group based on the ethnic origins of the individuals and 3 – using a combination of both processes. We found that using different methods of assigning ethnicity can result in different estimates of ethnic variation in cancer incidence. Combining ethnicity from multiple sources results in a more complete estimate of ethnicity than the use of one single source.\n\nhttps://medicinehealth.leeds.ac.uk/leeds-institute-cardiovascular-metabolic-medicine/doc/yorkshire-specialist-register-cancer-children-young-people",
    "url": "https://healthdatagateway.org/en/dataset/24",
    "uid": "93582beb-e45a-47b6-aeab-d26f583e4c3b",
    "datasource_id": 24,
    "source": "HDRUK"
  },
  {
    "id": 485,
    "name": "eLIXIR - Early Life Data Cross-Linkage in Research",
    "description": "The embryo, foetus and new born child are very sensitive to external influences during development. These can arise from problems with the mother's health, her lifestyle, her physical environment, medication, the placenta not working properly, complications during birth, or as consequence of being born too early. Adversity in these periods of developmental vulnerability can have persistent effects on the long-term health of the child, including physical and mental health disorders. We also know that if a mother has complications in pregnancy, that she herself may suffer from increased risk of ill-heath in later life, for example cardiovascular disease, diabetes or mental health problems. \n\neLIXIR is a prospective collection of blood samples from routine antenatal and neonatal appointments and is due to begin collecting in 2018.",
    "url": "https://healthdatagateway.org/en/dataset/886",
    "uid": "3c780d45-ed7b-4101-9c32-d50512cd9cfe",
    "datasource_id": 886,
    "source": "HDRUK"
  },
  {
    "id": 486,
    "name": "interNational Anaplastic Thyroid Cancer Tissue Bank (iNATT)",
    "description": "The primary objective is to establish an international anaplastic thyroid cancer tissue collection to facilitate research. Patients have the option to donate blood samples and clinical data. \nResearch proposals will be accepted from academic and industry research parties from the UK and internationally. All research proposals will be submitted to the multidisciplinary iNATT Steering Committee for assessment. \nAs the volume of tissue collected per patient is expected to be of small volume, by virtue of the specimen comprising core biopsy or fine needle aspirate material, research proposals will be prioritised according to the potential clinical benefits. Research proposals will require ethical approval and the relevant research and development permissions prior to commencement.\nClinicalTrials.gov Identifier: NCT01774279",
    "url": "https://healthdatagateway.org/en/dataset/495",
    "uid": "5ef75d71-73ce-4451-9d7a-a39dd82b712f",
    "datasource_id": 495,
    "source": "HDRUK"
  },
  {
    "id": 487,
    "name": "mHealth for pneumonia prevention",
    "description": "We aimed to appraise the available literature on CHW based mHealth approaches for caregivers to improve knowledge and management about common childhood infections and to develop a mobile application which lady health workers (LHWs) will use in their monthly visits to counsel caregivers on under five pneumonia.\n\nSouth Asian children are more at risk of dying due to infections attributed to highly prevalent deadly yet preventable childhood infections. Alongside concerns about the prevalence of these infections, there has been a renewed interest in involving community health workers (CHWs) in various public health programs. However, as CHWs are increasingly asked to take on different tasks there is a risk that their workload may become unmanageable. One approach to help reduce this burden is the use of mobile technology-based healthcare solutions (mHealth) in the community. We aimed to appraise the available literature on CHW based mHealth approaches for caregivers to improve knowledge and management about common childhood infections and to develop a mobile application which lady health workers (LHWs) will use in their monthly visits to counsel caregivers on inder five pneumonia.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/phd-studentships/hana-mahmood",
    "url": "https://healthdatagateway.org/en/dataset/249",
    "uid": "fc66381e-cc46-4bca-85fc-fc2ee09cc5dd",
    "datasource_id": 249,
    "source": "HDRUK"
  },
  {
    "id": 488,
    "name": "plasmaMATCH",
    "description": "The UK plasma based Molecular profiling of Advanced breast cancer to inform Therapeutic Choices (plasmaMATCH) Trial: \nA multiple parallel cohort,  open-label, multi-centre phase IIa clinical trial aiming to provide proof of principle efficacy for designated targeted therapies in patients with advanced breast cancer where the targetable mutation is identified through ctDNA screening.",
    "url": "https://healthdatagateway.org/en/dataset/494",
    "uid": "5618f65d-23fc-4bd5-9a4c-73e4b330c3a6",
    "datasource_id": 494,
    "source": "HDRUK"
  },
  {
    "id": 489,
    "name": "North West London Vulnerable Patient List (NWL VPL)",
    "description": "The NWL Vunerable Patient List linked table is for patients previously defined as vulnerable and now called shielded patient list by NHS England. These are patients who are registered with a North West London General Practice.",
    "url": null,
    "uid": "0cfa5b70-7ab0-4ecc-b027-ab6e2bffc7b7",
    "datasource_id": 489,
    "source": "HDRUK"
  },
  {
    "id": 490,
    "name": "ICT-based intervention for adult asthma with limited health literacy",
    "description": "We aim to develop and refine a mobile application for asthma self-management which tailored to health literacy needs for adults patients with asthma in primary care clinic in the Klang District, Selangor State, Malaysia. Dataset contains qualitative interviews from phase 2, responses to quantitative questionnaire and qualitative interviews in phase 3. \n\nSupported self-management for asthma (including action plans and regular review) is highly effective at improving asthma control, reducing acute attacks and the need for unscheduled healthcare. However, globally it is challenging to implement self-management in patients with asthma. One particular challenge is the need to tailor support for people with limited health literacy especially in low and middle income countries (LMICs) such as in Malaysia. In 2015,  it was estimated that more than 90% of the Malaysian general population have limited health literacy. A tailored asthma self-management intervention, paying attention to the needs of people with limited health literacy, potentially could lessen health inequalities in Malaysia. This study is conducted in 3 phases. Phase 1 is a systematic review looking at effectiveness of asthma self-management interventions which address health literacy needs. We found gap in this area of knowledge. In phase 2, we conducted mixed qualitative and Photovoice, an arts-based qualitative study to explore lived experience of people living with asthma and limited health literacy in Malaysia. We found rich data on challenges and enablers around psychosocial themes on how people views asthma, self-management and experienced health literacy.  We also explore the potential of technology i.e. mobile application to tailor asthma self-management support. Based on phase 2 findings, in phase 3, we developed and refined an asthma self-management mobile application with feedback from patients and health care professionals through a intervention design workshop which was conducted online. \n\nFor further information, see:\nhttps://www.ed.ac.uk/usher/respire/phd-studentships/hani-salim\nhttps://www.ed.ac.uk/usher/respire/health-literacy-asthma-malaysia           \n\nPhase 1: Salim, H., Young, I., Shariff Ghazali, S. et al. Protocol for a systematic review of interventions addressing health literacy to improve asthma self-management. npj Prim. Care Respir. Med. 29, 18 (2019). https://doi.org/10.1038/s41533-019-0125-y\n\nPhase 1: Salim H, Ramdzan SN, Ghazali SS, Lee PY, Young I, McClatchey K, Pinnock H; NIHR Global Health Research Unit on Respiratory Health (RESPIRE) collaborations. A systematic review of interventions addressing limited health literacy to improve asthma self-management. J Glob Health. 2020 Jun;10(1):010427. doi: 10.7189/jogh.10.010428.",
    "url": "https://healthdatagateway.org/en/dataset/235",
    "uid": "1d6b02f1-47b8-42ad-919d-f738b545cff7",
    "datasource_id": 235,
    "source": "HDRUK"
  },
  {
    "id": 491,
    "name": "Klang Asthma Cohort",
    "description": "We aim to assess the feasibility of delivering a supported self-management incorporating an adapted pictorial asthma action plan for adult patients with asthma in clinical practice in a public primary care clinic in the Klang District, Selangor State, Malaysia. Dataset contains follow-up responses at one-, three- and six-months post intervention. \n\nSupported self-management has been shown to reduce asthma-related morbidity and mortality in high-income countries, but poor health literacy (especially in low- and middle-income countries) is a potential barrier to its effectiveness. This study aims to assess the feasibility of delivering supported self-management incorporating an adapted pictorial asthma action plan for adult patients with asthma attending a public primary care clinic in Malaysia. This study will proceed in two phases: 1) adaptation of the pictorial asthma action plan (AAP); and 2) a feasibility study including  feasibility of assessing costs. Following the adaptation of the AAP, we assess the feasibility of using the AAP to support self-management in adults with asthma. In a pre-post study, 70 patients aged 18 years and above with physician-diagnosed asthma and currently prescribed inhaled corticosteroids will be recruited. Our proposed primary outcome is asthma control (Global Initiative for Asthma symptom control). A pre-piloted questionnaire will be used to collect baseline data on socio-demography, healthcare utilisation and expenditure, health literacy, clinical parameters (body mass index; peak expiratory flow rate).  Follow-up will be at 1, 3, and 6 months. \n\nThe protocol was approved by the Medical Research Ethics Committee, Ministry of Health Malaysia and registered with National Medical Research Registry (NMRR-18-2683-43494). The study trial registration number is ISRCTN87128530 (Pre-results).",
    "url": "https://healthdatagateway.org/en/dataset/268",
    "uid": "a2eddb27-c8bd-42ae-94f8-2a3026220da3",
    "datasource_id": 268,
    "source": "HDRUK"
  },
  {
    "id": 492,
    "name": "Education Wales (EDUW)",
    "description": "Schools and Pupil data for Wales which covers state funded learning centres. Contains information from the Pupil Level Annual School Census (PLASC) and the Welsh Examinations Database (WED). This describes learning centres, outcomes for learners, special educational needs (SEN), attendance summary (prior to 2020), and free school meals (FSM). See table and variable descriptions for further detail.\n\nAttendance data in EDUW was discontinued after 2019 and the Education Daily Attendance Dataset (EDAD) schema replaced it.\n\nData for 2019/2020 in EOTASPROVISION was found to be unreliable and has been removed by the data owner",
    "url": "https://healthdatagateway.org/en/dataset/314",
    "uid": "204fa806-071d-4410-9c2c-143017d32d24",
    "datasource_id": 314,
    "source": "HDRUK"
  },
  {
    "id": 493,
    "name": "Cystic Fibrosis Patient Liver Enzyme",
    "description": "The UK CF Registry is a centralised database of all 60 CF centres across the UK. Data are manually entered in calendar years by CF clinical teams for the 99% of people with a diagnosis of CF who consent to their data being donated to the Registry. Data are entered onto a secure web-portal. For more information please see www.cysticfibrosis.org.uk/registry and 'Data Resource Profile: The UK CF Registry' published in the International Journal of Epidemiology (2018 Feb 1;47(1)9-10e).",
    "url": "https://healthdatagateway.org/en/dataset/2",
    "uid": "f72e80fc-dcd3-4457-ae91-dd14449002dd",
    "datasource_id": 2,
    "source": "HDRUK"
  },
  {
    "id": 494,
    "name": "Cystic Fibrosis Patient Microbiology Cultures",
    "description": "The UK CF Registry is a centralised database of all 60 CF centres across the UK. Data are manually entered in calendar years by CF clinical teams for the 99% of people with a diagnosis of CF who consent to their data being donated to the Registry. Data are entered onto a secure web-portal. For more information please see www.cysticfibrosis.org.uk/registry and 'Data Resource Profile: The UK CF Registry' published in the International Journal of Epidemiology (2018 Feb 1;47(1)9-10e).",
    "url": "https://healthdatagateway.org/en/dataset/1",
    "uid": "9c1727f6-2032-413f-bf60-470769b2616e",
    "datasource_id": 1,
    "source": "HDRUK"
  },
  {
    "id": 495,
    "name": "Community perception on public health measures for COVID-19 prevention/control",
    "description": "Whereas most of the evidence generated from the start of the global pandemic, COVID-19, focuses on epidemiological, diagnostic and clinical aspects of the illness, importance of social aspects has also been highlighted. This has been included as one of the 34 knowledge gaps specified in the WHO R&D Blueprint WHO 2019 Novel Coronavirus Global research and innovation forum: towards a research roadmap published recently. One of the key approaches to containing COVID-19 has been self-quarantine and social distancing, which although is the need of the day but not easy to manage posing multiple challenges especially in a Pakistani community which follows strong cultural and social beliefs. It is, therefore important to identify the perception, practice and attitude of the community towards containing this disease. These insights are important for national health officials planning to implement control measures in an existing diverse socio-cultural system, an aspect which has been highlighted in the National Action Plan (NAP) for COVID-19 in Pakistan, published on 13th March 2020.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/covid-19/community-perception-public-health-measures",
    "url": "https://healthdatagateway.org/en/dataset/277",
    "uid": "6464fb4f-2838-495b-9963-4001204ed166",
    "datasource_id": 277,
    "source": "HDRUK"
  },
  {
    "id": 496,
    "name": "Reasons for delay in seeking care for pneumonia in children under five",
    "description": "Around 36% of deaths due to pneumonia in children under five occur at level of the household without ever reaching a healthcare provider. Our aim is to understand the factors responsible for delayed care seeking for pneumonia in selected communities in Pakistan with an objective to develop interventions involving counseling provided by LHW’s to bring about positive behavior change. Qualitative in depth interviews with mothers of under five children and focus group discussions with fathers and grandmothers of under five children were conducted.Data was collected from respondents residing in urban and rural settings from selected communities in Punjab, KPK, Sindh, Federal and AJK.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/acute-respiratory-disorders/care-seeking-pneumonia",
    "url": "https://healthdatagateway.org/en/dataset/251",
    "uid": "46f50b9b-b8e2-4ff1-a1b3-779174c3d032",
    "datasource_id": 251,
    "source": "HDRUK"
  },
  {
    "id": 497,
    "name": "Research on use of an online learning platform on under-five pneumonia",
    "description": "The aim of this fellowship is to use an online case management training application to facilitate final year medical students on the correct identification and management of pneumonia in children under five. Improved knowledge and skills of the undergraduate medical students in correct identification and treatment of childhood pneumonia will enable them to deal with cases more effectively, reducing pneumonia related morbidity and mortality in children under five. This innovative curriculum format could also be adapted to improve case management of other common paediatric illnesses.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/respire-fellowships/nomana-zeeshan",
    "url": "https://healthdatagateway.org/en/dataset/248",
    "uid": "a3d85fd2-9df3-4f6e-9035-0830a3488ddb",
    "datasource_id": 248,
    "source": "HDRUK"
  },
  {
    "id": 498,
    "name": "COVID-19 Consolidated Deaths (CDDS) - COPI",
    "description": "COVID-19 Consolidated Deaths dataset.\n\nThis dataset contains more Covid-19 related information than the standard deaths extract (ADDE).\n\nThis dataset is no longer being updated. The last update was approximately November 2022.\n\nCOPI - COVID-19 response projects only.",
    "url": "https://healthdatagateway.org/en/dataset/332",
    "uid": "70e37f44-5c3e-4c83-a42a-89b3476d1d45",
    "datasource_id": 332,
    "source": "HDRUK"
  },
  {
    "id": 499,
    "name": "Cafcass England (CAFE)",
    "description": "Cafcass (Children and Family Court Advisory and Support Service) England Family Justice dataset.\n\nThe Children and Family Court Advisory and Support Service (Cafcass) looks after the interests of children involved in family proceedings. It is independent of the courts and social services, but works under the rules of the Family Court and legislation to work with children and their families, and then advise the courts on what is considered to be in the best interests of individual children.\n\nUseful information about the Family Courts can be found here: https://www.gov.uk/government/statistics/family-court-statistics-quarterly-april-to-june-2023/guide-to-family-court-statistics",
    "url": "https://healthdatagateway.org/en/dataset/311",
    "uid": "8ee61578-e298-423a-be22-cb0438023e5c",
    "datasource_id": 311,
    "source": "HDRUK"
  },
  {
    "id": 500,
    "name": "CAFCASS Wales (CAFW)",
    "description": "Cafcass (Children and Family Court Advisory and Support Service) Wales Family Justice dataset.\n\nThe Children and Family Court Advisory and Support Service (Cafcass) looks after the interests of children involved in family proceedings. It is independent of the courts and social services, but works under the rules of the Family Court and legislation to work with children and their families, and then advise the courts on what is considered to be in the best interests of individual children.\n\nUseful information about the Family Courts can be found here: https://www.gov.uk/government/statistics/family-court-statistics-quarterly-april-to-june-2023/guide-to-family-court-statistics",
    "url": "https://healthdatagateway.org/en/dataset/328",
    "uid": "29a714e1-5289-4362-be24-2848c954344e",
    "datasource_id": 328,
    "source": "HDRUK"
  },
  {
    "id": 501,
    "name": "Welsh Dispensing Dataset (WDDS) - Legacy",
    "description": "WDDS was a project specific dataset for specific COVID-19 related projects only, which is not currently available to request. Prescription data are available from the WLGP dataset.\n\nThe data covers prescriptions that are prescribed in Wales by GPs (general medical practitioners) and non medical prescribers that have prescribed on behalf of the GP practice, that are then dispensed in the community within Wales. This is in contrast to prescription data which are contained in the WLGP dataset, which may not be dispensed.\n\nThe data includes all prescribed medicines, dressings and appliances that are dispensed each month. If a patient does not take a prescription to the pharmacy for dispensing, then the information will not be included in the dataset.  Information includes - the number of prescription items dispensed by each community pharmacy in Wales, broken down by the GP practice in which they prescribed, and also the number of prescription items prescribed in each GP practice in Wales broken down by the pharmacy that dispensed those items.",
    "url": "https://healthdatagateway.org/en/dataset/285",
    "uid": "50ef6443-ed4b-40f9-97fb-1cfd53be6579",
    "datasource_id": 285,
    "source": "HDRUK"
  },
  {
    "id": 502,
    "name": "Welsh Longitudinal General Practice Dataset (WLGP) - Welsh Primary Care",
    "description": "This dataset covers 86% of the population of Wales and 83% of GP practices in Wales. It is linkable with anonymised fields for individuals and GPs to other datasets, including bespoke project specific cohorts. Each GP practice uses a clinical information system to maintain an electronic health record for each of their patients; capturing the signs, symptoms, test results, diagnoses, prescribed treatment, referrals for specialist treatment and social aspects relating to the patients home environment.\n\nThe majority of the data is entered by the clinician during the patient consultation. Test results are electronically transferred from secondary care systems.\n\nThere are no standard rules for recording data within primary care clinical information systems. Therefore, each individual clinician can record information in their own way. The majority use Read Code Terminology, however, sometimes this is applied behind the scenes by the clinical system and sometimes local codes are used. Read codes are not as precise as ICD 10 or OPCS codes.\n\nCoding standards have been agreed on for conditions monitored by the QOF (Quality Outcomes Framework) returns. Since the implementation of QOF these conditions have been coded in a more consistent way.\n\nTime coverage varies between each practice.\n\nA link to the number of GP practices per local health board in this dataset can be found in the Associated media.",
    "url": "https://healthdatagateway.org/en/dataset/355",
    "uid": "33fc3ffd-aa4c-4a16-a32f-0c900aaea3d2",
    "datasource_id": 355,
    "source": "HDRUK"
  },
  {
    "id": 503,
    "name": "CPRD GOLD",
    "description": "The CPRD Gold database contains longitudinal routinely-collected electronic health records (EHR) from UK primary care practices using Vision® general practice patient management software. The database captures information on demographic characteristics; diagnoses and symptoms; drug exposures; vaccination history; laboratory tests; and referrals to hospital and specialist care.",
    "url": "https://healthdatagateway.org/en/dataset/694",
    "uid": "a29feafa-7bdd-44e9-b977-c9d26425e67f",
    "datasource_id": 694,
    "source": "HDRUK"
  },
  {
    "id": 504,
    "name": "CPRD Aurum",
    "description": "The CPRD Aurum database contains longitudinal routinely-collected electronic health records (EHR) from UK primary care practices using EMIS Web® general practice patient management software. The database captures information on demographic characteristics; diagnoses and symptoms; drug exposures; vaccination history; laboratory tests; and referrals to hospital and specialist care.",
    "url": "https://healthdatagateway.org/en/dataset/692",
    "uid": "1d574c14-1af0-490d-9c4e-be88cfd0e345",
    "datasource_id": 692,
    "source": "HDRUK"
  },
  {
    "id": 505,
    "name": "UK BioLAM",
    "description": "Lymphangioleiomyomatosis (LAM) is a rare lung and lymphatic disease categorised by infiltration of smooth muscle type cells in the lungs and lymphatics leading to progressive respiratory impairment. The disease only effects women and is caused by a defect in one of the two proteins associated with tuberous sclerosis, tuberin and hamartin.",
    "url": "https://healthdatagateway.org/en/dataset/1511",
    "uid": null,
    "datasource_id": 1511,
    "source": "HDRUK"
  },
  {
    "id": 506,
    "name": "Scarred Liver",
    "description": "Prospective, longitudinal cohort of patients with community liver disease.",
    "url": "https://healthdatagateway.org/en/dataset/1488",
    "uid": null,
    "datasource_id": 1488,
    "source": "HDRUK"
  },
  {
    "id": 507,
    "name": "Non-invasive assessment of portal hypertension using quantitative MRI (MRQuee)",
    "description": "40 patients prospectively underwent HVPG and MRI at 1.5T within 6 weeks",
    "url": "https://healthdatagateway.org/en/dataset/1487",
    "uid": null,
    "datasource_id": 1487,
    "source": "HDRUK"
  },
  {
    "id": 508,
    "name": "Inflammatory Bowel Disease datasets - Data profiling GWM",
    "description": "",
    "url": "https://healthdatagateway.org/en/dataset/1485",
    "uid": null,
    "datasource_id": 1485,
    "source": "HDRUK"
  },
  {
    "id": 509,
    "name": "MR measures of liver fibrosis (MRker)",
    "description": "Single centre prospective study comparing liver biopsies performed as part of clinical care and MRI at 1.5T",
    "url": "https://healthdatagateway.org/en/dataset/1486",
    "uid": null,
    "datasource_id": 1486,
    "source": "HDRUK"
  },
  {
    "id": 510,
    "name": "Crohn's Disease Metabolic Syndrome & Related Disease Outcomes",
    "description": "",
    "url": "https://healthdatagateway.org/en/dataset/1484",
    "uid": null,
    "datasource_id": 1484,
    "source": "HDRUK"
  },
  {
    "id": 511,
    "name": "Beta-blocker Stratification using MRI to assess portal pressure (B-SMaRT)",
    "description": "Beta-blocker Stratification using quantitative MRI techniques to assess portal pressure and Response to Treatment in patients with portal hypertension (B-SMaRT study).\r\n\r\nTo provide proof of concept that quantitative haemodynamic MRI markers can stratify the efficacy of carvedilol to lower clinically significant portal hypertension in patients with cirrhosis.\r\n\r\nDescriptive sequential pilot study of patients with cirrhosis, portal pressure measurement, research MRI (at 3T) and endoscopy with outcomes with repeat measures in those who are given Carvedilol.",
    "url": "https://healthdatagateway.org/en/dataset/1483",
    "uid": null,
    "datasource_id": 1483,
    "source": "HDRUK"
  },
  {
    "id": 512,
    "name": "3CN (Cirrhosis Cohort)",
    "description": "A Prospective, observational, single centre cohort study of patients with compensated cirrhosis",
    "url": "https://healthdatagateway.org/en/dataset/1482",
    "uid": null,
    "datasource_id": 1482,
    "source": "HDRUK"
  },
  {
    "id": 513,
    "name": "NUH Cancer Outcomes and Services Data set (COSD)",
    "description": "Secondary uses information required to support national cancer registration and associated analysis (at local, regional, national, and international level), as well as other national cancer audit programmes.",
    "url": "https://healthdatagateway.org/en/dataset/1481",
    "uid": null,
    "datasource_id": 1481,
    "source": "HDRUK"
  },
  {
    "id": 514,
    "name": "NUH Systemic Anti-Cancer Therapy Dataset (SACT)",
    "description": "Dataset includes patients receiving systemic anti-cancer therapies from Nottingham University Hospitals.",
    "url": "https://healthdatagateway.org/en/dataset/1480",
    "uid": null,
    "datasource_id": 1480,
    "source": "HDRUK"
  },
  {
    "id": 515,
    "name": "KeRNEL - Primary Care Problem",
    "description": "This table shows the problems which the patient has first presented when seeking medical advice and become headings/ titles within their record. E.g. the record may show Fracture of the left wrist as a 'problem' heading and then other events and activities may be associated with it, including diagnosis/ treatment codes and attendance at a fracture clinic.",
    "url": "https://healthdatagateway.org/en/dataset/1479",
    "uid": null,
    "datasource_id": 1479,
    "source": "HDRUK"
  },
  {
    "id": 516,
    "name": "Sussex Integrated Dataset - Primary Care Referral",
    "description": "This table represents either a referral of a patient for care by an external party or service or inbound referrals to provide a services related to the patient.",
    "url": "https://healthdatagateway.org/en/dataset/1478",
    "uid": null,
    "datasource_id": 1478,
    "source": "HDRUK"
  },
  {
    "id": 517,
    "name": "Sussex Integrated Dataset - Primary Care Problems",
    "description": "This table shows the problems which the patient has first presented when seeking medical advice and become headings/ titles within their record. E.g. the record may show Fracture of the left wrist as a 'problem' heading and then other events and activities may be associated with it, including diagnosis/ treatment codes and attendance at a fracture clinic.",
    "url": "https://healthdatagateway.org/en/dataset/1477",
    "uid": null,
    "datasource_id": 1477,
    "source": "HDRUK"
  },
  {
    "id": 518,
    "name": "Sussex Integrated Dataset - Primary Care Patient Demographics",
    "description": "This table contains a list of patients with associated demographic information including registered GP practice, ethnicity and estimates for year of birth and year of death. It will contain multiple records per patient each evaluated for a milestone date",
    "url": "https://healthdatagateway.org/en/dataset/1476",
    "uid": null,
    "datasource_id": 1476,
    "source": "HDRUK"
  },
  {
    "id": 519,
    "name": "Sussex Integrated Dataset - Primary Care Immunisation",
    "description": "This table records the GP's record of immunisations delivered to the patient. These will primarily but not exclusively have been delivered by staff working on behalf of the GP practice.",
    "url": "https://healthdatagateway.org/en/dataset/1475",
    "uid": null,
    "datasource_id": 1475,
    "source": "HDRUK"
  },
  {
    "id": 520,
    "name": "Sussex Integrated Dataset - Primary Care Encounter",
    "description": "This table records interactions between staff working on behalf of a GP practice and a patient's record.",
    "url": "https://healthdatagateway.org/en/dataset/1474",
    "uid": null,
    "datasource_id": 1474,
    "source": "HDRUK"
  },
  {
    "id": 521,
    "name": "Sussex Integrated Dataset - In-patients Dataset",
    "description": "This dataset consists of in-patient data used for creating SUS returns from acute providers sending data to the Sussex Integrated Dataset (SID). As at June 2024 acute providers to SID are ESHT and UHS (Brighton and West Sussex). The SID does not yet receive regular data feeds from QVH or SASH but these are planned to come in soon.",
    "url": "https://healthdatagateway.org/en/dataset/1473",
    "uid": null,
    "datasource_id": 1473,
    "source": "HDRUK"
  },
  {
    "id": 522,
    "name": "KeRNEL - Primary Care Appointment",
    "description": "This table records that activity has taken place between a patient and member of clinical staff working on behalf of a GP practice in an appointment 'slot', a pre-arranged opportunity in the rota for that clinician that was booked to be taken by this specific patient. GP practices sometimes use Clinic Lists that have slots but it is not possible to pre-book appointments into these slots, even though the clinical activity delivered in the time period may be exactly the same. Sometimes Clinic lists are retrospectively rearranged into appointments: e.g.when the GP has completed a ward round and only then knows who they have seen. This table also includes Did Not Attends (DNAs) which means that no patient contact took place.",
    "url": "https://healthdatagateway.org/en/dataset/1472",
    "uid": null,
    "datasource_id": 1472,
    "source": "HDRUK"
  },
  {
    "id": 523,
    "name": "Sussex Integrated Dataset - Primary Care Medication",
    "description": "This table records medications for particular patients. Note that there is no information in this table stating whether the medication was collected or taken by the patient.",
    "url": "https://healthdatagateway.org/en/dataset/1471",
    "uid": null,
    "datasource_id": 1471,
    "source": "HDRUK"
  },
  {
    "id": 524,
    "name": "KeRNEL - Primary Care Referral",
    "description": "This table represents either the referral of a patient for care by an external party or service or inbound referral to provide a services related to the patient. This is known as a referral in primary care.",
    "url": "https://healthdatagateway.org/en/dataset/1470",
    "uid": null,
    "datasource_id": 1470,
    "source": "HDRUK"
  },
  {
    "id": 525,
    "name": "KeRNEL - Primary Care Prescription",
    "description": "This table records the GP's record of immunisations delivered to the patient. These will primarily but not exclusively have been delivered by staff working on behalf of the GP practice.",
    "url": "https://healthdatagateway.org/en/dataset/1469",
    "uid": null,
    "datasource_id": 1469,
    "source": "HDRUK"
  },
  {
    "id": 526,
    "name": "KeRNEL - Primary Care Patient",
    "description": "This table contains a list of patients with demographic and geographic information known to the practice. These are supplied by the patient at registration and other later times. It will contain multiple records per patient that are considered 'active' and 'inactive' from a historical perspective by the GP practice.",
    "url": "https://healthdatagateway.org/en/dataset/1468",
    "uid": null,
    "datasource_id": 1468,
    "source": "HDRUK"
  },
  {
    "id": 527,
    "name": "KeRNEL - Primary Care Event",
    "description": "This table records activities and patient contacts between patients and clinical members of staff, plus tasks completed on behalf of the patient and noted in their clinical record at the GP practice. Showing e.g. entering a blood test result, tasks and referrals, coding added from a discharge letter.",
    "url": "https://healthdatagateway.org/en/dataset/1467",
    "uid": null,
    "datasource_id": 1467,
    "source": "HDRUK"
  },
  {
    "id": 528,
    "name": "KeRNEL - Primary Care Encounter",
    "description": "This table records interactions between staff working on behalf of a GP practice and a patient's record.",
    "url": "https://healthdatagateway.org/en/dataset/1466",
    "uid": null,
    "datasource_id": 1466,
    "source": "HDRUK"
  },
  {
    "id": 529,
    "name": "KeRNEL - Primary Care Staff",
    "description": "This table shows all Staff with their job title registered to a GP practice.",
    "url": "https://healthdatagateway.org/en/dataset/1465",
    "uid": null,
    "datasource_id": 1465,
    "source": "HDRUK"
  },
  {
    "id": 530,
    "name": "KeRNEL Primary Care Problem",
    "description": "This table shows the problems which the patient has first presented when seeking medical advice and become headings/ titles within their record. E.g. the record may show Fracture of the left wrist as a 'problem' heading and then other events and activities may be associated with it, including diagnosis/ treatment codes and attendance at a fracture clinic.",
    "url": "https://healthdatagateway.org/en/dataset/1463",
    "uid": null,
    "datasource_id": 1463,
    "source": "HDRUK"
  },
  {
    "id": 531,
    "name": "Sussex Integrated Dataset - Primary Care Appointment",
    "description": "This table records that activity has taken place between a patient and member of clinical staff working on behalf of a GP practice in an appointment 'slot', a pre-arranged opportunity in the rota for that clinician that was booked to be taken by this specific patient. GP practices sometimes use Clinic Lists that have slots but it is not possible to pre-book appointments into these slots, even though the clinical activity delivered in the time period may be exactly the same. Sometimes Clinic lists are retrospectively rearranged into appointments: e.g.when the GP has completed a ward round and only then knows who they have seen. This table also includes Did Not Attends (DNAs) which means that no patient contact took place.",
    "url": "https://healthdatagateway.org/en/dataset/1464",
    "uid": null,
    "datasource_id": 1464,
    "source": "HDRUK"
  },
  {
    "id": 532,
    "name": "Kent Integrated Dataset - GP Consultations",
    "description": "Primary Care Consultations information, recording the interactions that patients with medical staff at GP practices",
    "url": "https://healthdatagateway.org/en/dataset/1462",
    "uid": null,
    "datasource_id": 1462,
    "source": "HDRUK"
  },
  {
    "id": 533,
    "name": "Kent Integrated Dataset - GP Event",
    "description": "Primary Care Event information, recording the intereactions that patients with medical staff at GP practices and some read-coded numeric data",
    "url": "https://healthdatagateway.org/en/dataset/1461",
    "uid": null,
    "datasource_id": 1461,
    "source": "HDRUK"
  },
  {
    "id": 534,
    "name": "BELIEVE BangladEsh Longitudinal Investigation of Emerging Vascular Events",
    "description": "The BELIEVE (Bangladesh Early Life Interventions and Evaluation) study is a large-scale, population-based cohort that has recruited approximately 73,800 participants across rural and urban regions of Bangladesh. The study aims to understand risk factors and outcomes related to non-communicable diseases (NCDs), particularly cardiometabolic conditions, and provides a uniquely rich longitudinal dataset for global health research. We welcome potential collaboration with other researchers. Data is available on application to the study’s Data Access Committee.\n\nData and samples\nResearchers can request access to the following datasets collected as part of BELIEVE:\n\nPhenotypic and Questionnaire Data\n\nSociodemographic and socioeconomic status\nMedical and family history\nLifestyle factors: physical activity, diet (food frequency, food group usage), sleep, tobacco use\nReproductive history and anthropometry\nMobile phone use and digital access\nDisease outcomes:\nDiabetes (via HbA1c and self-report)\nHypertension\nCardiovascular disease\nStroke\nChronic kidney disease\nRespiratory disease\nOther self-reported symptoms and conditions\nBiomarker and Omics Data\n\nHbA1c: ~74,000 participants\nWhole blood counts: subset available\nLipid profile (total cholesterol, HDL, LDL, triglycerides, fructosamine): ~74,000\nEnvironmental exposures:\nArsenic in blood (n=500)\nArsenic in nails (n=500)\nHeavy metals in nails (n=4,000)\nGenomics:\nWhole Exome Sequencing (WES): ~73,000\nMethylation data: ~1,000 in pipeline\nProteomics:\nOlink (~3,300 samples)\nSomaLogic (~10,000 samples)\nMetabolomics:\nNightingale (1,300 analytes): ~74,000\nGlycomics: ~2,000 participants (IgG and plasma N-glycans)",
    "url": "https://healthdatagateway.org/en/dataset/1458",
    "uid": null,
    "datasource_id": 1458,
    "source": "HDRUK"
  },
  {
    "id": 535,
    "name": "Adult Social Care Client Level Data (Social Care CLD)",
    "description": "Client Level Data (CLD) has the potential to transform our understanding of peoples journeys through the social care system. As referenced in Data saves lives, the ability to link client level data from local authorities with NHS records for the same individuals will strengthen our understanding of how people move between health and social care, enabling better oversight of how services work together across the country and a better understanding to improve outcomes for individuals drawing on care.\n\nWith routine validation of the data, CLD will provide local authorities with a robust and consistent minimum core dataset that can be used to meet their local reporting requirements. Local authorities will also be able to request NHS number tracing and linked (pseudonymised) health records for greater commissioning insight into local health and care systems.\n\nThis data, particularly when linked with other NHS data, can be used to:\n- assess effective delivery of care\n- support local service planning\n- provide the basis for national indicators\n- develop, monitor and evaluate government policy\n- enable research\n- improve services",
    "url": "https://healthdatagateway.org/en/dataset/1513",
    "uid": null,
    "datasource_id": 1513,
    "source": "HDRUK"
  },
  {
    "id": 536,
    "name": "Histo-Mol GBM Collaborative (2021) Dataset",
    "description": "The Histo-Mol GBM Collaborative (2021) dataset is an international retrospective multi-centre cohort study of pathologically confirmed glioblastoma patients diagnosed during a 1-year time-period.\n\nThis dataset contains data from the majority of United Kingdom Neuro-oncology centres (44/49), every neuro-oncology centre in New Zealand (5/5) and the Republic of Ireland (2/2), plus 1 centre from Australia.\n\nThis dataset provides a national level overview of pathologically confirmed glioblastoma patients diagnosed between 01/01/2021 and 31/12/2021 with granular individual patient data comprising demographics, presenting symptoms, tumour characteristics, full treatment characteristics, post-treatment surveillance details, recurrence information including treatment at all recurrences, and survival data.",
    "url": "https://healthdatagateway.org/en/dataset/1412",
    "uid": null,
    "datasource_id": 1412,
    "source": "HDRUK"
  },
  {
    "id": 537,
    "name": "The PIND Study research database.",
    "description": "The database was originally an Access database used to record clinical information about children notified to the PIND Study.\n\nIt contains detailed data on presenting symptoms and signs in children with progressive intellectual and neurological deterioration (PIND), as well as records of all investigations carried out, including those key to making the final diagnosis.\n\nStudy Timeline\n==============\n\nStart date: **May 1997**\n\nNotification Trends\n-------------------\n\n- **First 20 months:** Large number of prevalent cases\n- **1999 to 2018:** 146 to 230 cases per year (stable rate despite COVID-19 disruption)\n- **2019 to 2023:** 121 to 171 cases per year\n- After active surveillance ended (end of 2023): 6 cases notified, last in April 2024\n\nBy April 2024 (after 27 years):\n\n- **Total notifications:** 5,222 cases\n\nCase Breakdown\n--------------\n\n- 2,540 cases did not meet PIND definition (duplicates, reporting errors, or missing info)\n- **PIND with no known diagnosis:** 309 cases\n- **vCJD cases:** 6 cases (4 definite, 2 probable)\n- **PIND with underlying diagnosis (excluding vCJD):** 2,367 cases\n\nLifetime Risk\n-------------\n\n- Diagnosis period: **May 1997 to April 2024**\n- UK live births (1997 to 2023): 19,598,293 births\n- **Calculated risk:** 0.1 per 1,000 live births\n\nDemographics\n------------\n\nOf the 2,367 diagnosed children (excluding vCJD):\n\n- **Gender:** Male 1,265 cases | Female 1,102 cases\n- **Ethnicity data (2,183 cases):**\n  - Asian or Asian British: 625 cases (28.6%)\n    - Indian: 60 cases\n    - Pakistani: 449 cases\n    - Bangladeshi: 54 cases\n    - Other Asian: 56 cases\n    - Chinese: 6 cases\n  - White: 1,342 cases (61.5%)\n  - Black: 72 cases (3.3%)\n  - Mixed: 67 cases (3.1%)\n  - Other: 77 cases (3.5%)\n\nComparison with 2021 Census (England and Wales):\n- Asian groups: 9.3%\n- White: 81.7%\n- Black: 4.0%\n- Mixed: 2.9%\n- Other: 2.1%\n\n**Observation:** Higher proportion of Asian British children in PIND study (28.6% vs census 9.3%)\n\nMany PIND-causing diseases are autosomal recessive.\nConsanguinity rates:\n- Pakistani: 67%\n- Bangladeshi: 48%\n- Indian: 13%\n- White: 3%\n\nInvestigations\n--------------\n\n**Neuropathology**\n- Brain biopsies: 14 cases (helpful in 13 cases: white matter disorders, Rasmussen encephalitis, mitochondrial diseases, etc.)\n\n**Molecular Genetics**\n- Results available: 1,312 cases\n- Diagnostic or confirmatory: 864 cases (66%)\n- Trend: Increasing role over time\n  - 1997 to 2001: 32 cases diagnosed\n  - 2019 to 2023: 206 cases diagnosed\n\n**Post Mortem**\n- Known deaths: 1,338 cases\n- Post mortem investigations: 39 cases (helpful in 25 cases)\n- Brain tissue available in 23 cases (diagnoses included Alpers disease, mitochondrial disorders, leukodystrophies, etc.)\n\nDisease Distribution\n--------------------\n\n- Total diagnosed diseases: 259 (excluding vCJD)\n- Inborn errors of metabolism: 61% of diseases (78% of diagnosed children)\n\nMajor groups:\n- Leukodystrophies: 444 cases\n- Mitochondrial diseases: 364 cases\n- Neuronal ceroid lipofuscinoses: 309 cases\n- Lysosomal diseases: 971 cases\n\nConclusion\n----------\n\nThe database is a unique resource for epidemiology and clinical features of more than 200 neurodegenerative childhood diseases.\n\nCase series can be identified by searching for specific diagnoses.",
    "url": "https://healthdatagateway.org/en/dataset/1411",
    "uid": null,
    "datasource_id": 1411,
    "source": "HDRUK"
  },
  {
    "id": 538,
    "name": "SAHSU - ONS England and Wales Death registrations",
    "description": "Death registration data for England and Wales include every death registered from 1981 onwards. Supplied to the Office for National Statistics (ONS) by the Local Registration Service in partnership with the General Register Office (GRO), these datasets record demographic details and cause of death, coded using the World Health Organization (WHO) International Classification of Diseases, 10th Revision (ICD-10).\n\nSAHSU holds a data sharing agreement with the ONS and has access to a restricted subset of variables from the full dataset.\n\nThe dataset may be internally linkable with SAHSU's environmental datasets, including air pollution and green space.",
    "url": "https://healthdatagateway.org/en/dataset/1338",
    "uid": null,
    "datasource_id": 1338,
    "source": "HDRUK"
  },
  {
    "id": 539,
    "name": "REal-time Assessment of Community Transmission (REACT-GE/Long COVID)",
    "description": "Clinical strand",
    "url": "https://healthdatagateway.org/en/dataset/1403",
    "uid": null,
    "datasource_id": 1403,
    "source": "HDRUK"
  },
  {
    "id": 540,
    "name": "STRIDES",
    "description": "The STRIDES study consists of blood cell data and linkage to electronic health records for 83,000 participants from the STRIDES BioResource. \n\nBackground and study aims\nThe STRIDES study aimed to reduce adverse events related to blood donation by implementing variations on the current interventions. Reducing adverse events is likely to increase donor retention and therefore the number of blood units donated for NHS patients. The study included changes to the material read prior to donation, the drink consumed prior to donation and the advice given during and after the donation experience. All blood donors in England who attended a blood donation session during the study period anonymously participated, with the option to opt-out. The results from this study will help inform national policies that should optimise blood collection procedures, minimise donor reactions, improve donor return rates, and improve donor (and staff) well-being and satisfaction. In addition to the main study, 83,000 donors joined the STRIDES BioResource, part of the NIHR BioResource. The STRIDES BioResource aims to provide additional biological (blood samples) and questionnaire data to address the overall aims of the STRIDES study",
    "url": "https://healthdatagateway.org/en/dataset/1402",
    "uid": null,
    "datasource_id": 1402,
    "source": "HDRUK"
  },
  {
    "id": 541,
    "name": "Cancer Waiting Times (CWT) - Oxford University Hospitals NHS Foundation Trust",
    "description": "The National Cancer Waiting Times Monitoring Data Set (known as Cancer Waiting Times or CWT) requires the submission of data to monitor NHS providers’ compliance with the government’s operational standards for ensuring that cancer services (diagnosis and treatment) are delivered to patients in a timely manner.\n\nIn particular the data is used to:\n\n- monitor timed pathways of care for cancer patients\n- manage pathways of care for cancer patients \n- performance manage elective services for cancer patients \n- report against the requirements of the NHS Operating Framework for cancer  waiting times \n- support the right of patients to access cancer services within the NHS Constitution \n- produce national, official and local statistics for cancer patients \n- support investment planning for cancer services",
    "url": "https://healthdatagateway.org/en/dataset/1260",
    "uid": null,
    "datasource_id": 1260,
    "source": "HDRUK"
  },
  {
    "id": 542,
    "name": "EU Farm Structure Survey Wales (EUFS)",
    "description": "The Farm Structure Survey provides harmonised data on the structure of agricultural holdings in the European Union, in particular on land use, livestock and farm labour force. Every ten years, a survey is carried out as an agricultural census. Two or three surveys are carried out between censuses in the form of a sample survey. The survey data helps to assess the agricultural situation across the EU, to monitor trends in the structure of holdings and to model the impact of external development of external developments of policy proposals.\n\nDefra (Department for Environment, Food and Rural Affairs - UK Government) is responsible for the collation and provision of the anonymised holding level dataset to Eurostat (the statistical office of the European Union). Responsibility for data collection in Wales lies with the Welsh Government. Much of the data is collected through the annual June Agricultural and Horticultural Survey with additional labour detail collected in farm structure survey years and other data through administrative sources.",
    "url": "https://healthdatagateway.org/en/dataset/290",
    "uid": "cc7aa34f-81df-42f8-ad69-80e136c48bf1",
    "datasource_id": 290,
    "source": "HDRUK"
  },
  {
    "id": 543,
    "name": "Scottish Trauma Audit Group (STAG)",
    "description": "STAG aims is to improve the quality of care, patient experience, and outcomes of severely injured patients through measuring compliance against Key Performance Indicators (KPIs) to support local quality improvement.\n\nhttps://publichealthscotland.scot/resources-and-tools/health-strategy-and-outcomes/scottish-national-audit-programme-snap/scottish-trauma-audit-group-stag/overview-of-stag/",
    "url": "https://healthdatagateway.org/en/dataset/1398",
    "uid": null,
    "datasource_id": 1398,
    "source": "HDRUK"
  },
  {
    "id": 544,
    "name": "NURTuRE Chronic Kidney Disease (NCKD)",
    "description": "The NURTuRE project was devised to create a national kidney biobank as recommended in the UK Renal Research Strategy 2016. Strategic Aims: To work towards achieving this NURTuRE will:\n\n- Create a national Kidney Bio Bank for collection and storage of biological samples from 3,000 CKD patients and up to 800 NS patients, to provide a strategic resource for fundamental and translational research.\n- Develop and implement proactive UK protocol driven cohort studies in CKD and NS to investigate determinants of and risk factors for clinically important adverse outcomes.\n- Engage patient cohorts, with consent to approach for any future research study. NURTuRE Objectives:\n- The provision of comprehensive clinical and laboratory data from cohort studies.\n- The provision of high quality bio-samples with centralised storage/retrieval.\n- To carry out core biomarker analysis of biopsy specimens in biofluids of all patients recruited and parallel assessment.\n- Follow-up specimen collection. First patient recruitment - By 31 June 2017\nCKD - baseline and 100 % follow up collections, over 2 years NS: baseline and 20% follow up - over 3 years. Healthy Volunteers - baseline\n\nBiological samples availability - Samples are available via the NURTuRE biobank - https://nurturebiobank.org/",
    "url": "https://healthdatagateway.org/en/dataset/1396",
    "uid": null,
    "datasource_id": 1396,
    "source": "HDRUK"
  },
  {
    "id": 545,
    "name": "Primary Care Appointments",
    "description": "An entry in the clinical systems calendar for time allocated to a specific patient. Some examples of this are a face to face appointment, a non face to face appointment and carrying out administration tasks relating to a patient",
    "url": "https://healthdatagateway.org/en/dataset/1512",
    "uid": null,
    "datasource_id": 1512,
    "source": "HDRUK"
  },
  {
    "id": 546,
    "name": "Scottish Health Survey (SHeS)",
    "description": "The main topics included in the survey are:\n1)\tgeneral health, long-term conditions & chronic pain\n2)\tmental wellbeing, loneliness & stress at work\n3)\tcardiovascular disease and use of services\n4)\tasthma, respiratory & COVID-19\n5)\tdiet, vitamin supplements & food insecurity\n6)\talcohol, smoking, drugs & gambling\n7)\tphysical activity\n8)\tdental health and services\n9)\tbiological measurements (including Body Mass Index) & prescribed medicines\n10)\taccidents & CPR training\n11)\tAdverse Childhood Experiences (ACE) & parental history\n12)\tunpaid caring\nNot all topics are included in the survey every year.\n\nData from the 1998, 2003 and 2008 to 2020 Scottish Health Surveys has now been linked to health record data on:\n\n1.\tvisits to hospital and length of stay\n2.\tdiagnosis, treatments and hospital stays for cancer, heart disease, stroke, diabetes and psychiatric episodes\n3.\tif the respondent has passed away, the date and cause of death\n4.\tCOVID-19 positive test results and vaccinations\n\nAccess is via a short application to be assessed by the PHS Data Protection Team. \n\nA full list of the variables included in the linked datasets is available on request.\n\nAny study requiring additional health record or Scottish Health Survey variables not included in the standard linked datasets will require an application to the relevant Public Benefit and Privacy Panel.",
    "url": "https://healthdatagateway.org/en/dataset/1377",
    "uid": null,
    "datasource_id": 1377,
    "source": "HDRUK"
  },
  {
    "id": 547,
    "name": "Health and Care Experience Survey",
    "description": "The  Scottish Health and Care Experience survey asks about peoples’ experiences of accessing and using their General Practice and other local healthcare services; receiving care, support and help with everyday living; and caring responsibilities.\nThe survey is run in partnership by the Scottish Government and Public Health Scotland. Both the Scottish Government and Public Health Scotland are involved in the planning and organisation of the survey, as well as analysing and reporting on the survey responses.\n\nThe survey covers five areas of health and care experience:\n\n1)Your General Practice\n2)Treatment or advice from your General Practice\n3)Out of hours healthcare\n4)Care, support and help with everyday living\n5) Caring responsibilities",
    "url": "https://healthdatagateway.org/en/dataset/1376",
    "uid": null,
    "datasource_id": 1376,
    "source": "HDRUK"
  },
  {
    "id": 548,
    "name": "NOVELTY Study (NOCD)",
    "description": "The NOVELTY study is a multi-country, multicentre, observational, prospective, longitudinal cohort study which will include patients with a physician diagnosis, or suspected diagnosis, of asthma and/or COPD. Patients will undergo clinical assessments and receive standard medical care as determined by the treating physician. All patients enrolled in the NOVELTY study will be followed up yearly by their treating physician for a total duration of three years. In addition, patients are expected to be followed up remotely once every quarter.\n\nIt is estimated that approximately 7,700 patients with suspected or primary diagnosis of asthma and 7,100 patients with suspected or primary diagnosis of COPD will be enrolled by a diverse set of physicians (e.g. primary care physicians, allergists, pulmonologists) from community and hospital outpatient settings within the countries targeted for NOVELTY.\n\nExposure(s):\nThe NOVELTY study is a longitudinal cohort study which does not involve or study a specific medicinal product; it will constitute a disease registry. Information about exposure to treatments as part of routine care will be collected (frequency, treatment, duration).\n\nSample Size Estimations:\nThe target minimum number of 100 patients per diagnostic label (asthma or COPD), physician-assessed severity level and country has been chosen to support many basic local reimbursement specific requirements with reasonable precision, and to provide large sample size for scientific questions applicable across severities and countries. Therefore, considering the targeted countries, it is estimated that approximately 7,700 patients with asthma and 7,100 patients with COPD will be enrolled.\n\nStatistical Analysis:\nAfter baseline data collection and each annual data collection, data will be summarized for the population overall and by pre specified subgroups, including by country, demographics, exposures, symptom history, treatment history, concurrent clinical features, treatment setting, socioeconomic setting and access to healthcare, where relevant.\n\nPatients’ changes regarding their treatment, disease or severity among and other variables that are observed between baseline and follow-up visits, will also be described.\n\nTo identify potential differences in disease diagnosis and severity classifications between physicians and guidelines, data collected on lung function results, symptom questionnaires, exacerbation occurrences and medication will allow the formal and consistent classification of the patients according to relevant international guidelines and other current and future phenotypic/diagnostic classifications.\n\nMore information on NOVELTY can be found here: https://www.astrazenecaclinicaltrials.com/study/D2287R00103/\n\nPlease note: the NOCD is a standalone dataset, and cannot be linked with other SAIL datasets.",
    "url": "https://healthdatagateway.org/en/dataset/1375",
    "uid": null,
    "datasource_id": 1375,
    "source": "HDRUK"
  },
  {
    "id": 549,
    "name": "Data First Cross-Justice System Linking Dataset (CJSL)",
    "description": "This linking dataset allows users to join up information in other Data First justice datasets. It does not itself contain information about people or their justice system interactions but acts as a lookup to identify which records in separate datasets refer to the same people. \n\nThe MOJ Data First datasets currently covered are:\n- magistrates' courts defendant (MACO)\n- Crown Court defendant  (CRCO)\n- prisoner custodial journey (PRIS)\n- probation (PROB)\n- family court (FACO)\n- civil court \n- offender assessments\n\nIt contains two tables: one acts as a lookup to group records together by person across all the datasets (xjs_link table), and the second acts as a lookup to link court cases between the Crown Court and the magistrates’ courts (mags_crown_journey table).\n\nXJS Link file - \nThis linking dataset allows users to join up information about people in Data First cross-justice system datasets. It acts as a lookup to identify which records in separate datasets refer to the same people. \n\nMags Crown Journey file - \nThis linking dataset allows users to join up information about people in Data First cross-justice system datasets. It acts as a lookup to identify which records in separate datasets refer to the same people. \nThis linking dataset allows users to join up information about cases in Data First magistrates' courts and Crown Court datasets. It acts as a lookup to identify which records refer to the same criminal case appearing in the two courts.\n\nMore information can be found at: https://www.gov.uk/guidance/ministry-of-justice-data-first#moj-cross-justice-system-datasets",
    "url": "https://healthdatagateway.org/en/dataset/1374",
    "uid": null,
    "datasource_id": 1374,
    "source": "HDRUK"
  },
  {
    "id": 550,
    "name": "COVID-19 Vaccination Status",
    "description": "https://digital.nhs.uk/services/data-access-request-service-dars/dars-products-and-services/data-set-catalogue/covid-19-vaccination-status",
    "url": "https://healthdatagateway.org/en/dataset/1373",
    "uid": null,
    "datasource_id": 1373,
    "source": "HDRUK"
  },
  {
    "id": 551,
    "name": "NHS Talking Therapies, for anxiety and depression programme (Improving Access to Psychological Therapies, IAPT)",
    "description": "The NHS Talking Therapies, for anxiety and depression programme (formerly known as Improving Access to Psychological Therapies - IAPT) was developed to improve the delivery of, and access to, evidence-based, NICE recommended, psychological therapies for depression and anxiety disorders within the NHS.\n\nNHS Talking Therapies, for anxiety and depression services are characterised by three key principles:\n\n1. All psychological therapies offered are evidence-based and delivered at the appropriate dose: where NICE recommended therapies are matched to the mental health problem, and the intensity and duration of delivery is designed to optimise clinical outcomes.\n2. All within the clinical workforce are appropriately trained and supervised: high-quality care is provided by clinicians who are trained to an agreed level of competence and accredited in the specific therapies they deliver, and they receive weekly outcomes focused supervision from senior clinical practitioners with the relevant competences to support continual improvement.\n3. Routine outcome monitoring via standardised measures is used on a session-by-session basis, so that the person having therapy and the clinician offering it have up-to-date information on the person's progress. The outcomes of all NHS Talking Therapies, for anxiety and depression services are published so that the sector can learn from variation in outcomes and public transparency about the benefits and limitations of the services is maintained. This helps guide the course of each person's treatment and provides a resource for service improvement, transparency, and public accountability.\nServices are delivered using a stepped-care model, which works according to the principle that people should be offered the least intrusive intervention appropriate for their needs first.\n\nNHS Talking Therapies, for anxiety and depression services provide treatment for people with the following common mental health problems: \nAgoraphobia\nBody Dysmorphic Disorder (BDD)\nDepression\nGeneralised anxiety disorder\nHealth anxiety (hypochondriasis)\nMixed depression and anxiety (the term for sub-syndromal depression and anxiety, rather than both depression and anxiety)\nObsessive-Compulsive Disorder (OCD)\nPanic disorder\nPost-traumatic Stress Disorder (PTSD)\nSocial anxiety disorder\nSpecific phobias (such as heights, flying, spiders etc.).",
    "url": "https://healthdatagateway.org/en/dataset/1514",
    "uid": null,
    "datasource_id": 1514,
    "source": "HDRUK"
  },
  {
    "id": 552,
    "name": "Secondary Uses Service (SUS)",
    "description": "https://digital.nhs.uk/services/secondary-uses-service-sus",
    "url": "https://healthdatagateway.org/en/dataset/1370",
    "uid": null,
    "datasource_id": 1370,
    "source": "HDRUK"
  },
  {
    "id": 553,
    "name": "National Waiting List",
    "description": "https://standards.nhs.uk/published-standards/national-waiting-list-weekly-minimum-data-set",
    "url": "https://healthdatagateway.org/en/dataset/1367",
    "uid": null,
    "datasource_id": 1367,
    "source": "HDRUK"
  },
  {
    "id": 554,
    "name": "Cheshire and Mersey Virtual Wards Occupancy",
    "description": "Unnamed NWSDE Data Asset",
    "url": "https://healthdatagateway.org/en/dataset/1366",
    "uid": null,
    "datasource_id": 1366,
    "source": "HDRUK"
  },
  {
    "id": 555,
    "name": "Medicines dispensed in Primary Care",
    "description": "https://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/medicines-dispensed-in-primary-care-nhsbsa-data#top",
    "url": "https://healthdatagateway.org/en/dataset/1365",
    "uid": null,
    "datasource_id": 1365,
    "source": "HDRUK"
  },
  {
    "id": 556,
    "name": "Local Dataset - NEAS Ambulance data - North East and North Cumbria",
    "description": "This dataset supports population health research, service evaluation, health system planning, and operational performance monitoring. It is particularly valuable for understanding emergency care demand, response patterns, and pre-hospital clinical interventions across the North East and North Cumbria region. The dataset is pseudonymised prior to being made available in the SDE and does not include directly identifiable patient information.",
    "url": "https://healthdatagateway.org/en/dataset/1363",
    "uid": null,
    "datasource_id": 1363,
    "source": "HDRUK"
  },
  {
    "id": 557,
    "name": "Alcohol dependence",
    "description": "https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/alcohol-dependence",
    "url": "https://healthdatagateway.org/en/dataset/1357",
    "uid": null,
    "datasource_id": 1357,
    "source": "HDRUK"
  },
  {
    "id": 558,
    "name": "Ambulance Data Set",
    "description": "https://www.england.nhs.uk/urgent-emergency-care/improving-ambulance-services/ambulance-data-set/",
    "url": "https://healthdatagateway.org/en/dataset/1356",
    "uid": null,
    "datasource_id": 1356,
    "source": "HDRUK"
  },
  {
    "id": 559,
    "name": "NHS e-Referral Service (e-RS)",
    "description": "https://www.england.nhs.uk/long-read/e-referrals/",
    "url": "https://healthdatagateway.org/en/dataset/1355",
    "uid": null,
    "datasource_id": 1355,
    "source": "HDRUK"
  },
  {
    "id": 560,
    "name": "NHS Continuing Health Care (CHC)",
    "description": "https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/continuing-health-care-data-set",
    "url": "https://healthdatagateway.org/en/dataset/1354",
    "uid": null,
    "datasource_id": 1354,
    "source": "HDRUK"
  },
  {
    "id": 561,
    "name": "Community Services Data Set (CSDS)",
    "description": "https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/community-services-data-set",
    "url": "https://healthdatagateway.org/en/dataset/1353",
    "uid": null,
    "datasource_id": 1353,
    "source": "HDRUK"
  },
  {
    "id": 562,
    "name": "Adult social care",
    "description": "https://digital.nhs.uk/services/data-services-for-commissioners/datasets/adult-social-care-data",
    "url": "https://healthdatagateway.org/en/dataset/1352",
    "uid": null,
    "datasource_id": 1352,
    "source": "HDRUK"
  },
  {
    "id": 563,
    "name": "Synthea OMOP (CDM) - North East and North Cumbria",
    "description": "Synthetic Primary Care Data ([Synthea](https://synthea.mitre.org/)) transformed into the Observational Medical Outcomes Partnership (OMOP) [Common Data Model](https://ohdsi.github.io/CommonDataModel/index.html) (CDM) \n\nData is sourced from https://synthea.mitre.org/downloads using the 100 sample patient CSV variant of available downloads. Data has been transformed using the ETL methods described by https://github.com/OHDSI/ETL-Synthea\n\nThis is a patient level dataset of Primary Care data covering 100 synthetic patients",
    "url": "https://healthdatagateway.org/en/dataset/1351",
    "uid": null,
    "datasource_id": 1351,
    "source": "HDRUK"
  },
  {
    "id": 564,
    "name": "Greater Manchester Virtual Wards Occupancy",
    "description": "Unnamed NWSDE Data Asset",
    "url": "https://healthdatagateway.org/en/dataset/1350",
    "uid": null,
    "datasource_id": 1350,
    "source": "HDRUK"
  },
  {
    "id": 565,
    "name": "Hospital Electronic Prescribing and Medicines Administration System (HEPMA)",
    "description": "HEPMA is a live clinical system and updated on a continuous basis. Data is automatically extracted from local systems at least weekly and\nin most cases, on a nightly basis, and integrated into the national HEPMA dataset. \nData is currently submitted by all health boards except NHS Fife and NHS Borders.",
    "url": "https://healthdatagateway.org/en/dataset/1349",
    "uid": null,
    "datasource_id": 1349,
    "source": "HDRUK"
  },
  {
    "id": 566,
    "name": "Mental Health Services Data Set (MHSDS)",
    "description": "https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/mental-health-services-data-set/about",
    "url": "https://healthdatagateway.org/en/dataset/1348",
    "uid": null,
    "datasource_id": 1348,
    "source": "HDRUK"
  },
  {
    "id": 567,
    "name": "SAHSU - ONS - England and Wales Birth Registrations (Live/Still)",
    "description": "Live and still births registration data for England and Wales include every death registered. Live birth records contain details such as date of birth, sex, birth weight, place of birth, and parental information. Still birth, defined as babies born dead after 24 complete weeks of pregnancy, contains details such as cause of deaths, birth weight, and parental information. \n\nSAHSU holds a data sharing agreement with the ONS and has access to a restricted subset of variables from the full dataset.\n\nThe dataset may be internally linkable with SAHSU's environmental datasets, including air pollution and green space.",
    "url": "https://healthdatagateway.org/en/dataset/1347",
    "uid": null,
    "datasource_id": 1347,
    "source": "HDRUK"
  },
  {
    "id": 568,
    "name": "Cancer Waiting Times Data Collection (CWT)",
    "description": "https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-collections/cancerwaitingtimescwt",
    "url": "https://healthdatagateway.org/en/dataset/1346",
    "uid": null,
    "datasource_id": 1346,
    "source": "HDRUK"
  },
  {
    "id": 569,
    "name": "NHS Talking Therapies, for anxiety and depression",
    "description": "https://www.england.nhs.uk/mental-health/adults/nhs-talking-therapies/service-standards/",
    "url": "https://healthdatagateway.org/en/dataset/1345",
    "uid": null,
    "datasource_id": 1345,
    "source": "HDRUK"
  },
  {
    "id": 570,
    "name": "National Cancer Registration and Analysis Service (NCRAS)",
    "description": "https://digital.nhs.uk/data-and-information/keeping-data-safe-and-benefitting-the-public/gdpr/gdpr-register/national-cancer-registration-dataset",
    "url": "https://healthdatagateway.org/en/dataset/1344",
    "uid": null,
    "datasource_id": 1344,
    "source": "HDRUK"
  },
  {
    "id": 571,
    "name": "Maternity Services Data Set (MSDS)",
    "description": "https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/maternity-services-data-set",
    "url": "https://healthdatagateway.org/en/dataset/1342",
    "uid": null,
    "datasource_id": 1342,
    "source": "HDRUK"
  },
  {
    "id": 572,
    "name": "Civil Registration of Deaths Dataset",
    "description": "Details of all registered deaths in England and Wales since 1993, as provided by the Office for National Statistics (ONS). It contains details of the registration and basic demographics of the deceased person. This data set may be used in cohort data disseminations, in which case submission of a patient participation list will be required.",
    "url": "https://healthdatagateway.org/en/dataset/1341",
    "uid": null,
    "datasource_id": 1341,
    "source": "HDRUK"
  },
  {
    "id": 573,
    "name": "Civil Registration of Births Dataset",
    "description": "Birth registrations are provided by local registries to the General Registry Office, then on to ONS for codification.",
    "url": "https://healthdatagateway.org/en/dataset/1340",
    "uid": null,
    "datasource_id": 1340,
    "source": "HDRUK"
  },
  {
    "id": 574,
    "name": "Diagnostic Imaging Dataset (DIDS)",
    "description": "https://digital.nhs.uk/services/data-services-for-commissioners/datasets/diagnostic-imaging-dataset-dids",
    "url": "https://healthdatagateway.org/en/dataset/1339",
    "uid": null,
    "datasource_id": 1339,
    "source": "HDRUK"
  },
  {
    "id": 575,
    "name": "London Ambulance Service Frequent Callers (LAS)",
    "description": "A list of the registered population that meet the LAS Frequent Callers criteria. \nThis includes:\n- seen and conveyed\n- seen and treated\n- treated over the phone",
    "url": "https://healthdatagateway.org/en/dataset/1516",
    "uid": null,
    "datasource_id": 1516,
    "source": "HDRUK"
  },
  {
    "id": 576,
    "name": "University College London Hospitals NHS OMOP dataset",
    "description": "UCLH has an OMOP extraction system (omop_es) that connects our Electronic Health Record (EHR) to an architecture that delivers high quality, standardised extracts meeting the OMOP CDM standards. Our EHR contains records for 6 million patients, 13 million diagnoses and 50 million medication events. These derive from the UCLH patient population which includes national referrals for tertiary and quaternary services (cancer, neurology etc.) and general medical admissions from an inner city teaching hospital that treats >1m outpatients per year, and has >100k inpatient admissions.\n\nUCLH has invested efforts and expertise to align international terminology systems e.g. SNOMED CT, LOINC, UCUM with NHS data standards, during EHR system build and post implementation. Our standardisation work has covered clinical domains i.e. Diagnosis and past medical history, Surgical and Ambulatory procedures, Diagnostic Imaging, Cardiac Echo, Lab Medicine including Biochemistry, Haematology, Microbiology, Immunology, Virology, Allergens, Medications (including route of administration); and Demographic information like Religion, Ethnicity. For some domains (e.g. diagnosis and surgical procedures) we have achieved 100%\nstandardisation, others are an ongoing task.\n\nOur data pipeline, the OMOP-Extraction System (OMOP-ES) is a modular, re-usable architecture written in over 20,000 lines of R. Extractions proceed through four stages.\n\n1. Standardisation - translates source data to OMOP concepts at full fidelity\n2. Projection - applies rules to redact, filter, transform & link\n3. Post-processing - allows linking of de-identified non-OMOP data\n4. Output - multiple formats & destinations incl. CSV, Parquet or SQLite for direct use or import in a TRE\n\nThe system is\n● configurable to a variety of OMOP projects via a settings file\n● reproducible and automated\n● queries EPIC EHR and other sources\n● automates filtering of sensitive data with safe defaults and ability for Information Governance teams to inspect settings before & after running\n● tests and reports quality of standardisation\n● being extended both by the 'core' team and by other trusts in an inner source fashion\n● has a small mock database for system development and testing",
    "url": "https://healthdatagateway.org/en/dataset/1336",
    "uid": null,
    "datasource_id": 1336,
    "source": "HDRUK"
  },
  {
    "id": 577,
    "name": "Analysing inequities in treatment of ethnic minority blood cancer patients receiving transplants",
    "description": "Cancer data from UHB.",
    "url": "https://healthdatagateway.org/en/dataset/1334",
    "uid": null,
    "datasource_id": 1334,
    "source": "HDRUK"
  },
  {
    "id": 578,
    "name": "CUREd+ Centre for Urgent and Emergency Care Research Database",
    "description": "Overview of CUREd+\nCUREd+ is a unique research database that links patient records from different NHS sources, including ambulance and hospital data, at an unprecedented scale. The database contains over 1 billion unique episodes of Urgent and Emergency Care (UEC) between 2011 and 2023. This enables researchers to gain a comprehensive understanding of patient journeys through UEC, providing valuable insights to improve service delivery, patient outcomes, and policy decisions. The database is currently approved until June 2026, but applications are underway to extend this.\n\nHistory\nCUREd+ builds upon the foundations laid by the original CUREd research database, developed by the University of Sheffield, containing data from UEC providers, including Yorkshire Ambulance Service (YAS), NHS hospital trusts, and mental health services. The data spanned from 2011 to 2017 and enabled critical research into patient flow through these services.\nThe renewal of CUREd involved significant amendments to expand its scope and impact. The establishment of CUREd+ has introduced key improvements, including:\n\nIncorporating data up to 2023\nExpansion of hospital data coverage to all of England\nInclusion of updated linked ambulance service data, now incorporating electronic patient records\nAddition of death registration data to support mortality-related research.\nStandardisation of hospital data formats to enable more robust research analysis.\nNHS England plays a key role in providing hospital data and facilitating the linkage process, ensuring high-quality and comprehensive data integration. Yorkshire Ambulance Service (YAS) remains a crucial data provider, contributing ambulance service records that enhance the understanding of patient care pathways.\n\nThe Aims of CUREd+\nAll research using data from the CUREd+ database must be focused on one or more of these aims:\n-Identifying and characterising the pathways of specific cohorts of patients before they enter the UEC system and their journey through it.\n-Understanding factors influencing UEC entry, including when, how, and why individuals from specific cohorts access urgent and emergency care.\n-Analysing performance indicators and quality measures, such as response times, waiting times, and risk-adjusted mortality rates.\n-Investigating demand management strategies, including the role of NHS 111 and other interventions designed to reduce avoidable attendances and admissions.\n-Examining the use of the urgent care workforce, including nurse practitioners and advanced paramedic practitioners, to assess workforce efficiency and impact.\n\nData Sources\nCUREd+ integrates data from multiple NHS sources to provide a comprehensive view of urgent and emergency care. Key data sources include:\nHospital Data: Provided by NHS England, covering Accident & Emergency, hospital admissions, outpatient care, mental health services, demographic data, and death registry records.\nAmbulance Service Data: Supplied by Yorkshire Ambulance Service, incorporating emergency call records, NHS 111 interactions, and electronic patient records.\n\nFunding\nCUREd+ is funded by the National Institute for Health and Care Research Yorkshire and Humber Applied Research Collaboration and the Yorkshire and Humber Secure Data Environment.\nAdditionally, the staff costs associated with managing CUREd+ and creating data extracts are recovered from research projects when data access is granted. This funding model ensures the sustainability of the database while supporting high-quality research.",
    "url": "https://healthdatagateway.org/en/dataset/1333",
    "uid": null,
    "datasource_id": 1333,
    "source": "HDRUK"
  },
  {
    "id": 579,
    "name": "The ADVANCE Study",
    "description": "The purpose of the study is to investigate the long-term outcomes of battlefield trauma casualties and to compare these outcomes to those of a similar group of non-battlefield trauma individuals.  The outcomes being investigated include medical (in particular cardiovascular disease and osteoarthritis) and psychosocial outcomes.  There is some evidence to suggest that battlefield trauma casualties may have some unfavourable outcomes but this evidence is limited.  Also, the types of injuries sustained in previous conflicts are different from those sustained in recent conflicts and therefore it is still unclear whether or how the type of injuries we are seeing from Afghanistan will affect the long-term outcome of injured servicemen.\nMore information can be found at https://www.advancestudydmrc.org.uk/",
    "url": "https://healthdatagateway.org/en/dataset/1332",
    "uid": null,
    "datasource_id": 1332,
    "source": "HDRUK"
  },
  {
    "id": 580,
    "name": "CUREd+ Linked Yorkshire Ambulance Service 111 calls [from CUREd]",
    "description": "This dataset contains structured records of NHS 111 calls handled by Yorkshire Ambulance Service. It includes information on call timing, presenting symptoms, triage outcomes, referral pathways, and additional metadata reflecting the structure of the legacy call handling system in use at the time.\n\nThis dataset should only be used for studies covering NHS 111 contacts between April 2011 and March 2017. It predates the introduction of the NHS 111 Minimum Data Set and differs structurally from later data extracts. Researchers should not attempt to combine it with post-2017 data unless there is a clear justification and defined harmonisation plan.",
    "url": "https://healthdatagateway.org/en/dataset/1318",
    "uid": null,
    "datasource_id": 1318,
    "source": "HDRUK"
  },
  {
    "id": 581,
    "name": "Leeds Teaching Hospitals OMOP Database",
    "description": "The Leeds Teaching Hospitals NHS Trust (LTHT) OMOP database is a robust, longitudinal dataset constructed using data from the electronic health records (EHR) of patients treated and diagnosed at Leeds Teaching Hospitals NHS Trust since 2003. This comprehensive resource is mapped to the OMOP CDM, ensuring interoperability with other OMOP databases, and enabling privacy-preserving, large-scale, multi-centre studies.\n\nEncompassing a wide array of clinical data, the database includes information on demographics, diagnoses, procedures, medications and laboratory results. A particular strength lies in its detailed cancer-specific data, which supports in-depth analyses of treatment outcomes, survival rates, and disease progression. This makes it an invaluable resource for researchers focusing on oncology, as well as those interested in broader secondary care settings.\n\nResearchers can draw insights from the LTHT OMOP database through federated analytics approaches as well as through the use of standardised OHDSI tools, which enable secure, privacy-preserving analyses across multiple institutions, eliminating the need to access individual-level patient data. \n\nNotably, the LTHT OMOP database has been instrumental in several high-profile studies:\n\n•\tHERON Network: LTHT is a member of the HERON network, funded by HDR UK, which focuses on enhancing the quality and impact of cancer research through federated analytics. LTHT participated in a study examining the use of antibiotics which are in the WHO watchlist for high risk of antimicrobial resistance.\n•\tDigiONE Pilot Studies: These studies analyse harmonised routine care data from OMOP databases in 6 digitally mature European hospitals. Three studies have been conducted to date, focusing on the impact of the COVID-19 pandemic on cancer care, on metastatic non-small cell lung cancer, and on HER2-/HR+ metastatic breast cancer.\n•\tFALCON-Lung Study: This study focused on the uptake of immune checkpoint inhibitors for metastatic non-small cell lung cancer across the world, and implemented a clinically validated line of therapy algorithm using systemic anti-cancer therapy data in the OMOP databases of 17 international institutions. \n\nIn summary, the LTHT OMOP database stands as a robust resource for secondary care research, particularly in oncology. Its comprehensive, high-quality data, combined with a commitment to national and international collaboration, positions it as a cornerstone for advancing healthcare research and improving patient outcomes.\n\nThe LTHT OMOP database consists of the following tables and data:\n\n•\tVisit occurrence: includes inpatient and outpatient admissions for all patients that are or have been part of the cancer pathway, as well as all in-patient admissions for all other patients. The visit_detail table has not been populated. \n•\tCondition occurrence: populated with all diagnoses in the Trust since 2003.\n•\tDrug exposure: populated. Includes all anti-cancer drugs (chemotherapy and immunotherapy), and selected antibiotics medication (all antibiotics that are in the WHO watchlist for antimicrobial resistance, as well as access antibiotics). Plans to extend this to all medication prescribed.\n•\tProcedure occurrence: populated. Includes surgical and radiotherapy procedures delivered to patients with cancer, as well as all surgical procedures delivered to all other patients. \n•\tMeasurement: populated with weight, height, TNM staging, performance status, and metastasis location data. \n•\tObservation: populated with ethnicity, IMD quintile, clinical trial participation (cancer only) and cancer histology data.\n•\tDevice exposure: not populated. \n•\tDeath: populated from ONS.",
    "url": "https://healthdatagateway.org/en/dataset/1320",
    "uid": null,
    "datasource_id": 1320,
    "source": "HDRUK"
  },
  {
    "id": 582,
    "name": "Acute Admissions - Regional Data Specification",
    "description": "HDRUK Acute Admissions, regional data specification. Patient records for all acute emergency admissions for adults (18 years + on day of admission) that had an admission date within the specified study period. Data delivery on demand. Please contact South West SDE for date ranges and extensions to geography.",
    "url": "https://healthdatagateway.org/en/dataset/1316",
    "uid": null,
    "datasource_id": 1316,
    "source": "HDRUK"
  },
  {
    "id": 583,
    "name": "Troponin and eGFR",
    "description": "Troponin and estimated Glomerular Filtration Rate (eGFR) blood test results taken by Pathology laboratory. Data delivery on demand. Please contact South West SDE for date ranges and extensions to geography.",
    "url": "https://healthdatagateway.org/en/dataset/1315",
    "uid": null,
    "datasource_id": 1315,
    "source": "HDRUK"
  },
  {
    "id": 584,
    "name": "Chest Pain - Medicine Discharge",
    "description": "Medicines at time of discharge information from Patient Administration system. HDR UK Chest Pain Pathways project. Data delivery on demand. Please contact South West SDE for date ranges and extensions to geography.",
    "url": "https://healthdatagateway.org/en/dataset/1314",
    "uid": null,
    "datasource_id": 1314,
    "source": "HDRUK"
  },
  {
    "id": 585,
    "name": "Virology",
    "description": "Virology results taken by Severn Pathology as reported by the WinPath system. Data to support population health management and applied research activities in BNSSG. Historical data shared from November 2016. Data delivery on demand. Please contact South West SDE for date ranges and extensions to geography.",
    "url": "https://healthdatagateway.org/en/dataset/1313",
    "uid": null,
    "datasource_id": 1313,
    "source": "HDRUK"
  },
  {
    "id": 586,
    "name": "Urines Bacterias",
    "description": "Urines Bacterias results taken by Severn Pathology as reported by the WinPath system. Data to support population health management and applied research activities in BNSSG. Historical data shared from November 2016. Data delivery on demand. Please contact South West SDE for date ranges and extensions to geography.",
    "url": "https://healthdatagateway.org/en/dataset/1312",
    "uid": null,
    "datasource_id": 1312,
    "source": "HDRUK"
  },
  {
    "id": 587,
    "name": "Urine Sensitivities",
    "description": "Urine Sensitivity results taken by Pathology laboratory. Data delivery on demand. Please contact South West SDE for date ranges and extensions to geography.",
    "url": "https://healthdatagateway.org/en/dataset/1311",
    "uid": null,
    "datasource_id": 1311,
    "source": "HDRUK"
  },
  {
    "id": 588,
    "name": "CDiff Bacteria",
    "description": "C-Difficile results taken by Pathology laboratory. Data delivery on demand. Please contact South West SDE for date ranges and extensions to geography.",
    "url": "https://healthdatagateway.org/en/dataset/1310",
    "uid": null,
    "datasource_id": 1310,
    "source": "HDRUK"
  },
  {
    "id": 589,
    "name": "Pathology - Bloods",
    "description": "Blood culture results taken by Pathology laboratory. Data delivery on demand. Please contact South West SDE for date ranges and extensions to geography.",
    "url": "https://healthdatagateway.org/en/dataset/1309",
    "uid": null,
    "datasource_id": 1309,
    "source": "HDRUK"
  },
  {
    "id": 590,
    "name": "Ward Movements",
    "description": "Ward movements information from Patient Administration system. HDR UK Better Care Partnership: ICU project. Data delivery on demand. Please contact South West SDE for date ranges and extensions to geography.",
    "url": "https://healthdatagateway.org/en/dataset/1308",
    "uid": null,
    "datasource_id": 1308,
    "source": "HDRUK"
  },
  {
    "id": 591,
    "name": "Medicine Discharge",
    "description": "Medicines at time of discharge information from Patient Administration system. HDR UK Better Care Partnership: ICU project. Data delivery on demand. Please contact South West SDE for date ranges and extensions to geography.",
    "url": "https://healthdatagateway.org/en/dataset/1307",
    "uid": null,
    "datasource_id": 1307,
    "source": "HDRUK"
  },
  {
    "id": 592,
    "name": "ICU Episodes Specification",
    "description": "Episodes Specification. HDR UK Better Care Partnership: ICU project. This file has one row per day of each admission to ICU/HDU and therefore reports at a day level. Data delivery is on demand. Please contact South West SDE for date ranges and extensions to geography.",
    "url": "https://healthdatagateway.org/en/dataset/1306",
    "uid": null,
    "datasource_id": 1306,
    "source": "HDRUK"
  },
  {
    "id": 593,
    "name": "ICU Daily Specification",
    "description": "Daily Specification. HDR UK Better Care Partnership: ICU project. This file has one row per day of each admission to ICU/HDU and therefore reports at a day level. Data delivery is on demand. Please contact South West SDE for date ranges and extensions to geography.",
    "url": "https://healthdatagateway.org/en/dataset/1305",
    "uid": null,
    "datasource_id": 1305,
    "source": "HDRUK"
  },
  {
    "id": 594,
    "name": "Emergency Admissions",
    "description": "Emergency admission information from Patient Administration system. HDR UK Better Care Partnership: ICU project. Data delivery is on demand. Please contact SWSDE for date ranges.",
    "url": "https://healthdatagateway.org/en/dataset/1304",
    "uid": null,
    "datasource_id": 1304,
    "source": "HDRUK"
  },
  {
    "id": 595,
    "name": "Chemotherapy clinical information",
    "description": "Chemotherapy prescribing and administration, held ARIA Medonc",
    "url": "https://healthdatagateway.org/en/dataset/1261",
    "uid": null,
    "datasource_id": 1261,
    "source": "HDRUK"
  },
  {
    "id": 596,
    "name": "Electronic Patient Record - Oxford University Hospitals NHS Foundation Trust",
    "description": "EPR patient demographics\nEPR emergency department attendances\nEPR emergency department escalations\nEPR bed stays\nEPR waiting lists - inpatient and outpatient\nEPR referral to treatment\nCapacity management information\nVital signs, procedures, problems, clinical scores, risk factors\nPrescribing, administration\nEPR inpatient spells and episodes, ward movements, diagnoses\nEPR outpatient attendances\nEPR free text in PowerForms\nEPR attachments (clinical letters etc)",
    "url": "https://healthdatagateway.org/en/dataset/1229",
    "uid": null,
    "datasource_id": 1229,
    "source": "HDRUK"
  },
  {
    "id": 597,
    "name": "VitPanc-1",
    "description": "A study by UHB.",
    "url": "https://healthdatagateway.org/en/dataset/1284",
    "uid": null,
    "datasource_id": 1284,
    "source": "HDRUK"
  },
  {
    "id": 598,
    "name": "PHOENIX Trial",
    "description": "A study by University of Birmingham.",
    "url": "https://healthdatagateway.org/en/dataset/1283",
    "uid": null,
    "datasource_id": 1283,
    "source": "HDRUK"
  },
  {
    "id": 599,
    "name": "Identifying and mitigating biases in perioperative prognostic models and clinical scoring systems",
    "description": "A study by UHB and University of Birmingham.",
    "url": "https://healthdatagateway.org/en/dataset/1282",
    "uid": null,
    "datasource_id": 1282,
    "source": "HDRUK"
  },
  {
    "id": 600,
    "name": "SGLT2i use in patients with diabetes and chronic kidney disease",
    "description": "A study by UHB and Aston University.",
    "url": "https://healthdatagateway.org/en/dataset/1281",
    "uid": null,
    "datasource_id": 1281,
    "source": "HDRUK"
  },
  {
    "id": 601,
    "name": "PhenoAge as a predictor of outcome in acute admissions with Covid-19",
    "description": "A study by University of Birmingham.",
    "url": "https://healthdatagateway.org/en/dataset/1280",
    "uid": null,
    "datasource_id": 1280,
    "source": "HDRUK"
  },
  {
    "id": 602,
    "name": "Comparison of two physiotherapy interventions",
    "description": "A study by Birmingham City University.",
    "url": "https://healthdatagateway.org/en/dataset/1265",
    "uid": null,
    "datasource_id": 1265,
    "source": "HDRUK"
  },
  {
    "id": 603,
    "name": "Congenital Conditions and Rare Diseases Registration and Information Service for Scotland​ (CARDRISS)",
    "description": "Babies with a condition are included if the pregnancy is live born and suspected and/or diagnosed within their first year; a spontaneous still birth at 24 weeks and over; miscarriage at 23 weeks and under or a termination of pregnancy at any gestation.\n\nThe register includes babies from pregnancies ending in January 2021 onwards, where the baby meets this inclusion criteria. The register will be extended in the future to also collect and hold information on other rare diseases.",
    "url": "https://healthdatagateway.org/en/dataset/1259",
    "uid": null,
    "datasource_id": 1259,
    "source": "HDRUK"
  },
  {
    "id": 604,
    "name": "Primary Care Data - North East and North Cumbria",
    "description": "This dataset is essential for population health research, service planning, and evaluation of primary care interventions. It enables linkage to other regional datasets (e.g. secondary care, ambulance, community services) to support end-to-end pathway analysis. All data are pseudonymised before being made available within the Secure Data Environment and exclude directly identifiable information.",
    "url": "https://healthdatagateway.org/en/dataset/1258",
    "uid": null,
    "datasource_id": 1258,
    "source": "HDRUK"
  },
  {
    "id": 605,
    "name": "Cheshire and Merseyside ICB Local Primary Care Data (OMOP)",
    "description": "This OMOP CDM is built from a flow of primary care data from Cheshire and Merseyside GPs who have signed the ICB Data Sharing Agreement for Population Health. Patients who have signalled that they wish to opt out of their records being shared for secondary uses (i.e. uses beyond Direct Patient Care) are removed as per national data opt-out policy.  The source data is refreshed weekly (Sunday evenings) and the data set includes a long list of fields relating to: NHS number, allergies, medications issued, Repeat medications, Covid-19 status, Active and Past Problems, GP Results, Vitals & Measurements (height/weight, BP, physiological function result), Lifestyle factors (smoking and alcohol), GP encounters, vaccinations and immunisations, Contraindications, OTC and Prophylactic Therapy, Family History, Child Health, Diabetes Diagnosis, Chronic Disease Monitoring.",
    "url": "https://healthdatagateway.org/en/dataset/1248",
    "uid": null,
    "datasource_id": 1248,
    "source": "HDRUK"
  },
  {
    "id": 606,
    "name": "Society of Acute Medicine Benchmarking Audit – National Frailty 2020-2022",
    "description": "The Society for Acute Medicine (SAM) Benchmarking Audit (SAMBA) is a national benchmark audit of acute medical care. The aim of SAMBA is to describe the severity of illness of acute medical patients presenting to Acute Medicine, the speed of their assessment, their pathway and progress \nat seven days after admission and to provide a comparison for each participating unit with the national average (or ‘benchmark’).\n\nSAMBA normally takes place at least once a year. Data are collected for patients admitted over a 24hour period, with follow up of clinical outcomes. The dataset consists of an audit of a day in Winter 2020, Summer 2021, and Summer 2022.\n\nThis audit focussing on frailty includes 22,938 patients in the SAMBA dataset, containing unit structure and staffing levels, patient demographics (age and gender), severity of illness at presentation using an early warning score (e.g. NEWS2), frailty and pathway of care through the hospital and readmission. \n\nGeography: Recruitment to SAMBA audits are open to all hospitals in the UK receiving acutely unwell medical patients. Non-acute and community hospitals were excluded.\nThe SAMBA dataset draws on contributions from across all four nations of the UK. Between the years 2020, 2021 and 2022, patient data was submitted by 157 hospitals in England, 5 in Northern Ireland, 10 in Scotland, and 8 in Wales. The dataset has grown each year, reflecting increasing engagement from healthcare providers across the country.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: To supplement the national audit data, PIONEER can provide more granular, longitudinal patient-level insights. This includes matched controls, ambulance and community data, and unstructured data (such as images). We can deliver the dataset in OMOP or other common data models, and we are able to generate synthetic data to meet bespoke requirements, ensuring a comprehensive, flexible data resource.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1247",
    "uid": null,
    "datasource_id": 1247,
    "source": "HDRUK"
  },
  {
    "id": 607,
    "name": "White Swan UK Cardiovascular Online Patient & Public Conversations Dataset",
    "description": "The dataset contains anonymised patient and public conversation which has taken place online regarding 17 cardiovascular disease areas (including: arrhythmia & tachycardia, cardiopulmonary disease and heart failure) in the UK. The dataset is segmented by cardiovascular conditions and topics conversed about such as testing, imaging and emotional impact.",
    "url": "https://healthdatagateway.org/en/dataset/1112",
    "uid": null,
    "datasource_id": 1112,
    "source": "HDRUK"
  },
  {
    "id": 608,
    "name": "Children and Young People Children in Need (CYP CIN)",
    "description": "Local Authority ‘Children in Need’ (CIN) data is linked with health data that is held in the NWL’s Whole Systems Integrated Care (WSIC) platform, to help better understand the needs of children within a Local Authority.\nBi-borough and Harrow are piloting this data sharing, with IG already in place and approved to enable the data to be shared directly to WSIC.\nCombining this data will help improve developmental, safeguarding, and wellbeing outcomes for children and young people, by providing analysis which gives a more informed understanding of children and young people. Analysis of the impacts of the wider determinants of health would also be possible, helping NHS Practitioners, DCSs, and their teams to identify and prioritise opportunities for development and improvement of the service offer, to best support the most vulnerable children and young people and their families.\nSpecific use cases could include:\n•Understanding the health needs of children in contact with children’s services, by analysing specific health events experienced across cohorts, such as, long-term conditions e.g., Mental Health, and A&E visits.\n•Geographically mapping the level of need within a Local Authority, using the combined data to inform the level of need, using data points such as the proportion of children: in contact with children’s services, with mental health conditions, visiting A&E, with care plans.",
    "url": "https://healthdatagateway.org/en/dataset/1517",
    "uid": null,
    "datasource_id": 1517,
    "source": "HDRUK"
  },
  {
    "id": 609,
    "name": "Sarcoidosis",
    "description": "Idiopathic Pulmonary Fibrosis (IPF) is a type of complex lung disease that affects roughly 50 in every 100,000 people. It causes the lungs to become scarred, leading to severe breathlessness and coughing, and owing to a lack of effective medicines and treatments, it currently has a survival time worse than most cancers. Applying cutting-edge AI tools, to unlock insights from existing IPF patient data, could help extend the lives of thousands of UK patients each year.",
    "url": "https://healthdatagateway.org/en/dataset/1244",
    "uid": null,
    "datasource_id": 1244,
    "source": "HDRUK"
  },
  {
    "id": 610,
    "name": "Data First Magistrates' Court defendant case level dataset (MACO)",
    "description": "One record per defendant per case giving details of defendant characteristics, offence categorisation, court proceedings, and outcomes. Details of the principle offence i.e., the offence with the most serious disposal, are given (please note that as the linked criminal courts datasets provide information on only the most serious offence at the point of committal to court and sentencing, they may not include the full range of offending that may be attributable to defendants, and therefore restricts the ability of researchers to explore any associations and correlations between different types of offences within the cases being heard before the courts).\n\nThe Research Accreditation Panel provides oversight of the framework that is used to accredit research projects, researchers and processing environments under the Digital Economy Act 2017 (DEA). Researchers are advised to liaise with SAIL support teams to understand the requirements and timelines involved with submitting a research project to the Research Accreditation Panel. https://uksa.statisticsauthority.gov.uk/digitaleconomyact-research-statistics/research-accreditation-panel/",
    "url": "https://healthdatagateway.org/en/dataset/350",
    "uid": "110b05d4-d2c1-4e02-aab8-105bec02bd66",
    "datasource_id": 350,
    "source": "HDRUK"
  },
  {
    "id": 611,
    "name": "White Swan UK Oncology Online Patient & Public Conversations Dataset",
    "description": "The dataset contains anonymised patient and public conversation which has taken place online regarding over 50 cancer types (This includes cancers most commonly experienced and rarer types). \n\nThe curation of the dataset is based on specific cancer types and cancer patient forums. It is not based on every social post about cancer within the online sources, which is often irrelevant to the patient experience.",
    "url": "https://healthdatagateway.org/en/dataset/1227",
    "uid": null,
    "datasource_id": 1227,
    "source": "HDRUK"
  },
  {
    "id": 612,
    "name": "Alcohol Dependence Dataset (England)",
    "description": "https://digital.nhs.uk/services/data-services-for-commissioners/datasets/alcohol-dependence",
    "url": "https://healthdatagateway.org/en/dataset/1224",
    "uid": null,
    "datasource_id": 1224,
    "source": "HDRUK"
  },
  {
    "id": 613,
    "name": "Lancashire Teaching Hospitals Acute OMOP Dataset",
    "description": "Activity data from Lancashire Teaching Hospitals conforming to the OMOP common data model.  For more information contact the North West Secure Data Environment.",
    "url": "https://healthdatagateway.org/en/dataset/1223",
    "uid": null,
    "datasource_id": 1223,
    "source": "HDRUK"
  },
  {
    "id": 614,
    "name": "Greater Manchester Virtual Wards",
    "description": "GM Virtual Wards contains activity data collected daily on each patient occupying a virtual ward bed.  This data is submitted by all NHS Provider trusts located in the Greater Manchester footprint.  Data collected includes patient demographics, admission and discharge dates, NHS provider code and diagnosis.",
    "url": "https://healthdatagateway.org/en/dataset/1222",
    "uid": null,
    "datasource_id": 1222,
    "source": "HDRUK"
  },
  {
    "id": 615,
    "name": "Demonstration only - August 2025",
    "description": "Description for demonstration dataset.",
    "url": "https://healthdatagateway.org/en/dataset/1221",
    "uid": null,
    "datasource_id": 1221,
    "source": "HDRUK"
  },
  {
    "id": 616,
    "name": "Dataset & BioSample for Gateway demonstration only",
    "description": "asfa sfasgaGASGASGASG AS GAS",
    "url": "https://healthdatagateway.org/en/dataset/912",
    "uid": null,
    "datasource_id": 912,
    "source": "HDRUK"
  },
  {
    "id": 617,
    "name": "Vaccinations",
    "description": "Collecting vaccination data for: COVID-19 07/12/2020, Cholera 27/04/2022, Herpes zoster (shingles) 05/03/2022, Flu 06/09/2021, Pertussis 01/07/2024, Pneumococcal 07/03/24, Typhoid fever 27/04/2022, Hepatitis A 27/04/2022, Respiratory Syncytial Virus infection 05/08/2024\n\nData is held in the National Clinical Data Store (NCDS), to which historic data for herpes zoster (shingles) and pneumococcal vaccinations from GPIT systems has also been imported.\n\nData is held relating to: vaccination events, patients, patient cohorts, patient eligibility, vaccination locations. \n\nAll vaccinations occurring in Scotland, and for Scottish residents vaccinated elsewhere.",
    "url": "https://healthdatagateway.org/en/dataset/1219",
    "uid": null,
    "datasource_id": 1219,
    "source": "HDRUK"
  },
  {
    "id": 618,
    "name": "Kids’ Environment and Health Cohort",
    "description": "The Kids’ Environment and Health Cohort will, for the first time, link administrative health and education data to longitudinal environmental exposures for children at national level in England. It will serve as a data resource to support research about the health and well-being of children via improved home and school environments.\n\nThe cohort will become a large data resource which will allow researchers to explore how children are affected by changes in environmental exposures over time, including children belonging to high-risk groups. The cohort will allow for cutting-edge research in environmental and social epidemiology, for example projects exploring long-term pandemic impacts or the effects of local climate change and Net Zero policies on children’s health and education.\n\nThe Kids’ Environment and Health Cohort will link birth and mortality records, health and educational attainment datasets, to maternal health (up to 12 months prior to their child’s birth), and environmental data for all children born in England from 2006 – approximately 11 million children at first build. A subset of children born between 2010 and 2012, and between 2020 and 2022 will be linked to their mothers’ 2011 or 2021 Census records, respectively. The cohort database will be held in, and accessed via, a trusted research environment (TRE) at the Office for National Statistics (ONS). All geographical identifiers in the cohort, allowing for linkage to further environmental data, will be securely held by the ONS, separately to the main cohort, and will be encrypted before being shared with researchers.\n\nBy linking children’s data to their mothers’ medical records, researchers will be able to explore the pathways between exposures and events during pregnancy and the health and education of children later on, for example associations between exposure to air pollution during pregnancy and subsequent child health. It will also be possible to link children to their siblings, allowing for sibling-control studies which have the potential to reduce bias when analysing cohort data by controlling for family-level risk factors.\n\nThe cohort will link the following datasets:\n\n- ONS birth and death registration data (Phase 1 delivery, early 2026)\n- Census 2011 and 2021 data: data on children born within two years of each Census (Phase 1, 2026)\n- NHS birth notification data (Phase 2)\n- Hospital Episode Statistics: data on hospital contacts (Phase 2)\n- Maternity Services Dataset: data on maternal health during pregnancy (Phase 2)\n- Mental Health Services Dataset: information on referrals to mental health services (Phase 2)\n- Community Dispensing Data: information on dispensed medicines, including for asthma (Phase 2)\n- National Pupil Database: data on all children in state school, including special educational needs provision and exam results (Phase 2)\n- Getting Information About Schools Data (school data) (Phase 2)\n\nIn addition to the linked administrative data, a number of open environmental datasets will also be mapped to the Kids’ Environment and Health Cohort at set up, including: \n- Department for Environment Food and Rural Affairs modelled Annual Air Pollution data (Phase 1, 2026)\n- Ordinance Survey Open Greenspace database (Phase 1, 2026)\n- Met Office data on air temperature (Phase 1, 2026)\n- Department for Levelling Up, Housing and Communities Energy Performance Certificate data (Phase 1, 2026)\n- Valuation Office Agency data on property type and valuation (Phase 1, 2026)\n- Ordinance Survey Distance to Roads and Traffic Flow data (Phase 2)\n\nThis will allow research on the impacts of air pollution, building characteristics, local neighbourhood, and road distance on child outcomes.",
    "url": "https://healthdatagateway.org/en/dataset/1215",
    "uid": null,
    "datasource_id": 1215,
    "source": "HDRUK"
  },
  {
    "id": 619,
    "name": "ECHILD - Education and Child Health Insights from Linked Data",
    "description": "Education & Child Health Insights from Linked Data (ECHILD) is a deidentified longitudinal population-based cohort of children & young people in England born between 01/09/1984.    \n\nIn England, information on a child’s journey through education and social care is recorded in administrative records held by the Department for Education (the National Pupil Database; NPD). NHS holds information about all NHS hospital contacts (captured in Hospital Episode Statistics; HES). HES records are generated for the purposes of service delivery, e.g., to support financial reimbursement for treatment relating to a hospital stay.\n\nWithin ECHILD, healthcare, education and social care records have been linked to create a longitudinal database that follows children over time. The database is very useful for research as health, education and social care trajectories are strongly interrelated from childhood to adulthood. ECHILD provides a valuable opportunity to explore these relationships and to generate evidence for policy and practice.\n\nECHILD will only be used for research that has a clear public benefit in England and Wales to improve the health and well-being of children and young people accessing health, education and social care services. The specific research purposes (permitted uses) are below with examples of relevant research questions.\n\n- Informing preventative strategies by Healthcare and Education services e.g., do disabled children attending schools, or living in areas that provide a good level of disability support in school or through social care services, have lower rates of unplanned hospital contacts compared with less supportive schools/areas?\n- Informing children and their parents e.g., about variation in special educational needs support and outcomes for children with chronic health conditions or disability.\n- Informing education and clinical practice e.g., investigating whether associations between chronic health conditions and lower school attainment are explained by school absence.\n- Identifying groups who could benefit from intervention e.g., what are the health outcomes of children post age 16 who have contact with social care services or have special educational needs?\n- Understanding the most effective methods for working with linked health and education data e.g., what are the most effective methods for working with linked health and education data?\n\nECHILD data are deidentified and further data linkage is not permitted.  Data can only be accessed by approved researchers in the ONS SRS, and researchers are not permitted to try to re-identify individuals. Furthermore, any results of analyses (tables or figures) are checked by ONS staff for potential disclosure risk before they can be exported from the ONS SRS.\n\nTo access ECHILD, the following steps are required:\n- Contact ECHILD Team: ich.echild@ucl.ac.uk\n- Complete ethics self-assessment\n-  Submit an application form to UCL\n- Obtain approval from UCL on feasibility and purpose\n- Submit the UCL-approved application form to the ONS Research Accreditation Panel (RAP)\n- Obtain approval from RAP for release under Digital Economy Act (DEA)\n- Sign Data Access Agreements with UCL & ONS Accredited Researcher Assurance Registration (ARAR) form.\n- UCL instructs ONS to provide access to a specified extract of ECHILD to named user(s)",
    "url": "https://healthdatagateway.org/en/dataset/1214",
    "uid": null,
    "datasource_id": 1214,
    "source": "HDRUK"
  },
  {
    "id": 620,
    "name": "Society of Acute Medicine Benchmarking Audit – England Region Summary 2019 to 2023",
    "description": "The Society for Acute Medicine (SAM) Benchmarking Audit (SAMBA) is a national benchmark audit of acute medical care. The aim of SAMBA is to describe the severity of illness of acute medical patients presenting to Acute Medicine, the speed of their assessment, their pathway and progress at seven days after admission and to provide a comparison for each participating unit with the national average (or ‘benchmark’).\n\nSAMBA normally takes place at least once a year. Data are collected for patients admitted over a 24-hour period, with follow up of clinical outcomes. The dataset consists of audit data for a single day of each year from 2019-2023.\n\nIt includes 36,057 patients in the SAMBA audit dataset containing unit structure and staffing levels, patient demographics (age and gender), severity of illness at presentation using an early warning score (e.g. NEWS2), frailty and pathway of care through the hospital and readmission. The dataset consists of audit data for a single day of each year from 2019-2023.\n\nGeography: Recruitment to SAMBA audits are open to all hospitals in the UK receiving acutely unwell medical patients. Non-acute and community hospitals were excluded.\n\nThe SAMBA dataset draws on contributions from across all four nations of the UK. Over the years, patient data was submitted by 167 hospitals in England. The dataset has grown each year, reflecting increasing engagement from healthcare providers across the country.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: To supplement the national audit data, PIONEER can provide more granular, longitudinal patient-level insights. This includes matched controls, ambulance and community data, and unstructured data (such as images). \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1213",
    "uid": null,
    "datasource_id": 1213,
    "source": "HDRUK"
  },
  {
    "id": 621,
    "name": "Society of Acute Medicine Benchmarking Audit – National SDEC 2023",
    "description": "The Society for Acute Medicine (SAM) Benchmarking Audit (SAMBA) is a national benchmark audit of acute medical care. The aim of SAMBA is to describe the severity of illness of acute medical patients presenting to Acute Medicine, the speed of their assessment, their pathway and progress at seven days after admission and to provide a comparison for each participating unit with the national average (or ‘benchmark’).\n\nSAMBA normally takes place at least once a year. Data are collected for patients admitted over a 24-hour period, with follow up of clinical outcomes. The dataset consists of an audit of a day in Summer 2023, and compares SDEC service structure, services and patient level data for >35,000 patient attendances at 188 hospitals in the UK. This data are from the largest evaluation of medical SDEC to date. \n\nIt includes 9,612 patients in the Society of acute medicine benchmarking audit dataset- containing unit structure and staffing levels, patient demographics (age and gender), SDEC admission details and investigations, severity of illness at presentation using an early warning score (e.g. NEWS2), frailty and pathway of care through the hospital and readmission. The data was collected on 22-06-2023.\n\nGeography: Recruitment to SAMBA audits are open to all hospitals in the UK receiving acutely unwell medical patients. Non-acute and community hospitals were excluded.\nThe SAMBA dataset draws on contributions from across all four nations of the UK. In 2023, patient data on SDEC attendance was submitted by 140 hospitals in England, 6 in Northern Ireland, 4 in Scotland, 9 in Wales, as well as 1 in Jersey.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: To supplement the national audit data, PIONEER can provide more granular, longitudinal patient-level insights. This includes matched controls, ambulance and community data, and unstructured data (such as images).\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1212",
    "uid": null,
    "datasource_id": 1212,
    "source": "HDRUK"
  },
  {
    "id": 622,
    "name": "Society of Acute Medicine Benchmarking Audit – National Summer 2023",
    "description": "The Summer Society for Acute Medicine Benchmarking Audit (SAMBA) 2023 provides a snapshot of the care provided for acutely unwell medical patients in the UK over a 24-hour period on Thursday 22nd June 2023.\n \nThe audit also highlights a growing reliance on Same Day Emergency Care (SDEC) services. These services consistently demonstrate superior performance compared to traditional Emergency Department pathways, offering a more effective alternative for managing acute care delivery.\n\nDespite these advancements, delays in consultant review persist for patients initially assessed in the Emergency Department. This highlights the need for targeted strategies to optimise patient pathways and further enhance overall care quality.\n\nIt includes 9,612 patients in the SAMBA dataset, containing unit structure and staffing levels, patient demographics (age and gender), severity of illness at presentation using an early warning score (e.g. NEWS2), frailty and pathway of care through the hospital and readmission. \n\nGeography: Recruitment to SAMBA audits are open to all hospitals in the UK receiving acutely unwell medical patients. Non-acute and community hospitals were excluded.\nThe SAMBA dataset draws on contributions from across all four nations of the UK. In 2023, patient data was submitted by 140 hospitals in England, 6 in Northern Ireland, 4 in Scotland, and 9 in Wales, as well as 1 hospital in Jersey. The dataset has grown each year, reflecting increasing engagement from healthcare providers across the country.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: To supplement the national audit data, PIONEER can provide more granular, longitudinal patient-level insights. This includes matched controls, ambulance and community data, and unstructured data (such as images). \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1210",
    "uid": null,
    "datasource_id": 1210,
    "source": "HDRUK"
  },
  {
    "id": 623,
    "name": "Society of Acute Medicine Benchmarking Audit – National Summer 2022",
    "description": "The Summer Society for Acute Medicine Benchmarking Audit (SAMBA) 2022 provides a snapshot of the care provided for acutely unwell medical patients in 149 hospitals   across the UK over a 24-hour period on Thursday 23rd June 2022. \n\nAt the time that SAMBA22 took place, urgent and emergency care services were already under increasing pressure. The number of patients waiting within the Emergency Department for over 12 hours for an inpatient bed has been rising, with all parts of the emergency, acute care and inpatient pathway needing to confront the increasingly complex challenge of maintaining the quality and of care provided.\n\nSAMBA22 aims to assess the same key clinical quality indicators as previously, with some aspects of data collection adapted to begin to expand our understanding of how acute medicine services perform and the care they provide in this changing landscape of urgent care services. It includes 8,344 patients in the SAMBA dataset- containing unit structure and staffing levels, patient demographics (age and gender), severity of illness at presentation using an early warning score (e.g. NEWS2), frailty and pathway of care through the hospital and readmission. \n\nGeography: Recruitment to SAMBA audits are open to all hospitals in the UK receiving acutely unwell medical patients. Non-acute and community hospitals were excluded.\nThe SAMBA dataset draws on contributions from across all four nations of the UK. In 2022, patient data was submitted by 128 hospitals in England, 4 in Northern Ireland, 8 in Scotland, and 6 in Wales. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: To supplement the national audit data, PIONEER can provide more granular, longitudinal patient-level insights. This includes matched controls, ambulance and community data, and unstructured data (such as images).\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1209",
    "uid": null,
    "datasource_id": 1209,
    "source": "HDRUK"
  },
  {
    "id": 624,
    "name": "Society of Acute Medicine Benchmarking Audit – National Summer 2021",
    "description": "The Society for Acute Medicine (SAM) Benchmarking Audit (SAMBA) is a national benchmark audit of acute medical care. The aim of SAMBA is to describe the severity of illness of acute medical patients presenting to Acute Medicine, the speed of their assessment, their pathway and progress at seven days after admission and to provide a comparison for each participating unit with the national average (or ‘benchmark’).\n\nSAMBA normally takes place at least once a year. Data are collected for patients admitted over a 24-hour period, with follow up of clinical outcomes. This dataset consists of a subset of SAMBA, only containing data from Thursday 17th June 2021.\n\nIt includes 8,973 patients in the SAMBA dataset- containing unit structure and staffing levels, patient demographics (age and gender), severity of illness at presentation using an early warning score (e.g. NEWS2), frailty and pathway of care through the hospital and readmission. \n\nGeography: Recruitment to SAMBA audits are open to all hospitals in the UK receiving acutely unwell medical patients. Non-acute and community hospitals were excluded.\nThe SAMBA dataset draws on contributions from across all four nations of the UK. In 2021, patient data was submitted by 140 hospitals in England, 4 in Northern Ireland, 5 in Scotland and 7 in Wales. The dataset has grown each year, reflecting increasing engagement from healthcare providers across the country.  \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: To supplement the national audit data, PIONEER can provide more granular, longitudinal patient-level insights. This includes matched controls, ambulance and community data, and unstructured data (such as images). \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1208",
    "uid": null,
    "datasource_id": 1208,
    "source": "HDRUK"
  },
  {
    "id": 625,
    "name": "Society of Acute Medicine Benchmarking Audit – National Summer 2019",
    "description": "The Society for Acute Medicine (SAM) Benchmarking Audit (SAMBA) is a national benchmark audit of acute medical care. The aim of SAMBA is to describe the severity of illness of acute medical patients presenting to Acute Medicine, the speed of their assessment, their pathway and progress at seven days after admission and to provide a comparison for each participating unit with the national average (or ‘benchmark’)\n\nSAMBA normally takes place at least once a year. Data are collected for patients admitted over a 24-hour period, with follow up of clinical outcomes. This dataset consists of a subset of SAMBA, only containing data from 27th June 2019.\n\nIt includes 7,173 patients in the Society of acute medicine benchmarking audit dataset- containing unit structure and staffing levels, patient demographics (age and gender), severity of illness at presentation using an early warning score (e.g. NEWS2), frailty and pathway of care through the hospital and readmission. \n\nGeography: Recruitment to SAMBA audits are open to all hospitals in the UK receiving acutely unwell medical patients. Non-acute and community hospitals were excluded.\nThe SAMBA dataset draws on contributions from across all four nations of the UK. In 2019, patient data was submitted by 116 hospitals in England, 4 in Northern Ireland, 7 in Scotland, 7 in Wales and 1 in the Isle of Man.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: To supplement the national audit data, PIONEER can provide more granular, longitudinal patient-level insights. This includes matched controls, ambulance and community data, and unstructured data (such as images). \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1207",
    "uid": null,
    "datasource_id": 1207,
    "source": "HDRUK"
  },
  {
    "id": 626,
    "name": "Society of Acute Medicine Benchmarking Audit – National Winter 2020",
    "description": "The Winter Society for Acute Medicine Benchmarking Audit (SAMBA) 2020 provides a snapshot of the care provided for acutely unwell medical patients in the UK over a 24-hour period on Thursday 30th January 2020. This was the first time SAMBA had been conducted during winter, to provide a comparison to the information provided by the annual SAMBA taking place in summer.\n \nIt includes 5,621 patients in the SAMBA dataset- containing unit structure and staffing levels, patient demographics (age and gender), severity of illness at presentation using an early warning score (e.g. NEWS2), frailty and pathway of care through the hospital and readmission. The dataset consists of an audit of a day on 30th January 2020.\n\nGeography: Recruitment to SAMBA audits are open to all hospitals in the UK receiving acutely unwell medical patients. Non-acute and community hospitals were excluded.\n\nThe SAMBA dataset draws on contributions from across all four nations of the UK. In 2020, patient data was submitted by 93 hospital in England, 3 in Northern Ireland, 4 in Scotland and 4 in Wales.\n \nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: To supplement the national audit data, PIONEER can provide more granular, longitudinal patient-level insights. This includes matched controls, ambulance and community data, and unstructured data (such as images).\n \nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1206",
    "uid": null,
    "datasource_id": 1206,
    "source": "HDRUK"
  },
  {
    "id": 627,
    "name": "Covid Vaccination Dataset (CVVD)",
    "description": "This dataset covers all patients and vaccinations administered or planned for Covid-19, in or funded by the NHS. Dataset is a de-identified record-level data of people who have received a vaccination for COVID-19, including details of the type of vaccine and date of vaccination.",
    "url": "https://healthdatagateway.org/en/dataset/309",
    "uid": "471f101c-a45e-4620-8710-be3036a46fba",
    "datasource_id": 309,
    "source": "HDRUK"
  },
  {
    "id": 628,
    "name": "Synthetic Data - Monoclonal Gammopathy of Undetermined Significance",
    "description": "Synthetic data generated from WM MGUS systems.",
    "url": "https://healthdatagateway.org/en/dataset/1204",
    "uid": null,
    "datasource_id": 1204,
    "source": "HDRUK"
  },
  {
    "id": 629,
    "name": "Synthetic Data - Early Warning Score",
    "description": "Synthetic data generated from WM EWS systems.",
    "url": "https://healthdatagateway.org/en/dataset/1203",
    "uid": null,
    "datasource_id": 1203,
    "source": "HDRUK"
  },
  {
    "id": 630,
    "name": "Synthetic Data - Pharmacy",
    "description": "50,000 records from UHB QE outpatient and HGS outpatient pharmacy data has been used to test the third party tool to generate the synthetic data.",
    "url": "https://healthdatagateway.org/en/dataset/1202",
    "uid": null,
    "datasource_id": 1202,
    "source": "HDRUK"
  },
  {
    "id": 631,
    "name": "Sea Hero Quest Spatial Navigation Dataset for Alzheimer's dementia",
    "description": "Sea Hero Quest® is a registered trademark of GLITCHERS LTD.\n\nThe future of Alzheimer’s disease can be preventable with early intervention. Sea Hero Quest is capable of spotting the earliest signs of spatial navigation decline by comparing individuals to the 4.3m data benchmark.\n\nSpatial performance is a powerful early indicator for many neurodegenerative diseases. We offer this tool and data set by the HDR UK platform to help turbo-charge research projects and help discover powerful new ways for early identification, prevention and detection.\n\nFind out more about Sea Hero Quest at glitchers.com. Sea Hero Quest is also available in conjunction with the Celest workbench as a commercial application for continuous monitoring. Please contact support@glitchers.com for pricing information.\n\nThe rest of this document is designed to outline the benchmark data.",
    "url": "https://healthdatagateway.org/en/dataset/1201",
    "uid": null,
    "datasource_id": 1201,
    "source": "HDRUK"
  },
  {
    "id": 632,
    "name": "Scottish Collaborative Optometry-Ophthalmology Network e-research (SCONe)",
    "description": "SCONe contains retinal images captured by community optometrists collated from participating practices across Scotland. \n\nAs they are CHI linked within the Public Health Scotland National Safe Haven, they can be linked to other routinely collected datasets.",
    "url": "https://healthdatagateway.org/en/dataset/1200",
    "uid": null,
    "datasource_id": 1200,
    "source": "HDRUK"
  },
  {
    "id": 633,
    "name": "Society of Acute Medicine Benchmarking Audit – National Summer 2019 to 2023",
    "description": "The Society for Acute Medicine (SAM) Benchmarking Audit (SAMBA) is a national benchmark audit of acute medical care. The aim of SAMBA is to describe the severity of illness of acute medical patients presenting to Acute Medicine, the speed of their assessment, their pathway and progress at seven days after admission and to provide a comparison for each participating unit with the national average (or ‘benchmark’).\nSAMBA normally takes place at least once a year. Data are collected for patients admitted over a 24-hour period, with follow up of clinical outcomes. The dataset consists of an audit of a day in Summer each year from 2019-2023 (excluding 2020).\nIt includes 34,103 patients in the SAMBA audit dataset containing unit structure and staffing levels, patient demographics (age and gender), severity of illness at presentation using an early warning score (e.g. NEWS2), frailty and pathway of care through the hospital and readmission. The dataset consists of an audit of a day in Summer 2019-2023 (excluding 2020).\n\nGeography: Recruitment to SAMBA audits are open to all hospitals in the UK receiving acutely unwell medical patients. Non-acute and community hospitals were excluded.\nThe SAMBA dataset draws on contributions from across all four nations of the UK. Over the years, patient data was submitted by more than 100 hospitals in England, Northern Ireland, Scotland and Wales, as well as Jersey. The dataset has grown each year, reflecting increasing engagement from healthcare providers across the country.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: To supplement the national audit data, PIONEER can provide more granular, longitudinal patient-level insights. This includes matched controls, ambulance and community data, and unstructured data (such as images). \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1182",
    "uid": null,
    "datasource_id": 1182,
    "source": "HDRUK"
  },
  {
    "id": 634,
    "name": "Scottish Linked Pregnancy and Baby Dataset (SLiPBD)",
    "description": "SLiPBD links and reconciles the following existing national datasets relating to ongoing and completed pregnancies:\n•\tGeneral hospital discharge records relating to care for a spontaneous pregnancy loss\nMaternity hospital discharge records relating to care for a spontaneous pregnancy loss, termination of pregnancy, or birth\n•\tTermination of pregnancy notifications\n•\tStatutory live and stillbirth registrations\nKey information included on all babies in SLiPBD includes estimated date of conception, end of pregnancy date, gestation, multiple pregnancy status, pregnancy outcome, and maternal sociodemographic characteristics. For live births, additional information on the birth, the baby's sociodemographic characteristics, and subsequent infant deaths is included",
    "url": "https://healthdatagateway.org/en/dataset/1181",
    "uid": null,
    "datasource_id": 1181,
    "source": "HDRUK"
  },
  {
    "id": 635,
    "name": "Welsh Neuroscience Research Tissue Bank (WNRTB)",
    "description": "The Welsh Neuroscience Research Tissue Bank is a collection of human (mainly fluid) biological samples donated from more than 3000 patients undergoing neurological investigations or who have known neurological diagnoses, as well as from healthy volunteers. Donors are recruited locally in South Wales, and from across the UK. Participants must be over the age of 16 and give consent for DNA analysis to take place. \nThe collection comprises samples (for example, but not limited to blood, serum, plasma, CSF, DNA, RNA) from disorders such as Multiple sclerosis, Epilepsy, MND, Idiopathic Intracranial hypertension, brain tumours, as well as healthy volunteers. To date more than 100,000 sample aliquots are available for neurological research.\nSamples are available for research into neurological disorders via an application process and governance panel review. The WNRTB is authorised by the Wales REC 3 to release samples to researchers following a review of the intended use of samples.  Researchers receiving samples from the WNRTB are deemed to have ethics approval and are therefore NOT required to have specific approval from an NHS ethics committee for the use of these samples, as samples will be provided anonymously with only the minimum data set. \nThe WNRTB infrastructure also acts as a guardian to several major clinical trials and specific research projects, each contributing to a large resource available to researchers.",
    "url": "https://healthdatagateway.org/en/dataset/1178",
    "uid": null,
    "datasource_id": 1178,
    "source": "HDRUK"
  },
  {
    "id": 636,
    "name": "Secondary Care EHR (UHB PICS)",
    "description": "PICS data from UHB.",
    "url": "https://healthdatagateway.org/en/dataset/1177",
    "uid": null,
    "datasource_id": 1177,
    "source": "HDRUK"
  },
  {
    "id": 637,
    "name": "Diabetic Eye Disease and Hospital Admission",
    "description": "Insight data from UHB.\n\nBackground : Diabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. More than 1 million people living with diabetes are acutely admitted to hospital due to complications of their illness every year. Complications include diabetic emergencies such as Diabetic comas, Hypoglycaemia, Diabetic ketoacidosis and Diabetic hyperosmolar hyperglycaemic state. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. This dataset includes acute all diabetic admissions to University Hospitals Birmingham NHS Trust from 2000 onwards with linked eye data including the national screening diabetic grade category (seven categories from R0M0 to R3M1) from the Birmingham, Solihull and Black Country DR screening program (a member of the National Health Service (NHS) Diabetic Eye Screening Programme) and the University Hospitals Birmingham NHS Trust Ophthalmology clinic at Queen Elizabeth Hospital, Birmingham .\n\nGeography : The West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData sources:\n\n1.  The Birmingham, Solihull and Black Country Dataset, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 diabetic patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic eye screening scheme in Europe.\n2.  The Electronic Health Records held at University Hospitals Birmingham NHS Foundation Trust is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. UHB runs a fully electronic healthcare record both for systemic disease as well as the Ophthalmology records.\n\nScope :  All hospitalised patients admitted to UHB with a diabetes related health concern from 2000 onwards. Longitudinal and individually linked with their diabetic eye care from primary screening data and secondary care ophthalmology data including • Demographic information (including age, sex and ethnicity) • Diabetes status • Diabetes type • Length of time since diagnosis of diabetes • Visual acuity • The national screening diabetic screening grade category (seven categories from R0M0 to R3M1) • Diabetic eye clinical features • Reason for sight and severe sight impairment • ICD-10 and SNOMED-CT codes pertaining to diabetes • Diagnosis for the acute/emergency admission • Co-morbid conditions • Medications • Outcome",
    "url": "https://healthdatagateway.org/en/dataset/1176",
    "uid": null,
    "datasource_id": 1176,
    "source": "HDRUK"
  },
  {
    "id": 638,
    "name": "Diabetic Screening and Secondary Care",
    "description": "Insight data from UHB.\n\nBackground : Diabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. This dataset includes the national screening diabetic grade category (seven categories from R0M0 to R3M1) from the Birmingham, Solihull and Black Country DR screening program (a member of the National Health Service (NHS) Diabetic Eye Screening Programme) and the Queen Elizabeth Hospital, University Hospitals Birmingham NHS Trust ophthalmology treatment and visual outcome data.\n\nGeography : The West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData sources:\n\n1. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 diabetic patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic eye screening scheme in Europe.\n2. The Electronic Health Records from the Ophthalmology clinic at Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation.\n\nScope : All Birmingham, Solihull and Black Country diabetic eye screened participants who have been seen in ophthalmology outpatients at University Hospitals Birmingham NHS Foundation from 2006 onwards. Longitudinal and individually linked with their diabetic eye care from primary screening data and secondary hospital eye care including • Demographic information (including age, sex and ethnicity) • Diabetes status • Diabetes type • Length of time since diagnosis of diabetes • Visual acuity • The national screening diabetic screening grade category (seven categories from R0M0 to R3M1) • Diabetic eye clinical features • Reason for sight and severe sight impairment • Ocular treatment including laser treatment and surgical treatment • Visual Outcome",
    "url": "https://healthdatagateway.org/en/dataset/1175",
    "uid": null,
    "datasource_id": 1175,
    "source": "HDRUK"
  },
  {
    "id": 639,
    "name": "Eye Disease and Cardiology",
    "description": "Insight data from UHB.\n\nBackground : Diabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. More than 1 million people living with diabetes are acutely admitted to hospital due to complications of their illness every year. Cardiovascuar disease is the most prevalent cause of morbidity and mortality in people with diabetes. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. This dataset includes the national screening diabetic grade category (seven categories from R0M0 to R3M1) from the Birmingham, Solihull and Black Country DR screening program (a member of the National Health Service (NHS) Diabetic Eye Screening Programme) and the University Hospitals Birmingham NHS Trust cardiac outcome data.\n\nGeography : The West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData sources:\n\n1.  The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 diabetic patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic eye screening scheme in Europe.\n2.  The Electronic Health Records held at University Hospitals Birmingham NHS Foundation Trust is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. UHB runs a fully electronic healthcare record for systemic disease.\n\nScope : All Birmingham, Solihull and Black Country diabetic eye screened participants who have been admitted to UHB with a cardiac related health concern from 2006 onwards. Longitudinal and individually linked with their diabetic eye care from primary screening data and secondary care hospital cardiac outcome data including • Demographic information (including age, sex and ethnicity) • Diabetes status • Diabetes type • Length of time since diagnosis of diabetes • Visual acuity • The national screening diabetic screening grade category (seven categories from R0M0 to R3M1) • Diabetic eye clinical features • Reason for sight and severe sight impairment • ICD-10 and SNOMED-CT codes pertaining to cardiac disease • Outcome\n\nWebsite: https://www.retinalscreening.co.uk/",
    "url": "https://healthdatagateway.org/en/dataset/1174",
    "uid": null,
    "datasource_id": 1174,
    "source": "HDRUK"
  },
  {
    "id": 640,
    "name": "UHB Insight Ophthalmology",
    "description": "Insight data from UHB.",
    "url": "https://healthdatagateway.org/en/dataset/1173",
    "uid": null,
    "datasource_id": 1173,
    "source": "HDRUK"
  },
  {
    "id": 641,
    "name": "Glaucoma",
    "description": "Insight data from UHB.\n\nBackground : Glaucoma is a worldwide leading cause of irreversible sight loss. Worldwide, an estimated 60 million people have glaucoma. Glaucoma is a condition of increased intraocular pressure in the eye. Because it may be asymptomatic until a relatively late stage, diagnosis is frequently delayed. There are four general categories of glaucoma: primary open-angle and angle-closure, and secondary open and angle-closure glaucoma.\n\nThe UHB glaucoma dataset is a longitudinal dataset consisting of routinely collected clinical metadata from patients receiving treatment for glaucoma at UHB, from 2007 to the present.\n\nThis dataset encompasses all patients at UHB who have received a diagnosis of primary or secondary glaucoma or ocular hypertension. Clinical metadata includes demographic information, visual acuities, central corneal thickness, intraocular pressure, optic nerve head findings, and mean deviation of the Humphrey visual fields.\n\nThis dataset is continuously updating, however, as of 1st October 2021, it consisted of 5065 people. This is a large single centre database from patients with glaucoma and covers more than a decade of follow-up for these patients.\n\nGeography : The Queen Elizabeth Hospital is one of the largest single-site hospitals in the United Kingdom, with 1,215 inpatient beds. Queen Elizabeth Hospital is part of one of the largest teaching trusts in England (University Hospitals Birmingham). Set within the West Midlands and it has a catchment population of circa 5.9million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData source : Ophthalmology department at Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.",
    "url": "https://healthdatagateway.org/en/dataset/1172",
    "uid": null,
    "datasource_id": 1172,
    "source": "HDRUK"
  },
  {
    "id": 642,
    "name": "Retinopathy Grades in Screening",
    "description": "Insight data from UHB.\n\nBackground : Diabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. The National Institute for Health and Care Excellence recommendations are for annual screening using digital retinal photography for all patients with diabetes aged 12 years and over until such time as specialist surveillance or referral to Hospital Eye Services (HES) is required.\n\nBirmingham, Solihull and Black Country DR screening program is a member of the National Health Service (NHS) Diabetic Eye Screening Programme. This dataset contains routine community annual longitudinal screening patient results of over 200,000 patients with screening results per patient ranging from 1 year to 15 years. Key data included in this imaging dataset are: • Fundal photographs • The national screening diabetic grade category (seven categories from R0M0 to R3M1) • Screening Outcome (digital surveillance and time; referral to HES)\n\nGeography : Birmingham, Solihull and Black Country is set within the West Midlands and has a population of circa 5.9million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData source : The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic screening scheme in Europe.",
    "url": "https://healthdatagateway.org/en/dataset/1171",
    "uid": null,
    "datasource_id": 1171,
    "source": "HDRUK"
  },
  {
    "id": 643,
    "name": "Diabetic Screening Service",
    "description": "Insight data from UHB.\n\nDiabetes mellitus affects over 3.9 million people in the UK, with over 2.6 million people in England alone. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. The National Institute for Health and Care Excellence recommendations are for annual screening using digital retinal photography for all patients with diabetes aged 12 years and over until such time as specialist surveillance or referral to Hospital Eye Services (HES) is required.\n\nBirmingham, Solihull and Black Country DR screening program is a member of the National Health Service (NHS) Diabetic Eye Screening Programme. This dataset contains routine community annual longitudinal screening patient results of over 200,000 patients with screening results per patient ranging from 1 year to 15 years. Key data included are: • Total number of patients screened and graded over a 15 year period. • Demographic information (including age, sex and ethnicity) • Diabetes status • Diabetes type • Length of time since diagnosis of diabetes • Visual acuity • The national screening diabetic screening grade category (seven categories from R0M0 to R3M1) • Diabetic eye clinical features • Reason for sight and severe sight impairment • Screening Outcome (digital surveillance and time; referral to HES)\n\nGeography : \nBirmingham, Solihull and Black Country is set within the West Midlands and has a population of circa 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData source : \nThe Birmingham, Solihull and Black Country Dataset, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic screening scheme in Europe.\n\nWebsite: https://www.retinalscreening.co.uk/\n\nPathway : \nThe Birmingham, Solihull and Black Country dataset is representative of the patient pathway for community screening and grading of diabetic eye disease. It covers standard UK Public Health England Diabetic Eye Screening requirements and will include patients receiving screening through the standard model, routine diabetic screening, surveillance and slit lamp examination.",
    "url": "https://healthdatagateway.org/en/dataset/1170",
    "uid": null,
    "datasource_id": 1170,
    "source": "HDRUK"
  },
  {
    "id": 644,
    "name": "Cataract Surgery",
    "description": "Insight data from UHB.\n\nBackground : \nCataract, opacification of the lens, is one of the commonest causes of loss of useful vision, with an estimated 16 million people worldwide affected. Cataract surgery is the most frequently performed surgical procedure in the NHS with over 300,000 operations annually in England alone. This dataset spans the full cataract care pathway at University Hospitals Birmingham NHS Trust. Information from the start of the hospital episode including the ophthalmological clinical assessment (details of ocular examination and vision), preoperative assessment (ocular biometry), moving to the surgery day and the chosen anaesthesia (type of anaesthetic), surgery (details of procedure, lens choice, any complications), postoperative recovery (postoperative events) and visual rehabilitation (refractive and visual outcomes).\n\nGeography :\nThe West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData source :\nThe Electronic Health Records held at University Hospitals Birmingham NHS Foundation Trust is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. UHB runs a fully electronic healthcare record for systemic disease.",
    "url": "https://healthdatagateway.org/en/dataset/1169",
    "uid": null,
    "datasource_id": 1169,
    "source": "HDRUK"
  },
  {
    "id": 645,
    "name": "Brain scan imaging",
    "description": "Brain imaging from UHB.",
    "url": "https://healthdatagateway.org/en/dataset/1168",
    "uid": null,
    "datasource_id": 1168,
    "source": "HDRUK"
  },
  {
    "id": 646,
    "name": "Cardiology ECG",
    "description": "Cardiology data from UHB.",
    "url": "https://healthdatagateway.org/en/dataset/1166",
    "uid": null,
    "datasource_id": 1166,
    "source": "HDRUK"
  },
  {
    "id": 647,
    "name": "Pulmonary Fibrosis",
    "description": "Fibrosis disease data from UHB.",
    "url": "https://healthdatagateway.org/en/dataset/1165",
    "uid": null,
    "datasource_id": 1165,
    "source": "HDRUK"
  },
  {
    "id": 648,
    "name": "Liver Viral Hepatitis",
    "description": "Viral Hepatitis data from UHB.",
    "url": "https://healthdatagateway.org/en/dataset/1164",
    "uid": null,
    "datasource_id": 1164,
    "source": "HDRUK"
  },
  {
    "id": 649,
    "name": "Biliary",
    "description": "Biliary data from UHB.",
    "url": "https://healthdatagateway.org/en/dataset/1163",
    "uid": null,
    "datasource_id": 1163,
    "source": "HDRUK"
  },
  {
    "id": 650,
    "name": "Gastric Cancer",
    "description": "A highly curated, multi-modal Gastric Cancer dataset to develop a deep learning algorithm capable of predicting the MSS/MSI phenotype in Gastric Cancer from histology slides.",
    "url": "https://healthdatagateway.org/en/dataset/1162",
    "uid": null,
    "datasource_id": 1162,
    "source": "HDRUK"
  },
  {
    "id": 651,
    "name": "Cardiology Echo",
    "description": "Cardiology data from UHB.",
    "url": "https://healthdatagateway.org/en/dataset/1161",
    "uid": null,
    "datasource_id": 1161,
    "source": "HDRUK"
  },
  {
    "id": 652,
    "name": "Rare Disease",
    "description": "Rare Disease data for 22,000 patients with over 100 different diseases.",
    "url": "https://healthdatagateway.org/en/dataset/1160",
    "uid": null,
    "datasource_id": 1160,
    "source": "HDRUK"
  },
  {
    "id": 653,
    "name": "Maternity (Badgernet) West Midlands Provider C",
    "description": "Maternity data held within Badgernet for West Midlands patients.",
    "url": "https://healthdatagateway.org/en/dataset/1159",
    "uid": null,
    "datasource_id": 1159,
    "source": "HDRUK"
  },
  {
    "id": 654,
    "name": "Community Healthcare Data",
    "description": "Mental Health data",
    "url": "https://healthdatagateway.org/en/dataset/1158",
    "uid": null,
    "datasource_id": 1158,
    "source": "HDRUK"
  },
  {
    "id": 655,
    "name": "HCM",
    "description": "The SOLENOID project, short for PredictorS Of cLinical outcomEs iN hypertrOphIc carDiomyopathy, which has a primary aim to enhance the prediction of disease progression in patients with hypertrophic cardiomyopathy a prevalent inherited heart condition affecting approximately 1 in 500 individuals.",
    "url": "https://healthdatagateway.org/en/dataset/1157",
    "uid": null,
    "datasource_id": 1157,
    "source": "HDRUK"
  },
  {
    "id": 656,
    "name": "MSI Colorectal Cancer",
    "description": "Colorectal cancer combined dataset.",
    "url": "https://healthdatagateway.org/en/dataset/1156",
    "uid": null,
    "datasource_id": 1156,
    "source": "HDRUK"
  },
  {
    "id": 657,
    "name": "Factors Predicting Serum HLA Antibody Persistence or Decline in Kidney Transplant Patients",
    "description": "In the UK, over 8,000 patients are on the waiting list for kidney transplants. Approximately 26% of these patients are classified as highly sensitised, which significantly extends their median waiting time to around six years. The presence of HLA antibodies complicates the transplant process, as potential donors with corresponding HLA mismatches are avoided. Understanding the factors that influence the persistence or decline of HLA antibodies can aid in making informed clinical decisions and enhance the management of waiting list patients. This information is crucial for optimizing patient management and improving the chances of successful transplantation for highly sensitized patients—those with high levels of HLA antibodies who face longer waiting times and fewer compatible donor options.",
    "url": "https://healthdatagateway.org/en/dataset/1155",
    "uid": null,
    "datasource_id": 1155,
    "source": "HDRUK"
  },
  {
    "id": 658,
    "name": "Prostate Cancer",
    "description": "Prostate Cancer from SDE Network project.",
    "url": "https://healthdatagateway.org/en/dataset/1154",
    "uid": null,
    "datasource_id": 1154,
    "source": "HDRUK"
  },
  {
    "id": 659,
    "name": "Ovarian Cancer",
    "description": "When patients see their GPs with symptoms that might indicate ovarian cancer, a CA125 blood test is carried out but this test does not identify all patients with ovarian cancer and wrongly identifies some patients as at risk of having ovarian cancer. It is possible that this can be improved by carrying out an additional test, HE4, on the same blood sample and calculating a score called the ROMA score: this is being investigated in the SONATA study with the aim of improving ovarian cancer diagnosis.",
    "url": "https://healthdatagateway.org/en/dataset/1153",
    "uid": null,
    "datasource_id": 1153,
    "source": "HDRUK"
  },
  {
    "id": 660,
    "name": "Musculoskeletal Conditions",
    "description": "Musculoskeletal conditions from Keele University dataset.",
    "url": "https://healthdatagateway.org/en/dataset/1152",
    "uid": null,
    "datasource_id": 1152,
    "source": "HDRUK"
  },
  {
    "id": 661,
    "name": "Whitehall II",
    "description": "The Whitehall II Study was established in 1985 to investigate the importance of socioeconomic circumstances for health by following a cohort of working men and women aged 35-55 at enrolment. Participants have taken part in eleven data collection phases, six of which have included a medical screening. The 12th is currently underway. The aim of the study is to understand the causes of age-related heterogeneity in health. By combining the existing 28 years of data on social inequalities and chronic disease with new clinical measures of cognitive function, mental disorders and physical functioning, Whitehall II has been transformed interdisciplinary study of ageing. In addition to providing insights into individual and social differences in the development of frailty, disability, dependence, and dementia, the study will enable the determination of optimal time windows and targets for interventions that maximise the potential for healthy-ageing and independent living.",
    "url": "https://healthdatagateway.org/en/dataset/1150",
    "uid": null,
    "datasource_id": 1150,
    "source": "HDRUK"
  },
  {
    "id": 662,
    "name": "PREVENT",
    "description": "The PREVENT Research Programme has established a cohort of individuals to explore differences in the brain and cognitive function in healthy people in mid-life (aged 40-59). People are grouped into high, mid and low risk based on their family history and APOE status (a well-known risk gene for Alzheimer’s disease). Participants are assessed on biological indicators including markers in blood, saliva, urine and spinal fluid as well as direct imaging of the brain's structure and function. Changes in all of these markers will be monitored at 2 years to work out if risks that predict these changes. One of the main aims of the study is to identify the earliest signs of changes in the brain whilst people are still in good health.",
    "url": "https://healthdatagateway.org/en/dataset/1149",
    "uid": null,
    "datasource_id": 1149,
    "source": "HDRUK"
  },
  {
    "id": 663,
    "name": "OxChronic",
    "description": "In depth Cognitive and mood assessments of long term stroke survivors (>2 years, average 4.5 years) for whom acute and 6 month cog data was available - subset of OCS screening study. REC Reference: 19/SC/0520",
    "url": "https://healthdatagateway.org/en/dataset/1148",
    "uid": null,
    "datasource_id": 1148,
    "source": "HDRUK"
  },
  {
    "id": 664,
    "name": "OCS-Care (Oxford Cognitive Screening Programme)",
    "description": "Cognitive, mood, and functional data of stroke survivors (approximately 6-months post-stroke) for whom acute cognitive data was available as part of a randomised trial design REC Reference: 12/WM/00335",
    "url": "https://healthdatagateway.org/en/dataset/1147",
    "uid": null,
    "datasource_id": 1147,
    "source": "HDRUK"
  },
  {
    "id": 665,
    "name": "OCS Recovery (Oxford Cogntiive Screening Programme)",
    "description": "Ongoing cogntiive  screening programme in Oxford acute stroke unit and oxfordshire community rehab setting with 6 month follow ups",
    "url": "https://healthdatagateway.org/en/dataset/1146",
    "uid": null,
    "datasource_id": 1146,
    "source": "HDRUK"
  },
  {
    "id": 666,
    "name": "NIMROD (Neuroimaging of Inflammation in MemoRy and Other Disorders)",
    "description": "The NIMROD (Neuroimaging of Inflammation in Memory and Other Disorders) study aims to understand the role of inflammation in several forms of dementia, memory loss and depression (Alzheimer's disease, AD, dementia with Lewy bodies, DLB, progressive supranuclear palsy, PSP, frontotemporal dementia, FTD, late life depression, LLD, vascular dementia, VaD, mild cognitive impairment, MCI). The study has been set up to investigate the extent and pattern of neuroinflammation in these disorders in relation to: key clinical symptoms, including cognitive impairment, peripheral markers of neuroinflammation, including cytokine levels and state-of-the-art immunoprofiling, structural changes on MRI, including atrophy and vascular (white matter lesion) changes, the extent and pattern of brain amyloid deposition as assessed with amyloid PET imaging, the extent and pattern of tau deposition as revealed with PET. A further aim is to investigate if neuroinflammation can predict subsequent clinical course, including cognitive and functional decline.",
    "url": "https://healthdatagateway.org/en/dataset/1145",
    "uid": null,
    "datasource_id": 1145,
    "source": "HDRUK"
  },
  {
    "id": 667,
    "name": "NICOLA (Northern Ireland Cohort for the Longitudinal study of Ageing)",
    "description": "NICOLA is a public health study involving men and women aged 50 years and over living in Northern Ireland who will voluntarily be followed over a number of years. Households were identified that had people aged 50 and over living in them, a sample representative of the population of Northern Ireland was drawn from this and then the employed market research company sought the participation from appropriate members within those selected households. The study, set up by the Centre for Public Health in Queen’s University Belfast aims to provide information on the health, social and economic circumstances of ~8500 men and women as they grow older in Northern Ireland and how their circumstances may change over those coming years. The primary objective is to collect longitudinal data on physical and mental health, well-being,disability, economic circumstances, social participation and connectedness as people plan for, move into and progress beyond retirement. Information is obtained through a fieldwork survey utilising a CAPI (Computer Assisted Personal Interview) and a Self Completion Questionnaire which the participant completes in their own time. These interviews will take place approximately every 2-3 years. The interviewers collect information on living arrangements, children, education, income and assets, physical and mental health, employment, lifelong learning, planning for retirement, care and social support. Questions will be asked about health, diet, work, family and social networks, income and benefit receipts, quality of life, cognitive functioning, daily activities, housing, retirement and pensions. The affect of 'the troubles' on the lives of people will also be explored. A physical health assessment is also planned for alternative waves (wave 1, wave 3, wave 5 etc.) covering cognitive measures, blood pressure, grip strength, anthropometric measurements, gait and balance, respiratory, bodystat, blood/urine samples and an ophthalmic component. Information gathered during this study will be used to inform and plan Northern Ireland Health and Social Care provision in the future, and to design other policies targeted towards older people. NICOLA will address longer term research goals to investigate the determinants of retirement behaviour and economic wellbeing, the impact of cognitive function and sensory disability on decision making, the determinants of disability trajectories, the influence of social participation on these and the interaction of genetic, biological and psychosocial determinants on health and mortality. At present there are no other studies in Northern Ireland that will look at such an array of topics that provide a fuller understanding of the ageing process.",
    "url": "https://healthdatagateway.org/en/dataset/1144",
    "uid": null,
    "datasource_id": 1144,
    "source": "HDRUK"
  },
  {
    "id": 668,
    "name": "MEMENTO",
    "description": "The Memento cohort is a research project implemented within the framework of the 2008-2012 Alzheimer's plan, with the aim of describing and better understanding the clinical evolution of patients with early signs that may evoke the onset of Alzheimer's or other related diseases. One of the main challenges of Memento is to measure, repeatedly in patients with early signs, a series of biomarkers (MRI of the brain, genetic analyses, molecular PET imaging, CSF extracted by lumbar puncture) and risk factors (lifestyle habits, drug treatments, quality of life) and to monitor the development of their neurocognitive performance in parallel. All of these observations should make it possible to understand which parameters explain why some people become sick and others do not. In the longer term, the knowledge accumulated thanks to the Memento study could make it possible to diagnose those with dementia at an earlier stage and to offer them, where available, treatments which will make it possible to stop or slow down the evolution of the disease.",
    "url": "https://healthdatagateway.org/en/dataset/1143",
    "uid": null,
    "datasource_id": 1143,
    "source": "HDRUK"
  },
  {
    "id": 669,
    "name": "Imperial APC (Imperial Amyloid PET Cohort)",
    "description": "The Imperial APC Study is an Alzheimer's Society funded project that was set up at the Imperial College London in 2019. The ovearching aim of the study is to investigate the diagnostic utility of amyloid PET and other more established biomarkers of AD in a clinical cohort of patients with atypical clinical presentations. In this sub-study, we have retrospectively collected the MRI scans of all individuals referred for amyloid PET imaging in our Centre between 2013 and 2021. Both MRI and amyloid PET scans were carried out for clinical purposes and referral to amyloid PET followed appropriate use criteria (Johnson et al., 2013 Alz & Dem).",
    "url": "https://healthdatagateway.org/en/dataset/1142",
    "uid": null,
    "datasource_id": 1142,
    "source": "HDRUK"
  },
  {
    "id": 670,
    "name": "ICICLE-PD (The Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation-PD)",
    "description": "The ICICLE-PD study aims to accurately characterise two independent cohorts of incident parkinsonsim in Newcastle-Gateshead and Cambridgeshire. A key objective is to identify patients who develop PD Dementia and the factors that predict its evolution. From this information, a simplified panel of tests that can be used to predict PDD will be established. ICICLE-PD will therefore provide a platform for studies investigating agents designed to help treat this complication of PD. Longitudinal follow up is on-going with assessments at 18 month intervals.",
    "url": "https://healthdatagateway.org/en/dataset/1141",
    "uid": null,
    "datasource_id": 1141,
    "source": "HDRUK"
  },
  {
    "id": 671,
    "name": "GS: SFHS (Generation Scotland: Scottish Family Health Study)",
    "description": "The aim of GS: SFHS is to establish a large, family-based intensively-phenotyped cohort recruited from the general population across Scotland, as a resource for studying the genetics of health areas of current and projected public health importance. It aims to identify genetic variants accounting for variation in levels of quantitative traits underlying the major common complex diseases (such as cardiovascular disease, cognitive decline, mental illness) in Scotland. DNA and non-identifiable information from this cohort will be made available to researchers in Scotland and international collaborators. Baseline data was collected at a single clinic visit. Longitudinal data is available by linkage to NHS medical records. Some participants are being invited to new clinic visits in 2015-17. This profile also includes scanning information from the Stratifying Resilience and Depression Longitudinally (STRADL) study to which approximately 3,000 GS participants have been invited for scanning.",
    "url": "https://healthdatagateway.org/en/dataset/1140",
    "uid": null,
    "datasource_id": 1140,
    "source": "HDRUK"
  },
  {
    "id": 672,
    "name": "Extend (The Exeter 10,000 project)",
    "description": "A re-contactable research volunteer register and sample biobank of local people with and without health problems who are willing to provide blood and urine samples, have their blood pressure taken and answer simple health and lifestyle questions. Half of the sample taken is used to measure diagnostic markers including blood sugar and cholesterol, which can be shared with the volunteer and the remaining half (requiring consent from the participant) is saved in a biobank. Participants without diabetes will be offered the opportunity to receive a copy of their health measures and blood tests. They may also choose to have these results copied to their doctor. Participants are invited to a return visit (after around three years) to replace samples that have been used up, and update the measurements and other health data recorded The Peninsula Research Bank (PRB) holds the data and banked samples of DNA, urine, serum, plasma and prospectively collected RNA/ whole blood for long term management. The Extend/PRB Biobank enables research into why some people are predisposed to develop a disease as they get older and why some people are protected. The EXTEND register volunteers who are identifiable by phenotype and genotype and agree that they can be contacted to participate in various research projects based on their genetic predisposition have the choice of whether they wish to be involved in studies about Dementia/Alzheimer's disease. Participants can give permission for Researchers to access their medical records.",
    "url": "https://healthdatagateway.org/en/dataset/1139",
    "uid": null,
    "datasource_id": 1139,
    "source": "HDRUK"
  },
  {
    "id": 673,
    "name": "EPINEF (Environmental Pollution-Induced Neurological Effects)",
    "description": "The EPINEF study was based on 6 prospective community-based cohorts (4 pre-existing and 2 newly established cohorts) involving areas with low, moderate, and high exposure risk (Figure 1). The 4 pre-existing cohorts included the Korean Genome and Epidemiology Study (KoGES) in Gangwha , the Korean Urban Rural Elderly cohort in Seoul , the Cardiovascular and Metabolic Diseases Etiology Research Center cohort in Seoul , and the Seoul incinerator cohort . The new cohorts were based in Namdong-gu, Incheon (where a large industrial complex is located) and Wonju/Pyeongchang (rural areas). The Seoul incinerator and Namdong cohorts, which include regions within a specific distance (300 m or 1 km) from the source of pollution (industrial complex region) were classified as having high exposure risk, whereas the KoGES and Wonju/Pyeongchang cohorts, which are considered as rural areas, were classified to have low exposure risk. The remaining cohorts were classified to have moderate exposure risk. By distinguishing between high exposure risk and moderate/low exposure risk based on the surrounding proximity of the industrial complex, we were able to obtain a basis for the classification of exposure and non-exposure to environmental pollutants and analyze differences between exposed and non-exposed regions. This multi-city design was chosen to obtain sufficient variation in the level of exposure to environmental pollutants. The survey centers were located at 3 university-based hospitals: Yonsei University Severance Hospital (Seoul and Gangwha), Gachon University Gil Medical Center (Incheon), and Wonju Severance Christian Hospital (Wonju and Pyeongchang).",
    "url": "https://healthdatagateway.org/en/dataset/1138",
    "uid": null,
    "datasource_id": 1138,
    "source": "HDRUK"
  },
  {
    "id": 674,
    "name": "BHC (Oxford Brain Health Clinic)",
    "description": "Inclusion criteria: Referral for memory clinic appointment, Exclusion Criteria: Not MRI compatible",
    "url": "https://healthdatagateway.org/en/dataset/1137",
    "uid": null,
    "datasource_id": 1137,
    "source": "HDRUK"
  },
  {
    "id": 675,
    "name": "BDR (Brains for  Dementia Research)",
    "description": "Living in the UK. Willingness to register for brain donation, and take part in widely used clinical and psychometric measures until the time for brain donation comes. People with a diagnosis of dementia or MCI can register at any age, and those with no diagnosis of memory impairment can register from 65 years old. Exclusion criteria are largely practical and related to the suitability of the donated brain tissue for research. This includes a diagnosis that indicates another brain bank might be more suitable, such as multiple sclerosis. Pre-existing conditions such as schizophrenia, bipolar disorder, brain tumours (including previous radiotherapy treatment to the head), major strokes, aneurysms, and other significant cerebrovascular incidents, can mean a person may not be an ideal ‘control’, or the brain structure is significantly changed. Infections such as meningitis, encephalitis, HIV, prion disease pose an infection risk to all coming in contact with the tissue, so also prevent brain donation.",
    "url": "https://healthdatagateway.org/en/dataset/1136",
    "uid": null,
    "datasource_id": 1136,
    "source": "HDRUK"
  },
  {
    "id": 676,
    "name": "AMPLE (AMyloid imaging for Phenotyping LEwy body dementia)",
    "description": "All Participants: Age 60+. Sufficient English to carry out cognitive testing. Controls: MMSE greater than or equal to 26. Lewy Body/AD Dementia: MMSE greater than or equal to 12. Meet criteria for probable LBD/AD. If taking anti-cholinesterase drugs or memantine, stable for at least 3 months. Presence of reliable informant sufficient to provide information for informant rated scales.Concurrent major psychiatric illness (e.g. major depression). Severe physical illness or comorbidity that may limit ability to fully participate in study. Past history of excess alcohol intake. Past history of neurological illness including stroke. Contra-indications for MR or PET. Psychotropic medications which may significantly interfere with cognitive testing (anti- dementia drugs not an exclusion criteria). \nControls: memory complaints or signs/ symptoms of dementia. \nPast history of Parkinson's disease. Psychotropic medications which may significantly interfere with cognitive testing (anti- dementia drugs not an exclusion criteria). \nControls: memory complaints or signs/ symptoms of dementia. \nPast history of Parkinson's disease. Alzheimer's patients: Past history of Parkinson's disease. All Participants: Contra-indications for MR or PET imaging.Past history of excessive alcohol intake. Past history of other neurological illness including, but not limited to stroke, intracerebral pathology. Psychotropic and other medications which may significantly interfere with cognitive testing (including but not limited to sedative antidepressants, benzodiazepines except low when used as hypnotics, centrally acting anticholinergic drugs). Use of anti dementia drugs (eg anti-cholinesterase drugs or memantine) is not an exclusion criterion. \nA relevant history of severe drug allergy or hypersensitivity. \nHave ever participated in an experimental study with an amyloid targeting agent (e.g. anti-amyloid immunotherapy, or γ-secretase inhibitor) unless it can be documented that the subject received only placebo during the course of the trial. Receiving any investigational medications, or participation in a trial with investigational medications within the last 30 days. A radiopharmaceutical imaging or treatment procedure within 7 days prior to the study imaging session.",
    "url": "https://healthdatagateway.org/en/dataset/1135",
    "uid": null,
    "datasource_id": 1135,
    "source": "HDRUK"
  },
  {
    "id": 677,
    "name": "Airwave (The Airwave Health Monitoring Study)",
    "description": "The Airwave Health Monitoring Study was established to evaluate possible health risks associated with the use of TETRA, a digital communication system used by the police forces and other emergency services in Great Britain since 2001. It is a long-term observational study following up the health of the police force with respect to TETRA exposure, and ability to monitor both cancer and non-cancer health outcomes. Baseline screening consisted of an enrolment questionnaire and a health screening.",
    "url": "https://healthdatagateway.org/en/dataset/1134",
    "uid": null,
    "datasource_id": 1134,
    "source": "HDRUK"
  },
  {
    "id": 678,
    "name": "ACONF (Aberdeen Children of the 1950s)",
    "description": "The Children of the 1950s study is a longitudinal cohort study managed by the University of Aberdeen. Comprising individuals born in Aberdeen, Scotland (UK) between 1950 and 1956, this cohort is based on over twelve thousand subjects who took part  in a detailed survey of all children attending an Aberdeen primary school in December 1962. The data collected include information on birth weight, childhood height and weight, tests of cognition and behavioural disorder, and a range of multi-level socio-economic indicators.\n\nThe process of revitalising the cohort was commenced in 1998.  The current vital status and whereabouts of 98.5% of the 12,150 subjects  with full baseline data was ascertained . A postal questionnaire to all surviving cohort members was distributed in 2001, with a response proportion of 63%. Updates on deaths are received on a regular basis, and linkage to routinely collected hospital admissions and prescriptions is possible. Some contextual work was carried out relating to neighbourhood ,household and school level data.",
    "url": "https://healthdatagateway.org/en/dataset/1133",
    "uid": null,
    "datasource_id": 1133,
    "source": "HDRUK"
  },
  {
    "id": 679,
    "name": "DECISIve Biorepository (Cardiff)",
    "description": "A repository of biosamples from DECISIve trial participants (optional participation). Purpose: to enable biomarker discovery studies\nto be conducted after full enrolment into the trial. Focusing on biomarkers that predict who will go on to develop MS, but may also include how that person’s MS will progress, how their MS will respond to treatment or to measure how well treatments are working at an early stage.\nThe samples were collected at the lumbar puncture visits from individuals who have presented with typical clinically isolated syndrome and are undergoing diagnostic evaluation of MS. The biorepository samples include:\n1. One sample of cerebrospinal fluid (of up to five millilitres). Unhaemolysed CSF was collected in polypropylene tubes, centrifuged, separated and the supernatant frozen in separate aliquots in polypropylene secondary tubes, preferably within one hour of the LP. The CSF was frozen and transported on dry ice. CSF has been kept frozen at -80 oC.\n2. One sample of blood (of up to 50 millilitres), drawn at the same time as the clinical blood test. This was collected in vacutainers with clotting activator and subsequently allowed to clot for at least 30 minutes at room temperature. Separation of serum was achieved by centrifugation. Samples were aliquoted in polypropylene screw cap vials and frozen at -80 oC. \n\nThe PIs are responsible for the samples’ storage and authorising access to the samples. The samples will be analysed within national or international centres of expertise.\n\nOther data available: \n year of birth\n gender\n ethnicity\n smoking status\n presenting symptom(s)\n date of first clinical symptom\n details of any subsequent suspected clinical events\n mode of presentation to MS team e.g. emergency admission, referral from ophthalmology/GP\n date of study enrolment\n baseline investigation results from blood tests and radiological investigation performed prior to\nenrolment.\n T2* MRI scan performed as a research MRI scan. The following two scans were acquired using a pre-defined protocol. 1) High resolution 3D T2* and 2) 3D FLAIR. The investigations took place as soon as possible after enrolment into the study with a limit on the time between the lumbar puncture and research MRI of eight weeks.",
    "url": "https://healthdatagateway.org/en/dataset/1113",
    "uid": null,
    "datasource_id": 1113,
    "source": "HDRUK"
  },
  {
    "id": 680,
    "name": "Anne Rowling Clinical Neurological Speech Corpus",
    "description": "This corpus is a longitudinal dataset of speech from people living with multiple neurodegenerative disorders, and controls who do not report to have a neurolical condition, accompanied with detailed and contemporaneous clinical annotation.",
    "url": "https://healthdatagateway.org/en/dataset/1111",
    "uid": null,
    "datasource_id": 1111,
    "source": "HDRUK"
  },
  {
    "id": 681,
    "name": "Demography, interventions & outcomes of older patients following stroke",
    "description": "This dataset includes 24,783 older people (aged 65 years and older) and 29,333 spells, designed to support research which improves cerebrovascular events and unplanned care for older people. \n\nAll patients admitted to hospital from year 2000 and onwards, curated to focus on Stroke. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (mortality, timings and wards). Along with presenting complaints, microbiology results, referrals, therapies, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations and others), all blood results (urea, albumin, platelets, white blood cells and others). Includes all prescribed & administered treatments and all outcomes.  Linked images are also available (radiographs, CT scans, MRI). \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1110",
    "uid": null,
    "datasource_id": 1110,
    "source": "HDRUK"
  },
  {
    "id": 682,
    "name": "A NIHR Midlands PSRC dataset of older patients with diabetic emergencies",
    "description": "Up to 30% of older adults (aged 65 years and older) have been diagnosed with Diabetes mellitus. Older diabetics are more likely to experience complications like heart disease, stroke, kidney problems, and nerve damage, leading to higher hospital admission rates.   When managing older diabetic patients, healthcare professionals need to consider factors like frailty, polypharmacy (multiple medications), and potential cognitive impairments.  \n\nThis dataset includes 83,303 people and 366,035 spells, designed to support research which improves diabetic emergency and unplanned care in older adults.  It includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process, presenting complaints, admissions, microbiology results, referrals, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations and others), all blood results (urea, albumin, platelets, white blood cells and others). Includes all prescribed & administered treatments and all outcomes.  Linked images are also available (radiographs, CT scans, MRI). \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size",
    "url": "https://healthdatagateway.org/en/dataset/1109",
    "uid": null,
    "datasource_id": 1109,
    "source": "HDRUK"
  },
  {
    "id": 683,
    "name": "NIHR Midlands PSRC dataset: Guideline Adherence in Community Acquired Pneumonia",
    "description": "Community acquired pneumonia (CAP) is a common causes of hospital admission with admission rates rising, especially in more socioeconomically deprived communities. Guideline adherence in CAP reduces hospital stay, is a vital part of effective antibiotic stewardship, and has been associated with lower mortality. However, adherence to guidelines in CAP is poor; it occurs for less than half of patients. Therefore, there is a significant opportunity to improve patient outcomes and broader public health outcomes by improving prescribing in CAP.\n\nThis dataset has been curated by PIONEER for the NIHR Midlands PSRC and includes admissions from 2018 to 2024.  It includes 31,417 patients admitted for CAP with demographics, comorbidities, Vital signs (blood pressure, respiratory rate, heart rate, temperature, BMI, NEWS2 Spo2 scale and others), assessments (MMS, AMT10, Continence assessment and Waterlow assessment), lab sample results (full blood count, liver function tests, urea & electrolytes, bone profile, coagulation, inflammatory markers and others), imaging, medications, intensive care admissions, sputum and blood culture results, mortality and readmissions.\n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size",
    "url": "https://healthdatagateway.org/en/dataset/1108",
    "uid": null,
    "datasource_id": 1108,
    "source": "HDRUK"
  },
  {
    "id": 684,
    "name": "A HDRUK Medicines Dataset:  Antibiotic allergies and prescribing on ICU",
    "description": "Approximately 10% of the population have a self-reported allergy label.  The symptoms causing patients to label themselves as being allergic arise from symptoms such as gastrointestinal upset, rash or altered taste. It is widely believed that only 10% of those reporting an allergy have a true, immune-mediated allergy. \nThe reporting of an allergy, irrespective of whether this is true allergy or not, can alter antibiotic prescribing. Alternative prescriptions may be less efficacious in serious infections and patients may be at increased risk of secondary infections and poor outcomes.  \nTo explore this, PIONEER, in collaboration with the HDRUK Medicines in Acute and Chronic Care Driver programme, has curated a dataset of over 41,000 admissions to intensive care, with self-reported allergies documented, as well as serial and highly granular data on presentation, acuity, physiology, investigations and all treatments and outcomes.\n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1106",
    "uid": null,
    "datasource_id": 1106,
    "source": "HDRUK"
  },
  {
    "id": 685,
    "name": "MFT Outpatient Dataset",
    "description": "The dataset includes both patient level demographic, and outpatient data. It includes  data on diagnosis, speciality and  appointment  for each episode of care for the patients.",
    "url": "https://healthdatagateway.org/en/dataset/1105",
    "uid": null,
    "datasource_id": 1105,
    "source": "HDRUK"
  },
  {
    "id": 686,
    "name": "MFT Acute Pharmacy Dataset",
    "description": "The dataset includes pharmacy prescription of patients and detailed data on the prescription,  therapeutic class, pharmaceutical class, and prescription strength. It also contains data on medication discontinuity. The dataset includes patient level demographic dataset.",
    "url": "https://healthdatagateway.org/en/dataset/1104",
    "uid": null,
    "datasource_id": 1104,
    "source": "HDRUK"
  },
  {
    "id": 687,
    "name": "MFT Inpatient Dataset",
    "description": "The dataset includes both patient level demographic, and inpatient data. It includes  data on diagnosis,  admission type and  department  for each episode of care for the patients.",
    "url": "https://healthdatagateway.org/en/dataset/1103",
    "uid": null,
    "datasource_id": 1103,
    "source": "HDRUK"
  },
  {
    "id": 688,
    "name": "MFT Radiology Dataset",
    "description": "The dataset includes the radiology examination of patients across different specialities such as Accident & Emergency, General Medicine, Paediatrics, Trauma & Orthopaedics e.t.c. The data includes both patient level demographic, inpatient/outpatient and radiology report data. Data has been sourced from Manchester Foundation Trust systems, and is patient level.",
    "url": "https://healthdatagateway.org/en/dataset/1102",
    "uid": null,
    "datasource_id": 1102,
    "source": "HDRUK"
  },
  {
    "id": 689,
    "name": "Connected Bradford - Secondary Care BRI OMOP database",
    "description": "This dataset is an extract from the Bradford Royal Infirmary EPR system. This contains current and some historical data, and is based on extracting the relevant tables from EPR, mapping to the OMOP schema and outputting in omop cdm 5.3 format.",
    "url": "https://healthdatagateway.org/en/dataset/1101",
    "uid": null,
    "datasource_id": 1101,
    "source": "HDRUK"
  },
  {
    "id": 690,
    "name": "CPRD Primary Care and Linked Data OMOP Common Data Model",
    "description": "The CPRD Primary Care and Linked Data OMOP CDM database contains longitudinal routinely-collected health records (EHR data) from UK primary care practices, and hospital episode data provided by NHS England. The data has been transformed into a common format (data model) using an open community data standard and structure from the OHDSI standardised vocabularies. The approach allows organisation, standardisation and common representation of medical terms and variables that have been obtained from various clinical data sources. Access to anonymised data from CPRD is subject to a full licence agreement containing detailed terms and conditions of use. Anonymised patient datasets can be extracted for researchers against specific study specifications, following protocol approval.",
    "url": "https://healthdatagateway.org/en/dataset/1099",
    "uid": null,
    "datasource_id": 1099,
    "source": "HDRUK"
  },
  {
    "id": 691,
    "name": "Barts Health NHS Trust OMOP Dataset",
    "description": "Barts Health NHS Trust OMOP Dataset",
    "url": "https://healthdatagateway.org/en/dataset/1098",
    "uid": null,
    "datasource_id": 1098,
    "source": "HDRUK"
  },
  {
    "id": 692,
    "name": "Mental Health Services Dataset (MHSDS) - North East and North Cumbria",
    "description": "\"https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/mental-health-services-data-set/about\"",
    "url": "https://healthdatagateway.org/en/dataset/1097",
    "uid": null,
    "datasource_id": 1097,
    "source": "HDRUK"
  },
  {
    "id": 693,
    "name": "Medicines Dispensed in Primary Care (NHSBSA Data) - North East and North Cumbria",
    "description": "\"https://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/medicines-dispensed-in-primary-care-nhsbsa-data\"}",
    "url": "https://healthdatagateway.org/en/dataset/1096",
    "uid": null,
    "datasource_id": 1096,
    "source": "HDRUK"
  },
  {
    "id": 694,
    "name": "Secondary Uses Service (SUS) Monthly - North East and North Cumbria",
    "description": "\"https://digital.nhs.uk/services/secondary-uses-service-sus/secondary-uses-services-sus-guidance\"",
    "url": "https://healthdatagateway.org/en/dataset/1095",
    "uid": null,
    "datasource_id": 1095,
    "source": "HDRUK"
  },
  {
    "id": 695,
    "name": "Personal Demographics Service (PDS) - North East and North Cumbria",
    "description": "https://digital.nhs.uk/services/personal-demographics-service#:~:text=PDS%20holds%20basic%20non%2Dclinical,phone%20number%20or%20email%20address",
    "url": "https://healthdatagateway.org/en/dataset/1093",
    "uid": null,
    "datasource_id": 1093,
    "source": "HDRUK"
  },
  {
    "id": 696,
    "name": "Patient Reported Outcome Measures (PROMS) - North East and North Cumbria",
    "description": "\"https://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/patient-reported-outcome-measures-proms\"",
    "url": "https://healthdatagateway.org/en/dataset/1092",
    "uid": null,
    "datasource_id": 1092,
    "source": "HDRUK"
  },
  {
    "id": 697,
    "name": "Maternity Services Data Set (MSDS) - North East and North Cumbria",
    "description": "\"https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/maternity-services-data-set/about-the-maternity-services-data-set\"",
    "url": "https://healthdatagateway.org/en/dataset/1085",
    "uid": null,
    "datasource_id": 1085,
    "source": "HDRUK"
  },
  {
    "id": 698,
    "name": "Improving Access to Psychological Therapies (IAPT) - North East and North Cumbria",
    "description": "\"https://digital.nhs.uk/data-and-information/information-standards/information-standards-and-data-collections-including-extractions/publications-and-notifications/standards-and-collections/dapb-1520-improving-access-to-psychological-therapies-data-set\"",
    "url": "https://healthdatagateway.org/en/dataset/1070",
    "uid": null,
    "datasource_id": 1070,
    "source": "HDRUK"
  },
  {
    "id": 699,
    "name": "Faster SUS - North East and North Cumbria",
    "description": "\"https://digital.nhs.uk/services/secondary-uses-service-sus/secondary-uses-services-sus-guidance\"",
    "url": "https://healthdatagateway.org/en/dataset/1068",
    "uid": null,
    "datasource_id": 1068,
    "source": "HDRUK"
  },
  {
    "id": 700,
    "name": "e-Referrals (ERS) - North East and North Cumbria",
    "description": "\"https://digital.nhs.uk/services/data-services-for-commissioners/datasets/e-referral-system-dataset-ersds\"",
    "url": "https://healthdatagateway.org/en/dataset/1067",
    "uid": null,
    "datasource_id": 1067,
    "source": "HDRUK"
  },
  {
    "id": 701,
    "name": "Emergency Care Dataset (ECDS) - North East and North Cumbria",
    "description": "https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/emergency-care-data-set-ecds",
    "url": "https://healthdatagateway.org/en/dataset/1066",
    "uid": null,
    "datasource_id": 1066,
    "source": "HDRUK"
  },
  {
    "id": 702,
    "name": "Elective Care Waiting List - North East and North Cumbria",
    "description": "\"https://www.england.nhs.uk/statistics/statistical-work-areas/rtt-waiting-times/wlmds/\"",
    "url": "https://healthdatagateway.org/en/dataset/1064",
    "uid": null,
    "datasource_id": 1064,
    "source": "HDRUK"
  },
  {
    "id": 703,
    "name": "Diagnostic Imaging Dataset (DIDS) - North East and North Cumbria",
    "description": "https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/diagnostic-imaging-dataset-did-data-product",
    "url": "https://healthdatagateway.org/en/dataset/1061",
    "uid": null,
    "datasource_id": 1061,
    "source": "HDRUK"
  },
  {
    "id": 704,
    "name": "Continuing Healthcare Data (CHC) - North East and North Cumbria",
    "description": "\"https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/continuing-health-care-data-set\"",
    "url": "https://healthdatagateway.org/en/dataset/1060",
    "uid": null,
    "datasource_id": 1060,
    "source": "HDRUK"
  },
  {
    "id": 705,
    "name": "Community Services Dataset (CSDS) - North East and North Cumbria",
    "description": "\"https://digital.nhs.uk/data-and-information/information-standards/information-standards-and-data-collections-including-extractions/publications-and-notifications/standards-and-collections/dapb1069-community-services-data-set\"}",
    "url": "https://healthdatagateway.org/en/dataset/1059",
    "uid": null,
    "datasource_id": 1059,
    "source": "HDRUK"
  },
  {
    "id": 706,
    "name": "Civil Registration of Births and Deaths Dataset - North East and North Cumbria",
    "description": "\"https://digital.nhs.uk/services/data-services-for-commissioners/datasets/civil-registration-of-births-dataset, https://digital.nhs.uk/services/data-access-request-service-dars/dars-products-and-services/data-set-catalogue/civil-registrations-of-death\"",
    "url": "https://healthdatagateway.org/en/dataset/1057",
    "uid": null,
    "datasource_id": 1057,
    "source": "HDRUK"
  },
  {
    "id": 707,
    "name": "Cancer Waiting Times (CWT) - North East and North Cumbria",
    "description": "Cancer Waiting Times Data Collection (CWT)\nThe national Cancer Waiting Times (CWT) system allows NHS providers to record data derived from patient care activity. This data can be used to monitor cancer waiting times targets or plan service improvements. As a patient moves through the stages of their treatment pathway data on referrals, treatments and diagnosis are derived from care records locally",
    "url": "https://healthdatagateway.org/en/dataset/1055",
    "uid": null,
    "datasource_id": 1055,
    "source": "HDRUK"
  },
  {
    "id": 708,
    "name": "Adult Social Care - North East and North Cumbria",
    "description": "Adult Social Care Client Level Data\nClient Level Data (CLD) is a curated data product containing details about Adult Social Care service users and informal carers, who are supported by local authorities in England.",
    "url": "https://healthdatagateway.org/en/dataset/1053",
    "uid": null,
    "datasource_id": 1053,
    "source": "HDRUK"
  },
  {
    "id": 709,
    "name": "Children and Young People Mental Health Services Data Set (CYP MHSDS)",
    "description": "All activity relating to Children and Young People who receive assessments and treatment from  Mental Health Services  is within the scope of the CYP Mental Health Services Data Set, where the patient has, or are thought to have:\n- a mental health condition and/or\n- a need for support with their mental wellbeing and/or\n- a learning disability and/or\n- Autism or any other neurodevelopmental condition\n- The scope of the Mental Health Services Data Set requires patient record level data submission from services as follows:\n\nFor each patient  attending a service located in England:\nIf the care is wholly funded by the NHS: the data submission for that patient is mandatory\nIf the care is partially funded by the NHS: the data submission for that patient is mandatory\nIf the care is wholly funded by any means that is not NHS: the data submission for that patient  is optional.\n\nFor each patient attending a service located outside England, but commissioned by an English Integrated Care Board or NHS England specialised commissioner, the data submission is optional.\n\nWe currently have the data from Central and North West London NHS Foundation Trust from financial year 2017 onwards.",
    "url": "https://healthdatagateway.org/en/dataset/1518",
    "uid": null,
    "datasource_id": 1518,
    "source": "HDRUK"
  },
  {
    "id": 710,
    "name": "Scottish Linked Congenital Conditions Dataset (SLiCCD)",
    "description": "The dataset contains a list of all pregnancies ending in Scotland affected by a major congenital condition as defined by EUROCAT and CARDRISS.",
    "url": "https://healthdatagateway.org/en/dataset/1032",
    "uid": null,
    "datasource_id": 1032,
    "source": "HDRUK"
  },
  {
    "id": 711,
    "name": "IQVIA Medical Research Data (IMRD)",
    "description": "IQVIA Medical Research Data, (IMRD) contains longitudinal non-identified patient electronic healthcare records (EHR) collected from UK General Practitioner (GP) clinical systems via the IQVIA Medical Research Extraction Scheme.",
    "url": "https://healthdatagateway.org/en/dataset/1031",
    "uid": null,
    "datasource_id": 1031,
    "source": "HDRUK"
  },
  {
    "id": 712,
    "name": "Somerset Cancer Registry",
    "description": "This is UHB Cancer Patients data extracted from Somerset system. This has patients data with complex and multiple Tumors. It is Cancer Data extracted along with Primary care data, MDT decisions, Surgery, Chemotherapy, Radiotherapy, Mets, Comorbidities, Previous medical history, CarePlan, Imaging, Pallcare and Tertiary_Info.",
    "url": "https://healthdatagateway.org/en/dataset/1029",
    "uid": null,
    "datasource_id": 1029,
    "source": "HDRUK"
  },
  {
    "id": 713,
    "name": "Ophthalmology",
    "description": "UHB Ophthalmology Data",
    "url": "https://healthdatagateway.org/en/dataset/1027",
    "uid": null,
    "datasource_id": 1027,
    "source": "HDRUK"
  },
  {
    "id": 714,
    "name": "Pharmacy",
    "description": "UHB Pharmacy Data",
    "url": "https://healthdatagateway.org/en/dataset/1025",
    "uid": null,
    "datasource_id": 1025,
    "source": "HDRUK"
  },
  {
    "id": 715,
    "name": "NT-proBNP in sepsis patients aged 65+: NIHR Birmingham BRC Dataset",
    "description": "Natriuretic peptides are special proteins produced by the heart, with the two key types being BNP and NT-proBNP. NT-proBNP is particularly useful in managing heart failure and assessing the risk of heart problems. This marker is not just for heart failure; studies have suggested that NT-proBNP levels during the acute phase of sepsis may be a useful indicator of higher risk of long-term impairments in physical function and muscle strength in sepsis survivors.\nThis dataset asset, created by PIONEER for the National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre (BRC) grant, includes detailed demographics, comorbidities, admission reasons, and care journeys for 254 ICU patients aged 65 and over. It contains serial physiological and blood test measurements reflecting disease severity, imaging, investigative results, treatments, and outcomes. This dataset is a valuable resource for evaluating the clinical utility of NT-proBNP as a prognostic marker for complications and mortality in older critically ill patients. A full dataset covering all ages is also available upon request.\nGeography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1024",
    "uid": null,
    "datasource_id": 1024,
    "source": "HDRUK"
  },
  {
    "id": 716,
    "name": "NIHR Birmingham BRC dataset: Outcomes for older adults on Critical Care",
    "description": "As our population ages, it is increasingly common for people presenting to hospital with critical illness to be older. Tools to identify people who may respond well to critical care interventions have not been optimised for older people and although age is a factor associated with poorer outcomes following admission to critical care; frailty and multimorbidity are also likely to be important.\n\nThis is a highly granular dataset of 8,656 critical care admissions for patients aged 65 and over, curated by the NIHR Birmingham Biomedical Research Centre Infection and Acute Care Theme in collaboration with PIONEER. It includes initial presentation, symptoms, and pre-calculated severity scores (SAPS, APACHE, SOFA). Data covers demographics, serial physiology, ventilatory parameters, investigations, treatments (drug, dose, route), diagnostic codes (ICD-10 & SNOMED-CT), and outcomes, with follow-up for one year. It can be supplemented with imaging (results and images) and linked to ambulance conveyance and community outcomes. Admissions span from 01-01-2017 to 01-03-2023. A full dataset for all ages is also available.\n \nGeography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1023",
    "uid": null,
    "datasource_id": 1023,
    "source": "HDRUK"
  },
  {
    "id": 717,
    "name": "Acute Care Patients Aged 65+ with Complex Multimorbidity: NIHR OPTIMAL Dataset",
    "description": "This dataset forms part of the OPTIMising therapies, discovering therapeutic targets and AI-assisted clinical management for patients Living with complex multimorbidity (OPTIMAL) NIHR funded programme.\n\nMultimorbidity is common, especially in older adults, and is associated with an increased risk of hospital admissions and poorer outcomes.  Caring for older adults with complex multimorbidity presenting acutely to hospital is challenging, as guidelines often do not focus on multimorbidity and older adults are especially susceptible to adverse effects from polypharmacy.\n\nPIONEER has curated this dataset of 15,950 patients aged 65 and over with multimorbidity who were acutely admitted to hospital and had an inpatient stay. It contains longitudinal data on serial physiology readings, frailty scores, blood results, medications, comorbidities, drug allergies, treatments, procedures, and mortality outcomes up to a year post-discharge. While this subset focuses on older adults, a full dataset covering all ages is also available.\n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAll data uses should name both PIONEER and the NIHR Optimal programme in data outputs. This will be specified in the Data Licensing Agreement.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1022",
    "uid": null,
    "datasource_id": 1022,
    "source": "HDRUK"
  },
  {
    "id": 718,
    "name": "Investigation pathways for headache patients aged 65+ in acute hospital settings",
    "description": "Headaches are common and often do not require hospitalisation. However, headaches account for 1–4% of all emergency department visits and here, headaches are one of the most challenging complaints to assess. Potentially life-threatening causes are rare and imaging using CT or MRI is usually not indicated.  However, neuro-imaging remains the most common investigation, especially in older adults, where concerns about intracranial pathologies are highest. Pathways to stratify neuro-imaging and admission would be helpful.\n\nThis dataset includes 5,352 patients aged 65 and over who presented with headaches. It contains detailed data on demographics, vital signs (e.g., blood pressure, respiratory rate, heart rate, temperature, NEWS2 score, BMI), comorbidities, assessments, lab results, medical events, medications (including dose and route), imaging (films and reports), onward referrals, treatments (including surgery), mortality, and readmissions (short- and long-term). Data spans from 2013 to 2023. While this subset focuses on older adults, a full dataset covering all ages is also available.\n \nGeography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1021",
    "uid": null,
    "datasource_id": 1021,
    "source": "HDRUK"
  },
  {
    "id": 719,
    "name": "Developing Tools to Prevent Avoidable Admissions for Patients Aged 65 and Older",
    "description": "The NHS long term plan includes prioritising work to avoid hospital admissions. Emergency admissions are costly and frequently unpleasant experiences for patients. Many hospital admissions could potentially be avoided if there were care pathways available to support the patient before this became an urgent care concern. These conditions are known as ambulatory care sensitive (ACS) and urgent care sensitive (UCS) conditions. As many patients presenting to emergency departments are aged over 65 years, identifying ACS and UCS conditions in older adults is an NHS priority.\n\nPIONEER, in collaboration with HDR UK, has curated a highly granular dataset of 130,753 Emergency Department admissions specifically for patients aged 65 and older. The dataset includes demographics, presentation, symptoms, investigations, treatments, procedures, operations, and outcomes. Also, care process data such as ward stays, clinical review times, and the healthcare professionals involved in care decisions. While this subset focuses on older adults, a full dataset covering all ages is available. Admissions span from 01-10-2021 to 30-09-2023, with the potential for expansion to other periods of interest.\n\nGeography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1018",
    "uid": null,
    "datasource_id": 1018,
    "source": "HDRUK"
  },
  {
    "id": 720,
    "name": "Improving Care for Suspected Myocardial Infarction in Patients Aged 65 and Over",
    "description": "The NHS long term plan highlights the need for better care for common long-term conditions. With advances in prevention and medical care the UK mortality rate from heart diseases has declined by more than three quarters in the last 40 years. But cardiovascular disease remains the biggest cause of premature death.  Furthermore, chest pain remains the commonest cause of presentation to acute medical services (up to 40% of unscheduled admissions).  It is known that older adults can have atypical presentations of myocardial infarctions, leading to diagnostic delay and poorer outcomes. Care models to rapidly treat older people with suspected heart disease remain a priority across services.\n\nThis highly granular dataset includes 16,638 patients aged 65 and over attending an Emergency Department or Acute Medical Unit with a possible myocardial infarction. It contains demographics, comorbidities, presenting symptoms, serial physiology, acuity, lab results, imaging, ECGs, procedures, medications (dose and route), onward care journeys, mortality, and readmissions. While this subset focuses on older adults, a full dataset covering all ages is also available.\n\nGeography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1017",
    "uid": null,
    "datasource_id": 1017,
    "source": "HDRUK"
  },
  {
    "id": 721,
    "name": "An HDRUK dataset: Linked Health and Air Quality Data for older adults",
    "description": "The environmental determinants of health are increasingly recognised with descriptions of higher hospital admissions and poorer outcomes for patients during periods of low air quality.  Older adults are known to be especially prone to adverse health events during extremes of temperature  but the acute impact of poor air quality is less well known. \n\nPIONEER has curated a highly granular dataset of 3,175,299 admissions for patients aged 65 and over, linking DEFRA air pollution data to patients' registered addresses. It includes demographics, admission details, diagnostic codes (ICD-10 & SNOMED-CT), respiratory data, medications, and presenting complaints. This dataset enables analysis of the short- and long-term impacts of air quality on health outcomes, providing a unique resource for research in environmental health, epidemiology, and multidisciplinary studies. Covering admissions from 01-01-2000 to 31-08-2023, it offers opportunities to expand to other timelines or include all age groups if required. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1016",
    "uid": null,
    "datasource_id": 1016,
    "source": "HDRUK"
  },
  {
    "id": 722,
    "name": "Synthetic dataset of hospitalised patients with an acute exacerbation of asthma",
    "description": "To support respiratory research, a synthetic asthma dataset was generated based on a real-world data, originally documenting 381 patients with physician-confirmed asthma who were admitted to secondary care at a single centre in 2019. The dataset is highly detailed, covering demographics, structured physiological data, medication records, and clinical outcomes. The synthetic version extends to 561 patients admitted over a year, offering insights into patient patterns, risk factors, and treatment strategies.  \n\nThe dataset was created using the Synthetic Data Vault package, specifically employing the GAN synthesizer. Real data was first read and pre-processed, ensuring datetime columns were correctly parsed and identifiers were handled as strings. Metadata was defined to capture the schema, specifying field types and primary keys. This metadata guided the synthesizer in understanding the structure of the data. The GAN synthesizer was then fitted to the real data, learning the distributions and dependencies within. After fitting, the synthesizer generated synthetic data that mirrors the statistical properties and relationships of the original dataset. \n\nGeography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Real world data. Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can provide real-world data upon request. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1015",
    "uid": null,
    "datasource_id": 1015,
    "source": "HDRUK"
  },
  {
    "id": 723,
    "name": "An NIHR Midlands PSRC dataset of SDEC acute medical services for Older Patients",
    "description": "Emergency care services face increasing pressure. NHS England (NHSE) has prioritised pathways for patients which avoid admission, including Same Day Emergency Care (SDEC) services. The NHS Long Term Plan recommends SDEC assessment for one third of medical attendances. The impact of these new care models for older adults is as yet unknown, but data from the Society for Acute Medicine suggests older adults may benefit less from these pathways. \n\n​Care quality indicators (CQI) include times from arrival to assessment by senior clinical teams. Performance measured against these CQI are impacted by other factors, such as delays in referrals, awaiting investigation results.​  \n\nPIONEER has curated a highly granular dataset of 4,617 Same Day Emergency Care (SDEC) spells for patients aged 65 and older. This dataset includes detailed patient-level information such as demography, comorbidities, presenting symptoms, serial physiology, investigations, treatments, and outcomes. Additionally, it provides data on the wider clinical environment on the day of admission, offering valuable insights into the care pathways and systemic factors influencing outcomes for older adults in SDEC settings.\n\nGeography:The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \nData set availability:Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \nAvailable supplementary data:Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \nAvailable supplementary support:Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size",
    "url": "https://healthdatagateway.org/en/dataset/1014",
    "uid": null,
    "datasource_id": 1014,
    "source": "HDRUK"
  },
  {
    "id": 724,
    "name": "An NIHR Birmingham BRC dataset of Community-Acquired Pneumonia in Older Adults",
    "description": "Community acquired pneumonia (CAP) is a leading cause of hospital admission and has high rates of mortality and complications, especially in older people. Data from PIONEER examining CAP admissions in winter 19/20 and winter 20/21 demonstrated that hospital admissions due to CAP fell by 40% in 20/21 compared to 19/20 but the 30-day mortality rate almost doubled in winter 20/21 compared to 19/20. Frailty was thought to be a determinant of poor outcomes. \n\nTo explore this further, PIONEER, working with the NIHR Midlands Biomedical Research Centre Infections and acute care theme, have curated a highly granular dataset of 1,701 community acquired pneumonia admissions for a focused cohort of adults aged 65 years old and over.  The data includes demography, comorbidities, Charlson comorbidity index, Manchester mobility score (MMS), clinical frailty score (CFS) and symptoms on presentation, serial physiology and acuity, investigations, CURB-65 assessments, intensive care, treatments (drug, dose, route), diagnostic codes (ICD-10 & SNOMED-CT), outcomes (death and readmissions).  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1013",
    "uid": null,
    "datasource_id": 1013,
    "source": "HDRUK"
  },
  {
    "id": 725,
    "name": "Surgical Virtual Ward Outcomes for Patients Aged 65 and Older in New Care Models",
    "description": "Virtual wards (VW) provide care at home with remote monitoring for people who do not need admission to hospital, but require hospital-led care.  NHS England (NHSE) has requested an extension of a VW model of care,with a national ambition of developing 40-50 VW ‘beds’ per 100,000 population. It is important that these new models of care benefit older adults,as they make up the majority of unplanned hospital admissions. \n\n​The Surgical Assessment Unit VW manages patients who are clinically suitable for home while waiting for investigation or treatment for an acute surgical condition.   \n\nTo support a better evidence base for surgical VW, PIONEER has curated a highly granular dataset of 451,306 spells for patients aged 65 and older, eligible for the Virtual Surgical Assessment Unit (VSAU). The dataset includes a proportion of patients admitted to the VSAU and those remaining in traditional care pathways. It covers demography, comorbidities, presenting symptoms, serial physiology, diagnoses, investigations, treatments (including procedures), and outcomes. Admissions span from 2018 to 2023, with potential for expansion to other timelines of interest.\n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1012",
    "uid": null,
    "datasource_id": 1012,
    "source": "HDRUK"
  },
  {
    "id": 726,
    "name": "Influenza Hospitalisation Outcomes in Smokers vs Non-Smokers Aged 65 and Older",
    "description": "Smoking is a leading preventable cause of chronic diseases, including circulatory disease, cancer, and chronic lung conditions, and worsens outcomes in acute illnesses. Despite public health efforts, 13-16% of the UK population still smoke, with higher rates among hospital admissions, especially in older adults who also experience poorer outcomes.  \n\nInfluenza can cause severe complications, such as ICU admission and death, particularly in older adults and those with chronic respiratory conditions. Smoking further increases the risks of mortality and ICU admission, yet UK-specific data on seasonal influenza in this context remains limited. \n\nThis dataset includes 13,524 influenza-related hospital admissions from January 2018 to July 2024, focusing on individuals aged 65 and older. It contains demographics, serial physiology, clinical assessments, diagnostic codes (ICD-10 and SNOMED-CT), initial presentations, ventilation, ICU transfers, prescriptions, and outcomes. While a dataset for all ages is available, this subset emphasizes older adults, who are at greater risk of severe complications, particularly from smoking. \n\nGeography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1011",
    "uid": null,
    "datasource_id": 1011,
    "source": "HDRUK"
  },
  {
    "id": 727,
    "name": "NIHR Applied Research Collaborative dataset of ambulance chest pain conveyances.",
    "description": "A highly granular dataset of 49,785 Chest pain admissions for patients calling West Midlands Ambulance Service or presenting directly to hospital, curated by the NIHR Applied Research Collaborative West Midlands Acute Theme in collaboration with PIONEER. The data includes demography, serial physiology, assessments, diagnostic codes (ICD-10 & SNOMED-CT), initial presentation, presenting symptoms, West Midlands Ambulance Service (WMAS) data (conveyed/non conveyed), procedures (OPCS4 & SNOMED-CT), Imaging, Prescriptions, Ward locations and outcomes. The current dataset includes admissions from 01-04-2023 to 01-04-2024 but can be expanded to assess other timelines of interest. WMAS data provides information relating to categorisation of call, time from call to conveyance (if applicable), assessments and interventions prior to arrival at hospital.  This has been linked to secondary care electronic health record data.  \n\n​​Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. The West Midlands Ambulance Service (WMAS) covers this entire area. This dataset is linked to University Hospitals Birmingham NHS Foundation Trust (UHB) data on an individual patient basis. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. WMAS and UHB both run a fully electronic healthcare record (EHR). \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1010",
    "uid": null,
    "datasource_id": 1010,
    "source": "HDRUK"
  },
  {
    "id": 728,
    "name": "Birmingham Inflammation and joint pain study",
    "description": "Background: Synovial inflammation is associated with pain severity in patients with knee osteoarthritis (OA). The aim here was to determine in a population with knee OA, whether synovial tissue from patient-reported sites associated with pain exhibited different synovial fibroblast transcriptomes, compared to synovial tissue from patient-matched non-painful sites. A further aim was to compare differences between early and end-stage disease synovial fibroblasts.\n\nMethods: Patients undergoing arthroscopy or total joint replacement, categorised as early knee OA (n=30) and end-stage knee OA (n=41) respectively, were recruited. Patient reported pain was recorded using EQ5D, Oxford Knee Score (OKS), Visual Analogue Scale (VAS) questionnaires and using an anatomical knee pain map where patient marked painful and non-painful sites. Proton density fat suppressed MRI axial and sagittal sequences were analysed and scored for synovitis. Synovial tissue was obtained from the medial and lateral parapatellar and suprapatellar sites. RNA sequencing was performed using Illumina’s NextSeq 500 (GSE176223) and single-RNA seq performed using 10x (GSE176308). Transcriptomes were functionally characterised using Ingenuity Pathway Analysis.\n\nFindings: Parapatellar synovitis was significantly associated with increased OA pain perception. Functional pathway analysis revealed that early OA painful sites mediate immune cell recruitment and promote the formation and development of neurites.\n\nConclusion: OA disease progression and the presence of pain in early OA is associated with different synovial pathotypes. Further interrogation of these pathotypes will increase our understanding of the role of synovitis in OA joint pain and provide a rationale for the therapeutic targeting to alleviate pain in patients. \n\nFull study details can be found in our publication DOI: 10.1016/j.ebiom.2021.103618 (PMID: 34628351 PMCID: PMC8511845)\n\nOn going: \n-\tintegrating proteomic data from synovial tissues and matched synovial fluids to further investigate the underlying cellular mechanisms between synovial fibroblasts and neurones that mediate nociceptor activity\n-\ttesting antisense oligonucleotides (ASOs) designed to silence candidate genes and their efficacy in modulating the fibroblast pain pathotype and reducing the growth and sensory function of neurons\n-\tintra-articular delivery of ASO into the synovial joint tissues and its analgesic efficacy in an experimental model of OA pain will be evaluated",
    "url": "https://healthdatagateway.org/en/dataset/1009",
    "uid": null,
    "datasource_id": 1009,
    "source": "HDRUK"
  },
  {
    "id": 729,
    "name": "NNUH heart failure hospital admissions",
    "description": "This dataset contains information about hospital admissions for heart failure patients. It describes their previous medical history and all treatments, procedures, measurements, and lab results performed during their hospital stay. The researchers gathered the data from Norfolk and Norwich University Hospitals Foundation Trust over ten years. It is used to study the prevalence of heart failure readmissions and the common risk factors associated with a readmission.",
    "url": "https://healthdatagateway.org/en/dataset/1006",
    "uid": null,
    "datasource_id": 1006,
    "source": "HDRUK"
  },
  {
    "id": 730,
    "name": "CUH heart failure hospital admissions",
    "description": "This dataset contains information about hospital admissions for heart failure patients. It describes their previous medical history and all treatments, procedures, measurements, and lab results performed during their hospital stay. The researchers gathered the data from Cambridge University Hospitals Foundation Trust over ten years. It is used to study the prevalence of heart failure readmissions and the common risk factors associated with a readmission.",
    "url": "https://healthdatagateway.org/en/dataset/922",
    "uid": null,
    "datasource_id": 922,
    "source": "HDRUK"
  },
  {
    "id": 731,
    "name": "Synthetic Dataset of Hospital Admissions for Patients with Type 1 and 2 Diabetes",
    "description": "Type 1 Diabetes is an autoimmune disease impacting on insulin production.   Type 2 Diabetes is caused by insulin resistance.  Both are chronic conditions associated with serious complications such as heart disease, kidney failure, vision loss, and neuropathy. In the UK, 10% of the NHS budget is spent on managing diabetes. The demand for care is rising, with an increasing number of acute hospital admissions.  \n\nThis highly granular synthetic dataset represents approximately 159,800 diabetes patients acutely admitted between 2004 and 2022.  Data includes demography, socioeconomic status, co-morbidities, “time stamped” serial acuity, physiology and treatments, investigations (structured and unstructured data), hospital care processes, and outcomes. \n\nThe dataset was created using the Synthetic Data Vault (SDV) package, specifically employing the GAN synthesizer. The real data was read and pre-processed, ensuring datetime columns were correctly parsed and identifiers were handled as strings. Metadata was defined to capture the schema, specifying field types and primary keys. This metadata guided the synthesizer in understanding the structure of the data. The GAN synthesizer was then fitted to the real data, learning the distributions and dependencies within. After fitting, the synthesizer generated synthetic data that mirrors the statistical properties and relationships of the original dataset.  \n\nGeography: This synthetic dataset is based on patient data from the West Midlands.  The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity.  \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build different synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1004",
    "uid": null,
    "datasource_id": 1004,
    "source": "HDRUK"
  },
  {
    "id": 732,
    "name": "Synthetic Dataset of Hospital Admissions for an acute Stroke",
    "description": "Strokes can be ischaemic or haemorrhagic in nature, leading to debilitating symptoms which are dependent on the location of the stroke in the brain and the severity of the insult.  Stroke care is centred around Hyper-acute Stroke Units (HASU), Acute Stroke and Brain Injury Units (ASU/ABIU) and specialist stroke services.  Early presentation enables the use of more invasive treatments to clear blood clots, but commonly strokes present late, preventing their use.    \n\nThis synthetic dataset represents approximately 29,000 stroke patients.  Data includes demography, socioeconomic status, co-morbidities, “time stamped” serial acuity, physiology and treatments, investigations (structured and unstructured data), hospital care processes, and outcomes. \n\nThe dataset was created using the Synthetic Data Vault (SDV) package, specifically employing the GAN synthesizer. Real. data was first read and pre-processed, ensuring datetime columns were correctly parsed and identifiers were handled as strings. Metadata was defined to capture the schema, specifying field types and primary keys. This metadata guided the synthesizer in understanding the structure of the data. The GAN synthesizer was then fitted to the real data, learning the distributions and dependencies within. After fitting, the synthesizer generated synthetic data that mirrors the statistical properties and relationships of the original dataset. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute stroke services & specialist care across four hospital sites.  \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1003",
    "uid": null,
    "datasource_id": 1003,
    "source": "HDRUK"
  },
  {
    "id": 733,
    "name": "Synthetic Data: Acute Atrial Fibrillation Patient Profiles, Clinical Insights",
    "description": "Atrial fibrillation (AF) is a common abnormal heart rhythm that causes the heart to beat irregularly and often too fast. AF increases the risk of stroke and heart failure. AF primarily affects older adults and individuals with chronic conditions such as heart disease, high blood pressure, or obesity. Additional factors include congenital heart disease, and cardiomyopathy.  AF can be treated by ablation or controlled using medication.  The risk of stroke can be reduced using anti-coagulants. \n\nThis synthetic AF dataset comprises of 24.8k “patients” including demographics, co-morbidities, presenting symptoms and medical events during hospital stays, coded with ICD-10 and SNOMED-CT.  \n\nUsing the Synthetic Data Vault package with a GAN synthesizer, a synthetic dataset was generated from real clinical data. The dataset includes demographic information and hospital admission details. The real data was pre-processed for correct datetime parsing and metadata was defined to capture schema structure, guiding the synthesizer in learning data distributions and relationships. The resulting synthetic dataset closely mirrors the statistical properties of the original, supporting privacy-preserving analysis and model training. \n\nGeography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1002",
    "uid": null,
    "datasource_id": 1002,
    "source": "HDRUK"
  },
  {
    "id": 734,
    "name": "Synthetic Dataset of Acute Admissions for Patients of Intentional Drug Overdose",
    "description": "This synthetic dataset includes 16,276 patients admitted for drug overdose from 2016 to 2022, featuring comprehensive patient demographics, comorbidities coded by ICD-10 and SNOMED-CT, and detailed admission data from the index event onward. Information on clinical outcomes, primary diagnoses, psychiatric referrals, and all treatments (e.g., fluids, blood products, procedures) is included. \n\nThe dataset was generated using the SDV package's HMA1 synthesizer. The real data was pre-processed, with metadata defining schema, primary/foreign keys, and inter-table relationships, guiding the synthesizer in learning data structure and dependencies. This approach produced synthetic data that mirrors the original’s statistical properties, supporting privacy-preserving analysis and model training. \n\nGeography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1001",
    "uid": null,
    "datasource_id": 1001,
    "source": "HDRUK"
  },
  {
    "id": 735,
    "name": "Maternity (Badgernet) West Midlands Provider B",
    "description": "Maternity data held within Badgernet for West Midlands patients.",
    "url": "https://healthdatagateway.org/en/dataset/999",
    "uid": null,
    "datasource_id": 999,
    "source": "HDRUK"
  },
  {
    "id": 736,
    "name": "DECOVID: Data derived from UCLH and UHB during the COVID pandemic",
    "description": "DECOVID, a multi-centre research consortium, was founded in March 2020 by two United Kingdom (UK) National Health Service (NHS) Foundation Trusts (comprising three acute care hospitals) and three research institutes/universities: University Hospitals Birmingham (UHB), University College London Hospitals (UCLH), University of Birmingham, University College London and The Alan Turing Institute. The original aim of DECOVID was to share harmonised electronic health record (EHR) data from UCLH and UHB to enable researchers affiliated with the DECOVID consortium to answer clinical questions to support the COVID-19 response.   \n​​   \n​​The DECOVID database has now been placed within the infrastructure of PIONEER, a Health Data Research (HDR) UK funded data hub that contains data from acute care providers, to make the DECOVID database accessible to external researchers not affiliated with the DECOVID consortium.   \n \nThis highly granular dataset contains 256,804 spells and 165,414 hospitalised patients. The data includes demographics, serial physiological measurements, laboratory test results, medications,  procedures, drugs, mortality and readmission. \n \nGeography: UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UCLH provides first-class acute and specialist services in six hospitals in central London, seeing more than 1 million outpatient and 100,000 admissions per year.  Both UHB and UCLH have fully electronic health records. Data has been harmonised using the OMOP data model. \nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/998",
    "uid": null,
    "datasource_id": 998,
    "source": "HDRUK"
  },
  {
    "id": 737,
    "name": "An NIHR BRC dataset: Antimicrobial stewardship and antimicrobial resistance.",
    "description": "Tackling antimicrobial resistance is a priority for the World Health Organisation and national governments.  A challenge driving resistance is the inappropriate use of broad-spectrum antibiotics caused by diagnostic delays in identifying the cause of suspected infections.  Predictive models may support better antibiotic stewardship, but there is often a lack of time stamped data which maps timelines between presentation, symptoms, samples being sent and granular medication administration. \n\nTo address this, PIONEER, working with the NIHR Birmingham Biomedical Research Centre, has curated a highly granular dataset of 273,437 admissions including demography, presenting symptoms, co-morbidities, serial physiology, laboratory tests, diagnoses (ICD10, SNOMED CT), procedures (OPCS4), images, prescriptions and administrations (dose and route), microbiology results (including resistance patterns) and outcomes.  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/997",
    "uid": null,
    "datasource_id": 997,
    "source": "HDRUK"
  },
  {
    "id": 738,
    "name": "HDRUK Medicines: Self-reported Penicillin Allergy & outcomes on Intensive Care",
    "description": "Approximately 10% of the population have a penicillin allergy label which is mostly self-reported.  The symptoms causing patients to label themselves as being allergic arise from symptoms such as gastrointestinal upset, rash or altered taste. It is widely believed that only 10% of those with a SRPA have a true, immune-mediated penicillin allergy.  \n\n The reporting of a penicillin allergy, irrespective of whether this is true allergy or not, can alter antibiotic prescribing. Alternative prescriptions of non-beta-lactam antibiotics may be less efficacious in serious infections and patients may be at increased risk of secondary infections and poor outcomes.   \n\nTo explore this, PIONEER, in collaboration with the HDRUK Medicines in Acute and Chronic Care Driver programme, has curated a dataset of over 37,000 admissions to intensive care, with self-reported allergies documented, as well as serial and highly granular data on presentation, acuity, physiology, investigations and all treatments and outcomes. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/996",
    "uid": null,
    "datasource_id": 996,
    "source": "HDRUK"
  },
  {
    "id": 739,
    "name": "Medications on Intensive Care:  A HDRUK Medicines Driver Programme dataset",
    "description": "Delirium is a clinical syndrome of acute confusion which is particularly common in critically ill Intensive Care patients. Delirium on ICU is associated with adverse health outcomes including increased mortality, length of stay, readmission, discharge to long term care, dementia, falls and pressure sores. It can be distressing for both the person affected and their family and carers.  Delirium on ICU can be triggered by medications, especially those with an anticholinergic effect.   \n\nPIONEER has curated a highly granular dataset of 9,100 spells admitted to intensive care. This includes timestamped medication prescriptions and administration with cognition scores. Also, demography, serial physiology, consult specialties, diagnostic codes (ICD-10 & SNOMED-CT), initial presentation, presenting symptoms, serial physiology, all investigations, all medications (name, dose, route), anticholinergic burden (AEC) scores, and outcomes. The current dataset includes admissions from 01-01-2021 to 31-12-2023 but can be expanded to assess other timelines of interest.  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/995",
    "uid": null,
    "datasource_id": 995,
    "source": "HDRUK"
  },
  {
    "id": 740,
    "name": "Cardiac disease following critical illness and intensive care admission",
    "description": "Clinical studies have suggested a link between critical illness and cardiac events.  Pre-clinical research has also suggested that critical illness is associated with the development of acute cardiac disease, with mechanisms including inflammation, changeable physiology and the use of medications and fluids which are associated with vascular damage. Understanding who might be at risk for poor cardiac outcomes following critical illness and how to predict cardiac events in real time might support new treatment strategies.   \n\n​To explore this further, PIONEER has curated a dataset of over 800 patients who have experienced an acute myocardial event during or shortly after an admission to intensive care.  This includes serial, time-stamped physiology, blood results, investigations including ECGs and imaging, other cardiac diagnostic tests, all medications before, during and after the event, and outcomes, including death, length of stay and readmission.  Data also include ventilatory support, SOFA score and APACHE II scores.  This can be matched with a control population. \n\n \nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/994",
    "uid": null,
    "datasource_id": 994,
    "source": "HDRUK"
  },
  {
    "id": 741,
    "name": "Our Future Health Clinical Measurements Data",
    "description": "Our Future Health is a prospective, observational cohort study of the general adult population of the United Kingdom (UK). The programme aims to support a wide range of observational health research. We gather personal, health and lifestyle information from each participant through a self-completed baseline health questionnaire and at an in-person clinic visit. We will further link this data to other health-related data sets. Participants have also given consent for us to recontact them, for example to invite them to take part in further or repeat data collections, or other embedded studies such as clinical trials.  \n\nThe Our Future Health programme is currently open to all adults (18 years and older) living in the UK. In July 2022, we started recruiting participants in England and will continue to expand across the rest of the UK. The data we’ve gathered so far (December 2025) includes clinical measurements from 1,456,410 participants.\n\nThe current data available contains responses from our baseline health questionnaire only and an indicator if a blood sample was provided and stored. The current data is split into 2 categories:\n•\tparticipant data - which contains baseline demographic information collected across all consented participants\n•\tquestionnaire data - which contains self-reported health information, details about participants' household, socioeconomic status, work and education history, and family history\n\n\nAdditional linked datasets are available: \n -  ‘Linked NHS England Health Records Data  which contains linked clinical data from NHS England for 1,668,668 participants.\n-   ‘Genotype Data’ which includes genotype array data on 707,522 variants from a subset of 775,118 participants\n- The ‘Imputed Genotype’ dataset include data on 159,587,100 variants and 550,000 participants. \n- Clinical Measurements Data which contains clinical data from 1,456,410 participants.\n\nThe data is stored in the Our Future Health Trusted Research Environment. We de-identify all participant data we gather before it’s available for use. All researchers will need to become registered researchers at Our Future Health and have an approved research study before they're given access to the data.\n\nWe aim to collect a variety of data types from up to 5 million adult participants from across the UK. We hope to make more data types available on a quarterly basis.\nWe aim to collect a variety of data types from up to 5 million adult participants from across the UK. We hope to make more data types available on a quarterly basis.",
    "url": "https://healthdatagateway.org/en/dataset/993",
    "uid": null,
    "datasource_id": 993,
    "source": "HDRUK"
  },
  {
    "id": 742,
    "name": "Barts Health NHS Trust Cerner Millenium Electronic Patient Record",
    "description": "Barts Health NHS Trust Cerner Millenium Electronic Patient Record",
    "url": "https://healthdatagateway.org/en/dataset/992",
    "uid": null,
    "datasource_id": 992,
    "source": "HDRUK"
  },
  {
    "id": 743,
    "name": "Akrivia Health Database: Secondary Mental Healthcare",
    "description": "The Akrivia Health database contains anonymised data derived from the electronic health records of secondary mental health and dementia care patients across England and Wales. Structured data, such as patient demographics and service pathways, are harmonised across different Healthcare Organisation providers, with research-relevant information also being extracted from free-text progress notes using natural language processing. Data access is provided via Akrivia’s secure data environment.",
    "url": "https://healthdatagateway.org/en/dataset/990",
    "uid": null,
    "datasource_id": 990,
    "source": "HDRUK"
  },
  {
    "id": 744,
    "name": "Outcomes of Influenza related hospitalisations in smokers vs. non-smokers",
    "description": "Smoking is a leading preventable cause of chronic diseases like circulatory disease, cancer, and chronic lung conditions, worsening outcomes in acute illnesses. Despite reductions from public health campaigns, 13-16% of the UK population still smoke, with higher rates in hospital admissions. Smoking-related admissions cost over £870,000 annually, prompting a focus on smoking cessation, particularly in secondary care, where targeted interventions are effective.\nInfluenza often leads to severe complications in hospitals, such as ICU admission and death, especially in older adults and those with chronic respiratory conditions. Smoking increases risks of mortality and ICU admission in influenza cases, but UK-specific data, especially on seasonal influenza, is limited. Updated data on high-risk groups, including smokers, is crucial to guide interventions.\nThis dataset of 26,047 admissions between Jan 2018 and Jul 2024 with influenza, includes demography, serial physiology, assessments, diagnostic codes (ICD-10 & SNOMED-CT), initial presentation, ventilation, ICU transfers, prescriptions and outcomes. \nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/948",
    "uid": null,
    "datasource_id": 948,
    "source": "HDRUK"
  },
  {
    "id": 745,
    "name": "The interactions of frailty, age and illness severity during COVID-19.",
    "description": "Frailty is a syndrome of increased vulnerability to incomplete resolution of homeostasis (healing or return to baseline function) following a stressor event (such as an infection or fall) and it is associated with poor outcomes including increased mortality and reduced quality of life.  The pathophysiology of frailty is poorly understood. Age and frailty have been proven to be independently predictive of outcomes in patients admitted with an acute illness. In COVID-19, routine frailty identification informed decision making about treatment. \n\nThis dataset from 01-03-2020 to 01-04-2022 of 327,346 patients admitted during all waves of the COVID pandemic both with and without COVID-19.  The dataset includes granular demographics, frailty scores, physiology and vital signs, all care contacts and investigations (including imaging), all medications including dose and routes, care outcomes, length of stay and outcomes including readmission and mortality. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.  \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.  \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.  \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/947",
    "uid": null,
    "datasource_id": 947,
    "source": "HDRUK"
  },
  {
    "id": 746,
    "name": "NHS priority challenge: Improving care for suspected Myocardial Infarction",
    "description": "The NHS long term plan highlights the need for better care for common long-term conditions.  With advances in prevention and medical care the UK mortality rate from heart diseases has declined by more than three quarters in the last 40 years.  But cardiovascular disease remains the biggest cause of premature mortality and the rate of improvement has slowed.  Furthermore, chest pain remains the commonest cause of presentation to acute medical services (up to 40% of unscheduled admissions).  Care models to identify and rapidly provide treatment to people with suspected heart disease remain a priority across services. \n\nThis highly granular dataset includes 43,587 patients attending an Emergency Department or Acute Medical Unit with a possible myocardial infarction.  The data includes demographics, comorbidities, presenting symptoms, serial physiology and acuity, laboratory results, imaging, ECGs, procedures, medications (dose and route), onward care journeys, mortality and readmission. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/946",
    "uid": null,
    "datasource_id": 946,
    "source": "HDRUK"
  },
  {
    "id": 747,
    "name": "NHS priority challenge: Developing tools to prevent avoidable admissions",
    "description": "The NHS long term plan includes prioritising work to avoid hospital admissions. Emergency admissions are costly and frequently unpleasant experiences for patients.  Many hospital admissions could potentially be avoided if there were care pathways available to support the patient before this became an urgent care concern.  These conditions are known as ambulatory care sensitive (ACS) and urgent care sensitive (UCS) conditions, but at a systems level, we only have vague tools to identify what an ACS and UCS condition is. \n\nPIONEER, working with HDRUK, has curated a highly granular dataset of 503,154 Emergency Department and inpatient admissions. The data includes admission details, demography, initial presentation, presenting symptoms, investigations, treatments (all, including medicine dose and route), procedures, operations and outcomes. It also includes care process data, including ward stays, clinical review times and the healthcare professionals involved in care decisions.  The current dataset includes admissions from 01-10-2021 to 30-09-2023 but can be expanded to assess other timelines of interest.  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/945",
    "uid": null,
    "datasource_id": 945,
    "source": "HDRUK"
  },
  {
    "id": 748,
    "name": "HDRUK Medicine dataset: Digoxin repurposing as a senolytic in pneumonia",
    "description": "Senescence is defined as a deterioration of function with age.  Senolytic drugs clear senescent (ageing) cells from the body and reduce inflammation. These, and other geroprotector drugs are of increasing interest in preventing or reducing the negative effects of ageing on organs, tissues and cells.  Digoxin is a drug commonly used to control atrial fibrillation.  Animal models suggest digoxin is a senolytic.  If digoxin was used as a senolytic, it would be a repurposed use of the drug, where digoxin is used for another indication rather than the one it is commonly prescribed for. \n\nCommunity acquired pneumonia (CAP) is a common cause of hospitalisation in older adults and is increasingly recognised as a severe consequence of senescence. There is some evidence to suggest people on digoxin are protected from severe consequences of CAP.  \n\nPIONEER, working with HDRUK Medicines programme, have curated a highly granular dataset of 63,664 CAP admissions. The data includes demography, comorbidities, presenting symptoms, serial physiology, investigations, medications and outcomes. It focuses on a cohort who are and are not taking digoxin.   \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/943",
    "uid": null,
    "datasource_id": 943,
    "source": "HDRUK"
  },
  {
    "id": 749,
    "name": "NHS Priority Challenge for new models of care:  A Surgical virtual ward",
    "description": "Virtual wards provide care at home with remote monitoring for people who do not need admission to hospital, but require hospital-led care.  NHS England (NHSE) has requested an extension of a virtual ward model of care, with a national ambition of developing 40-50 virtual ward ‘beds’ per 100,000 population.   \n\n​The Surgical Assessment Unit Virtual Ward manages patients who are clinically suitable for home while waiting for investigation or treatment for an acute surgical condition.   \n\nTo support a better evidence base for surgical virtual wards, PIONEER has curated a highly granular dataset of 1,655,756 spells eligible for Virtual Surgical Assessment Unit (VSAU), with a proportion of patients being admitted to the VSAU, and a proportion remaining in tradition care pathways. The data includes demography, co-morbidities, presenting symptoms, serial physiology, diagnoses, investigations, treatments (including procedures) and outcomes. The current dataset includes admissions from 2018 to 2023 but can be expanded to assess other timelines of interest.  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/942",
    "uid": null,
    "datasource_id": 942,
    "source": "HDRUK"
  },
  {
    "id": 750,
    "name": "Socioeconomic and environmental factors and outcomes of acutely unwell patients",
    "description": "Social and environmental determinants of health are of critical importance, but are rarely captured in health data.  To address this, PIONEER have curated a highly granular dataset of 8,803 spells admitted to Intensive care unit. The dataset not only includes detailed demography, presenting symptoms, comorbidities, serial physiology and acuity, all investigations and treatments (including all medicines, their doses and routes) but also all ventilatory parameters.  This is mapped on an individual patient level to weather data, geolocation and measures of social deprivation. The current dataset includes admissions from 01-02-2020 to 28-02-2022 but can be expanded to assess other timelines of interest.  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/941",
    "uid": null,
    "datasource_id": 941,
    "source": "HDRUK"
  },
  {
    "id": 751,
    "name": "NIHR dataset: Penicillin allergy and the presence of antimicrobial resistance",
    "description": "Allergy to penicillin is commonly reported. In the United Kingdom, 14% of hospitalised patients are reported to have a penicillin allergy however only about 5% of patients reporting a penicillin allergy are found to be truly allergic when this is investigated.  \n\n​Penicillin allergy has a significant impact on antibiotic prescribing. Here, broad spectrum non-penicillin antibiotics are more commonly used but these antibiotics are increasingly less effective during infections due to antimicrobial resistance. Research to understand any association between stated penicillin allergy, antimicrobial resistant and health outcomes is needed.  \n\nTo investigate this (and related questions) PIONEER has curated a highly granular dataset of 17444 acute blood steam infection admissions in 15069 patients, supported by the NIHR.  The data includes demography, diagnostic codes (ICD-10 & SNOMED-CT), presenting symptoms, serial physiology, investigations including microbiology, treatments and medications, stated allergies, intensive care,) and outcomes. The current dataset includes admissions from 2015 to 2020 but can be expanded to assess other timelines of interest.  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/940",
    "uid": null,
    "datasource_id": 940,
    "source": "HDRUK"
  },
  {
    "id": 752,
    "name": "A NIHR Birmingham BRC Dataset: Macrolide use in patients with Viral Pneumonia",
    "description": "Viral pneumonia is common, caused by a variety of pathogens including seasonal influenza and respiratory syncytial virus.  Secondary bacterial infections are common and can account for increased morbidity and mortality.  This may be due to viral-mediated immunosuppression of the host innate immune system.   \n\n​Antibiotics are usually given for pneumonia, most commonly penicillins and macrolides.  There is some evidence that macrolides may improve outcomes from influenza however, this has not been explored in large studies. \n\n​PIONEER and the NIHR Birmingham BRC Infections and Acute Care theme have curated a highly granular dataset of 2,692 viral pneumonia admissions, working with Respiratory specialists.  The data includes demography, serial physiology, investigations, treatments (drug, dose, route), diagnostic codes (ICD-10 & SNOMED-CT), initial presentation, presenting symptoms, outcomes, and several severity scoring systems including National Early Warning Score (NEWS2), Clinical Frailty Score (CFS), Glasgow Coma Score (GCS) and AVPU score (Alert, Voice, Pain, Unresponsive). It also includes results from viral swabs and standard microbiological tests. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.",
    "url": "https://healthdatagateway.org/en/dataset/939",
    "uid": null,
    "datasource_id": 939,
    "source": "HDRUK"
  },
  {
    "id": 753,
    "name": "NHS Priority Challenge: Identifying patients for Same Day Emergency Care",
    "description": "Emergency care services face increasing pressure. NHS England (NHSE) has prioritised pathways for patients which avoid admission, including Same Day Emergency Care (SDEC) services.   The NHS Long Term Plan recommends SDEC assessment for one third of medical attendances and new NHSE guidelines suggest an initial assessment should be completed within 1 hour on arrival to SDEC.  Selecting the right patients for these services will be critical. \n\nScoring systems to help selection include the Glasgow admission prediction score (GAPS) and Ambulatory care score (AMBs), but these are rarely used in clinical practice and may require modification. \n\n​PIONEER has curated this highly granular dataset of 348,191 Emergency Department spells, including demography, serial physiology and acuity, laboratory tests, initial presentation, presenting symptoms, Glasgow admission prediction score (GAPS), Ambulatory care score (AMBs), medical imaging, medications, ward locations and outcomes. The current dataset includes admissions from 01-04-2023 to 31-03-2024 but can be expanded to assess other timelines of interest. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/938",
    "uid": null,
    "datasource_id": 938,
    "source": "HDRUK"
  },
  {
    "id": 754,
    "name": "Investigation pathways for patients presenting to hospitals with headaches",
    "description": "Headaches are common and often do not require hospitalisation. However, headaches account for 1–4% of all emergency department visits and here, headaches are one of the most challenging complaints to assess.  Potentially life-threatening causes are rare and imaging using CT or MRI is usually not indicated.  However, neuro-imaging remains the most common investigation. Pathways to stratify neuro-imaging and admission would be helpful.   \n\nThis dataset is a highly granular dataset of >33,000 patients who have presented to hospital with headache to University Hospitals Birmingham.   The data includes demographics, vital signs (blood pressure, respiratory rate, heart rate, temperature, NEWS2 score, BMI and others), comorbidities, assessments, laboratpry sample results), medical events, medications given including dose and route, imaging (films and reports), onward referrals, treatments including surgery, mortality and readmissions both in the short and longer term.  The data ranges from 2013 to 2023. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/937",
    "uid": null,
    "datasource_id": 937,
    "source": "HDRUK"
  },
  {
    "id": 755,
    "name": "NHS Priority Challenge: Optimising pathways to enable care in SDEC services",
    "description": "A highly granular dataset of 16,052 Same day emergency care (SDEC) spells with a focus on care pathways.  It includes demography, co-morbidities, presenting symptoms, serial physiology, investigations, and outcomes. \n\nDescription (3000 Characters) – Current 2540 (with spaces) \n\nEmergency care services face increasing pressure. NHS England (NHSE) has prioritised pathways for patients which avoid admission, including Same Day Emergency Care (SDEC) services.   The NHS Long Term Plan recommends SDEC assessment for one third of medical attendances.   \n\n​Care quality indicators (CQI)  include times from arrival to assessment by senior clinical teams. Performance measured against these CQI are impacted by other factors, such as delays in referrals, awaiting investigation results.​  \n\nPIONEER has curated a highly granular dataset of 16,052 Same day emergency care (SDEC) spells, including not only detailed patient level information, but data about the wider clinical environment on the day of admission.   \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/936",
    "uid": null,
    "datasource_id": 936,
    "source": "HDRUK"
  },
  {
    "id": 756,
    "name": "NIHR Midlands ARC Dataset:  Outcomes from out-of-hospital cardiac arrest",
    "description": "Resuscitation to Recovery is the national framework to improve care of people with Out of hospital cardiac arrests (OHCA).  Despite this, survival rates continue to be around 10%. Recently an OHCA care pathway been developed by the British Cardiovascular Interventional Society, aiming to reduce unwarranted variation in interventional cardiovascular practice for OHCA. However, little research has tracked the care OHCA patients receive along the whole pathway.  \n\nTo support a better understanding of OHCA care pathways, PIONEER, working with the NIHR Midlands Applied Research Collaboration and West Midlands Ambulance Service, has curated a highly granular dataset of 1588 OHCA events. The data includes demography, comorbidities, initial presentation, serial physiology, assessments, treatment provided both before and after West Midlands Ambulance Service arrival, onward hospital investigations, management and outcomes, including future healthcare use.  The current dataset includes OHCA from 2018 to 2022 but can be expanded to assess other timelines of interest.  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/935",
    "uid": null,
    "datasource_id": 935,
    "source": "HDRUK"
  },
  {
    "id": 757,
    "name": "A NIHR Birmingham BRC Dataset: Hospital Acquired Pneumonia & Antimicrobial Use",
    "description": "Hospital Associated Pneumonia (HAP) is a common healthcare associated infection, thought to affect 1-2% of all UK hospital admissions. Patients with HAP are more likely to need intensive care support and have increased length of stay and mortality rates. Unlike in community-acquired pneumonia, tools to stratify risk or severity are lacking. While there is some understanding of risk-factors that predispose people to HAP, prognostic factors are less well defined.  Treatment guidelines suggest broad spectrum antibiotics but there is little understanding of the causative organisms which cause HAP.   \n\n​To explore HAP, PIONEER, with the NIHR Birmingham BRC Infection and Acute Care theme, have curated a highly granular dataset of 22,580 hospital acquired pneumonia spells. The data includes demography, co-morbidities including Charlson comorbidity index, symptoms, serial physiology and acuity, investigations including microbiology, imaging, medications (dose and route), ward locations including intensive care details and outcomes. The current dataset includes admissions from 01-01-2018 to 31-12-2022 but can be expanded to assess other timelines of interest.  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/934",
    "uid": null,
    "datasource_id": 934,
    "source": "HDRUK"
  },
  {
    "id": 758,
    "name": "NIHR Birmingham BRC Dataset: Predicting the need for broad spectrum antibiotics",
    "description": "Sepsis presents a significant medical challenge with high morbidity and mortality rates, necessitating prompt and effective management. Sepsis is not always easy to diagnose, especially in its early stages.  To improve outcomes from sepsis, those with suspected, severe infections receive antibiotics, with guidelines highlighting that these should be given as early as possible.  However, this must be balanced with appropriate antibiotic stewardship.  Broad spectrum antibiotic use is associated with severe side effects and adverse outcomes, especially in older people, and antibiotic resistance increases with increasing antibiotic use. \n\nThe NIHR Birmingham Biomedical Research Centre Infections and acute care theme has curated a highly granular dataset of 1,589,709 hospital spells, containing 647,934 unique patients with suspected infections, with PIONEER. The data includes demographics, co-morbidities, presenting symptoms, serial physiology and acuity, all investigations and treatments and all outcomes including admission to intensive care, death and readmission.  All medications are included with dose and route. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/933",
    "uid": null,
    "datasource_id": 933,
    "source": "HDRUK"
  },
  {
    "id": 759,
    "name": "NIHR Patient Safety Research Collaboration: Medicines & kidney injury dataset",
    "description": "Many medications are cleared by the body through the kidneys. In people with renal impairment, this process can be slowed, potentially leading to toxic levels of a drug building up in the body. To avoid this, medicines excreted by the kidneys have dosing recommendations.   Renal function can change over time but in acute care settings, this can be dynamic, with some patients experiencing a rapid decline in renal function during their acute illness.   Here, drug errors are common with only 40% of medicines which need a dose adjustment having this made when first prescribed.  This can impact on patient safety. \n\n​To improve how medicines are prescribed during acute kidney injury in urgent and emergency care settings, PIONEER, with the NIHR Midlands Patient Safety Research Collaboration,  has curated a highly granular dataset containing 449,472 hospitalised patients. The data includes demographics, vital signs (early warning score components and BMI), comorbidities, laboratory results and all medications, including dose and route.  The dataset captures any prescribing alerts, and outcomes, including dialysis, mortality and readmission. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/932",
    "uid": null,
    "datasource_id": 932,
    "source": "HDRUK"
  },
  {
    "id": 760,
    "name": "ADMISSION programme data: Multiple long-term conditions in hospital patients",
    "description": "Improving outcomes for people with multiple long term conditions is a priority as set out in the NHS long term plan.  ADMISSION is a Research Collaborative funded by UK Research and Innovation and the National Institute for Health Research and Care Research that brings together scientists, clinicians and patients from five UK universities and hospitals (Newcastle University and Newcastle Hospitals NHS Foundation Trust, University of Birmingham (PIONEER – the Health Data Research UK Acute Care Hub),  Manchester Metropolitan University, University of Dundeeand University College London) to transform understanding of multiple long-term conditions in hospital patients. \n\nAs part of this, PIONEER has curated a highly granular dataset of 119,815 unique hospitalised patients focusing on the impact of multiple long term conditions. The data includes admission details, demography, initial presentation, presenting symptoms, diagnoses, treatments, therapy, medications, imaging, wards, investigations, procedures, operations and outcomes. The current dataset includes admissions from 01-01-2000 to 07-02-2024 but can be expanded to assess other timelines of interest.  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/931",
    "uid": null,
    "datasource_id": 931,
    "source": "HDRUK"
  },
  {
    "id": 761,
    "name": "NHS Priority Challenge: Improving medical care in Unplanned, Emergency Services",
    "description": "There is increasing demand on urgent and emergency healthcare services, with rising presentations to hospital Emergency Departments. This has led to overcrowding in acute care services, long delays and poor outcomes for patients. Most patients requiring hospital-led care require review by general and acute internal medical teams.  Annual benchmarking audits by the Society for Acute Medicine suggest performance against key quality indicators is deteriorating.  New care models are needed to improve care, a priority highlighted by the NHS Long Term plan. \n\nTo help address this, PIONEER has curated a highly granular dataset of 35,419 Acute medical unit (AMU) spells. The data includes demography, serial physiology, acuity assessments, initial presentation, presenting symptoms, standard and non-standard Blood tests, Imaging, Ward locations and outcomes. The current dataset includes admissions from 01-01-2023 to 31-12-2023 but can be expanded to assess other timelines of interest.  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/930",
    "uid": null,
    "datasource_id": 930,
    "source": "HDRUK"
  },
  {
    "id": 762,
    "name": "A NIHR Birmingham BRC dataset of community acquired pneumonia (CAP) and frailty",
    "description": "Community acquired pneumonia (CAP) is a leading cause of hospital admission and has high rates of mortality and complications, especially in older people with frailty. Data from PIONEER examining CAP admissions in winter 19/20 and winter 20/21 demonstrated that hospital admissions due to CAP fell by 40% in 20/21 compared to 19/20 but the 30-day mortality rate almost doubled in winter 20/21 compared to 19/20. Frailty was thought to be a determinant of poor outcomes. \n\nTo explore this further, PIONEER, working with the NIHR Midlands BRC Infections and acute care theme have curated a highly granular dataset of 2,158 community acquired pneumonia admissions.  The data includes demography, comorbidities, Charlson comorbidity index, Manchester mobility score (MMS), clinical frailty score (CFS) and symptoms on presentation, serial physiology and acuity, investigations, CURB-65 assessments, intensive care, treatments (drug, dose, route), diagnostic codes (ICD-10 & SNOMED-CT), outcomes (death and readmissions).  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/929",
    "uid": null,
    "datasource_id": 929,
    "source": "HDRUK"
  },
  {
    "id": 763,
    "name": "Identifying and Addressing Avoidable Readmissions Following Hip Fracture",
    "description": "Hip fractures remain a significant public health issue, despite improvements in survival rates. While mortality has decreased, readmission rates within 30 days of discharge have steadily increased. This is concerning as re-admissions are associated with increased mortality and substantial healthcare costs. \n\nFactors contributing to readmissions are complex and multifaceted. While some, such as age, are non-modifiable, others, like complications related to care delivery, are potentially avoidable. However, there is a lack of consensus on what constitutes an \"avoidable\" readmission. \n\nBirmingham Heartlands Hospital treats a high volume of hip fracture patients. This population is often characterized by socioeconomic disparities, which can exacerbate health inequalities and impact outcomes. Understanding how to reduce avoidable readmissions in this context is a priority.   \n\nThis dataset offers opportunities to understand risk associated with hip fracture and it management, and build strategies to avoid poor outcomes and readmissions.  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/928",
    "uid": null,
    "datasource_id": 928,
    "source": "HDRUK"
  },
  {
    "id": 764,
    "name": "Virtual wards for Exacerbations of Chronic Obstructive Pulmonary Disease",
    "description": "There is increasing interest in care pathways for acute exacerbations of disease, which are safe but avoid hospital admission.   A virtual ward is a system where people who may otherwise be admitted to hospital receive hospital-led care in their home with observations and reviews conducted remotely by a specialist team.  A virtual ward for COPD exacerbations has been recommended by NHS England, promoted following a rapid evaluation report published in 2022 and a number of small studies.  To support the evaluation of this new service, PIONEER has developed a highly granular dataset of 6,973 Respiratory Virtual Ward admissions. The data includes demography, serial physiology, assessments, diagnostic codes (ICD-10 & SNOMED-CT), initial presentation, presenting symptoms, Imaging, Prescriptions, Ward locations and outcomes including mortality, readmissions and out patient follow up. The current dataset includes admissions from January 2019 to December 2023.  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/923",
    "uid": null,
    "datasource_id": 923,
    "source": "HDRUK"
  },
  {
    "id": 765,
    "name": "South East London Community Health Study (SELCoH)",
    "description": "The South East London Community Health (SELCoH) study aims to examine the impact socioeconomic factors such as income or education have on people’s health, as well to understand if other demographic factors such as age, culture, ethnicity and/or residence make a difference for people’s wellbeing. The population of Southwark and Lambeth is highly diverse in terms of ethnicity and wealth, ensuring that the study encompasses as wide a range of health service users as possible. \n\nThe study is a community survey of psychiatric and physical morbidity of 1,698 adults, aged 16 years and over from 1,075 randomly selected households in the south London boroughs of Southwark and Lambeth. In the two boroughs, there is higher deprivation than the England average, but similar proportions of economically active and inactive residents in comparison to greater London. The boroughs are also ethnically diverse, with a greater number of Black Caribbean residents but fewer South Asian residents than other areas of London. The SELCoH sample resided in a community setting served by South London and Maudsley National Health Service Foundation Trust (SLaM), and the partnership between King's College London and SLaM allows this and other research to inform and benefit clinical treatment.",
    "url": "https://healthdatagateway.org/en/dataset/909",
    "uid": "e282c783-0d91-4e66-b063-12a5a272326e",
    "datasource_id": 909,
    "source": "HDRUK"
  },
  {
    "id": 766,
    "name": "Connected Bradford - Secondary Care Bradford Royal Infirmary  - FDM",
    "description": "The Connected Bradford Bradford Royal Infirmary : Flexible Data Model (FDM) Contains routinely collected data for approximately 800,000 patients. The dataset has been fully anonymised, but can link to other Connected Bradford FDM's.\n\nThe Bradford Royal Infirmary FDM last build date was 2024-05-09 and contains data up to : 2023-11-16.. The observation period for this data is: 1933-08-28 to 2024-04-22\n\n\nThe FDM is made up of 32 source table  using routinely collected data for 822,440\n\nindividuals.\n\nThe  tables are as supplied with minimal reformatting. \n\nThe source tables are largely populated by fields with the tbl_ where there is a person and a start and end date, and cb_ where there is no identifiable person (these are typically lookup tables) \n\nIt includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications, theatre, spells, pharmacy etc",
    "url": "https://healthdatagateway.org/en/dataset/908",
    "uid": "2461aa14-93fb-4d91-9e86-e010c7615511",
    "datasource_id": 908,
    "source": "HDRUK"
  },
  {
    "id": 767,
    "name": "Connected Bradford - Secondary Care Calderdale - FDM",
    "description": "The Connected Bradford Calderdale: Flexible Data Model (FDM) Contains routinely collected data for approximately 400,000 patients. The dataset has been fully anonymised, but can link to other Connected Bradford FDM's.\n\nThe Calderdale FDM last build date was 2024-05-09 and contains data up to : 2023-11-16.. The observation period for this data is: 2017-06-18\tto\t2023-11-16\n\nThe FDM is made up of 9 source table  using routinely collected data for 438,564\nindividuals.\n\nThe  tables are as supplied with minimal reformatting, but as the overall format for each file type has changed there are different versions of files. For example there are 3 ECDS formats used as the ECDS format has changed 3 times since we started receiving it. \n\nThe source tables are largely populated by fields with the tbl_ where there is a person and a start and end date, and cb_ where there is no identifiable person (these are typically lookup tables) \n\nIt includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications and vaccinations",
    "url": "https://healthdatagateway.org/en/dataset/907",
    "uid": "4d5bdfec-5290-4d79-b271-b9ab6e5a1e6b",
    "datasource_id": 907,
    "source": "HDRUK"
  },
  {
    "id": 768,
    "name": "Connected Bradford - Secondary Care Airedale - FDM",
    "description": "The Connected Bradford Airedale: Flexible Data Model (FDM) Contains routinely collected data for approximately 300,000 patients. The dataset has been fully anonymised, but can link to other Connected Bradford FDM's.\n\nThe Airedale FDM last build date was 2024-05-09 and contains data up to : 2024-04-15.. The observation period for this data is: 2010-04-01 to 2024-04-15\n\nThe FDM is made up of 10 source table  using routinely collected data for 330,091\nindividuals.\n\nThe  tables are as supplied with minimal reformatting, but as the overall format for each file type has changed there are different versions of files. For example there are 4 APC formats used as the APC format has changed 4 times since we started receiving it. \n\nThe source tables are largely populated by fields with the tbl_ where there is a person and a start and end date, and cb_ where there is no identifiable person (these are typically lookup tables) \n\nIt includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications and vaccinations",
    "url": "https://healthdatagateway.org/en/dataset/906",
    "uid": "1889f317-96a1-4fed-9c93-1ea5de49fcac",
    "datasource_id": 906,
    "source": "HDRUK"
  },
  {
    "id": 769,
    "name": "Connected Bradford - System One GP Primary Care - FDM",
    "description": "The Connected Bradford Primary Care: Flexible Data Model (FDM) Contains routinely collected data for approximately 1 million patients. The dataset has been fully anonymised, but can link to other Connected Bradford FDM's.\n\nThe Primary Care FDM last build date was 2024-02-08 and contains data up to : 2024-02-08 . The observation period for this data is: 1900-01-01 to 2024-02-08\n\nThe FDM is made up of 48 source table  using routinely collected data for 1,185,326 individuals.\n\nThe source tables are largely populated by fields with the tbl_ where there is a person and a start and end date, and cb_ where there is no identifiable person (these are typically lookup tables) \n\nIt includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications and vaccinations",
    "url": "https://healthdatagateway.org/en/dataset/905",
    "uid": "8f01465c-10e0-4fe6-9611-704a8199c474",
    "datasource_id": 905,
    "source": "HDRUK"
  },
  {
    "id": 770,
    "name": "Connected Bradford - Secondary Care : Maternity FDM",
    "description": "The Connected Bradford Bradford Royal Infirmary Maternity  : Flexible Data Model (FDM) Contains routinely collected data for approximately 68,000 patients. The dataset has been fully anonymised, but can link to other Connected Bradford FDM's.\n\nThe Bradford Royal Infirmary Maternity FDM last build date was 2024-05-17 and contains data up to : 2024-004-20.. The observation period for this data is: 2016-03-15 to 2024-04-20\n\n\nThe FDM is made up of 1 source table  using routinely collected data for 68,352\nindividuals.\n\nThe  tables are as supplied with minimal reformatting. \n\nIt includes basic patient demographics, and maternity unit events.",
    "url": "https://healthdatagateway.org/en/dataset/904",
    "uid": "756ea1a7-ca14-4481-bbea-22064d8639d1",
    "datasource_id": 904,
    "source": "HDRUK"
  },
  {
    "id": 771,
    "name": "Connected Bradford - Secondary Care Airedale - Emergency Care FDM",
    "description": "The Connected Bradford Airedale Emergency Care : Flexible Data Model (FDM) Contains routinely collected data for approximately 185,000 patients. The dataset has been fully anonymised, but can link to other Connected Bradford FDM's.\n\nThe Airedale Emergency Care FDM last build date was 2024-09-07 and contains data up to : 2024-04-07.. The observation period for this data is: 2010-04-01 to 2024-04-07\n\nThe FDM is made up of 4 source table  using routinely collected data for 185,571\nindividuals.\n\nThe  tables are as supplied with minimal reformatting, but as the overall format for each file type has changed there are different versions of files. For example there are 4 AE formats used as the AE format has changed 4 times since we started receiving it. \n\nThe source tables are largely populated by fields with the tbl_ where there is a person and a start and end date, and cb_ where there is no identifiable person (these are typically lookup tables) \n\nIt includes basic patient demographics, information about emergency care consultation events, medical history including diagnoses and investigations",
    "url": "https://healthdatagateway.org/en/dataset/903",
    "uid": "3b90807b-928d-43ab-a3a5-528844438ca4",
    "datasource_id": 903,
    "source": "HDRUK"
  },
  {
    "id": 772,
    "name": "Connected Bradford - Secondary Care : Autism",
    "description": "The Connected Bradford - Autism Flexible Data Model (FDM) Contains routinely collected data for approximately 5,000 patients. The dataset has been fully anonymised, but can link to other Connected Bradford FDM's.\n\nThe Autism FDM last build date was 2024-06-06 and contains data up to : 2022-05-15.. The observation period for this data is: 2015-02-27 to 2022-05-15\n\n\nThe FDM is made up of 1 source table  using routinely collected data for 5,106\nindividuals, aged 4 to 24.\n\nThe  tables are as supplied with minimal reformatting. \n\nIt includes basic patient demographics, and information about consultations.",
    "url": "https://healthdatagateway.org/en/dataset/902",
    "uid": "0a78c5e9-d304-469c-9aff-453cb95283cd",
    "datasource_id": 902,
    "source": "HDRUK"
  },
  {
    "id": 773,
    "name": "Connected Bradford - Intensive Care National Audit & Research Centre  - FDM",
    "description": "The Connected Bradford ICNARC : Flexible Data Model (FDM) Contains routinely collected data for approximately 13,000 patients. The dataset has been fully anonymised, but can link to other Connected Bradford FDM's.\n\nThe Bradford Royal Infirmary FDM last build date was 2023-05-15 and contains data up to : 2019-10-22. The observation period for this data is: 2002-07-21\tto\t2019-10-22\n\nThe FDM is made up of 2source table  using routinely collected data for 13,936 individuals.\n\nThe  tables are as supplied with minimal reformatting. \n\nThe source tables are largely populated by fields with the tbl_ where there is a person and a start and end date, and cb_ where there is no identifiable person (these are typically lookup tables) \n\nIt includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications, theatre, spells, pharmacy etc as per the ICNARC standard.",
    "url": "https://healthdatagateway.org/en/dataset/901",
    "uid": "28fa7bba-7fac-46e1-88b7-66bf764bae27",
    "datasource_id": 901,
    "source": "HDRUK"
  },
  {
    "id": 774,
    "name": "Connected Bradford - Secondary Care : Death Certificate",
    "description": "The Connected Bradford - Death Certificate Flexible Data Model (FDM) Contains routinely collected data for approximately 200,000 patients. The dataset has been fully anonymised, but can link to other Connected Bradford FDM's.\n\nThe Autism FDM last build date was 2023-11-15 . The observation period for this data is:1904-01-15 to\t2023-08-22\n\nThe FDM is made up of 1 source table  using routinely collected data for 218,276 \nindividuals, aged 0 to 100+\n\nThe  tables are as supplied with minimal reformatting. \n\nIt includes basic patient demographics, and information about consultations.",
    "url": "https://healthdatagateway.org/en/dataset/900",
    "uid": "4c0f0cc4-5449-4480-bd4c-af6088b7b642",
    "datasource_id": 900,
    "source": "HDRUK"
  },
  {
    "id": 775,
    "name": "Connected Bradford - Secondary Care : Blood Pressure Readings",
    "description": "The Connected Bradford - Blood Pressure Flexible Data Model (FDM) Contains routinely collected data for approximately 9,000 patients. The dataset has been fully anonymised, but can link to other Connected Bradford FDM's.\n\nThe Autism FDM last build date was 2024/09/06 . The observation period for this data is:1998-06-12 to 2024-06-28 , with and average of 102 readings per patient.\n\nThe FDM is made up of 2 source table  using routinely collected data for 9,408 individuals, aged 5 to 100+ \n\nThe  tables are as supplied with minimal reformatting. \n\nIt includes basic patient demographics in addition to the source tables.",
    "url": "https://healthdatagateway.org/en/dataset/899",
    "uid": "04fb196a-a72b-4797-80c3-00a7f1df685c",
    "datasource_id": 899,
    "source": "HDRUK"
  },
  {
    "id": 776,
    "name": "ConnectedBradford-ProactiveCareteam",
    "description": "Description\nThe Proactive Care team (PACT) is a specialist team of health professionals. We help adults who have complex health problems to live more positively in the present, and to face the challenges of tomorrow with more confidence.\n\nWho is the Proactive Care team for?\nWe support people over the age of 18 who have a long-term condition which impacts on their wellbeing. They may be finding it difficult to access the right NHS services, or require support with symptom management.\n\nOften, we work with older people who need support to remain independent or to plan for end of life. We also work with carers and families to help them with in supporting someone who has dementia.\n\nWhat services does the Proactive Care team offer?\nWe offer services in addition to those already provided by local GPs, primary care wellbeing services and community services.\n\nOur services are short-term to address a person’s particular needs and priorities, so they can live well and avoid unnecessary visits to their GP or A&E or urgent hospital admissions. We can provide interpreters if required.\n\nOur services include physiotherapy, psychological support, speech and language therapy, dietary advice and occupational therapy.  We also offer longer term support for people with dementia and their families and carers from specialist Admiral Nurses.",
    "url": "https://healthdatagateway.org/en/dataset/898",
    "uid": "05e3a976-76c3-4eea-8f6b-ec9a97219627",
    "datasource_id": 898,
    "source": "HDRUK"
  },
  {
    "id": 777,
    "name": "Connected Bradford - Secondary Care : Lab Results  - FDM",
    "description": "The Connected Bradford Bradford Royal Infirmary Lab Results  : Flexible Data Model (FDM) Contains routinely collected data for approximately 9,000 patients. The dataset has been fully anonymised, but can link to other Connected Bradford FDM's.\n\nThe Bradford Royal Infirmary FDM last build date was 2024-004-23 and contains data up to : 2024-04-17.. The observation period for this data is: 2017-03-02 to 2023-05-24\n\n\nThe FDM is made up of 1 source table  using routinely collected data for8,691\nindividuals.\n\nThe  tables are as supplied with minimal reformatting. \n\nIt includes basic patient demographics, and information about tests including results",
    "url": "https://healthdatagateway.org/en/dataset/897",
    "uid": "a11282b7-69db-43bc-bc53-25a626d5179c",
    "datasource_id": 897,
    "source": "HDRUK"
  },
  {
    "id": 778,
    "name": "iCARE Secure Data Environment",
    "description": "The iCARE SDE is a cloud-based, big data analytics platform sitting within Imperial College Healthcare NHS Trust (ICHT) NHS infrastructure. This, combined with the iCARE Team’s robust method of data de-identification, make the Environment an incredibly secure platform. The fact that it can be accessed remotely using the Trust’s Virtual Desktop Infrastructure means that researchers can perform their work remotely and are therefore not constrained by location.\n(imperial.dcs@nhs.net)\n\nThe iCARE SDE enables clinicians, researchers and data scientists to access large-scale, highly curated databases for the purposes of research, clinical audit and service evaluation. The iCARE SDE enables advanced data analytics through a scalable virtual infrastructure supporting Azure Machine Learning, Python, R and STATA and a large variety of snowflake SQL tooling.\n\nThe main iCARE data model is a HRA REC approved database covering all routinely captured information from Imperial College Healthcare Trust (ICHT) Electronic Health Record and 39 linked (at the patient-level) clinical and non-clinical systems. It contains data for all patients from 2015 onwards and is updated weekly as a minimum, and close to real-time when required. It includes inpatient, outpatient, A&E, pathology, cancer, imaging treatments, e-prescribing, procedures, clinical notes, Consent, clinical trials, tissue bank samples, Patient safety and incidents, Patient experience, Staffing and environment data.\n\nData can also be linked to primary care data for the 2.8million population in Northwest London, HRA REC approved, Whole Systems Integrated Care (WSIC) hosted database and other health and social care providers when approved.\n\nOn a project-by-project basis the model can be expanded to curate and include new data (including multi-modality data), that is either captured routinely or through approved research and clinical trials. There are streamlined processes to approve and curate new data (imperial.dataaccessrequest@nhs.net) and data will always remain hosted in the SDE.",
    "url": "https://healthdatagateway.org/en/dataset/896",
    "uid": "23b4b8eb-b224-4748-8e42-89a4fb9e082d",
    "datasource_id": 896,
    "source": "HDRUK"
  },
  {
    "id": 779,
    "name": "British Regional Heart Study (BRHS)",
    "description": "The British Regional Heart Study (BRHS) is a long-term cohort investigating the causes of cardiovascular disease (CVD) in men and seeking to understand the effect of co-morbidities on CVD and ageing.  The BRHS provides a geographically and socially representative cohort for the prospective investigation of CVD in British men spanning over four decades. Established in 1978-80, with 7735 male participants, the BRHS study has benefited from four repeated assessments of the men at ages in middle (40-59 years) and later life (60-79, 72-91 and 79-98 years). Participants have been followed up at four life stages for a wide range of health outcomes, including all-cause mortality and CVD morbidity, physical disability and frailty using GP records and participant questionnaires. This unique ageing cohort with extensive phenotyping, genotyping and detailed follow-up will contribute and allow us to study healthy cardiovascular ageing including prevention of CVD, heart failure, stroke, diabetes and related disabilities (frailty, dementia) in older age and to add to our understanding of the biological ageing process on CVD risk.\n\nThe survey data are accessible to bona fide researchers by applying direct to the study - https://www.ucl.ac.uk/epidemiology-health-care/research/primary-care-and-population-health/research/ageing/british-regional-heart-study-brhs/brhs-2\n\nFor further information on study description and data dictionaries please see the study webpages - https://www.ucl.ac.uk/epidemiology-health-care/research/primary-care-and-population-health/research/brhs",
    "url": "https://healthdatagateway.org/en/dataset/893",
    "uid": "f6bbe978-f223-4f2f-8ff4-06cd281e18fd",
    "datasource_id": 893,
    "source": "HDRUK"
  },
  {
    "id": 780,
    "name": "Million Women Study",
    "description": "The Million Women Study started recruiting participants in 1996. The initial stimulus was to obtain robust prospective information on the risk of breast cancer associated with use of different types of menopausal hormone therapy. When planning the necessary large-scale prospective study, an equally important aim was to obtain reliable information on the effects of other potentially modifiable factors that affect women’s health as they age.\n\nThe study includes 1 in 4 of all UK women born between 1935 and 1950, recruited through NHS breast screening centres in England and Scotland in 1996-2001. The 66 NHS breast screening centres that recruited participants covered about half of the UK population.\n\nWhile the initial stimulus was to study the risk of breast cancer and other conditions associated with the use of menopausal hormone therapy, most of the women who joined the study had reached adulthood in the 1960s and had considerably different lifestyles to those of previous generations. For example, large proportions had begun smoking and using oral contraceptives as teenagers and young adults. The prevalence of obesity was also increasing and there were claims that other lifestyle factors also had important effects on health. To answer questions about the effects of these factors on health reliably requires large scale population-based evidence. The Million Women Study is therefore investigating the short-term and long-term effects of these and many other factors on women’s risk of developing or dying from different types of cancer, heart disease, stroke, dementia and other mental and neuro-degenerative disorders, and many other conditions in middle and in old age.\n\nFor further details on the study design, recruitment, data collection and other aspects of the Million Women Study, please visit https://www.ceu.ox.ac.uk/research/the-million-women-study",
    "url": "https://healthdatagateway.org/en/dataset/892",
    "uid": "226d734c-3a73-438c-bd9e-f6b9356894e3",
    "datasource_id": 892,
    "source": "HDRUK"
  },
  {
    "id": 781,
    "name": "Our Future Health Genotype Data",
    "description": "Our Future Health is a prospective, observational cohort study of the general adult population of the United Kingdom (UK). The programme aims to support a wide range of observational health research. We gather personal, health and lifestyle information from each participant through a self-completed baseline health questionnaire and at an in-person clinic visit. We will further link this data to other health-related data sets. Participants have also given consent for us to recontact them, for example to invite them to take part in further or repeat data collections, or other embedded studies such as clinical trials.  \n\nThe Our Future Health programme is currently open to all adults (18 years and older) living in the UK. In July 2022, we started recruiting participants in England and will continue to expand across the rest of the UK. The data we’ve gathered so far (September 2025) includes genotype array data on 707,522 variants and 775,118 participants. .\n\nThese data were obtained using a custom Illumina Infinium Excalibur beadchip array, designed by Our Future Health in collaboration with Illumina. The array includes variants related to a wide range of health phenotypes, blood typing, pharmacogenetics, selected copy number variants, clinically relevant variants, and a “backbone” of variants to support imputation.\n\nAn imputed genotype dataset on 159,587,100 variants and 550,000 participants is also available (from December 2025). These participants are also included in the genotype array dataset.\n\n\nA separate, linked dataset is available that provides participant baseline demographic information and responses to our baseline health questionnaire. Clinical measurements data is also available from participants.\nAdditionally linked NHS England data that provides clinical information on participants is also available.  \n\nThe data is stored in the Our Future Health Trusted Research Environment. We de-identify all participant data we gather before it’s available for use. All researchers will need to become registered researchers at Our Future Health and have an approved research study before they're given access to the data.\n\nWe aim to collect a variety of data types from up to 5 million adult participants from across the UK. We hope to make more data types available on a quarterly basis.",
    "url": "https://healthdatagateway.org/en/dataset/890",
    "uid": "86fdd5a9-4be1-481d-92a8-7768ec708248",
    "datasource_id": 890,
    "source": "HDRUK"
  },
  {
    "id": 782,
    "name": "Our Future Health Linked Health Records Data",
    "description": "Our Future Health is a prospective, observational cohort study of the general adult population of the United Kingdom (UK). The programme aims to support a wide range of observational health research. We gather personal, health and lifestyle information from each participant through a self-completed baseline health questionnaire and at an in-person clinic visit. We will further link this data to other health-related data sets. Participants have also given consent for us to recontact them, for example to invite them to take part in further or repeat data collections, or other embedded studies such as clinical trials.  \n\nThe Our Future Health programme is currently open to all adults (18 years and older) living in the UK. In July 2022, we started recruiting participants in England and will continue to expand across the rest of the UK. The data we’ve gathered so far (December 2025) includes linked NHS England clinical data on 1,665,668 participants\n\n\n-\tAdditional linked datasets are available: \n -  ‘Baseline Health Questionnaire Data’ which contains baseline demographic information and responses to our health questionnaire from 1,929,752 participants. \n-  ‘Genotype Data’ which includes genotype array data on 707,522 variants from a subset of 775,118 participants\n-\t- Imputed Genotype Dataset which include data on 159,587,100 variants and 550,000 participants. \n- Clinical Measurements Data which contains clinical data from 1,456,410 participants.\n\n\nThe data is stored in the Our Future Health Trusted Research Environment. We de-identify all participant data we gather before it’s available for use. All researchers will need to become registered researchers at Our Future Health and have an approved research study before they're given access to the data.\n\nWe aim to collect a variety of data types from up to 5 million adult participants from across the UK. We hope to make more data types available on a quarterly basis.",
    "url": "https://healthdatagateway.org/en/dataset/889",
    "uid": "be266ce1-3b00-4d9c-8f8b-ecaefda7a97a",
    "datasource_id": 889,
    "source": "HDRUK"
  },
  {
    "id": 783,
    "name": "Our Future Health Baseline Health Questionnaire Data",
    "description": "Our Future Health is a prospective, observational cohort study of the general adult population of the United Kingdom (UK). The programme aims to support a wide range of observational health research. We gather personal, health and lifestyle information from each participant through a self-completed baseline health questionnaire and at an in-person clinic visit. We will further link this data to other health-related data sets. Participants have also given consent for us to recontact them, for example to invite them to take part in further or repeat data collections, or other embedded studies such as clinical trials.  \n\nThe Our Future Health programme is currently open to all adults (18 years and older) living in the UK. In July 2022, we started recruiting participants in England and will continue to expand across the rest of the UK. The data we’ve gathered so far (September 2025) includes responses from 1,929,752 participants.\n\nThe current data available contains responses from our baseline health questionnaire only and an indicator if a blood sample was provided and stored. The current data is split into 2 categories:\n•\tparticipant data - which contains baseline demographic information collected across all consented participants\n•\tquestionnaire data - which contains self-reported health information, details about participants' household, socioeconomic status, work and education history, and family history\n•\tClinical Measurements Data which contains clinical data from participants.\n\n\n\n\nAdditional linked datasets are available: \n -  ‘Linked NHS England Health Records Data  which contains linked clinical data from NHS England for 1,668,668 participants.\n-   ‘Genotype Data’ which includes genotype array data on 707,522 variants from a subset of 775,118 participants\n- The ‘Imputed Genotype’ dataset include data on 159,587,100 variants and 550,000 participants. \n- Clinical Measurements Data which contains clinical data from 1,456,410 participants.\n\nThe data is stored in the Our Future Health Trusted Research Environment. We de-identify all participant data we gather before it’s available for use. All researchers will need to become registered researchers at Our Future Health and have an approved research study before they're given access to the data.\n\nWe aim to collect a variety of data types from up to 5 million adult participants from across the UK. We hope to make more data types available on a quarterly basis.",
    "url": "https://healthdatagateway.org/en/dataset/888",
    "uid": "fdbfc1ac-9ec7-4f37-8729-f2b9e7253164",
    "datasource_id": 888,
    "source": "HDRUK"
  },
  {
    "id": 784,
    "name": "eLIXIR Born in South London- Early Life Data Cross-Linkage in Research- Data",
    "description": "Investment in the earliest stages of life is increasingly recognised to improve health across the life-course, beginning with the health of parents before pregnancy, in embryonic life, through to infancy, childhood, and into adulthood. eLIXIR  BiSL combines information from routine maternity and neonatal health records and blood samples at two acute NHS Trust hospitals, along with mental health and primary care data. The study is able to address relationships between maternal and child physical health, and to investigate interactions with mental health. Participants are predominantly residents of South London, in areas with high levels of deprivation and ethnic diversity.\n\nThe BiSL data-linkage project uses opt-out consent to collect routine maternity and neonatal clinical patient data (GSTT and KCH NHS Trusts), mental health data from the SLaM CRIS platform, and primary care data from the LDN platform, for those registered with a GP in Lambeth. We hold the approval to also link with emergency and admissions data (HES), national fertility data (HFEA), and immunisation records (NIMS), as well as expanding primary care data to other boroughs in South London, namely: Southwark, Lewisham, and Bromley; the process to link these new data sources is currently ongoing.\n\nAt present, eLIXIR holds over 50,000 records. All records are deidentified, including masking of identifying information in open-text fields and use of pseudonymised identifiers. The data refresh process occurs every 6 months, and each update includes all retrospective data since conception of the cohort (October 2018), thus building a dynamic cohort.\n\nThe BiSL team includes members King’s College London Faculty of Life Sciences and Medicine and the Institute of Psychiatry, Psychology and Neurosciences (IoPPN), along with services users and patient representatives. \n\nThe eLIXIR Born in South London project has now been successfully awarded a MRC Longitudinal Population Study Grant which will enable us to operate for the next 5 years and continue building this dynamic mother-child database. BiSL is part of the MIREDA Study Partnership bringing together birth cohort data across the UK.",
    "url": "https://healthdatagateway.org/en/dataset/887",
    "uid": "4990a806-060e-4297-878b-34dce94a8c99",
    "datasource_id": 887,
    "source": "HDRUK"
  },
  {
    "id": 785,
    "name": "MuMPreDiCT (Pregnancy and postpartum outcomes of mothers and their offspring)",
    "description": "MuM-PreDiCT is a research collaboration across the UK that will conduct data-driven research to characterise and understand the determinants and consequences of pre-existing multimorbidity (MM) in pregnant women, and to predict and prevent MM and its adverse consequences in women and their offspring. The multidisciplinary approach undertaken, using existing quantitative data and new stakeholder data, aims to detail the burden of pre-existing MM in pregnant women, understand how morbidities accumulate and cluster from the pre-pregnancy stage through the maternity journey to their long-term healthcare, and then investigate what determinants should be targeted to influence MM through early interventions; explore women's experiences, and current health service provision to inform recommendations for practice; investigate the impact of pre-existing MM and multiple prescriptions on pregnancy, postpartum and long-term outcomes for mothers and their offspring; and investigate the extent to which pregnancy complications predict future MM in risk prediction models.\n\nA significant outcome of this collaboration will be the creation of a comprehensive dataset on pregnancy and postpartum outcomes for mothers and their children, directly contributing to the core vision and objectives of the MIREDA Partnership. Specifically, the database will include pregnancy and birth records of English mothers aged 15-50 and their offspring, derived from electronic health records that link primary and secondary care data from the Clinical Practice Research Datalink (CPRD, GOLD, and Aurum) and linked to Hospital Episode Statistics (HES). This will be achieved through a federated analysis model in collaboration with the Centre for Health Data Science at the Institute of Applied Health Research, University of Birmingham.",
    "url": "https://healthdatagateway.org/en/dataset/884",
    "uid": "8c56c152-e8e2-43ce-b947-9e7eebb2e3e4",
    "datasource_id": 884,
    "source": "HDRUK"
  },
  {
    "id": 786,
    "name": "Born in Scotland (BiS)",
    "description": "Born in Scotland is an ongoing observational longitudinal study set to capture a contemporary and representative cohort of mothers in Scotland and provide a valuable research resource to assess current clinical issues and health disparities and investigate the drivers of long-term maternal and child wellbeing. The current pilot study is open to recruitment and is testing consent models. The scale-up study intends to include 100,000 pregnant women and their children, constituting a diverse, flexible, and nationally representative maternity cohort. It is embedded within the NHS services, capitalising on capturing routinely collected data and biological samples, and allowing linkage to additional clinical and demographic data through the unique Community Health Index (CHI) number.\n\nThe pilot study currently targets all women aged 18-50 years old, living in Edinburgh and the Lothians and the Borders, and who are planning to give birth in Scotland, offering recruitment during any of the routine antenatal booking appointments. Data from the participants is extracted from the electronic maternity records, neonatal units, and clinical and diagnostic results. Biological samples are retrieved from hospital laboratories using samples that would otherwise be discarded after clinical use or collected at birth. The aim is to use the cohort to link to future maternal and child health and social care records to address key research questions to improve maternal and child health in Scotland.",
    "url": "https://healthdatagateway.org/en/dataset/883",
    "uid": "1b238377-7f51-4b86-aa35-4d0f7ca5af7d",
    "datasource_id": 883,
    "source": "HDRUK"
  },
  {
    "id": 787,
    "name": "Understanding Society",
    "description": "Understanding Society: The UK Household Longitudinal Study follows the lives of thousands of individuals within households over time.  It is an internationally recognised study and provides vital evidence for scientists and policymakers on the causes and consequences of deep-rooted social problems.\n\nThe study commenced in 2009, building on and incorporating the long-running British Household Panel Survey (BHPS). Covering all the regions and nations of the UK, it has an initial sample of 39,802 households at Wave 1. Sample members are followed when they leave a household, and new people join the Study as they become part of existing sample member households.\n\nUnderstanding Society has a number of unique design features. It covers the whole population, with boost samples to ensure it is representative of immigrant and ethnic minority groups, and its large sample enables sub-population groups to be examined. Researchers can use the household context to explore how lives link, and the relationships between family members. Its annual data collection means that changes in people’s lives are more accurately captured over time.\n\nInformation is collected directly from everyone aged over 10 years, with parents providing information on younger children. Understanding Society asks people about things like their home and family, work and school, health and wellbeing, financial situation and their social and political attitudes. The information people share helps us to understand what people think, feel and do. It also helps us see how society is changing over time. The Study covers everyone in a household, from children to adults, so researchers can understand the experiences of the whole family over time. By 2020, nine Waves of Understanding Society data were available, with 27 years of data available for a significant sub-group who were part of BHPS. Information from the Study is used by researchers to investigate how changes in economic, social and health events affect individuals, households and communities. Evidence from the Study is extensively used by government departments, devolved administrations, agencies, charities, and think tanks.",
    "url": "https://healthdatagateway.org/en/dataset/882",
    "uid": "edbf3381-1a95-42d7-af30-84f9d731a00b",
    "datasource_id": 882,
    "source": "HDRUK"
  },
  {
    "id": 788,
    "name": "Cancer Registration Data",
    "description": "The National Cancer Registration and Analysis Service (NCRAS) at Public Health England supplies cancer registration data to NHS Digital. This data is available to be linked to other data held by NHS Digital in order to provide notifications on an individual's cancer status, be available to support research studies and to identify potential research participants for clinical trials.\n\nNCRAS is the population-based cancer registry for England. It collects, quality assures and analyses data on all people living in England who are diagnosed with malignant and pre-malignant neoplasms, with national coverage since 1971.\n\nThe Cancer Registration dataset comprises England data to the present day, and Welsh data up to April 2017.\n\nTimescales for dissemination of agreed data can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process) [Standard response](https://web.www.healthdatagateway.org/dataset/2ed7bbbc-80db-46c2-a45b-632dda40794b)",
    "url": "https://healthdatagateway.org/en/dataset/880",
    "uid": "6904a2ff-ab44-4dd6-b9ec-25d3cad29e43",
    "datasource_id": 880,
    "source": "HDRUK"
  },
  {
    "id": 789,
    "name": "Improving Access to Psychological Therapies Data Set",
    "description": "The adult Improving Access to Psychological Therapies (IAPT) programme began in the NHS in 2008 and has transformed the treatment of anxiety disorders and depression in adults in England. Further information about the programme can be found on the NHS England Adult IAPT Programme web pages. The IAPT Data Set was developed with the IAPT Programme as a patient level, output based, secondary uses data set which aims to deliver robust, comprehensive, nationally consistent and comparable information for patients accessing NHS-funded IAPT Services in England. This national data set has been collected since April 2012 and is a mandatory submission for all NHS funded care, including care delivered by independent sector healthcare providers.",
    "url": "https://healthdatagateway.org/en/dataset/876",
    "uid": "aad63d57-1277-4139-9511-6d9f3c689bbb",
    "datasource_id": 876,
    "source": "HDRUK"
  },
  {
    "id": 790,
    "name": "COVID-19 Vaccination Adverse Reaction",
    "description": "Includes: Patient demographics, Source Organisation, Adverse reaction details. Its scope covers: Anyone vaccinated within England and anyone vaccinated in a Devoted Administration where this information is subsequently passed to England.\n\nSettings include hospital hubs - NHS providers vaccinating on site local vaccine services – community or primary care led services which could include primary care facilities, retail, community facilities, temporary structures or roving teams vaccination centres – large sites such as sports and conference venues set up for high volumes of people\n\nTimescales for dissemination can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process)",
    "url": "https://healthdatagateway.org/en/dataset/873",
    "uid": "a7917c7c-13a7-4f07-b613-d9c4259ddeca",
    "datasource_id": 873,
    "source": "HDRUK"
  },
  {
    "id": 791,
    "name": "COVID-19 Vaccination Status",
    "description": "Includes: Patient demographics, Source Organisation, vaccination details and vaccine batch events. Its scope covers: Anyone vaccinated within England Anyone vaccinated in a Devoted Administration where this information is subsequently passed to England.\n\nSettings include: hospital hubs - NHS providers vaccinating on site local vaccine services – community or primary care led services which could include primary care facilities, retail, community facilities, temporary structures or roving teams vaccination centres – large sites such as sports and conference venues set up for high volumes of people\n\nTimescales for dissemination can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process)",
    "url": "https://healthdatagateway.org/en/dataset/872",
    "uid": "6dd0ad40-ac34-4a14-b20c-53b3c9d250a7",
    "datasource_id": 872,
    "source": "HDRUK"
  },
  {
    "id": 792,
    "name": "Medicines dispensed in Primary Care (NHSBSA data)",
    "description": "Since July 2020 NHS Digital has established a collection of data from electronic and paper prescriptions submitted to the NHSBSA for reimbursement each month.\n\nThe data comprises prescriptions for medicines that are dispensed or supplied by community pharmacists, appliance contractors and dispensing doctors in England.\n\nThe data also includes:\n\nprescriptions submitted by prescribing doctors, for medicines personally administered in England prescriptions written in England and dispensed outside of England prescriptions written in Wales, Scotland, Northern Ireland, the Isle of Man, Jersey and Guernsey but dispensed in England\n\nData includes prescriptions issued by prescribers in:\n\ngeneral practice community clinics hospital clinics dentists community nursing services.\n\nThere are around 90 to 100 million rows of patient-level data in this collection per month. Each row represents each medicine or appliance on a prescription and includes personal data (for example NHS number) and special category data (data concerning health).\n\nTimescales for dissemination of agreed data can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process) [Standard response](https://web.www.healthdatagateway.org/dataset/f201b68f-d995-4a70-a9ee-aa3510232777)",
    "url": "https://healthdatagateway.org/en/dataset/867",
    "uid": "1c84067c-b7c7-440f-b35b-ae3deef59efd",
    "datasource_id": 867,
    "source": "HDRUK"
  },
  {
    "id": 793,
    "name": "Covid-19 UK Non-hospital Antigen Testing Results",
    "description": "COVID-19 UK Non-hospital Antigen Testing Results (Pillar 2) data is required by NHS Digital to support COVID-19 requests for linkage, analysis and dissemination to other organisations. These requests are often urgent and in support of direct care and service monitoring, planning and research. These are all functions that NHS Digital have been asked to deliver as a national resource in response to COVID-19, through the recent direction from the SoS.\n\nAntigen test results relate to subjects who have had swab testing in the community at drive through test centres, walk in centres, home kits returned by posts, care homes, prisons etc.\n\nThe dataset is composed of:\n\n• Patient identity and contact details\n\n• Testing centre and laboratory details\n\n• Test results • Test kit types (manufacturer)\n\nThe data cover the UK and is collected under SoS Covid Direction under s254 of the HSCA 2012 and s255 requests from devolved administrations for Scotland, Northern Ireland and Wales. This is an expansion of the original scope which only included data for welsh patients tested in other parts of the UK.\n\nData is currently available for dissemination through the NHS Digital DARS service for England. If your extract is to include data from the devolved administrations their approval will also be required.\n\nTimescales for dissemination can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process)",
    "url": "https://healthdatagateway.org/en/dataset/864",
    "uid": "edc109fe-7b62-40a3-921f-dfecbbfb61fd",
    "datasource_id": 864,
    "source": "HDRUK"
  },
  {
    "id": 794,
    "name": "Covid-19 UK Non-hospital Antibody Testing Results",
    "description": "The Covid-19 UK Non-hospital Antibody Testing Results (Pillar 3) dataset, also referred to as iElisa, documents individuals that have undergone a finger prick test for antibodies from having had Covid-19. The dataset is UK wide and contains positive, negative and void results. It also contains demographic data. Data available is in relation to specified cohorts which differ across geography and time. Data does not include the NHS Antibody tests as NHS Digital does not hold this data.\n\nData is currently available for dissemination through the NHS Digital DARS service for England. If your extract is to include data from the devolved administrations their approval will also be required.\n\nTimescales for dissemination can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process)",
    "url": "https://healthdatagateway.org/en/dataset/858",
    "uid": "0389ba02-98ce-4a9c-bde6-a2e5bbb5269b",
    "datasource_id": 858,
    "source": "HDRUK"
  },
  {
    "id": 795,
    "name": "Community Services Data Set",
    "description": "Providers of publicly-funded community services are legally mandated to collect and submit community health data, as set out by the Health and Social Care Act 2012. The Community Services Data Set (CSDS) expands the scope of the Children and Young People's Health Services Data Set (CYPHS) data set, by removing the 0-18 age restriction. The CSDS supersedes the CYPHS data set, to allow adult community data to be submitted. The structure and content of the CSDS remains the same as the CYPHS data set. The Community Information Data Set (CIDS) has been retired, to remove the need for a separate local collection and reduce burden on providers. Reports from the CSDS are available to download from the Community Services Data Set reports webpage\n\nTimescales for dissemination can be found under 'Our Service Levels' at the following link: [https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process](https://digital.nhs.uk/services/data-access-request-service-dars/data-access-request-service-dars-process)",
    "url": "https://healthdatagateway.org/en/dataset/850",
    "uid": "5525b2cd-744e-4926-95ac-dbfa00d8c425",
    "datasource_id": 850,
    "source": "HDRUK"
  },
  {
    "id": 796,
    "name": "MFT Pancreatic Cancer - Early Detection Prediction",
    "description": "The dataset includes patients either diagnosed with pancreatic cancer (PC) or deemed at risk of PC. Risk factors include certain, clinician-validated features such as previous GI referrals, chronic pancreatitis diagnosis etc. The dataset includes both patient level demographic, inpatient/outpatient and laboratory data. The variables included have been carefully identified as being potentially risk factors for PC. Data has been sourced from Manchester Foundation Trust systems, and is patient level.",
    "url": "https://healthdatagateway.org/en/dataset/845",
    "uid": "9d4b5360-c92c-4c00-8be0-047369dfb117",
    "datasource_id": 845,
    "source": "HDRUK"
  },
  {
    "id": 797,
    "name": "NHS Greater Glasgow and Clyde Diabetes",
    "description": "Diabetes registry information for patients registered to NHS Greater Glasgow and Clyde health board. Data is derived from SCI-Diabetes, an integrated shared electronic patient record to support treatment of NHS Scotland patients with Diabetes. SCI Diabetes pulls data from Primary and Secondary Care Clinicians, and specialist services like Paediatrics, Podiatry, Diabetes Specialist Nursing and Dietetics. See https://www.sci-diabetes.scot.nhs.uk/",
    "url": "https://healthdatagateway.org/en/dataset/844",
    "uid": "e76cb037-7779-470f-9934-914e213e25c4",
    "datasource_id": 844,
    "source": "HDRUK"
  },
  {
    "id": 798,
    "name": "NHS Greater Glasgow and Clyde Intellectual Disability and Pain",
    "description": "This project dataset investigated pain medication prescribing patterns in adults with intellectual disabilities compared to a general population cohort. It was used to examine the impact of multimorbidity, polypharmacy and clinical/demographic factors on prescribing. It was also used to assess whether pain medication prescribing is associated with health outcomes like hospitalization and mortality. The cohort consists of over 10,700 adults with intellectual disabilities. The aim of the dataset was to increase understanding of pain medication use and its implications considering multimorbidity and polypharmacy.",
    "url": "https://healthdatagateway.org/en/dataset/843",
    "uid": "d94fb37b-dfc6-4d30-8583-e1d8b3d3f467",
    "datasource_id": 843,
    "source": "HDRUK"
  },
  {
    "id": 799,
    "name": "NHS Greater Glasgow and Clyde Outpatient",
    "description": "Outpatients appointments and attendances at NHS Greater Glasgow and Clyde outpatient clinics, for new and follow-up appointments across specialities. Derived from SMR00 records supplied to Public Health Scotland. Includes information about patients, healthcare facilities, procedures, and main relevant diagnoses.",
    "url": "https://healthdatagateway.org/en/dataset/842",
    "uid": "76182e7f-c282-4cb0-bd21-e19e8e2c7729",
    "datasource_id": 842,
    "source": "HDRUK"
  },
  {
    "id": 800,
    "name": "NHS Greater Glasgow and Clyde BMI",
    "description": "BMI data for patients registered to NHS Greater Glasgow and Clyde, collated from multiple clinical dataset sources including SCI Diabetes, TRAK, and weight management services.",
    "url": "https://healthdatagateway.org/en/dataset/841",
    "uid": "b60c1233-5914-4dbd-8488-caa08b1bd298",
    "datasource_id": 841,
    "source": "HDRUK"
  },
  {
    "id": 801,
    "name": "NHS Greater Glasgow and Clyde Inpatient",
    "description": "Hospital inpatient and day case discharges from acute specialities from hospitals at NHS Greater Glasgow and Clyde. Derived from SMR01 records supplied to Public Health Scotland. Includes information about admissions types, diagnoses, and discharge destinations.",
    "url": "https://healthdatagateway.org/en/dataset/840",
    "uid": "33897ce7-6c55-4e11-9519-dc2b5ebff3d3",
    "datasource_id": 840,
    "source": "HDRUK"
  },
  {
    "id": 802,
    "name": "NHS Greater Glasgow and Clyde Prescribing",
    "description": "Community dispensed prescription items for patients registered to NHS Greater Glasgow and Clyde health board. Drugs are coded to the British National Formulary (BNF) code and include details of strength, unit, and dose. Does not include hospital prescribing, although the West of Scotland Safe Haven intends to make this available in 2024.",
    "url": "https://healthdatagateway.org/en/dataset/839",
    "uid": "395f8974-4ceb-429f-a680-8e27564f2eac",
    "datasource_id": 839,
    "source": "HDRUK"
  },
  {
    "id": 803,
    "name": "NHS Greater Glasgow and Clyde Deaths",
    "description": "Deaths for patients registered to NHS Greater Glasgow and Clyde health board, from National Records of Scotland registry sources.\n\nPre-2015 deaths data was not seeded with a Community Health Index (CHI) number. Post-processing and manual entry means the dataset represent most, but not necessarily all, deceased persons.",
    "url": "https://healthdatagateway.org/en/dataset/838",
    "uid": "2056fe58-d793-4bc1-a25a-7fa9c3c60b14",
    "datasource_id": 838,
    "source": "HDRUK"
  },
  {
    "id": 804,
    "name": "NHS Greater Glasgow and Clyde Radiology",
    "description": "Radiology test requests for patients registered to NHS Greater Glasgow and Clyde health board, including X-rays and CT scans. Requests are to all radiology services across the Health Board, and transformed to a single source database in Glasgow (knows as 'SCI Store'). Accession numbers can be used to identify source imaging on PACS.",
    "url": "https://healthdatagateway.org/en/dataset/837",
    "uid": "c1949af3-0632-4b74-8f00-8973c1cf89d0",
    "datasource_id": 837,
    "source": "HDRUK"
  },
  {
    "id": 805,
    "name": "NHS Greater Glasgow and Clyde Ethnicity",
    "description": "Ethnicity data for patients registered to NHS Greater Glasgow and Clyde, collated from multiple sources including hospital attendance data and immunization records.",
    "url": "https://healthdatagateway.org/en/dataset/836",
    "uid": "8c37cde8-a120-40ed-93d5-730c12349a5a",
    "datasource_id": 836,
    "source": "HDRUK"
  },
  {
    "id": 806,
    "name": "NHS Greater Glasgow and Clyde Laboratory Tests",
    "description": "Laboratory testing for patients registered to NHS Greater Glasgow and Clyde health board, including disciplines like haematology, biochemistry, immunology and virology. Dataset includes requests from secondary care services, as well as requests sent to main labs from primary care. Data is sourced from multiple LIMS and core systems, and transformed to a single source database in Glasgow (knows as 'SCI Store'). Radiology requests available separately.",
    "url": "https://healthdatagateway.org/en/dataset/835",
    "uid": "c5c8aaf7-d5e1-4989-96d4-f557ff5537ab",
    "datasource_id": 835,
    "source": "HDRUK"
  },
  {
    "id": 807,
    "name": "NHS Greater Glasgow and Clyde Demographics",
    "description": "Demographic data for patients registered to NHS Greater Glasgow and Clyde Health Board data, for linkage to West of Scotland Safe Haven data packages. The demography dataset contains a single record for each patient in a study cohort, with details for the most recent time the person was treated at NHS Greater Glasgow and Clyde. Data includes gender, age, and Scottish Index of Multiple Deprivation (SIMD) zone information.",
    "url": "https://healthdatagateway.org/en/dataset/834",
    "uid": "5e378476-f9f7-4167-9ca3-4d9e9ad83863",
    "datasource_id": 834,
    "source": "HDRUK"
  },
  {
    "id": 808,
    "name": "Evaluating the Genetic Factors Associated with Non-Alcoholic Steatohepatitis.",
    "description": "https://nddcbru.org.uk/Fatty-Liver-and-Steatohepatitis-Study",
    "url": "https://healthdatagateway.org/en/dataset/833",
    "uid": "c0e3e61c-000c-43bb-9dd1-55698198a8b6",
    "datasource_id": 833,
    "source": "HDRUK"
  },
  {
    "id": 809,
    "name": "MRC National Survey of Health and Development (NSHD, 1946 British Birth Cohort)",
    "description": "The Medical Research Council (MRC) National Survey of Health and Development (NSHD) is the oldest and longest running of the British birth cohort studies.\n\nFrom an initial maternity survey of 13,687 of all births recorded in England, Scotland and Wales during one week of March, 1946, a socially stratified sample of 5,362 singleton babies born to married parents was selected for follow-up. This sample comprises the NSHD cohort.\nThe study members have been followed up in the course of 27 data collections. Regular interviews with the mothers were conducted by health visitors, with additional assessments by school doctors and teachers.  In adult life, research nurses conducted home visits at ages 26,36,43,53 and 69, a detailed clinic visit took place between ages 60-64, as well as clinical sub studies focusing on the heart (Myofit46) and brain (Insight46).  At the latest home visit at age 69, the participation rate was 80% (N=2149). In addition to regular postal questionnaires throughout life, there have been annual questionnaires to women (47-54 years) to capture the menopause transition and 3 waves of a COVID-19 questionnaires.\nMultiple datasets cover over 20,000 variables, including biological samples which have been used to generate a variety of omics data. Data have A range of imaging and wearable data have been collected in clinical and remote environments. \nThe MRC National Survey for Health and Development (NSHD) has governance and access arrangements that comply with MRC data sharing policy. The survey data are accessible to bona fide researchers by applying through the NSHD data sharing platform, Skylark (https://skylark.ucl.ac.uk/) .  This data can be made available to researchers, for more information please email MRCLHA.swiftinfo@ucl.ac.uk.",
    "url": "https://healthdatagateway.org/en/dataset/832",
    "uid": "a2017e30-5257-40ec-8bae-9b221724121e",
    "datasource_id": 832,
    "source": "HDRUK"
  },
  {
    "id": 810,
    "name": "University College London - Edinburgh-Bristol (UCLEB) multi-omics consortium",
    "description": "The UCLEB consortium brings together well established prospective observational studies comprising over 30000 individuals from across the UK to interrogate genetic and biomarker associations underlying cardiovascular and other diseases. Participating studies include: Whitehall-II (WHII), British Regional Heart Study (BRHS), English Longitudinal Study of Ageing (ELSA), MRC National Survey of Health and Development (MRC NSHD), 1958 Birth Cohort (1958BC), Edinburgh Artery Study (EAS), Edinburgh Type 2 Diabetes Study (ET2DS), Edinburgh Heart Disease Prevention Study (EHDPS), the Aspirin for Asymptomatic Atherosclerosis Trial (AAAT), Caerphilly Prospective Study (CaPS), the British Women's Heart and Health Study (BWHHS), Southall and Brent Revisited (SABRE) and a subset of the UK Longitudinal Women's Cohort.  The consortium enables research into the genomic and other determinants of disease using a range of genotyping panels (Cardiochip, n=8000; Metabochip, n=20,000; and the Infinium Human Core DrugDev array, n=20,000), as well as the Nightingale NMR metabolomics platform (>200 lipidomic and metabolomic measures) and the Somalogic Proteomics platform (>5000 circulating proteins)  \n\nGenotypes are currently imputed to the Haplotype Reference Consortium panel (HRC) providing a rich source of genetic variants used in Mendelian Randomisation and Drug Target Validation pipelines.  The genotyping arrays have overlapping SNP content providing extensive and dense coverage of key areas of the human genome.   \n\nThese data are complemented by a wide range of survey data collected by each study over several years including clinical surveys with a range of individually assayed biomarkers, as well as measures of cognitive, respiratory, cardiac, and renal function.   There is also linkage to NHS health outcomes. \n\nData is hosted at UCL on the secure, access restricted servers with sensitive identifiable data on the UCL ISO27001 Data Safe Haven.  \n\nIndividual cohort studies maintain independent data governance and ethics for core data.  Data Access for UCLEB consortium data is granted via application to the UCLEB Steering group indicating which studies and datasets are required for proposed research.  In some instances a further application to the individual study is required for bespoke datasets.",
    "url": "https://healthdatagateway.org/en/dataset/831",
    "uid": "cd8f4e44-0228-4abd-9286-0509afe9427e",
    "datasource_id": 831,
    "source": "HDRUK"
  },
  {
    "id": 811,
    "name": "Capture-24: Activity tracker dataset for human activity recognition",
    "description": "This dataset contains Axivity AX3 wrist-worn activity tracker data that were collected from 151 participants in 2014-2016 around the Oxfordshire area. Participants were asked to wear the device in daily living for a period of roughly 24 hours, amounting to a total of almost 4,000 hours. Vicon Autograph wearable cameras and Whitehall II sleep diaries were used to obtain the ground truth activities performed during the period (e.g. sitting watching TV, walking the dog, washing dishes, sleeping), resulting in more than 2,500 hours of labelled data. Accompanying code to analyse this data is available at https://github.com/activityMonitoring/capture24. The following papers describe the data collection protocol in full: i.) Gershuny J, Harms T, Doherty A, Thomas E, Milton K, Kelly P, Foster C (2020) Testing self-report time-use diaries against objective instruments in real time. Sociological Methodology doi: 10.1177/0081175019884591; ii.) Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A. (2018) Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Scientific Reports. 8(1):7961. Regarding Data Protection, the Clinical Data Set will not include any direct subject identifiers. However, it is possible that the Data Set may contain certain information that could be used in combination with other information to identify a specific individual, such as a combination of activities specific to that individual (\"Personal Data\"). Accordingly, in the conduct of the Analysis, users will comply with all applicable laws and regulations relating to information privacy. Further, the user agrees to preserve the confidentiality of, and not attempt to identify, individuals in the Data Set.",
    "url": "https://healthdatagateway.org/en/dataset/830",
    "uid": "5713fd1a-4358-4485-bed5-83fb25ec2fce",
    "datasource_id": 830,
    "source": "HDRUK"
  },
  {
    "id": 812,
    "name": "The Aberdeen Children of the 1950's (ACONF)",
    "description": "The Aberdeen Children of the 1950s (ACONF) is a dataset gathered through a longitudinal study from 12 150 participants born in Aberdeen between 1950 - 1956 that was repeated in 1990s, 2000s and is still ongoing. The most recent part of the study was conducted in 2021 and examined views on Covid-19.\n\nThe initial goal was to find the cause of learning disabilities among school children however, the existing dataset contents have been expanded over years to aid research in multiple fields such as health-related studies. The data includes invaluable information about the prevalence of heart disease, pregnancy details, intelligence, schooling, housing, and mental health across the generations.\n\nBack in the 1950's, children in Aberdeen primary schools were tested by the University of Aberdeen with the Aberdeen Child Development Survey (ACDS) in maths and reading tests in December 1962. Four decades later, the survey was sent by post to all the now-adult participants with more questions about their personal life, health and living situation to draw correlation between the examined factors. The study yielded 7000 responses and was enriched by consulting the medical records. It has been possible to confirm vital status and place of residence for 98.5% of the 12,150 subjects from which 81% still lived in Scotland and 73% in the Grampian, including Aberdeen. 1431 subjects have been confirmed to be deceased (as at March 2018). \n\nLinkages to hospital admissions and other health endpoints captured through the routine Scottish Morbidity Records system resulted in links to 41,159 hospital admission records, 1,258 cancer registrations and 1,084 psychiatric admissions (as at March 2008) and include an intergenerational linkage to 7928 deliveries in Scotland occurring to female members of the study population. A postal questionnaire to all surviving cohort members has also been distributed in 2001, with a response success rate of 63%.",
    "url": "https://healthdatagateway.org/en/dataset/820",
    "uid": "4ccb0964-d74a-47b8-96e7-ba5e564c1681",
    "datasource_id": 820,
    "source": "HDRUK"
  },
  {
    "id": 813,
    "name": "Aberdeen Maternity and Neonatal Databank (AMND)",
    "description": "The Aberdeen Maternity and Neonatal Databank (AMND) was initiated in the department of Obstetrics and Gynaecology, University of Aberdeen, in 1950, by the late Professor Sir Dugald Baird, in collaboration with the Medical Research Council’s (MRC’s) Medical Sociology Unit. It was originally set up as a resource for the study of the physiology, pathology and sociology of pregnancy, but the usefulness of the AMND has extended significantly beyond this through linkage with other health and social care records as well as intergenerational and family linkages. \n\nThe AMND is an invaluable resource for life-course epidemiology, especially since it is one of the earliest and most comprehensive obstetric databases. From the year 1950 to the present, this unique database has been recording all the obstetric and fertility-related events occurring in women residing in Aberdeen, Scotland, UK.\n\nData are collected from every pregnancy event occurring in Aberdeen Maternity Hospital which is part of the National Health Services (NHS) Grampian. \n\nAberdeen Maternity Hospital is the only maternity hospital in the city of Aberdeen and serves the Grampian region as well as the Northern Isles, Shetland and Orkney, for tertiary maternity care. A dedicated midwives’ unit also based at the hospital provides shared maternity care for uncomplicated pregnancies. The hospital provides antenatal and postnatal care, with about 4000–5000 babies born every year. In addition, an early pregnancy unit based at the hospital manages complications such as miscarriage and other pregnancy loss. The AMND also captures data from these units. \n\nThe AMND population coverage varies according to different areas. It covers about 99% of Aberdeen and about 97% of the entire Grampian region. This differential coverage is due to a small proportion of home births and deliveries in peripheral hospitals.\n\nThis description references the International Journal of Epidemiology, Volume 45, Issue 2, April 2016, Pages 389–394, https://doi.org/10.1093/ije/dyv356",
    "url": "https://healthdatagateway.org/en/dataset/819",
    "uid": "5ece7fd9-f9fb-43ae-9021-4dc406fcfd70",
    "datasource_id": 819,
    "source": "HDRUK"
  },
  {
    "id": 814,
    "name": "Tomosynthesis Moderate Risk Patient Journey",
    "description": "Cohort of screening attendances and screening outcomes for  moderate risk family history patients",
    "url": "https://healthdatagateway.org/en/dataset/818",
    "uid": "tomosynthesis_moderate_risk_patient_journey",
    "datasource_id": 818,
    "source": "HDRUK"
  },
  {
    "id": 815,
    "name": "The EPIC-Oxford Study",
    "description": "EPIC-Oxford is the Oxford component of the European Prospective Investigation into Cancer and Nutrition (EPIC), a large multi-centre cohort study with participants enrolled from 10 European countries. The EPIC-Oxford study began in the 1990s and follows the health of 65,000 men and women living throughout the UK, many of whom are vegetarian. The main objective of EPIC Oxford is to examine how diet influences the risk of cancer, particularly for the most common types of cancer in Britain, as well as the risks of other chronic diseases. \n\nEPIC-Europe was initiated in 1992. It involves over 500,000 people from 23 centres in 10 European countries. It is coordinated by the World Health Organization International Agency for Research on Cancer in Lyon, and supported by the European Union and national funding agencies. \n\nEPIC-Oxford is one of two EPIC cohorts in the UK, the other is EPIC-Norfolk.\n\nFor further details on the study design, recruitment, data collection and other aspects of the EPIC-Oxford study, please visit https://www.ceu.ox.ac.uk/research/epic-oxford-1",
    "url": "https://healthdatagateway.org/en/dataset/817",
    "uid": "985be746-2350-40aa-9dd3-14c2e452765b",
    "datasource_id": 817,
    "source": "HDRUK"
  },
  {
    "id": 816,
    "name": "King's College Hospital MedCAT NLP 2011-2019",
    "description": "This dataset contains Natural Language Processing (NLP) output from the MedCAT library applied to the full text content of the King's College Hospital electronic health record available through CogStack. Documents were annotated with SNOMED codes and meta-annotations for experiencer, negation and temporality. \n\nResearch use of the dataset is governed by the patient-led KERRI committee, and requires a KCH principal investigator.",
    "url": "https://healthdatagateway.org/en/dataset/816",
    "uid": "4e8d4fed-69d6-402c-bd0a-163c23d6b0ee",
    "datasource_id": 816,
    "source": "HDRUK"
  },
  {
    "id": 817,
    "name": "OPTIMAM Mammographic Image Database",
    "description": "The development of artificial intelligence software to improve the outcomes of breast screening relies on the availability of well-curated image databases. The OPTIMAM Mammography Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The initial reason for creating the database was for the Cancer Research United Kingdom–funded projects OPTIMAM (2008–2013) and OPTIMAM2 (2013–2018), which evaluated how various factors affect breast cancer detection on mammograms. The images are derived from screening centers in the United Kingdom and combined with systematically collected data on the current screening episode, as well as previous and subsequent episodes. In the United Kingdom, the National Health Service Breast Screening Programme (NHSBSP) invites women to attend breast screening every 3 years between the ages of 50 and 70 years. A screening episode is one attendance at screening by a woman and includes any immediate workup imaging (assessment) if she was recalled for further investigation of a suspicious region on the screening mammograms. Any pathologic finding is also included, and the episode ends with histologic diagnosis or treatment for all lesions. Our objective was to collect mammograms for women with screen-detected cancers as well as representative samples of normal and benign screening cases.\n\n“For processing” and “for presentation” screening mammograms and prior mammograms have been collected for all screen-detected and interval cancers from several screening centres since 2011. All mammography images and data associated with initial screening attendance, further assessment, and surgical outcomes were collected as a screening episode. In addition to continuous collection of cancers, images and clinical data were collected for all women screened during 2014, and for a random selection of 25% of all women screened in 2012, 2013, and 2015 at two of the three sites. Collection into the database is ongoing, and each case is updated with new information and further screening episodes. \n\nThe associated data comprise radiologic, clinical, and pathologic information extracted from NBSS. Information on screening history, previous occurrences of cancer, biopsy results, and surgical procedures are collected from NBSS. The exact radiologic locations of lesions are not stored in NBSS. However, such information, important for training and evaluating algorithms, is collected in OMI-DB. Experienced (UK accredited) mammography readers at their own site (radiologists and advanced practice radiographers) annotate the images with reference to records made at the time of initial mammography interpretation and at further (assessment) workup (magnification views, US, and biopsy). This information is used to define rectangular regions of interest indicating the location and area of lesions and other attributes, such as radiologic appearance and conspicuity.",
    "url": "https://healthdatagateway.org/en/dataset/815",
    "uid": "2a9df760-704d-48ce-bbec-48dd47c74034",
    "datasource_id": 815,
    "source": "HDRUK"
  },
  {
    "id": 818,
    "name": "NHS Lothian Secondary Care - Lothian extract from Scottish Morbidity Records",
    "description": "NHS Lothian extracts for national reporting to Scottish Morbidity Records; Outpatients (SMR00), General/Acute Inpatients/Day Cases (SMR01), Maternity Inpatients/Day Cases (SMR02), Mental Health Inpatients/Day Cases (SMR04), Scottish Cancer Registry (SMR06).",
    "url": "https://healthdatagateway.org/en/dataset/814",
    "uid": "3aeeee52-e5f8-462c-b1f3-fe43e3aa1e10",
    "datasource_id": 814,
    "source": "HDRUK"
  },
  {
    "id": 819,
    "name": "DataLoch Respiratory",
    "description": "DataLoch has collaborated with the BREATHE Health Data Research Hub and others at Health Data Research UK (HDRUK) to create a respiratory-related database of South-East Scotland residents (NHS Lothian) with chronic respiratory conditions, specifically Asthma, Chronic Obstructive Pulmonary Disease (COPD) and Interstitial Lung Disease (ILD). DataLoch's respiratory registry is one of three related registries alongside others in England (Clinical Practice Research Datalink (CPRD) Aurum) and Wales (Secure Anonymised Information Linkage Databank (SAIL)). In addition to these conditions, the DataLoch respiratory registry includes Cystic Fibrosis (CF) and Wheeze (a common respiratory symptom).\n\nThis database of respiratory patients also contains research-ready data related to these patients' demographics, diagnoses, condition events, measurements and medications, expertly curated with clinical input and as much harmonisation as possible across the different UK registries using methodology in a soon-to-be-published paper. The data is presented in tables where each dataset has had its data chosen and derived from multiple sources and ran against algorithms to remove the noise from the raw data.",
    "url": "https://healthdatagateway.org/en/dataset/813",
    "uid": "ae0726a1-149c-4d0a-bb0a-4babf785a999",
    "datasource_id": 813,
    "source": "HDRUK"
  },
  {
    "id": 820,
    "name": "Lothian Community Prescribing",
    "description": "Drugs and medical devices dispensed by community pharmacies since 2009 where the prescriber is located within NHS Lothian. Includes drug strength, formulation and dispensed quantity data. Dispensed prescriptions are coded using British National Formulary (BNF) hierarchy.",
    "url": "https://healthdatagateway.org/en/dataset/812",
    "uid": "89b6688c-2575-4baa-9332-f718c23d7b15",
    "datasource_id": 812,
    "source": "HDRUK"
  },
  {
    "id": 821,
    "name": "Deaths - National Records of Scotland",
    "description": "Cause of death data from National Records Scotland (NRS, formerly General Registrar Office (GRO) including ICD-9/ICD-10 codes.  Cause of death records are subject to change potentially long after the date of death.",
    "url": "https://healthdatagateway.org/en/dataset/811",
    "uid": "e73bb8ed-df18-46b1-bfc2-a91e81e5fe91",
    "datasource_id": 811,
    "source": "HDRUK"
  },
  {
    "id": 822,
    "name": "NHS Lothian Critical Care",
    "description": "NHS Lothian Regional Patients' data for visits to critical care (High Dependency or Intensive Care), as recorded in the Ward Watcher systems, at NHS Lothian hospitals: Royal Infirmary Edinburgh (RIE), Western General Hospital (WGH) or St John's Hospital at Howden. A limited extract of the regional database contributes to the Scottish Intensive Care Society Audit Group (SICSAG) database of patients admitted to adult general Intensive Care Units (ICU) in Scotland. Dataset include episodes, admission specialties, severity of illness (APACHE acute physiology), treatments (such as therapies, ACP, drugs, trials) and infections (Healthcare Associated Infections also known as Hospital Acquired Infections or nosocomial infections) and obstetrics.",
    "url": "https://healthdatagateway.org/en/dataset/810",
    "uid": "e790ea58-e3b3-4186-8de0-f0556ed43564",
    "datasource_id": 810,
    "source": "HDRUK"
  },
  {
    "id": 823,
    "name": "NHS Lothian GP data",
    "description": "Patient registration information and coded interactions (Read2) between NHS Lothian registered patients and GP practices (using EMIS or Vision) which participate in the DataLoch. This includes all clinical and non-clinical events the GPs have recorded for the patient.",
    "url": "https://healthdatagateway.org/en/dataset/809",
    "uid": "837a761e-a27a-45d9-ae61-bc3fc0b9f12a",
    "datasource_id": 809,
    "source": "HDRUK"
  },
  {
    "id": 824,
    "name": "Lothian Primary and Secondary Care with Phenotypes",
    "description": "Primary care, secondary care (Scottish Morbidity Records) and deaths data (NRS) where ICD10, OPCS and Read2 codes are mapped to Caliber phenotypes. Only records that can be mapped to Caliber Phenotypes are included in this dataset.",
    "url": "https://healthdatagateway.org/en/dataset/808",
    "uid": "6c3c2424-eccb-4f78-b862-46f807f13c53",
    "datasource_id": 808,
    "source": "HDRUK"
  },
  {
    "id": 825,
    "name": "South East Scotland Cancer Database  - Core Oncology Dataset",
    "description": "SESCD oncology data is recorded and curated by a dedicated team of clinical coders using paper-based patient case notes, electronic patient records, Scottish morbidity registers, and secondary healthcare databases. In addition, there are a number of automatic feeds from various national, regional and bespoke databases and EPRs which are quality checked by the coding team. Clinical expertise is provided by a dedicated team of clinical and research professionals.",
    "url": "https://healthdatagateway.org/en/dataset/807",
    "uid": "13c0e5fb-005f-467d-8b3b-5b652b9e76ba",
    "datasource_id": 807,
    "source": "HDRUK"
  },
  {
    "id": 826,
    "name": "NHS Lothian Secondary Care -  Patient Management System extracts",
    "description": "Extracts include demographics, emergency attendances, inpatient admissions, ordered tests and investigations. These are complementary to the Lothian extracts from Scottish Morbidity Records.",
    "url": "https://healthdatagateway.org/en/dataset/806",
    "uid": "e852fd5f-d05c-4281-9f89-e60410d65972",
    "datasource_id": 806,
    "source": "HDRUK"
  },
  {
    "id": 827,
    "name": "Edinburgh Ovarian Cancer Database",
    "description": "The Edinburgh Ovarian Cancer Database was founded by Professor John Smyth in 1984 with the main aim of tracking the disease course of every ovarian cancer patient in the South-East of Scotland (Lothian, Fife, Borders and Dumfries and Galloway). Clinical, pathological, genetic, surgical and treatment information is recorded. The database tracks the patient’s disease course including therapies, responses to treatment, progression episodes, radiological investigations, tumour marker results and ultimately cause of death.  It has been and continues to be a huge resource for retrospective research, sample collection and uniform prospective data collection. The data helps identify patients suitable for particular therapy options and clinical trials. There are over 4500 patients documented to date. Data is curated by a team of 2 data managers who source data from patient case notes, electronic patient records, SCI-Store, APEX, the Scottish morbidity registers and from Scotland’s genetic services. Going forward some areas of the database will be populated using automated feeds from various national, regional and bespoke databases and EPRs.",
    "url": "https://healthdatagateway.org/en/dataset/805",
    "uid": "e7bea308-da74-4762-99d7-1bcadd042d46",
    "datasource_id": 805,
    "source": "HDRUK"
  },
  {
    "id": 828,
    "name": "DataLoch Core",
    "description": "DataLoch works with data in several ways, including: collaborating with clinicians to improve the data quality; linking datasets to enable broad insights; translating data into common standard definitions; and maintaining a high-quality metadata dictionary. Critical to this work is the involvement of clinical experts from NHS Scotland who have a detailed understanding of routine data in health care and help the DataLoch team make sure the data are research-ready. \n \nOur initial focus was on building a COVID-19 dataset to support clinicians and NHS partners in their ongoing COVID-19 response. These data have proven to be an invaluable resource enabling researchers and clinicians to generate new knowledge and insights. Feedback from our early contributors has helped inform improvements to the process and development of the data to support research beyond COVID-19.",
    "url": "https://healthdatagateway.org/en/dataset/804",
    "uid": "d62939c4-e4a8-4fca-91ed-54f7f87fb89e",
    "datasource_id": 804,
    "source": "HDRUK"
  },
  {
    "id": 829,
    "name": "NHS Lothian Lab Results",
    "description": "Laboratory test results for specimens processed in Lothian 'Blood Science' Laboratories. Numeric laboratory test results for 'Blood Sciences' throughout Lothian (Edinburgh Royal Infirmary, Western General Hospital and St John's Hospital at Howden), primarily from Haematology, Biochemistry and Immunology disciplines.\nOnly tests analysed in Lothian Laboratories are included: testing undertaken by Lighthouse (some covid-19 testing), SNBTS (some blood grouping and tissue typing) and some point of care testing (e.g. capillary glucose or ketones) are not included.",
    "url": "https://healthdatagateway.org/en/dataset/803",
    "uid": "f67fe6c6-f431-4fc5-88ed-8b23d11be458",
    "datasource_id": 803,
    "source": "HDRUK"
  },
  {
    "id": 830,
    "name": "CovPall - Survey of Palliative Care Services",
    "description": "During the outbreak of the COVID-19 pandemic, the World Health Organization rapidly issued guidance on maintaining essential health services during the pandemic, highlighting prevention, maternity, emergency care and chronic diseases, without mention of palliative care. Palliative care is multidisciplinary, holistic and person-centered treatment, care and support for people with life-limiting illness, and those important to them, such as family and friends. In the COVID-19 pandemic, palliative care has an important role in ensuring symptom control, training of nonspecialists in symptom management and care of dying patients, compassionate communication, psychosocial support for patients, carers and health care professionals, advance care planning and bereavement support, supporting patients wherever they want to be cared for.\n\nThe CovPall (Rapid evaluation of the COVID-19 pandemic response in palliative and end of life care: national delivery, workforce and symptom management) study aimed to understand the response of and challenges faced by palliative care services during the COVID-19 pandemic and to identify factors associated with challenges experienced, in particular shortages of equipment, medicines and staff. It is the first multinational survey on the response of and challenges to palliative care services during the pandemic. \n\nThe study included a cross-sectional online survey of palliative care services and hospices, and a multicentre cohort study of COVID-19 patients seen and treated by palliative care services. It is made up of two work packages.\n\nWork package 1 aimed to identify how palliative care and hospice services changed, how their staff, volunteers and others adapted their practices, and their challenges and innovations.\n\nWork package 2 determined which symptoms and problems patients had, how they changed over time, and which treatments/therapies were used and seemed to work best.\n\nThis dataset covers WP1, the online survey of palliative care services, the first main component of CovPall. The survey opened on April 23rd, 2020 and closed on July 31st, 2020. 458 valid responses were collected: 277 UK, 85 rest of Europe, 95 rest of the world, 1 missing country. Overall, 261 services provided inpatient palliative care units, 261 home care teams, 217 hospital palliative care teams, and 119 home nursing teams. Services were usually publicly, or charity managed, and many services offered care in more than one setting.\n\nMore information regarding data collected during the survey, as well as a copy of the questionnaire used for data collection, can be found at the link below: \n\nhttps://www.jpsmjournal.com/cms/10.1016/j.jpainsymman.2021.01.138/attachment/23618c62-a710-4cad-8a27-326f24ed6f1a/mmc3.pdf",
    "url": "https://healthdatagateway.org/en/dataset/802",
    "uid": "8ba190b7-4c29-4558-a256-50e1f2242874",
    "datasource_id": 802,
    "source": "HDRUK"
  },
  {
    "id": 831,
    "name": "HES Admitted Patient Care data for QResearch",
    "description": "Linked Hospital Episode Statistics Admitted Patient Care (HES APC) data contains details of all admissions to, or attendances at English NHS healthcare providers. This includes private patients treated in NHS hospitals, patients resident outside of England and care delivered by treatment centres (including those in the independent sector) funded by the NHS. All NHS healthcare providers in England, including acute hospital trusts, primary care trusts and mental health trusts provide data. HES APC data includes the complete set of hospital episode information (admission and discharge dates, diagnoses (identifying primary diagnosis), specialists seen under and procedures undertaken) for each linked patient with a hospitalisation record. In addition, Augmented care data (intensive and/or high dependency levels of care) and Maternity data are available.",
    "url": "https://healthdatagateway.org/en/dataset/801",
    "uid": "69996d55-ead8-4c8a-a2b6-eb5cdc0e7d75",
    "datasource_id": 801,
    "source": "HDRUK"
  },
  {
    "id": 832,
    "name": "HES Emergency Care (Accident & Emergency) data for QResearch",
    "description": "Hospital Episode Statistics Accident and Emergency (HES A&E) data consists of individual records of patient care administered in the accident and emergency setting in England. These data are a subset of national A&E data collected by NHS England to monitor the national standard that 95% of patients attending A&E should wait no longer than 4 hours from arrival to admission, transfer or discharge. A&E data are submitted by A&E providers of all types in England. Data collected includes details about patients’ attendance, outcomes of attendance, waiting times, referral source, A&E diagnosis, A&E treatment (drugs prescribed not recorded), A&E investigations and Health Resource Group. HES A&E may be used to clarify the health care pathway, to quantity health resource use and costs in the emergency setting, and to assess variations in the uptake of emergency services over time.",
    "url": "https://healthdatagateway.org/en/dataset/800",
    "uid": "9fc11eb5-3e64-491d-9b50-43a84bd76fee",
    "datasource_id": 800,
    "source": "HDRUK"
  },
  {
    "id": 833,
    "name": "HES Maternity for QResearch",
    "description": "",
    "url": "https://healthdatagateway.org/en/dataset/799",
    "uid": "30ec0315-5242-40cb-93ad-2c6f244caad4",
    "datasource_id": 799,
    "source": "HDRUK"
  },
  {
    "id": 834,
    "name": "HES Critical Care data for QResearch",
    "description": "Hospital Episode Statistics (HES) is a database containing details of all admissions, A and E attendances and outpatient appointments at NHS hospitals in England.\n\nAdult Critical Care (ACC) is a subset of APC data. An Intensive Care Unit (ICU) or High Dependency Unit (HDU) ward in a hospital, known as a critical care unit, provides support, monitoring and treatment for critically ill patients requiring constant support and monitoring to maintain function in at least one organ, and often in multiple organs. Medical equipment is used to take the place of patients’ organs during their recovery.\n\nSome critical care units are attached to condition-specific treatment units, such as heart, kidney, liver, breathing, circulation or nervous disorders. Others specialise in neonatal care (babies), paediatric care (children) or patients with severe injury or trauma.\n\nInitially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver.\n\nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the HES data set.\n\nHES data covers all NHS Clinical Commissioning Groups (CCGs) in England, including:\n\nprivate patients treated in NHS hospitals patients resident outside of England care delivered by treatment centres (including those in the independent sector) funded by the NHS Each HES record contains a wide range of information about an individual patient admitted to an NHS hospital, including:\n\nclinical information about diagnoses and operations patient information, such as age group, gender and ethnicity administrative information, such as dates and methods of admission and discharge geographical information such as where patients are treated and the area where they live We apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published HES data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.",
    "url": "https://healthdatagateway.org/en/dataset/798",
    "uid": "87966461-0162-4649-84e3-76adc5cd0274",
    "datasource_id": 798,
    "source": "HDRUK"
  },
  {
    "id": 835,
    "name": "COVID Therapeutics data for QResearch",
    "description": "",
    "url": "https://healthdatagateway.org/en/dataset/797",
    "uid": "891150d2-85b5-4bed-88a8-7d65f75bb020",
    "datasource_id": 797,
    "source": "HDRUK"
  },
  {
    "id": 836,
    "name": "NIMS - National immunisation data for QResearch",
    "description": "",
    "url": "https://healthdatagateway.org/en/dataset/796",
    "uid": "65b04cbe-1b5c-44d7-b640-ce63fb106401",
    "datasource_id": 796,
    "source": "HDRUK"
  },
  {
    "id": 837,
    "name": "ICNARC - Intensive Care National Audit and Research Centre for QResearch",
    "description": "",
    "url": "https://healthdatagateway.org/en/dataset/795",
    "uid": "0e6aaabb-0efc-4a69-a9a7-aa98888c04bd",
    "datasource_id": 795,
    "source": "HDRUK"
  },
  {
    "id": 838,
    "name": "Cancer registry data for QResearch",
    "description": "",
    "url": "https://healthdatagateway.org/en/dataset/793",
    "uid": "fd876055-ab0f-4562-9e8a-35efa3928b02",
    "datasource_id": 793,
    "source": "HDRUK"
  },
  {
    "id": 839,
    "name": "SACT - systemic anticancer treatment  for QResearch",
    "description": "Linked Systemic Anti-Cancer Treatment (SACT) data covers chemotherapy treatment for all solid tumour and haematological malignancies, including those in clinical trials. Information is included about programme and regime of treatment, and the outcome for each treatment.",
    "url": "https://healthdatagateway.org/en/dataset/792",
    "uid": "c7219fcb-cf19-474c-bdbf-8d72b84c9f0e",
    "datasource_id": 792,
    "source": "HDRUK"
  },
  {
    "id": 840,
    "name": "SGSS data for QResearch",
    "description": "Second Generation Surveillance System (SGSS) data contains SARS-CoV-2 testing (swab samples, PCR test method) offered to those in hospital and NHS key workers (i.e. Pillar 1) and includes positive tests results only.",
    "url": "https://healthdatagateway.org/en/dataset/791",
    "uid": "8a438697-7cdd-4716-8824-8685fa8eb314",
    "datasource_id": 791,
    "source": "HDRUK"
  },
  {
    "id": 841,
    "name": "ONS Death Registration data for QResearch",
    "description": "Linked Death Registration data from the Office for National Statistics (ONS) include information on the official date and causes of death.",
    "url": "https://healthdatagateway.org/en/dataset/790",
    "uid": "0d9db5af-972a-4597-ae5b-f9c2fdeddeff",
    "datasource_id": 790,
    "source": "HDRUK"
  },
  {
    "id": 842,
    "name": "HES Outpatients data for QResearch",
    "description": "Hospital Episode Statistics Outpatient (HES OP) data are a collection of individual records of outpatient appointments occurring in England. The data includes information on the type of outpatient consultation appointment dates, the main specialty and treatment specialty under which the patient was treated, referral source, waiting times, clinical diagnosis and procedures performed. HES OP data can be used to support health resource utilisation studies, clarify clinical health care pathways and enable variations in the uptake of services to be evaluated, for example by gender and age.",
    "url": "https://healthdatagateway.org/en/dataset/789",
    "uid": "8321071e-88ef-4d7e-8cf8-ab826c00096c",
    "datasource_id": 789,
    "source": "HDRUK"
  },
  {
    "id": 843,
    "name": "RTDS - Radiotherapy treatment data set for QResearch",
    "description": "QResearch linked National Radiotherapy Dataset (RTDS) data contain records of radiotherapy services provided since April 2018, including teletherapy and brachytherapy. All radiotherapy delivered in England to cancer patients in NHS facilities, or in private facilities where delivery was funded by the NHS, is included.",
    "url": "https://healthdatagateway.org/en/dataset/788",
    "uid": "3a65467a-61a1-4801-ba73-1e062de75c85",
    "datasource_id": 788,
    "source": "HDRUK"
  },
  {
    "id": 844,
    "name": "Vaccine Adverse Reactions data for QResearch",
    "description": "",
    "url": "https://healthdatagateway.org/en/dataset/787",
    "uid": "9ad92ce4-7410-4edc-a5ac-2c0c64915f4c",
    "datasource_id": 787,
    "source": "HDRUK"
  },
  {
    "id": 845,
    "name": "dhis2 service delivery/mortality indicators Mexico 2019/20",
    "description": "This dataset contains a series of service delivery and institutional mortality indicators from the Mexican Institute for Social Security (IMSS) health information system for the period of January 2019 to December 2020. This monthly dataset includes 15 months pre-COVID and 9 months during the pandemic. The unit of analysis is the delegation (35 IMSS delegations across 32 Mexican states including one delegation per state except in Mexico City, the State of Mexico and Veracruz where there are two delegations per state). The dataset covers all delegations over 24 months. This data can be accessed from: Arsenault, Catherine, 2021, \"Service delivery at IMSS during the COVID-19 pandemic\", https://doi.org/10.7910/DVN/XSHQYB, Harvard Dataverse, V1",
    "url": "https://healthdatagateway.org/en/dataset/786",
    "uid": "74480c90-8297-4ff7-9460-26843185eb26",
    "datasource_id": 786,
    "source": "HDRUK"
  },
  {
    "id": 846,
    "name": "ICODA Safety and Efficacy of clinical trials driver project data - Versions 1-3",
    "description": "This metadata describes the data format for data contributions to the International COVID-19 Data Alliance (ICODA) driver project investigating the safety and efficacy of clinical trials. The first data dictionary was published in December 2020, newer versions are available.\n\nSeveral thousand clinical COVID-19 trials were in progress globally. As these trials were all evaluating the benefit/risk of potential COVID-19 treatment options, it was vital that the scientific community could interrogate this data as it emerged.\n\nThe summary level data from some of these trials across industry, academia and government was included in the ICODA Workbench. In order to provide near-immediate access to results and data from the trials, ICODA has partnered with Certara to provide curated and digitised summary level data from key trials as they were reported in the public domain. In addition, several data contributing organisations provided enriched summary-level data within 5-30 days post top-line reporting of the trial results which allowed a more in depth evaluation of the results.\n\nThis Driver project used a Data Dictionary to harmonise variable definitions and subgroup classifications from all trials. This allowed side by side interrogation of the data from these trials making the data readily useable to interpret findings. Researchers could also view data from individual trials in the context of other available trials thus expanding their insights. Our visual analytics and meta-analyses tools further enhanced the researchers’ ability to work quickly.",
    "url": "https://healthdatagateway.org/en/dataset/785",
    "uid": "2b1accf8-6a82-47f0-a825-619e348bd2e0",
    "datasource_id": 785,
    "source": "HDRUK"
  },
  {
    "id": 847,
    "name": "Risk and vulnerabilities variables related to COVID-19 in Brazil - PAMEpi data",
    "description": "The data includes demographic, clinical, and socioeconomic variables of hospitalised SRAS-CoV-2 infections in Brazil from February 2020 to November 2021 and was primarily prepared for use in the analysis performed in our titled manuscript \"Profile of COVID-19 in Brazil: Risk factors and socioeconomic vulnerability associated with disease outcome\", currently available as a preprint. The raw data can be freely downloaded directly at the OpenData SUS website (Link https://opendatasus.saude.gov.br/dataset/srag-2020 and https://opendatasus.saude.gov.br/dataset/srag-2021-e-2022) or through a Python code available at our GitHub directory https://github.com/PAMepi/PAMepi_scripts_datalake.git. \n\nThe data process to obtain the specific data described here is available at https://github.com/PAMepi/PAMEpi-Reproducibility-of-published-results.git. \n\nThis work can be cited as: 1. Platform For Analytical Models in Epidemiology. (2022). PAMEpi-Reproducibility-of-published-results (v1.0). Zenodo. https://doi.org/10.5281/zenodo.6385254. or 2. Pereira, Felipe AC, Arthur R. de Azevedo, Guilherme L. de Oliveira, Renzo Flores-Ortiz, Luis Iván O. Valencia, Moreno Rodrigues, Pablo IP Ramos, Nívea B. da Silva, and Juliane Fonseca Oliveira. \"Profile of COVID-19 in Brazil: Risk Factors and Socioeconomic Vulnerability Associated with Disease Outcome.\" Available at SSRN 4081979.",
    "url": "https://healthdatagateway.org/en/dataset/784",
    "uid": "51ce7193-a439-4d0f-95ef-491b3f4ff155",
    "datasource_id": 784,
    "source": "HDRUK"
  },
  {
    "id": 848,
    "name": "dhis2 service delivery/mortality indicators Ghana 2019/20",
    "description": "This dataset contains a series of service delivery and institutional mortality indicators from the Ghana dhis2 for the period of January 2019 to December 2020. This monthly dataset includes 15 months pre-COVID and 9 months during the pandemic.\n\nThe unit of analysis is the region (N=16). The dataset covers all regions over 24 months.\n\nPermissions to use this dataset must be obtained from the Policy, Planning, Monitoring and evaluation department of Ghana Health Services.",
    "url": "https://healthdatagateway.org/en/dataset/783",
    "uid": "f940b9f5-82e2-474a-a786-e35648e351c2",
    "datasource_id": 783,
    "source": "HDRUK"
  },
  {
    "id": 849,
    "name": "COVID-19 Transmission Chains for using Epidemiological Survey Data from Asia",
    "description": "This line-list dataset contains confirmed COVID-19 cases with specific information that can link them to others, such as close contacts and cooccurrence in the same place. The linkage information can be used for transmission chain reconstruction. The data are curated from the publicly available case reports disclosed by global governments or collected from published studies.\n\n\nPlease acknowledge Dr. Xiaofan Liu and the DP-CHAIN team if you use this dataset.",
    "url": "https://healthdatagateway.org/en/dataset/782",
    "uid": "216ecfb2-5126-4323-bf2c-3d787abbc03e",
    "datasource_id": 782,
    "source": "HDRUK"
  },
  {
    "id": 850,
    "name": "ISARIC Global COVID-19 dataset",
    "description": "Clinical data from patients hospitalised with COVID19 globally shared as a part of the ISARIC Clinical Characterisation Group collaboration.\n\nIn collaboration with The International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC), The Infectious Diseases Data Observatory (IDDO) has assembled the world’s largest global database on COVID-19 clinical data with detailed individual patient data on 657,312 hospitalised individuals from 1,297 institutions across 45 countries.\n\nThe full dataset is available to all institutions contributing data via ncov@isaric.org. Individuals and institutions who have not contributed data to the dataset may apply for access via https://www.iddo.org/covid19/data-sharing/accessing-data under the following license https://www.iddo.org/document/covid-19-data-transfer-agreement",
    "url": "https://healthdatagateway.org/en/dataset/781",
    "uid": "107b1f77-2654-424f-a91c-59f443ebdc34",
    "datasource_id": 781,
    "source": "HDRUK"
  },
  {
    "id": 851,
    "name": "International Perinatal Outcomes in the Pandemic (iPOP) study, multi-country",
    "description": "Data from the International Perinatal Outcomes in the Pandemic (iPOP) study [https://ipopstudy.com](https://ipopstudy.com). The iPOP study is investigating the impact of pandemic lockdowns on preterm birth and stillbirth rates. Aggregate population-based birth data provided by collaborators will be analysed using quasi-experimental methods including Interrupted Time Series Analysis (ITSA) and Difference in Differences [DID] design. \n\nThis dataset definition describes the data provided by collaborators and the study outputs. The [Study protocol](https://wellcomeopenresearch.org/articles/6-21) provides more details on the scope and design of the study. The data collection templates used are provided in the Resources section below.\n\nIn order to have an access request for this dataset considered, you must be part of the iPOP community. See iPOP website for instructions as to how to join.",
    "url": "https://healthdatagateway.org/en/dataset/780",
    "uid": "b4c49228-ed67-40ab-97e4-a5a4f892a2a5",
    "datasource_id": 780,
    "source": "HDRUK"
  },
  {
    "id": 852,
    "name": "COVID-19 ESUS Confirmed cases and death episodes, Brazil",
    "description": "This dataset comprises data on new and accumulated confirmed cases and death episodes for each Brazilian municipality, by epidemiological week. \n\nCriteria used for confirmed cases (mild and moderate cases): * Laboratory * Clinical epidemiological * Clinical criterion * Clinical image Death episodes refer to COVID-19 confirmed cases that progressed to death. Reference date for cases: * symptom onset date (preferably) * notification or testing date (for missing data) Reference date for deaths: * death or case closing date * notification or testing date (for missing data) Age groups follow a five-year window. Phase and peak variables according to the epidemiological week in which the cases and deaths occurred.\n\nThis dataset was used as part project - Evaluating Effects of Social Inequalities on the COVID-19 Pandemic in Brazil. Maria Yury Ichihara and colleagues at the Centre for Data and Knowledge Integration for Health (Cidacs) at Fiocruz in Brazil created a social disparities index to measure inequalities relevant to the COVID-19 pandemic, such as unequal access to healthcare, to identify regions that are more vulnerable to infection and to better focus prevention efforts. \n\nIn Brazil, markers of inequality are associated with COVID-19 morbidity and mortality. They developed the index with available COVID-19 surveillance data, hosted on the Cidacs platform, and built a public data visualisation dashboard to share the index and patterns of COVID-19 incidence and mortality with the broader community. This enabled health managers and policymakers to monitor the pandemic situation in the most vulnerable populations and target social and health interventions.\n\nPermissions to use this dataset must be obtained from the Ministry of Health Brazil.",
    "url": "https://healthdatagateway.org/en/dataset/779",
    "uid": "10d64421-66a1-4f00-982a-9b09327a857f",
    "datasource_id": 779,
    "source": "HDRUK"
  },
  {
    "id": 853,
    "name": "Brazilian Vaccination, Deaths, Cases in a Municipality Level",
    "description": "",
    "url": "https://healthdatagateway.org/en/dataset/778",
    "uid": "8bba8231-ec5f-4353-a79a-414517aa6938",
    "datasource_id": 778,
    "source": "HDRUK"
  },
  {
    "id": 854,
    "name": "dhis2 service delivery/mortality indicators Ethiopian 2019/20",
    "description": "This dataset contains a series of service delivery and institutional mortality indicators from the Ethiopian dhis2 for the period of January 2019 to December 2020. This monthly dataset includes 15 months pre-COVID and 9 months during the pandemic. \n\nThe unit of analysis is the health facility, woreda, or primary health care unit (PHCU). The dataset covers all regions over 24 months, for the exception of Tigray which stopped reporting in October 2020 due to the ongoing conflict in the region.\n\nData cleaning included: imputing 0s for missing mortality indicator values if the service that it relates to is non-missing (e.g., facility deliveries and maternal deaths, neonatal deaths and stillbirths, inpatient admissions and inpatient deaths). For any given indicator, removing any unit that reports less than 15 out of 24 months. Set to missing any value greater than 3.5 standard deviation from the mean over 24 months.\n\nPermissions to use this dataset must be obtained from the Ministry of Health of Ethiopia.",
    "url": "https://healthdatagateway.org/en/dataset/777",
    "uid": "74e550ab-6097-4c1d-96c7-c6d00bb78d45",
    "datasource_id": 777,
    "source": "HDRUK"
  },
  {
    "id": 855,
    "name": "COVID-19 SRAG Cases and Death Episodes, Brazil",
    "description": "This dataset comprises new and accumulated cases and death episodes for each Brazilian municipality, by epidemiological week. \n\nCriteria for confirmed cases: * Final classification (variable CLASSI_FIN) = 5 * Antigenic test result (variable AN_SARS2) = 1 * RT-PCR test result (variable AN_SARS2) = 1 For death episodes: * confirmed cases that progressed to death (variable EVOLUCAO = 2) * death from other causes (variable EVOLUCAO = 3) Reference date for cases: * symptom onset date (variable DT_SIN_PRI) Reference date for death episodes: * case evolution date (variable DT_EVOLUCA) * for missing dates, the closest date was used: case closing date, ICU discharge date, ICU entry date, testing date, notification date Age groups follow a five-years interval Phase and peak variables were created based on epidemiological weeks.\n\nThis dataset was used as part project - Evaluating Effects of Social Inequalities on the COVID-19 Pandemic in Brazil. Maria Yury Ichihara and colleagues at the Centre for Data and Knowledge Integration for Health (Cidacs) at Fiocruz in Brazil created a social disparities index to measure inequalities relevant to the COVID-19 pandemic, such as unequal access to healthcare, to identify regions that are more vulnerable to infection and to better focus prevention efforts. \n\nIn Brazil, markers of inequality are associated with COVID-19 morbidity and mortality. They developed the index with available COVID-19 surveillance data, hosted on the Cidacs platform, and built a public data visualisation dashboard to share the index and patterns of COVID-19 incidence and mortality with the broader community. This enabled health managers and policymakers to monitor the pandemic situation in the most vulnerable populations and target social and health interventions.\n\nPermissions to use this dataset must be obtained from the Ministry of Health Brazil.",
    "url": "https://healthdatagateway.org/en/dataset/776",
    "uid": "3865ec77-f7cb-4a17-b8d8-dfc81152c495",
    "datasource_id": 776,
    "source": "HDRUK"
  },
  {
    "id": 856,
    "name": "Pandemic Respiratory Infection Emergency System Triage. UK, South Africa, Sudan",
    "description": "This test dataset consists of one table of variables collected in PRIEST dataset. The PRIEST (Pandemic Respiratory Infection Emergency System Triage) Study for Low and Middle-Income Countries (DP – PRIEST)\n\nTo ensure hospitals in low- and middle- income countries are not overwhelmed during the COVID-19 pandemic by developing a risk assessment tool for clinicians to quickly decide whether a patient needs emergency care or can be safely sent home.\n\nCarl Marincowitz and colleagues at the University of Sheffield in the United Kingdom and the University of Cape Town in South Africa have developed a risk assessment tool to help emergency clinicians quickly decide whether a patient with suspected COVID-19 needs emergency care or can be safely treated at home to avoid overburdening hospitals particularly in low- and middle- income countries (LMICs). They have used existing data to which they have access on 50,000 patients with suspected COVID-19 infection who sought emergency care in the United Kingdom, South Africa, and Sudan to develop prediction models for specific COVID-19 related outcomes in all income settings. These prediction models have been used to develop risk stratification tools, which enable providers to identify the right level of care and services for distinct subgroups of patients. These have been developed with input from patient and clinical stakeholders. The team have tested the performance of their risk assessment tools for identifying high-risk patients with existing triage methods.",
    "url": "https://healthdatagateway.org/en/dataset/775",
    "uid": "1e3407af-dd72-4aa5-87af-5d4641ec3ad4",
    "datasource_id": 775,
    "source": "HDRUK"
  },
  {
    "id": 857,
    "name": "Incidence and Risk Factors for COVID-19 for pregnancy and infants, Uganda",
    "description": "The research was a secondary analysis of data collected among women who received antenatal and delivery services at Kawempe National Referral Hospital in Kampala, Uganda for the period before during and immediately post the COVID19 outbreak (i.e. Hospital antenatal, maternal and neonatal attendance for the period January 2020 to October 2021). The objective of the analysis was to assess for the adverse pregnancy and infant outcomes that can be attributed to the COVID19 pandemic. The period of analysis was stratified into pre-COVID, COVID and Immediate post-COVID. Pregnancy and infant outcomes were compared across the three time points to assess the adverse outcomes attributable to the pandemic. Results from the analysis was also intended to inform the development of a visualization dashboard that would be available and used by healthcare workers to inform their decisions during service delivery.\n\nThe data dictionary was compiled to provide reference for anyone accessing and using the data from the Electronic Health Record. The antenatal and labour & delivery registers used by healthcare workers to record patient information while offering services at the hospital were reviewed to identify variables included in the registers. The registers are part of the Health Management Information System tools developed by Ministry of Health and are used in all health facilities country-wide. \n\nThe variables were reviewed during a stakeholder’s workshop that comprised of Ministry of Health officials, Kawempe Hospital management, heads of departments and selected health workers with the objective of identifying those that would be useful for monitoring pregnancies with potential adverse pregnancy and infant outcomes. During the stakeholder meetings, variable definitions were documented to provide clarity and consistency when using the data from the Electronic Health Record.",
    "url": "https://healthdatagateway.org/en/dataset/774",
    "uid": "cbb04ab0-ffa3-4068-a7de-5c7e87a66bda",
    "datasource_id": 774,
    "source": "HDRUK"
  },
  {
    "id": 858,
    "name": "dhis2 service delivery/mortality indicators KwaZulu-Natal, South Africa 2019/20",
    "description": "This dataset contains a series of service delivery and institutional mortality indicators from the Ethiopian dhis2 for the period of January 2019 to December 2020. This monthly dataset includes 15 months pre-COVID and 9 months during the pandemic. \n\nThe unit of analysis is the health facility, woreda, or primary health care unit (PHCU). The dataset covers all regions over 24 months, for the exception of Tigray which stopped reporting in October 2020 due to the ongoing conflict in the region.\n\nData cleaning included: imputing 0s for missing mortality indicator values if the service that it relates to is non-missing (e.g., facility deliveries and maternal deaths, neonatal deaths and stillbirths, inpatient admissions and inpatient deaths). For any given indicator, removing any unit that reports less than 15 out of 24 months. Set to missing any value greater than 3.5 standard deviation from the mean over 24 months.\n\nPermissions to use this dataset must be obtained from the Ministry of Health of Ethiopia.",
    "url": "https://healthdatagateway.org/en/dataset/773",
    "uid": "93c268c8-d61f-41da-a12d-14bbb2497104",
    "datasource_id": 773,
    "source": "HDRUK"
  },
  {
    "id": 859,
    "name": "COVID-19 Community Mobility Reports, Brazil",
    "description": "This dataset measures the mobility trend in different dimensions (location categories) for Brazil, Federation Units and Municipalities. It is based on Google's Mobility Report. Location categories are: * supermarkets and pharmacies * parks * public transport stations * retail and leisure places * working places * dwelling For aggregation purposes, daily measurements were transformed into weekly averages (by epidemiological week).\n\nThis dataset was used as part project - Evaluating Effects of Social Inequalities on the COVID-19 Pandemic in Brazil. Maria Yury Ichihara and colleagues at the Centre for Data and Knowledge Integration for Health (Cidacs) at Fiocruz in Brazil created a social disparities index to measure inequalities relevant to the COVID-19 pandemic, such as unequal access to healthcare, to identify regions that are more vulnerable to infection and to better focus prevention efforts. \n\nIn Brazil, markers of inequality are associated with COVID-19 morbidity and mortality. They developed the index with available COVID-19 surveillance data, hosted on the Cidacs platform, and built a public data visualisation dashboard to share the index and patterns of COVID-19 incidence and mortality with the broader community. This enabled health managers and policymakers to monitor the pandemic situation in the most vulnerable populations and target social and health interventions.\n\nFind this dataset through Google - https://www.google.com/covid19/mobility/",
    "url": "https://healthdatagateway.org/en/dataset/772",
    "uid": "5754b02f-a2e5-42e0-98ad-f8b482e25b4b",
    "datasource_id": 772,
    "source": "HDRUK"
  },
  {
    "id": 860,
    "name": "Aggregated Brazilian Covid-19 data surveillance - PAMEpi data",
    "description": "The current file contains community-level aggregate information extracted from health, human mobility, population inequality, and non-pharmaceutical interventions. The integration of variables from different sources facilitates the data analysis and epidemiological studies once the data set is aligned and represents a single entry for each city and day since the beginning of the pandemic in Brazil. \n\nThe data includes, for example, the daily time series of mild to moderate cases resulting from the Flu Syndrome database, hospital occupancy and deaths from the Severe Acute Respiratory Syndrome database, vaccine doses administered daily, etc. \n\nTo familiarize yourself with the data, a data explorer and dictionary are also available at https://pamepi.rondonia.fiocruz.br/en/aggregated_ en.html, and codes used to create the data set can be found on our GitHub directory https://github.com/PAMepi/PAMepi_scripts_datalake.git. \n\nThis work can be cited as: 1. Platform For Analytical Modelis in Epidemiology. (2022). GitHub directory: https://github.com/PAMepi/PAMepi_scripts_datalake.git. PAMepi/PAMepi_scripts_datalake: v1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.6384641",
    "url": "https://healthdatagateway.org/en/dataset/771",
    "uid": "a9d4b159-a1c5-4d8c-bfc0-2446d4181706",
    "datasource_id": 771,
    "source": "HDRUK"
  },
  {
    "id": 861,
    "name": "dhis2 service delivery/mortality indicators Nepal 2019/20",
    "description": "This dataset contains a series of service delivery and institutional mortality indicators from the Nepal dhis2 for the period of January 2019 to December 2020. This monthly dataset includes 15 months pre-COVID and 9 months during the pandemic. \n\nThe unit of analysis is the palika (lowest subnational administrative division in Nepal). The dataset covers all palikas (N=753) over 24 months. Data cleaning included: imputing 0s for missing mortality indicator values if the service that it relates to is non-missing (e.g., facility deliveries and maternal deaths, neonatal deaths and stillbirths, inpatient admissions and inpatient deaths). For any given indicator, removing any unit that reports less than 15 out of 24 months. Set to missing any value greater than 3.5 standard deviation from the mean over 24 months. \n\nPermissions to use this dataset must be obtained from the Ministry of Health and Population of Nepal.",
    "url": "https://healthdatagateway.org/en/dataset/770",
    "uid": "55d1075b-6cb9-4533-80a9-35abc650da41",
    "datasource_id": 770,
    "source": "HDRUK"
  },
  {
    "id": 862,
    "name": "COVID-19 impact on patient healthcare use/outcomes Haiti, Malawi, Mexico, Rwanda",
    "description": "Title: The impact of COVID-19 on chronic care patients health care utilization and health outcomes in Haiti, Malawi, Mexico and Rwanda Original data source: Electronic Medical Records Date range: March 1st, 2019-Feb 28th, 2021 Geographic region: Non-representative subnational regions of Haiti, Malawi, Mexico, and Rwanda Clinical populations: Diabetes, HIV, and hypertension patients Level of data: Aggregated by country, sex, age category, clinical population, and pre- vs post-COVID-19 period Size of the data: 35 KB Research question/s that use the dataset 1. Has the COVID-19 pandemic changed the risk of poor clinical outcomes among chronic care patients living with HIV, cardiovascular disease and diabetes programs in Haiti, Malawi, Mexico and Rwanda? 2. Among these patients, how has care utilization changed during the COVID-19 pandemic? Useful Links https://icoda-research.org/project/dp-pih-covco/ \n\nData access information: In order to request access to data, please contact Jean Claude Mugunga, jmugunga@pih.org, with a description of your study team, your research questions, and which countr(ies) and clinical program(s) you would like data for. Note that Dr. Mugugna will reach out to representatives from each country you request data from for approval before sharing the data.",
    "url": "https://healthdatagateway.org/en/dataset/769",
    "uid": "f190dfd0-2080-440b-a75e-992e0a0354fa",
    "datasource_id": 769,
    "source": "HDRUK"
  },
  {
    "id": 863,
    "name": "dhis2 service delivery/mortality indicators Lao 2019/20",
    "description": "This dataset contains a series of service delivery and institutional mortality indicators from the Lao People's Democratic Republic dhis2 for the period of January 2019 to December 2020. This monthly dataset includes 15 months pre-COVID and 9 months during the pandemic. The unit of analysis is the health facility. \n\nThe dataset covers all regions over 24 months, and includes all public sector health facilities in the country. Data cleaning included: imputing 0s for missing mortality indicator values if the service that it relates to is non-missing (e.g., facility deliveries and maternal deaths, neonatal deaths and stillbirths, inpatient admissions and inpatient deaths). For any given indicator, removing any unit that reports less than 15 out of 24 months. Set to missing any value greater than 3.5 standard deviation from the mean over 24 months. \n\nPermissions to use this dataset must be obtained from the Ministry of Health Lao PDR.",
    "url": "https://healthdatagateway.org/en/dataset/768",
    "uid": "0bdfaeb2-43af-4adb-b54f-9de704c1dc8b",
    "datasource_id": 768,
    "source": "HDRUK"
  },
  {
    "id": 864,
    "name": "dhis2 service delivery/mortality indicators Haiti 2019/20",
    "description": "This dataset contains a series of service delivery and institutional mortality indicators from the Haiti dhis2 for the period of January 2019 to December 2020. This monthly dataset includes 15 months pre-COVID and 9 months during the pandemic.\n\nThe unit of analysis is the health facility. The dataset covers all health facilities in Haiti over 24 months.\n\nData cleaning included: imputing 0s for missing mortality indicator values if the service that it relates to is non-missing (e.g., facility deliveries and maternal deaths, neonatal deaths and stillbirths, inpatient admissions and inpatient deaths). For any given indicator, removing any unit that reports less than 15 out of 24 months. Set to missing any value greater than 3.5 standard deviation from the mean over 24 months.\n\nPermissions to use this dataset must be obtained from the Ministry of Public Health and Population of Haiti.",
    "url": "https://healthdatagateway.org/en/dataset/767",
    "uid": "4b97012c-2788-4361-8f28-72542e2c4138",
    "datasource_id": 767,
    "source": "HDRUK"
  },
  {
    "id": 865,
    "name": "Health and Employment After Fifty (HEAF)",
    "description": "HEAF is a prospective cohort study of older people drawn from the patient registers of 24 English general practices which contribute data to the Clinical Practice Research Datalink (CPRD). \n\nThe HEAF study aims to assess the health benefits and risks of remaining in work at older ages and their predictors, and thereby the potential health impact of policies to extend working life and maximise employment in later working life and to  identify occupational, social and personal co-factors which modify this relationship, as possible targets for intervention.\n\nOver 8,000 participants born between 1948 and 1962 were recruited and represent every decile of deprivation. Annual questionnaires are used to collect information concerning participants' mental and physical health, work status, thoughts about work and retirement, and other demographic factors (e.g. age, gender, ethnicity) and lifestyle choices (e.g. smoking and physical activity). Health information in the form of coded data is provided by the CPRD.",
    "url": "https://healthdatagateway.org/en/dataset/766",
    "uid": "ea2d54a2-a6d4-4e82-b2e4-95184f00bbdc",
    "datasource_id": 766,
    "source": "HDRUK"
  },
  {
    "id": 866,
    "name": "Next Steps",
    "description": "Next Steps is a longitudinal cohort study following the lives of young people in England born in 1989-90. Previously known as the Longitudinal Study of Young People in England (LSYPE), Next Steps began in 2004 when cohort members were 14 years old with an original sample of 15,770 people. \n\nOriginally managed by the Department for Education, the study was designed to explore young people’s experiences through secondary school, and on to further education, training or the workplace. Cohort members were surveyed annually until 2010, and the next sweep after this was when they were aged 25, in 2015-16. \n\nNext Steps has collected information about cohort members’ education and employment, economic circumstances, family life, physical and emotional health and wellbeing, social participation and attitudes. The Next Steps data has also been linked to National Pupil Database (NPD) records, which include the cohort members’ individual scores at Key Stage 2, 3 and 4 and more administrative linkages are planned. The study is now based at the Centre for Longitudinal Studies (CLS) and has become a multidisciplinary study providing invaluable insights into the different aspects of the lives of millennials.",
    "url": "https://healthdatagateway.org/en/dataset/765",
    "uid": "0ad7a3dd-e720-49ef-9d27-c394a32b0326",
    "datasource_id": 765,
    "source": "HDRUK"
  },
  {
    "id": 867,
    "name": "Lothian Birth Cohort 1921 (LBC1921)",
    "description": "The original Lothian Birth Cohorts of 1921 (LBC1921) and 1936 (LBC1936) were designed as follow-up studies to the Scottish Mental Surveys of 1932 (SMS1932) and 1947 (SMS1947), respectively. The SMS1932 took place simultaneously across schools in Scotland on the 1st of June, 1932, and used the Moray House Test (No. 12; MHT) of general intelligence. Almost every child attending school and born in 1921 (N = 87,498) was tested. The same MHT was administered to almost every child born in 1936 and attending school on the 4th of June, 1947 for the SMS1947 (N = 70,805). Decades later, participants of both Surveys, mostly living in Edinburgh and the surrounding area (the Lothians) in older age, were invited to participate in the Lothian Birth Cohort (LBC) studies. Between 1999 and 2001, 550 of the SMS1932 were recruited to Wave 1 of the LBC1921 study, at a mean age of 79 years. Between 2004 and 2007, 1,091 members of SMS1947 were recruited to Wave 1 of the LBC1936 study, at a mean age of 70 years. Both cohorts re-sat the MHT at initial follow-up. In addition, a large amount of other cognitive, psychosocial, lifestyle, medical, biomarker, genetic, brain imaging and other data were collected. \n\nThe LBC studies set out principally to examine the nature and determinants of non-pathological cognitive ageing from childhood to older age, and within in older age. However, in recent years the scope of the studies has extended to identifying more risk and protective factors that have the potential to be interventions to reduce the risk of cognitive loss in later life. At each visit, participants submit a wide range of data, leading to a database of thousands of data points for every single participant, including cognitive, lifestyle, social and psychological, genetic and epigenetic, health and physical fitness, biological, and brain and vascular imaging data.",
    "url": "https://healthdatagateway.org/en/dataset/764",
    "uid": "df288318-6663-417d-9870-666e75920bed",
    "datasource_id": 764,
    "source": "HDRUK"
  },
  {
    "id": 868,
    "name": "English Longitudinal Study of Ageing (ELSA)",
    "description": "The English Longitudinal Study of Ageing (ELSA) is a panel study of a representative cohort of men and women living in England aged ≥50 years. The study collects objective and subjective measures of physical and mental health, wellbeing, finances and attitudes around ageing and how these change over time. \n\nELSA was designed as a sister study to the Health and Retirement Study in the USA and is multidisciplinary in orientation, involving the collection of economic, social, psychological, cognitive, health, biological and genetic data. The original sample was drawn from households that had previously responded to the Health Survey for England (HSE) between 1998 and 2001. A pilot study was conducted in 2001 before main fieldwork began in March 2002. The same group of respondents have been interviewed at two-yearly interviews to measure changes in their health, economic and social circumstances. Younger age groups are replaced or refreshed to retain the panel. The sample has been refreshed using HSE participants in waves 3, 4, 6, 7 and 9. Data are collected using computer-assisted personal interviews and self-completion questionnaires, with additional nurse visits for the assessment of biomarkers every 4 years. The original sample consisted of 11,391 members ranging in age from 50 to 100 years. ELSA is harmonized with ageing studies in other countries to facilitate international comparisons, and is linked to financial and health registry data.\n\nMore than 18,000 people have taken part in the study since it started in 2002, with the same people re-interviewed every two years. Data from ELSA participants informs policy across all aspects of ageing including health and social care, retirement and pensions policy, and social and civic participation.",
    "url": "https://healthdatagateway.org/en/dataset/763",
    "uid": "f12cb023-1402-49d1-aa27-e0fe4ed19fd7",
    "datasource_id": 763,
    "source": "HDRUK"
  },
  {
    "id": 869,
    "name": "1958 National Child Development Study (NCDS)",
    "description": "NCDS started in 1958 as the Perinatal Mortality Survey.\n\nThe initial birth survey captured information on 17,415 babies born in a single week – or 98 per cent of total births across England, Scotland and Wales. Since then, the cohort has been followed up ten times at ages 7, 11, 16, 23, 33, 42, 44, 46, 50, and most recently at age 55, when 9,137 cohort members took part. At 7, 11 and 16, the sample was augmented with those who had been born overseas in the relevant week and subsequently moved to Great Britain. This resulted in a total sample of 18,558 cohort members, who have been followed ever since. The tenth sweep of the NCDS originally behan in January 2020, when the cohort members were age 62, with data becoming available for researchers to use from early 2024. \n\nOver the course of cohort members’ lives, information has been collected on their physical and educational development, economic circumstances, employment, family life, health behaviour, wellbeing, social participation and attitudes. The main data collection methods used during the study have included questionnaires, cognitive assessments, clinical assessments and nurse measurements. Questionnaires have been used to gather a variety of information about study members, including social and family background, mental health and wellbeing, income and housing, and marriage and employment status. Cognitive assessments have measured verbal and language ability in childhood, as well as literacy and numeracy from adolescence into later life. Medical examinations and nurse measurements have provided information about bone development in the early years to heart problems in middle age. The study has also collected blood samples to see how people’s health is linked to their genes.",
    "url": "https://healthdatagateway.org/en/dataset/762",
    "uid": "0ceaaedc-4904-4820-8b74-d6a8e679fb11",
    "datasource_id": 762,
    "source": "HDRUK"
  },
  {
    "id": 870,
    "name": "Resilience, Ethnicity & Adolescent Mental Health (REACH)",
    "description": "REACH is an accelerated cohort study of adolescent mental health in two inner city London boroughs, Lambeth and Southwark. The study aims to investigate the impact of social, psychological, and biological risk and protective factors on the occurrence and persistence of mental health problems over time in large, ethnically diverse cohorts of adolescents.\n\nREACH has recruited three cohorts—age 11–12 (cohort 1; school year 7), 12–13 (cohort 2; year 8) and 13–14 (cohort 3; year 9) from 12 mainstream secondary schools in the two boroughs. These boroughs are among the most densely-populated, socioeconomically and ethnically diverse areas in England,13–15 and have high rates of adult mental health problems. To investigate novel questions on the developmental origins of mental health problems in adolescents, extensive data is collected at each time point. Participants provide detailed information, each year, about their mental health, social circumstances, and experiences via questionnaires. A subset (approx. 20%) of participants also complete face-to-face interviews and cognitive assessments.",
    "url": "https://healthdatagateway.org/en/dataset/761",
    "uid": "0f754b8b-353e-4e8d-9ff0-03d5e0817e02",
    "datasource_id": 761,
    "source": "HDRUK"
  },
  {
    "id": 871,
    "name": "Determinants of Adolescent Social Wellbeing & Health (DASH)",
    "description": "The DASH study was designed to systematically examine the influence of social conditions (particularly aspects of family life, school life and neighbourhoods) on the health and well-being of ethnic minority young people. A key aim of the DASH study is to investigate why some ethnic groups experience higher rates of certain diseases than other ethnic groups in adulthood.\n\nThe cohort contains over 6,500 pupils recruited from 51 schools across 10 inner-London boroughs. Pupils were aged 11-13 years old at the start of the study in 2003, and were followed up at 2005/06, 2010-13 and 2012/13. DASH was designed to have a sizeable number of respondents from the major ethnic minority groups and  has had high response rates in conventionally thought ‘hard to reach’ populations. Cultural differences within South Asian groups are well known but very little is known about these issues among those of African origin. In DASH, Black Caribbeans, Nigerians, Ghanaians and other Africans can be identified separately so that differences in health and well-being can be explored. The cohort is now in their early 20s and around 81% of the baseline sample has been traced via postal, electronic and web-based strategies. \n\nSocio-demographic, area, family life, social support, health (illness and health behaviour) and psychosocial data were collected from children at baseline and follow-up via self-complete questionnaires. Physical measurements included anthropometry, blood pressure, pubertal stage, lung function and salivary cotinine.  \n\nThe DASH study provides unique opportunities to better understand the complex interplay of social, biological, and environmental factors for individuals from ethnic minority groups.",
    "url": "https://healthdatagateway.org/en/dataset/760",
    "uid": "9d242d12-2d2e-4941-93e4-9ec40d239dd1",
    "datasource_id": 760,
    "source": "HDRUK"
  },
  {
    "id": 872,
    "name": "Lothian Birth Cohort 1936 (LBC1936)",
    "description": "The original Lothian Birth Cohorts of 1921 (LBC1921) and 1936 (LBC1936) were designed as follow-up studies to the Scottish Mental Surveys of 1932 (SMS1932) and 1947 (SMS1947), respectively. The SMS1932 took place simultaneously across schools in Scotland on the 1st of June, 1932, and used the Moray House Test (No. 12; MHT) of general intelligence. Almost every child attending school and born in 1921 (N = 87,498) was tested. The same MHT was administered to almost every child born in 1936 and attending school on the 4th of June, 1947 for the SMS1947 (N = 70,805). Decades later, participants of both Surveys, mostly living in Edinburgh and the surrounding area (the Lothians) in older age, were invited to participate in the Lothian Birth Cohort (LBC) studies. Between 1999 and 2001, 550 of the SMS1932 were recruited to Wave 1 of the LBC1921 study, at a mean age of 79 years. Between 2004 and 2007, 1,091 members of SMS1947 were recruited to Wave 1 of the LBC1936 study, at a mean age of 70 years. Both cohorts re-sat the MHT at initial follow-up. In addition, a large amount of other cognitive, psychosocial, lifestyle, medical, biomarker, genetic, brain imaging and other data were collected. \n\nThe LBC studies set out principally to examine the nature and determinants of non-pathological cognitive ageing from childhood to older age, and within in older age. However, in recent years the scope of the studies has extended to identifying more risk and protective factors that have the potential to be interventions to reduce the risk of cognitive loss in later life. At each visit, participants submit a wide range of data, leading to a database of thousands of data points for every single participant, including cognitive, lifestyle, social and psychological, genetic and epigenetic, health and physical fitness, biological, and brain and vascular imaging data.",
    "url": "https://healthdatagateway.org/en/dataset/759",
    "uid": "e63613a9-92e9-4147-8a76-40e4a87ec131",
    "datasource_id": 759,
    "source": "HDRUK"
  },
  {
    "id": 873,
    "name": "The 1970 British Cohort Study (BCS70)",
    "description": "The 1970 British Cohort Study (BCS70) follows the lives of more than 17,000 people born in England, Scotland and Wales in a single week of 1970. Over the course of cohort members lives, the BCS70 has collected information on health, physical, educational and social development, and economic circumstances among other factors.\n\nSince the birth survey in 1970, there have been eight ‘sweeps’ of all cohort members at ages 5, 10, 16, 26, 30, 34, 38, 42 and 46. A new sweep of the cohort is planned at age 51.\n\nThe main data collection methods used during the study have included questionnaires, cognitive assessments, clinical assessments and nurse measurements.\n\nQuestionnaires have been used to gather a variety of information about study members, including social and family background, mental health and wellbeing, income and housing, and marriage and employment status. Cognitive assessments have measured verbal and language ability in early childhood, as well as literacy and numeracy from adolescence to middle age. Medical examinations and nurse measurements have provided information about different health conditions experienced by the study members, from bone development in childhood to heart problems in middle age. \n\nBy collecting information on various aspects of life, BCS70 has become a vital source of evidence on key policy areas such as social mobility, education, training and employment, and economic insecurity.\n\nBCS70 is part of CLOSER (Cohort & Longitudinal Studies Enhancement Resources) which aims to maximise the use, value and impact of the UK’s longitudinal studies.",
    "url": "https://healthdatagateway.org/en/dataset/758",
    "uid": "929186f3-e230-4b52-a9f5-0c20af7f5ce2",
    "datasource_id": 758,
    "source": "HDRUK"
  },
  {
    "id": 874,
    "name": "The Airwave Health Monitoring Study",
    "description": "Please refer to the study website for more information about the study documents available (https://police-health.org.uk/study-data-documentation)",
    "url": "https://healthdatagateway.org/en/dataset/757",
    "uid": "98bfe17f-be12-47b1-943a-2d3a621b7618",
    "datasource_id": 757,
    "source": "HDRUK"
  },
  {
    "id": 875,
    "name": "Growing up in Bradford (BiB)",
    "description": "**What is Growing Up in Bradford?**\n\nGrowing Up in Bradford is a follow up to the initial Born in Bradford (BiB) cohort study. BiB was established to examine the determinants of health and development during childhood and throughout adult life, and recruited 12,453 mothers who experienced 13,858 births. The Growing Up study is the first full follow up of the cohort and aims to investigate the determinants of primary school aged children’s health and development, with a focus on both parents health and wellbeing and the exposure in childhood that may influence future health. The age of children included in this follow up are between seven and 11 years old.\n\n**Recruitment process**\n\nThe study recruited from the pool of individuals who had taken part in the original BiB study, with as many mothers, partners and children from the original cohort recruited as possible. 6,502 children, 5,291 mothers and 826 partners completed the study.\n\n**Available data**\n\nEach child completed an age appropriate questionnaire, with one of the child’s parents completing a questionnaire about themselves and their partner and a separate questionnaire about their child. Topics included in the adults questionnaire included residential environment characteristics and satisfaction, socio-economic circumstances, social circumstances, and health and behaviour.  The adult completed child survey asked questions about their child’s health, development and behaviour. The child completed child questionnaire asked questions regarding physical activity and diet.\n\nTwo subsets of adults were asked further questions. One was asked additional questions regarding their child’s diet and physical activity, and the adult’s views on parenting.  The other was asked questions about their child’s experience of asthma and allergies.\n\nParticipants (adults and children) could also volunteer to provide a range of biological measures and samples. As a result, the Growing Up data also contains samples/results of blood tests, blood pressure reading, renal analyses and DEXA scans.",
    "url": "https://healthdatagateway.org/en/dataset/756",
    "uid": "fe1684ef-69ee-4490-9ab8-b26e990c9060",
    "datasource_id": 756,
    "source": "HDRUK"
  },
  {
    "id": 876,
    "name": "BiB4All SystmOne HV Primary Care CTV3",
    "description": "BiB4All is a data linkage cohort study of babies born in Bradford and their mothers. Community midwives within Bradford invite every woman due to have their baby in Bradford to the project during their routine maternity appointments. The study aims to link routine data together from a variety of health, education and social care datasets in Bradford. For example; GPs and Dentists, NHS Digital, Education and Schools, Dept. of Work and Pensions, Voluntary Organisations, Local Authority and Social Care, and Other Research Studies. This will allow us to build up a clearer image of people's lives and answer questions to improve health, care and services for families.  \n\nThis primary care dataset covers all participants from the BiB4All study, which means it can be linked to all BiB4All datasets. This includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications and vaccinations for ~10,000 BiB4All participants. \n\nSystmOne data is easily searchable by using Read CTV3 or SNOMED CT codes.",
    "url": "https://healthdatagateway.org/en/dataset/755",
    "uid": "c15e2873-93d6-4f26-b47f-e6f2681ae06f",
    "datasource_id": 755,
    "source": "HDRUK"
  },
  {
    "id": 877,
    "name": "Born in Bradford: SystmOne GP Primary Care CTV3",
    "description": "This primary care dataset covers all participants from the BiB longitudinal birth cohort study UK, which means it can be linked to all BiB surveys and data. This includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications and vaccinations for ~29,000 BiB participants. \n\nSystmOne data is easily searchable by using Read CTV3 or BNF drug codes.",
    "url": "https://healthdatagateway.org/en/dataset/754",
    "uid": "f89c25ab-a22d-4cb3-83b1-72ace104eb9e",
    "datasource_id": 754,
    "source": "HDRUK"
  },
  {
    "id": 878,
    "name": "BiB4All SystmOne GP Primary Care Medication",
    "description": "BiB4All is a data linkage cohort study of babies born in Bradford and their mothers. Community midwives within Bradford invite every woman due to have their baby in Bradford to the project during their routine maternity appointments. The study aims to link routine data together from a variety of health, education and social care datasets in Bradford. For example; GPs and Dentists, NHS Digital, Education and Schools, Dept. of Work and Pensions, Voluntary Organisations, Local Authority and Social Care, and Other Research Studies. This will allow us to build up a clearer image of people's lives and answer questions to improve health, care and services for families.  \n\nThis primary care dataset covers all participants from the BiB4All study, which means it can be linked to all BiB4All datasets. This includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications and vaccinations for ~10,000 BiB4All participants. \n\nSystmOne data is easily searchable by using BNF, DM+D or SNOMED codes.",
    "url": "https://healthdatagateway.org/en/dataset/753",
    "uid": "d1fddac0-f742-4124-bfb4-1adbc127377b",
    "datasource_id": 753,
    "source": "HDRUK"
  },
  {
    "id": 879,
    "name": "BiB4All Child Health Services Vaccinations",
    "description": "BiB4All is a data linkage cohort study of babies born in Bradford and their mothers. Community midwives within Bradford invite every woman due to have their baby in Bradford to the project during their routine maternity appointments. The study aims to link routine data together from a variety of health, education and social care datasets in Bradford. For example; GPs and Dentists, NHS Digital, Education and Schools, Dept. of Work and Pensions, Voluntary Organisations, Local Authority and Social Care, and Other Research Studies. This will allow us to build up a clearer image of people's lives and answer questions to improve health, care and services for families.  \n\nThis dataset contains vaccinations performed on individuals from the BiB4All birth cohort by  Child Health Services. Recorded in SystmOne and coded by Read CTV3, it contains attendance and clinical information for all Child Health vaccination interactions.",
    "url": "https://healthdatagateway.org/en/dataset/752",
    "uid": "021c2730-f05b-46e3-88ac-661749270648",
    "datasource_id": 752,
    "source": "HDRUK"
  },
  {
    "id": 880,
    "name": "BiB4All SystmOne GP Primary Care Vaccination",
    "description": "BiB4All is a data linkage cohort study of babies born in Bradford and their mothers. Community midwives within Bradford invite every woman due to have their baby in Bradford to the project during their routine maternity appointments. The study aims to link routine data together from a variety of health, education and social care datasets in Bradford. For example; GPs and Dentists, NHS Digital, Education and Schools, Dept. of Work and Pensions, Voluntary Organisations, Local Authority and Social Care, and Other Research Studies. This will allow us to build up a clearer image of people's lives and answer questions to improve health, care and services for families.  \n\nThis primary care dataset covers all participants from the BiB4All study, which means it can be linked to all BiB4All datasets. This includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications and vaccinations for ~10,000 BiB4All participants. \n\nSystmOne data is easily searchable.",
    "url": "https://healthdatagateway.org/en/dataset/751",
    "uid": "9e533e86-533b-42c0-8f6e-ce49b294f5c6",
    "datasource_id": 751,
    "source": "HDRUK"
  },
  {
    "id": 881,
    "name": "BiB4All SystmOne GP Primary Care CTV3",
    "description": "BiB4All is a data linkage cohort study of babies born in Bradford and their mothers. Community midwives within Bradford invite every woman due to have their baby in Bradford to the project during their routine maternity appointments. The study aims to link routine data together from a variety of health, education and social care datasets in Bradford. For example; GPs and Dentists, NHS Digital, Education and Schools, Dept. of Work and Pensions, Voluntary Organisations, Local Authority and Social Care, and Other Research Studies. This will allow us to build up a clearer image of people's lives and answer questions to improve health, care and services for families.  \n\nThis primary care dataset covers all participants from the BiB4All study, which means it can be linked to all BiB4All datasets. This includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications and vaccinations for ~10,000 BiB4All participants. \n\nSystmOne data is easily searchable by using Read CTV3 or SNOMED.",
    "url": "https://healthdatagateway.org/en/dataset/750",
    "uid": "5d5d791b-8ef8-4619-928c-3af7dd8d81e1",
    "datasource_id": 750,
    "source": "HDRUK"
  },
  {
    "id": 882,
    "name": "BiB4All SystmOne HV Primary Care Medication",
    "description": "BiB4All is a data linkage cohort study of babies born in Bradford and their mothers. Community midwives within Bradford invite every woman due to have their baby in Bradford to the project during their routine maternity appointments. The study aims to link routine data together from a variety of health, education and social care datasets in Bradford. For example; GPs and Dentists, NHS Digital, Education and Schools, Dept. of Work and Pensions, Voluntary Organisations, Local Authority and Social Care, and Other Research Studies. This will allow us to build up a clearer image of people's lives and answer questions to improve health, care and services for families.  \n\nThis primary care dataset covers all participants from the BiB4All study, which means it can be linked to all BiB4All datasets. This includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications and vaccinations for ~10,000 BiB4All participants. \n\nSystmOne data is easily searchable by using BNF, DM+D or SNOMED CT codes.",
    "url": "https://healthdatagateway.org/en/dataset/749",
    "uid": "3c8df2d3-f685-4c70-b745-45e92d4aa7a8",
    "datasource_id": 749,
    "source": "HDRUK"
  },
  {
    "id": 883,
    "name": "BiB4All SystmOne CH Primary Care CTV3",
    "description": "BiB4All is a data linkage cohort study of babies born in Bradford and their mothers. Community midwives within Bradford invite every woman due to have their baby in Bradford to the project during their routine maternity appointments. The study aims to link routine data together from a variety of health, education and social care datasets in Bradford. For example; GPs and Dentists, NHS Digital, Education and Schools, Dept. of Work and Pensions, Voluntary Organisations, Local Authority and Social Care, and Other Research Studies. This will allow us to build up a clearer image of people's lives and answer questions to improve health, care and services for families.  \n\nThis primary care dataset covers all participants from the BiB4All study, which means it can be linked to all BiB4All datasets. This includes basic patient demographics, information about consultation events, medical history including diagnoses and investigations, laboratory results, medications and vaccinations for ~10,000 BiB4All participants. \n\nSystmOne data is easily searchable by using Read CTV3 or SNOMED CT codes.",
    "url": "https://healthdatagateway.org/en/dataset/748",
    "uid": "6b911118-d336-4dbf-8582-b806e7b8a6fd",
    "datasource_id": 748,
    "source": "HDRUK"
  },
  {
    "id": 884,
    "name": "HIV-POGO",
    "description": "HIV POGO is a cross sectional deep profiling study examining sensory profiles, cognitive function and patient reported outcome measures. 148 participants were recruited.  It includes multiple measures including: \n•\tPatient reported pain outcome measures \n•\tSites of pain \n•\tPsychophysical pain measures (quantitative sensory testing and conditioned pain modulation) \n•\tObjective cognitive function measures \n•\tPatient reported measures of mood, sleep, quality of life and function",
    "url": "https://healthdatagateway.org/en/dataset/747",
    "uid": "e171a080-a4cd-4e64-9c2b-e72beb7cac78",
    "datasource_id": 747,
    "source": "HDRUK"
  },
  {
    "id": 885,
    "name": "CAPS",
    "description": "A longitudinal profiling study using a multimodal battery of measures which included the following:\n•\telectrophysiological measures of nerve conduction\n•\tparameters of pain, pain interference and symptom severity\n•\tsomatosensory function and quantitative sensory testing derived sensory phenotype\n•\tsleep impairment\n•\tmood\n•\tpain-related worrying\n•\tfunctional impairment\n•\t76 participants were recruited and completed baseline data; 3 month post-surgical data n=65; 6 month post-surgical data n=61\n•\tQST results for median nerve distribution was compared to a convenience sample on 54 healthy volunteers (QST healthy volunteer study N=106)",
    "url": "https://healthdatagateway.org/en/dataset/746",
    "uid": "a4c595a6-cb41-4743-bea3-feb4d4c72091",
    "datasource_id": 746,
    "source": "HDRUK"
  },
  {
    "id": 886,
    "name": "DOLORISK",
    "description": "The DOLORisk cohort at the University of Oxford and Imperial College London is a cross-sectional deeply phenotyped cohort of nearly 1,000 participants with painful and painless neuropathy, focussing on diabetic polyneuropathy and extreme/rare neuropathic pain disorders. The dataset comprises detailed phenotypic data from questionnaires, nerve excitability testing, quantitative sensory testing and clinical examination.",
    "url": "https://healthdatagateway.org/en/dataset/745",
    "uid": "0a467180-a898-44d8-a586-ca4762de362e",
    "datasource_id": 745,
    "source": "HDRUK"
  },
  {
    "id": 887,
    "name": "BathTAP Headache Cohort 2",
    "description": "People who experience frequent headaches completed a battery of attention-based computer tasks on two separate occasions: once when they were experiencing a headache, and again when headache free. The tasks were: a choice reaction time task, flanker task, n-back task, attentional switching task (cued and uncued), and dual task. Outcomes are speed of response and/or accuracy scores on these tasks.",
    "url": "https://healthdatagateway.org/en/dataset/744",
    "uid": "2d79feab-3046-4735-a4ec-a4c11c9c1aa8",
    "datasource_id": 744,
    "source": "HDRUK"
  },
  {
    "id": 888,
    "name": "HIV PiNS",
    "description": "A cross-sectional deep profiling study examining HIV-SN was conducted in people\nliving with HIV in a high resource setting using a battery of measures which included the following:\n- parameters of pain and sensory symptoms\n- sensory innervation\n- psychological state \n- quality of life\n36 healthy volunteers and 66 HIV infected participants were recruited for this study.",
    "url": "https://healthdatagateway.org/en/dataset/743",
    "uid": "1abc859e-4ac6-431a-b550-af1afb36df1b",
    "datasource_id": 743,
    "source": "HDRUK"
  },
  {
    "id": 889,
    "name": "BathTAP Switching Cohort 3",
    "description": "Cross-sectional study of people recruited online from the general population. Participants reported their pain status (no pain, current, recurrent pain) and completed four different versions of a remotely delivered computerized version of the switching task (alternating binary response, alternating numerical response, cued binary response, cued numerical response). Outcomes were speed of response and/or accuracy scores on these four tasks. Study 3.",
    "url": "https://healthdatagateway.org/en/dataset/742",
    "uid": "a20643c7-ef15-4f7d-9e48-5a9f23a645c1",
    "datasource_id": 742,
    "source": "HDRUK"
  },
  {
    "id": 890,
    "name": "Neuropathic Pain Profiling in Retroviral Infection Study (NIPPR)",
    "description": "The neuropathic pain profiling in retroviral infection Study: The NIPPR Study. This study contains pain related information reported by people living with Human T-lymphotropic cell virus (HTLV-1) infection. It includes those with and without neuropathic pain. \nNIPPR is a cross sectional deep profiling study examining sensory profiles and patient reported outcome measures. It also includes skin biopsy data.  22/40 participants have currently been recruited.  It includes multiple measures including: \n•\tPatient reported pain outcome measures \n•\tSites of pain \n•\tPsychophysical pain measures (quantitative sensory testing and conditioned pain modulation) \n•\tIntra-epidermal nerve fibre density \n•\tPatient reported measures of mood, sleep, quality of life and function.",
    "url": "https://healthdatagateway.org/en/dataset/741",
    "uid": "41f5c96c-a27a-4015-be50-af967b6c4bac",
    "datasource_id": 741,
    "source": "HDRUK"
  },
  {
    "id": 891,
    "name": "BathTAP Switching Cohort 2",
    "description": "Cross-sectional study of people recruited online from the general population. Participants reported their pain status (no pain, current, recurrent pain) and completed three different versions of a remotely delivered computerized version of the switching task (random, cued, uncued). Outcomes were speed of response and/or accuracy scores on these tasks. Study 2.",
    "url": "https://healthdatagateway.org/en/dataset/740",
    "uid": "ea1aca1f-6ddb-44a0-b530-0645a26b2bee",
    "datasource_id": 740,
    "source": "HDRUK"
  },
  {
    "id": 892,
    "name": "Decode-ME",
    "description": "DecodeME aims to find genetic causes of why people become ill with ME/CFS. It aims to recruit 25,000 participants in the UK who have been diagnosed with ME/CFS by a health professional. Participants are assessed according to Canadian Consensus and Institute of Medicine (NAM) criteria. Participant data has been collected using the CureME Questionnaire with additional questions. 5,000 of these participants will have been diagnosed with ME/CFS following COVID-19 infection. Phenotype data include information relating to post-exertional malaise, fatigue and pain symptoms.",
    "url": "https://healthdatagateway.org/en/dataset/739",
    "uid": "a71d0b2a-620c-4c04-b7bb-45843461db9f",
    "datasource_id": 739,
    "source": "HDRUK"
  },
  {
    "id": 893,
    "name": "BathTAP Updating Cohort 1",
    "description": "Cross-sectional study of people recruited online from the general population. Participants reported their pain status (no pain, acute pain, chronic pain) and completed a remotely delivered computerized version of the n-back task. Outcomes were speed of response and/or accuracy scores on these tasks.",
    "url": "https://healthdatagateway.org/en/dataset/738",
    "uid": "90131a34-3cc8-4a74-b72f-d9600cfa2808",
    "datasource_id": 738,
    "source": "HDRUK"
  },
  {
    "id": 894,
    "name": "BathTAP Headache Cohort 1",
    "description": "People who experience frequent headaches completed a battery of attention-based computer tasks on two separate occasions: once when they were experiencing a headache, and again when headache free. The tasks were: a flanker task, n-back task, attentional switching task, and dual task. Outcomes are speed of response and/or accuracy scores on these tasks.",
    "url": "https://healthdatagateway.org/en/dataset/737",
    "uid": "9cff3fc9-25d6-49fe-8218-dbbc3b3bc1ab",
    "datasource_id": 737,
    "source": "HDRUK"
  },
  {
    "id": 895,
    "name": "Oxford Carpal Tunnel Syndrome (CTS) Cohort",
    "description": "This is a cohort study in patients with carpal tunnel syndrome. It contains both cross sectional (pre-surgery) as well as longitudinal data from patients undergoing decompression surgery (pre and 6 months post surgery). The dataset contains phenotypic data (quantitative sensory testing, electrodiagnostic testing, clinical examination, questionnaires) and data derived from bio-samples (e.g., skin biopsies).",
    "url": "https://healthdatagateway.org/en/dataset/736",
    "uid": "cd3555b0-514a-4693-8f68-c8267a8e9939",
    "datasource_id": 736,
    "source": "HDRUK"
  },
  {
    "id": 896,
    "name": "1958 National Child Development Study - Pain data",
    "description": "The 1958 National Child Development Study (NCDS) is following the lives of an initial 17,415 people born in England, Scotland and Wales in a single week of 1958. It started in 1958 at birth, as the Perinatal Mortality Survey. Over the course of cohort members’ lives, information has been collected on their physical and educational development, economic circumstances, employment, family life, health behaviour, wellbeing, social participation and attitudes.\n\nThis collection specifically relates to a short pain survey (self-completion booklet) completed by NCDS participants in 2002-3.",
    "url": "https://healthdatagateway.org/en/dataset/735",
    "uid": "33092502-12ef-4bc0-8400-5f63915cbf33",
    "datasource_id": 735,
    "source": "HDRUK"
  },
  {
    "id": 897,
    "name": "Optimum Patient Care Research Database (OPCRD)",
    "description": "About OPCRD\n\nOptimum Patient Care Research Database (OPCRD) is a real-world, longitudinal, research database that provides anonymised data to support scientific, medical, public health and exploratory research. OPCRD is established, funded and maintained by Optimum Patient Care Limited (OPC) – which is a not-for-profit social enterprise that has been providing quality improvement programmes and research support services to general practices across the UK since 2005.\n\nKey Features of OPCRD\n\nOPCRD has been purposefully designed to facilitate real-world data collection and address the growing demand for observational and pragmatic medical research, both in the UK and internationally. Data held in OPCRD is representative of routine clinical care and thus enables the study of ‘real-world’ effectiveness and health care utilisation patterns for chronic health conditions.\n\nOPCRD unique qualities which set it apart from other research data resources:\n•\tDe-identified electronic medical records of more than 24.9 million patients\n•\tOPCRD covers all major UK primary care clinical systems\n•\tOPCRD covers approximately 35% of the UK population\n•\tOne of the biggest primary care research networks in the world, with over 1,175 practices\n•\tLinked patient reported outcomes for over 68,000 patients including Covid-19 patient reported data\n•\tLinkage to secondary care data sources including Hospital Episode Statistics (HES)\n\nData Available in OPCRD\n\nOPCRD has received data contributions from over 1,175 practices and currently holds de-identified research ready data for over 24.9 million patients or data subjects. This includes longitudinal primary care patient data and any data relevant to the management of patients in primary care, and thus covers all conditions. The data is derived from both electronic health records (EHR) data and patient reported data from patient questionnaires delivered as part of quality improvement. OPCRD currently holds over 68,000 patient reported questionnaire data on Covid-19, asthma, COPD and rare diseases. \n\nApprovals and Governance\n\nOPCRD has NHS research ethics committee (REC) approval to provide anonymised data for scientific and medical research since 2010, with its most recent approval in 2020 (NHS HRA REC ref: 20/EM/0148). OPCRD is governed by the Anonymised Data Ethics and Protocols Transparency committee (ADEPT). All research conducted using anonymised data from OPCRD must gain prior approval from ADEPT. Proceeds from OPCRD data access fees and detailed feasibility assessments are re-invested into OPC services for the continued free provision of patient quality improvement programmes for contributing practices and patients.\n\nFor more information on OPCRD please visit: https://opcrd.co.uk/",
    "url": "https://healthdatagateway.org/en/dataset/734",
    "uid": "ac755244-4143-4629-9170-a9e8fc98bb1e",
    "datasource_id": 734,
    "source": "HDRUK"
  },
  {
    "id": 898,
    "name": "BRIAN App - Patient data; treatments, PROMs, multi-modal data and demographics",
    "description": "# Summary of data captured and available for analysis in BRIAN:\n\n## Tumour Log\n\nDetails of users' condition and how it changes over time.  \n\n*   Tumour Type, grade, location, status, date of diagnosis\n*   Historical view of changes over time\n\n## Demographics (primary)\n\nAll BRIAN users provide the following information when signing up to the app.\n\n*   Month / year of birth\n*   Month / year of death (where patient is deceased)\n*   Sex\n*   Country of residence\n*   UK County\n\n## Demographics (secondary)\n\nThese fields are non-mandatory so not as well populated as the primary demographics.\n\n*   Qualifications\n*   Job Status\n*   Income\n*   Marital Status\n\n## Treatments & Appointments\n\nUsers can use BRIAN to track all of their health visits.\n\n*   Health visit description\n*   Start / End dates\n*   Number of treatments\n*   Radiotherapy dosage info\n\n## Medications\n\nBRIAN allows its users to set up a medication schedule and uses mobile notifications to remind users to take them.\n\n*   Schedule: medication types, start/end dates, dosage, and the schedule for taking the medication\n*   Reminders: reminder times, and the times logged by users for when they actually took the medication\n\n## Wearables\n\nUsers can opt in to sharing wearable data with the app. N.B. the quantity data gathered varies by the users’ wearable device.\n\n*   Activity data: steps, distance travelled, active minutes, flights stairs climbed\n*   Sleep data: total sleep, light sleep, deep sleep, REM sleep, wake sleep, time in bed\n*   Heart data: average, min, max & resting heart rate\n*   Height & Weight data: height, weight, BMI\n\n## Challenges\n\nThese small games / activities were created to capture some unique data points that could be used for analysis.\n\n*   Stability Challenge: users have to hold device one-handed and try to keep a ball in a target using very fine motor movements. Scores over time can be shared.\n*   Memory Challenge: users are shown a series of images one after another and are required to indicated whether the current image was the same as the last image. Scores over time can be shared.\n*   Speech Challenge: users read aloud a passage of text & the audio is captured. Voice clips & Azure audio analysis data can be shared.\n*   Selfie challenge: users take a photo of their face each day. Azure Cognizant analysis can be shared.\n\n## Quality of Life\n\nThere are two means for users to track quality of life in BRIAN:\n\n*   QoL Check-ins; a regular check in where users record how they’re feeling physically, cognitively & emotionally, and track symptoms they’re experiencing.\n*   EORTC-QLQ-C30 + BN20 (Adults) and PEDSQL (Paediatrics)\n\n## Clinical Trial History\n\n*   Record of clinical trials attended, as entered by users\n\n## Benefits Claimed\n\n*   Record of state benefits patient is claiming, as entered by users\n\n## Diet Survey\n\n*   An internally authored survey that gathers information on dietary behaviours in patients.",
    "url": "https://healthdatagateway.org/en/dataset/733",
    "uid": "37c82bb7-eaae-4f16-97b6-045373b7ba18",
    "datasource_id": 733,
    "source": "HDRUK"
  },
  {
    "id": 899,
    "name": "GoDARTS",
    "description": "DARTS (Diabetes Audit and Research in Tayside Scotland) was started in 1996, and aimed to identify all patients with diabetes within the wider Tayside region, through electronic record linkage, in order to improve health care over and above that which was practical through existing general practice lists alone.\n\nIn 1998, consenting patients within this electronic database were recruited to the Genetics of DARTS (GoDARTS) study and invited to provide a blood sample for DNA extraction, for research purposes. At the same time, they were invited to provide phenotypic data (clinical and lifestyle factors), through questionnaires and clinical examination.\n\nThis resource was intended to help identify the relative contribution of specific genetic and environmental factors that are associated with disease onset, progression and response to treatment.",
    "url": "https://healthdatagateway.org/en/dataset/732",
    "uid": "8ca05f55-af73-42bd-b682-851c2c54a6cc",
    "datasource_id": 732,
    "source": "HDRUK"
  },
  {
    "id": 900,
    "name": "The Fenland Study",
    "description": "Information for researchers web page can be found at: https://doi.org/10.22025/2017.10.101.00001\n\nData sharing portal page, including data dictionary links: https://epi-meta.mrc-epid.cam.ac.uk/studies/fenland/release.shtml",
    "url": "https://healthdatagateway.org/en/dataset/731",
    "uid": "8ca5cc9c-bb4e-4fa4-b430-165720cd9634",
    "datasource_id": 731,
    "source": "HDRUK"
  },
  {
    "id": 901,
    "name": "The EPIC-Norfolk Study",
    "description": "For study description and data dictionary please see https://www.epic-norfolk.org.uk/",
    "url": "https://healthdatagateway.org/en/dataset/730",
    "uid": "fe610510-fe78-461e-9c73-54dae67cade4",
    "datasource_id": 730,
    "source": "HDRUK"
  },
  {
    "id": 902,
    "name": "Better Rx BRIT2 Patient GP Dataset",
    "description": "BetterRx utilises primary care electronic health records of frail and elderly (65 years+) people in the UK; containing basic patient demographics such as year of birth, sex, ethnicity, BMI and smoking history. The dataset includes medical history to identify known health conditions (e.g. diabetes diagnosis), prescription history to identify all known prescribed medications over time, linked outcomes as defined in the primary care record, hospital episode statistics (HES) and mortality (ONS), linked area deprivation defined using linked practice and patient level index of multiple deprivation (IMD). \nThe temporal start date of GP data varies by practice, two year historic data at the point of sign up.",
    "url": "https://healthdatagateway.org/en/dataset/729",
    "uid": "656b2c22-9e57-4f15-9b3c-1969da6495ca",
    "datasource_id": 729,
    "source": "HDRUK"
  },
  {
    "id": 903,
    "name": "2019 UK Parkinson's Audit - Occupational therapy",
    "description": "The UK Parkinson’s Audit assesses care provided to people with Parkinson's by a range of clinical specialties against evidence-based guidelines.\n\nThe occupational therapy audit includes services providing care to people specifically in connection with their Parkinson’s. Services submit data on their model of service delivery and the assessments which are routinely carried out (service audit) and data on the care provided over the last 12 months to at least ten of their consecutive patients seen during the five-month data collection period (patient audit). No patient identifiers are collected. \n\nThe audit is open to services across the UK - secondary care (non-acute) and community services. A service is self-defined - for example an individual therapist or a group of therapists seeing the same cohort of patients. There can therefore be more than one service taking part in any Trust or setting.\n\nThe 2019 audit includes data from 82 occupational therapy services covering 958 patient cases.\n\nThe data is available by UK Parkinson’s Excellence Network region. The UK Parkinson's Excellence Network is a network of health and social care professionals working to improve Parkinson's services administered with support from Parkinson’s UK. Professionals can join the Network to access resources, increase their knowledge of Parkinson's and collaborate with people affected by Parkinson's to transform health and care services. \n\nThe audit is the recognised quality improvement tool for the Excellence Network, providing an important baseline against which progress can be measured, informing national, regional and local service improvement priorities and plans to achieve better services for people living with the condition. \n\nThe regions are: \n\nEngland:\nCheshire and Merseyside\nEast of England\nEast Midlands\nGreater Manchester, Lancashire and South Cumbria\nLondon\nNorth East and Cumbria\nPeninsula\nSouth East Coast\nSouth West\nThames Valley\nWessex\nWest Midlands\nYorkshire and Humber\n\nScotland:\nNorth Scotland\nSouth and East Scotland\nWest Scotland\n\nNorthern Ireland:\nNorthern Ireland\n\nWales:\nNorth and Mid Wales\nSouth Wales\n\nOther:\nGuernsey\nIsle of Man\nRepublic of Ireland\n\nInformation about the regions can be found here:  https://www.parkinsons.org.uk/professionals/local-parkinsons-excellence-network-groups",
    "url": "https://healthdatagateway.org/en/dataset/727",
    "uid": "9890eee9-44d4-49f7-8674-16c05a5f82e0",
    "datasource_id": 727,
    "source": "HDRUK"
  },
  {
    "id": 904,
    "name": "2019 UK Parkinson's Audit - Physiotherapy",
    "description": "The UK Parkinson's Audit assesses care provided to people with Parkinson's by a range of clinical specialties against evidence-based guidelines.  \n\nThe physiotherapy audit includes services which provide care to people in connection with their Parkinson's. Services submit data on their model of service delivery and the assessments which are routinely carried out (service audit) and data on the care provided over the last 12 months to at least twenty of their consecutive patients seen during the five-month data collection period (patient audit). No patient identifiers are collected.\n\nThe audit is open to services across the UK - secondary care (non-acute) and community services. A service is self-defined and can consist of a single therapist or a group of therapists seeing the same cohort of patients. There can therefore be more than one service taking part in any Trust or setting.\n\nThe 2019 audit includes data from 153 physiotherapy services covering 2099 patient cases.\n\nThe data is available by UK Parkinson's Excellence Network region. The UK Parkinson's Excellence Network is a network of health and social care professionals working to improve Parkinson's services administered with support from Parkinson's UK. Professionals can join the Network to access resources, increase their knowledge of Parkinson's and collaborate with people affected by Parkinson's to transform health and care services.\n\nThe audit is the recognised quality improvement tool for the Excellence Network, providing an important baseline against which progress can be measured, informing national, regional and local service improvement priorities and plans to achieve better services for people living with the condition.\n\nThe regions are: \n\nEngland:\nCheshire and Merseyside\nEast of England\nEast Midlands\nGreater Manchester, Lancashire and South Cumbria\nLondon\nNorth East and Cumbria\nPeninsula\nSouth East Coast\nSouth West\nThames Valley\nWessex\nWest Midlands\nYorkshire and Humber\n\nScotland:\nNorth Scotland\nSouth and East Scotland\nWest Scotland\n\nNorthern Ireland:\nNorthern Ireland\n\nWales:\nNorth and Mid Wales\nSouth Wales\n\nOther:\nGuernsey\nIsle of Man\nRepublic of Ireland\n\nInformation about the regions can be found here:  https://www.parkinsons.org.uk/professionals/local-parkinsons-excellence-network-groups",
    "url": "https://healthdatagateway.org/en/dataset/726",
    "uid": "27690c58-a5da-4359-909a-8e19ff6ca463",
    "datasource_id": 726,
    "source": "HDRUK"
  },
  {
    "id": 905,
    "name": "2019 UK Parkinson's Audit - Elderly care and neurology",
    "description": "The UK Parkinson’s Audit assesses care provided to people with Parkinson's by a range of clinical specialities against evidence-based guidelines.\n\nThe audit of patient management covers elderly care and neurology services and the routine care they provide to people in connection with their Parkinson’s. Services submit data on their model of service delivery and the assessments which are routinely carried out (service audit) and data on the care provided over the last 12 months to at least twenty of their consecutive patients seen during the five-month data collection period (patient audit). No patient identifiers are collected. \n\nThe audit is open to services across the UK which see patients for the routine review of their Parkinson’s - secondary care (non-acute) and community services. A service is self-defined - for example an individual consultant, a consultant and a specialist nurse, a group of consultants seeing the same cohort of patients, or a specialist nurse working in a community setting. There can therefore be more than one service taking part in any Trust or hospital.\n\nThe 2019 audit includes data from 266 elderly care and neurology services covering 6256 patient cases.\n\nThe data is available by UK Parkinson’s Excellence Network region. The UK Parkinson's Excellence Network is a network of health and social care professionals working to improve Parkinson's services administered with support from Parkinson’s UK. Professionals can join the Network to access resources, increase their knowledge of Parkinson's and collaborate with people affected by Parkinson's to transform health and care services. \n\nThe audit is the recognised quality improvement tool for the Excellence Network, providing an important baseline against which progress can be measured,  informing national, regional and local service improvement priorities and plans to achieve better services for people living with the condition. \n\nThe regions are: \n\nEngland:\nCheshire and Merseyside\nEast of England\nEast Midlands\nGreater Manchester, Lancashire and South Cumbria\nLondon\nNorth East and Cumbria\nPeninsula\nSouth East Coast\nSouth West\nThames Valley\nWessex\nWest Midlands\nYorkshire and Humber\n\nScotland:\nNorth Scotland\nSouth and East Scotland\nWest Scotland\n\nNorthern Ireland:\nNorthern Ireland\n\nWales:\nNorth and Mid Wales\nSouth Wales\n\nOther:\nGuernsey\nIsle of Man\nRepublic of Ireland\n\nInformation about the regions can be found here:  https://www.parkinsons.org.uk/professionals/local-parkinsons-excellence-network-groups",
    "url": "https://healthdatagateway.org/en/dataset/725",
    "uid": "0cac3870-dd13-44fc-97c2-4bdb67550b39",
    "datasource_id": 725,
    "source": "HDRUK"
  },
  {
    "id": 906,
    "name": "2019 UK Parkinson's Audit - Speech and language therapy",
    "description": "The UK Parkinson’s Audit assesses care provided to people with Parkinson's by a range of clinical specialties against evidence-based guidelines.\n\nThe speech and language therapy audit includes services providing care to people specifically in connection with their Parkinson’s. Services submit data on their model of service delivery and the assessments which are routinely carried out (service audit) and data on the care provided over the last 12 months to at least ten of their consecutive patients seen during the five-month data collection period (patient audit). No patient identifiers are collected. \n\nThe audit is open to services across the UK - secondary care (non-acute) and community services. A service is self-defined - for example an individual therapist or a group of therapists seeing the same cohort of patients. There can therefore be more than one service taking part in any Trust or setting.\n\nThe 2019 audit includes data from 79 speech and language therapy services covering 1022 patient cases.\n\nThe data is available by UK Parkinson’s Excellence Network region. The UK Parkinson's Excellence Network is a network of health and social care professionals working to improve Parkinson's services administered with support from Parkinson’s UK. Professionals can join the Network to access resources, increase their knowledge of Parkinson's and collaborate with people affected by Parkinson's to transform health and care services. \n\nThe audit is the recognised quality improvement tool for the Excellence Network, providing an important baseline against which progress can be measured, informing national, regional and local service improvement priorities and plans to achieve better services for people living with the condition. \n\nThe regions are: \n\nEngland:\nCheshire and Merseyside\nEast of England\nEast Midlands\nGreater Manchester, Lancashire and South Cumbria\nLondon\nNorth East and Cumbria\nPeninsula\nSouth East Coast\nSouth West\nThames Valley\nWessex\nWest Midlands\nYorkshire and Humber\n\nScotland:\nNorth Scotland\nSouth and East Scotland\nWest Scotland\n\nNorthern Ireland:\nNorthern Ireland\n\nWales:\nNorth and Mid Wales\nSouth Wales\n\nOther:\nGuernsey\nIsle of Man\nRepublic of Ireland\n\nInformation about the regions can be found here:  https://www.parkinsons.org.uk/professionals/local-parkinsons-excellence-network-groups",
    "url": "https://healthdatagateway.org/en/dataset/724",
    "uid": "c7f7541b-5030-40b9-962a-4aba44af7e1a",
    "datasource_id": 724,
    "source": "HDRUK"
  },
  {
    "id": 907,
    "name": "2019 UK Parkinson's Audit - Patient Reported Experience Measure (PREM)",
    "description": "The UK Parkinson’s Audit assesses care provided to people with Parkinson's by a range of clinical specialties against evidence-based guidelines. The audit is open to services across the UK - secondary care (non-acute) and community services.\n\nThe audit includes a Patient Reported Experience Measure (PREM) which takes the form of a patient questionnaire which can be completed by any individual who attends one of the services (elderly care, neurology, physiotherapy, occupational therapy and speech and language therapy) participating in the audit. The questionnaire can be completed by the person with Parkinson’s or by a carer on their behalf.  No patient identifiers are collected. \n\nThe PREM questions cover the individual’s experiences of the whole of their Parkinson’s service, not only the specialty which has handed them the questionnaire to complete.\n\nThe questionnaire is completed and sealed by the participant - the service does not have sight of the completed questionnaire. Each service must return at least ten questionnaires in order for their PREM data to be included in the analysis.\n\nThe 2019 audit received 8,247 completed PREM questionnaires from 451 (77.7%) of the participating services.\n\nThe data is available by UK Parkinson’s Excellence Network region. The UK Parkinson's Excellence Network is a network of health and social care professionals working to improve Parkinson's services administered with support from Parkinson’s UK. Professionals can join the Network to access resources, increase their knowledge of Parkinson's and collaborate with people affected by Parkinson's to transform health and care services. \n\nThe audit is the recognised quality improvement tool for the Excellence Network, providing an important baseline against which progress can be measured, informing national, regional and local service improvement priorities and plans to achieve better services for people living with the condition. \n\nThe regions are: \n\nEngland:\nCheshire and Merseyside\nEast of England\nEast Midlands\nGreater Manchester, Lancashire and South Cumbria\nLondon\nNorth East and Cumbria\nPeninsula\nSouth East Coast\nSouth West\nThames Valley\nWessex\nWest Midlands\nYorkshire and Humber\n\nScotland:\nNorth Scotland\nSouth and East Scotland\nWest Scotland\n\nNorthern Ireland:\nNorthern Ireland\n\nWales:\nNorth and Mid Wales\nSouth Wales\n\nOther:\nGuernsey\nIsle of Man\nRepublic of Ireland\n\nInformation about the regions can be found here:  https://www.parkinsons.org.uk/professionals/local-parkinsons-excellence-network-groups",
    "url": "https://healthdatagateway.org/en/dataset/723",
    "uid": "6a6c4568-59e9-4760-89d6-07558a24944c",
    "datasource_id": 723,
    "source": "HDRUK"
  },
  {
    "id": 908,
    "name": "UK Renal Registry - CKD and AKI on Dialysis Dataset",
    "description": "The dataset contains patient-level data for adults under the care of NHS hospital kidney centres in the UK who have chronic kidney disease (CKD - mostly stages 4 and 5), as well as adults and children on kidney replacement therapy (KRT) for end-stage kidney disease (ESKD), and adults with an acute kidney injury (AKI) on dialysis at a kidney centre. The data are collected daily, quarterly or annually and include patient identifiable information, socio-demographic and clinical data. Since 2007 the UKRR has had 100% coverage of all people under the care of kidney centres in the UK on KRT and each year collects data on over 70,000 people on KRT. Coverage of people with CKD under the care of renal centres (excluding those on KRT) is lower - data collection commenced in 2016 and has increased from almost 17,000 adults recorded with CKD stages 4 and 5 up to the end of 2016, to more than 80,000 adults by the end of 2022. These numbers will continue to rise as more kidney centres submit data. In 2022 the UKRR received data from only 18 of the 67 UK adult kidney centres. In total, this dataset holds information on over 300,000 people on KRT or with advanced CKD. See here for further information: https://ukkidney.org/audit-research/data-permissions/data/ukrr-ckdaki-clinical-dataset.",
    "url": "https://healthdatagateway.org/en/dataset/722",
    "uid": "b7152f21-0f2c-4ed9-a013-51e725242f94",
    "datasource_id": 722,
    "source": "HDRUK"
  },
  {
    "id": 909,
    "name": "UK Renal Registry - Kidney PREM dataset",
    "description": "The dataset contains self-reported patient-level data for adults  adults and young people or their parents/carers with CKD who are under the care of NHS hospital renal centres in England, Northern Ireland, Scotland and Wales. The data are collected annually via a national survey. Kidney PREM data are anonymised and include socio-demographic information and 39 questions measuring patient experience of kidney care. The kidney PREM has been collected since 2016 and in 2019 and almost 16,500 people treated at almost all of the UK adult renal centres participated. See here for further information: https://renal.org/audit-research/data-permissions/data/ukrr-ckd-patient-measures-dataset/kidney-prem-data",
    "url": "https://healthdatagateway.org/en/dataset/721",
    "uid": "ea068fd4-1a96-4ed5-a010-8930afb968bb",
    "datasource_id": 721,
    "source": "HDRUK"
  },
  {
    "id": 910,
    "name": "UK Renal Registry - AKI Lab Alerts Dataset",
    "description": "The dataset contains patient-level data for adults and children who trigger a laboratory e-alert for an acute kidney injury (AKI) in primary or secondary care in England. The data are collected monthly and include patient identifiable information, and limited socio-demographic and clinical data. Data collection began in 2015 and by 2022, 98% of the 192 laboratories in England submitted data that could be used in the UKRR annual AKI report that was published in Dec 2023.  Each year the UKRR collects data on about 500,000 people with a suspected AKI in England - this includes more than 1.5 million AKI e-alerts. This data collection is mandated by NHS England and alerts are generated by an algorithm embedded in the laboratory information management systems. See here for further information: https://ukkidney.org/audit-research/data-permissions/data/ukrr-lab-aki-dataset",
    "url": "https://healthdatagateway.org/en/dataset/720",
    "uid": "c38541a7-cf43-4bfd-928d-75613abc8e3f",
    "datasource_id": 720,
    "source": "HDRUK"
  },
  {
    "id": 911,
    "name": "UK Renal Registry - Your Health Survey dataset",
    "description": "The dataset contains self-reported patient-level data for adults with chronic kidney disease (CKD) who are under the care of NHS hospital renal centres in England. The data are collected using a survey called Your Health Survey that includes identifiable information, socio-demographic information, a quality of life measure (EQ5D-5L), symptom measure (POS-S Renal) and patient activation measure (PAM). In 2016 and 2017 over 3,000 Your Health Surveys were collected by the UKRR as part of the quality improvement project Transforming participation in chronic kidney disease, and in 2018 Transforming participation 2 used the surveys to measure a coaching intervention in over 200 patients. See here for further information: https://renal.org/audit-research/data-permissions/data/ukrr-ckd-patient-measures-dataset/pam-and-prom-data",
    "url": "https://healthdatagateway.org/en/dataset/719",
    "uid": "14ed4ab3-470b-45c8-b48f-27fd382a912e",
    "datasource_id": 719,
    "source": "HDRUK"
  },
  {
    "id": 912,
    "name": "UK Renal Registry - COVID-19 dataset",
    "description": "The dataset contains patient-level data for adults and children with chronic kidney disease (CKD) or adults with an acute kidney injury (AKI) on dialysis who are under the care of NHS hospital renal centres in England, Northern Ireland and Wales and who have a positive laboratory test for SARS-CoV-2. The data were collected weekly, but this is now moving to monthly, and include patient identifiable information and limited socio-demographic and clinical data. Public Health Scotland submits aggregate data to the UKRR on a monthly basis. Data collection commenced in March 2020 and coverage of renal centres is very good (see COVID-19 surveillance reports here: https://ukkidney.org/audit-research/publications-presentations/report/covid-19-surveillance-reports).",
    "url": "https://healthdatagateway.org/en/dataset/718",
    "uid": "2a3dba77-34ff-4781-b273-1b3d54d11f32",
    "datasource_id": 718,
    "source": "HDRUK"
  },
  {
    "id": 913,
    "name": "Investigating Musculoskeletal Health and Wellbeing (IMH&W)",
    "description": "In an ageing population, pain, frailty and disability frequently coexist across a wide range of musculoskeletal diagnoses, but their associations remain incompletely understood. The Investigating Musculoskeletal Health and Wellbeing (IMH&W) study aims to measure and characterise the development and progression of pain, frailty and disability, and to identify discrete subgroups and their associations. The survey will form a longitudinal context for nested research, permitting targeted recruitment of participants for qualitative, observational and interventional studies; helping to understand recruitment bias in clinical studies; and providing a source cohort for cohort randomised controlled trials.",
    "url": "https://healthdatagateway.org/en/dataset/716",
    "uid": "f3dbec9c-8b53-4f8f-b5d6-8858ef022180",
    "datasource_id": 716,
    "source": "HDRUK"
  },
  {
    "id": 914,
    "name": "internet-based exercises aimed at treating knee osteoarthritis (iBEAT-OA)",
    "description": "Knee osteoarthritis (OA) is the most common joint disease worldwide. As of today, there are no disease-modifying drugs, but there is evidence that muscle strengthening exercises can substantially reduce pain and improve function in this disorder, and one very well tested physiotherapy protocol is the ‘Better Management of Patients with Osteoarthritis’ developed in Sweden. Given the high prevalence of knee OA, a potentially cost-effective, digitally delivered approach to treat knee OA should be trialled. This study aims to explore the benefits of iBEAT-OA (Internet-Based Exercise programme Aimed at Treating knee Osteoarthritis) in modulating pain, function and other health-related outcomes in individuals with knee OA.",
    "url": "https://healthdatagateway.org/en/dataset/715",
    "uid": "50d81a60-8feb-4314-baec-bf68be93e512",
    "datasource_id": 715,
    "source": "HDRUK"
  },
  {
    "id": 915,
    "name": "PANTHER study",
    "description": "The PANTHER (PANdemic Tracking of Healthcare woRkers) study, is hosted at University of Nottingham, Nottingham University Hospitals NHS Trust and supported by the Nottingham BRC. It aims to deepen the understanding of susceptibility to, immunity from and transmission of Sars-Cov2 (the virus that causes Covid-19) in an at risk population.\nThe PANTHER study measures chemical substances in blood (such as cytokines) that indicate inflammation, and other molecules that fight infection. Additionally, it will study genes to understand if these contribute to the risk of becoming become sick.\nThe PANTHER Study is supported by around 600 volunteers from both hospitals in Nottingham who have been participating in weekly blood draws since the peak of the pandemic and have at the heart of the study a unique resource of rich biological, genetic and other information kindly provided by our cohort of volunteer front line healthcare workers. The study samples are stored in the Nottingham Tissue Bank under a research licence, which is a Human Tissue Authority regulated tissue bank.",
    "url": "https://healthdatagateway.org/en/dataset/714",
    "uid": "596077a9-298b-44cc-aa7a-81f04cf5dcb3",
    "datasource_id": 714,
    "source": "HDRUK"
  },
  {
    "id": 916,
    "name": "Webex Cohort",
    "description": "Knee osteoarthritis (OA) is the most common joint disease worldwide. As of today, there are no disease-modifying drugs, but there is evidence that muscle strengthening exercises can substantially reduce pain and improve function in this disorder, and one very well tested physiotherapy protocol is the 'Better Management of Patients with Osteoarthritis' developed in Sweden. Given the high prevalence of knee OA, a potentially cost-effective, digitally delivered approach to treat knee OA should be trialled. This study aims to explore the benefits of iBEAT-OA (Internet-Based Exercise programme Aimed at Treating knee Osteoarthritis) in modulating pain, function and other health-related outcomes in individuals with knee OA.\n\nA randomised controlled trial was designed to evaluate the efficacy of a web-based exercise programme in a population with knee OA compared with standard community care provided by general practitioners (GPs) in the UK. We anticipate recruiting participants into equal groups. The intervention group (n=67) will exercise for 20-30 min daily for six consecutive weeks, whereas the control group (n=67) will follow GP-recommended routine care. The participants will be assessed using a Numerical Rating Scale, the Western Ontario and McMaster Universities Osteoarthritis Index, the Arthritis Research UK Musculoskeletal Health Questionnaire, the Pittsburgh Sleep Quality Index, 30 s sit to stand test, timed up and go test, quantitative sensory testing, musculoskeletal ultrasound scan, muscle thickness assessment of the vastus lateralis, and quadriceps muscles force generation during an isokinetic maximum voluntary contraction (MVC). Samples of urine, blood, faeces and synovial fluid will be collected to establish biomarkers associated with changes in pain and sleep patterns in individuals affected with knee OA. Standard parametric regression methods will be used for statistical analysis.",
    "url": "https://healthdatagateway.org/en/dataset/713",
    "uid": "9254f941-db57-4a97-922e-880b84855a21",
    "datasource_id": 713,
    "source": "HDRUK"
  },
  {
    "id": 917,
    "name": "Nottingham Trauma Registry",
    "description": "Nottingham hosts an orthopaedic trauma audit registry which has been carefully designed to measure the process of care and relevant outcomes. Initiated in 2016 this registry holds information on injuries, their management, their complications and other confounding factors relevant to patients and outcomes. The registry hosts over 30000 records of prospectively collected injury data.",
    "url": "https://healthdatagateway.org/en/dataset/712",
    "uid": "7fa76997-dff7-43c3-9681-86b4f18b2494",
    "datasource_id": 712,
    "source": "HDRUK"
  },
  {
    "id": 918,
    "name": "Genetics of Osteoarthritis and Lifestyle (GOAL)",
    "description": "X-rays at baseline for 2000 hip and knee OA patients and 1200 controls, serum and urine samples for all, WOMAC pain questionnaires, follow up pain, QoL, medical history surgery history 10 years later for a subset of n=1200 participants. Includes age, sex, body mass index, knee or hip OA, history of joint replacement, anxiety, and depression. \n\nCan be used to examine the association between adrenergic blocker prescription and at least moderate joint pain (WOMAC score <75) and use of prescription analgesics",
    "url": "https://healthdatagateway.org/en/dataset/711",
    "uid": "563c0bfc-437e-4c10-b144-b392d983017c",
    "datasource_id": 711,
    "source": "HDRUK"
  },
  {
    "id": 919,
    "name": "VIKING GENES",
    "description": "The dataset is described on the study website at https://www.ed.ac.uk/viking\nThe data dictionary for the VIKING II study has DOI https://doi.org/10.7488/ds/3145.",
    "url": "https://healthdatagateway.org/en/dataset/708",
    "uid": "43636f80-27fd-43d0-8556-d0a2de0cd819",
    "datasource_id": 708,
    "source": "HDRUK"
  },
  {
    "id": 920,
    "name": "Studies Supported by NIHR CRN, ICD10 Coded, with Summary Site Recruitment Data",
    "description": "This dataset comprises summary records of all non-commercial clinical research projects which have been supported* by the National Institute for Research Clinical Research Network [CRN]. The majority of these studies have been coded according to the disease(s) or condition(s) they are investigating. This classification has been done by a clinical coder using ICD10 and HRCS** codes.\n\nAccompanying this information is a list of all the NHS Trusts and GP Practices which have recruited participants for these studies with the number of participants recruited each month. The Trusts and GP Practices are identified by name and ODS Code.\n\n\n*Studies receive CRN support through a formal process based on criteria set by DHSC (https://www.nihr.ac.uk/documents/eligibility-for-nihr-clinical-research-network-support/23746). For the purposes of this extract it includes all studies added to the NHR CRN Portfolio since 1st January 2008.\n**UKCRC Health Research Classification System Health Categories.",
    "url": "https://healthdatagateway.org/en/dataset/707",
    "uid": "02397c83-62e6-4ec2-8366-89d211739564",
    "datasource_id": 707,
    "source": "HDRUK"
  },
  {
    "id": 921,
    "name": "VIVALDI 2",
    "description": "The study will be expanding to other providers and care homes across England and will provide a detailed picture of prevalence, seroprevalence, transmission and potential immunity over time.By testing around 6500 staff and 5000 residents across >100 care homes in England, we will estimate the proportion who have been infected with COVID-19 in the past and have antibodies, and the proportion who are infected now. These tests will be repeated over time to learn how COVID-19 spreads in care homes and how long the antibody response lasts and whether this helps to prevent re-infection with the virus.  In those who are currently infected, we will also collect information on who is experiencing symptoms to help us to understand how this affects spread of infection within care homes. We will find out about how infection spreads between care homes, the community and hospitals by linking the information we collect to national data on hospital admissions and deaths.\n\nN.B.: The data within the VIVALDI 2 dataset is being examined and cleaned to improve its quality, this is ongoing work.",
    "url": "https://healthdatagateway.org/en/dataset/702",
    "uid": "12f1ffbc-0ddd-4d6e-b126-2cccf6738658",
    "datasource_id": 702,
    "source": "HDRUK"
  },
  {
    "id": 922,
    "name": "COVIDsortium",
    "description": "COVID-19 has caused the greatest pandemic in living memory. Alongside providing excellent clinical care in the most challenging of environments, there is also a critical need for clinical research to better understand this disease. This will equip us to better deal with the current pandemic as well future ones.\n\nWe need to establish why some people develop severe disease and others never get ill despite infection. We need to know whether there are targets for drug development to treat the disease or to give people who are exposed. We will look at genetic influences and immunology (including prior protective viral exposure), seek neutralising antibodies, understand the cellular responses and assess ethnic and sociological factors – all by collecting a biorepository of over 200,000 samples taken from our own healthcare staff weekly over the next 4 months. These samples will then be divided up and sent to the UKs best academic and pharmaceutical research institutions for collaborative, swift science maximising the yield of the consortium to answer the questions in such urgent need of answers.\n\nDr Charlotte Manisty, Professor James Moon and their team embarked on a pioneering project with Barts NHS Health Trust, in collaboration with University College London (UCL) and Queen Mary’s University London (QMLU). Their research focused on gathering blood samples and health data from frontline healthcare workers, rather than patients admitted to hospital with COVID-19. This was because healthcare workers have high exposure rates to the disease – it also allows researchers to compare samples from each person before, during and after their exposure to COVID-19, and to investigate the disease in people who develop only mild symptoms or are asymptomatic.",
    "url": "https://healthdatagateway.org/en/dataset/703",
    "uid": "529f126b-660c-4c31-aeb2-765dac1d9df0",
    "datasource_id": 703,
    "source": "HDRUK"
  },
  {
    "id": 923,
    "name": "ISARIC4C COVID-19 Clinical Information Network (CO-CIN)",
    "description": "Data and samples collected from patients of all ages requiring admission to hospital with covid-19, and patients in hospital subsequently diagnosed with covid-19 in England, Scotland and Wales.",
    "url": "https://healthdatagateway.org/en/dataset/699",
    "uid": "84336be3-23bd-466e-b29b-df48db897665",
    "datasource_id": 699,
    "source": "HDRUK"
  },
  {
    "id": 924,
    "name": "Pregnancy Register for CPRD Aurum",
    "description": "The CPRD Aurum Pregnancy Register contains a list of all pregnancy episodes recorded in CPRD Aurum. The CPRD Pregnancy Registers are derived from the primary care data based on an algorithm. Each version of the Pregnancy Register is built from a corresponding CPRD Aurum database release.\n\nEach record within the Pregnancy Register represents a unique pregnancy episode with a number of variables provided including details of the start and end of the pregnancy, trimester dates and the outcome of the pregnancy. There may be more than one episode per woman.",
    "url": "https://healthdatagateway.org/en/dataset/697",
    "uid": "6fac32d2-c815-4480-9831-afff128b670b",
    "datasource_id": 697,
    "source": "HDRUK"
  },
  {
    "id": 925,
    "name": "CPRD GOLD SGSS",
    "description": "Second Generation Surveillance System (SGSS) is the national laboratory reporting system used in England to capture routine laboratory data on infectious diseases and antimicrobial resistance. The SARS-CoV-2 testing started in UK laboratories on 24/02/2020, with the SGSS data reflecting testing (swab samples, PCR test method) offered to those in hospital and NHS key workers (i.e. Pillar 1). The CPRD-SGSS linked data currently contain positive tests results only.",
    "url": "https://healthdatagateway.org/en/dataset/695",
    "uid": "7325c42d-6974-4f1b-acd6-5a3c9d22f4a1",
    "datasource_id": 695,
    "source": "HDRUK"
  },
  {
    "id": 926,
    "name": "CPRD Aurum ICNARC",
    "description": "The Intensive Care National Audit & Research Centre (ICNARC) COVID-19 dataset covers adult patients (aged 18 years and over) in adult, general critical care units (intensive care and combined intensive care/high dependency units) who had a laboratory confirmed COVID-19 case in England since 12th December 2019.",
    "url": "https://healthdatagateway.org/en/dataset/696",
    "uid": "d1490c18-b6a0-46a2-915f-d7145f02e36f",
    "datasource_id": 696,
    "source": "HDRUK"
  },
  {
    "id": 927,
    "name": "CPRD Aurum Ethnicity Record",
    "description": "The CPRD Ethnicity Records are comprised of a single derived ethnicity category for each patient in CPRD Aurum. The CPRD Ethnicity Records draw ethnicity data from the primary care databases and, for linkage eligible patients, Hospital Episode Statistics (HES) datasets.",
    "url": "https://healthdatagateway.org/en/dataset/689",
    "uid": "44a49deb-3254-4e56-971b-e7fff08b96d0",
    "datasource_id": 689,
    "source": "HDRUK"
  },
  {
    "id": 928,
    "name": "Quality of Life of Colorectal Cancer Survivors: Patient Reported Outcomes (GOLD)",
    "description": "CPRD GOLD linked Quality of Life of Colorectal Cancer Survivors in England: Patient Reported Outcome Measures (PROMs) survey, is a national survey that was commissioned by the Department of Health as a follow-on from the pilot study in July 2011 undertaken to confirm the value of collecting PROMs data on breast, prostate, colorectal and non-Hodgkin’s lymphoma. It includes survey data from 34,467 patients aged 16 years and over with an incident colorectal cancer diagnosis during Jan 2010 Dec 2011. Outcome items in the survey are made up of Euroqol 5-level (EQ-5D), Functional Assessment of Cancer Therapy (FACT), and Social Difficulties Inventory (SDI) items.",
    "url": "https://healthdatagateway.org/en/dataset/690",
    "uid": "61a607a9-b404-41e0-a52e-0c6d0ce8c2be",
    "datasource_id": 690,
    "source": "HDRUK"
  },
  {
    "id": 929,
    "name": "CPRD COVID-19 Symptoms and Risk Factors Synthetic Dataset",
    "description": "This wholly synthetic dataset is based on real anonymised primary care patient data extracted from the CPRD Aurum database. Researchers will not be able to access the real anonymised patient data extract which were used as the basis for the synthetic dataset generation to preserve patient privacy.\n\nThe dataset focuses on patients presenting to primary care with symptoms indicative of COVID-19 (confirmed/suspected COVID-19) and control patients with negative COVID-19 test results. The dataset includes data on sociodemographic and clinical risk factors. The ‘ground truth’ CPRD Aurum data extract used as the basis for generating this synthetic dataset included data till 13/04/2021 on patients with either suspected or confirmed COVID-19 as ascertained from the primary care record. The ground truth data extract was subject to data pre-processing and as such, the synthetic dataset based on this, does not reflect the structure of the source CPRD Aurum database.\n\nThe development of this synthetic dataset was funded by NHS X using the synthetic data generation and evaluation framework developed by CPRD under a grant from the Regulators’ Pioneer Fund launched by The Department for Business, Energy and Industrial Strategy (BEIS) and managed by Innovate UK. The methodology used to generate and evaluate this synthetic dataset is outlined in Wang et al. 2019 (DOI Bookmark:10.1109/CBMS.2019.00036).",
    "url": "https://healthdatagateway.org/en/dataset/685",
    "uid": "a48c65e2-a4e0-4d05-9e9c-fc84e050dea0",
    "datasource_id": 685,
    "source": "HDRUK"
  },
  {
    "id": 930,
    "name": "CPRD Cardiovascular Disease Synthetic Dataset",
    "description": "This wholly synthetic dataset is based on real anonymised primary care patient data extracted from the CPRD Aurum database and focuses on cardiovascular disease risk factors.\nResearchers will not be able to access the real anonymised patient data extract which was used as the basis for the synthetic dataset generation to preserve patient privacy.\nThe ground truth data extract was subject to data pre-processing and as such, the synthetic dataset, which is based on this, does not reflect the structure of the source CPRD Aurum\ndatabase. This synthetic dataset was developed as part of a project funded by the Regulators’ Pioneer Fund launched by The Department for Business, Energy and Industrial Strategy (BEIS) and\nmanaged by Innovate UK. The methodology used to generate and evaluate this synthetic dataset is outlined in Wang et al. 2019.",
    "url": "https://healthdatagateway.org/en/dataset/686",
    "uid": "780c0a71-0264-4843-939d-1336eee60d40",
    "datasource_id": 686,
    "source": "HDRUK"
  },
  {
    "id": 931,
    "name": "CPRD Aurum CHESS",
    "description": "The former Public Health England (PHE) established COVID-19 Hospitalisation in England Surveillance System (CHESS) across all NHS Trusts in England on 15/03/2020 to collect epidemiological data on COVID-19 infection in persons requiring hospitalisation and ICU/HDU admission. Trends in hospital and critical care admission rates need to be interpreted in the context of testing recommendations, which changed over time.",
    "url": "https://healthdatagateway.org/en/dataset/683",
    "uid": "0c67a669-7a19-4cb8-b7b0-42a19f84d56d",
    "datasource_id": 683,
    "source": "HDRUK"
  },
  {
    "id": 932,
    "name": "Quality of Life of Colorectal Cancer Survivors:Patient Reported Outcomes (Aurum)",
    "description": "CPRD Aurum linked Quality of Life of Colorectal Cancer Survivors in England: Patient Reported Outcome Measures (PROMs) survey, is a national survey that was commissioned by the Department of Health as a follow-on from the pilot study in July 2011 undertaken to confirm the value of collecting PROMs data on breast, prostate, colorectal and non-Hodgkin’s lymphoma. It includes survey data from 34,467 patients aged 16 years and over with an incident colorectal cancer diagnosis during Jan 2010 Dec 2011. Outcome items in the survey are made up of Euroqol 5-level (EQ-5D), Functional Assessment of Cancer Therapy (FACT), and Social Difficulties Inventory (SDI) items.",
    "url": "https://healthdatagateway.org/en/dataset/682",
    "uid": "bfb90d2b-746d-44a6-a896-4979d73d9276",
    "datasource_id": 682,
    "source": "HDRUK"
  },
  {
    "id": 933,
    "name": "CPRD GOLD CHESS",
    "description": "The former Public Health England (PHE) established COVID-19 Hospitalisation in England Surveillance System (CHESS) across all NHS Trusts in England on 15/03/2020 to collect epidemiological data on COVID-19 infection in persons requiring hospitalisation and ICU/HDU admission. Trends in hospital and critical care admission rates need to be interpreted in the context of testing recommendations, which changed over time.",
    "url": "https://healthdatagateway.org/en/dataset/676",
    "uid": "1b677d33-8e87-4951-9658-e99ae73d963b",
    "datasource_id": 676,
    "source": "HDRUK"
  },
  {
    "id": 934,
    "name": "CPRD GOLD ICNARC",
    "description": "The Intensive Care National Audit & Research Centre (ICNARC) COVID-19 dataset covers adult patients (aged 18 years and over) in adult, general critical care units (intensive care and combined intensive care/high dependency units) who had a laboratory confirmed COVID-19 case in England since 12th December 2019.",
    "url": "https://healthdatagateway.org/en/dataset/664",
    "uid": "0e47bbf0-0d60-4c7c-85a5-543957635471",
    "datasource_id": 664,
    "source": "HDRUK"
  },
  {
    "id": 935,
    "name": "CPRD GOLD Ethnicity Record",
    "description": "The CPRD Ethnicity Records are comprised of a single derived ethnicity category for each patient in CPRD GOLD. The CPRD Ethnicity Records draw ethnicity data from the primary care databases and, for linkage eligible patients, Hospital Episode Statistics (HES) datasets.",
    "url": "https://healthdatagateway.org/en/dataset/667",
    "uid": "8ae187cd-a1d3-4f22-9cef-a435d26c6597",
    "datasource_id": 667,
    "source": "HDRUK"
  },
  {
    "id": 936,
    "name": "CPRD Aurum SGSS",
    "description": "Second Generation Surveillance System (SGSS) is the national laboratory reporting system used in England to capture routine laboratory data on infectious diseases and antimicrobial resistance. The SARS-CoV-2 testing started in UK laboratories on 24/02/2020, with the SGSS data reflecting testing (swab samples, PCR test method) offered to those in hospital and NHS key workers (i.e. Pillar 1). The CPRD-SGSS linked data currently contain positive tests results only.",
    "url": "https://healthdatagateway.org/en/dataset/669",
    "uid": "981b8787-45be-4f61-8625-3e3aae6ef10a",
    "datasource_id": 669,
    "source": "HDRUK"
  },
  {
    "id": 937,
    "name": "Quality of Life of Cancer Survivors: Pilot Patient Reported Outcomes(Aurum)",
    "description": "CPRD Aurum linked Quality of Life of Cancer Survivors in England: Pilot Survey (2011) was commissioned by the Department of Health as part of the National Cancer Survivorship Initiative (NCSI). The survey was conducted by Quality Health in conjunction with three cancer registries in England. The survey measured the overall quality of life of representative samples of cancer survivors with breast, colorectal cancer, prostate cancer and non-Hodgkin’s lymphoma (NHL) diagnosed during July 2006 - July 2010. Quality of life was assessed at four different time points after diagnosis at approximately one, two, three or five years. As this was a pilot survey, numbers are small and data governance issues will need to be carefully considered on a study by study basis. Outcome items in the survey are made up of Euroqol 5-level (EQ-5D), Functional Assessment of Cancer Therapy (FACT), and Social Difficulties Inventory (SDI) items.",
    "url": "https://healthdatagateway.org/en/dataset/657",
    "uid": "3bc43876-507b-4dff-bd2b-7df147322e15",
    "datasource_id": 657,
    "source": "HDRUK"
  },
  {
    "id": 938,
    "name": "Quality of Life of Cancer Survivors: Pilot Patient Reported Outcomes (GOLD)",
    "description": "CPRD GOLD linked Quality of Life of Cancer Survivors in England: Pilot Survey (2011) was commissioned by the Department of Health as part of the National Cancer Survivorship Initiative (NCSI). The survey was conducted by Quality Health in conjunction with three cancer registries in England. The survey measured the overall quality of life of representative samples of cancer survivors with breast, colorectal cancer, prostate cancer and non-Hodgkin’s lymphoma (NHL) diagnosed during July 2006 - July 2010. Quality of life was assessed at four different time points after diagnosis at approximately one, two, three or five years. As this was a pilot survey, numbers are small and data governance issues will need to be carefully considered on a study by study basis. Outcome items in the survey are made up of Euroqol 5-level (EQ-5D), Functional Assessment of Cancer Therapy (FACT), and Social Difficulties Inventory (SDI) items.",
    "url": "https://healthdatagateway.org/en/dataset/658",
    "uid": "83059be4-071b-4a36-8ab1-9fde1e32e75e",
    "datasource_id": 658,
    "source": "HDRUK"
  },
  {
    "id": 939,
    "name": "Barts Research Data Extract",
    "description": "Collecting information about people in contact with adult psychological therapy services in England. The IAPT data set was developed with the IAPT programme as a patient level, output based, secondary uses data set which aims to deliver robust, comprehensive, nationally consistent and comparable information for patients accessing NHS-funded IAPT services in England. This national data set has been collected since April 2012 and is a mandatory submission for all NHS funded care, including care delivered by independent sector healthcare providers. Data collection on patients with depression and anxiety disorders that are offered psychological therapies, so that we can improve the delivery of care for these conditions.Providers of NHS-funded IAPT services are required to submit data to NHS Digital on a monthly basis.As a secondary uses data set the IAPT data set re-uses clinical and operational data for purposes other than direct patient care. It defines the data items, definitions and associated value sets extracted or derived from local information systems and sent to NHS Digital for analysis purposes.",
    "url": "https://healthdatagateway.org/en/dataset/648",
    "uid": "ac6cded4-cba4-4d9a-8b84-1b53fa4f8be5",
    "datasource_id": 648,
    "source": "HDRUK"
  },
  {
    "id": 940,
    "name": "Wellcome Sanger Institute: Whole Exome Sequencing",
    "description": "There is a substantial overlap between the participants in the NIHR IBD BioResource and the long-running IBD UK Genetics Consortium (IBDGC).  The Wellcome Sanger Institute performs the sequencing work for the IBDGC. The NIHR BioResource provides DNA samples to this initiative. For those samples, and where there is an additional overlap, e.g. because a participant has been seen before and therefore no new sample is required, this data is being provided to the Gut Reaction Hub by the Wellcome Sanger Institute.",
    "url": "https://healthdatagateway.org/en/dataset/628",
    "uid": "cd20517c-2f75-40e9-9ee8-563d32624b55",
    "datasource_id": 628,
    "source": "HDRUK"
  },
  {
    "id": 941,
    "name": "NIHR IBD BioResource: Health and Lifestyle Questionnaire",
    "description": "The NIHR IBD Bioresource comprises ~34k participants with Inflammatory Bowel Disease (IBD). The NIHR IBD BioResource requests a self-reported health and lifestyle questionnaire from all participants at recruitment. About 70% of participants complete the questionnaire. Typically variables include height, weight, smoking history and alcohol consumption, with addition of questions relating to disease history and current medications. This is used to pre-screen or match participants when inviting them to take part in experimental medicine studies. De-identified versions of this data are available to researchers investigating the feasibility of future studies.",
    "url": "https://healthdatagateway.org/en/dataset/629",
    "uid": "18bfe765-5733-41f3-8114-0a1ec2524b5b",
    "datasource_id": 629,
    "source": "HDRUK"
  },
  {
    "id": 942,
    "name": "NIHR IBD BioResource: Case report form",
    "description": "The NIHR IBD Bioresource comprises ~34k participants with Inflammatory Bowel Disease (IBD). The NIHR IBD BioResource acquires a case report form at recruitment for each participant recruited on the basis of their disease. Typically variables include questions that may be used to partition or group patients by their disease course and severity, and include disease history and medication use. This is used to pre-screen or match participants when inviting them to take part in experimental medicine studies. De-identified versions of this data are available to researchers investigating the feasibility of future studies.",
    "url": "https://healthdatagateway.org/en/dataset/630",
    "uid": "877a7275-b99d-4ed4-9691-de577e0ab49e",
    "datasource_id": 630,
    "source": "HDRUK"
  },
  {
    "id": 943,
    "name": "NIHR IBD BioResource: SNP chip data",
    "description": "The NIHR IBD Bioresource comprises ~34k participants with Inflammatory Bowel Disease (IBD). The NIHR IBD BioResource extracts DNA from blood and saliva samples taken at recruitment, and measures a panel of SNPs on each DNA sample, using a commodity SNP genotyping array from e.g. Illumina or Affymetrix (now Thermofisher). This is used to pre-screen or match participants when inviting them to take part in experimental medicine studies. De-identified versions of this data is available to researchers investigating the feasibility of future studies. The Technical Metadata describes a SNP annotation file – i.e. what the chip is measuring. The file itself has as many rows as there are SNPs represented on the chip, and is proprietary to the manufacturer, although deeply familiar to researchers.",
    "url": "https://healthdatagateway.org/en/dataset/624",
    "uid": "3c749cfc-4008-4916-b64f-969d6a754c0f",
    "datasource_id": 624,
    "source": "HDRUK"
  },
  {
    "id": 944,
    "name": "NIHR IBD BioResource: Contact detail",
    "description": "The NIHR IBD Bioresource comprises ~34k participants with Inflammatory Bowel Disease (IBD). The NIHR IBD BioResource acquires contact details - name, address, email address, phone/mobile number - from participants at recruitment. This is used to recontact participants to invite them to take part in experimental medicine studies, although sample-only and data-only studies are permitted. We also record NHS number, where known, to allow linkage to healthcare records. Recruitment takes place at disease clinics. A participant is not considered a member of the NIHR IBD BioResource without contact details.",
    "url": "https://healthdatagateway.org/en/dataset/625",
    "uid": "02ddc5c9-03d4-40e4-9934-e60817ec8f3e",
    "datasource_id": 625,
    "source": "HDRUK"
  },
  {
    "id": 945,
    "name": "NIHR IBD BioResource: SNP imputation data",
    "description": "The NIHR IBD Bioresource comprises ~34k participants with Inflammatory Bowel Disease (IBD). SNP chip data can be used to impute many of the (non-rare) SNPs not included on the chips.  The NIHR BioResource is using a modified version of the UK Biobank protocol to improve the options for recall.",
    "url": "https://healthdatagateway.org/en/dataset/626",
    "uid": "7dee2bd6-733a-4b2e-a942-7f9eaf8ebbc2",
    "datasource_id": 626,
    "source": "HDRUK"
  },
  {
    "id": 946,
    "name": "NIHR IBD BioResource: NHS Trust data",
    "description": "The NIHR IBD BioResource comprises ~34k participants with Inflammatory Bowel Disease (IBD). For the Gut Reaction programme, 10 NHS Trusts have been asked to provide detailed data on the participants in their Trust.  Categories of data requested include: test results; prescribing; imaging; digital pathology; data from disease-specific databases and registries; and discharge summaries. While the formats and contents will vary, the hope is that this will be a much richer source of data than nationally collated datasets, like NHS Digital.",
    "url": "https://healthdatagateway.org/en/dataset/627",
    "uid": "ae1898da-7fe5-4fe0-b10d-9f06cdec1735",
    "datasource_id": 627,
    "source": "HDRUK"
  },
  {
    "id": 947,
    "name": "IBD Registry COVID-19",
    "description": "The Registry has captured a consented research dataset from 9,800 patients with a further 30,000 ethically permissioned records for research related to IBD and COVID-19. This includes patient demographics, medications; plus vaccinations, responses and care received during COVID-19 period April 2020-June 2021.  The original source of the data was the IBD Registry's COVID-19 IBD Risk Tool, which was launched at the start of the pandemic (1 April 2020) to allow people with IBD to self-assess their risk.  It had a high uptake, with over 16,000 people completing it in the first week alone; by the first end of shielding in August 2020 ovr 37,000 people with IBD had completed it.  Ethical permission was sought and received to re-contact participants for use of this data in research relate to IBD and COVID-19, along with a follow-on survey to give a second timepoint about one year later, which was by June 2021. 9,800 people consented and completed the follow-on (second timepoint) survey, with the ethical permissions allowing the original dataset (first timepoint only) to also be used in research under more restricted permissions providing all requests for withdrawal fulfilled.",
    "url": "https://healthdatagateway.org/en/dataset/620",
    "uid": "ffdf167b-f3a3-4d78-a228-2865aea90509",
    "datasource_id": 620,
    "source": "HDRUK"
  },
  {
    "id": 948,
    "name": "NIHR IBD BioResource: Demographic",
    "description": "The NIHR IBD Bioresource comprises ~34k participants with Inflammatory Bowel Disease (IBD). The NIHR IBD BioResource acquires demographic details – e.g. age, sex, ethnicity - from participants at recruitment. This is used to pre-screen or match participants when inviting them to take part in experimental medicine studies. De-identified versions of this data are available to researchers investigating the feasibility of future studies.",
    "url": "https://healthdatagateway.org/en/dataset/621",
    "uid": "b4121feb-f91b-4e20-a19b-fa49c7a5c1c1",
    "datasource_id": 621,
    "source": "HDRUK"
  },
  {
    "id": 949,
    "name": "NIHR IBD BioResource: Sample holding",
    "description": "The NIHR IBD BioResource comprises ~34k participants with Inflammatory Bowel Disease (IBD). The NIHR IBD BioResource acquires samples at recruitment for each participant recruited. The preferred sample collection is 3 blood tubes – see https://bioresource.nihr.ac.uk/recruiters/sample-collection-recruiters/ – from which DNA can be extracted, and plasma and sera stored. In many clinics, the NIHR BioResource blood draw follows immediately the blood collection for routine healthcare purposes. In the case of children, less blood will be taken, down to 1ml. The Technical Metadata describes the standard used by RD-Connect, the EU-wide Rare Disease biobank consortium - https://samples.rd-connect.eu/menu/main/home. During 2021, this will be replaced by an API from the UK CRC Tissue Directory, as using this discovery tool is a condition of running a Research Tissue Bank in the UK.",
    "url": "https://healthdatagateway.org/en/dataset/622",
    "uid": "f2af20f9-af84-40ce-8a07-7d095948653e",
    "datasource_id": 622,
    "source": "HDRUK"
  },
  {
    "id": 950,
    "name": "NIHR IBD BioResource: Consent records",
    "description": "The NIHR IBD Bioresource comprises ~34k participants with Inflammatory Bowel Disease (IBD). The NIHR IBD BioResource records consent details – form, version and date - from participants at many points of recontact.  In addition, participants are empowered to contact the study directly to express their preferences. The primary use of consent records is as an exclusion criterion when participants are invited to take part in experimental medicine studies.  However, consent is also factored in to the release of samples and data for sample-only and data-only studies.  One implication, is that data releases have freezes that capture a snapshot of the current consent statuses.  How the withdrawal process is managed, and implications for whether data can be removed rapidly from already published datasets is described in our Participant Privacy Notice, online at https://bioresource.nihr.ac.uk/about-us/governance-and-ethics/privacy-notice/",
    "url": "https://healthdatagateway.org/en/dataset/623",
    "uid": "44ca2cf8-679e-447c-a9eb-55b32159679a",
    "datasource_id": 623,
    "source": "HDRUK"
  },
  {
    "id": 951,
    "name": "National Neonatal Research Database - Artificial Intelligence (NNRD-AI)",
    "description": "The National Neonatal Research Database is an award-winning resource, a dynamic relational database containing information extracted from the electronic patient records of babies admitted to NHS neonatal units in England, Wales and Scotland (Northern Ireland is currently addressing regulatory requirements for participation). The NNRD-AI is a version of the NNRD curated for machine learning and artificial intelligence applications.  \n\nA team led by Professor Neena Modi at the Chelsea and Westminster Hospital campus of Imperial College London established the NNRD in 2007 as a resource to support clinical teams, managers, professional organisations, policy makers, and researchers who wish to evaluate and improve neonatal care and services. Recently, supported by an award from the Medical Research Council, the neonatal team and collaborating data scientists at the Institute for Translational Medicine and Therapeutics, Data Science Group at Imperial College London, created NNRD-AI. \n\nThe NNRD-AI is a subset of the full NNRD with around 200 baby variables, 100 daily variables and 450 additional aggregate variables. The guiding principle underpinning the creation of the NNRD-AI is to make available data that requires minimal input from domain experts. Raw electronic patient record data are heavily influenced by the collection process. Additional processing is required to construct higher-order data representations suitable for modelling and application of machine learning/artificial intelligence techniques. In NNRD-AI, data are encoded as readily usable numeric and string variables. Imputation methods, derived from domain knowledge, are utilised to reduce missingness. Out of range values are removed and clinical consistency algorithms applied. A wide range of definitions of complex major neonatal morbidities (e.g. necrotising enterocolitis, bronchopulmonary dysplasia, retinopathy of prematurity), aggregations of daily data and clinically meaningful representations of anthropometric variables and treatments are also available.",
    "url": "https://healthdatagateway.org/en/dataset/618",
    "uid": "cf4c4419-7c10-4376-a246-77b0411f9928",
    "datasource_id": 618,
    "source": "HDRUK"
  },
  {
    "id": 952,
    "name": "Oxford Healthcare Workers",
    "description": "OUH offers both symptomatic and asymptomatic SARS-CoV-2 staff testing programs, defining a Health Care Worker (HCW) as anyone working at its four teaching hospital sites in Oxfordshire, UK. 12,411 healthcare workers (HCWs) have undergone serological testing to date; this analysis includes all 3217 HCWs who attended more than once for antibody testing. SARS-CoV-2 PCR testing of nasal and oropharyngeal swabs for all symptomatic (new persistent cough, fever ?37.8°C, anosmia/ageusia) staff was offered from 27-March-2020 onwards. PCR-positive results from community-based symptomatic testing of OUH HCWs forwarded by public health agencies were also included.",
    "url": "https://healthdatagateway.org/en/dataset/616",
    "uid": "9de2f365-4c5d-4252-acee-6270b113aa93",
    "datasource_id": 616,
    "source": "HDRUK"
  },
  {
    "id": 953,
    "name": "National Waiting List Open Pathways",
    "description": "Restoration of elective activity is one of the highest priorities for NHS England and NHS Improvement following the impact of the Covid-19 pandemic. Understanding the composition of the waiting list is critical to managing restoration within North West London. \n\nData will be collected via data submissions made by each individual provider of NHS Acute healthcare services in North West London. This dataset includes data from Imperial College Healthcare NHS Trust, Chelsea and Westminster NHS Foundation Trust, London North West Healthcare NHS Trust and The Hillingdon Hospital NHS Trust. Data will be processed under an Information Sharing Agreement between North West London CCG and each organisation. Data submissions will be processed and used for the following purposes:\n  1.  Developing a visual display of the waiting list composition (Elective Waiting List Data Dashboard).\n  2.  Developing a data quality improvement programme with providers.\n\nAll open (incomplete) RTT & not current RTT pathways as at 23:59 on the Sunday of the reporting period.",
    "url": "https://healthdatagateway.org/en/dataset/540",
    "uid": "c7b1e328-f2d7-4d11-b38f-ce9930b95ca0",
    "datasource_id": 540,
    "source": "HDRUK"
  },
  {
    "id": 954,
    "name": "National Waiting List Clock Stops",
    "description": "Restoration of elective activity is one of the highest priorities for NHS England and NHS Improvement following the impact of the Covid-19 pandemic. Understanding the composition of the waiting list is critical to managing restoration within North West London. \n\nData will be collected via data submissions made by each individual provider of NHS Acute healthcare services in North West London. This dataset includes data from Imperial College Healthcare NHS Trust, Chelsea and Westminster NHS Foundation Trust, London North West Healthcare NHS Trust and The Hillingdon Hospital NHS Trust. Data will be processed under an Information Sharing Agreement between North West London CCG and each organisation. Data submissions will be processed and used for the following purposes:\n\n  1.  Developing a visual display of the waiting list composition (Elective Waiting List Data Dashboard).\n  2.  Developing a data quality improvement programme with providers.\n\nAll RTT pathways with a clock stop date after 23:59 on Sunday 4th April 2021 and before 23:59 on the Sunday of the reporting period and not recorded to date (in a previous submission).",
    "url": "https://healthdatagateway.org/en/dataset/534",
    "uid": "3f073707-50b4-48cf-93b6-76843519a5c0",
    "datasource_id": 534,
    "source": "HDRUK"
  },
  {
    "id": 955,
    "name": "National Waiting List Diagnostics",
    "description": "Restoration of elective activity is one of the highest priorities for NHS England and NHS Improvement following the impact of the Covid-19 pandemic. Understanding the composition of the waiting list is critical to managing restoration within North West London. \n\nData will be collected via data submissions made by each individual provider of NHS Acute healthcare services in North West London. This dataset includes data from Imperial College Healthcare NHS Trust, Chelsea and Westminster NHS Foundation Trust, London North West Healthcare NHS Trust and The Hillingdon Hospital NHS Trust. Data will be processed under an Information Sharing Agreement between North West London CCG and each organisation. Data submissions will be processed and used for the following purposes:\n  1.  Developing a visual display of the waiting list composition (Elective Waiting List Data Dashboard).\n  2.  Developing a data quality improvement programme with providers.\n\nAll open (incomplete) and planned diagnostic waits for modalities in scope DM01 before 23:59 on Sunday of the reporting period.",
    "url": "https://healthdatagateway.org/en/dataset/535",
    "uid": "e508951d-e118-4217-84a4-722e50fdd400",
    "datasource_id": 535,
    "source": "HDRUK"
  },
  {
    "id": 956,
    "name": "Weekly SUS Outpatients Dataset",
    "description": "Initially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver. \n\nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the SUS data set.\n\nSUS data covers all NHS Clinical Commissioning Groups (CCGs) in England, including:\n1. private patients treated in NHS hospitals\n2. patients resident outside of England\n3. care delivered by treatment centres (including those in the independent sector) funded by the NHS\n\nEach SUS record contains a wide range of information about an individual patient admitted to an NHS hospital, including:\n1. clinical information about diagnoses and operations\n2. patient information, such as age group, gender and ethnicity\n3. administrative information, such as dates and methods of admission and discharge\n4. geographical information such as where patients are treated and the area where they live\n\nNHS Digital apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published SUS data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\n\nWho SUS is for:\nSUS provides data for the purpose of healthcare analysis to the NHS, government and others including:\n\nThe Secondary Users Service (SUS) database is made up of many data items relating to A&E care delivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data Set (CDS), and are generated by the patient administration systems within each hospital.\n1. national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n2. local Clinical Commissioning Groups (CCGs)\n3. provider organisations\n4. government departments\n5. researchers and commercial healthcare bodies\n6. National Institute for Clinical Excellence (NICE)\n7. patients, service users and carers\n8. the media\n\nUses of the statistics\nThe statistics are known to be used for:\n1. national policy making\n2. benchmarking performance against other hospital providers or CCGs  \n3. academic research\n4. analysing service usage and planning change\n5. providing advice to ministers and answering a wide range of parliamentary questions\n6. national and local press articles\n7. international comparison\n\nMore information can be found at \nhttps://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/hospital-episode-statistics\nhttps://digital.nhs.uk/data-and-information/publications/statistical/hospital-accident--emergency-activity\"",
    "url": "https://healthdatagateway.org/en/dataset/530",
    "uid": "572cd3e2-9119-40ff-ab2e-49e8a0a02ab8",
    "datasource_id": 530,
    "source": "HDRUK"
  },
  {
    "id": 957,
    "name": "Air Pollution Exposure Estimates",
    "description": "Modelled concentrations (µg/m3) of annual average nitrogen dioxide (NO2) and particulate matter with diameter <2.5µm (PM2.5) were linked to postcode centroids in Greater London. Air pollution exposure estimates (i.e. concentrations) were derived using models developed for the year 2015 by overlaying the x,y location of postcode centroids with NO2 and PM2.5 maps (25m x 25m resolution) within a geographic information system (GIS). Following analysis of changes in air pollution measurements over time, using routine monitoring data from the DEFRA-run Automatic Urban and Rural Network (AURN), we made adjustments to the 2015 modelled concentrations to estimate 2010 to 2019 exposures using a method know as ‘differencing’ (Gulliver et al. 2013).",
    "url": "https://healthdatagateway.org/en/dataset/533",
    "uid": "584cbecc-43ef-4015-adf7-ff70178d7abe",
    "datasource_id": 533,
    "source": "HDRUK"
  },
  {
    "id": 958,
    "name": "North West London Acute Patient level Data (NWL Acute PLD)",
    "description": "SLAM is comprehensive transactional commissioning toolkit, showing CCGs and providers including planned and actual reports to support NHS commissioning (tariff, non-tariff & block contracts). Acute PLD shows A&E, OP & IP data for NWL contracted providers.",
    "url": "https://healthdatagateway.org/en/dataset/522",
    "uid": "0d5a40e6-52c0-43c4-bd9f-f2a803cc8ff6",
    "datasource_id": 522,
    "source": "HDRUK"
  },
  {
    "id": 959,
    "name": "Weekly SUS Inpatient Dataset",
    "description": "Initially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the SUS data set.\nSUS data covers all NHS Clinical Commissioning Groups (CCGs) in England, including:\n1. private patients treated in NHS hospitals\n2. patients resident outside of England\n3. care delivered by treatment centres (including those in the independent sector) funded by the NHS\n\nEach SUS record contains a wide range of information about an individual patient admitted to an NHS hospital, including:\n1. clinical information about diagnoses and operations\n2. patient information, such as age group, gender and ethnicity\n3. administrative information, such as dates and methods of admission and discharge\n4. geographical information such as where patients are treated and the area where they live\n\nNHS Digital apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published SUS data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\n\nWho SUS is for\nSUS provides data for the purpose of healthcare analysis to the NHS, government and others including:\n\nThe Secondary Users Service (SUS) database is made up of many data items relating to A&E care\ndelivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data Set (CDS), and are generated by the patient administration systems within each hospital.\n1. national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n2. local Clinical Commissioning Groups (CCGs)\n3. provider organisations\n4. government departments\n5. researchers and commercial healthcare bodies\n6. National Institute for Clinical Excellence (NICE)\n7. patients, service users and carers\n8. the media\n\nUses of the statistics\nThe statistics are known to be used for:\n1. national policy making\n2. benchmarking performance against other hospital providers or CCGs  \n3. academic research\n4. analysing service usage and planning change\n5. providing advice to ministers and answering a wide range of parliamentary questions\n6. national and local press articles\n7. international comparison\n\nMore information can be found at \nhttps://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/hospital-episode-statistics\nhttps://digital.nhs.uk/data-and-information/publications/statistical/hospital-accident--emergency-activity\"",
    "url": "https://healthdatagateway.org/en/dataset/523",
    "uid": "3858647c-5161-45dc-ba49-66a4fc4499c2",
    "datasource_id": 523,
    "source": "HDRUK"
  },
  {
    "id": 960,
    "name": "Weekly SUS ECDS Dataset",
    "description": "Initially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the SUS data set.\nSUS data covers all NHS Clinical Commissioning Groups (CCGs) in England, including:\n1. private patients treated in NHS hospitals\n2. patients resident outside of England\n3. care delivered by treatment centres (including those in the independent sector) funded by the NHS\n\nEach SUS record contains a wide range of information about an individual patient admitted to an NHS hospital, including:\n1. clinical information about diagnoses and operations\n2. patient information, such as age group, gender and ethnicity\n3. administrative information, such as dates and methods of admission and discharge\n4. geographical information such as where patients are treated and the area where they live\n\nNHS Digital apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published SUS data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\n\nWho SUS is for\nSUS provides data for the purpose of healthcare analysis to the NHS, government and others including:\n\nThe Secondary Users Service (SUS) database is made up of many data items relating to A&E care delivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data\nSet (CDS), and are generated by the patient administration systems within each hospital.\n1. national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n2. local Clinical Commissioning Groups (CCGs)\n3. provider organisations\n4. government departments\n5. researchers and commercial healthcare bodies\n6. National Institute for Clinical Excellence (NICE)\n7. patients, service users and carers\n8. the media\n\nUses of the statistics\nThe statistics are known to be used for:\n1. national policy making\n2. benchmarking performance against other hospital providers or CCGs  \n3. academic research\n4. analysing service usage and planning change\n5. providing advice to ministers and answering a wide range of parliamentary questions\n6. national and local press articles\n7. international comparison\n\nMore information can be found at \nhttps://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/hospital-episode-statistics\nhttps://digital.nhs.uk/data-and-information/publications/statistical/hospital-accident--emergency-activity\"",
    "url": "https://healthdatagateway.org/en/dataset/524",
    "uid": "f75b02f0-a45f-433a-a669-420c8bd40720",
    "datasource_id": 524,
    "source": "HDRUK"
  },
  {
    "id": 961,
    "name": "North West London COVID-19 Patient Level Situation Report (NWL COVID19 SITREP)",
    "description": "The Daily Situation Report collects data on:\nthe number of urgent operations cancelled, including those cancelled for the 2nd time or more, throughout the month\ncritical care capacity, including adult, paediatric and neonatal available and occupied critical care beds",
    "url": "https://healthdatagateway.org/en/dataset/527",
    "uid": "ed08c844-343e-44fa-a02d-3ad5ecf25184",
    "datasource_id": 527,
    "source": "HDRUK"
  },
  {
    "id": 962,
    "name": "National Waiting List Clock Starts",
    "description": "Restoration of elective activity is one of the highest priorities for NHS England and NHS Improvement following the impact of the Covid-19 pandemic. Understanding the composition of the waiting list is critical to managing restoration within North West London. \n\nData will be collected via data submissions made by each individual provider of NHS Acute healthcare services in North West London. This dataset includes data from Imperial College Healthcare NHS Trust, Chelsea and Westminster NHS Foundation Trust, London North West Healthcare NHS Trust and The Hillingdon Hospital NHS Trust. Data will be processed under an Information Sharing Agreement between North West London CCG and each organisation. Data submissions will be processed and used for the following purposes:\n\n  1.  Developing a visual display of the waiting list composition (Elective Waiting List Data Dashboard).\n  2.  Developing a data quality improvement programme with providers.\n\nAll RTT pathways with a clock start date after 23:59 on Sunday 4th April 2021 and before 23:59 on the Sunday of the reporting period and not recorded to date (in a previous submission).",
    "url": "https://healthdatagateway.org/en/dataset/520",
    "uid": "2b847fd2-0f65-43b7-84e1-10cec99d699e",
    "datasource_id": 520,
    "source": "HDRUK"
  },
  {
    "id": 963,
    "name": "Generation Scotland: Scottish Family Health Study",
    "description": "A collaboration between the Universities of Edinburgh, Glasgow, Dundee and Aberdeen, and NHS Scotland, to provide resources for genetic and medical research.\n\nThe Generation Scotland: Scottish Family Health Study (GS:SFHS) is a collection of genetic, medical, family history and lifestyle information from over 24,000 volunteers. It is an intensively phenotyped, family-based cohort for the study of the genetic basis of common complex diseases and response to treatments. The cohort includes just over 24,000 participants recruited 2006-11, with most consenting to record linkage and recontact. Participants gave blood or saliva samples (for processing, biochemistry and cryopreservation) and a urine sample.  The blood or saliva samples were processed to DNA and extensive genotyping was carried out on over 20,000 participants by 2015, with DNA methylation assayed on 10,000 by 2019. Follow up is primarily through record linkage and includes linkage to GP records and also via online questionnaires, including CovidLife.y history and lifestyle information from over 24,000 volunteers.  it is an intensively phenotyped, family-based cohort for the study of the genetic basis of common complex diseases and response to treatments. The cohort includes just over 24,000 participants recruited 2006-11, with most consenting to record linkage and recontact. Participants gave blood or saliva samples (for processing, biochemistry and cryopreservation) and a urine sample.  The blood or saliva samples were processed to DNA and extensive genotyping was carried out on over 20,000 participants by 2015, with DNA methylation assayed on 10,000 by 2019.",
    "url": "https://healthdatagateway.org/en/dataset/413",
    "uid": "f4f0ef1a-46cf-4734-8516-3adb0b62ae6d",
    "datasource_id": 413,
    "source": "HDRUK"
  },
  {
    "id": 964,
    "name": "Labour Force Survey (Household)",
    "description": "The primary purpose of the Labour Force Survey (LFS) is \"providing good quality point in time and change estimates for various labour market outputs and related topics” (National Statistics Quality Review (NSQR) of Labour Force Survey 2014). The labour market covers all aspects of people's work, including the education and training needed to equip them for work, the jobs themselves, job-search for those out of work and income from work and benefits.  Output from the LFS is quarterly since 1992. Each quarter’s sample is made up of 5 waves. The sample is made up of approximately 40,000 responding UK households and 100,000 individuals per quarter. This dataset only coveres household responses. Respondents are interviewed for 5 successive waves at 3-monthly intervals and 20% of the sample is replaced every quarter. The LFS is intended to be representative of the entire population of the UK.",
    "url": "https://healthdatagateway.org/en/dataset/410",
    "uid": "0b630200-1e7c-4b19-ad53-bcc8863f7ade",
    "datasource_id": 410,
    "source": "HDRUK"
  },
  {
    "id": 965,
    "name": "Labour Force Survey (Person)",
    "description": "The primary purpose of the Labour Force Survey (LFS) is \"providing good quality point in time and change estimates for various labour market outputs and related topics” (National Statistics Quality Review (NSQR) of Labour Force Survey 2014). The labour market covers all aspects of people's work, including the education and training needed to equip them for work, the jobs themselves, job-search for those out of work and income from work and benefits.  Output from the LFS is quarterly since 1992. Each quarter’s sample is made up of 5 waves. The sample is made up of approximately 40,000 responding UK households and 100,000 individuals per quarter. This dataset includes only individual responses. Respondents are interviewed for 5 successive waves at 3-monthly intervals and 20% of the sample is replaced every quarter. The LFS is intended to be representative of the entire population of the UK.",
    "url": "https://healthdatagateway.org/en/dataset/411",
    "uid": "8c60a308-1e47-4a0c-8b69-9259f05338cb",
    "datasource_id": 411,
    "source": "HDRUK"
  },
  {
    "id": 966,
    "name": "Labour Force Survey (Longitudinal)",
    "description": "The primary purpose of the Labour Force Survey (LFS) is \"providing good quality point in time and change estimates for various labour market outputs and related topics” (National Statistics Quality Review (NSQR) of Labour Force Survey 2014). The labour market covers all aspects of people's work, including the education and training needed to equip them for work, the jobs themselves, job-search for those out of work and income from work and benefits.  Output from the LFS is quarterly since 1992. Each quarter’s sample is made up of 5 waves. The sample is made up of approximately 40,000 responding UK households and 100,000 individuals per quarter. Respondents are interviewed for 5 successive waves at 3-monthly intervals and 20% of the sample is replaced every quarter. The LFS is intended to be representative of the entire population of the UK. This dataset is the lognitudinal LFS data",
    "url": "https://healthdatagateway.org/en/dataset/412",
    "uid": "4ac15572-fe9a-46d3-860d-a46b616181f7",
    "datasource_id": 412,
    "source": "HDRUK"
  },
  {
    "id": 967,
    "name": "Death Registration Data - Finalised Extracts",
    "description": "These datasets include all deaths registered in England and Wales for the time periods specified.\n\nData are supplied to ONS by the Local Registration Service, in partnership with the General Register Office (GRO). Coding for cause of death is carried out according to the World Health Organization (WHO) International Classification of Diseases (ICD-10) and internationally agreed rules, allowing for international comparisons. Deaths registered in England and Wales to those usually resident outside of England and Wales are included. Deaths registered outside of England and Wales to those usually resident in England and Wales are excluded.\n\nThis data comprises the finalised annual Death Registration data which covers the period 1993-2019. For the latest Death Registration data (2020-2021), please see 'Death registration data - Provisional.'",
    "url": "https://healthdatagateway.org/en/dataset/406",
    "uid": "fdfa5116-4f44-457d-99ef-e8c1852aa858",
    "datasource_id": 406,
    "source": "HDRUK"
  },
  {
    "id": 968,
    "name": "Virus Watch",
    "description": "Virus Watch will provide data relevant to a wide range of audiences involved in pandemic response. Virus Watch has collected personal and special category data.\n\nBaseline survey – the baseline survey collects basic demographic information including sex, date of birth, age in years, ethnicity. It also includes details of the household structure, socioeconomic status including household income. The survey also collects health data used for the study including existing medical conditions (general and COVID-related) and access to health during the pandemic.\n\nWeekly survey – the weekly survey collects data about any illnesses within the household during each week and the results of any COVID tests performed. The survey collects information on behaviours during illness of the household. Since Jan 2021, the weekly survey has also collected data on vaccination status of household members.\n\nMonthly surveys – the monthly surveys collect regular data on contact patterns of household throughout the pandemic, regardless of symptoms or illnesses in the household. Each month additional bespoke questions have been asked in the monthly surveys in order to inform important policy questions at the time.\n\nLaboratory data – the laboratory data includes information on the results of antibody tests for a subset of participants including nucleocapsid and spike antibody levels.  It also includes PCR results for participants that took part in home COVID testing for Virus Watch.",
    "url": "https://healthdatagateway.org/en/dataset/407",
    "uid": "28bb192d-d990-4f7a-b552-345684fa8704",
    "datasource_id": 407,
    "source": "HDRUK"
  },
  {
    "id": 969,
    "name": "Covid-19 Infection Survey",
    "description": "The purpose of this dataset is to understand the prevalence of the coronavirus in the UK population, using longitudinal data and including not only cross-sectional data but the inclusion of an antibody test for a sub-sample of people. Demographic information is also included allowing for analyse by different variables to identify patterns and trends.\n\nParticipants have three options open to them; can have just have one visit, can have a visit every week for a month or, can have a visit every week for a month and then continue to have visits every month for one year in total from when you joined the study. This is entirely voluntary.\n\nAt each visit a field worker conducts a questionnaire, and supervises swab tests. A proportion of visits also include a blood sample being taken. The swab and blood samples are tested at laboratories.\n\nThe overall purpose of this study is to understand how many people across the UK have or may already have had the coronavirus. This will help the government manage the pandemic moving forwards.\n\nThe COVID-19 Community Infection Survey includes information on:\n• how many people across England and Wales (extending to Scotland and Northern Ireland) test positive for COVID-19 at a given point in time, regardless of whether they report experiencing symptoms\n• the average number of new infections per week over the course of the study\n• the number of people who test positive for antibodies, to indicate how many people are ever likely to have had the virus\n• key demographic information (sex, age, occupation)",
    "url": "https://healthdatagateway.org/en/dataset/408",
    "uid": "628e9ad9-28c5-4d58-9ec5-04a63be9b4cf",
    "datasource_id": 408,
    "source": "HDRUK"
  },
  {
    "id": 970,
    "name": "Opinions and Lifestyle Survey - Great Britain",
    "description": "The Opinions and Lifestyle Survey (OPN) is an omnibus survey collecting data on a range of subjects commissioned by both internal Office for National Statistics (ONS) and external clients (limited to; other government departments, charities, non-profit organisations and academia). Data is collected from 1 adult selected from each sampled private household. Personal data include person, family, address, household, income, education plus responses and opinions on a variety of subjects within commissioned modules. The dataset includes a standard set of demographic variables and a single commissioned module.\n\nIn March 2020, the OPN was adapted to become a weekly survey used to collect data on the impact of the coronavirus pandemic on day-to-day life in Great Britain. From 25 August 2021, as COVID-19 restrictions began to be lifted across Great Britain, the OPN moved to a fortnightly data collection with the sample size at around 5,000 households in each period to help ensure the survey remains sustainable.\n\nPrior to the changes in frequency to the OPN survey during the coronavirus pandemic, there had been on-going improvements to the OPN. In recent years, work has been undertaken to change the design of the OPN from a face-to-face survey to a mixed mode design (online first with telephone follow-up). Mixed mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers.\n\nThe questionnaire collects timely data for research and policy analysis evaluation into the impact that the coronavirus pandemic has had on individuals and households in Great Britain.",
    "url": "https://healthdatagateway.org/en/dataset/409",
    "uid": "e253257c-48c2-491b-b3b9-205161a16a7e",
    "datasource_id": 409,
    "source": "HDRUK"
  },
  {
    "id": 971,
    "name": "Covid-19 Infection Survey linked with VOA and EPC data",
    "description": "The purpose of this dataset is to understand the prevalence of COVID-19 in the UK population, including swab results, antibody tests and demographic information. COVID-19 Infection Survey households have been linked, where a match can be found, to VOA and EPC data to provide additional information on property attributes.",
    "url": "https://healthdatagateway.org/en/dataset/402",
    "uid": "2193ad56-6671-4ecc-9ecf-6da84e23e9fd",
    "datasource_id": 402,
    "source": "HDRUK"
  },
  {
    "id": 972,
    "name": "Public Health Research Database (PHRD)",
    "description": "The Public Health Research Database (PHRD) is a linked asset which currently includes Census 2011 data; Mortality Data; Hospital Episode Statistics (HES); GP Extraction Service (GPES) Data for Pandemic Planning and Research data. Researchers may apply for these datasets individually or any combination of the current 4 datasets.\n\nThe purpose of this dataset is to enable analysis of deaths involving COVID-19 by multiple factors such as ethnicity, religion, disability and known comorbidities as well as age, sex, socioeconomic and marital status at subnational levels. 2011 Census data for usual residents of England and Wales, who were not known to have died by 1 January 2020, linked to death registrations for deaths registered between 1 January 2020 and 8 March 2021 on NHS number. The data exclude individuals who entered the UK in the year before the Census took place (due to their high propensity to have left the UK prior to the study period), and those over 100 years of age at the time of the Census, even if their death was not linked. The dataset contains all individuals who died (any cause) during the study period, and a 5% simple random sample of those still alive at the end of the study period. For usual residents of England, the dataset also contains comorbidity flags derived from linked Hospital Episode Statistics data from April 2017 to December 2019 and GP Extraction Service Data from 2015-2019.",
    "url": "https://healthdatagateway.org/en/dataset/403",
    "uid": "a325f33e-bac8-49af-896f-1e025941dae8",
    "datasource_id": 403,
    "source": "HDRUK"
  },
  {
    "id": 973,
    "name": "Business Insights and Conditions Survey (BICS)",
    "description": "Based on user feedback, from Wave 24 the \"Business Impact of Coronavirus (COVID-19) Survey\" has changed its name to the \"Business Insights and Conditions Survey\" (BICS). The purpose remains the same, to collect real-time information on important issues such as the coronavirus (COVID-19) pandemic and the end of the EU transition period.\n\nThe indicators and analysis presented are based on responses from the voluntary fortnightly business survey, which captures business’ views on impact on turnover, workforce prices, trade and business resilience. Estimates from the Business Insights and Conditions Survey (BICS) are now weighted, with only regional tables unweighted.",
    "url": "https://healthdatagateway.org/en/dataset/404",
    "uid": "8e739a01-bcfa-4b75-b7f8-e694ee185f8d",
    "datasource_id": 404,
    "source": "HDRUK"
  },
  {
    "id": 974,
    "name": "Death Registration Data - Provisional Monthly Extracts",
    "description": "These datasets include all deaths registered in England and Wales for the time periods specified.\nData are supplied to ONS by the Local Registration Service, in partnership with the General Register Office (GRO). Coding for cause of death is carried out according to the World Health Organization (WHO) International Classification of Diseases (ICD-10) and internationally agreed rules, allowing for international comparisons.\n\nDeaths registered in England and Wales to those usually resident outside of England and Wales are included. Deaths registered outside of England and Wales to those usually resident in England and Wales are excluded.\n\nThis dataset was created to provide timely statistics needed for COVID-19 analysis. Therefore, the provisional death registration data is only to be used only for COVID-19 accredited projects.  The data are provisional, and cover the period 2020-2021. For Death registration data prior to this period, please see 'Death registration data - Finalised Extracts.'",
    "url": "https://healthdatagateway.org/en/dataset/405",
    "uid": "487222b7-5c13-4a92-8b41-044796048720",
    "datasource_id": 405,
    "source": "HDRUK"
  },
  {
    "id": 975,
    "name": "COVID-19 Staff Testing of Antibody Responses Study (Co-STARS)",
    "description": "The cohort contains repeated serological antibody testing of at least 1000 healthcare workers at Great Ormond Street Hospital. Within this cohort, a subset of 150-250 staff members with confirmed (PCR positive) SARS-CoV-2 disease will be followed with intensive monthly testing for 6 months to determine whether antibody levels in the blood are maintained or decrease during this time. \nData is sourced from EPIC LIMS and ELECTRONIC SURVEY",
    "url": "https://healthdatagateway.org/en/dataset/401",
    "uid": "435bfcda-f378-44f6-9387-042d959eb945",
    "datasource_id": 401,
    "source": "HDRUK"
  },
  {
    "id": 976,
    "name": "NIHR BioResource: Case report form",
    "description": "The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022.  The NIHR BioResource acquires a case report form at recruitment for each participant recruited on the basis of their disease. Typically variables include questions that may be used to partition or group patients by their disease course and severity, and include disease history and medication use. This is used to pre-screen or match participants when inviting them to take part in experimental medicine studies. De-identified versions of this data is available to researchers investigating the feasibility of future studies.",
    "url": "https://healthdatagateway.org/en/dataset/399",
    "uid": "4b8675d3-dd87-4cca-804c-aaf0e4ff8140",
    "datasource_id": 399,
    "source": "HDRUK"
  },
  {
    "id": 977,
    "name": "Juvenile Dermatomyositis Cohort and Biomarker Study",
    "description": "Established in 2000, the Juvenile Dermatomyositis Cohort Biomarker Study and Repository (JDCBS) is the largest cohort study with linked biobank of its kind for juvenile dermatomyositis (JDM) and related childhood onset inflammatory myositis conditions. The JDCBS has 17 centres from around the United Kingdom contributing data and samples and as such supports multiple national and international clinical, genetic, immunology, antibody, and muscle pathology studies in JDM and myositis.  \n\nData and samples are gathered from patients during routine clinical care on standardised proforma and results of investigations and biospecimens (DNA, serum, PBMC, plasma and quadriceps muscle biopsy  where available) are then submitted tocollected into the cohort biomarker study and repositorybiobank. This occurs at presentation and at three to six monthly intervals thereafter for 3 years and then annually or when whilst the patient has active disease. \n\nThe dataset* contains information collected in three clinic protocol forms (form A, form B and form C). Form A records demographic and diagnostic information and is completed once, at the initial clinic visit. The second is for recording clinic visit information on disease activity, and is repeated at each 3-6 monthly clinic visit, including the initial visit. Form C is completed annually, including the initial visit, and records information relating to disease damage. There is an additional flare form that collects clinical data of any flares the patient may have had over the year, and a medications form.\n\nFor further information regarding this study, visit: https://juveniledermatomyositis.org.uk/  \n\n*The dataset described is not currently in use. Migration of the current dataset to the described dataset is underway and will become live in 2022.",
    "url": "https://healthdatagateway.org/en/dataset/400",
    "uid": "juvenile_dermatomyositis_cohort_and_biomarker_study",
    "datasource_id": 400,
    "source": "HDRUK"
  },
  {
    "id": 978,
    "name": "NIHR BioResource: Whole Genome Sequencing",
    "description": "The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022. The NIHR BioResource ran the pilot for GEL's 100,000 Genomes Project. Most of the participants with rare diseases were recruited on the basis of having no known diagnosis, and have had extensive work upon WGS data, including reporting to the clinical team.",
    "url": "https://healthdatagateway.org/en/dataset/395",
    "uid": "ecb90b4a-6f4f-4398-a4f6-d64891c4a137",
    "datasource_id": 395,
    "source": "HDRUK"
  },
  {
    "id": 979,
    "name": "NIHR BioResource: NHS Trust data",
    "description": "The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022.  NHS Trust data were received as part of the HDR UK Sprint Exemplar project, on up to 1600 participants with one of 3 rare diseases (BPD, PAH, PID) at one of the 5 NHS Trusts (Cambridge, Leeds, Liverpool, Newcastle and Papworth).",
    "url": "https://healthdatagateway.org/en/dataset/396",
    "uid": "3731006a-ee7c-4d15-bc07-4eeb29a3d031",
    "datasource_id": 396,
    "source": "HDRUK"
  },
  {
    "id": 980,
    "name": "NIHR BioResource: Returned datasets",
    "description": "The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022.",
    "url": "https://healthdatagateway.org/en/dataset/397",
    "uid": "261bcf76-8621-4f0d-b2e6-310993d73daa",
    "datasource_id": 397,
    "source": "HDRUK"
  },
  {
    "id": 981,
    "name": "NIHR BioResource: Contact detail",
    "description": "The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022.  The NIHR BioResource acquires contact details - name, address, email address, phone/mobile number - from participants at recruitment. This is used to recontact participants to invite them to take part in experimental medicine studies, although sample-only and data-only studies are permitted. We also record NHS number, where known, to allow linkage to healthcare records. Recruitment takes place at blood donor centres, disease clinics, online (particularly for the Mental Health BioResource) but also from more public settings. A participant is not considered a member of the NIHR BioResource without contact details. NHS number availability depends on recruitment method: blood donors and those recruited through clinics will have these to hand, general members of the public will not.",
    "url": "https://healthdatagateway.org/en/dataset/398",
    "uid": "fc2fcaec-bd5b-4d43-9e0c-2fed36eff0e4",
    "datasource_id": 398,
    "source": "HDRUK"
  },
  {
    "id": 982,
    "name": "NIHR BioResource: SNP chip data",
    "description": "The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022.  The NIHR BioResource extracts DNA from blood and saliva samples taken at recruitment, and measures a panel of SNPs on each DNA sample, using a commodity SNP genotyping array from e.g. Illumina or Affymetrix (now Thermofisher). This is used to pre-screen or match participants when inviting them to take part in experimental medicine studies. De-identified versions of this data is available to researchers investigating the feasibility of future studies. The Technical Metadata describes a SNP annotation file – i.e. what the chip is measuring. The file itself has as many rows as there are SNPs represented on the chip, and is proprietary to the manufacturer, although deeply familiar to researchers.",
    "url": "https://healthdatagateway.org/en/dataset/391",
    "uid": "c388676c-a5e5-419c-967c-239c1f0c3c5c",
    "datasource_id": 391,
    "source": "HDRUK"
  },
  {
    "id": 983,
    "name": "NIHR BioResource: Health and Lifestyle Questionnaire",
    "description": "The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022.  The NIHR BioResource acquires a self-reported health and lifestyle questionnaire from all participants at recruitment, save those recruited in rare disease clinics. Typically variables include height, weight, smoking history and alcohol consumption, with addition of questions relating to disease history and current medications. This is used to pre-screen or match participants when inviting them to take part in experimental medicine studies. De-identified versions of this data are available to researchers investigating the feasibility of future studies.",
    "url": "https://healthdatagateway.org/en/dataset/392",
    "uid": "321e3ad6-96b9-4f19-bbc4-d06dfe2a59d5",
    "datasource_id": 392,
    "source": "HDRUK"
  },
  {
    "id": 984,
    "name": "NIHR BioResource: Sample holding",
    "description": "The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022.  The NIHR BioResource acquires samples at recruitment for each participant recruited. With the exception of the GLAD study, which posts saliva kits direct to participants’ homes, the preferred sample collection is 3 blood tubes – see https://bioresourcesupport.org.uk/live-samples/ - from which DNA can be extracted, and plasma and sera stored. In many clinics, the NIHR BioResource blood draw follows immediately the blood collection for routine healthcare purposes. In the case of children, less blood will be taken, down to 1mL. The Technical Metadata describes the standard used by RD-Connect, the EU-wide Rare Disease biobank consortium - https://samples.rd-connect.eu/menu/main/home. During 2020, this will be replaced by an API from the UK CRC Tissue Directory, as using this discovery tool is a condition of running a Research Tissue Bank in the UK.",
    "url": "https://healthdatagateway.org/en/dataset/393",
    "uid": "56993820-96e4-4a1f-9de1-bad53108c201",
    "datasource_id": 393,
    "source": "HDRUK"
  },
  {
    "id": 985,
    "name": "NIHR BioResource: Demographic",
    "description": "The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022.  The NIHR BioResource acquires demographic details – e.g. age, sex, ethnicity - from participants at recruitment. This is used to pre-screen or match participants when inviting them to take part in experimental medicine studies. De-identified versions of this data are available to researchers investigating the feasibility of future studies.",
    "url": "https://healthdatagateway.org/en/dataset/394",
    "uid": "d2c486bb-b95b-4093-bb58-b196cfc08c7b",
    "datasource_id": 394,
    "source": "HDRUK"
  },
  {
    "id": 986,
    "name": "NIHR BioResource: Consent records",
    "description": "The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022. The NIHR BioResource records consent details – form, version and date - from participants at many points of recontact.  In addition, participants are empowered to contact the study direct to express their preferences. The primary use of consent records is as an exclusion when participants are invited to take part in experimental medicine studies.  However consent is also factored in to the release of samples and data for sample-only and data-only studies.  One implication, is that data releases have freezes that capture a snapshot of the current consent statuses.  How the withdrawal process is managed, and implications for whether data can be removed rapidly from already published datasets is described in our Participant Privacy Notice, online at https://bioresource.nihr.ac.uk/privacy/",
    "url": "https://healthdatagateway.org/en/dataset/387",
    "uid": "1fb479b1-98d4-4d7b-91a2-8368ce3dc77c",
    "datasource_id": 387,
    "source": "HDRUK"
  },
  {
    "id": 987,
    "name": "NIHR BioResource: Metabolite data",
    "description": "The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022.  Some of the general population participants have had sera or plasma measured for metabolites, using Metabolon technology.",
    "url": "https://healthdatagateway.org/en/dataset/388",
    "uid": "dec09f2e-a93f-41e2-8217-72431ea4a19e",
    "datasource_id": 388,
    "source": "HDRUK"
  },
  {
    "id": 988,
    "name": "NIHR BioResource: SNP imputation data",
    "description": "The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022.  SNP chip data can be used to impute many of the (non-rare) SNPs not included on the chips.  The NIHR BioResource is using a modified version of the UK Biobank protocol to improve the options for recall.",
    "url": "https://healthdatagateway.org/en/dataset/389",
    "uid": "39d7ad29-be30-4e12-a7c8-d8757141de85",
    "datasource_id": 389,
    "source": "HDRUK"
  },
  {
    "id": 989,
    "name": "NIHR BioResource: Full Blood Counts",
    "description": "The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022.  Blood donor studies are particularly exercised about the characteristics of blood received from donors, and measures FBCs using Sysmex machines at the NIHR National Biosample Centre, which is where all samples are sent.",
    "url": "https://healthdatagateway.org/en/dataset/390",
    "uid": "c20c2d81-c2d3-47cc-be38-93074467544c",
    "datasource_id": 390,
    "source": "HDRUK"
  },
  {
    "id": 990,
    "name": "Genomics England - Bioinformatics",
    "description": "To identify and enrol participants for the 100,000 Genomes Project we have created NHS Genomic Medicine Centres (GMCs). Each centre includes several NHS Trusts and hospitals. GMCs recruit and consent patients. They then provide DNA samples and clinical information for analysis.\r\n\r\nIllumina, a biotechnology company, have been commissioned to sequence the DNA of participants. They return the whole genome sequences to Genomics England. We have created a secure, monitored, infrastructure to store the genome sequences and clinical data. The data is analysed within this infrastructure and any important findings, like a diagnosis, are passed back to the patient’s doctor.\r\n\r\nTo help make sure that the project brings benefits for people who take part, we have created the Genomics England Clinical Interpretation Partnership (GeCIP). GeCIP brings together funders, researchers, NHS teams and trainees. They will analyse the data – to help ensure benefits for patients and an increased understanding of genomics. The data will also be used for medical and scientific research. This could be research into diagnosing, understanding or treating disease.\r\n\r\nTo learn more about how we work you can read the 100,000 Genomes Project protocol. It has details of the development, delivery and operation of the project. It also sets out the patient and clinical benefit, scientific and transformational objectives, the implementation strategy and the ethical and governance frameworks.",
    "url": "https://healthdatagateway.org/en/dataset/381",
    "uid": "6f94b0f4-7e4d-5ee1-0000-00000000df8b",
    "datasource_id": 381,
    "source": "HDRUK"
  },
  {
    "id": 991,
    "name": "Genomics England - Secondary Data - PHE/NCRAS",
    "description": "av_patient\t\nPatient information - demographics and death details.\n\nav_tumour\t\nTumour catalogue and characterisation for all patients with registerable tumour. Table's anon_tumour_id is used to link treatment tables also available in NCRAS. One row per tumour (av* table specific anon_tumour_id), per participant at the point of registration of that cancer/tumour with NCRAS.\n\nav_treatment\t\nTumour linked catalogue of treatments and sites that provided them for all patients with registerable tumour.\n\nav_imd\t\nThe Income Deprivation Domain (IMD table) measures the proportion of the population experiencing deprivation relating to low income. The definition of low income used includes both those people that are out-of-work and those that are in work but who have low earnings.\n\nav_rtd\t\nRoutes to Diagnosis: cancer registration data are combined with Administrative Hospital Episode Statistics data, Cancer Waiting Times data and data from the cancer screening programmes. Using these datasets cancers registered in England which were diagnosed in 2006 to 2016 are categorised into one of eight Routes to Diagnosis. The methodology is described in detail in the British Journal of Cancer article 'Routes to Diagnosis for cancer - Determining the patient journey using multiple routine datasets'.\n\ncwt\t\nThe National Cancer Waiting Times Monitoring Data Set supports the continued management and monitoring of waiting times.\n\nsact\t\nSystemic Anti-Cancer Therapy (chemotherapy detail) data for cancer participants from NHSE covering regimens between 04/2012 and 08/2022. One row per chemotherapy cycle, per tumour (SACT-specific anon_tumour_id), per participant.\n\nrtds\t\nThe Radiotherapy Data Set (RTDS) standard (SCCI0111) is an existing standard that has required all NHS Acute Trust providers of radiotherapy services in England to collect and submit standardised data monthly against a nationally defined data set since 2009. The purpose of the standard is to collect consistent and comparable data across all NHS Acute Trust providers of radiotherapy services in England in order to provide intelligence for service planning, commissioning, clinical practice and research and the operational provision of radiotherapy services across England. Data is available from 01/04/2009. The data is linked at a patient level and can be linked to the latest available av_patient table.\n\nncras_did\t\nThe Diagnostic Imaging Dataset (DID) is a central collection of detailed information about diagnostic imaging tests carried out on NHS patients, extracted from local radiology information systems and submitted monthly. The DID captures information about referral source, details of the test (type of test and body site), demographic information such as GP registered practice, patient postcode, ethnicity, gender and date of birth, plus data items about different events (date of imaging request, date of imaging, date of reporting, which allows calculation of time intervals.\n\nlucada_2013\t\nThe National Lung Can",
    "url": "https://healthdatagateway.org/en/dataset/382",
    "uid": "6f94b0f4-7e4d-5ee1-0000-00000000d210",
    "datasource_id": 382,
    "source": "HDRUK"
  },
  {
    "id": 992,
    "name": "Genomics England - Secondary Data - MHSDS",
    "description": "Mental Health Datasets contain historic data on patients receiving care in NHS specialist mental health services.",
    "url": "https://healthdatagateway.org/en/dataset/383",
    "uid": "6f94b0f4-7e4d-5ee1-0000-00000000aa10",
    "datasource_id": 383,
    "source": "HDRUK"
  },
  {
    "id": 993,
    "name": "Genomics England - Secondary Data - NHSE",
    "description": "Secondary data tables are the corpus of curated data we receive from national data warehouses for all eligible participants not belonging in a data restricting cohort and not registered in Northern Ireland, Wales or Scotland. They are mostly longitudinal in nature and agnostic to the recruited disease. Data at the point of release captures all activity contained in the period covered within each of the datasets up to the latest quarter published by NHSE and end of calendar year for PHE/NCRAS.\n\nPlease Note: The linking files MH_bridge and DID_bridge will no longer be provided as part of the main programme release. Participant id is already been included in all tables making these files redundant.\n\n- HES: Hospital Episode Statistics containing details of all commissioned activity during admissions, outpatient appointments and A&E attendances.\n- DID: Metadata (demographics, modalities, ordering entity and dates) on diagnostic imaging tests collated from local radiology information systems.\n- MORTALITY/CANCER_REGISTRY: Office of National Statistics registry data for cancer registrations and deaths inside and outside hospitals. Issue of death certificates and cancer network registrations are a requirement for an entry to these manifests.\n- COVID: Data on covid test results for 100K participants. Pre Data Release V14 this data was found in the frequent release folder. For more information please see Clinical and phenotype data Secondary Data - COVID.\n- MHMDS: Data on patients receiving care in NHS specialist mental health services. Reporting care period for this dataset is up to March '14.\n- MHLDDS: Data on patients receiving care in NHS specialist mental health services. Reporting care period for this dataset is from March '14 to March '16.\n- MHSDS: Data on patients receiving care in NHS specialist mental health services. Reporting care period for this dataset us from March '16 to March '19.",
    "url": "https://healthdatagateway.org/en/dataset/384",
    "uid": "6f94b0f4-7e4d-5ee1-0000-00000000b210",
    "datasource_id": 384,
    "source": "HDRUK"
  },
  {
    "id": 994,
    "name": "Genomics England - Research Community Provided Data",
    "description": "Data provided by or on behalf of the wider research community. This can include data resources from both GeCIP and Discovery Forum members. Researchers can reach out to Genomics England via the Service Desk for more information or if they wish to share their data with the wider community.",
    "url": "https://healthdatagateway.org/en/dataset/377",
    "uid": "6f94b0f4-7e4d-5ee1-0000-00000000a7f0",
    "datasource_id": 377,
    "source": "HDRUK"
  },
  {
    "id": 995,
    "name": "Genomics England - Quick View",
    "description": "Quickviews bring together data from several LabKey tables for convenient access, including:\r\n\r\nrare_disease_analysis Data for all rare disease participants including: sex, ethnicity, disease recruited for and relationship to proband; latest genome build, QC status of latest genome, path to latest genomes and whether tiering data are available; as well as family selection quality checks for rare disease genomes on GRCh38, reporting abnormalities of the sex chromosomes, family relatedness, Mendelian inconsistencies and reported vs genetic sex summary checks. Please note that only sex checks are unpacked into individual data fields; a final status is shown in the “genetic vs reported results” column.\r\n\r\ncancer_analysis Data for all cancer participants whose genomes have been through Genomics England bioinformatics interpretation and passed quality checks, including: sex, ethnicity, disease recruited for and diagnosis; tumour ID, build of latest genome, QC status of latest genome and path to latest genomes; as well file paths to the genomes. This table includes information derived from laboratory_sample and cancer_participant_tumour.",
    "url": "https://healthdatagateway.org/en/dataset/378",
    "uid": "6f94b0f4-7e4d-5ee1-0000-000000009df0",
    "datasource_id": 378,
    "source": "HDRUK"
  },
  {
    "id": 996,
    "name": "Genomics England - Cancer",
    "description": "Cancer data are presented for either the patient level cancer diagnosis or “disease type” or the tumour specific sample details of participants in the Cancer arm of the 100,000 Genomes Project.\r\n\r\nData Relating to Cancer Participants:\r\n\r\ncancer_participant_disease\r\n\r\nFor each cancer participant in the 100,000 Genomes Project, this table includes data about their cancer disease type and subtype.\r\n\r\ncancer_participant_tumour\r\n\r\nFor each cancer participant’s tumour in the 100,000 Genomes Project, this table contains data that characterises the tumour, e.g. staging and grading; morphology and location; recurrence at time of enrolment; and the basis of diagnosis.\r\n\r\ncancer_participant_tumour_ metastatic_site\r\n\r\nFor each cancer participant in the 100,000 Genomes Project, this table contains the site of their metastatic disease in the body (if applicable) at diagnosis.\r\n\r\ncancer_care_plan\r\n\r\nFor a proportion of cancer participants in the 100,000 Genomes Project, this table contains information from their NHS cancer care plan on their treatment and care intent, in particular outcomes of MDT meetings and coded connected data (e.g. diagnoses from scans).\r\n\r\ncancer_surgery\r\n\r\nFor a proportion of cancer participants in the 100,000 Genomes Project, this table contains details of what surgical procedures were had, as well as the specific location of the intervention.\r\n\r\ncancer_risk_factor_general\r\n\r\nFor a proportion of cancer participants in the 100,000 Genomes Project, this table contains data on general cancer risk factors, namely smoking status, height, weight and alcohol consumption. This table was compiled with input from GeCIP members.\r\n\r\ncancer_risk_factor_cancer_specific:\r\n\r\nFor a proportion of cancer participants in the 100,000 Genomes Project, this table contains data on specific risk factors related to particular cancer types. This table was compiled with input from GeCIP members.\r\n\r\ncancer_invest_imaging:\r\n\r\nFor a proportion of cancer participants in the 100,000 Genomes Project, this table contains: coded data on imaging investigations characterising the scan, its modality, anatomical site and outcome; as well as the outcome of the imaging report in free text form.\r\n\r\nData derived from or relating to tumour samples:\r\n\r\ncancer_invest_sample_pathology:\r\n\r\nFor a proportion of cancer participants in the 100,000 Genomes Project, this table contains full pathology reports and other related data on and from their tumour samples around diagnosis and characterisation of the cancer. Please note that much of this information is also found in the clinic_sample and cancer_participant_tumour tables.\r\n\r\ncancer_specific_pathology:\r\n\r\nFor a proportion tumours from cancer participants in the 100,000 Genomes Project, this table contains pathology data specific to that participant’s cancer type. This may provide additional data to the cancer_invest_sample_pathology and cancer_participant_tumour tables.\r\n\r\ncancer_systemic_anti_cancer_therapy:\r\n\r\nFor a proportion tumour",
    "url": "https://healthdatagateway.org/en/dataset/379",
    "uid": "6f94b0f4-7e4d-5ee1-0000-00000000da10",
    "datasource_id": 379,
    "source": "HDRUK"
  },
  {
    "id": 997,
    "name": "Genomics England - Rare Disease",
    "description": "Rare Disease data are presented at the level of Rare Disease families (families of probands), Rare Disease pedigrees, and participants. Participants are individuals who have consented to be part of the project with the expectation that a sample of their DNA will be obtained and their genome sequenced. Pedigree members are extended members of the proband’s family, this includes participants as well a small amounts of deidentified data recorded to allow a full picture of the proband’s extended family. This additional information is extracted from the proband’s medical record.\r\n\r\nAll Rare Disease table names are prefixed with “rare_diseases_”.\r\n\r\nData at the Level of Rare Disease Families:\r\n\r\nrare_diseases_family:\r\n\r\nData describing the families of rare disease probands participating in the 100,000 Genomes Project. It includes the family group type, the status of the family’s pre-interpretation clinical review and the settings that were chosen for the interpretation pipeline at the clinical review.\r\n\r\nrare_diseases_pedigree:\r\n\r\nData describing the Rare Disease participants, linking pedigrees to probands and their family members.\r\n\r\nrare_diseases_pedigree_member:\r\n\r\nData describing the Rare Disease pedigree members, similar to the data about each individual participant in the participant table (common data view, see section 8.2). It may also include additional data, such as the age of onset of predominant clinical features; data on links to other family members; as well as data collected only for Phenotypes.\r\n\r\nData at the Level of Rare Disease Participants.\r\n\r\nThe data presented in these tables provides information on disease progression and pertinent medical history:\r\n\r\nrare_diseases_participant_disease:\r\n\r\nData describing the rare disease participants' disease type/subtype assigned to them upon enrolment, and the date of diagnosis.\r\n\r\nrare_diseases_participant_phenotype:\r\n\r\nData describing the Rare Disease participants’ phenotypes. For each Rare Disease participant in the 100,000 Genomes Project, there are data about whether a phenotypic abnormality as defined by an HPO term is present and what the HPO term is, as well as the age of onset, the severity of manifestation, the spatial pattern in the body and whether it is progressive or not. Please note that these data are only available for a subset of the rare disease participants.\r\n\r\nrare_diseases_gen_measurement:\r\n\r\nFor Rare Disease participants in the 100,000 Genomes Project, this table contains general measurements relevant to the disease, alongside the date that the measurements were taken on. Please note that these data are only available for a subset of the rare disease participants.\r\n\r\nrare_diseases_early_childhood_observation:\r\n\r\nFor Rare Disease participants in the 100,000 Genomes Project, this table contains measurements and milestones provided by the GMCs, related to childhood development. Please note that these data are only available for a subset of the rare disease participants.\r\n\r\nr",
    "url": "https://healthdatagateway.org/en/dataset/380",
    "uid": "6f94b0f4-7e4d-5ee1-0000-000000009ff0",
    "datasource_id": 380,
    "source": "HDRUK"
  },
  {
    "id": 998,
    "name": "Genomics England - OMOP CDM",
    "description": "Genomics England 100k data in OMOP CDM v5.4 format. Includes 100k data and PHE NCRAS data.",
    "url": "https://healthdatagateway.org/en/dataset/373",
    "uid": "6f94b0f4-7e4d-5ee1-0000-00000000e38e",
    "datasource_id": 373,
    "source": "HDRUK"
  },
  {
    "id": 999,
    "name": "Genomics England - Long Read Sequencing",
    "description": "Contains tables related to long-reads sequencing data for 100,000 Genomes Project participants.\r\n\r\n- lrs_laboratory_sample: Data describing the characteristics and processing methods (DNA to library preparation) of samples from participants in the 100,000 Genomes Project for which long-reads sequencing has been carried out.\r\n- lrs_sequencing_data: This table includes data describing long-read sequencing of a subset of 100,000 Genomes Project participants and associated output, including paths to raw and BAM files.\r\n- cancer_ont_cohorts: Table listing participant ids, sample data, file paths and sequencing statistics for Oxford Nanopore cancer cohorts available in the Research Environment, along with corresponding matched germline and Illumina short reads files where available\r\n- rare_disease_pacbio_pilot: This is a dataset of 91 rare disease samples from the 100k genome project re-sequenced with Pacific Biosciences (PacBio) as an example dataset to to demonstrate the utility of their HiFi technology.",
    "url": "https://healthdatagateway.org/en/dataset/374",
    "uid": "6f94b0f4-7e4d-5ee1-0000-000000009cf0",
    "datasource_id": 374,
    "source": "HDRUK"
  },
  {
    "id": 1000,
    "name": "Genomics England - Common",
    "description": "Data views that are common to both the rare disease and the cancer domains. This data pertains to sample handling, genome sequencing, and participant data.\r\n\r\nData Relating to Participants:\r\n\r\n- participant: Data on each individual participant in the 100,000 Genomes Project, e.g. personal information (such as relatives or self-reported ethnicity); points of contact with the Project (e.g. handling Genomic Medicine Centre or Trust); and a record of the status of their clinical review.\r\n- death_details: Data on participant deaths submitted by GMCs, likely less complete than the data collected by ONS and NHSE.\r\n\r\nData Relating to Samples:\r\n\r\n- clinic_sample:\tData describing the taking and handling of participant samples at the Genomic Medicine Centres, i.e. in the clinic, as well as the type of samples obtained. Because of the complexities of handling and managing tumour tissues samples in a clinical setting, there are many fields that are cancer-specific.\r\n- clinic_sample_quality_check_result: Data describing the quality control of obtaining and handling participant samples at the Genomic Medicine Centres, i.e. in the clinic.\r\n- laboratory_sample: Data describing the handling of samples at the biorepository and in preparation for sequencing, as well as the type of sample.\r\n- plated_sample: Data describing the handling and QC of samples at Illumina (the sequencing provider).\r\n- laboratory_sample_omics_availability: Availability of samples collected from participants in the 100,000 Genomes Project for the purpose of omics research. Data includes: Participant ID, Sample Type (e.g. Serum, RNA Blood), the number of aliquots of that sample type for that participant, and the availability status - whether the sample has already been used for a research project. Research proposals for the use of these samples can be submitted, via the GECIP team, to the Scientific Advisory Committee and Access Review Committee.",
    "url": "https://healthdatagateway.org/en/dataset/375",
    "uid": "6f94b0f4-7e4d-5ee1-0000-000000009bf0",
    "datasource_id": 375,
    "source": "HDRUK"
  },
  {
    "id": 1001,
    "name": "Genomics England - Secondary Data - Cancer Specific Curated Datasets - Pilot",
    "description": "Genomics England are striving to improve the clinical data provided for its researchers. We understand the value of accurate and granular clinical data, especially in the context of cancer.\n\nIn order to deliver this, we are planning a series of pilot datasets, aiming to incorporate additional clinical data provided by Public Health England cancer registry (NCRAS). Genomics England will aim to deliver cancer specific datasets, with the initial focus being on providing a broad pathological understanding. This will aim to incorporate data points such as molecular mutations and resection margins in pathology reports. The focus will then incorporate radiological imaging reports and finally focus on live/ up-to-date clinical data. In addition, we are also including the date each participant was last seen alive (data provided up to October 2020) and dates and causes of death to aid with outcomes.\n\nIt must be stressed that this work is a development process, and we are working in unison with NCRAS to progress this. Whilst we do not possess the extensive experience and resource of Public Health England, we are developing a natural language based algorithm for focused data extraction. NCRAS have a dedicated team to curating clinical data and the gold standard remains the NCRAS curated tables. However, for this dataset to improve and move forward, Genomics England are keen for feedback and for you to highlight areas for improvement.\n\nYou will note subtle differences to the structure of the table compared to the curated NCRAS tables and thus additional data dictionaries have been provided. Genomics England hopes to continue developing this uncurated live dataset with feedback and look forward to hearing your thoughts. Please reach out to us with related thoughts and suggestions via the Genomics England Service Desk, including \"cancer_specific_datasets_pilot\" in the title of your enquiry.\n\nWith the addition of the new pathology_reports dataset introduced in v16, the aml_path_reports and testes_path_reports datasets have been deprecated in v17.",
    "url": "https://healthdatagateway.org/en/dataset/376",
    "uid": "6f94b0f4-7e4d-5ee1-0000-00000000a810",
    "datasource_id": 376,
    "source": "HDRUK"
  },
  {
    "id": 1002,
    "name": "Looked After Children Adoption (LACA)",
    "description": "Children who are 'looked after' by the State are considered one of the most vulnerable groups in society. Being in State care is associated with poor social, educational and health outcomes. Exploring how to improve the system and better support children in care is key to improving these outcomes. When children and young people come to the attention of children's social services a significant amount of information about their care experience is routinely collected by local authorities. In Wales, routine data are captured in the 'Children Looked After' Census which is submitted annually to the Welsh Government. This dataset includes details of all adoptions of looked after children in Wales. \n\nThis dataset is a subset of the primary Looked After Children in Wales (LACW) dataset. Other subsets include: Looked After Children Care Leavers aged 16 and over (LACC); Looked After Children Birthday 19 (LACB: 1999 - 2016); and Looked after Children - Education Qualifications (LACE). LACE was discontinued in 2016 (1999 - 2016) and included within LACW. \n\nDue to the small number of looked after children with an Anonymised Linkage Field (ALF, 37%), a two-stage algorithm was developed. This algorithm utilises other datasets within SAIL to allocate children within the LACW ALF, increasing the overall ALF match rate to 61%. The improved ALF matches are available in the LAC ALF DERIVED table, part of the LACW dataset, and can be obtained by combined the SYSTEM_ID and LOCAL_AUTHORITY_CODE.",
    "url": "https://healthdatagateway.org/en/dataset/372",
    "uid": "6679cde9-b3ab-43a1-bb11-ad25786655cb",
    "datasource_id": 372,
    "source": "HDRUK"
  },
  {
    "id": 1003,
    "name": "Looked After Children Education (LACE) - Static",
    "description": "Children who are &amp;amp;amp;#039;looked after&amp;amp;amp;#039; by the State are considered one of the most vulnerable groups in society. Being in State care is associated with poor social, educational and health outcomes. Exploring how to improve the system and better support children in care is key to improving these outcomes. When children and young people come to the attention of children&amp;amp;amp;#039;s social services a significant amount of information about their care experience is routinely collected by local authorities. In Wales, routine data are captured in the &amp;amp;amp;#039;Children Looked After&amp;amp;amp;#039; Census which is submitted annually to the Welsh Government.\n\nThis dataset contains the qualifications that care leavers had gained at the point they ceased to be looked after. The dataset was discontinued in 2016 and it is now published as part of the primary Looked After Children (LACW) dataset. Other LACW subsets include: Looked After Children Adoption (LACA); Looked After Children Care Leavers aged 16 and over (LACC); and Looked After Children Birthday 19 (LACB: 1999 - 2016).\n\nDue to the small number of looked after children with an Anonymised Linkage Field (ALF, 37%), a two-stage algorithm was developed. This algorithm utilises other datasets within SAIL to allocate children within the LACW ALF, increasing the overall ALF match rate to 61%. The improved ALF matches are available in the LAC ALF DERIVED table, part of the LACW dataset, and can be obtained by combined the SYSTEM_ID and LOCAL_AUTHORITY_CODE.\n\nPlease note: the LACE dataset is static and is no longer refreshed.",
    "url": "https://healthdatagateway.org/en/dataset/371",
    "uid": "365b9657-e1a8-4b2f-8942-a39eea7d1a79",
    "datasource_id": 371,
    "source": "HDRUK"
  },
  {
    "id": 1004,
    "name": "Data First Probation Dataset (PROB)",
    "description": "The probation data included in this dataset is sourced from National Delius (nDelius). nDelius is used for the management of offenders on Probation, or in the community. A service user (offender) is referred to nDelius by a court and an event is created in nDelius. Broadly, events are either a sentence or pre-sentence. An event can only ever have one sentence outcome (disposal). One court case can receive multiple sentences/disposals so more than one event may run at the same time.\n\nA service user will receive one offender_id per court case however duplicates can happen by mistake. The variable estimated_offender_id uses a process of data deduplication to eliminate these duplicates and group them under the same service user cluster.\n\nThe accuracy of the source data is dependent on the quality assurance processes and local recording practices intrinsic to the source data systems used by HMPPS staff (nDelius).\n\nThe Research Accreditation Panel provides oversight of the framework that is used to accredit research projects, researchers and processing environments under the Digital Economy Act 2017 (DEA). Researchers are advised to liaise with SAIL support teams to understand the requirements and timelines involved with submitting a research project to the Research Accreditation Panel. https://uksa.statisticsauthority.gov.uk/digitaleconomyact-research-statistics/research-accreditation-panel/",
    "url": "https://healthdatagateway.org/en/dataset/362",
    "uid": "f93ffd0a-137a-4f90-98aa-dc2136397164",
    "datasource_id": 362,
    "source": "HDRUK"
  },
  {
    "id": 1005,
    "name": "Welsh Demographic Service Dataset (WDSD) - Legacy three-view version",
    "description": "Legacy metadata for the discontinued three-view WDSD version. The views now provisioned to new projects have &amp;#039;SINGLE&amp;#039; in their title and are found in the main WDSD metadata entry, which is separate to this one.\n\nAdministrative information about individuals in Wales that use NHS services; such as address and practice registration history. It replaced the NHS Wales Administrative Register (NHSAR) in 2009.\n\nData drawn from GP practices via Exeter System.\n\nThis dataset provides linkage from anonymous individual to anonymous residences, thus enable to group households of individuals.",
    "url": "https://healthdatagateway.org/en/dataset/365",
    "uid": "2c43322e-aa9b-4b55-9eb9-c968262f2f22",
    "datasource_id": 365,
    "source": "HDRUK"
  },
  {
    "id": 1006,
    "name": "School Workforce Annual Census (SWAC)",
    "description": "The School Workforce Annual Census (SWAC) is an electronic collection of individual level data on the school workforce in local authority maintained settings in Wales. The first collection was introduced in 2019 and collects information at November each year.\n\nThe SWAC is split into two parts: SWAC School return and SWAC Pay, HR and absences return. Information relating to codes can be found on the Welsh Government (see links below). \n\nThe SWAC School return is completed by all local authority maintained school settings in Wales, including Pupil Referral Units (PRUs). Schools record data on the workforce throughout the year in their Management Information System (MIS) software. This part of the return collects information on workforce characteristics (including Welsh language, ethnicity and disability), staff roles and curriculum taught.\n\nThe SWAC Pay, HR and Absences return is completed by each local authority, as well as schools which have opted-out of payroll and / or human resource (HR) service level agreements with their local authority. The data is maintained throughout the year in their HR and payroll systems. This return collects information on staff contracts, including salary and any additional payments they receive. This approach ensures that data for all relevant staff who work at local authority maintained schools is captured.",
    "url": "https://healthdatagateway.org/en/dataset/366",
    "uid": "5692eade-5738-48ed-8527-39be9caaa7b5",
    "datasource_id": 366,
    "source": "HDRUK"
  },
  {
    "id": 1007,
    "name": "Cancer Network Information System (CNIS / CANISC)",
    "description": "Cancer Network Information System Cymru (CaNISC) includes multidisciplinary team diagnosis, proposed treatments, and a system-generated summary of the patient&#039;s cancer record.\n\nThe core system used across Wales has been CaNISC. This system provided the cancer patient record of treatment, the patient administration system for Velindre Cancer Centre, and was the primary source of cancer data for waiting times, clinical audit, and cancer registration.\n\nVelindre Cancer Centre successfully migrated from CaNISC onto the new Cancer Informatics System (CIS) in November 2022.\n\nWhy is CaNISC being replaced?\n\nWhilst CaNISC allowed multiple organisations to record the diagnosis, treatment and follow-up care information for a patient, it could only be accessed by approximately 2000 authorised health care professionals. Patients receive care in many settings outside of the cancer centres and their cancer care clinical information was often not available to other health professionals treating patients for other health related issues in other care settings.\n\nCaNISC approached the end of its viable service as it could no longer be updated to keep pace with changing clinical practices and processes. The 2018 Cancer Delivery Plan for Wales included an action to explore the replacement of CaNISC, resulting in a business case being submitted to the Welsh Government to develop a new Cancer Information System for Wales.\n\nWelsh Government agreed a &amp;amp;amp;pound;6.5 million investment from the Digital Priorities Investment Fund in 2019 spanning three financial years, working with Digital Health and Care Wales (DHCW), health boards and trusts to deliver the Cancer Informatics Programme.\n\nA Data Explained report on CNIS can be found here: https://adrwales.org/wp-content/uploads/2025/02/Data_Explained_CNIS.pdf",
    "url": "https://healthdatagateway.org/en/dataset/358",
    "uid": "da91f0eb-1761-485b-8b7f-9121b6a1d004",
    "datasource_id": 358,
    "source": "HDRUK"
  },
  {
    "id": 1008,
    "name": "ONS 2021 Census (CENS)",
    "description": "Every ten years since 1801 the nation has set aside one day for the census - a count of all people and households. It is the most complete source of information about the population that we have. The latest census was held on Sunday 21 March 2021.\n\nEvery effort is made to include everyone, and that is why the census is so important. It is the only survey which provides a detailed picture of the entire population, and is unique because it covers everyone at the same time and asks the same core questions everywhere. This makes it easy to compare different parts of the country.\n\nThe information the census provides allows central and local government, health authorities and many other organisations to target their resources more effectively and to plan housing, education, health and transport services for years to come.\n\nIn England and Wales, the census is planned and carried out by the Office for National Statistics. Elsewhere in the UK, responsibility lies with the National Records of Scotland and the Northern Ireland Statistics and Research Agency.\n\nA usual resident is anyone who on Census Day, 21 March 2021 was in the UK and had stayed or intended to stay in the UK for a period of 12 months or more, or had a permanent UK address and was outside the UK and intended to be outside the UK for less than 12 months.\n\nThe ONS have three processes for checking and resolving duplicate responses so that the main census data should simply be one record for each person:\n\n1. The ONS resolve duplicates coming in for the same postcode using a process called Resolve Multiple Responses (RMR). For instance, if two people both fill in a form for their whole household, or someone from a household also submits an individual response unknown to the main submission. They have rules for checking they are duplicates, and rules for which to keep.\n\n2. The ONS also do an over coverage check on a sample basis for duplicates across the rest of the country, and then factor the findings into their coverage estimation calculations. This sampling focuses on the types of population which are more likely to be duplicated (people who have indicated they have a second residence on the census, students aged 18-25, armed forces personnel, children, adults enumerated at a communal establishment, etc.) but also samples from the remaining population.\n\n3. The ONS ask parents to fill in basic demographic information for any children who are away studying, and when they get to the question on their term-time address, if they answer that the term-time address is elsewhere, we then use that to filter those out-of-term students out of the main database. Then when that student does respond actually at their term-time address, they only include them there.\n\nPlease note: variables RELAT06, RELAT11, RELAT16, RELAT21, RELAT26, GENDER_IDENTITY are not available in the data.\n\nThe Research Accreditation Panel provides oversight of the framework that is used to accredit research projects, researchers and processing environments under the Digital Economy Act 2017 (DEA). Researchers are advised to liaise with SAIL support teams to understand the requirements and timelines involved with submitting a research project to the Research Accreditation Panel. https://uksa.statisticsauthority.gov.uk/digitaleconomyact-research-statistics/research-accreditation-panel/",
    "url": "https://healthdatagateway.org/en/dataset/361",
    "uid": "fc24482c-0f9b-445f-af7a-8598627c3b15",
    "datasource_id": 361,
    "source": "HDRUK"
  },
  {
    "id": 1009,
    "name": "Covid Lateral Flow Test (CVLF) - Static",
    "description": "Dataset no longer updated after 29/02/2024.\nThe test for people without coronavirus symptoms is called a rapid lateral flow test. Rapid lateral flow tests help to find cases in people who may have no symptoms but are still infectious and can give the virus to others.",
    "url": "https://healthdatagateway.org/en/dataset/354",
    "uid": "ce60d677-6263-4d41-9d45-d3c7ebb3ee36",
    "datasource_id": 354,
    "source": "HDRUK"
  },
  {
    "id": 1010,
    "name": "Data First Crown Court defendant case level dataset (CRCO)",
    "description": "One record per defendant per case giving details of defendant characteristics, offence categorisation, court proceedings, and outcomes. Where multiple offence and disposal types were recorded for a case, only details of the most serious offence and most serious disposal are given (please note that as the linked criminal courts datasets provide information on only the most serious offence at the point of committal to court and sentencing, they may not include the full range of offending that may be attributable to defendants, and therefore restricts the ability of researchers to explore any associations and correlations between different types of offences within the cases being heard before the courts). The fields populated for appeals differ from other case types.\n\nThe Research Accreditation Panel provides oversight of the framework that is used to accredit research projects, researchers and processing environments under the Digital Economy Act 2017 (DEA). Researchers are advised to liaise with SAIL support teams to understand the requirements and timelines involved with submitting a research project to the Research Accreditation Panel. https://uksa.statisticsauthority.gov.uk/digitaleconomyact-research-statistics/research-accreditation-panel/",
    "url": "https://healthdatagateway.org/en/dataset/356",
    "uid": "e3f96792-2eed-425d-8d84-1d57247b3796",
    "datasource_id": 356,
    "source": "HDRUK"
  },
  {
    "id": 1011,
    "name": "Data First Prisoner Custodial Journey Dataset (PRIS)",
    "description": "The prisoner dataset extracts from the Prison National Offender Management Information System (p-NOMIS) and the Offender Assessment System (OASys), operational databases used in prisons for the management of offenders.\n\nThe Research Accreditation Panel provides oversight of the framework that is used to accredit research projects, researchers and processing environments under the Digital Economy Act 2017 (DEA). Researchers are advised to liaise with SAIL support teams to understand the requirements and timelines involved with submitting a research project to the Research Accreditation Panel. https://uksa.statisticsauthority.gov.uk/digitaleconomyact-research-statistics/research-accreditation-panel/",
    "url": "https://healthdatagateway.org/en/dataset/351",
    "uid": "c1d8b38f-9ed2-4539-bd67-c39dce27c6c3",
    "datasource_id": 351,
    "source": "HDRUK"
  },
  {
    "id": 1012,
    "name": "Data First Family Courts Case Management System (FACO)",
    "description": "This dataset covers people involved in family court cases in England and Wales.  Three tables have been created to join together information stored across multiple tables in the raw Family Court database: \nCases - contains information about cases as a whole, including case type, key dates, related cases and originating court. There is one row per case.\nEvents - contains information about events within a case, for example, hearings, applications, orders and administrative processes. There is one row per event within the case, which can be joined to cases table on the case_number_hash.\n\nUseful information about the Family Courts can be found here: https://www.gov.uk/government/statistics/family-court-statistics-quarterly-april-to-june-2023/guide-to-family-court-statistics\n\nThe Research Accreditation Panel provides oversight of the framework that is used to accredit research projects, researchers and processing environments under the Digital Economy Act 2017 (DEA). Researchers are advised to liaise with SAIL support teams to understand the requirements and timelines involved with submitting a research project to the Research Accreditation Panel. https://uksa.statisticsauthority.gov.uk/digitaleconomyact-research-statistics/research-accreditation-panel/",
    "url": "https://healthdatagateway.org/en/dataset/352",
    "uid": "63ac335b-77d5-4857-bdce-3ebdedb9285c",
    "datasource_id": 352,
    "source": "HDRUK"
  },
  {
    "id": 1013,
    "name": "Education Daily Attendance Dataset (EDAD)",
    "description": "This dataset provides detailed information about daily educational attendance within Wales.\n\nAttendance data in the EDUW schema was discontinued after 2019 and the Education Daily Attendance Dataset (EDAD) schema replaced it. This dataset contains more detailed information on attendance than was previously available in EDUW.",
    "url": "https://healthdatagateway.org/en/dataset/339",
    "uid": "17056e83-ee5a-4fe7-86e1-f09563256be1",
    "datasource_id": 339,
    "source": "HDRUK"
  },
  {
    "id": 1014,
    "name": "Student Health and Wellbeing Survey (SHWS)",
    "description": "The Student Health and Wellbeing (SHW) survey is carried out by the School Health Research Network (SHRN). Established in 2013, SHRN brings together secondary schools and academic researchers, policymakers and practitioners from health, education, and social care to improve young people&amp;amp;rsquo;s health and wellbeing in the school setting. It is a partnership between the Centre for Development, Evaluation, Complexity, and Implementation in Public Health Improvement (DECIPHer) at Cardiff University, Welsh Government, and Public Health Wales, funded by Welsh Government.\n\nThe SHW survey is a biennial cross-sectional survey administered to 11&amp;amp;ndash;16-year-olds attending SHRN member schools and was developed from the World Health Organisation&amp;amp;rsquo;s Health Behaviour in School aged Children (HBSC) survey. The survey is completed alongside a School Environment Questionnaire (SEQ), which all participating schools must complete on their health policies and practices.\n\nThe SHW survey provides in-depth understanding of the health and wellbeing of young people. Content includes mental health and wellbeing, substance use and gambling, physical activity and diet, school life, family and social life, and relationships.\n\n2021 &amp;amp;amp; 2023 survey years are available in SAIL",
    "url": "https://healthdatagateway.org/en/dataset/340",
    "uid": "6de12c10-1fb6-4151-906d-ddf881f8bed9",
    "datasource_id": 340,
    "source": "HDRUK"
  },
  {
    "id": 1015,
    "name": "Healthcare Workers Risk Assessment (HWRA)",
    "description": "This dataset provides limited information about which NHS workers completed Risk Assessments in the course of normal work.",
    "url": "https://healthdatagateway.org/en/dataset/341",
    "uid": "ed131fee-0635-4613-a453-a383d5dd7cec",
    "datasource_id": 341,
    "source": "HDRUK"
  },
  {
    "id": 1016,
    "name": "NHS 111 Dataset (NHSO)",
    "description": "NHS 111 / Integrated Urgent Care data describes a range of statistics including NHS 111 and Out of Hours services, which aim to ensure a seamless patient experience with minimum handoffs and access to a clinician where required.",
    "url": "https://healthdatagateway.org/en/dataset/342",
    "uid": "3d6b69dc-cb3c-4b9a-bdea-a798e7d5b5cf",
    "datasource_id": 342,
    "source": "HDRUK"
  },
  {
    "id": 1017,
    "name": "Intensive Care National Audit and Research Centre (ICCD) - Legacy - COVID only",
    "description": "Intensive care case mix and activity; this is the weekly COVID-only version of ICNC dataset. Includes information preceding admission to intensive care e.g. date of hospital admission, and information about min and max values of critical indicator (e.g. lowest heart rate, highest serum glucose).\n\nICCD was a project specific dataset for specific COVID-19 related projects only, which is not currently available to request. However, the ICNC dataset is still available.",
    "url": "https://healthdatagateway.org/en/dataset/343",
    "uid": "fa8bf269-4769-491b-b398-fa63137d14ab",
    "datasource_id": 343,
    "source": "HDRUK"
  },
  {
    "id": 1018,
    "name": "COVID-19 Test Trace and Protect (CTTP) - Static",
    "description": "This dataset details the Covid-19 Test Trace and Protect programme implemented across various parts of the UK.\n\nDataset is static and no longer updated after 31/05/2024.",
    "url": "https://healthdatagateway.org/en/dataset/336",
    "uid": "c0fc84af-aeab-4c7e-aa0b-8264baff55d9",
    "datasource_id": 336,
    "source": "HDRUK"
  },
  {
    "id": 1019,
    "name": "Daily Situation Report Data (DSRD) - Legacy",
    "description": "Dataset no longer updated after 26/07/2023.\nDaily Situation Report dataset.\nDaily situation report for healthcare equipment, staff, activity, capacity and usage.\nDSRD daily view moved to a weekly flow from 13/02/2023",
    "url": "https://healthdatagateway.org/en/dataset/337",
    "uid": "c74a9b9e-1fea-4aac-99fd-54e30668e085",
    "datasource_id": 337,
    "source": "HDRUK"
  },
  {
    "id": 1020,
    "name": "UK Cystic Fibrosis Registry (CYFI)",
    "description": "The UK Cystic Fibrosis Registry is a national, secure, centralized database sponsored and managed by the Cystic Fibrosis Trust, with UK National Health Service (NHS) research ethics approval and consent from each person for whom data are collected. First established in 1995, it records longitudinal health data on all people with cystic fibrosis (CF) in England, Wales, Scotland and Northern Ireland, and to date has captured data on over 12,000 individuals.\n\nIf you are interested in using the CYFI dataset in the SAIL Databank, please contact SAIL via the website, along with also discussing your project with the Cystic Fibrosis Registry team for further advice via email at: registry@cysticfibrosis.org.uk",
    "url": "https://healthdatagateway.org/en/dataset/338",
    "uid": "0628fb77-057d-4838-b80e-18245a8f535d",
    "datasource_id": 338,
    "source": "HDRUK"
  },
  {
    "id": 1021,
    "name": "SARS-CoV-2 viral sequencing data (COG-UK data)-Lineage/Variant Data-Wales (CVSD)",
    "description": "The dataset provides information about SARS-CoV-2 variants of concern and\nvariants under investigation in Wales. This includes phylogenetic and mutational information about samples.",
    "url": "https://healthdatagateway.org/en/dataset/333",
    "uid": "602cbc9b-0113-4712-8c4a-0efe8d86b77c",
    "datasource_id": 333,
    "source": "HDRUK"
  },
  {
    "id": 1022,
    "name": "ONS 2011 Census Wales (CENW)",
    "description": "Every ten years since 1801 the nation has set aside one day for the census - a count of all people and households. It is the most complete source of information about the population that we have. The latest census was held on Sunday 27 March 2011.\n\nEvery effort is made to include everyone, and that is why the census is so important. It is the only survey which provides a detailed picture of the entire population, and is unique because it covers everyone at the same time and asks the same core questions everywhere. This makes it easy to compare different parts of the country.\n\nThe information the census provides allows central and local government, health authorities and many other organisations to target their resources more effectively and to plan housing, education, health and transport services for years to come.\n\nIn England and Wales, the census is planned and carried out by the Office for National Statistics. Elsewhere in the UK, responsibility lies with the National Records of Scotland and the Northern Ireland Statistics and Research Agency.\n\nAll 2011 Census data for &amp;lsquo;Welsh&amp;rsquo; records are defined as those:\n- Currently resident in Wales\n- With a second address in Wales\n- With a previous Years Address in Wales\n- With a term-time address in Wales\n- Who work in Wales (but live in England)\n- In Armed Forces Establishments in Wales\n- Who are visitors in Wales\n- Who are Welsh language speakers (including those who live and work outside of Wales).\n\nThe ONS have three processes for checking and resolving duplicate responses so that the main census data should simply be one record for each person:\n\n1. The ONS resolve duplicates coming in for the same postcode using a process called Resolve Multiple Responses (RMR). For instance, if two people both fill in a form for their whole household, or someone from a household also submits an individual response unknown to the main submission. They have rules for checking they are duplicates, and rules for which to keep.\n\n2. The ONS also do an over coverage check on a sample basis for duplicates across the rest of the country, and then factor the findings into their coverage estimation calculations. This sampling focuses on the types of population which are more likely to be duplicated (people who have indicated they have a second residence on the census, students aged 18-25, armed forces personnel, children, adults enumerated at a communal establishment, etc.) but also samples from the remaining population.\n\n3. The ONS ask parents to fill in basic demographic information for any children who are away studying, and when they get to the question on their term-time address, if they answer that the term-time address is elsewhere, we then use that to filter those out-of-term students out of the main database. Then when that student does respond actually at their term-time address, they only include them there.\n\nPlease note: Variables RELAT06, RELAT11, RELAT16, RELAT21, RELAT26 are not available in the data.\n\nThe Research Accreditation Panel provides oversight of the framework that is used to accredit research projects, researchers and processing environments under the Digital Economy Act 2017 (DEA). Researchers are advised to liaise with SAIL support teams to understand the requirements and timelines involved with submitting a research project to the Research Accreditation Panel. https://uksa.statisticsauthority.gov.uk/digitaleconomyact-research-statistics/research-accreditation-panel/",
    "url": "https://healthdatagateway.org/en/dataset/335",
    "uid": "87485535-07a0-4b5c-a61d-72403191b7a5",
    "datasource_id": 335,
    "source": "HDRUK"
  },
  {
    "id": 1023,
    "name": "Children In Need Census Wales (CINW) - Static",
    "description": "Numbers and characteristics of children in need, including parental circumstances, health and educational outcomes.\n\nFollowing the commencement of the Social Services and Well-being (Wales) Act in April 2016, the children in need census was discontinued and replaced by the children receiving care and support census (CRCS). Therefore this data source is static and no longer updated, but it is available.",
    "url": "https://healthdatagateway.org/en/dataset/329",
    "uid": "e4f6d9a8-88d0-4781-b192-cd165451b272",
    "datasource_id": 329,
    "source": "HDRUK"
  },
  {
    "id": 1024,
    "name": "Careers Wales Dataset (CARW)",
    "description": "Careers Wales dataset.\n\nThe data supplied to SAIL represents interactions and services provided by Careers Wales to clients throughout Wales over the period 01/04/2012 to 31/03/2020. At the start of this period, the Careers Wales had just been formed from six regional Careers companies, and their CRM system was initially populated with imports from the preceding companies&amp;amp;rsquo; systems. Between 2012 and the present Careers Wales has worked to rationalise the varied approaches to recording, enhance the quality and suitability of the data they gather, and to simplify the data entry requirements for their staff as far as possible. Therefore users of the data will very likely notice changes over time in the nature of the data.\n\nCareers Wales stores information on all its interactions, services provided, etc. as entities called histories. Within their CRM, different on-screen fields can be enabled or disabled for different history types &amp;amp;ndash; therefore a history representing a Group Session would contain information on the duration of the session, the topic and the venue; whereas a history for a Traineeship Referral Form completion would not display any of these fields.\n\nAll the numerically coded columns in Careers Wales&amp;amp;rsquo; supplied data can be decoded via the accompanying lookup file - HISTORIES_LOOKUP_1545_20200821.CSV.",
    "url": "https://healthdatagateway.org/en/dataset/331",
    "uid": "e4004e0e-6adb-4b11-82c2-af0caf61d751",
    "datasource_id": 331,
    "source": "HDRUK"
  },
  {
    "id": 1025,
    "name": "Higher Education Statistics Agency (HESA) Student Data",
    "description": "The HESA Student record is collected from subscribing Higher Education Providers (HEPs) throughout the devolved administrations of the United Kingdom. The data collected as part of the Student record is used extensively by various stakeholders and is fundamental in the formulation of: Funding, Performance Indicators, Publications (including UNISTATS), League tables.\n\nThe Student record collects individualised data about students active during the reporting period. A wide range of data items are collected, including: the student&amp;#039;s entry profile and personal characteristics, module and course level data, funding information and qualifications awarded. \n\nAll HESA records are collected on the basis of the HESA reporting period that determines the time period that the data being returned relates to. This ensures consistency across the data streams collected. The reporting period is from 01 August year 1 to 31 July year 2, for example, the 2016/2017 Student record was collected in respect of the activity which took place between 01 August 2016 and 31 July 2017.\n\nFurther information on the HESA Student record can be found on the HESA website: https://www.hesa.ac.uk/collection/archive",
    "url": "https://healthdatagateway.org/en/dataset/320",
    "uid": "68b04d3b-17b4-4a04-a8a3-e2a93ed231df",
    "datasource_id": 320,
    "source": "HDRUK"
  },
  {
    "id": 1026,
    "name": "Blood Cancer Cardiff and the Vale Dataset (BCCV)",
    "description": "All patients receiving systemic anti-cancer therapies in or funded by the NHS are covered by the dataset.\n\nThe data is from eFroms filled in during a patient visit in a Myeloma clinic.",
    "url": "https://healthdatagateway.org/en/dataset/324",
    "uid": "7349d31e-1061-4b85-86f7-30978b3af98a",
    "datasource_id": 324,
    "source": "HDRUK"
  },
  {
    "id": 1027,
    "name": "Intensive Care National Audit and Research Centre (ICNC)",
    "description": "Intensive care case mix and activity. Includes information preceding admission to intensive care e.g. date of hospital admission, and information about min and max values of critical indicator (e.g. lowest heart rate, highest serum glucose).\n\nData Collection Manual Version 4 can be found online here:\nhttps://d7g406zpx7bgk.cloudfront.net/x/5498b78713/data-collection-manual-icnarc-case-mix-programme-dataset-specification-icmpds-_v4-inicua.pdf\n\nOr by going to ICNARC website directly and requesting latest version:\nhttps://account.icnarc.org/Our-Audit/Audits/Cmp/Resources",
    "url": "https://healthdatagateway.org/en/dataset/317",
    "uid": "add6226b-0f21-439a-84a6-51dc26cdc425",
    "datasource_id": 317,
    "source": "HDRUK"
  },
  {
    "id": 1028,
    "name": "Millennium Cohort Study Dataset (MCSD)",
    "description": "The Millennium Cohort Study (MCS) is a multi-disciplinary research project following the lives\nof around 19,000 children born in the UK in 2000-01. It is the most recent of Britain&rsquo;s world renowned national longitudinal birth cohort studies. The study has been tracking the Millennium children through their early childhood years and plans to follow them into adulthood. It collects information on the children&rsquo;s siblings and parents. MCS&rsquo;s field of inquiry covers such diverse topics as parenting; childcare; school choice; child behaviour and cognitive development; child and parental health; parent&rsquo;s employment and education; income and poverty; housing, neighbourhood and residential mobility; and social capital and ethnicity.\n\nThe study is core funded by the Economic and Social Research Council (ESRC) and a consortium of Government departments.",
    "url": "https://healthdatagateway.org/en/dataset/312",
    "uid": "a380bd6b-009f-4b78-b638-ba9ee62be2eb",
    "datasource_id": 312,
    "source": "HDRUK"
  },
  {
    "id": 1029,
    "name": "Welsh Health Survey Dataset (WHSD) - Static",
    "description": "The Welsh Health Survey informs local government, NHS, and nationwide health strategy.\n\nThe Welsh Health Survey (WHS) collects information on the health and health-related lifestyles of people living in Wales. It is a major source of information about the health of people in Wales, the way the NHS is used, and behaviours that can affect health, such as smoking and alcohol consumption.\n\nData for the WHS is collected via face-to-face-interviews and self-completion questionnaires. The sampling unit for the WHS are households, however all adults within households were asked to take part. Families with children under the age of 16 are eligible, however where the household has 3 or more children, up to two children between the ages of 0 and 15 are randomly selected for inclusion in the study. Interviews are used to collect data at the household level, with questionnaires distributed to household members. Information on the household type and employment status of the household reference person are collected, and the interviewer is asked to comment on the condition of the property. Separate self-completion questionnaires are used to collect data for adults and young people (aged 13-15), whilst adults/guardians are required to complete questionnaires on behalf of children younger than 13 years old.\n\nThe WHS data provided to SAIL relates to survey years 2011, 2013 and 2014 covering only adults - aged 16 and older - who have consented to allow their data to be linked, with consent to data link data being included on a trial basis for 2011. As a result WHS data in SAIL can be analysed only at the individual adult level (and with a very limited number of records for 2011). By contrast WHS data in the UK Data Archive allows for adult-child records to be combined for research exploring &amp;amp;lsquo;household&amp;amp;rsquo; health or the links between parental and child health, for example. \n\nDerived variables are those which have been created as an additional value based on responses to other variables, primarily for facilitate further analysis.\n\nPlease note: From April 2016 health and health-related lifestyles are reported in in SAIL by the National Survey for Wales Dataset. Therefore this data source is static and is no longer updated, but it is available.",
    "url": "https://healthdatagateway.org/en/dataset/313",
    "uid": "e4d8edb3-6e8e-435d-9732-60c83027d8f9",
    "datasource_id": 313,
    "source": "HDRUK"
  },
  {
    "id": 1030,
    "name": "Welsh Ambulance Services NHS Trust (WASD)",
    "description": "The ambulance services statistics show monthly data for Wales on the number of calls and the time taken to respond to an incident. Information is available by Local Health Board (LHB) and for Wales. The latest and past versions of the release are available on the ambulance services page. Since April 2014, Local Health Boards have been responsible for providing emergency ambulance services (999 calls) for their local residents; the Welsh Ambulance Services NHS Trust (WAST) is commissioned to deliver emergency ambulance services on their behalf.\n\nThere are two categories of ambulance service:\nEmergency Medical Services (EMS) - the Emergency Medical Service deals with emergency and urgent cases, and is accessed by dialling 999; and\nPatient Care Services (PCS) &amp;ndash; provides transport for patients to a variety of planned hospital appointments and outpatient clinics.\n\nThe DF_AMBULANCEPCR view is currently not flowing and updates aren&amp;#039;t available.",
    "url": "https://healthdatagateway.org/en/dataset/310",
    "uid": "4dd97609-2753-46ed-8066-3482d6dbdb34",
    "datasource_id": 310,
    "source": "HDRUK"
  },
  {
    "id": 1031,
    "name": "Lifelong Learning Wales Record (LLWR)",
    "description": "The LLWR contains the following four datasets: the Learner (LN) dataset which includes information about the learner such as name, date of birth, ethnic origin and gender; the Learning Programme (LP) dataset which gives information about the current programme of learning being undertaken by the learner and any characteristics which may change over time; the Learning Activity (LA) dataset which collects data on the individual activities or courses undertaken by the learner on his/her programme of learning; and the Award (AW) dataset, which provides information on the awards for which the learner is entered and those achieved. From 1 August 2017, two new datasets will be added to collect Programme and Activity data directly from the National Centre for Learning Welsh. These new datasets relate specifically to the delivery of Welsh for Adults provision and will be collated and submitted centrally by the National Centre for Learning Welsh.",
    "url": "https://healthdatagateway.org/en/dataset/302",
    "uid": "cba62a8d-5917-4912-ad4d-340472f7b904",
    "datasource_id": 302,
    "source": "HDRUK"
  },
  {
    "id": 1032,
    "name": "Diabetic Eye Screening Wales (DESW)",
    "description": "The Diabetic Eye Screening Wales service checks for eye problems caused by having diabetes. Eye screening looks for damage to the back of the eye (diabetic retinopathy) which can lead to permanent sight loss.\n\nThe Dataset contains details of the screening carried out for Diabetic patients.\n\nPlease see the &#039;Technical Details&#039; section for details of the variables.",
    "url": "https://healthdatagateway.org/en/dataset/303",
    "uid": "82cbc342-59e6-402b-b58e-91f44de06c56",
    "datasource_id": 303,
    "source": "HDRUK"
  },
  {
    "id": 1033,
    "name": "Health and Attainment of Pupils in a Primary Education Network (HAPN)",
    "description": "HAPPEN is a primary school network in Wales which brings together education, health, and research in line with the new curriculum proposals for health and wellbeing.\n\nAll pupils in years 4, 5, and 6 can complete the HAPPEN survey: a health and wellbeing questionnaire focused on physical and mental health.\n\nHAPPEN has been collecting data since 2016 and has more than 35,000 responses from children across Wales. Of this number, 35% are duplicate entries from children at different time points. There are more than 500 schools registered to take part in HAPPEN. \n\nSchools can take part in the HAPPEN survey throughout the academic year to provide snapshots, track change, and evaluate practice. Having completed the survey, schools receive an individual school report aligned with the new curriculum showing the overall picture of health and wellbeing in the school. HAPPEN was co-developed following interviews with headteachers who called for a better understanding of school needs in the development of health interventions and advocated for a more collaborative approach to improving child health through schools.",
    "url": "https://healthdatagateway.org/en/dataset/304",
    "uid": "464d8653-4e69-406b-93fd-d68c29d5dfdc",
    "datasource_id": 304,
    "source": "HDRUK"
  },
  {
    "id": 1034,
    "name": "Annual Population Survey - Welsh Records (APSW)",
    "description": "The Annual Population Survey is a dataset that is made up of Labour Force Survey responses in the first and fifth waves of interviewing, plus a boost sample where respondents are interviewed annually for up to four consecutive years. The datasets consist of around 250,000 individuals, each of whom only appear once in the dataset (the use of the first and fifth waves avoids response being counted twice). The larger sample makes the data better suited for estimates of employment (etc.) at local authority level. Comprehensive information regarding the surveys is available via the user guidance and also the QMI pages for both the LFS and AP.\n\nThe Research Accreditation Panel provides oversight of the framework that is used to accredit research projects, researchers and processing environments under the Digital Economy Act 2017 (DEA). Researchers are advised to liaise with SAIL support teams to understand the requirements and timelines involved with submitting a research project to the Research Accreditation Panel. https://uksa.statisticsauthority.gov.uk/digitaleconomyact-research-statistics/research-accreditation-panel/",
    "url": "https://healthdatagateway.org/en/dataset/305",
    "uid": "88e8809f-7f6f-4bec-8481-296a52ff36e0",
    "datasource_id": 305,
    "source": "HDRUK"
  },
  {
    "id": 1035,
    "name": "Looked After Children Birthdays (LACB) - Static",
    "description": "Children who are &amp;amp;amp;#039;looked after&amp;amp;amp;#039; by the State are considered one of the most vulnerable groups in society. Being in State care is associated with poor social, educational and health outcomes. Exploring how to improve the system and better support children in care is key to improving these outcomes. When children and young people come to the attention of children&amp;amp;amp;#039;s social services a significant amount of information about their care experience is routinely collected by local authorities. In Wales, routine data are captured in the &amp;amp;amp;#039;Children Looked After&amp;amp;amp;#039; Census which is submitted annually to the Welsh Government.\n\nThe dataset LACB includes all care leavers who turned 19 years old between 1st April 2002 and 31st March 2016. There are no updates past March 2016. It is a subset of the primary Looked After Children in Wales (LACW) dataset. Other subsets include: Looked After Children Adoption (LACA); Looked After Children Care Leavers aged 16 and over (LACC); and Looked after Children - Education Qualifications (LACE). LACE was discontinued in 2016 (1999 - 2016) and included within LACW. \n\nDue to the small number of looked after children with an Anonymised Linkage Field (ALF, 37%), a two-stage algorithm was developed. This algorithm utilises other datasets within SAIL to allocate children within the LACW ALF, increasing the overall ALF match rate to 61%. The improved ALF matches are available in the LAC ALF DERIVED table, part of the LACW dataset, and can be obtained by combined the SYSTEM_ID and LOCAL_AUTHORITY_CODE.\n\nPlease note: the LACB dataset is static and is no longer refreshed.",
    "url": "https://healthdatagateway.org/en/dataset/297",
    "uid": "85a369d9-58b3-49a9-94e1-9dc05de8ea49",
    "datasource_id": 297,
    "source": "HDRUK"
  },
  {
    "id": 1036,
    "name": "Looked After Children Care Leavers (LACC)",
    "description": "Children who are &#039;looked after&#039; by the State are considered one of the most vulnerable groups in society. Being in State care is associated with poor social, educational and health outcomes. Exploring how to improve the system and better support children in care is key to improving these outcomes. When children and young people come to the attention of children&#039;s social services a significant amount of information about their care experience is routinely collected by local authorities. In Wales, routine data are captured in the &#039;Children Looked After&#039; Census which is submitted annually to the Welsh Government.\n\nThis dataset is a subset of the primary Looked After Children in Wales (LACW) dataset, and contains information relating to all children who ceased to be looked after if they were aged 16 years or older at the time of care ending. Other subsets include: Looked After Children Adoption (LACA); Looked After Children Birthday 19 (LACB: 1999 - 2016); and Looked after Children - Education Qualifications (LACE). LACE was discontinued in 2016 (1999 - 2016) and included within LACW.\n\nDue to the small number of looked after children with an Anonymised Linkage Field (ALF, 37%), a two-stage algorithm was developed. This algorithm utilises other datasets within SAIL to allocate children within the LACW ALF, increasing the overall ALF match rate to 61%. The improved ALF matches are available in the LAC ALF DERIVED table, part of the LACW dataset, and can be obtained by combined the SYSTEM_ID and LOCAL_AUTHORITY_CODE.",
    "url": "https://healthdatagateway.org/en/dataset/298",
    "uid": "c7729362-c81f-41c9-a298-1c00b65ad6f5",
    "datasource_id": 298,
    "source": "HDRUK"
  },
  {
    "id": 1037,
    "name": "Domiciliary Social Care Worker (DSCW)",
    "description": "Domiciliary care workers provide care and support for individuals in their own homes. Workers provide a wide range of support from preventative services, reablement, support for independent living, support with social activities, education and employment, practical assistance with personal care and domestic tasks to end of life care. Workers may work in specialist services or with individuals with particular needs.",
    "url": "https://healthdatagateway.org/en/dataset/300",
    "uid": "0e38bbf4-c788-4a1a-9af8-a24821250154",
    "datasource_id": 300,
    "source": "HDRUK"
  },
  {
    "id": 1038,
    "name": "Welsh Ambulance Services NHS Trust (WAST) - Legacy",
    "description": "The ambulance services statistics show monthly data for Wales on the number of calls and the time taken to respond to an incident. Information is available by Local Health Board (LHB) and for Wales. The latest and past versions of the release are available on the ambulance services page. Since April 2014, Local Health Boards have been responsible for providing emergency ambulance services (999 calls) for their local residents; the Welsh Ambulance Services NHS Trust (WAST) is commissioned to deliver emergency ambulance services on their behalf.\n\nThere are two categories of ambulance service:\nEmergency Medical Services (EMS) - the Emergency Medical Service deals with emergency and\nurgent cases, and is accessed by dialling 999; and\nPatient Care Services (PCS) &amp;ndash; provides transport for patients to a variety of planned hospital\nappointments and outpatient clinics.",
    "url": "https://healthdatagateway.org/en/dataset/294",
    "uid": "d415a5c5-8432-4a81-937f-12135ade6de7",
    "datasource_id": 294,
    "source": "HDRUK"
  },
  {
    "id": 1039,
    "name": "Radiotherapy Dataset (RTDS)",
    "description": "The Radiotherapy Data Set&amp;nbsp;(RTDS) allows for the routine collection of clinically and managerially relevant ACTIVITY data from Radiotherapy facilities, in order to commission or monitor Radiotherapy Services in an evidence-based manner.\n\nA Data Explained report on RTDS can be found here: https://adrwales.org/wp-content/uploads/2025/02/Data_Explained_RTDS.pdf",
    "url": "https://healthdatagateway.org/en/dataset/295",
    "uid": "f102ca50-d1e4-44ec-924e-be89446652af",
    "datasource_id": 295,
    "source": "HDRUK"
  },
  {
    "id": 1040,
    "name": "Systemic Anti-Cancer Therapy Dataset (SACT)",
    "description": "All patients receiving systemic anti-cancer therapies in or funded by the NHS are covered by the dataset.\n\nMore information about SACT can be found at (NHS Wales): https://www.datadictionary.wales.nhs.uk/index.html#!WordDocuments/nationalcancerdatastandardsforwalessystemicanticancertherapysact1.htm\nand https://executive.nhs.wales/functions/networks-and-planning/cancer/clinical-hub/systemic-anti-cancer-therapies-sact/",
    "url": "https://healthdatagateway.org/en/dataset/296",
    "uid": "6c4ed998-0f05-415c-a3e4-42430bb62d24",
    "datasource_id": 296,
    "source": "HDRUK"
  },
  {
    "id": 1041,
    "name": "SAIL Dementia e-Cohort (SDEC)",
    "description": "The SAIL Dementia e-Cohort is a population-based electronic cohort (e-cohort) containing health-related information on people with and without diagnosed dementia.\n\nBy applying coding algorithms to linked routinely-collected datasets, a novel Dementia Platform UK (DPUK) cohort was developed to maximise generalisability and utility for a broad range of research questions and methodologies. It aims to minimise duplication of effort, increase reproducibility, reduce costs, and allow a broader range of researchers to apply to use SAIL data.",
    "url": "https://healthdatagateway.org/en/dataset/289",
    "uid": "f48b7c05-6a44-480c-84c6-4f7a684f29f8",
    "datasource_id": 289,
    "source": "HDRUK"
  },
  {
    "id": 1042,
    "name": "Antiviral Dataset (AVDS)",
    "description": "The Antiviral Dataset contains details of Antiviral and Monoclonal Antibody drugs prescribed to patients to fight Covid-19.  \n\nThe Dataset is made up of two tables. &amp;#039;Service notifications&amp;#039; which gives details of patients and the type of drug prescribed and &amp;#039;Treatment&amp;#039; which gives details of the treatment administered. Further information on variables can be found under the &amp;#039;Technical details&amp;#039; menu.  \n\nThe &amp;#039;Service Notifications&amp;#039; table contains 10863 records of which 10082 are distinct, covering the period 11/12/2021 - 24/07/2022.\nThe &amp;#039;Treatment&amp;#039; table contains 2649 records, of which 2576 are distinct, covering the period 21/01/2021 - 26/04/2022.",
    "url": "https://healthdatagateway.org/en/dataset/291",
    "uid": "6ee9fa47-1002-4710-8621-68ce2219c92b",
    "datasource_id": 291,
    "source": "HDRUK"
  },
  {
    "id": 1043,
    "name": "Welsh Results Reports Service (WRRS)",
    "description": "The Welsh Results Reporting Service (WRRS) allows health care professionals (HCPs) across Wales to access, enter, and view laboratory results for pathology requests and any other associated results across all health boards in Wales, from both primary and secondary care, regardless of where they were requested, tested or provided. WRRS aims to save time, reduce test duplication, and improve patient safety.\n\nThe Welsh Clinical Portal (WCP) is a digital patient record across NHS Wales, which is available to all HCPs with appropriate permissions in relevant organisations. The WCP makes available patient information from several sources with a single log-on. The WCP gives clinicians pathology results (e.g. blood tests) for patients wherever they had their test taken, meaning patients can utilise mobile units or local centres rather than having to travel far.\n\nA Data Explained report on WRRS can be found here: https://saildatabank.com/wp-content/uploads/2022/04/Data-Explained-The-Welsh-Results-Reports-Service-WRRS-Data.pdf",
    "url": "https://healthdatagateway.org/en/dataset/292",
    "uid": "71d37610-ac55-432d-82a3-bdb04407acd8",
    "datasource_id": 292,
    "source": "HDRUK"
  },
  {
    "id": 1044,
    "name": "Rural Payments Wales (RPWD)",
    "description": "Rural Payments Wales (RPWD) - Rural Payments Wales is a safety net for farmers as a supplement to their main farm business income.",
    "url": "https://healthdatagateway.org/en/dataset/286",
    "uid": "9dc76035-dd6c-4246-8686-403087ed8890",
    "datasource_id": 286,
    "source": "HDRUK"
  },
  {
    "id": 1045,
    "name": "Suspected Cancer Pathway Monthly (SCPM)",
    "description": "This dataset covers all patients informed they do not have cancer, or patients who started their first definitive treatment.  Dataset provides context to cancer performance measures included in the NHS activity and performance release, and contains measures about the suspected cancer pathway.\n\nData is received monthly from DHCW, it undergoes a quarterly refresh, at DHCW where the data is complete refreshed for the quarter.\nThe sscpclockstop_dt changes are in the quarter (8,9,10 of 2022).\n\nA Data Explained report on SCPM can be found here: https://adrwales.org/wp-content/uploads/2025/02/Data_Explained_SCPM.pdf",
    "url": "https://healthdatagateway.org/en/dataset/287",
    "uid": "fcf5a844-f48d-428e-aa06-abe64e66185e",
    "datasource_id": 287,
    "source": "HDRUK"
  },
  {
    "id": 1046,
    "name": "Asymptomatic COVID19 in Education Cohort (ACEC)",
    "description": "At the start of the 2020/2021 academic year some education providers established in-house testing facilities.These specialised sites were created in response to the sharp increase in asymptomatic COVID-19 infection rates among the student population. Their services ran alongside those provided by the UK government, which mainly monitored self-referred symptomatic cases of infection.\n\nSeeing the potential highly controlled cohorts such as these had, the Universities of Nottingham, Cambridge and Cardiff combined resources to create the ACE cohort, which is made up of students and staff who have consented for research.\n\nThe cohort’s differences in region, demographics and living arrangement provides unique data that are particularly valuable when investigating the development of immunity against SARS-CoV-2. It is also helpful to studies examining underappreciated aspects of COVID-19 infection, such as the impact of virus mutation on the development of immune response.",
    "url": "https://healthdatagateway.org/en/dataset/288",
    "uid": "fb642457-42e4-4f47-8039-e97769352ea8",
    "datasource_id": 288,
    "source": "HDRUK"
  },
  {
    "id": 1047,
    "name": "Effects of second-hand smoke on pregnant women: a phenomenological study",
    "description": "More than 40% of all pregnant women in Pakistan are exposed to second-hand smoke (SHS) – causing approximately 17,000 still births in a year. SHS exposure in non-smoking pregnant women has increased the risk of stillbirth and congenital malformation along with behavioral and cognitive issues in children. Partner’s support during pregnancy is important for developing a better maternal health. Therefore, an evidence-informed conceptual framework will be developed to propose a behaviour change communication intervention, which will target poor or risky behaviors to promote behaviour modification that may result in positive maternal and fetal health outcomes. This will be a qualitative study incorporating an initial stage of systematic review followed by formative research.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/acute-respiratory-disorders/second-hand-smoke",
    "url": "https://healthdatagateway.org/en/dataset/284",
    "uid": "4a5c610f-019d-450a-90c5-0fe315027f56",
    "datasource_id": 284,
    "source": "HDRUK"
  },
  {
    "id": 1048,
    "name": "Asthma self-management behaviour in Bangladesh and Pakistan",
    "description": "This dataset consists of:\n\ni) systematic review - narratively synthesised 16 RCTs (17 papers) in South Asian and Black populations to explore the extent to which variance in self-management is due to ethnicity and/or various sociocultural contexts.\n\nii) qualitative study - semi-structured interviews with 27 Bangladeshis and Pakistanis with asthma to understand the role of culture in self-management. \n\niii) qualitative study - semi-structured interviews with nine healthcare professionals to understand their perspective on providing supported self-management to these communities.",
    "url": "https://healthdatagateway.org/en/dataset/282",
    "uid": "3b378225-48e1-45fa-b62d-fc4ed4b39bec",
    "datasource_id": 282,
    "source": "HDRUK"
  },
  {
    "id": 1049,
    "name": "Theory of Planned Behaviour Intervention: respiratory disease in South India",
    "description": "Chronic respiratory diseases (CRDs) in low resource settings are neglected and often poorly diagnosed, leading to missed opportunities for early initiation of treatment and poor patient pathways. For example, in India, lung cancer often emerges on a background of chronic respiratory symptoms and is often not diagnosed at all – or at a very late stage. Because of late presentation and a range of other factors, survival from lung cancer in India is very low. At present we know very little about how knowledge and attitudes relating to CRD in poor, rural populations in India might be influenced by health and behavioural interventions. Psychological theory-based interventions must be culturally appropriate and grounded in the local context. As such, it’s vital we develop this understanding if we are to change behaviours and reduce exposure to common risk factors, such as smoking and indoor cooking smoke.\n\nThe PhD study aims to gather evidence through systematic review of literature about TPB based health interventions and conduct qualitative studies to inform the which constructs of TPB are important in developing an educational intervention which can effectively change the knowledge, attitude and health behaviour of people with CRD in these low resource settings. \n\nFor further information, see: https://www.ed.ac.uk/usher/respire/phd-studentships/biswajit-paul",
    "url": "https://healthdatagateway.org/en/dataset/279",
    "uid": "7d23e729-72b0-4dd1-820d-07697b49acd0",
    "datasource_id": 279,
    "source": "HDRUK"
  },
  {
    "id": 1050,
    "name": "Prevention, detection, treatment: adult lung disease/cancer in rural Tamil Nadu",
    "description": "Chronic respiratory diseases (CRDs) in low resource settings are neglected and often poorly diagnosed, leading to missed opportunities for early initiation of treatment and poor patient pathways. For example, in India, lung cancer often emerges on a background of chronic respiratory symptoms and is often not diagnosed at all – or at a very late stage. Because of late presentation and a range of other factors, survival from lung cancer in India is very low. At present we know very little about how knowledge and attitudes relating to CRD in poor, rural populations in India might be influenced by health and behavioural interventions. Similarly, compliance with treatments offered is poorly understood. Psychological theory-based interventions must be culturally appropriate and grounded in the local context. As such, it’s vital we develop our understanding if we are to change behaviours and reduce exposure to common risk factors, such as smoking and indoor cooking smoke.                         \n\nThe project will determine the feasibility of an intervention study of Health Care Worker (HCW)-delivered respiratory package including health education drawing on psychological theory of planned behaviour in the intervention arm and Health Belief Model in control arm and delivery of treatment and follow-up. \n\nFor further information, see: https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/adult-lung-disease",
    "url": "https://healthdatagateway.org/en/dataset/280",
    "uid": "2581994d-5be2-4465-bb62-60c4c97e8e2b",
    "datasource_id": 280,
    "source": "HDRUK"
  },
  {
    "id": 1051,
    "name": "Strategy introducing Pulmonary Rehabilitation for COPD management in rural India",
    "description": "Chronic obstructive pulmonary disease (COPD) is the biggest cause of death in rural India and its prevalence continues to increase due to widespread air-pollution.\n\nPulmonary Rehabilitation (PR) is proven to be the most effective strategy to improve shortness of breath, health status and exercise tolerance as well as reducing re-admissions and mortality in patients with recent exacerbations.\n\nDespite this, there is no existing programme for PR being systemically introduced in rural India and anecdotal evidence points towards inadequate knowledge and practice of PR in rural areas of the Pune district.\n\nThe aim is to assess the need for PR from both a caregiver and care recipient’s perspective, while formulating and testing a PR strategy for COPD management in a rural Indian setting.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/pulmonary-rehabilitation",
    "url": "https://healthdatagateway.org/en/dataset/275",
    "uid": "e8afaf39-86fa-4085-a075-6118cc25fae0",
    "datasource_id": 275,
    "source": "HDRUK"
  },
  {
    "id": 1052,
    "name": "At-Risk Registers Integrated into primary care to Stop Asthma crises in the UK",
    "description": "<p>It is a randomised cluster controlled trial of a brief training of GP practice staff and of the identification and flagging of records of high risk asthma patients to determine if this it may reduce the occurrence of severe asthma related events. In this study design it is the GP practice that is randomised.</p>\n\n<p>The study data is from primary care electronic health records, Hospital Episode Statistics, and Mortality data of the asthma patients at the practice.</p>",
    "url": "https://healthdatagateway.org/en/dataset/269",
    "uid": "36a5da45-772c-4c67-8ec3-84c9e67332d3",
    "datasource_id": 269,
    "source": "HDRUK"
  },
  {
    "id": 1053,
    "name": "Teleconsultation in remote rural India: management of COPD and Asthma",
    "description": "Remote rural places have always experienced inequity in access to health care facilities and services. Even where places are equipped with facilities, the availability of trained health care providers is a challenge.\n\nRecent advances in technology have enabled clinicians to deploy telemedicine in remote locations. Telehealthcare provides a holistic approach to health and wellbeing by improving access. However, multiple barriers still exist to implement telehealthcare and to scale-up the available technology.\n\nUse of telehealthcare for the management of Chronic Respiratory Disorders (CRDs) in India is not yet proven, hence a feasibility study will help to explore barriers and facilitators to the successful implementation of teleconsultation. This could be done by documenting views, opinions and experiences of opinion leaders and by understanding the perception of the stakeholders, including providers and patients, towards doctor-to-doctor teleconsultation.\n\nThis study will generate evidence for creating a policy on ‘Management of overall chronic diseases at remote rural area in India using teleconsultation’.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/phd-studentships/rutuja-patil",
    "url": "https://healthdatagateway.org/en/dataset/270",
    "uid": "de496590-26b3-4e74-bc5e-1ff5c83f5b43",
    "datasource_id": 270,
    "source": "HDRUK"
  },
  {
    "id": 1054,
    "name": "Effect of personal air pollution exposure on asthma control in children",
    "description": "<p>This study aimed to recruit asthmatic children and to assess whether it was feasible to reduce their air pollution exposure levels by intervening with advice to decrease exposure. The following steps had been taken to assess the aim: First, this study assessed the participants’ BC exposure data collected before and after the intervention. In particular, the assessments explored how children would react within a week and 5 weeks after receiving the intervention. Second, the study explored when and where the exposure reduction occurred. This was done by dividing each BC data into a set of four predetermined microenvironments, i.e. home, commute, school and other microenvironments. The purpose of the four microenvironments was to simplify and consolidate participants’ actions during each monitoring period, which would identify which microenvironment had achieved the most significant effects because of the intervention. Finally, the study assessed the participants’ time weighted exposure levels. This was done by calculating the participants’ exposure levels for each microenvironment weighted with the time spent in them. In addition to the main objective, the study has also explored other variables, included participants’ paediatric asthma quality of life, asthma control tests and their lung function, as well as their NO2 exposure levels before and after the intervention. Overall, there was a significant (p<0.05) reduction in participants’ BC exposure after the intervention. Questionnaires revealed the participants’ asthma control had significantly improved after the intervention and the analysis of their cotinine and urinary particle loading levels were also declined after the intervention. As such, the findings from this study confirm the main research aim, which argued that reduced exposure to air pollution in asthmatic children is achievable via increased awareness of the health impacts that air pollution can have on them and that mitigating personal exposure is achievable via simple behavioural changes. </p>",
    "url": "https://healthdatagateway.org/en/dataset/271",
    "uid": "57efab41-65fc-4069-8b9d-e5ced13cb4d3",
    "datasource_id": 271,
    "source": "HDRUK"
  },
  {
    "id": 1055,
    "name": "Peer-led, Professionals Assisted Pulmonary Rehabilitation (PLPAPR): pilot study",
    "description": "In low and middle income countries (LMICs) like India, pulmonary rehabilitation (PR) is sporadically available either at a tertiary care centres or in teaching hospitals located in urban settings. There are several barriers that exist in implementation (labor intensive, time consuming, lack of trained health care professionals, high drop-out rate and cost of rehabilitation) as well as accessing (lack of awareness among CRD patients on PR, unavailability of a PR program and unwilling to spend money on PR) a PR program which hampers the uptake of PR in such settings. Christian Medical College (CMC), Vellore, India, a partner site of RESPIRE research conducted a feasibility intervention trial on chronic respiratory disease (CRD) \"Prevention, detection and treatment of adult lung disease including lung cancer in a poor, rural population in Tamil Nadu: feasibility study in rural south India\". About 1 year through the study, some have reported remarkable improvement to standard treatment and a significant number have shown suboptimal response to standard treatment. With a need to revamp our CRD patients' health and also to develop a low cost feasible model to endorse and encourage PR in resource-poor settings, the follow-up study was undertaken with the objective to test the feasibility of a peer-led community based comprehensive PR assisted by professionals in a rural Indian setting. Both quantitative and qualitative data were collected to test the feasibility of the study. The quantitative data collected were demographic details, medical history, smoking history, occupation history, hemoglobin level, BMI and treatment details. The baseline and endline assessment included physical activity assessment using the International Physical Activity Questionnaire, activities of daily living using London Chest Activity of Daily Living scale. The nutritional status was assessed using 24 hour recall for a week day and weekend by the Nutritionist. Anxiety and depression was assessed using the Hospital Anxiety and Depression Scale (HADS). Exercise capacity using a six-minute walk test (6-MWT), upper limb strength was assessed using the hand held dynamometer and lower limb endurance was assessed using cycle ergometer. After 8 weeks of pulmonary rehabilitation program, endline assessment was done. The qualitative data was collected at the begining and end of the PR training program. Indepth interviews were conducted among patients and peer volunteers. Data collected was transcribed and translated into English.\n\nAt the end of 8 weeks PR program, thirty patients with chronic respiratory disease should see a significant improvement in exercise capacity (mean difference in 6-minute walk test, 56.35 m; P <0.01), endurance (mean difference in cycling time, 71.4 s; P <0.01), and upper limb strength (mean difference in Hand dynamometer, 5.1 Kg; P <0.01). The results showed that a model with a center based PR in the community is feasible.",
    "url": "https://healthdatagateway.org/en/dataset/272",
    "uid": "dec5d8da-2d63-4ab8-927e-4a5a659dc541",
    "datasource_id": 272,
    "source": "HDRUK"
  },
  {
    "id": 1056,
    "name": "Respiratory rate counters in paediatric pneumonia diagnosis",
    "description": "Fast breathing is the most common sign of childhood pneumonia. It is identified by observing the child’s chest and counting respiratory rate (RR). However, manual count of RR is challenging for the health workers often resulting in misdiagnosis of pneumonia. The availability of novel RR counters (e.g., ChARM, Masimo Rad-G, uPM60) can support health workers by detecting fast breathing automatically. However, the absence of an appropriate reference standard to evaluate the performance of these devices is a challenge. The commonly used reference standard is manual RR counts by a human expert which might be biased. If good quality videos could be captured and RR interpretation from these videos could be systematically conducted, it could be an ideal and non-biased reference standard. This study is designed to develop a video expert panel (VEP) as a reference standard to evaluate automated RR counters to identify pneumonia in children. The study will assess the performance of ChARM device in terms of accuracy of counting RR, duration to take the count and any potential influence of the device on RR counts. The study will record the child’s chest movements, and the recorded videos will be interpreted by the VEP. A mechanism to interpret RR from the recorded videos and maintain ongoing quality control will be established. This study will be carried out in Bangladesh in two phases. Eligible children will be 0-2 months old presenting with any illness and 2-59 months old presenting with suspected pneumonia (cough and/or breathing difficulty). In Phase-I, we will develop a process of videography of the child’s chest movements and interpretation of RR by a VEP. We will establish the process of capturing good quality videos, making a set of reference videos and will train and standardise the VEP members using those reference videos. We will record videos from a hospital in Dhaka. We will take videos of child’s chest movements both with and without using of ChARM. In Phase-II, we will conduct a cross-sectional study in rural Sylhet for evaluation of the performance of ChARM device. We will enrol children presenting at first level health facilities and a sub-district hospital. This study will provide evidence to establish the videography of child’s chest movements and its interpretation by a VEP as an appropriate and non-biased reference standard to evaluate the performance of novel RR counters. For further information please visit https://www.ed.ac.uk/usher/respire/phd-studentships/ahad-mahmud-khan.",
    "url": "https://healthdatagateway.org/en/dataset/263",
    "uid": "beeee4fb-55ab-41b0-9287-33f008e9d6bf",
    "datasource_id": 263,
    "source": "HDRUK"
  },
  {
    "id": 1057,
    "name": "The Malaysian Asthma Hajj Study",
    "description": "This study will assess the prevalence of asthma amongst hajj pilgrims, and risk of asthma-related events during the hajj using routine data from the hajj medical service. We will observe current organisational and clinical routines associated with the provision of hajj medicals, explore the perceptions of clinicians and managers of the hajj service, and to develop an intervention and implementation strategy to reduce the risk of asthma events.\n\nFor more information, please see : https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/the-malaysian-asthma-hajj-study",
    "url": "https://healthdatagateway.org/en/dataset/264",
    "uid": "8187f77f-65e3-435e-ad7e-d7f22e2fcf80",
    "datasource_id": 264,
    "source": "HDRUK"
  },
  {
    "id": 1058,
    "name": "Use of digital auscultation to improve diagnosis of paediatric pneumonia",
    "description": "Integrated Community Case Management (iCCM) is a World Health Organization (WHO) approach in which community health workers deliver basic healthcare services in the community setting, including childhood pneumonia treatment.\n\nThe WHO pneumonia guidelines are sensitive but non-specific, in order to ensure that children with possible pneumonia receive antibiotic treatment. As a result, while the guidelines miss few children with pneumonia (high sensitivity), many children who do not have pneumonia incorrectly receive antibiotics (low specificity), resulting in antibiotic overuse.\n\nThe WHO guidelines do not include lung auscultation (listening to lung sounds) in their pneumonia definition for frontline healthcare workers, likely due to its high inter-observer variability, regardless of healthcare providers’ training level. Digital auscultation by electronic stethoscopes may help to overcome these limitations. Inclusion of lung auscultation in the current algorithm could enhance the specificity of the guidelines.\n\nThis study aims to improve the diagnostic accuracy of child pneumonia by using automated lung sound classification through digital auscultation.\n\nThe embedded PhD will use the study data to (i) assess the consistency of lung sounds recorded by primary health care workers from under-five children using a digital stethoscope against pre-defined quality thresholds and (ii) determine the reliability and performance of the interpretations of recorded lung sounds by the Smartscope analysis system compared to reference interpretations by a paediatric listening panel.\n\nFor further information, see associated media\n\nhttps://www.ed.ac.uk/usher/respire/phd-studentships/salahuddin-ahmed",
    "url": "https://healthdatagateway.org/en/dataset/265",
    "uid": "198bd2cc-ca40-4dff-992c-a9480703fc37",
    "datasource_id": 265,
    "source": "HDRUK"
  },
  {
    "id": 1059,
    "name": "Assessment of ASHA’s workload and its determinants",
    "description": "The ever evolving role of ASHA demands an up-to-date comprehensive assessment of the workload, incentives and understanding of the work profile from the perspectives of the health system, community and ASHA herself in order to guide successful future implementation as well as sustainability of the programme. This study had a broad interest in both the full range of tasks and the different situations in which ASHA work and the changing context in which their role is interpreted. This study therefore used a mixed-methods approach (MMA) to assess and explore ASHAs’ perspectives of their workload alongside that of local healthcare colleagues in both rural and village contexts.\n\nBackground: \nGlobally, Community Health Workers (CHWs) are integral contributors to many health systems. In India, Accredited Social Health Activists (ASHAs) have been deployed since 2005. Engaged in multiple health care activities, they are a key link between the health system and population. ASHAs are expected to participate in new health programmes, prompting interest in their current workload from the perspective of the health system, community and their family.   \n  \nMethods: \nThis MMA design  was conducted in rural and tribal Primary Health Centers (PHCs), in Pune district, Western Maharashtra, India. All ASHAs affiliated with these PHCs were invited to participate in the quantitative study, those agreeing to contribute in-depth interviews (IDI) were enrolled in an additional qualitative study. Key informants’ interviews  were conducted with the Auxiliary Nurse Midwife, Block Facilitators and Medical Officers of the same PHCs. Quantitative data were analysed using descriptive statistics. Qualitative data were analysed thematically.\n\nResults: \nWe recruited 67 ASHAs from the two PHCs.  ASHAs worked up to 20 hours/week in their village of residence, serving populations of  approximately 800-1200, embracing an increasing range of activities, despite a workload that contributed to feelings of being rushed and constant tiredness. They juggled household work, other paid  jobs and their ASHA activities. Practical problems with travel added to time involved, especially in tribal areas where transport is lacking. Their sense of benefiting the community and respect and recognition in village brought happiness and job satisfaction. They were willing to take on new tasks. ASHAs perceived themselves as voluntary community health workers rather than as \"health activists.\"\n\nConclusions:\nASHAs were struggling to balance their significant ASHA workload, and domestic tasks. They were proud of their role as CHWs and willing to take on new activities. Strategies to recruit, train, enhance skills, incentivise, and retain ASHAs, need to be prioritised.\n\nfor more information, please see :\nhttps://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/asha-workload",
    "url": "https://healthdatagateway.org/en/dataset/257",
    "uid": "1a02bc11-a378-4f9e-82f9-c52674b388bd",
    "datasource_id": 257,
    "source": "HDRUK"
  },
  {
    "id": 1060,
    "name": "Optimising the treatment of Bangladeshi adults with severe pneumonia or ARDS",
    "description": "\"The number of COVID-19 patients hospitalised with severe breathing difficulty is increasing. Unfortunately, there is acute scarcity in the availability of mechanical ventilators to meet the increasing demand of hospitalised COVID-19 patients requiring assisted breathing. The situation is particularly alarming in countries with limited resources like Bangladesh. Low- and middle-income countries urgently need low-cost adaptive technologies to provide CPAP. A trial by the lead investigator demonstrated that a locally made low-cost bubble CPAP was effective in significantly reducing the mortality in children with severe pneumonia. Bubble CPAP device comprises (a) an interface; nasal seal, (b) oxygen delivery piping, connectors and nasal cannula, (c) appropriately sized transparent plastic bottles (containing sterilized water). The safety of the pediatric device and its use in children with acute respiratory disease has already been approved by the Directorate General of Drug Administration (DGDA), Government of Bangladesh. Adapting this technology, if safe and scaled up, could potentially reduce the need for mechanical ventilation and subsequently averting deaths among adult COVID-19 patients. \n\nOur adaptations have included ensuring adequate seal for the interface (mask for nose) when delivering oxygen at high pressure to meet the demand of hypoxaemia in adult lungs. To prevent nasal air leak around the mask, we developed and successfully tested a prototype in healthy individuals which is silicon-based and ergonomically designed considering the variations in size and direction of adult nasal cavities as well as the comfort of patients. This adaptive version of nasal canula was made with the help of Bangladesh University of Engineering and Technology (BUET).\n\nThe design phase has demonstrated that (a) using a collaborative approach we can consistently produce adult silicone nasal seals, (b) we can deliver effective PEEP with a mean pressure that is overall within +/-12% of target pressure, with most loss only at the highest pressure setting (15cmH2O), (c) the device is well tolerated by participants with no in-study adverse events of note and no skin injury or undue discomfort in relation to the cannula or flow.  \n\nThe safety phase initiated on 1st November 2020 and ended on 30th April 2021. We have enrolled twelve patients (ten RT-PCR negatives for COVID-19 and rest of the two RT-PCR positives for COVID-19) in this trial. One participant withdrew from the study by voluntary approach. The rest of the patients were successfully discharged. None of them developed any adverse event(s). \n\nThis study aims to evaluate the barriers and operational challenges related to the introduction of adult bubble CPAP oxygen therapy along with the acceptability and usability of introduction of adult bubble CPAP in tertiary level hospitals of Bangladesh\n\"\n\"CNN : The plastic bottle saving babies from pneumonia\n\nhttps://www.youtube.com/watch?v=IU-QzOVPEj4\n\"",
    "url": "https://healthdatagateway.org/en/dataset/258",
    "uid": "224b1065-340b-4c01-842f-d422911d9334",
    "datasource_id": 258,
    "source": "HDRUK"
  },
  {
    "id": 1061,
    "name": "Developing and evaluating interventions to improve asthma care: HEAL ASTHMA",
    "description": "We aim to assess the feasibility of delivering a supported self-management incorporating an adapted pictorial asthma action plan for adult patients with asthma in clinical practice in a public primary care clinic in the Klang District, Selangor State, Malaysia. Dataset contains follow-up responses at one-, three- and six-months post intervention. \n\nSupported self-management has been shown to reduce asthma-related morbidity and mortality in high-income countries, but poor health literacy (especially in low- and middle-income countries) is a potential barrier to its effectiveness. This study aims to assess the feasibility of delivering supported self-management incorporating an adapted pictorial asthma action plan for adult patients with asthma attending a public primary care clinic in Malaysia. This study will proceed in two phases: 1) adaptation of the pictorial asthma action plan (AAP); and 2) a feasibility study including  feasibility of assessing costs. Following the adaptation of the AAP, we assess the feasibility of using the AAP to support self-management in adults with asthma. In a pre-post study, 70 patients aged 18 years and above with physician-diagnosed asthma and currently prescribed inhaled corticosteroids will be recruited. Our proposed primary outcome is asthma control (Global Initiative for Asthma symptom control). A pre-piloted questionnaire will be used to collect baseline data on socio-demography, healthcare utilisation and expenditure, health literacy, clinical parameters (body mass index; peak expiratory flow rate).  Follow-up will be at 1, 3, and 6 months. \n\nThe protocol was approved by the Medical Research Ethics Committee, Ministry of Health Malaysia and registered with National Medical Research Registry (NMRR-18-2683-43494). The study trial registration number is ISRCTN87128530 (Pre-results).",
    "url": "https://healthdatagateway.org/en/dataset/259",
    "uid": "216924f0-585e-4842-a165-78cf9e0af0aa",
    "datasource_id": 259,
    "source": "HDRUK"
  },
  {
    "id": 1062,
    "name": "Hypertonic saline nasal irrigation and gargling for suspected COVID-19",
    "description": "COVID-19 and the ‘common cold’ are both caused by viruses that get into the respiratory tract. Some studies with patients with the common cold have found that nasal washing and gargling with salt-water may be helpful in reducing the length of the illness . However, we do not know if this same benefit is also seen in patients with suspected or confirmed COVID-19. This study will help us find out if nasal washing and gargling with salt-water are helpful in COVID-19. The study involved randomly selected individuals who fulfil the criteria of having the following symptoms: (i) Respiratory symptoms, such as cough and shortness of breath, (ii) Fever, (iii) Muscle pain, (iv) Headache, (v) Sore throat, (vi) New loss of taste or smell, (vii) Severe fatigue. \n\nIndividuals who have one or more symptoms of SARS-CoV2 are encouraged to signup for the study on a website. They fill a few forms are then divided randomly into a control and treatment group. Control group participants are advised to follow the standard treatment suggested by Government of Pakistan. The treatment group also follows the standard recommendations but along it perform saline nasal wash a few times a day. The participants of both groups fill a form everyday to record their symptoms for 14 days and then an end of study form. The data shared records the responses of the participants that were collected digitally.",
    "url": "https://healthdatagateway.org/en/dataset/260",
    "uid": "574cf7bc-dd70-4508-a66d-ac59d8d06639",
    "datasource_id": 260,
    "source": "HDRUK"
  },
  {
    "id": 1063,
    "name": "Deriving and validating a clinical prediction rule for the diagnosis of asthma in primary care",
    "description": "<p>Asthma is common in the UK, causing considerable illness, healthcare usage, and public expense. Accurate diagnosis is essential for good asthma management. Yet, uncertainty about the best way to diagnose asthma can lead to missed diagnoses and under-treatment, or over-diagnosis leading to unnecessary treatment and healthcare costs. To make it easier for doctors and nurses to identify and interpret important information gathered from patient suspected of having asthma, existing research database 'Avon Longitudinal Study of Parents and Children' (ALSPAC) will be used to identify feautres which predict who has asthma. This data contains information on a wide-range of socioeconomic, lifestyle, clinical, anthropometric and biological data on all family members repeatedly. The rule will be tested on anonymous routine data from UK GP's 'Optimum Patient Care Research Database'.</p>\n\n<p>ALPSAC: http://www.bristol.ac.uk/alspac\nOPCRD: https://opcrd.co.uk/</p>",
    "url": "https://healthdatagateway.org/en/dataset/261",
    "uid": "b0a68ee7-742d-45dc-aae9-4be4e1655fc0",
    "datasource_id": 261,
    "source": "HDRUK"
  },
  {
    "id": 1064,
    "name": "Bangladesh Asthma Hajj Study",
    "description": "This study will identify the gaps in the existing pre-Hajj medical check-up services to address the risk of asthma among Hajj pilgrims, will explore perceptions of medical staff and Hajj service managers, who are involved in pre-Hajj medical check-ups and management, about the risk posed by asthma to the health of Hajj pilgrims, and the potential acceptability of an intervention to guide recommended care (including self-management) and identify the opportunities and barriers in integrating that intervention within the existing pre-Hajj medical check-up system.\n\nFor more information please see: https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/the-bangladesh-asthma-hajj-study",
    "url": "https://healthdatagateway.org/en/dataset/252",
    "uid": "27e1ffc7-5804-40e2-83e2-93f570906127",
    "datasource_id": 252,
    "source": "HDRUK"
  },
  {
    "id": 1065,
    "name": "The Consequence of Respiratory Syncytial Virus (RSV) Infection in Young Infants",
    "description": "Each year, an estimated 33.1 million episodes of RSV-associated acute lower respiratory infections occur among under-five children globally, leading to 3.2 million hospitalisations and 118,200 deaths. The burden of RSV-associated severe acute lower respiratory tract infection is ten times higher in developing countries compared to that in developed countries (36.1 per 1000 life birth vs 3.2 per 1000 life birth, respectively). \n\nIn addition to acute mortality and morbidity, RSV infection has a long-term effect on children’s health. RSV infection can induce a state of bronchial hyper-reactivity that has an association with the development of asthma in later life, which, in turn, is a significant risk factor of chronic obstructive pulmonary disease in adulthood.\n\nIn this study, we are collecting longitudinal data of a group of 2,274 children aged between 6-8 years in three South Asian countries, namely Bangladesh, India and Pakistan.  Among these children, 402 had RSV infection in their first two months of life; the remaining 1,873 children did not have such conditions at the same period confirmed by laboratory testing.\n\nHealth workers are routinely visiting households of eligible children. Children whose parents provided consent are visited three times in one year period, at baseline, after six months and the end of the year. In the first visit, a research assistant explained the study objectives and procedures to a family member (primarily the mother) of the eligible children to provide consent for enrolling the children in the study.  The parents of the consented children were being interviewed using a structured questionnaire to record the current and previous health status of children. The asthma sign and symptoms were recorded using a questionnaire designed following the International Study of Allergies and Asthma in Childhood (ISAAC). During the second and third visits, similar questionnaires of the first visit are being planned to use for data collection. All children are intended to assess by a research physician to record the physical growth, lung function, exercise tolerance, and blood eosinophil level.\n\nFor more information please see: https://www.ed.ac.uk/usher/respire/acute-respiratory-disorders/rsv-infection",
    "url": "https://healthdatagateway.org/en/dataset/253",
    "uid": "51d41006-727e-43d4-bdb6-f8b427c295c6",
    "datasource_id": 253,
    "source": "HDRUK"
  },
  {
    "id": 1066,
    "name": "Estimate the cost of care, quality of life and wider societal burden due to COPD",
    "description": "The burden to the patient with Chronic obstructive pulmonary disease (COPD) is high, both in terms of health-related quality of life and health status. Exacerbations are associated with significant mortality, lead to frequent hospitalisation, and are a major determinant of COPD costs. Due to the natural history of COPD and its relentlessly progressive nature it needs long-term care, which means the range of medical and/or social services designed to help people with disabilities or chronic care needs. Hence designing the method to measure direct medical cost, direct non-medical cost, indirect cost and loss of productivity collectively, with reference to COPD, would be value-added information.\n\nIn this study, family, health system and societal perspective will be adopted which will be useful for informing policy decisions to local stakeholders and this data will be used to generate evidence to write further large studies. Interviews would be conducted for key stakeholders in the community to understand the burden of COPD to the society. Mixed method approach will give a comprehensive data to design the measure for estimating cost of illness for COPD for future cost of illness studies.\n\nFor more information, please see : https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/estimating-the-costs-of-care-quality-of-life-and-w",
    "url": "https://healthdatagateway.org/en/dataset/254",
    "uid": "ee6013d6-5584-4275-b124-3113ee5ad37d",
    "datasource_id": 254,
    "source": "HDRUK"
  },
  {
    "id": 1067,
    "name": "Treatment of Bangladeshi children with severe pneumonia",
    "description": "This study aims to explore whether bubble CPAP is able to improve outcomes of children with severe pneumonia and hypoxaemia who receive care in non-tertiary, district hospitals. We also aim to understand the feasibility and acceptability of introducing and using locally made innovative low-cost bubble CPAP in these real-life settings in two selected district hospitals, prior to commencing our multicentre trial.\n\nHypoxaemia, a low level of oxygen in the blood, is one of the main risk factors for death due to pneumonia among children.\n\nDistrict hospitals in Bangladesh are considered as secondary level referral hospitals and usually provide care of paediatric patients in paediatric wards, including children with pneumonia and severe pneumonia.\n\nDistrict hospitals do not have additional respiratory support available to children who are failing to improve following treatment with low-flow oxygen supplementation, the World Health Organization standard. As a result, these children may die due to lack of availability of additional respiratory support.\n\nBubble CPAP (a low-cost, locally made device to deliver oxygen) improved survival rates when provided to treat severe pneumonia in tertiary hospitals. This study will explore whether the same technology made available in district hospitals could provide the same patient benefit.\n\nThis study aims to explore whether bubble CPAP is able to improve outcomes of children with severe pneumonia and hypoxaemia who receive care in non-tertiary, district hospitals. We also aim to understand the feasibility and acceptability of introducing and using locally made innovative low-cost bubble CPAP in these real-life settings in two selected district hospitals, prior to commencing our multicentre trial.\n\nFor further information please see:  https://www.ed.ac.uk/usher/respire/acute-respiratory-disorders/bubble-cpap",
    "url": "https://healthdatagateway.org/en/dataset/255",
    "uid": "2f4c56b3-ca6a-4b30-a7f0-d83e68192c5b",
    "datasource_id": 255,
    "source": "HDRUK"
  },
  {
    "id": 1068,
    "name": "CuT-AsthMa: Culturally tailored school-based intervention for asthma in Malaysia",
    "description": "We used a qualitative method to explore the views of stakeholders on asthma and school-based intervention of asthma in Malaysia. We involved school staff, healthcare professionals and policymakers in focus groups/interviews to inform the development of Culturally-Tailored school-based for Asthma in Malaysia (CuT-AsthMa) programme. We had 52 participants: 39 school staff, 11 healthcare professionals and two policymakers. The summary of our data is described using a codebook consisting of codes and the description used to analyse the focus groups/interviews. The codebook was developed using open codes, categories (collation of open codes) and themes. We used this codebook to aid in constructing a schema to inform the development of the CuT-AsthMa programme. Other documents include an invitation letter to participants, a set of participant information sheet and consent. The data in this study is to support part of the PhD work at the Usher Institute, the University of Edinburgh. Due to the sensitivity of the data and to protect the confidentiality of the participants involved in this study, the raw data will be deposited in DataVault at the University of Edinburgh.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/phd-studentships/siti-nurkamilla-ramdzan",
    "url": "https://healthdatagateway.org/en/dataset/256",
    "uid": "009bcb87-05f3-46e6-8866-7e4df698cb19",
    "datasource_id": 256,
    "source": "HDRUK"
  },
  {
    "id": 1069,
    "name": "Exploring Personal Preparedness and Self-Protective Measures during COVID-19",
    "description": "Frontline health care workers (FHCWs) face a number of safety concerns when providing services during the current COVID-19 pandemic, with a significant number catching the virus themselves.\n\nAs the pandemic escalates, there is a need to establish an effective, and socially and culturally sensitive, protection system to protect FHCWs.\n\nThere is a lack of research relating to low and middle-income country FHCWs’ perceived behaviour, including their understanding of personal preparation and self-protective measures in a pandemic.\n\nThere are particular needs exemplified by working in refugee settings of intense humanitarian crises.\n\nThere is currently no global information on how COVID-19 will impact refugee settings. FHCWs in these settings face exceptional challenges, working with limited resources, with a population who are fearful of external authority systems, and who have endured many atrocities.\n\nThis project will explore the perceptions and feelings of a community of FHCWs (including doctors, nurses and community health workers) in Cox’s Bazar, about personal preparation and protective measures for FHCWs during the COVID-19 pandemic.\n\nFor more information please see: https://www.ed.ac.uk/usher/respire/covid-19/personal-preparedness-health-care-workers",
    "url": "https://healthdatagateway.org/en/dataset/247",
    "uid": "680a7d41-0fd1-4b32-a2cd-190e338f8ac7",
    "datasource_id": 247,
    "source": "HDRUK"
  },
  {
    "id": 1070,
    "name": "4 Country ChrOnic: estimating respiratory disease burden in adults, South India",
    "description": "Informed by a scoping review of existing questionnaires and protocols used in Low- and Middle-Income Countries (LMICs) to identify Chronic Respiratory Diseases, we will undertake a pilot survey to explore feasibility of using screening process in four countries (Bangladesh, India, Malaysia, and Pakistan). Our pilot findings will inform a future fully powered survey to determine the prevalence of asthma, COPD and other CRD in the community, using robust random sampling strategies, and quality assured spirometry undertaken by field workers.\n\nFor further details, see: https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/estimating-chronic-respiratory-disease-burden",
    "url": "https://healthdatagateway.org/en/dataset/241",
    "uid": "2f1244b8-7cbf-4fa3-8534-433efd769d48",
    "datasource_id": 241,
    "source": "HDRUK"
  },
  {
    "id": 1071,
    "name": "Computational Framework to Interpret Chest X-rays and Diagnose Pneumonia",
    "description": "It is estimated that 95% of the two million deaths due to pneumonia occur in developing countries. In Bangladesh alone, six million cases of pneumonia are diagnosed every year.  Unfortunately, diagnostic methods to date lack sensitivity or are difficult to fully standardized. The lack of a reliable diagnostic hampers the execution of evidence based interventions, impacting the monitoring of interventions, like vaccines.\n\nThe “gold standard” for defining pneumonia are chest X-rays. However, the interpretations are subjective, sometimes requiring multiple radiologists/clinicians to reach a conclusive diagnosis. As there are few well-trained radiologists/clinicians in resource-poor settings, having a tool to aid in the diagnosis of pneumonia would be invaluable in the impact monitoring of interventions.\n\nThe aim of the project is to construct a computational framework to automatically and systematically interpret paediatric chest X-rays to diagnose pneumonia.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/acute-respiratory-disorders/interpret-chest-x-rays",
    "url": "https://healthdatagateway.org/en/dataset/243",
    "uid": "cf9bc775-1db7-44e4-9a16-317d5bb063a2",
    "datasource_id": 243,
    "source": "HDRUK"
  },
  {
    "id": 1072,
    "name": "Seasonal Influenza Vaccine Effectiveness II",
    "description": "<p>Please see: <a href=\"https://bmjopen.bmj.com/content/7/2/e014200\">this link for full description</a></p>",
    "url": "https://healthdatagateway.org/en/dataset/244",
    "uid": "50b47298-d935-4990-a002-e26b6bb26d30",
    "datasource_id": 244,
    "source": "HDRUK"
  },
  {
    "id": 1073,
    "name": "Enhancing access to pulmonary rehabilitation through implementation research",
    "description": "As respiratory infection is better controlled in Bangladesh, chronic respiratory diseases such as Chronic Obstructive Pulmonary Disease, are emerging as the major health threat. This project aims to enhance access to pulmonary rehabilitation through implementation research in Bangladesh.\n\nPulmonary Rehabilitation (PR) is proven to be the most effective strategy to improve shortness of breath, health status and exercise tolerance as well as reducing re-admissions and mortality in patients with recent exacerbations.\n\nWhile PR uptake is significant in developed countries, in Bangladesh it is a relatively new intervention.\n\nThe aim of this PhD is to develop and pilot a PR programme that could be scaled up in Bangladesh.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/phd-studentships/monsur-habib",
    "url": "https://healthdatagateway.org/en/dataset/245",
    "uid": "6265a41f-9444-457b-b401-1d0978f5c58a",
    "datasource_id": 245,
    "source": "HDRUK"
  },
  {
    "id": 1074,
    "name": "Childhood Pneumonia Governance in Bangladesh",
    "description": "This project aims to map and analyse global and national governance of childhood pneumonia (CP) control through examination of existing health policies, financing mechanisms and health system analysis. Lois will investigate the extent of neglect at the international level and look at the extent of neglect at the domestic level, uncovering if and why there are differences when comparing global to national prioritisation, what the local effects of this are and how Bangladesh’s policies have adapted to tackle CP.\n\nGlobally, childhood pneumonia (CP) remains the leading infectious cause of death among children under five. However, the disease receives less prioritisation and funding in global health when compared to other infectious diseases such as tuberculosis, malaria and polio. Thus, CP can be described as a neglected health issue at the global level due to lack of synergy among key stakeholders and current agenda-setting practices between influential global health actors.\n\nBangladesh is typically seen as a success story due to huge reductions in CP in the last few decades. Nevertheless, it remains the 13th highest burden country globally for CP. As such, Bangladesh offers a unique example of what governments are doing to address CP, despite the lack of global prioritisation. Due to the complex wider determinants of health that need to be considered when tackling CP, this disease is a useful litmus test of how well a health system is functioning.\n\nThis project aims to map and analyse global and national governance of CP control through examination of existing health policies, financing mechanisms and health system analysis. Lois will investigate the extent of neglect at the international level and look at the extent of neglect at the domestic level, uncovering if and why there are differences when comparing global to national prioritisation, what the local effects of this are and how Bangladesh’s policies have adapted to tackle CP.\n\nFor further information, see: \"https://www.ed.ac.uk/usher/respire/phd-studentships/lois-king\"",
    "url": "https://healthdatagateway.org/en/dataset/246",
    "uid": "5df03d24-ad95-4d64-96df-0684a4f26e58",
    "datasource_id": 246,
    "source": "HDRUK"
  },
  {
    "id": 1075,
    "name": "Developing and Evaluating Interventions to Improve Asthma Care",
    "description": "Asthma is one the most prevalent non-communicable chronic disease among children and adults in Malaysia. It is, however, neglected. Most patients have poor knowledge about asthma and despite the availability of effective treatment, asthma control is reported to be unsatisfactory. Disease management and prevention is suboptimal and has an impact on the economic burden. As not much is known about what actually contributes to poor asthma control in our region, improving understanding on factors related to disease burden could contribute significantly to improved asthma morbidity. The aim of this project is to develop a cohort of adults and children with asthma in Klang district in Malaysia to enable epidemiology, disease management and economic burden of asthma to be studied and interventions directed at patients and health care practice level to be carried out to improve asthma care. \n\nFor further information, see: https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/asthma-care",
    "url": "https://healthdatagateway.org/en/dataset/238",
    "uid": "d57d7956-d13e-49c4-bb31-889b8e560596",
    "datasource_id": 238,
    "source": "HDRUK"
  },
  {
    "id": 1076,
    "name": "Development of ELISA and Rapid Testing Kits for COVID-19",
    "description": "The electronic data will consist of documenting the different diagnostic reagents imported, with their technical details, specifications and expiry dates. A record will be kept of the use of these reagents, and their performance characteristics in the research laboratory. The second main data set will involve the development of the diagnostic kits, and documentation of all physical, chemical and immunological parameters around each diagnostic procedure, and the outcomes, and whether success or failure. The third set of data will be generated when the developed diagnostic kits are tested against the known samples received from the commercial (Metropole) laboratories and the results compared.",
    "url": "https://healthdatagateway.org/en/dataset/239",
    "uid": "6b2aca2c-b03a-44ba-80a1-883b744421b6",
    "datasource_id": 239,
    "source": "HDRUK"
  },
  {
    "id": 1077,
    "name": "Air Quality Study: An ecological analysis of asthma health outcomes in Malaysia",
    "description": "The aim of the study is to assess the feasibility of using the eDPSEEA (ecosystems-enriched Drivers, Pressures, State, Exposure, Effects, Actions) model in Air Quality RESPIRE projects and pilot the approach to determine the influence of potential environmental factors on asthma events in Malaysia over a 4-year period.\n\nFor further details, see: https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/ecological-analysis-asthma",
    "url": "https://healthdatagateway.org/en/dataset/240",
    "uid": "95f385b1-4f5c-4a88-8e79-9613acc66938",
    "datasource_id": 240,
    "source": "HDRUK"
  },
  {
    "id": 1078,
    "name": "Exploring psychological issues of primary care teams in Malaysia amidst COVID-19",
    "description": "We aim to explore the impact of the COVID-19 pandemic on psychological stress and well-being of primary healthcare workers (HCWs) in Malaysia. This research will provide information on psychological issues faced by primary care team who are the front liner facing this pandemic. It will provide insight to approaches to support their psychosocial needs, which in turn will help prepare other countries yet to face this pandemic and the next unforeseen pandemic and provide equitable access to the needs of these HCWs. Quick dissemination to policy holders on the findings will influence early support given to HCWs after the current first wave and potentially in a future second wave.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/covid-19/psychological-issues-primary-care-teams",
    "url": "https://healthdatagateway.org/en/dataset/231",
    "uid": "64f78a5f-268f-4951-9c91-f7e7130baffa",
    "datasource_id": 231,
    "source": "HDRUK"
  },
  {
    "id": 1079,
    "name": "Salford Lung Study",
    "description": "SLS Asthma - Primary Effectiveness Analysis (PEA) - This population is defined as all ITT subjects (all randomised subjects who receive at least one prescription of study medication [e.g. FF/VI or Usual Care]) who have an ACT total score of < 20 at baseline (Day 0).\nThis population will be based on the treatment to which the subject was randomised.\n\nSLS COPD - Primary Effectiveness Analysis (PEA) - This population comprised all ITT subjects (all randomised subjects who received at least one prescription for study medication [e.g. FF/VI or usual care]  who had at least one moderate/severe exacerbation in the year prior to Visit 2 (randomisation), as recorded on the eCRF.",
    "url": "https://healthdatagateway.org/en/dataset/236",
    "uid": "86838cad-d5d3-4b65-9c37-29b0c8c36309",
    "datasource_id": 236,
    "source": "HDRUK"
  },
  {
    "id": 1080,
    "name": "Respiratory social media database",
    "description": "<p>The QMUL-LSI-RSM respiratory social media is a database for multidisciplinary research aimed to improve understanding of unmet needs of people with asthma and asthma self-management. It includes 875,151 anonymised posts from 23,182 users of the Asthma UK (10 years of data) and British Lung Foundation online communities (4 years of data). </p>\n \n<p>The datasets includes publicly shared posts and their metadata (ie, the anonymized user ID numbers), user roles (eg, user, administrator, or moderator), date of posting, the hierarchical level of the post within the corresponding thread, and the dates in which the users joined and left the community. </p>\n\n<ul>\n<li>Asthma UK: Date from to 02/03/2006-06/09/2016 (548 weeks)</li>\n<li>Total number of users, n = 3,345</li>\n<li>Total number of posts, n = 32,780</li>\n<li>Number of posts per user, mean (SD) = 14.2 (55.0)</li>\n<li>Posts contributed by top 1% superusers, n (%) = 10,457 (31.9)</li> \n<li>Number of connections per user, mean (SD) = 2.1 (5.9) </li>\n<li>Number of connections per user, median (SD) = 1.0 (69.0)</li>\n</ul>\n<ul>\n<li>British Lung Foundation  Date from to 13/04/2012-06/09/2016 (230 weeks)</li>\n<li>Total number of users, n = 19,837</li>\n<li>Total number of posts, n = 875,151</li>\n<li>Number of posts per user, mean (SD) = 66.9 (75.1)</li>\n<li>Posts contributed by top 1% superusers, n (%) = 426,198 (48.7)</li>\n<li>Number of connections per user, mean (SD) = 17.6 (69.0)</li>\n<li>Number of connections per user, median (SD) = 1.0 (69.0) </li>\n</ul>\n\n<p>See complete information in Table 1 at https://www.jmir.org/2018/7/e238/</p>",
    "url": "https://healthdatagateway.org/en/dataset/228",
    "uid": "43fc768a-9d2c-4c6d-952f-d8570c487b4a",
    "datasource_id": 228,
    "source": "HDRUK"
  },
  {
    "id": 1081,
    "name": "Blended learning for primary care physicians on COPD in Bangladesh",
    "description": "The aim is to explore the use of both face-to-face and online methods (blended learning) for the training of primary care doctors in COPD in Bangladesh.  We will assess the feasibility of the blended approach, comparing exam scores with usual face-to-face training and exploring participants’ thoughts following the training.\n\nIn Bangladesh, more than 6.5 million people over the age of 40 have Chronic Obstructive Pulmonary Disease (COPD). COPD is often under diagnosed and not well managed by doctors in primary care practices in Bangladesh.\n\nCOPD training for doctors takes about 40 hours and because primary care is so busy, doctors often can’t attend training as there would be no-one to see patients while they are away from the practice.\n\nCombining online learning, with the standard face-to-face training, would reduce the time away from practice.\n\nThe aim is to explore the use of both face-to-face and online methods (blended learning) for the training of primary care doctors in COPD in Bangladesh. We will assess the feasibility of the blended approach, comparing exam scores with usual face-to-face training and exploring participants’ thoughts following the training.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/respire-fellowships/md-nazim-uzzaman",
    "url": "https://healthdatagateway.org/en/dataset/229",
    "uid": "3907a55f-104b-4533-ba21-69bd5f08ce8f",
    "datasource_id": 229,
    "source": "HDRUK"
  },
  {
    "id": 1082,
    "name": "Development of spirometry predictive values for Indian population",
    "description": "Spirometry is considered as a gold standard to detect the obstructive airway diseases (OADs) such as asthma and COPD. In India, use of spirometry among physicians and researchers to diagnose OADs is increasing day by day. Predictive values generated based on healthy population are used in interpretation of spirometry data. Currently, in India, most of the predicted values used in the spirometry data interpretation are based on predictive values from western populations. As no predictive values are available for the Indian population, physicians and researchers from India depend on the western countries guidelines such as ERS, ECHRS for the predictive values. A few attempts have been made to generate the predictive values for Indian population, but no conclusion has been drawn. We propose to use spirometry data from 2,500 adults from Vadu Health and Demographic Surveillance System (Vadu HDSS) population to develop the predictive values for the Western Indian population.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/spirometry-predictive-values",
    "url": "https://healthdatagateway.org/en/dataset/230",
    "uid": "59521b01-6adb-4dd3-8b40-dbeb51171e4f",
    "datasource_id": 230,
    "source": "HDRUK"
  },
  {
    "id": 1083,
    "name": "Developing Patient and Public Involvement in Research in Malaysia",
    "description": "Background: Patient and public involvement (PPI) in research is a movement that has gained increasing acceptance worldwide and is currently a requirement for many research funding and publication agencies. However, the concept of PPI is still novel in Malaysia and the South East Asian region.\nObjective: We aim to explore the development of PPI in our local research namely the RESPIRE project.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/respire-fellowships/su-may-liew",
    "url": "https://healthdatagateway.org/en/dataset/221",
    "uid": "153c0ba6-f2ec-4fb4-b5a7-33e11cbdf7fc",
    "datasource_id": 221,
    "source": "HDRUK"
  },
  {
    "id": 1084,
    "name": "Exploring the provision of supportive care for patients with COPD in Malaysia",
    "description": "We aim to explore the views and healthcare experiences of patients in Malaysia with severe, potentially life-threatening COPD, health care providers and policy makers regarding the services that provide (or not) supportive and palliative care for these patients. The findings will inform how the current service provision gap for this patient group may be addressed to meet their currently unmet needs. It has potential to inform palliative care guidelines in Malaysia and ultimately the quality of service provision for these patients. \n\nFor further information, see: https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/palliative-care-copd",
    "url": "https://healthdatagateway.org/en/dataset/222",
    "uid": "af8cdc87-789e-4bea-9d8b-927aa689d211",
    "datasource_id": 222,
    "source": "HDRUK"
  },
  {
    "id": 1085,
    "name": "Projections of global, regional and national prevalence of asthma from 2018-2040",
    "description": "<p>Determine appropriate models for estimating and projecting the prevalence and disease burden of asthma. Derive valid and reproducible estimates of the global, regional and national prevalence and disease burden of asthma and make projections in relation to these estimates for the years up to 2030. Develop automated approaches to updating these estimates on an annual basis.</p>\n\n<p>The dataset contains projected asthma population (number of people having asthma) from 2018 to 2040 for 187 countries, 8 United Nations (UN) Sustainable Development Goal (SDG) regions, 22 sub-regions and the world as a whole. The dataset contains projected age-sex-and-location specific prevalence of asthma for five years age groups including all ages and both sexes.</p>",
    "url": "https://healthdatagateway.org/en/dataset/223",
    "uid": "0911f991-6c0f-4412-b805-1ee0ded93a12",
    "datasource_id": 223,
    "source": "HDRUK"
  },
  {
    "id": 1086,
    "name": "Estimating Chronic Respiratory Disease burden in adults in Asian LMICs",
    "description": "Informed by a scoping review of existing questionnaires and protocols used in Low- and Middle-Income Countries (LMICs) to identify Chronic Respiratory Diseasess, we will undertake a pilot survey to explore feasibility of using screening process in four countries (Bangladesh, India, Malaysia, and Pakistan). Our pilot findings will inform a future fully powered survey to determine the prevalence of asthma, COPD and other CRD in the community, using robust random sampling strategies, and quality assured spirometry undertaken by field workers.\n\nFor further details, see: https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/estimating-chronic-respiratory-disease-burden",
    "url": "https://healthdatagateway.org/en/dataset/225",
    "uid": "56130df6-1880-4600-a7a4-afa9a4bcc895",
    "datasource_id": 225,
    "source": "HDRUK"
  },
  {
    "id": 1087,
    "name": "Assessing the feasibility and effectiveness of introducing pulse oximetry",
    "description": "Integrated Management of Childhood Illness (IMCI) is a global strategy, developed by WHO and UNICEF, for the management of common childhood illnesses, including pneumonia, in low-resource settings. IMCI guides a service provider to follow a step-by-step approach in history taking, clinical assessment, classification of the illness and treatment for a sick child. IMCI classifications depend on the clinical assessment skills of service providers and this subjectivity might lead to misclassification and inappropriate referral/treatment.\n\nHypoxemia (low levels of oxygen in the blood) is one of the strongest predictors of mortality due to pneumonia in children. Pulse oximetry (PO) is a non-invasive method for monitoring a person's blood oxygen level. The integration of PO in existing IMCI services could improve the accuracy of pneumonia diagnosis and treatment.\n\nThere are several health systems barriers and operational challenges associated with introducing a new technology, like PO, in developing country settings.\n\nThe aim of the project is to assess the feasibility, acceptability and operational challenges of introducing PO in IMCI services through routine providers at first-level primary care health facilities in Bangladesh.\n\nThe embedded PhD aims to assess the feasibility, acceptability and operational challenges of introducing PO in IMCI services at first-level primary care health facilities in Bangladesh, as well as evaluating its effectiveness and cost-effectiveness.\n\nFor further information, see associated media section.\n\nalso https://www.ed.ac.uk/usher/respire/phd-studentships/ahmed-ehsanur-rahman for further details",
    "url": "https://healthdatagateway.org/en/dataset/226",
    "uid": "2f36b7b3-e50f-459f-a691-f62dde65e2bd",
    "datasource_id": 226,
    "source": "HDRUK"
  },
  {
    "id": 1088,
    "name": "Introducing pulse oximetry in IMNCI, primary health facilities in Pune India",
    "description": "The aim of the project is to assess the feasibility of introducing pulse oximetry (PO) in the Integrated Management of Childhood Illness (IMCI) programme in Pune and explore barriers to and facilitators of effective implementation with assessment of the readiness of the public health system.\n\nPneumonia is a leading cause of childhood mortality, accounting for 16% of under-five deaths globally – with the majority occurring in low- and middle-income countries.\n\nFor the management of common childhood illnesses, including pneumonia, in low-resource settings, adoption of Integrated Management of Childhood Illness (IMCI) is globally recommended. IMCI classifications depend on the clinical assessment skills of service providers and this subjectivity might lead to misclassification and inappropriate referral/treatment.\n\nHypoxemia (low levels of oxygen in the blood) is one of the strongest predictors of pneumonia mortality. Hypoxemia can be measured simply and effectively using pulse oximetry (PO).\n\nThe latest IMCI guidelines, from the World Health Organization (WHO), include the use of PO to classify severity of pneumonia in children and manage accordingly. Current IMCI practices in Pune district do not include PO in the treatment algorithms.\n\nThe aim of the project is to assess the feasibility of introducing PO in the IMCI programme in Pune and explore barriers to and facilitators of effective implementation with assessment of the readiness of the public health system.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/acute-respiratory-disorders/pulse-oximetry-india\"",
    "url": "https://healthdatagateway.org/en/dataset/216",
    "uid": "dae5b81c-db28-414b-9328-5596632a8732",
    "datasource_id": 216,
    "source": "HDRUK"
  },
  {
    "id": 1089,
    "name": "Care-seeking practices for childhood pneumonia in non/tribal areas of Pune India",
    "description": "The doctoral thesis aims to determine care-seeking practices and identify influencing factors of and barriers to care-seeking for pneumonia in children aged between 2-59 months residing in tribal and non-tribal rural areas in Pune district, India.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/phd-studentships/sudipto-roy",
    "url": "https://healthdatagateway.org/en/dataset/218",
    "uid": "4346676d-6108-47de-b579-6cef74c6acef",
    "datasource_id": 218,
    "source": "HDRUK"
  },
  {
    "id": 1090,
    "name": "Estimating Chronic Respiratory Disease (Asthma and COPD) Burden in Adults",
    "description": "The dataset contains quantitative and qualitative datasets. Quantitative dataset have questionnaire and spirometry data collected from randomly selected adult population. Whereas the qualitative data contains in depth interview data collected from patient, physician and different stakeholders from private and public health system in rural part of India. The purpose of this dataset collected is to assess the burden of CRD and its social and economic impact on the community and health system.\n\nChronic Respiratory Diseases (CRD) especially asthma & Chronic Obstructive Pulmonary Disease (COPD) are common public health problems with high prevalence and mortality rates across the world. Although the majority of deaths and disability occur in developing countries, there is very little data on the true prevalence of asthma and COPD in these countries. Chronic respiratory symptoms are common in the general population but weak primary health care systems in resource-poor countries are often unable to diagnose the underlying disease condition. Factors contributing to low rates of diagnosis include limited access to, negative perceptions of, and lack of diagnostic capability in healthcare facilities. Determining the prevalence of asthma & COPD in the community has remained a challenge because of the poor sensitivity and specificity of the widely used questionnaire-based research tools, while spirometry, which is the gold standard diagnostic test, is a challenge to use in community-based epidemiological surveys. \n\nInformed by a scoping review of existing questionnaires and protocols used in Low- and Middle-Income Countries (LMICs) to identify CRDs, we conducted a pilot survey to explore feasibility of using screening process in four countries (Bangladesh, India, Malaysia, and Pakistan). Our pilot findings will inform a future fully powered survey to determine the prevalence of asthma, COPD and other CRD in the community, using robust random sampling strategies, and quality assured spirometry undertaken by field workers.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/chronic-respiratory-disorders/estimating-chronic-respiratory-disease-burden",
    "url": "https://healthdatagateway.org/en/dataset/219",
    "uid": "490747bf-b268-41f3-8f35-f6bc4a4bdbfa",
    "datasource_id": 219,
    "source": "HDRUK"
  },
  {
    "id": 1091,
    "name": "Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II)",
    "description": "Introduction: Following the emergence of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019 and the ensuing COVID-19 pandemic, population-level surveillance and rapid assessment of the effectiveness of existing or new therapeutic or preventive interventions are required to ensure that interventions are targeted to those at highest risk of serious illness or death from COVID-19. We aim to repurpose and expand an existing pandemic reporting platform to determine the attack rate of SARS-CoV-2, the uptake and effectiveness of any new pandemic vaccine (once available) and any protective effect conferred by existing or new antimicrobial drugs and other therapies.\n\nMethods and analysis: A prospective observational cohort will be used to monitor daily/weekly the progress of the COVID-19 epidemic and to evaluate the effectiveness of therapeutic interventions in approximately 5.4?million individuals registered in general practices across Scotland. A national linked dataset of patient-level primary care data, out-of-hours, hospitalisation, mortality and laboratory data will be assembled. The primary outcomes will measure association between: (A) laboratory confirmed SARS-CoV-2 infection, morbidity and mortality, and demographic, socioeconomic and clinical population characteristics; and (B) healthcare burden of COVID-19 and demographic, socioeconomic and clinical population characteristics. The secondary outcomes will estimate: (A) the uptake (for vaccines only); (B) effectiveness; and (C) safety of new or existing therapies, vaccines and antimicrobials against SARS-CoV-2 infection. The association between population characteristics and primary outcomes will be assessed via multivariate logistic regression models. The effectiveness of therapies, vaccines and antimicrobials will be assessed from time-dependent Cox models or Poisson regression models. Self-controlled study designs will be explored to estimate the risk of therapeutic and prophylactic-related adverse events.\n\nhttps://bmjopen.bmj.com/content/10/6/e039097",
    "url": "https://healthdatagateway.org/en/dataset/220",
    "uid": "25bc9862-5b6c-4dda-80b2-46642d9e518a",
    "datasource_id": 220,
    "source": "HDRUK"
  },
  {
    "id": 1092,
    "name": "Sero-surveillance to monitor the trend of SARS-CoV-2 infection in rural India",
    "description": "\"This globally relevant study aims to estimate the seroprevalence of SARS-CoV-2 infection in a rural population in India. The study will help to determine the burden of COVID-19 infection at the community level and monitor the trends in transmission of the infection. The study will also examine the risk factors for infection, and the morbidity status and quality of life in individuals with SARS-CoV-2 seropositivity.\n\nThe project findings will be useful to guide the design and implementation of appropriate interventions and containment measures in India, with the potential to inform other low-and-middle income country responses.\n\nFor further details, see: https://www.ed.ac.uk/usher/respire/covid-19/sero-surveillance-monitor-transmission\"",
    "url": "https://healthdatagateway.org/en/dataset/212",
    "uid": "185e9c5c-e686-4174-bcba-5e2f584b5cbb",
    "datasource_id": 212,
    "source": "HDRUK"
  },
  {
    "id": 1093,
    "name": "Exogenous sex steroid hormones and asthma in females of reproductive age",
    "description": "<p>Background: Despite well-described sex differences in asthma incidence, there remains uncertainty about the role of female sex hormones in the development of asthma. Objective: We sought to investigate whether hormonal contraceptive use, its subtypes, and duration of use were associated with new-onset asthma in reproductive-age women. Methods: Using the Optimum Patient Care Research Database, a UK national primary care database, we constructed an open cohort of 16- to 45-year-old women (N = 564,896) followed for up to 17 years (ie, January 1, 2000, to December 31, 2016). We fitted multilevel Cox regression models to analyze the data. Results: At baseline, 26% of women were using any hormonal contraceptives. During follow-up (3,597,146 person-years), 25,288 women developed asthma, an incidence rate of 7.0 (95% CI, 6.9-7.1) per 1000 person-years. Compared with nonuse, previous use of any hormonal contraceptives (hazard ratio [HR], 0.70; 95% CI, 0.68-0.72), combined (HR, 0.70; 95% CI, 0.68-0.72), and progestogen-only therapy (HR, 0.70; 95% CI, 0.67-0.74) was associated with reduced risk of new-onset asthma. For current use, the estimates were as follows: any (HR, 0.63; 95% CI, 0.61-0.65), combined (HR, 0.65; 95% CI, 0.62-0.67), and progestogen-only therapy (HR, 0.59; 95% CI, 0.56-0.62). Longer duration of use (1-2 years: HR, 0.83; 95% CI, 0.81-0.86; 3-4 years: HR, 0.64; 95% CI, 0.61-0.67; 5+ years: HR, 0.46; 95% CI, 0.44-0.49) was associated with a lower risk of asthma onset than nonuse. Conclusions: Hormonal contraceptive use was associated with reduced risk of new-onset asthma in women of reproductive age. Mechanistic investigations to uncover the biological processes for these observations are required. Clinical trials investigating the safety and effectiveness of hormonal contraceptives for primary prevention of asthma will be helpful to confirm these results. </p>\n\nFor more information, please see: https://pubmed.ncbi.nlm.nih.gov/32305347/?from_term=Nwaru+bi&from_sort=date&from_pos=2",
    "url": "https://healthdatagateway.org/en/dataset/214",
    "uid": "bc5d6d69-ec03-42fe-a63b-638c25c5258c",
    "datasource_id": 214,
    "source": "HDRUK"
  },
  {
    "id": 1094,
    "name": "4 Country ChrOnic Respiratory Disease study: PILOT PHASE, Islamabad",
    "description": "\"In this project, we collected clinical data of two groups of patients. We initially recruited 100 participants and collected some initial data. This data was then passed to the team led by Sandeep Salvi in India, who requested AAIP to collect further details from 43 of these 100 participants. Hence the 43 participants from whom further data was collected was a subset of the overall 100 participants that were initially recruited by AAIP.  We will describe them one by one below:\nGroup of 100 persons\nFor this group of persons, we collected the following information:\n•\tClinical histories of study participants stored in excel files including sensitive information like gender and age.\n•\tThe results of their breathing tests (Spirometry readings) as excel graphs converted to PDFs.\nSubgroup of 43 patients\nOut of these 100 people, 43 were recommended by the researchers (Prof Sandeep Salvi) for further investigation, and the following additional data was collected. \n1.\tX-ray reports as image files.\n2.\tX-ray images\n3.\tClinical notes of specialist doctors when they examined these group of patients. These will be image files of their handwritten notes. \n\nAll highly sensitive information in clinical histories like name and phone number, address etc have been replaced with numeric codes to avoid any risk to privacy\"",
    "url": "https://healthdatagateway.org/en/dataset/209",
    "uid": "a6ea6185-2e2e-4738-b7f1-dc052eae902e",
    "datasource_id": 209,
    "source": "HDRUK"
  },
  {
    "id": 1095,
    "name": "EMEP4UK Pollution",
    "description": "The EMEP4UK model framework consists of an atmospheric chemistry transport model (ACTM) which simulates hourly to annual average atmospheric composition and deposition of various pollutants and the weather research and forecast model (WRF). Dry and wet deposition of pollutants are routinely calculated by the model. Coverage is comprehensive - EMEP4UK is capable of representing the UK hourly atmospheric composition at a horizontal scale ranging from 100 km to 1 km on pollutants.",
    "url": "https://healthdatagateway.org/en/dataset/206",
    "uid": "eb76598f-e228-413c-82f3-231809062611",
    "datasource_id": 206,
    "source": "HDRUK"
  },
  {
    "id": 1096,
    "name": "Asthma and COPD: practices and perceptions of GPs in rural India",
    "description": "This datasets contains quantitative and qualitative data. Quantitative data is collected from GPs practising in study area to understand their qualification and type of practice and their involvement in asthma & COPD diagnosis and management. Qualitative data is collected from few GPs (using in depth interview tools) in study area who has good experience about diagnosis and management of asthma & COPD. Their perceptions and practices about their clinical practice are documented.\n\nLong-term lung conditions, such as asthma and chronic obstructive pulmonary disease (COPD), are very common and can affect people’s well-being, and ability to work and care for their families.\n\nRespiratory symptoms are common but weak primary health care systems in resource-poor countries are often unable to diagnose the underlying disease condition, leading to inappropriate treatment. A lack of diagnosis also means the true burden of the disease is not appreciated.\n\nTo improve treatment of these conditions, we first need to understand how primary care doctors (who provide the majority of healthcare for rural populations in India) diagnose and treat people with chest symptoms, what makes it difficult for them to provide good care and how they think care can be improved.\n\nTo work with primary care doctors in the Pune district in India, to determine their thoughts on the diagnosis and management of lung conditions.\n\nFor further information, see: https://www.ed.ac.uk/usher/respire/respire-fellowships/dhiraj-agarwal",
    "url": "https://healthdatagateway.org/en/dataset/207",
    "uid": "c15bc3ad-c053-496b-863f-1d54075d5bd4",
    "datasource_id": 207,
    "source": "HDRUK"
  },
  {
    "id": 1097,
    "name": "REal-time Assessment of Community Transmission (REACT-2)",
    "description": "REal-time Assessment of Community Transmission (REACT-2) started in May 2020 to determine the prevalence of and trends in antibodies levels in study participants. This study involves approximately 150,000 unique people who use a finger prick test over 6 week periods, with additional information collected on contact with known cases to assess an infection point prevalence at national, regional and local levels. Within REACT 2 there is also a study on usability and efficacy of different tests.\n\nImperial College London is leading a major programme of home testing for COVID-19 to track the progress of the infection across England. Called REACT, the programme was commissioned by the Department of Health and Social Care, and is being carried out in partnership with Imperial College Healthcare NHS Trust and Ipsos MORI.\n\nREACT-2 is a world largest surveillance study undertaken in England that examines the prevalence of antibodies in the community. The study focusses on finger prick self-testing at home by individuals aged 18 or over.The findings will provide the government with a better understanding of the use of antibody tests at home as well as assess the trends in antibody levels and how they vary across different population subgroups. This will inform government policies to protect health and save lives.",
    "url": "https://healthdatagateway.org/en/dataset/204",
    "uid": "25003f25-384c-4264-9c8f-dd8734ee5595",
    "datasource_id": 204,
    "source": "HDRUK"
  },
  {
    "id": 1098,
    "name": "REal-time Assessment of Community Transmission (REACT-1)",
    "description": "REal-time Assessment of Community Transmission (REACT-1) is one of the largest population surveillance studies in the world.  It started in April 2020 to measure the prevalence of SARS-CoV-2 in the general population in England. Each month around 150,000 people completed a questionnaire and returned a PCR test.  \n\nThe study tracked the progress of infection across England. It was commissioned by the Department of Health and Social Care and was carried out between April 2020 and April 2022 in partnership with Imperial College Healthcare NHS Trust and Ipsos MORI.\n\nThe partner study, REal-time Assessment of Community Transmission (REACT-2), started in May 2020 to determine the prevalence of and trends in antibodies levels in study participants. This study is also described in the Innovation Gateway.\n\n \n\nFor further information and the study questionnaires, please see the REACT study website.\n\n\nWe present the full meta-data catalogue in the Innovation Gateway. Researchers external to Imperial College London can apply to access subsets of anonymised data from REACT participants. To access the full REACT dataset on Imperial&#039;s TRE, researchers require an affiliation or collaboration with Imperial College London.\n\nREACT has a data sharing agreement with NHS England, details of which can be found under agreement DARS-NIC-431352-G7F1M-v2.2 via https://digital.nhs.uk/services/data-access-request-service-dars/data-uses-register",
    "url": "https://healthdatagateway.org/en/dataset/205",
    "uid": "59a98ace-623d-47ce-83ec-8fd63ffb839c",
    "datasource_id": 205,
    "source": "HDRUK"
  },
  {
    "id": 1099,
    "name": "COMPARE",
    "description": "The COMPARE dataset comprises of genomic and blood data from the COMPARE clinical trial run by the University of Cambridge and NHS Blood and Transplant. \n\nBackground: To safeguard donors, blood services measure haemoglobin concentration in advance of each donation. NHS Blood and Transplant&amp;amp;amp;amp;amp;amp;#039;s (NHSBT) customary method have been capillary gravimetry (copper sulphate), followed by venous spectrophotometry (HemoCue) for donors failing gravimetry. However, NHSBT&amp;amp;amp;amp;amp;amp;#039;s customary method results in 10% of donors being inappropriately bled (ie, with haemoglobin values below the regulatory threshold).\n\nMethods: We compared the following four methods in 21 840 blood donors (aged &amp;amp;amp;amp;amp;amp;ge;18 years) recruited from 10 NHSBT centres in England, with the Sysmex XN-2000 haematology analyser, the reference standard: (1) NHSBT&amp;amp;amp;amp;amp;amp;#039;s customary method; (2) &amp;amp;amp;amp;amp;amp;quot;post donation&amp;amp;amp;amp;amp;amp;quot; approach, that is, estimating current haemoglobin concentration from that measured by a haematology analyser at a donor&amp;amp;amp;amp;amp;amp;#039;s most recent prior donation; (3) &amp;amp;amp;amp;amp;amp;quot;portable haemoglobinometry&amp;amp;amp;amp;amp;amp;quot; (using capillary HemoCue); (4) non-invasive spectrometry (using MBR Haemospect or Orsense NMB200). We assessed sensitivity; specificity; proportion who would have been inappropriately bled, or rejected from donation (&amp;amp;amp;amp;amp;amp;quot;deferred&amp;amp;amp;amp;amp;amp;quot;) incorrectly; and test preference.\n\nResults: Compared with the reference standard, the methods ranged in test sensitivity from 17.0% (MBR Haemospect) to 79.0% (portable haemoglobinometry) in men, and from 19.0% (MBR Haemospect) to 82.8% (portable haemoglobinometry) in women. For specificity, the methods ranged from 87.2% (MBR Haemospect) to 99.9% (NHSBT&amp;amp;amp;amp;amp;amp;#039;s customary method) in men, and from 74.1% (Orsense NMB200) to 99.8% (NHSBT&amp;amp;amp;amp;amp;amp;#039;s customary method) in women. The proportion of donors who would have been inappropriately bled ranged from 2.2% in men for portable haemoglobinometry to 18.9% in women for MBR Haemospect. The proportion of donors who would have been deferred incorrectly with haemoglobin concentration above the minimum threshold ranged from 0.1% in men for NHSBT&amp;amp;amp;amp;amp;amp;#039;s customary method to 20.3% in women for OrSense. Most donors preferred non-invasive spectrometry.\n\nConclusion: In the largest study reporting head-to-head comparisons of four methods to measure haemoglobin prior to blood donation, our results support replacement of NHSBT&amp;amp;amp;amp;amp;amp;#039;s customary method with portable haemoglobinometry.",
    "url": "https://healthdatagateway.org/en/dataset/200",
    "uid": "8cbb73e2-a2c4-4caf-8dcd-6ac3795bb996",
    "datasource_id": 200,
    "source": "HDRUK"
  },
  {
    "id": 1100,
    "name": "INTERVAL",
    "description": "In over 100 years of blood donation practice, INTERVAL is the first randomised controlled trial to assess the impact of varying the frequency of blood donation on donor health and the blood supply. It provided policy-makers with evidence that collecting blood more frequently than current intervals can be implemented over two years without impacting on donor health, allowing better management of the supply to the NHS of units of blood with in-demand blood groups. INTERVAL was designed to deliver a multi-purpose strategy: an initial purpose related to blood donation research aiming to improve NHS Blood and Transplant&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;rsquo;s core services and a longer-term purpose related to the creation of a comprehensive resource that will enable detailed studies of health-related questions.\n\nApproximately 50,000 generally healthy blood donors were recruited between June 2012 and June 2014 from 25 NHS Blood Donation centres across England. Approximately equal numbers of men and women; aged from 18-80; ~93% white ancestry. All participants completed brief online questionnaires at baseline and gave blood samples for research purposes. Participants were randomised to giving blood every 8/10/12 weeks (for men) and 12/14/16 weeks (for women) over a 2-year period. ~30,000 participants returned after 2 years and completed a brief online questionnaire and gave further blood samples for research purposes. \n\nThe baseline questionnaire includes brief lifestyle information (smoking, alcohol consumption, etc), iron-related questions (e.g., red meat consumption), self-reported height and weight, etc. The SF-36 questionnaire was completed online at baseline and 2-years, with a 6-monthly SF-12 questionnaire between baseline and 2-years.\n\nAll participants have had the Affymetrix Axiom UK Biobank genotyping array assayed and then imputed to 1000G+UK10K combined reference panel (80M variants in total). 4,000 participants have 50X whole-exome sequencing and 12,000 participants have 15X whole-genome sequencing. Whole-blood RNA sequencing has commenced in ~5,000 participants.\n\nThe dataset also contains data on clinical chemistry biomarkers, blood cell traits, &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt;200 lipoproteins, metabolomics (Metabolon HD4), lipidomics, and proteomics (SomaLogic, Olink), either cohort-wide or is large sub-sets of the cohort.",
    "url": "https://healthdatagateway.org/en/dataset/201",
    "uid": "c1fa9b47-a954-4587-a0c2-bae0f3ec2b9b",
    "datasource_id": 201,
    "source": "HDRUK"
  },
  {
    "id": 1101,
    "name": "TRACK-COVID",
    "description": "Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus stimulates a rapid antibody response in people with symptomatic and asymptomatic infection. Seroprevalence of SARS-CoV-2 antibodies in a population can serve as a useful measure of exposure and spread.To help plan key aspects of the public health response (e.g., shielding; eventual vaccination implementation strategies), decision-makers need regularly updated data on the evolution of SARS-CoV-2 infection (and potential immunity) in the community. The rationale is that serial longitudinal seroepidemiological surveys can help to quantify and monitor the proportion of the population that has antibodies against SARS-CoV-2, providing information on the proportion of the population exposed and the cumulative incidence of infection in the population. Hence, there is a strategic need for data from serial sero-survey studies of SARS-CoV-2 in UK.\n\nTo provide scientists and national decision-makers with detailed information to help control and understand the novel coronavirus pandemic (“COVID-19”), this study aims to track up to 90,000 individuals across England during the coming the year or so. Individuals who participated in the INTERVAL, COMPARE or STRIDES BioResource studies will be invited to take part.\n\nThe TRACK-COVID study is being conducted by researchers at the University of Cambridge to investigate why some people have symptoms of the new coronavirus (SARS-CoV-2 virus) and others don’t will help to determine the extent of infection in the general population as well as it will help to design new ways to prevent and treat such infections.\n\nThe aim of this research is to determine risk factors for infection of the novel coronavirus (COVID-19). The secondary aim to investigate why some people who carry the virus are symptomatic while others never are.\n\nThe research will provide a better understanding of the biological and environmental determinants of COVID-19 virus.",
    "url": "https://healthdatagateway.org/en/dataset/202",
    "uid": "9e89af6b-bfa8-43dc-a2d4-5540c899a670",
    "datasource_id": 202,
    "source": "HDRUK"
  },
  {
    "id": 1102,
    "name": "Hospitalised Community Acquired Pneumonia granular pathway and outcome data",
    "description": "Background \n\nCommunity acquired pneumonia (CAP) is a leading cause of hospital admission, and in older adults has high rates of mortality and complications. CAP is associated with increased long-term mortality and loss of independence for older adults. CAP typically affects older adults with co-morbidities.  Complications such as sepsis, and empyema (infected fluid around the lung) prolong hospital admission, result in additional interventions in hospital and have higher mortality than CAP alone. The causative agents for CAP are often poorly identified in real world clinical practice.  \n\nThe treatment of patients with CAP is complex. Key decisions relate to the antibiotics used, the way antibiotics are given (in a tablet or by a drip) and the place of care (home, hospital and in hospital, a normal ward or intensive care).  These data will allow analyses on differing antimicrobial treatments and outcomes, as well as differing pathways of care.  This data has been constructed to support machine learning including algorithm generation and testing models. \n\nPIONEER geography \nThe West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix.  \n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”. \n\nScope: All patients admitted to hospital from 2000 because of Community Acquired Pneumonia. Longitudinal and individually linked, so that the preceding and subsequent health journey can be mapped and healthcare utilisation prior to and after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to process of care (timings and admissions), presenting complaints, therapy, ventilation route, assessments components (AMT10, falls, MMS, thrombosis and waterlow), physiology readings (temperature, blood pressure, respiratory rate, NEWS2 score, oxygen saturations, AVPU scale and others), Sample analysis results (bilirubin, urea, albumin, platelets, white blood cells and others) drug administered and all outcomes. Linked images available (radiographs, CT scans, MRI). \n\nAvailable supplementary data: CAP admission data from 2000 onwards. Matched controls; ambulance, OMOP data, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation and refinement; A.I.; Data partner support for ETL (extract, transform and load) process, Clinical expertise, Patient and end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/198",
    "uid": "6225bc5d-ace2-498f-8a90-2d671771bc65",
    "datasource_id": 198,
    "source": "HDRUK"
  },
  {
    "id": 1103,
    "name": "Antimicrobial prescribing surveillance data during the COVID-19 pandemic",
    "description": "The use of antimicrobial drugs is linked to antimicrobial resistance which can lead to infections that are harder to treat and may be associated with worse outcomes for the patient.  \n\nThe use of antibiotics changed in hospital during the different waves of the COVID-19 pandemic, as data was used to assess if antibiotic therapy was associated with better health outcomes for patients with confirmed COVID-19. Looking at changes in health outcomes linked to antibiotic therapy across the whole hospital instead of only patients with COVID-19 over time will help understand if changes to antibiotic use during the pandemic may have had an impact on the risk of antibiotic resistance. \n\nPIONEER geography The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix.  \n\nEHR: UHB is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”. \n\nScope:  Longitudinal and individually linked, so that the preceding and subsequent health journey can be mapped and healthcare utilisation prior to and after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, wards and admissions), surgery procedures, microbiology reports, COVID results, prescriptions, drug administered and all outcomes. \n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation and refinement; A.I.; Data partner support for ETL (extract, transform and load) process, Clinical expertise, Patient and end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/194",
    "uid": "a95beeb8-9316-442c-8699-22fc4b2170e5",
    "datasource_id": 194,
    "source": "HDRUK"
  },
  {
    "id": 1104,
    "name": "A dataset of hospitalised patients with Sarcoma",
    "description": "Background \n\nSarcomas are uncommon cancers that can affect any part of the body. There are many different types of sarcoma and subtypes can be grouped into soft tissue or bone sarcomas. About 15 people are diagnosed every day in the UK. 3 in every 200 people with cancer in the UK have sarcoma. \n\nA highly granular dataset with a confirmed sarcoma event including hospital presentation, serial physiology, demography, treatment prescribed and administered, prescribed and administered drugs. The infographic includes data from 27/12/2004 to 31/12/2021 but data is available from the past 10 years+.  \n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.  \n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nScope: All hospitalised patients from 2004 onwards, curated to focus on Sarcoma. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards and triage). Along with presenting complaints, outpatients admissions, microbiology results, referrals, procedures, therapies, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations and others), and all blood results (urea, albumin, platelets, white blood cells and others). Includes all prescribed & administered treatments and all outcomes.  Linked images are also available (radiographs, CT scans, MRI). \n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/195",
    "uid": "6dcd9c0e-d5f8-46f9-9d4a-335943e46c21",
    "datasource_id": 195,
    "source": "HDRUK"
  },
  {
    "id": 1105,
    "name": "Hospitalised and ventilator acquired pneumonia severity, treatments, outcomes",
    "description": "Background: Hospital-Acquired pneumonia (HAP) and Ventilator-Associated pneumonia is an infection of the lungs that is contracted by a patient 2 or more days after an admission.  HAP is often more serious than other lung infections due to the nature of the bacteria present in hospital settings, as they are more resistant to treatment than those in the community. HAP typically affects older patients with co-morbidities, those with weakened immune systems or a long-term chronic lung disease.  Ventilator-Associated pneumonia is the most common infection associated with a stay in intensive care, with increased long-term mortality and length of stays.  These data allow the investigation of the sensitivities of the bacteria, which antibiotics were administered and patient outcomes.  The period of data available allows for studying pre- and post- COVID-19 and the impact of ventilation.  \nPIONEER geography: The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix. \nEHR: UHB is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”.\nScope: All hospitalised patients from 2000 onwards, curated to focus on Hospital and ventilator acquired pneumonia. Longitudinal and individually linked, so that the preceding and subsequent health journey can be mapped and healthcare utilisation prior to and after admission understood. The dataset includes highly granular patient demographics and co-morbidities taken from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards and triage). Along with presenting complaints, outpatients admissions, microbiology results, referrals, procedures, therapies,  all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations and others), and all blood results(urea, albumin, platelets, white blood cells and others). Includes all prescribed and administered treatments and all outcomes.  Linked images are also available (radiographs, CT scans, MRI).\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data.\nAvailable supplementary support: Analytics, Model build, validation and refinement; A.I.; Data partner support for ETL (extract, transform and load) process, Clinical expertise, Patient and end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/196",
    "uid": "99dc4159-00d0-4874-b158-fe30244b53a1",
    "datasource_id": 196,
    "source": "HDRUK"
  },
  {
    "id": 1106,
    "name": "A synthetic dataset of 15,000 \"patients\" with Community Acquired Pneumonia (CAP)",
    "description": "Community Acquired Pneumonia (CAP) is the leading cause of infectious death and the third leading cause of death globally. Disease severity and outcomes are highly variable, dependent on host factors (such as age, smoking history, frailty and comorbidities), microbial factors (the causative organism) and what treatments are given. Clinical decision pathways are complex and despite guidelines, there is significant national variability in how guidelines are adhered to and patient outcomes.\n\nFor clinicians treating pneumonia in the hospital setting, care of these patients can be challenging. Key decisions include the type of antibiotics (oral or intravenous), the appropriate place of care (home, hospital or intensive care), and when it is appropriate to stop antibiotics. Decision support tools to help inform clinical management would be highly valuable to the clinical community.\n\nThis dataset is synthetic, formed from statistical modelling using real patient data, and represents a population with significant diversity in terms of patient demography, socio-economic status, CAP severity, treatments and outcomes. It can be used to develop code for deployment on real data, train data analysts and increase familiarity with this disease and its management.\n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. \n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.  This synthetic dataset has been modelled to reflect data collected from this EHR.\n\nScope: A synthetic dataset which has been statistically modelled on all hospitalised patients admitted to UHB  with Community Acquired Pneumonia. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care including timings, admissions, escalation of care to ITU, discharge outcomes, physiology readings (heart rate, blood pressure, AVPU score and others), blood results and drug prescribing and administration.\n\nAvailable supplementary data: Matched synthetic controls; ambulance, OMOP data, real patient CAP data.\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/197",
    "uid": "a6575d98-0b2b-4c06-b9c6-88c1729257b9",
    "datasource_id": 197,
    "source": "HDRUK"
  },
  {
    "id": 1107,
    "name": "An NIHR Birmingham BRC dataset of severity scores and outcomes in critical care.",
    "description": "A highly granular dataset of 21,581 critical care admissions, curated by the NIHR Birmingham Biomedical Research Centre Infection and Acute Care Theme in collaboration with PIONEER. The data includes initial presentation, presenting symptoms, and several pre-calculated severity scoring systems including Simple Acute Physiology Score (SAPS), the Acute Physiology and Chronic Health Evaluation (APACHE) and the Sequential Organ Failure Assessment (SOFA) score. Data includes demography, serial physiology, ventilatory parameters, investigations, treatments (drug, dose, route), diagnostic codes (ICD-10 & SNOMED-CT) and outcomes, following patients for one year. This can be supplemented with imaging (results and images) and linked to ambulance conveyance and longer-term outcomes in the community. The current dataset includes admissions from 2017 to 2023 but can be expanded to assess other timelines of interest.\n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n    \nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/188",
    "uid": "ea03d4e1-73e8-4d84-b93a-a41febf73fb4",
    "datasource_id": 188,
    "source": "HDRUK"
  },
  {
    "id": 1108,
    "name": "Immune Checkpoint Inhibitors synthetic data: HDR UK Medicines Programme resource",
    "description": "This highly granular synthetic dataset created as an asset for the HDR UK Medicines programme includes information on 680 cancer patients over a period of three years. Includes simulated patient-related data, such as demographics & co-morbidities extracted from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to acute care process (readmissions, survival), primary diagnosis, presenting complaint, physiology readings, blood results (infection, inflammatory markers) and acuity markers such as AVPU Scale, NEWS2 score, imaging reports, prescribed & administered treatments including fluids, blood products, procedures, information on outpatient admissions and survival outcomes following one-year post discharge.\n\nThe data was generated using a generative adversarial network model (CTGAN). A flat real data table was created by consolidating essential information from various key relational tables (medications, demographics). A synthetic version of the flat table was generated using a customized script based on the SDV package (N. Patki, 2016), that replicated the real distribution and logic relationships.\n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and provide the real-data via application.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/189",
    "uid": "43b11885-5eb4-4e08-943d-c915526a16c7",
    "datasource_id": 189,
    "source": "HDRUK"
  },
  {
    "id": 1109,
    "name": "Urinary tract infections acute presentations: microbiology, treatment, outcome",
    "description": "Background: A urinary tract infection (UTI) is a common infection that affects the bladder (cystitis), urethra (urethritis) or kidneys (pyelonephritis). They are more prevalent in women, and the incidence in women over 65 years old is double the rate compared with the overall female population. Catheterisation is known to affect the likelihood of infection. The National Institute for Clinical Excellence suggests that UTIs in the elderly are often over-diagnosed and over-treated. This has led to NHS England requiring a reduction in the number of Trimethoprim prescriptions prescribed to patients over 70 years old. \n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix.  \n\nEHR: UHB is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”. \n\nScope: All hospitalised patients from 2000 onwards, curated to focus on Urinary tract infection. Longitudinal and individually linked, so that the preceding and subsequent health journey can be mapped and healthcare utilisation prior to and after admission understood. The dataset includes highly granular patient demographics and co-morbidities taken from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards and triage). Along with presenting complaints, outpatients admissions, microbiology results, referrals, procedures, therapies, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations and others), all blood results (urea, albumin, platelets, white blood cells and others). This dataset includes all prescribed and administered treatments including antibiotics, bacterial resistance patterns from microbiology assessments and outcomes. Linked images are also available (radiographs, CT scans, MRI). \n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation and refinement; A.I.; Data partner support for ETL (extract, transform and load) process, Clinical expertise, Patient and end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/190",
    "uid": "ae2192ce-7d75-4cec-8ebd-e07c2eacb7c3",
    "datasource_id": 190,
    "source": "HDRUK"
  },
  {
    "id": 1110,
    "name": "The impact of COVID on hospitalised patients with COPD; a dataset in OMOP",
    "description": "Background.\nChronic obstructive pulmonary disease (COPD) is a debilitating lung condition characterised by progressive lung function limitation.   COPD is an umbrella term and encompasses a spectrum of pathophysiologies including chronic bronchitis, small airways disease and emphysema.  COPD caused an estimated 3 million deaths worldwide in 2016, and is estimated to be the third leading cause of death worldwide.  The British Lung Foundation (BLF) estimates that the disease costs the NHS around £1.9 billion per year. COPD is therefore a significant public health challenge.  This dataset explores the impact of hospitalisation in patients with COPD during the COVID pandemic.\n\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK.  There are particularly high rates of physical inactivity, obesity, smoking & diabetes.  The West Midlands has a high prevalence of COPD, reflecting the high rates of smoking and industrial exposure. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.\n\nEHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nScope: All hospitalised patients admitted to UHB during the COVID-19 pandemic first wave, curated to focus on COPD.  Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood.  The dataset includes ICD-10 & SNOMED-CT codes pertaining to COPD and COPD exacerbations, as well as all co-morbid conditions.  Serial, structured data pertaining to process of care (timings, staff grades, specialty review, wards), presenting complaint, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, nebulisers, antibiotics, inotropes, vasopressors, organ support), all outcomes.  Linked images available (radiographs, CT).\n\nAvailable supplementary data:\nMore extensive data including wave 2 patients in non-OMOP form. Ambulance, 111, 999 data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/191",
    "uid": "323dc330-aad9-472a-b579-ab66f5f75906",
    "datasource_id": 191,
    "source": "HDRUK"
  },
  {
    "id": 1111,
    "name": "Longitudinal hospital prescribing data for >48,000 deeply phenotyped patients",
    "description": "Background. The Healthcare Safety Investigation Branch (HSIB) published a report in 2020 reviewing the need to have a better method of identifying and preventing medication errors. 237 million medications errors occur in England per year. 5% of hospital admissions are related to medication errors, side effects or drug/drug interactions. Older patients, those with multiple long-term conditions and polypharmacy are most likely to experience the worse outcomes from medicine related harm. This dataset provides highly detailed medicine prescribing, indication, administration and patient outcome data, focusing on hospitalised patients in acute care.\n\nPIONEER geography. The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix.\n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”. \n\nScope: All hospitalised patients in UHB Acute Medicine (AMU) and Emergency Departments (ED) from November 2017 to October 2020, curated to focus on medicines reconciliation. Longitudinal and individually linked, so that the preceding and subsequent health journey can be mapped and healthcare utilisation prior to and after admission understood. The dataset includes highly granular patient demographics and co-morbidities taken from ICD-10. Serial, structured data pertaining to acute care process (timings and wards). Along with presenting complaints, physiology readings (NEWS 2 and SEWS score). Includes all prescribed treatments, drug history, medication history and pharmacy interventions.\n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation and refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/192",
    "uid": "6c8894f0-3675-4e65-baa8-88ce539b3fd1",
    "datasource_id": 192,
    "source": "HDRUK"
  },
  {
    "id": 1112,
    "name": "Hospitalised patients with diabetic emergencies & acute diabetic health concerns",
    "description": "Background. \n\nDiabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. Each year more than 1 million people with diabetes are acutely admitted to hospital due to complications of their illness. This includes Diabetic emergencies such as Diabetic Comas, Hypoglycaemia, Diabetic ketoacidosis, and Diabetic Hyperosmolar Hyperglycaemic State.   Diabetic emergency management is often not compliant with national guidelines, and there is a pressing need to improve patient care. This dataset includes 65,506 people and 168,706 spells, designed to support research which improves diabetic emergency and unplanned care.\n\nOther causes for admission include diabetic ulcers, neuropathies, kidney disease and associated co-morbidities such as infection, cerebrovascular disease and cardiovascular disease. This dataset includes acute all diabetic admissions to University Hospitals Birmingham NHS Trust from 2000 onwards  refreshed to include new admissions as they occur.\n\nPIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.\n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nScope: All patients admitted to hospital from year 2002 and onwards, curated to focus on Diabetes. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards and triage). Along with presenting complaints, outpatients admissions, microbiology results, referrals, procedures, therapies,  all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations and others), all blood results(urea, albumin, platelets, white blood cells and others). Includes all prescribed & administered treatments and all outcomes.  Linked images are also available (radiographs, CT scans, MRI).\n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/193",
    "uid": "0d556d7e-be27-4979-a09e-d419b2e838f3",
    "datasource_id": 193,
    "source": "HDRUK"
  },
  {
    "id": 1113,
    "name": "Air Quality & Health data: Longitudinal impact of a clean air zone on asthma",
    "description": "This dataset, curated by PIONEER, encompasses a detailed collection of 181,207 asthma admissions from 1st June 2016 to 31st May 2022, offering a comprehensive analysis tool for researchers examining the effects of air quality on respiratory health. It includes extensive patient demographics, serial physiological measurements, assessments, diagnostic codes (ICD-10 and SNOMED-CT), initial presentations, symptoms, and outcomes. Additionally, it integrates DEFRA air pollution data, geographically linked t individual health data, allowing for a nuanced exploration of environmental impacts on asthma incidence and severity. The dataset includes 4 years of data prior to and currently 1 year post introduction of the clean air zone.\n \nThe dataset invites longitudinal studies to evaluate the Clean Air Zones' effectiveness. Timelines post-introduction of the clean air zone can be expanded to include data up to 2024.   Its granular detail provides invaluable insights into emergency medicine, public health policy, and environmental science, supporting targeted interventions and policy formulations aimed at reducing asthma exacerbations and improving air quality standards.\n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/184",
    "uid": "9e8cf85e-0bf6-4d74-94e3-fd6d75832db7",
    "datasource_id": 184,
    "source": "HDRUK"
  },
  {
    "id": 1114,
    "name": "Intentional Self-Poisoning Emergency Admissions presenting to hospital",
    "description": "A highly granular dataset of 11,267 deliberate self-poisoning admissions curated by PIONEER. The data includes demography, diagnostic codes (ICD-10 & SNOMED-CT), presenting symptoms, procedures (OPCS4 & SNOMED-CT, prescriptions, referrals, follow-ups, and outcomes. The current dataset includes admissions from 03-12-2015 to 30-12-2023 but can be expanded to assess other timelines of interest. This dataset provides a clear understanding of self-poisoning in relation to different patient characteristics. The data also provides the type of drug taken (narcotic, non-opiod etc) and where (home, street, industrial areas etc) . Furthermore, assessments can be made on the impact of mental health service referrals and the likelihood of readmission with a subsequent overdose.  \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/185",
    "uid": "c0dadf8d-7821-43dd-bebb-1e1c1f4d5878",
    "datasource_id": 185,
    "source": "HDRUK"
  },
  {
    "id": 1115,
    "name": "Synthetic Dataset- Patients at risk of sudden death: hypertrophic cardiomyopathy",
    "description": "Background:\n\nA PIONEER synthetic dataset of 20,000 ethnically diverse hypertrophic cardiomyopathy patients created using CT-GAN generative AI. Data includes clinical & biological phenotyping, co-morbidities, investigations (ECG, ECHO), procedures & outcomes.\n\nWell-created synthetic data establishes a governance risk-free environment for algorithm development & experimentation. This includes evaluating new treatment models, care management systems, clinical decision support, and more. Synthetic data is of particular use in rare diseases, where real data may be in short supply, or to replicate disease in less common patient demographics (e.g. ethnicities).\n \nFamilial hypertrophic cardiomyopathy (HCM) is a rare genetic condition characterised by thickening (hypertrophy) of the cardiac muscle, usually of the interventricular septum. Arrhythmias can be life threatening and HCM is associated with an increased risk of sudden death. Some affected individuals develop potentially fatal heart failure, which may require heart transplantation. Approximately 130,000 people have HCM in the UK, but there is a significant burden of undiagnosed disease and diagnostic delay.\n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can provide real world data to meet bespoke requirements.\n \nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/186",
    "uid": "7b1c1f1a-4fb2-452d-9a3c-7748992b1fa4",
    "datasource_id": 186,
    "source": "HDRUK"
  },
  {
    "id": 1116,
    "name": "The emergency health care needs of >40,000 patients with complex multimorbidity",
    "description": "This dataset forms part of the OPTIMising therapies, discovering therapeutic targets and AI-assisted clinical management for patients Living with complex multimorbidity (OPTIMAL) NIHR funded programme. \n\nThe dataset includes >40,000 adult patients with multimorbidity who were acutely admitted to hospital and had an inpatient stay.  Longitudinal data includes serial physiology readings, frailty scores, blood results, medications, comorbidities, drug allergies, treatments, procedures and mortality outcomes up to a year post discharge.\n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.  \n\nAll data uses should name both PIONEER and the NIHR Optimal programme in data outputs.  This will be specified in the Data Licensing Agreement.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/187",
    "uid": "5764caa8-c7ca-41b2-89d5-f38f7d977aa7",
    "datasource_id": 187,
    "source": "HDRUK"
  },
  {
    "id": 1117,
    "name": "Emergency hospital admissions in patients from care home settings",
    "description": "Nearly 340,000 older people in England live in residential or nursing care homes. Older people living in care homes often have complex health problems which make them more likely to need hospital care in hospital if their health suddenly deteriorates. People living in care homes account for 185,000 emergency admissions to hospital each year and spend over 1.46 million days in hospital beds. Improving care for older patients living in care homes will directly benefit patients while reducing the demand for hospital beds and reduce the risk of hospital overcrowding. \n\nA significant proportion of hospital admissions from care homes are unnecessary and could be avoided if their needs were addressed differently.  The hospital environment and can be distressing for some older people living in care homes and the burden of transferring patients from their home to hospital can be significant. These factors have driven a search for alternative ways of providing better care. \n\nThis highly granular dataset of 128,000 admissions from care home provides a unique opportunity to understand reasons, pathways and outcomes from acute presentations to hospital.\n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. \n\nElectronic Heath Record. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nScope: Acute care episodes amongst patients aged over 65 from care homes. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards), presenting complaint, physiology readings (heart rate, BMI, blood pressure, respiratory rate, NEWS2 score, oxygen saturations and clinical frailty scale), Charlson comorbidity index, Lab analysis results(e.g. urea, albumin, platelets, white blood cells) microbiology results, procedures, outpatients admissions, oxygen delivery methods, drug administered and all outcomes. Linked images available (radiographs, CT scans, MRI).\n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/177",
    "uid": "6009f720-3cd5-449d-b36b-e2ccf3fbc73a",
    "datasource_id": 177,
    "source": "HDRUK"
  },
  {
    "id": 1118,
    "name": "Granular ICU data focussing on the impact of lactate readings on outcomes",
    "description": "Lactate is a chemical produced by the body as cells consume energy - in times of stress more lactate is produced. In the past, we thought that lactate was just a waste product, but more recently we have learned that lactate has an important role to play in the body. \n\nPeople suffering from certain severe illnesses may have a high ‘lactate’ level in their blood. This is particularly common in the following: \n\nSevere infections which the body cannot properly control (sepsis) \n\nPeople who have sustained severe injuries (traumatic injury) \n\nPeople who are critically unwell with other illnesses (needing treatment in an intensive care unit) \n\nSome patients will develop a high lactate level when they are in hospital. Doctors recognise that this indicates the patient is becoming more unwell, but it is often challenging to know exactly what is causing the lactate level to be raised. \n\nRaised lactate level has been associated with worse outcome in other syndromes, including major trauma and undifferentiated critical illness; however healthy individuals may generate very high lactate levels during strenuous exercise from which they recover without any harm. It is unclear whether lactate in itself is harmful to patients. This dataset provides unique insight into the potential role of lactate as not only a biomarker but a therapeutic target in acute illness. \n\nPIONEER geography The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix.  \n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”. \n\nScope: Longitudinal and individually linked, so that the preceding and subsequent health journey can be mapped and healthcare utilisation prior to and after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards), presenting complaint, physiology readings (BMI, temperature and weight), Sample analysis results (blood sodium level, lactate, haemoglobin, oxygen saturations, and others) drug administered and all outcomes.    \n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform and load) process, Clinical expertise, Patient and end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/178",
    "uid": "edfc20b7-5a5e-40c4-afd0-a7a5bba77003",
    "datasource_id": 178,
    "source": "HDRUK"
  },
  {
    "id": 1119,
    "name": "Cholecystectomy and post-operative complications from over 9000 surgical cases",
    "description": "Cholecystectomy is a common surgical procedure to remove the gall bladder, with over 1.2 million procedures performed in the USA each year. The most common indication is recurrent gall stones. The main operative incidents are haemorrhage, iatrogenic perforation of the gallbladder, and common bile duct (CBD) injuries. The main post-operative complications are sepsis, sub-hepatic abscess, haemorrhage, bile leakage and retained bile duct stones, with sepsis the most common post-operative complications.\n\nSepsis post-surgery is costly to the individual, associated with a reduced quality of life, increased length of stay, pain, loss of function and mortality.  Although risk factors for developing sepsis are recognised, these cannot be applied at an individual level, making it difficult to predict who might develop sepsis, in order to implement mitigation strategies.  \n\nThis dataset includes 9,400 individual surgical cases for cholecystectomy, including both elective and emergency surgery.  The data includes detailed patient demography, measures of socio-economic deprivation, co-morbidities, the surgical indication, all physiological and pathological measurements, the surgery performed, anaesthetic used, medications given, complications and outcomes.   \n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.  \n\nElectronic Health Record: UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nScope: Patients that had an emergency or elective Cholecystectomy procedure during their hospital stay. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards), presenting complaint, physiology readings (e.g. heart rate, blood pressure, respiratory rate, NEWS2 score and oxygen saturations), Lab analysis results (Alanine Transferase, albumin, EGFR, Creatine Kinase, White Blood Cells and others), microbiology results, surgery, medications, complications and all outcomes.   \n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/179",
    "uid": "4abba8ec-9a67-4334-91fc-e624173f3055",
    "datasource_id": 179,
    "source": "HDRUK"
  },
  {
    "id": 1120,
    "name": "Demographic risk factors, biomarkers, physiology for Acute Compartment Syndrome",
    "description": "Acute compartment syndrome (ACS) is an emergency orthopaedic condition wherein a rapid rise in compartmental pressure compromises blood perfusion to the tissues leading to ischaemia and muscle necrosis.  This serious condition is often misdiagnosed or associated with significant diagnostic delay, and can lead to limb amputations and death.\n\nThe most common causes of ACS are high impact trauma, especially fractures of the lower limbs which account for 40% of ACS cases. ACS is a challenge to diagnose and treat effectively, with differing clinical thresholds being utilised which can result in unnecessary osteotomy.  The highly granular data for over 800 patients with ACS provide the following key parameters to support critical research into this condition:\n\n1)\tPatient data (injury type, location, age, sex, pain levels, pre-injury status and comorbidities)\n2)\tPhysiological parameters (intracompartmental pressure, pH, tissue oxygenation, compartment hardness)\n3)\tMuscle biomarkers (creatine kinase, myoglobin, lactate dehydrogenase)\n4)\tBlood vessel damage biomarkers (glycocalyx shedding markers, endothelial permeability markers)\n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. \n\nUHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nScope: Enabling data-driven research and machine learning models towards improving the diagnosis of Acute compartment syndrome. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, physiological parameters, muscle biomarkers, blood biomarkers and co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings and admissions), presenting complaint, lab analysis results (creatinine, eGFR, troponin, CRP, INR, ABG glucose), systolic and diastolic blood pressures, procedures and surgery details. \n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/180",
    "uid": "60c1557a-4541-429f-bbe5-f9edbdb34f8a",
    "datasource_id": 180,
    "source": "HDRUK"
  },
  {
    "id": 1121,
    "name": "Investigations, interventions, and outcomes for acute coronary syndrome",
    "description": "Background. Following an acute coronary syndrome (ACS), the annual risk of recurrent acute coronary syndrome or death is approximately 6-9% despite contemporary treatments (percutaneous coronary intervention, dual antiplatelet therapy and standard secondary prevention, including statins, beta blockers and ACE inhibitors). \n\nIn recent years, many new strategies have been shown to further reduce the risk of cardiovascular death in patients with coronary artery disease and these are recommended in the 2019 ESC guideline for chronic coronary syndromes. \n\nNovel medications for diabetes (SGLT2 inhibitors and GLP-1 agonists) and lipids (ezetimibe and PCSK-9 inhibitors) have demonstrated reductions in adverse cardiovascular events in patients with coronary artery disease. \n\nWith treatment regimens becoming increasingly complex, it can be unclear which drugs are being used for each patient. This highly granular dataset of >61,000 patients under investigation for or with confirmed ACS would enable projects to assess guideline compliance, drug related adverse events and modelling to identify responder and non-responder subgroups.   \n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.  \n\nEHR: UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 100 ITU bed capacity including a dedicated cardiac HDU and ITU. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nScope: ALL patients being investigated or treated for coronary artery disease and acute coronary syndromes focusing on myocardial infarction and unstable angina from 2019 onwards. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards), presenting complaint, physiology readings (blood pressure, respiratory rate, heart rate, height, weight), Lab analysis results (EGFR, cholesterol, lactate, platelets, white blood cells and others), drug allergies, drug administered and all outcomes.  \n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/181",
    "uid": "b8f2c611-21d0-424e-a1ed-6976b1c207ec",
    "datasource_id": 181,
    "source": "HDRUK"
  },
  {
    "id": 1122,
    "name": "A dataset of monitored patient safety indicators in 3 acute hospital settings",
    "description": "Background. Electronic Health Records (EHRs) embedded into hospital systems have been reported to have benefits including reductions in patient safety events.\n\nThe dataset includes the summarised performance of three key clinical indicators. These nursing indicators are based on clinical quality and patient safety. Data is provided prior and post migration to a fully integrated Electronic Health System (EHS) with monthly numerator and denominators. The data covers the implementation at three hospital sites in Birmingham. Further supporting data can be requested from PIONEER to analyse the impact on patient safety and key outcomes such a mortality, length of stay, escalation to intensive care and readmissions.\n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.\n\nElectronic Health Record. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems) and this record includes the adoption of the EHR at two new hospital sites, a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nScope: The dataset includes summarised data for performance of three clinical indicators, primarily:\n\n- Late/Missed Antibiotics – this is the prescribed dose administered on time, late or missed by ward area each month.\n\n- Late/Missed Non-Antibiotics – this is the prescribed medication (excluding antibiotics) which have been administered on time, administered late or missed by ward area each month.\n\n- 12 Hour Observations – a measure to assess that a patient has a full set of physiological observations taken every 12 hours. These include temperature, blood pressure, respiratory rate and oxygen saturations. The data within this dataset are only the compliance count, but PIONEER has full results available on request.\n\nAvailable supplementary data: Matched controls; synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/182",
    "uid": "4985feee-3c91-47be-a122-2e13073bdac9",
    "datasource_id": 182,
    "source": "HDRUK"
  },
  {
    "id": 1123,
    "name": "Cancer and cerebrovascular events: frequency, cancer types and outcomes",
    "description": "Common causes of cerebrovascular events include arrhythmias such as atrial fibrillation,  damage to the small vessels of the brain termed ‘small vessel disease’, large vessel disease and haemorrhage. \n\nAnecdotally, clinicians have described an increased prevalence of newly diagnosed cancers in people presenting with cerebrovascular disease. However, there is limited information on how common cancer is associated with stroke, what types of cancers are most commonly diagnosed, and how this effects prognosis both in relation to the stroke and the cancer.  \n\nFurthermore, it is unclear how people with cancer-related strokes should be treated; including if standard treatments are still beneficial or whether a more tailored approach is required.  \n\nThis is a highly granular dataset of >16,000 patients with a confirmed cerebrovascular event including hospital presentation, serial physiology, every treatment prescribed and administered, and outcomes for the subsequent 12 months. It differentiates patients into those with a known or newly diagnosed malignancy and those without, and cancer types can be linked to pathology staining information, if needed.    \n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix.  \n\nUHB is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”. \n\nScope: Investigating the relationship between cancer and stroke and whether a cancer related stroke is associated with a worse clinical outcome compared with patients with non-cancer related stroke. Longitudinal and individually linked, so that the preceding and subsequent health journey can be mapped and healthcare utilisation prior to and after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to process of care (timings and admissions), presenting complaint, procedures, physiology readings (blood pressure, respiratory rate, heart rate, oxygen saturations, swallow screening), Lab analysis results (blood sodium level, estimated Glomerular filtration rate (GFR), urea, albumin, cholesterol, full blood counts and others), drug administered and all outcomes.\n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation and refinement; A.I.; Data partner support for ETL (extract, transform and load) process, Clinical expertise, Patient and end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/183",
    "uid": "ae3fd121-141d-466b-b002-06c3959116d0",
    "datasource_id": 183,
    "source": "HDRUK"
  },
  {
    "id": 1124,
    "name": "Clinical characteristics of hospitalised primary biliary cholangitis patients",
    "description": "A highly granular dataset of 7,156 Primary biliary cholangitis (PBC) hospital spell admissions containing 3,520 patients curated by PIONEER.  \n\nThe dataset contains highly granular data including patient demographics, structured serial physiology, assessments, liver function tests, diagnostic codes (ICD-10 & SNOMED-CT), initial presentation, presenting symptoms, procedures (OPCS4 & SNOMED-CT), medical imaging, medications, consultations, and outcomes. The current dataset includes admissions from 30-03-2000 to 30-01-2024 but can be expanded to assess other timelines of interest. \n\nBackground: PBC is a rare, autoimmune cholestatic liver disease that puts patients at risk of life-threatening complications. PBC is primarily a disease of women, affecting approximately one in 1,000 women over the age of 40. \n\nWith an estimated total prevalence in the UK of ~3.9 per 10,000 of the population, equating to around 19,175 people in England, the survival of PBC patients is significantly worse than the general population if left untreated. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/172",
    "uid": "ee315149-c131-4b7c-b88a-9d4a3e1f7948",
    "datasource_id": 172,
    "source": "HDRUK"
  },
  {
    "id": 1125,
    "name": "Deeply phenotyped clinical data for hospitalised Atrial Fibrillation patients",
    "description": "Atrial fibrillation (AF) is a condition of the heart where the heart control rhythm changes from the normal sinus mode to a rapid activity. It is an irregular and often very rapid heart rhythm, known as a type of arrhythmia that can lead to thrombotic events and cardiac dysfunction. AF increases the risk of stroke, heart failure and other heart-related complications.\n\nThe cause is not fully understood, but it tends to affect certain groups of people, such as older people and people living with long-term (chronic) conditions such as heart disease, high blood pressure or obesity. Congenital heart disease, pericarditis, cardiomyopathy, physical and mental stress, also contribute to disease pathogenesis. \n\nThis highly granular dataset includes patient demographics, key lifestyle and underlying health status information, procedures (catheter ablation, electrical cardioversion), medications (beta-blockers, calcium channel blockers, anticoagulants), risk factors and co-morbidities.\n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. \n\nUHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nScope: Thematic dataset of Atrial Fibrillation and Atrial Flutter. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards, attending practitioner change and triage), presenting complaint, assessments, bed moves, consultation, DNAR TEAL, electrocardiogram, events, follow ups, physiology readings (heart rate, BMI, blood pressure, respiratory rate, NEWS2 score, oxygen saturations and clinical frailty scale and others), Lab analysis results (urea, albumin, platelets, potassium, white blood cell count, Covid 19 test and others) microbiology results, procedures, outpatients admissions, surgeries, therapies,  ventilation, drug administered and all outcomes.  Linked images available (radiographs, CT scans, MRI).\n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/173",
    "uid": "572b3ccd-4213-4a41-9255-3096952a2954",
    "datasource_id": 173,
    "source": "HDRUK"
  },
  {
    "id": 1126,
    "name": "Admission patterns in Multiple Long-Term Conditions: NIHR/UKRI ADMISSION dataset",
    "description": "This dataset forms part of ADMISSION, the NIHR and UKRI funded programme of work to better understand and to design healthcare services which are better able to care for patients with multiple long term conditions.\n  \nThis dataset includes >70,000 adult patients who were acutely admitted to hospital. It includes longitudinal and detailed data for these people over 10-years, designed to trace the accrual, progression and impact of multiple co-morbidities as well as acute health care use and outcomes. \n\nThe dataset contains highly granular data regarding patient demographics and the specific co-morbidities associated with each patient, categorised according to ICD-10 codes. It provides a structured sequence of data related to the acute care and inpatient process, capturing critical timings, acuity, medications, laboratory results, observations, readmissions, and mortality rates. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  Data access must reference ADMISSION and the papers which describe this resource.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/174",
    "uid": "799dcdc6-8df9-4ebb-8f0f-3e8ee9e5b443",
    "datasource_id": 174,
    "source": "HDRUK"
  },
  {
    "id": 1127,
    "name": "A granular assessment of the day-to-day variation in emergency presentations",
    "description": "The acute-care pathway (from the emergency department (ED) through acute medical units or ambulatory care and on to wards) is the most visible aspect of the hospital health-care system to most patients. Acute hospital admissions are increasing yearly and overcrowded emergency departments and high bed occupancy rates are associated with a range of adverse patient outcomes. Predicted growth in demand for acute care driven by an ageing population and increasing multimorbidity is likely to exacerbate these problems in the absence of innovation to improve the processes of care.   \n\nKey targets for Emergency Medicine services are changing, moving away from previous 4-hour targets. This will likely impact the assessment of patients admitted to hospital through Emergency Departments.  \n\nThis data set provides highly granular patient level information, showing the day-to-day variation in case mix and acuity.  The data includes detailed demography, co-morbidity, symptoms, longitudinal acuity scores, physiology and laboratory results, all investigations, prescriptions, diagnoses and outcomes.  It could be used to develop new pathways or understand the prevalence or severity of specific disease presentations.   \n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.  \n\nElectronic Health Record: University Hospital Birmingham is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nScope: All patients with a medical emergency admitted to hospital, flowing through the acute medical unit. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards and readmissions), physiology readings (NEWS2 score and clinical frailty scale), Charlson comorbidity index and time dimensions. \n\nAvailable supplementary data: Matched controls; ambulance data, OMOP data, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/175",
    "uid": "ef850406-2993-43ec-80b9-ef504734f2ac",
    "datasource_id": 175,
    "source": "HDRUK"
  },
  {
    "id": 1128,
    "name": "The impact of multimorbidity on care pathways during COPD hospitalisations",
    "description": "Many patients admitted to hospital have multiple long-term conditions (MLTCs), also known as multimorbidity. Despite this, care delivery in hospital is designed for the treatment of single conditions. Often, the care of patients with multimorbidity can be unsatisfactory, inefficient and expensive.\n\nChronic Obstructive Pulmonary Disease (COPD) is associated with a high burden of co-morbidities which tend to co-exist in specific disease clusters. Recognising their presence enables holistic patient management, but also offers opportunities to identify common biological mechanisms across diseases which might be therapeutically targetable.    \n\nThe most common comorbidities in COPD include cardiovascular disease, diabetes, depression and osteoporosis.  Often presentations are badged as exacerbations and alternative causes of breathlessness are missed.   \n\nThis dataset includes 846 patients with COPD admitted to hospital. The infographic includes data from 01/01/2018 to 31/12/2018, but data is available from the past 10 years+.  Data includes detailed demography, presenting symptoms, co-morbidities, admission diagnosis, laboratory tests, serial physiology, prescribed and administered drugs, use of non-invasive and invasive ventilation, and outcomes. Data can be matched to lung function for a proportion of patients. \n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.\n\nUHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nScope: All patients admitted to hospital for COPD exacerbations. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards), presenting complaint, physiology readings (temperature, BMI, blood pressure, respiratory rate, NEWS2 score, oxygen saturations and others), Sample analysis results (urea, albumin, platelets, white blood cells and others) drug administered and all outcomes.  Linked images available (radiographs, CT scans, MRI). \n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/176",
    "uid": "76167567-9221-4024-bccf-dfdfb9e4bb07",
    "datasource_id": 176,
    "source": "HDRUK"
  },
  {
    "id": 1129,
    "name": "Synthetic dataset - Hospitalised patients with Thromboembolic diagnosis",
    "description": "Background \n\n​Annually in the UK, around 60,000 people develop a pulmonary embolism (PE) and 200,000 a deep vein thrombosis (DVT) and the number of emergency admissions for suspected PE and DVT is increasing. Diagnosing PE and DVT remains a challenge due to the non-specific nature of presenting symptoms. Further tests are often required and each year the number of CTPAs and USS performed for suspected VTE increases. \n\nThere is great interest in finding better tools to identify those with the highest likelihood of a DVT and PE, so that precious screening services can be focused where needed most. A number of tools have been suggested but few have been adopted in clinical practice. \n\nMethods such as age-adjusted D-dimer tests and 4PEPs and 4D scores aim to predict PE and DVT more accurately. Implementing a more precise system could revolutionise how we diagnose and treat these dangerous conditions. This dataset enables an exploration of VTE to better understand disease, identify patients at most risk of the poorest outcomes and to improve health services through the development of new prognostic tools.  \n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, and 2,750 beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health.”  \n\nMethodology: A specific pipeline was designed for the generation of the synthetic version of thromboembolic events dataset including data pre-processing, synthetising, and post-process steps. In brief, a generative adversarial network model (CTGAN) in the SDV package (N. Patki, 2016) was employed to generate synthetic dataset which is statistically equivalent to a real dataset. Pre-process and post-process steps were customised to improve the realisticity of the synthetic data. \n\nScope: Enabling data-driven research and machine learning models towards improving the diagnosis of Thromboembolic events (PE/DVT). Real-world dataset linked. The dataset includes large patient demographics, clinical scores, and medical conditions for PE/DVT patients, alongside outcomes taken from ICD-10 & SNOMED-CT codes. \n\nAvailable supplementary data: real-world PE/DVT cohort. \n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/167",
    "uid": "52674ccb-877c-4c85-9b09-50ec9335f1d5",
    "datasource_id": 167,
    "source": "HDRUK"
  },
  {
    "id": 1130,
    "name": "Risk and outcomes of coagulopathies in acutely unwell patients",
    "description": "Risk and outcomes of coagulopathies & arterial/venous thrombosis in acutely unwell patients\nDataset number 12.0\n\nCoagulopathies and bleeding disorders can reflect hereditary conditions such as Haemophilia or von Willebrand disease, be associated with other diseases such as liver conditions, sepsis, trauma or be iatrogenic, related to therapies or their side effects.   Hospital associated venous thromboembolic (VTE) events remain common despite well known risk factors and effective prophylactic treatments. There are a number of blood biomarkers associated with coagulopathies, as well as genetic tests and treatments.  This dataset focuses on the acute presentation of coagulopathies, including in people with known bleeding/clotting disorders and in people for present with a new clotting or bleeding events during an acute presentation.\n\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.  University Hospitals Birmingham NHS Foundation Trust is a Comprehensive Care Haemophilia Centre (CCC) and cares for a wide range of inherited bleeding disorders, including Haemophilia A and B, von Willebrand Disorder and other clotting factor and platelet disorders.  UHB also has a mandated VTE risk and prescription prompt in the medical clerking, capturing risk factors and contraindications for anticoagulation.\n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.  \n\nScope: All patients admitted with coagulopathy or bleeding disorders (chronic or acute) from 2000 onwards.  The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes.  Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards), presenting complaint, diagnosis of TE or bleeds, clotting parameters, D-Dimers, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, imaging reports, all prescribed & administered treatments (fluids, blood products, procedures), all outcomes.  \n\nAvailable supplementary data:  Matched controls; ambulance, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/168",
    "uid": "bf3fe16c-5377-4571-b5a5-7aa7e796594c",
    "datasource_id": 168,
    "source": "HDRUK"
  },
  {
    "id": 1131,
    "name": "NT-proBNP in critically ill patients with sepsis: a NIHR Birmingham BRC Dataset",
    "description": "A dataset of 552 patients  who have been cared for on ICU.\n\nNatriuretic peptides are special proteins produced by the heart, with the two key types being BNP and NT-proBNP. NT-proBNP is particularly useful in managing heart failure and assessing the risk of heart problems. This marker is not just for heart failure; studies have suggested that NT-proBNP levels during the acute phase of sepsis may be a useful indicator of higher risk of long-term impairments in physical function and muscle strength in sepsis survivors.\n\nThis dataset includes detailed demographic information, comorbidities and admission reasons and journeys for patients who have required an intensive care admission.  The data includes serial physiological and blood test measurements reflecting the severity of the patient’s condition, imaging and other investigative results, treatments, and outcomes. This data is invaluable in identifying the clinical utility of NT-proBNP as a potential prognostic marker for future complications and mortality.\n\nPIONEER geography: The West Midlands (WM) has a population of 6.2 million & includes a diverse ethnic & socio-economic mix. \nEHR: UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health.”\n\nScope:  Patients without chronic heart failure or acute heart failure diagnoses admitted to ICU with sepsis, acute respiratory failure (specifically ARDS) or polytrauma without severe head injury; with at least one measurement of NT-proBNP at any time from 3-months pre-ICU admission to 6 months post-ICU discharge\n\nLongitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, hospital mortality, Length of stays, readmissions), Intensive care details, primary diagnosis, SOFA score & APACHE II scores, NT-proBNP results & outcomes.\n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data.\n\nAvailable supplementary support: PIONEER can also offer analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.\n\nThis research is supported by the National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre (BRC), specifically the Infections and Acute Care theme.",
    "url": "https://healthdatagateway.org/en/dataset/169",
    "uid": "5afad0d5-bb01-4683-8d12-b69886bf2988",
    "datasource_id": 169,
    "source": "HDRUK"
  },
  {
    "id": 1132,
    "name": "Age-Adjusted D-Dimers: Enhancing Diagnosis & Patient Safety in Thromboembolism",
    "description": "Background \n\nAnnually in the UK, around 60,000 people develop a pulmonary embolism (PE) and 200,000 a deep vein thrombosis (DVT) and the number of emergency admissions for suspected PE and DVT is increasing. Diagnosing PE and DVT remains a challenge due to the non-specific nature of presenting symptoms. Further tests are often required and each year the number of CTPAs and USS performed for suspected VTE increases. \n\nThere is great interest in finding better tools to identify those with the highest likelihood of a DVT and PE, so that precious screening services can be focused where needed most. A number of tools have been suggested but few have been adopted in clinical practice. \n\nMethods such as age-adjusted D-dimer tests and 4PEPs and 4D scores aim to predict PE and DVT more accurately. Implementing a more precise system could revolutionise how we diagnose and treat these dangerous conditions. This dataset enables an exploration of VTE to better understand disease, identify patients at most risk of the poorest outcomes and to improve health services through the development of new prognostic tools.  \n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, and 2,750 beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health.”  \n\nScope: Enabling data-driven research and machine learning models towards improving the diagnosis of Thromboembolic events (PE/DVT). Real-world dataset linked. The dataset includes patient demographics, clinical scores, and medical conditions for PE/DVT patients, alongside outcomes taken from ICD-10 & SNOMED-CT codes.  \n\nAvailable supplementary data: A synthetic version of thromboembolic events dataset including data pre-processing, synthetising, and post-process steps. \n\nAvailable supplementary support: PIONEER can also offer analytics, model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services. \n\nThis research is supported by the National Institute for Health and Care Research (NIHR) Midlands Patient Safety Research Collaboration (PSRC). \n\nThis dataset was prepared by and is available through PIONEER, with the support of the NIHR Applied Research Collaboration West Midlands (NIHR ARC WM).",
    "url": "https://healthdatagateway.org/en/dataset/170",
    "uid": "f21ef3fd-8193-447f-bcda-191243ca4b12",
    "datasource_id": 170,
    "source": "HDRUK"
  },
  {
    "id": 1133,
    "name": "Assessing acuity scores – NEWS2 in acute illnesses with component markers",
    "description": "Background\n\nEarly warning systems (EWS) are bedside tools used to assess basic physiological parameters to identify patients with potential or established critical illness. Evidence suggests that they may predict risk of intensive care admission, death and length of hospital stay. In 2017, the Royal College of Physicians (RCP) published an updated National Early Warning Score, referred to as NEWS2, based upon six physiological parameters (heart rate, blood pressure, respiratory rate, peripheral oxygen saturations, temperature and level of consciousness). It is associated with specific clinical response recommendations in which a step change occurs at a threshold NEWS2 score >5, requiring an urgent clinical response no matter what the presenting complaint or condition. \n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix. \n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”. \n\nScope: Serial NEWS2 scores of acutely unwell patients recorded during their hospital stay with each individual component of NEWS2 reported. Longitudinal and individually linked, so that the preceding and subsequent health journey can be mapped and healthcare utilisation prior to and after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions), presenting complaint, physiology readings (e.g. heart rate, blood pressure, respiratory rate, NEWS2 score and oxygen saturations), Lab analysis results (Alanine Transferase, albumin, Hb, Creatine Kinase, White Blood Cells and others), microbiology results, medications and all outcomes.   \n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation and refinement; A.I.; Data partner support for ETL (extract, transform and load) process, Clinical expertise, Patient and end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/171",
    "uid": "bc6a661a-4447-40ad-bc92-3007e89688c9",
    "datasource_id": 171,
    "source": "HDRUK"
  },
  {
    "id": 1134,
    "name": "Laboratory turnaround times processing electronic blood test orders in the NHS",
    "description": "Pathology services are a fundamental core of healthcare services and are essential in the delivery of many national priorities. A Report of the Review of NHS Pathology Services in England, chaired by Lord Carter of Coles, estimated that 70-80 per cent of all healthcare decisions affecting diagnosis or treatment involve a pathology investigation. With the increased demand on acute care services there is a growing requirement for rapid laboratory results to facilitate the decision to discharge or admit, including the escalation of care. Laboratory turn around times (LTAT) are defined as the interval between when a test is requested to the time the results are available to the clinical team. LTAT is considered one of the most noticeable markers of a laboratory service and is often used as a key performance indicator in healthcare settings. \n\nComputerised Provider Order Entry (CPOE) systems are computer-assisted systems that are designed to replace a hospital’s paper-based ordering system. When configured correctly CPOE systems should increase efficiency and improve patient care.  \n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.  \n\nUHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nScope: Clinical and operational pathway data for 323,899 blood tests ordered pre and post implementation of a CPOE system. Data on the time the new system was implemented. Date and time fields are provided for the specimens from the point they were requested through to processing times in the laboratory and finally the date/time results are reported back via the Electronic Health System. Data on the ward and specialty are provided.  \n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/162",
    "uid": "971e2827-3aa4-4af3-bdce-3c8419897654",
    "datasource_id": 162,
    "source": "HDRUK"
  },
  {
    "id": 1135,
    "name": "Immune Checkpoint Inhibitors: HDR UK Medicines Programme cancer-related resource",
    "description": "A highly granular, medicines-focused dataset of approximately 1,000 patients over 3 years. Includes patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, readmissions, survival), primary diagnosis, presenting complaint, physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations and others),  extensive blood results (infection, inflammatory markers) and acuity markers such as AVPU Scale, NEWS2 score, SEWS score, imaging reports, consultation, therapy, referrals, complete documentation of all prescribed & administered treatments including fluids, blood products, procedures, information on outpatient admissions and survival outcomes following one year post discharge.\n\n\nThis medicines-focused dataset is an invaluable resource for researchers aiming to analyse and compare the effects of checkpoint inhibitors on patients. It offers an opportunity to understand treatment pathways and healthcare utilisation in this specific patient cohort. Dive into this rich data source to uncover new insights and contribute to the evolving field of cancer immunotherapy.\n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size",
    "url": "https://healthdatagateway.org/en/dataset/163",
    "uid": "832b9428-da74-4bed-b35f-5a797bafd67e",
    "datasource_id": 163,
    "source": "HDRUK"
  },
  {
    "id": 1136,
    "name": "Investigating the impact of frailty, age and illness severity during COVID-19",
    "description": "Frailty is a syndrome of increased vulnerability to incomplete resolution of homeostasis (healing or return to baseline function) following a stressor event (such as an infection or fall) and it is associated with poor outcomes including increased mortality and reduced quality of life. Prevalence increases with age, but it should not be considered an inevitable consequence of ageing.\n\nThe pathophysiology of frailty is poorly understood but the immune and endocrine systems appear to be involved in its development or response. Age and frailty have been proven to be independently predictive of outcomes in patients admitted with an acute illness. \n\nIn COVID-19, routine frailty identification has been used to inform decision making about high level of treatment. This is because frailty usually moderates the effect of age on mortality. Anecdotally, this effect has not been recognised by clinicians looking after older COVID-19 patients. Four papers have been published so far on the effect of frailty on COVID-19 with differing results.  However, all papers show the independent predictive value of age when controlling for frailty, which is not usually seen in studies of age and frailty in other acute illnesses.\n\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. \n\nEHR: UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nScope: All patients aged 18 years and above admitted for an acute illness in hospitals within University Hospitals Birmingham NHS trust during the COVID-19 pandemic. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, imaging reports, all prescribed & administered treatments (fluids, blood products, procedures), all outcomes.\n\nAvailable supplementary data: Matched controls; ambulance, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/164",
    "uid": "108ba1b0-994d-449c-b192-2331698e477d",
    "datasource_id": 164,
    "source": "HDRUK"
  },
  {
    "id": 1137,
    "name": "Specialist bronchiectasis management and acute care presentations to hospital",
    "description": "Bronchiectasis is a chronic lung condition characterised by the permanent dilation and damage of the airways, leading to frequent respiratory infections, persistent cough, and significant morbidity. Acute exacerbations of bronchiectasis are critical events that often necessitate hospital care, representing a period of heightened risk for patients and a substantial burden on healthcare systems. \n\nThis NIHR Respiratory Translational Research Collaboration-associated dataset of 51,166 bronchiectasis admissions from 17k unique patients offers a comprehensive collection of hospital presentation data related to acute bronchiectasis episodes, capturing key clinical details, patient demographics, treatment interventions, and outcomes. The data includes also includes, serial physiology, assessments, diagnostic codes (ICD-10 & SNOMED-CT), initial presentation, presenting symptoms, procedures (OPCS4 & SNOMED-CT), imaging, microbial cultures, prescriptions & medications, ward locations and outcomes. The current dataset includes admissions from 01-01-2000 to 29-05-2024 but can be filtered accordingly.\n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/165",
    "uid": "16b1b424-9b2a-4dc6-a46c-78b2e6e1a193",
    "datasource_id": 165,
    "source": "HDRUK"
  },
  {
    "id": 1138,
    "name": "Identification of Medical Admissions suitable for Same Day Emergency Care (SDEC)",
    "description": "Emergency hospital admissions in the UK have been rising steadily. Medical emergencies account for the largest proportion of unplanned admissions.  Same Day Emergency Care (SDEC) is one of many ways the NHS are working to provide the right care, in the right place and at the right time.  The national SDEC model builds on the previous work undertaken in ambulatory emergency care (AEC) services across the NHS , which was aimed at providing a consistent approach to patient pathways. \n\nA proportion of medical admissions are suitable for SDEC, where they are assessed and treated, but do not require overnight admission to an inpatient bed. This is beneficial for patients, as hospital admission and its associated risks can be avoided. As inpatient admissions increase, it is also important to consider alternative methods of care to reduce pressure on inpatient services. SDEC is highlighted in the NHS Long Term Plan, recommending a third of patients in acute services should receive SDEC.\n\nThe number of medical patients receiving SDEC varies between centres. This may relate to local patient populations, but also local availability of services. SDEC is often delivered through Ambulatory Emergency Care, as well as the Acute Medical Unit, and multiple additional services can aid delivery, including hospital at home services, and early outpatient review in specialist clinics. These services vary between hospitals.\n\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. \n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nScope: All patients admitted with unplanned medical admissions who receive Same Day Emergency Care from 2004 onwards. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, imaging reports, all prescribed & administered treatments (fluids, blood products, procedures), all outcomes.\n\nAvailable supplementary data: Matched controls; ambulance, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/166",
    "uid": "7af96f17-5ab6-4275-b8ef-61a3e053d342",
    "datasource_id": 166,
    "source": "HDRUK"
  },
  {
    "id": 1139,
    "name": "Environmental determinants of health; linked health and DEFRA air quality data",
    "description": "A highly granular large dataset of 10,908,440 admissions, curated by PIONEER to look at matching DEFRA’s air pollution data to the patients registered address.  The data includes demography, admission details, diagnostic codes (ICD-10 & SNOMED-CT), respiratory data, medications, presenting complaints all linked to DEFRA air quality. This dataset offers an exceptional resource for researchers seeking to understand the short- and long-term impacts of air quality on health outcomes.  This dataset synergises DEFRA air pollution data with anonymised health records to offer an opportunity for multidisciplinary research in environmental health, epidemiology and beyond. The current dataset includes admissions from 01-01-2000 to 31-08-2023 but can be expanded to assess other timelines of interest. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nDEFRA\n\nAir quality data has been extracted from sampling stations in the Birmingham area, hourly rates of volatile and non-volatile particulates, hydrocarbons, sulphur dioxide, ozone, carbon monoxide and nitrogen oxides. Each station covers a subset of the pollutants, so this may not necessarily be the full set.  © Crown 2024 copyright Defra via uk-air.defra.gov.uk, licenced under the Open Government Licence (OGL).\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/158",
    "uid": "4191bc7e-48c3-40ef-b95a-dc7318551c47",
    "datasource_id": 158,
    "source": "HDRUK"
  },
  {
    "id": 1140,
    "name": "UHB 2020 Winter Society of Acute Medicine Benchmarking Audit",
    "description": "Background\nThe Society for Acute Medicine (SAM) Benchmark Audit (SAMBA) is a national benchmark audit of acute medical care. The aim of SAMBA20 is to describe the severity of illness of acute medical patients presenting to Acute Medicine within UK hospitals, speed of assessment, pathway and progress seven days after admission and to provide a comparison for each participating unit with the national average (or ‘benchmark’). On average >150 hospitals take part in this audit per year.\nSAMBA20 winter audit measured adherence to some of the standards for acute medical care. Acute Medical Units work 24-hours per day and 365 days a year. They are the single largest point of entry for acute hospital admissions and most patients are at their sickest within the first 24-hours of admission. \nThis dataset includes\n•\tTotal number of patients assessed by acute medicine across ED, AMU and Ambulatory Care. \n•\tMedical and nursing levels \n•\tSeverity of illness \n•\tTimeliness in processes of care \n•\tClinical outcomes 7 days after admission\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9million & includes a diverse ethnic, socio-economic mix. There is a higher than average % of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. WM has a high prevalence of COPD, reflecting the high rates of smoking and industrial exposure. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. This is the SAMBA dataset from 4 NHS hospitals.\nEHR University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\nScope: These data come from Queen Elizabeth Hospitals Birmingham, Good Hope Hospital, Solihull Hospital and Heartlands Hospital. All admissions in a  pre-defined 24-hour period, the severity of illness, patient demographics, co-morbidity, acuity scores, serial, structured data pertaining to care process (timings, staff grades, specialty review, wards) all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.  \nAvailable supplementary data:\nMore extensive data including granular serial physiology, bloods, conditions, interventions, treatments. Ambulance, 111, 999 data, synthetic data.\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services",
    "url": "https://healthdatagateway.org/en/dataset/159",
    "uid": "5bf9bf39-3cd7-4a54-994b-9914405f945e",
    "datasource_id": 159,
    "source": "HDRUK"
  },
  {
    "id": 1141,
    "name": "UHB 2019 Summer Society of Acute Medicine Benchmarking Audit",
    "description": "Background\nThe Society for Acute Medicine (SAM) Benchmark Audit (SAMBA) is a national benchmark audit of acute medical care. The aim of SAMBA19 is to describe the severity of illness of acute medical patients presenting to Acute Medicine within UK hospitals, speed of assessment, pathway and progress seven days after admission and to provide a comparison for each participating unit with the national average (or ‘benchmark’). On average >150 hospitals take part in this audit per year.\nSAMBA19 summer audit measured adherence to some of the standards for acute medical care. Acute Medical Units work 24-hours per day and 365 days a year. They are the single largest point of entry for acute hospital admissions and most patients are at their sickest within the first 24-hours of admission. \nThis dataset includes\n•\tTotal number of patients assessed by acute medicine across ED, AMU and Ambulatory Care. \n•\tMedical and nursing levels \n•\tSeverity of illness \n•\tTimeliness in processes of care \n•\tClinical outcomes 7 days after admission\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9million & includes a diverse ethnic, socio-economic mix. There is a higher than average % of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. WM has a high prevalence of COPD, reflecting the high rates of smoking and industrial exposure. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. This is the SAMBA dataset from 4 NHS hospitals.\nEHR University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\nScope: These data come from Queen Elizabeth Hospitals Birmingham, Good Hope Hospital, Solihull Hospital and Heartlands Hospital. All admissions in a  pre-defined 24-hour period, the severity of illness, patient demographics, co-morbidity, acuity scores, serial, structured data pertaining to care process (timings, staff grades, specialty review, wards) all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.  \nAvailable supplementary data:\nMore extensive data including granular serial physiology, bloods, conditions, interventions, treatments. Ambulance, 111, 999 data, synthetic data.\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services",
    "url": "https://healthdatagateway.org/en/dataset/160",
    "uid": "5acfadf9-21a7-4517-a1b2-620d20469611",
    "datasource_id": 160,
    "source": "HDRUK"
  },
  {
    "id": 1142,
    "name": "Investigating Interactions between Mycobacterium Tuberculosis and SARS-CoV-2",
    "description": "Tuberculosis (TB) is caused by a bacterium called Mycobacterium tuberculosis.  TB remains a significant global health problem. The UK has one of the highest rates of TB in Europe, with almost 5000 new cases notified in 2019. Within the UK, Birmingham and the West Midlands are particular hotspots for TB, with over 300 cases of active disease and approximately 10 times that of new latent infections diagnosed each year. \n\nBirmingham and the West Midlands have experienced particularly high rates of COVID-19 during the pandemic and there is increasing evidence that individuals of Black, Asian and minority ethnicities (BAME) experience the most significant morbidity and highest mortality rates due to COVID-19. These groups also experience the highest burdens of TB, both in the UK and overseas. \n\nEpidemiological data suggests that current and previous tuberculosis (TB) increase the risk of COVID-19 mortality and severe disease. There is also evidence of immunopathogenic overlap between the two infections with in vitro studies finding that SARS-CoV-2 infection is increased in human macrophages cultured in the inflammatory milieu of TB-infected macrophages. \n\nThis dataset would enable a deeper analysis of demography and clinical outcomes associated with COVID-19 in patients with concurrent TB. \n\nPIONEER geography: the West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.  \n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nScope: All hospitalised patients admitted to UHB during the COVID-19 pandemic, curated to focus on Mycobacterium tuberculosis and SARS-CoV-2. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (A&E, triage, IP, ITU admissions), presenting complaint, DNAR teal, all physiology readings (AVPU scale, Covid CFS, blood pressure, respiratory rate, oxygen saturations and others), all blood results, imaging reports, all prescribed & administered treatments, all outcomes. \n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/161",
    "uid": "ea2d242d-6a16-4dcd-b1b0-832e97b41582",
    "datasource_id": 161,
    "source": "HDRUK"
  },
  {
    "id": 1143,
    "name": "Transplants in Renal Disease: outcomes and the effects of immunosuppression",
    "description": "A highly granular dataset of 868 patients on immunosuppressive treatments who underwent a kidney transplant. The data includes demography, serial physiology, assessments, diagnostic codes (ICD-10 & SNOMED-CT), initial presentation, presenting symptoms, dialysis, post-transplant chronic kidney disease, procedures (OPCS4 & SNOMED-CT), imaging, prescriptions, outpatient appointments and outcomes. The current dataset includes admissions from 1st January 2000 to 1st July 2024 to observe and improve the long-term outcomes of patients but can be expanded to assess other timelines of interest. \n\nEnd-Stage Renal Disease (ESRD) is the advanced stage of kidney disease requiring renal replacement therapy. Kidney transplantation offers improved outcomes but requires immunosuppressive treatment to prevent organ rejection. However, these medications pose risks such as infections and metabolic complications, highlighting the complexity of managing ESRD post-transplantation. \n\nGeography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/154",
    "uid": "1768559b-4df5-4bef-a7db-eccc87a82acc",
    "datasource_id": 154,
    "source": "HDRUK"
  },
  {
    "id": 1144,
    "name": "Deeply phenotyped sepsis patients within hospital: onset, treatments & outcomes",
    "description": "Deeply phenotyped sepsis patients within hospital: onset, treatments & outcomes \n\nSepsis is life-threatening organ dysfunction due to a dysregulated host response to infection & is a global health challenge. In 2017, 48•9 million incident cases of sepsis were recorded worldwide with 11million sepsis-related deaths, representing 19•7% of all global deaths. There are >123,000 sepsis cases diagnosed in Engl& each year with an estimated 36,800 sepsis-associated deaths.   Sepsis is treatable, & timely, targeted interventions improve outcomes.  The World Health Assembly identified sepsis as a global health priority. \n\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK.  Birmingham has the highest birth rate in England.  It also has the highest infant mortality rate. WM life expectancy is 1.8 years less than in London. There are particularly high rates of physical inactivity, obesity, smoking & diabetes.  Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.\n\nEHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nScope: All hospitalised patients to UHB from 2000 – current day.  Updated monthly.  Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after sepsis understood.\nThe dataset includes ICD-10 & SNOMED-CT codes pertaining to sepsis & suspected sepsis.  Serial, structured data pertaining to process of care (timings, staff grades, specialty review, wards), presenting complaint, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.  Linked images available (radiographs, CT, MRI, ultrasound). Includes COVID-19 wave 1 and wave 2 data.\n\nAvailable supplementary data:\nMatched “non-sepsis” controls; ambulance, 111, 999 data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/155",
    "uid": "38ff49e2-dffa-4103-87a8-386f9f424d22",
    "datasource_id": 155,
    "source": "HDRUK"
  },
  {
    "id": 1145,
    "name": "The impact of COVID on hospitalised patients with COPD and hospital services",
    "description": "Chronic obstructive pulmonary disease (COPD) is a debilitating lung condition characterised by progressive lung function limitation. COPD is an umbrella term and encompasses a spectrum of pathophysiologies including chronic bronchitis, small airways disease and emphysema. COPD caused an estimated 3 million deaths worldwide I each year, and is estimated to be the third leading cause of death worldwide. The British Lung Foundation (BLF) estimates that the disease costs the NHS around £1.9 billion per year. COPD is therefore a significant public health challenge. This dataset explores the impact of hospitalisation and service use in patients with COPD during the COVID pandemic.\n\nPIONEER geography \nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. \n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nScope: All hospitalised patients admitted to UHB during the COVID-19 pandemic and elective service users, curated to focus on COPD. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, imaging reports, all prescribed & administered treatments (fluids, blood products, procedures), all outcomes.\n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/156",
    "uid": "a1c4dda9-d34b-443f-aaaf-231f2e0c31fa",
    "datasource_id": 156,
    "source": "HDRUK"
  },
  {
    "id": 1146,
    "name": "Hospitalised Community Acquired Pneumonia before & during the COVID-19 pandemic",
    "description": "Community acquired pneumonia (CAP) is a leading cause of hospital admission, and in older adults has high rates of mortality and complications. CAP is associated with increased long-term mortality and loss of independence for older adults.  CAP typically affects older adults with co-morbidities- a group who have largely shielded throughout the winter period. This seems to have reduced rates of transmissible disease in vulnerable people. Complications such as sepsis, and empyema (infected fluid around the lung) prolong hospital admission, result in additional interventions in hospital and have higher mortality than CAP alone. The causative agents for CAP are often poorly identified in real world clinical practice.\nThese data allow the investigation of the different ways in which COVID-19 has impacted on existing health conditions, how often causative agents were identified in real-world practice and the sensitivities of the bacteria, which antibiotics were used and patient outcomes.  \n\nPIONEER geography \nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix (42% non-white within Birmingham). \n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nScope: All hospitalised patients admitted to UHB before and during the COVID-19 pandemic, curated to focus on Community Acquired Pneumonia. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, readmissions and discharge outcomes, physiology readings (heart rate, blood pressure, NEWS2 score, SEWS score, AVPU score), blood results and flags for microbiology and surgical data. Comparing the burden of hospitalised community acquired pneumonia (CAP) before and during COVID-19 pandemic. \n\nAvailable supplementary data: Matched controls; ambulance, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/157",
    "uid": "1861b3dd-4969-490e-9faf-c57809f56bc1",
    "datasource_id": 157,
    "source": "HDRUK"
  },
  {
    "id": 1147,
    "name": "Ventilatory strategies, medications and outcomes for patients with COVID",
    "description": "Background: Coronavirus disease 2019 (COVID-19) was identified in January 2020.  Currently, there have been more than 125 million cases, and more than 2.7 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonitis, adult respiratory distress syndrome (ARDS) and death.  Many patients required ventilatory support including high flow oxygen, continuous positive airway pressure and intubated with or without tracheotomy. There was considerable learning on how to manage COVID-19 during the pandemic and new drugs became available during the different waves. This secondary care COVID dataset contains granular ventilatory, demographic, morbidity, serial acuity, medications and outcome data in COVID-19 across all waves and will be continuously refreshed.\n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day, more than 100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions across all waves.\n\nElectronic Health Records (EHR): University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. ITU capacity increased to 250 beds during the COVID pandemic. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”. UHB has cared for more than 10,000 COVID admissions to date.  \n\nScope: All COVID swab confirmed hospitalised patients to UHB from January 2020 to the current date.  The dataset includes highly granular patient demographics and co-morbidities taken from ICD-10 and SNOMED-CT codes.  Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), severity, ventilatory requirements, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed and administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support, dexamethasone, remdesivir, tocilizumab), all outcomes.  \n\nAvailable supplementary data: Ambulance, 111, 999 data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation and refinement; A.I.; Data partner support for ETL (extract, transform and load) process, Clinical expertise, Patient and end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/147",
    "uid": "55a744c4-a2d4-4ea0-b0eb-1af6e1465383",
    "datasource_id": 147,
    "source": "HDRUK"
  },
  {
    "id": 1148,
    "name": "Machine Learning Frailty Index estimates with routine test results in acute care",
    "description": "Background. \n\nFrailty is a critical measure in health care for evidence-based clinical decision making and an accurate electronic Frailty Index (eFI) at admission will be beneficial to both patients and medical service for prompt and appropriate assessment and management in acute care.\n\nAn accurate and valid eFI will be beneficial to both patients and emergency care services for prompt and appropriate assessment and management.\n\nSince 2020, Clinical Frailty Scale (CFS) was introduced to QE as a way to screen and quantify frailty and fitness of individual patients with Covid 19 at admission. Essentially, CFS is a value derived from 92 baseline variables (also referred to as deficits) using a cumulative model, although studies have shown feasibility of reducing the number of variables without loss of predictive ability. In addition, CFS is judgement-based and requires specially trained clinicians to perform a series of measurements and determine the presence or absence of each deficit.\n\nAn eFI that was derived from 31 routinely collected test results showed that it has promising identification power for high risk frailty patients in aged cohort (>65), indicating the potential of having a simpler and more efficient model for frailty estimation.\n\nPIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. \n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nScope:  All hospitalised patients admitted to UHB during the COVID-19 pandemic, curated to focus on patients with frailty score. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, discharge locations), presenting complaint, all physiology readings (Clinical Frailty Scale, pulse, blood pressure, respiratory rate, oxygen saturations), blood results (albumin, glucose, platelets, sodium, c-reactive protein, urea and others) and all outcomes.\n\nAvailable supplementary data: Matched controls; ambulance, OMOP data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/148",
    "uid": "ea334a9e-f6fa-44eb-b9e3-d180d3ae14da",
    "datasource_id": 148,
    "source": "HDRUK"
  },
  {
    "id": 1149,
    "name": "The acute presentation of pregnant women to non-maternity Emergency departments",
    "description": "Each year, there are audits to assess maternal & foetal outcomes across the UK. In 2016-18, 217 women died during or up to six weeks after pregnancy, from causes associated with their pregnancy, among 2,235,159 women giving birth in the UK. 9.7 women per 100k died during pregnancy or up to six weeks after childbirth or the end of pregnancy. There was an increase in the overall maternal death rate in the UK between 2013-15 & 2016-18. Assessors judged that 29% of women who died had good care. However, improvements in care which may have made a difference to the outcome were identified for 51% of women who died. Birmingham has a higher than average maternal & foetal death rate. This dataset includes detailed information about the reasons pregnant women seek acute care, & their care pathways & outcomes.\nPIONEER geography: The West Midlands (WM) has a population of 5.9m & includes a diverse ethnic, socio-economic mix. There is a higher than average % of minority ethnic groups. WM has the youngest population in the UK with a higher than average birth rate. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. 51.2% of babies born in Birmingham have at least one parent born outside of the UK, this compares with 34.7% for England. Each day >100k people are treated in hospital, see their GP or are cared for by the NHS.\nEHR: University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\nScope: Pregnant or post-partum women from 2015 onwards who attended A&E in Queen Elizabeth hospital. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics (including gestation & postpartum period), co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (admissions, wards, practitioner changes & discharge outcomes), presenting complaints, physiology readings (temperature, blood pressure, NEWS2, SEWS, AVPU), referrals, all prescribed & administered treatments & all outcomes.\nAvailable supplementary data: More extensive data including granular serial physiology, bloods, conditions, interventions, treatments. Ambulance, 111, 999 data, synthetic data.\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/149",
    "uid": "86632207-1e57-4272-b178-ea069a13cae1",
    "datasource_id": 149,
    "source": "HDRUK"
  },
  {
    "id": 1150,
    "name": "The impact of hospitalised patients with COPD: from admission to outcome",
    "description": "The impact of hospitalised patients with COPD: from admission to outcome\n\nDataset 13.0\n\nBackground.\nChronic obstructive pulmonary disease (COPD) is a debilitating lung condition characterised by progressive lung function limitation.   COPD is an umbrella term and encompasses a spectrum of pathophysiologies including chronic bronchitis, small airways disease and emphysema.  COPD caused an estimated 3 million deaths worldwide in 2016, and is estimated to be the third leading cause of death worldwide.  People with COPD experience flares in their symptoms, termed exacerbations.  Exacerbations are associated with increased mortality, morbidity, a faster decline in lung function and other systemic illness such as heart attacks and strokes.  Despite this impact, COPD exacerbations are poorly characterised and have been without novel treatments for >30 years.\n\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. The West Midlands has a high prevalence of COPD, reflecting the high rates of smoking and industrial exposure. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.\n\nEHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nScope: All hospitalised patients admitted to UHB with an exacerbation of COPD from Jan 2000 - 2021.  Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood.  The dataset includes ICD-10 & SNOMED-CT codes pertaining to COPD and COPD exacerbations, as well as all co-morbid conditions.  Serial, structured data pertaining to process of care (timings, staff grades, specialty review, wards), presenting complaint, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (NIV, nebulisers, antibiotics), all outcomes.  Linked images available (radiographs, CT).\n\nAvailable supplementary data:\nAmbulance, 111, 999 data, synthetic data. Non-COPD “controls”\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user accs, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/150",
    "uid": "780bf7d0-9ee1-4641-a74f-dc148e361970",
    "datasource_id": 150,
    "source": "HDRUK"
  },
  {
    "id": 1151,
    "name": "Ventilation strategies for patients on intensive care",
    "description": "Ventilatory strategies and outcomes for patients acutely admitted to hospital\n\nDataset 14.0\nVersion 1.0 15.2.2021\n\nBackground.\nAcute respiratory failure is commonly encountered in the emergency department (ED). Early treatment can have positive effects on long-term outcome. Non-invasive ventilation is commonly used for patients with respiratory failure during acute exacerbations of chronic obstructive lung disease and congestive heart failure.  For other patients, including neuromuscular dysfunction, mechanical ventilation may be needed. For refractory hypoxemia, new rescue therapies have emerged to help improve the oxygenation, and in some cases mortality. This dataset summarises the demography, admitting complaint, serial physiology, treatments and ventilatory strategies in patients admitted with hypoxaemia. Management options and rescue therapies including extracorporeal membrane oxygenation are included.\n\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK.  Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.  \n\nEHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds.  ITU capacity increased to 250 beds during the COVID pandemic. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.  The electronic record captures ventilatory parameters.\n\nScope: All hospitalised patients with hypoxaemia requiring ventilatory support  from 2000 onwards. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes.  Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), severity, ventilatory requirements, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.  \n\nAvailable supplementary data:\nSynthetic data.  Post discharge care contacts.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/151",
    "uid": "1a66dbc1-4f06-455c-85fc-68b372575cad",
    "datasource_id": 151,
    "source": "HDRUK"
  },
  {
    "id": 1152,
    "name": "Characterisation of hospitalised COPD exacerbations using real world data",
    "description": "Chronic respiratory diseases remain one of the leading causes of death from non-communicable disease, with the majority of deaths due to Chronic Obstructive Pulmonary Disease (COPD). COPD presents a significant healthcare burden and is detrimental to quality of life. Currently, there are no disease modifying treatments.\n\nFurther to the burden of stable COPD, patients experience acute exacerbations (AECOPD), defined as an acute worsening of symptoms which requires a change in treatment.  These are important events, associated with increased mortality, morbidity and long-term health impacts. Patients who exacerbate frequently are more likely to have a faster decline in lung function, have a lower quality of life and experience adverse cardiovascular events.  Whilst there are therapies to reduce exacerbation frequency and treat the acute event, options have limited efficacy and have not changed in overall drug class for many years.\n\nExacerbations are defined by the severity of the symptoms and the treatments involved – so a severe exacerbation is one which requires hospitalisation.  However, in our ageing and increasingly frail population, hospitalisation can be required for even a minor event, if a person is already struggling to cope at home.\n\nPIONEER geography \nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. \n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nScope: All hospitalised patients admitted to UHB between January 2018 to January 2020 curated to focus on COPD exacerbations. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, imaging reports, all prescribed & administered treatments (fluids, blood products, procedures), all outcomes.\n\nAvailable supplementary data: Matched controls; ambulance, synthetic data, differing time periods including/excluding COVID-19 pandemic periods.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/152",
    "uid": "f6f2978d-4a49-4dd9-8615-78d28e6dc83c",
    "datasource_id": 152,
    "source": "HDRUK"
  },
  {
    "id": 1153,
    "name": "Demography, interventions & outcomes of patients with Cerebrovascular Disease",
    "description": "PIONEER geography\nThe West Midlands (WM) has a population of 5.9million & includes a diverse ethnic, socio-economic mix. There is a higher than average % of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. WM has a high prevalence of COPD, reflecting the high rates of smoking and industrial exposure. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. This is the SAMBA dataset from 4 NHS hospitals.\nEHR\nUniversity Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\nScope: All patients from 2015 onwards, curated to focus on Stroke. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (admissions, wards and discharge outcomes), presenting complaints, therapies, all physiology readings (pulse, temperature, blood pressure, screening for dysphagia, all sample analysis results (urine specimens, blood specimens), all prescribed & administered treatments and all outcomes. \nAvailable supplementary data:  \nMore extensive data including granular serial physiology, bloods, conditions, interventions, treatments. Ambulance, 111, 999 data, synthetic data.\nAvailable supplementary support: \nAnalytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services",
    "url": "https://healthdatagateway.org/en/dataset/153",
    "uid": "ddf7f82f-b453-442d-b562-bb0157593831",
    "datasource_id": 153,
    "source": "HDRUK"
  },
  {
    "id": 1154,
    "name": "Ventilatory strategies and outcomes for patients with COVID: a dataset in OMOP",
    "description": "Background.\nCoronavirus disease 2019 (COVID-19) was identified in January 2020.  Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide.  Some individuals experience severe manifestations of infection, including viral pneumonitis, adult respiratory distress syndrome (ARDS) & death.  Many patients required ventilatory support including high flow oxygen, continuous positive airway pressure and intubated with or without tracheotomy.  Different centres took different approaches to care delivery depending on ITU bed availability. This secondary care COVID OMOP dataset contains granular ventilatory, demographic, morbidity, serial acuity and outcome data in COVID-19.\n\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK.  Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.\n\nEHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds.  ITU capacity increased to 250 beds during the COVID pandemic. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.  UHB has cared for >5000 COVID admissions to date.  This data is in the OMOP format.\n\nScope: All COVID swab confirmed hospitalised patients to UHB from January – September 2020.  The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes.  Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), severity, ventilatory requirements, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.  \n\nAvailable supplementary data:\nMore extensive data including wave 2 patients in non-OMOP form. Ambulance, 111, 999 data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/142",
    "uid": "cacba424-6c2c-4b57-8f5b-64e92a78370a",
    "datasource_id": 142,
    "source": "HDRUK"
  },
  {
    "id": 1155,
    "name": "The impact of ethnicity and multi-morbidity on C19 hospitalised outcomes",
    "description": "PIONEER:  The impact of ethnicity and multi-morbidity on COVID-related outcomes; a primary care supplemented hospitalised dataset\nDataset number 3.0\n\nCoronavirus disease 2019 (COVID-19) was identified in January 2020.  Currently, there have been more than 65million cases and more than 1.5 million deaths worldwide.  Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) and death.  Evidence suggests that older patients, those from some ethnic minority groups and those with multiple long-term health conditions have worse outcomes.  This secondary care COVID dataset contains granular demographic and morbidity data, supplemented from primary care records, to add to the understanding of patient factors on disease outcomes. \n\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK.  Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 and 2.\n\nEHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.  UHB has cared for >5000 COVID admissions to date.  \n\nScope: All COVID swab confirmed hospitalised patients to UHB from January – May 2020.  The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes but also primary care records and clinic letters.  Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.  Linked images available (radiographs, CT, MRI, ultrasound). \n\nAvailable supplementary data:\nHealth data preceding and following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/143",
    "uid": "cfa1ef13-8ae0-4827-8a0f-9d2df5244725",
    "datasource_id": 143,
    "source": "HDRUK"
  },
  {
    "id": 1156,
    "name": "Coagulopathies & arterial/venous thrombosis in COVID patients: an OMOP dataset",
    "description": "In December 2019, the first case of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) was described and by March 2020, the World Health Organization had declared the disease (Coronavirus disease 2019, COVID-19) a pandemic.  Whilst respiratory symptoms are the fundamental feature of the disease, evidence indicates that the disease is associated with coagulation dysfunction which predisposes patients to an increased risk of both venous and arterial thromboembolism (TE) and potentially increased mortality risk as a consequence.  Biomarkers associated with TE (D-dimers) are often raised in people with COVID but without clear evidence of TE.  It is important to understand who is at most risk of TE, to manage disease effectively.  This dataset (in OMOP) describes patients with and without COVID who were admitted to UHB including all those with and without TE. \n\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.  Birmingham was hard hit by all COVID waves and University Hospitals Birmingham NHS Foundation Trust (UHB) had >8000 COVID admissions by the end of December 2020.\n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.  \n\nScope: All patients admitted during the first wave of the COVID-19, both with and without COVID.  The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes.  Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards), presenting complaint, SARS-CoV-2 swab result, diagnosis of TE, clotting parameters, D-Dimers, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, imaging reports, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.  \n\nAvailable supplementary data:  Matched controls; ambulance, synthetic data. \n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/144",
    "uid": "dd4629c0-14b6-4cba-8dcf-31ec5fea68bb",
    "datasource_id": 144,
    "source": "HDRUK"
  },
  {
    "id": 1157,
    "name": "Deeply-phenotyped hospital COVID patients: severity, acuity, therapies, outcomes",
    "description": "PIONEER:  Deeply-phenotyped hospital COVID patients: severity, acuity, therapies, outcomes\nDataset number 4.0\n\nCoronavirus disease 2019 (COVID-19) was identified in January 2020.  Currently, there have been more than 6 million cases& more than 1.5 million deaths worldwide.  Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS)& death.  There is a pressing need for tools to stratify patients, to identify those at greatest risk.  Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care.  There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.\n\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK.  Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.\n\nEHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.  UHB has cared for >5000 COVID admissions to date.\n\nScope: All COVID swab confirmed hospitalised patients to UHB from January – May 2020.  The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes but also primary care records& clinic letters.  Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.  Linked images available (radiographs, CT, MRI, ultrasound). \n\nAvailable supplementary data:\nHealth data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/145",
    "uid": "16a09f1b-3654-4ec3-9913-fb70cb1e2364",
    "datasource_id": 145,
    "source": "HDRUK"
  },
  {
    "id": 1158,
    "name": "Longitudinal C-reactive protein concentrations in COVID-19: an OMOP dataset",
    "description": "C-reactive protein (CRP) is the classical acute-phase protein produced by the liver at rates regulated by pro-inflammatory cytokines, notably IL-6. Acute phase CRP production is non-specific but generally reflects the extent and severity of whatever infective, inflammatory, traumatic and neoplastic conditions have triggered it (Pepys, M. B. & Hirschfield, G. M. J. Clin. Invest. 111, 1805-1812 (2003). CRP binds specifically to dead or dying cells and then activates complement, leading to enhanced inflammation and exacerbation of pre-existing tissue damage (Griselli, M. et al. J. Exp. Med. 190, 1733-1739 (1999). Large amounts of CRP in the blood can also increase damage to tissues that are already injured.  CRP may thus contribute to disease severity and death in COVID-19.   \n\nCirculating CRP values in COVID-19 patients are closely associated with disease activity, severity and outcome (for example: L. Yan et al. (2020) https://doi.org/10.1038/s42256-020-0180-7). However, the published studies are of moderate size with only one or few CRP measurements per patient.  \n\nIn this OMOP dataset, we present longitudinal CRP measurements for a cohort of over 4500 hospitalised COVID-19 patients, from admission to discharge, including severity of disease, co-morbidities, treatments given, complications, ITU admissions and patient outcomes. \n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. \n\nEHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.\n\nScope: All hospitalised patients admitted to Queen Elizabeth Hospital, Birmingham with positive SARS-Cov2 tests reported, transformed into an extended set of tables based on OMOP. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care including timings, admissions, escalation of care to ITU, discharge outcomes, physiology readings (heart rate, blood pressure, AVPU score and others), blood results (especially C-Reactive Protein (CRP) measurements) and drug prescribing and administration data.\n\nAvailable supplementary data: Matched controls; ambulance, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/146",
    "uid": "597524d4-038e-4fc2-aff9-1c9b87d3e16c",
    "datasource_id": 146,
    "source": "HDRUK"
  },
  {
    "id": 1159,
    "name": "Synthetic dataset - Using data-driven ML towards improving diagnosis of ACS",
    "description": "Background\nAcute compartment syndrome (ACS) is an emergency orthopaedic condition wherein a rapid rise in compartmental pressure compromises blood perfusion to the tissues leading to ischaemia and muscle necrosis.  This serious condition is often misdiagnosed or associated with significant diagnostic delay, and can lead to limb amputations and death. \n\nThe most common causes of ACS are high impact trauma, especially fractures of the lower limbs which account for 40% of ACS cases.  ACS is a challenge to diagnose and treat effectively, with differing clinical thresholds being utilised which can result in unnecessary osteotomy.  The highly granular synthetic data for over 900 patients with ACS provide the following key parameters to support critical research into this condition: \n \n1.\tPatient data (injury type, location, age, sex, pain levels, pre-injury status and comorbidities) \n2.\t Physiological parameters (intracompartmental pressure, pH, tissue oxygenation, compartment hardness) \n3.\tMuscle biomarkers (creatine kinase, myoglobin, lactate dehydrogenase) \n4.\tBlood vessel damage biomarkers (glycocalyx shedding markers, endothelial permeability markers) \n\nPIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nScope: Enabling data-driven research and machine learning models towards improving the diagnosis of Acute compartment syndrome. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, physiological parameters, muscle biomarkers, blood biomarkers and co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings and admissions), presenting complaint, lab analysis results (eGFR, troponin, CRP, INR, ABG glucose), systolic and diastolic blood pressures, procedures and surgery details.  \n  \nAvailable supplementary data: ACS cohort, Matched controls; ambulance, OMOP data.\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/138",
    "uid": "bdd9a3bf-e88a-4681-95cd-ae2afb2c9f2c",
    "datasource_id": 138,
    "source": "HDRUK"
  },
  {
    "id": 1160,
    "name": "OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes",
    "description": "OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes\nDataset number 2.0\n\nCoronavirus disease 2019 (COVID-19) was identified in January 2020.  Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide.  Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) & death.  There is a pressing need for tools to stratify patients, to identify those at greatest risk.  Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID OMOP dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.\n\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK.  Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.\n\nEHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.  UHB has cared for >5000 COVID admissions to date.  This is a subset of data in OMOP format.\n\nScope: All COVID swab confirmed hospitalised patients to UHB from January – August 2020.  The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes.  Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.  \n\nAvailable supplementary data:\nHealth data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data. Further OMOP data available as an additional service.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/139",
    "uid": "0eb789bb-0047-46ac-83ff-631f31e98da0",
    "datasource_id": 139,
    "source": "HDRUK"
  },
  {
    "id": 1161,
    "name": "UHB 100K Genomics patient clinical data including their acute care contacts",
    "description": "1 in 17 people are born with or develop a rare disease during their lifetime. 80% of rare diseases have an identified genetic component. However, there are usually significant diagnostic delays. The 100k Genome project was established to collect clinical data, genomic sequencing and samples from people with cancer and rare diseases, to better understand disease and find novel treatments and interventions. This includes rare cardiovascular, ciliopathy, endocrine, gastroenterological, haematological, metabolic, neurological, renal, respiratory skeletal and rheumatological disorders and cancers. \n\nThe PIONEER University Hospital Birmingham (UHB) secondary care 100k genomics dataset contains granular demographic, morbidity, treatment and outcome data, supplemented with acute care contacts with serial physiology, blood biomarker data from UHB patients recruited to this programme, to better understand the acute healthcare needs of this group of patients.\n\nPIONEER geography: The West Midlands has a population of 5.9M and includes a diverse ethnic and socio-economic mix. There is a higher than average percentage of minority ethnic groups and a higher than average proportion of patients with rare diseases. Birmingham is home to the first Centre for Rare Diseases for adults and children, treating more than 500 rare diseases and 9000 patients per year.\n\nElectronic Health Records: University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2M patient episodes per year, 2750 beds and 100 ITU beds. \n\nScope: All patients recruited to the 100K genome project from UHB. This includes all routinely collected health data for all these patients, but data is uniquely supplemented with all acute care contacts through UHB. The dataset includes highly granular patient demographics and co-morbidities taken from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed and administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.\n\nAvailable supplementary data: Matched controls; ambulance, synthetic data.\nAvailable supplementary support: Analytics, Model build, validation and refinement; A.I.; Data partner support for ETL process, Clinical expertise, Patient and end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.",
    "url": "https://healthdatagateway.org/en/dataset/140",
    "uid": "ceae4fe0-4e98-4e3c-85db-e86539aade81",
    "datasource_id": 140,
    "source": "HDRUK"
  },
  {
    "id": 1162,
    "name": "Clinical response thresholds (acuity) in acutely unwell patients: onset-outcome",
    "description": "Clinical response thresholds (acuity) in acutely unwell patients: onset-outcome\n\nEarly warning systems (EWS) are bedside tools used to assess basic physiological parameters to identify patients with potential or established critical illness. Evidence suggests that they may predict risk of intensive care admission, death and length of hospital stay.  In 2017, the Royal College of Physicians (RCP) published an updated National Early Warning Score, referred to as NEWS2, based upon six physiological parameters. It is associated with specific clinical response recommendations in which a step change occurs at a threshold NEWS2 score ?5, requiring an urgent clinical response no matter what the presenting complaint or condition.\n\nPIONEER geography\nThe West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK.  Birmingham has the highest birth rate in England.  It also has the highest infant mortality rate. WM life expectancy is 1.8 years less than in London. There are particularly high rates of physical inactivity, obesity, smoking & diabetes.  Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.\n\nEHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. \n\nScope: All hospitalised patients to UHB from 2000 – current day.  Updated monthly.  Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after acute admission interrogated.\nThe dataset includes all admission ICD-10 & SNOMED-CT codes including medical, surgical, paediatrics.  Serial, structured data pertaining to process of care (timings, staff grades, specialty review, wards), presenting complaint, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.  Linked images available (radiographs, CT, MRI, ultrasound). Includes but not limited to COVID-19 wave 1 and wave 2 data.\n\nAvailable supplementary data:\nMatched ‘elective’ controls; ambulance, 111, 999 data, synthetic data.\n\nAvailable supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen",
    "url": "https://healthdatagateway.org/en/dataset/141",
    "uid": "a9018475-452b-4864-bbc3-80eda2067820",
    "datasource_id": 141,
    "source": "HDRUK"
  },
  {
    "id": 1163,
    "name": "Sars-CoV-2 Immunity & REinfection EvaluatioN (SIREN)",
    "description": "This is a large-scale study testing blood samples and swabs obtained from healthcare workers who work in a clinical setting to see if infection with COVID-19 protects them from future episodes of infection. It was set up by PHE in April 2020 in response to the need to assess the risk of re-infection in those who have tested positive for antibodies. Since the introduction of COVID-19 vaccination among healthcare workers, it is also a leading source of information on vaccine effectiveness.\n\nThe aim of SIREN is to establish whether healthcare workers who test positive for COVID-19 antibodies are protected from future episodes of infection.  We will follow healthcare workers for at least a year and study their immune response to the virus causing COVID-19. We will do this by collecting baseline data, such as their history of COVID-19 infection and potential exposure to the virus via an online survey.  Continued details of exposure and any new symptoms is monitored in regular follow-up surveys. A swab test (PCR) is completed every other  every other week in order to detect mild cases of COVID-19 or cases without symptoms.  Blood samples are taken each month to determine whether individuals have antibodies.  Since January 2021, vaccination data has been collected, in order to inform vaccine effectiveness estimates among healthcare workers.\n\nPublic Health England is not currently accepting requests to access this dataset.",
    "url": "https://healthdatagateway.org/en/dataset/136",
    "uid": "8e0221d4-dcfa-4580-834b-f44421fca3b5",
    "datasource_id": 136,
    "source": "HDRUK"
  },
  {
    "id": 1164,
    "name": "ECHO",
    "description": "The dataset collects all the values obtained during the scan and the operator's assessment of the cardiac chambers and valves, together with a free text field to describe the operator's overall opinion of the images.",
    "url": "https://healthdatagateway.org/en/dataset/131",
    "uid": "b54d9224-a03f-4d53-8de8-c253b65a8172",
    "datasource_id": 131,
    "source": "HDRUK"
  },
  {
    "id": 1165,
    "name": "SHARE",
    "description": "SHARE is an NHS Research Scotland initiative. It has been created to establish a register of people, aged 11 and over, interested in participating in health research. https://www.registerforshare.org/",
    "url": "https://healthdatagateway.org/en/dataset/132",
    "uid": "232c528c-48b5-4ff5-8119-5e9340bdc167",
    "datasource_id": 132,
    "source": "HDRUK"
  },
  {
    "id": 1166,
    "name": "SCI Diabetes",
    "description": "SCI Diabetes brings together the functionality of SCI-DC Network and SCI-DC Clinical and also includes specialty modules for Paediatrics, Podiatry, Diabetes Specialist Nursing and Dietetics\nSCI-Diabetes is developed, maintained and supported by the SCI-DC Development Team based in the Clinical Technology Centre, Ninewells Hospital, Tayside.",
    "url": "https://healthdatagateway.org/en/dataset/133",
    "uid": "e179bd5b-0153-46d4-9e1b-4febd3f1153f",
    "datasource_id": 133,
    "source": "HDRUK"
  },
  {
    "id": 1167,
    "name": "Follow-COVID",
    "description": "As COVID-19 is a new disease, there is a need to identify the long term consequences and future care needs of COVID19 survivors. Follow-COVID has recruited over 100 patients and has used  blood testing, urine, respiratory samples, and tests of blood vessel function to measure the long term consequences of this disease.\n\nThe study consists of a preliminary patient visit, followed by a second in-person visit several months later. There are then phone call consultations 1 year, 2 years and 5 years after the first visit, giving a longitudinal view of the progression of symptoms and long covid.",
    "url": "https://healthdatagateway.org/en/dataset/134",
    "uid": "e50d2b20-ef95-43fe-bbf0-83224d88e480",
    "datasource_id": 134,
    "source": "HDRUK"
  },
  {
    "id": 1168,
    "name": "SIMD",
    "description": "This Dataset is compiled be the Health Informatic Centre to combine a persons data with postal code, healthboard and SIMD Decile and Quintiles",
    "url": "https://healthdatagateway.org/en/dataset/124",
    "uid": "5e1ee577-f1d1-48ae-861e-6a5f2f510773",
    "datasource_id": 124,
    "source": "HDRUK"
  },
  {
    "id": 1169,
    "name": "Lighthouse",
    "description": "ECOSS is a database that holds surveillance data on various microorganisms (e.g. influenza virus, coronavirus) and infections reported from NHS diagnostic and reference laboratories and Pillar 2 facilities/Lighthouse laboratories [high-throughput facilities dedicated to COVID-19 viral Reverse Transcription-Polymerase Chain Reaction (RT-PCR) testing for the National Testing Programme]. Data on laboratory results for all SARS-CoV-2 RT-PCR tests carried out in Scotland are being collated by ECOSS and can be linked to other data sources",
    "url": "https://healthdatagateway.org/en/dataset/125",
    "uid": "62903826-acfc-4d60-9010-cd70b4f556c1",
    "datasource_id": 125,
    "source": "HDRUK"
  },
  {
    "id": 1170,
    "name": "SAS(Scottish Ambulance Service) Hypoglycemic",
    "description": "This data set contains Hypoglycemic Events and is provided for the whole of Scotland.\nThis may be CHI'ed to determine HB residency of patients. \nData is refreshed every 6 months and routine uploads are July and December.",
    "url": "https://healthdatagateway.org/en/dataset/118",
    "uid": "6a419752-42b0-485f-a0eb-08baa820920a",
    "datasource_id": 118,
    "source": "HDRUK"
  },
  {
    "id": 1171,
    "name": "MATCH",
    "description": "Up to 20% of cases of COVID-19 disease in some countries have been reported to be healthcare workers.  The prevalence of HCW infections has not been properly documented due to lack of comprehensive studies.  This information is crucial for infection control and to maintain the workforce during a pandemic.  In addition, as HCWs are frequently exposed to infected patients they represent an ideal population in which to characterise immune responses to the infection.  Discovering the frequency and impact of HCW infections will allow better workforce planning in the NHS as well as establishing whether a positive antibody test can allow staff to safely return to work with a low risk of infection.  2000 HCW in NHS Tayside and from social care will have blood samples tested with a validated laboratory assay to detect antibodies to SARS-CoV-2.  The aims of the study are to identify undiagnosed asymptomatic healthcare worker infections with COVID-19, to understand the burden of undetected infections and to identify in patients with detectable antibodies can be reinfected with COVID-19.",
    "url": "https://healthdatagateway.org/en/dataset/119",
    "uid": "5429fc2a-3d72-414d-9393-4079c4c6c0fa",
    "datasource_id": 119,
    "source": "HDRUK"
  },
  {
    "id": 1172,
    "name": "Covid-19 Tests",
    "description": "Health Protection Scotland (HPS), part of Public Health Scotland (PHS), is leading the Enhanced Surveillance of COVID-19 in Scotland (ESoCiS) programme on behalf of Scottish Government gathering a wide variety of data about COVID-19 from a range of sources, to learn more about the virus and gain an understanding of how it is spreading through the population in Scotland.   \nData is via ECOSS (health protection system) and the Test and Protect datasets. NHS Digital have been providing a feed of the UK Gov data into NSS IT which then populates the systems for a full picture.",
    "url": "https://healthdatagateway.org/en/dataset/112",
    "uid": "53585c9c-f588-41f8-b493-3d271a2794a8",
    "datasource_id": 112,
    "source": "HDRUK"
  },
  {
    "id": 1173,
    "name": "Covid 19 Accident and Emergency Statistics",
    "description": "Contains data on episode level new and unscheduled attendances at Emergency Departments and some smaller community hospitals and minor injury units (MIUs).",
    "url": "https://healthdatagateway.org/en/dataset/106",
    "uid": "c319c402-7ca7-4e2b-bd17-a1abacf9f84c",
    "datasource_id": 106,
    "source": "HDRUK"
  },
  {
    "id": 1174,
    "name": "Unscheduled Care",
    "description": "Feed was initially set up in May 2020 to cover COVID-19 epademic as this system was used within the emergency COVID hubs that were set up prior to being admitted to hospital.\nFeed included 2 years of back data, from January 2019 onwards.\nData Dictionary and other ISD documentation here: https://www.isdscotland.org/Products-and-Services/eDRIS/COVID-19/",
    "url": "https://healthdatagateway.org/en/dataset/107",
    "uid": "b37c01bf-59ba-412b-a08e-f8f8add92eab",
    "datasource_id": 107,
    "source": "HDRUK"
  },
  {
    "id": 1175,
    "name": "Diabetic Eye Screening: the Birmingham, Solihull and Black Country Data Set",
    "description": "Diabetes mellitus affects over 3.9 million people in the UK, with over 2.6 million people in England alone. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. The National Institute for Health and Care Excellence recommendations are for annual screening using digital retinal photography for all patients with diabetes aged 12 years and over until such time as specialist surveillance or referral to Hospital Eye Services (HES) is required. \n\nBirmingham, Solihull and Black Country DR screening program is a member of the National Health Service (NHS) Diabetic Eye Screening Programme.  This dataset contains routine community annual longitudinal screening patient results of over 200000 patients with screening results per patient ranging from 1 year to 15 years. Key data included are:\n• Total number of patients screened and graded over a 15 year period. \n• Demographic information (including age, sex and ethnicity)\n• Diabetes status\n• Diabetes type\n• Length of time since diagnosis of diabetes \n• Visual acuity\n• The national screening diabetic screening grade category (seven categories from R0M0 to R3M1)\n• Diabetic eye clinical features\n• Reason for sight and severe sight impairment\n• Screening Outcome (digital surveillance and time; referral to HES)\n\nGeography\nBirmingham, Solihull and Black Country is set within the West Midlands and has a population of circa 5.9million.  The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData source:  The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic screening scheme in Europe. \n\n\nWebsite: https://www.retinalscreening.co.uk/\n\nPathway: The Birmingham, Solihull and Black Country dataset is representative of the patient pathway for community screening and grading of diabetic eye disease. It covers standard UK Public Health England Diabetic Eye Screening requirements and will include patients receiving screening through the standard model, routine diabetic screening, surveillance and slit lamp examination.",
    "url": "https://healthdatagateway.org/en/dataset/97",
    "uid": "36886b21-12ff-45e7-82bc-fb5308c12450",
    "datasource_id": 97,
    "source": "HDRUK"
  },
  {
    "id": 1176,
    "name": "UHB Linked Diabetic Eye Disease in Acute Diabetic Hospital Admissions",
    "description": "Background:\nDiabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. More than 1 million people living with diabetes are acutely admitted to hospital due to complications of their illness every year. Complications include Diabetic emergencies such as Diabetic Comas, Hypoglycaemia, Diabetic ketoacidosis and Diabetic Hyperosmolar Hyperglycaemic State. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. This dataset includes acute all diabetic admissions to University Hospitals Birmingham NHS Trust from 2000 onwards with linked eye data including the national screening diabetic grade category (seven categories from R0M0 to R3M1) from the Birmingham, Solihull and Black Country DR screening program (a member of the National Health Service (NHS) Diabetic Eye Screening Programme) and the University Hospitals Birmingham NHS Trust Ophthalmology clinic at Queen Elizabeth Hospital, Birmingham .  \n\nGeography:\nThe West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData sources:  \n1. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 diabetic patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic eye screening scheme in Europe. \n2. The Electronic Health Records held at University Hospitals Birmingham NHS Foundation Trust is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. UHB runs a fully electronic healthcare record both for systemic disease as well as the Ophthalmology records.\n\nScope: \nAll hospitalised patients admitted to UHB with a diabetes related health concern from 2000 onwards. Longitudinal and individually linked with their diabetic eye care from primary screening data and secondary care ophthalmology data including \n• Demographic information (including age, sex and ethnicity)\n• Diabetes status\n• Diabetes type\n• Length of time since diagnosis of diabetes \n• Visual acuity\n• The national screening diabetic screening grade category (seven categories from R0M0 to R3M1)\n• Diabetic eye clinical features\n• Reason for sight and severe sight impairment\n• ICD-10 and SNOMED-CT codes pertaining to diabetes\n• Diagnosis for the acute/emergency admission\n• Co-morbid conditions\n• Medications\n• Outcome",
    "url": "https://healthdatagateway.org/en/dataset/98",
    "uid": "865c99c4-bbe9-45de-83b9-290fb54d5470",
    "datasource_id": 98,
    "source": "HDRUK"
  },
  {
    "id": 1177,
    "name": "Moorfields Dry-AMD Dataset 006",
    "description": "The Moorfields Dry-AMD Dataset encompasses all patients with advanced dry AMD, specifically geographic atrophy (GA), and without choroidal neovascularization, at Moorfields Eye Hospital - a leading provider of eye health services in the UK and a world-class centre of excellence for ophthalmic research and education. A detailed description of the dataset definitions can be found below.\n\nInclusion criteria:\n- a posterior free hand draw label of ‘Geographic’ or (ii) at least one reference in their clinical notes to ‘geographic atrophy’\n\nExclusion criteria:\n- Patients under the age of 50 at their most-recent imaging event\n- Patients with at least one reference in their clinical notes to ‘Stargardt’\n- Patients with a record of injections of any type\n- Patients with a posterior free hand draw label of ‘Choroidal neovascularization’\n\nThe dataset will include any imaging or clinical metadata that is available for these patients. In the instance where only one eye meets the criteria above, both eyes are included in the dataset. For these reasons, the dataset will include longitudinal data from where a patient underwent monitoring during the early and intermediate stages of the disease.\n\nClinical metadata includes information regarding:\n- patient demographics\n- visual acuities (predominantly measured with Early Treatment Diabetic Retinopathy Study (ETDRS) charts)\n- ocular surgeries\n\nAdditional information is provided in the ‘technical details’ tab. \n\nThe Dry-AMD dataset includes eye imaging modalities, such as:\n- Optical coherence tomography (CSO, Heidelberg, Optos, Topcon, Zeiss)\n- Colour fundus photographs (Topcon, Zeiss)\n- Ultra-wide field photographs (Optos, Zeiss)\n- Iris photographs (CSO, Zeiss)\n- Keratoscope topography (CSO)\n- Infrared photographs (Heidelberg, Topcon, Zeiss)\n- Fluorescein angiography (Heidelberg, Optos, Topcon, Zeiss)\n- Indocyanine green angiography (Heidelberg, Optos, Topcon)\n- Fundus autofluorescence (Heidelberg, Optos, Zeiss)\n\nImaging data from CSO is subject to additional approvals.\n\nAs of July 2024, the dataset consisted of 11,168 eyes and over 688,018 ophthalmic images.",
    "url": "https://healthdatagateway.org/en/dataset/99",
    "uid": "5e594b63-908c-4b47-a123-5d71bae5a0a1",
    "datasource_id": 99,
    "source": "HDRUK"
  },
  {
    "id": 1178,
    "name": "UHB Linked Diabetic Eye Disease and Cardiac Outcomes",
    "description": "www.insight.hdrhub.org/about-us \n\nBackground:\nDiabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. More than 1 million people living with diabetes are acutely admitted to hospital due to complications of their illness every year. Cardiovascuar disease is the most prevalent cause of morbidity and mortality in people with diabetes. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. This dataset includes the national screening diabetic grade category (seven categories from R0M0 to R3M1) from the Birmingham, Solihull and Black Country DR screening program (a member of the National Health Service (NHS) Diabetic Eye Screening Programme) and the University Hospitals Birmingham NHS Trust cardiac outcome data.  \n\nGeography:\nThe West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData sources:  \n1. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 diabetic patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic eye screening scheme in Europe. \n2. The Electronic Health Records held at University Hospitals Birmingham NHS Foundation Trust is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. UHB runs a fully electronic healthcare record for systemic disease.\n\nScope: \nAll Birmingham, Solihull and Black Country diabetic eye screened participants who have been admitted to UHB with a cardiac related health concern from 2006 onwards. Longitudinal and individually linked with their diabetic eye care from primary screening data and secondary care hospital cardiac outcome data including \n• Demographic information (including age, sex and ethnicity)\n• Diabetes status\n• Diabetes type\n• Length of time since diagnosis of diabetes \n• Visual acuity\n• The national screening diabetic screening grade category (seven categories from R0M0 to R3M1)\n• Diabetic eye clinical features\n• Reason for sight and severe sight impairment\n• ICD-10 and SNOMED-CT codes pertaining to cardiac disease\n• Outcome\n\nWebsite: https://www.retinalscreening.co.uk/",
    "url": "https://healthdatagateway.org/en/dataset/100",
    "uid": "4385ed4c-1c42-4ba5-b57f-9ab18e5ffbe1",
    "datasource_id": 100,
    "source": "HDRUK"
  },
  {
    "id": 1179,
    "name": "Moorfields DMO Dataset 003",
    "description": "The Moorfields DMO Dataset encompasses all patients who have received at least one injection of either Lucentis (ranibizumab) or Eylea (aflibercept) to treat diabetic macular oedema (DMO) at Moorfields Eye Hospital - a leading provider of eye health services in the UK and a world-class centre of excellence for ophthalmic research and education.\n\nThese therapies began at Moorfields in 2013, however, the dataset will include any imaging or clinical metadata that is available for these patients prior to that time (for example in patients who were initially monitored for the early forms of the disease prior to receiving treatment). Also of note, this dataset will include data from both eyes in each case - for example, it will include data from fellow eyes that are not receiving injections. For these reasons, the dataset will include longitudinal data from a wide range of diabetic eye disease. \n\nClinical metadata includes information regarding:\n- patient demographics\n- visual acuities (predominantly measured with Early Treatment Diabetic Retinopathy Study (ETDRS) charts)\n- diabetic retinopathy grading\n- intravitreal therapies and ocular surgeries\n\nAdditional information is provided in the ‘technical details’ tab. \n\nThe DMO dataset includes eye imaging modalities, such as:\n- Optical coherence tomography (CSO, Heidelberg, Optos, Topcon, Zeiss)\n- Colour fundus photographs (Topcon, Zeiss)\n- Ultra-wide field photographs (Optos, Zeiss)\n- Iris photographs (CSO, Zeiss)\n- Keratoscope topography (CSO)\n- Infrared photographs (Heidelberg, Topcon, Zeiss)\n- Fluorescein angiography (Heidelberg, Optos, Topcon, Zeiss)\n- Indocyanine green angiography (Heidelberg, Optos, Topcon)\n- Fundus autofluorescence (Heidelberg, Optos, Zeiss)\n\nImaging data from CSO is subject to additional approvals.\n\nAs of July 2024, the dataset consisted of 5,873 eyes receiving Lucentis or Eylea for DMO, over 58,808 injection episodes, and over 1,304,395 ophthalmic images. This is one of the largest single centre databases from patients with DMO and covers more than a decade of follow-up for these patients.",
    "url": "https://healthdatagateway.org/en/dataset/101",
    "uid": "120106fb-49b4-4bc0-b30c-50a59c5c6253",
    "datasource_id": 101,
    "source": "HDRUK"
  },
  {
    "id": 1180,
    "name": "Cataract surgery and visual outcomes at QE Hospital, Birmingham, UK",
    "description": "Background\nCataract, opacification of the lens, is one of the commonest causes of loss of useful vision, with an estimated 16 million people worldwide affected. Cataract surgery is the most frequently performed surgical procedure in the NHS with over 300,000 operations annually in England alone. This dataset spans the full cataract care pathway at University Hospitals Birmingham NHS Trust.  Information from the start of the hospital episode including the ophthalmological clinical assessment (details of ocular examination and vision), preoperative assessment (ocular biometry), moving to the surgery day and the chosen anaesthesia (type of anaesthetic), surgery (details of procedure, lens choice, any complications), postoperative recovery (postoperative events) and visual rehabilitation (refractive and visual outcomes). \n\nGeography\nThe West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData source:  \n1. The Electronic Health Records held at University Hospitals Birmingham NHS Foundation Trust is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. UHB runs a fully electronic healthcare record for systemic disease.",
    "url": "https://healthdatagateway.org/en/dataset/102",
    "uid": "4352ef1c-67d7-4f2e-b256-1efaac61dacd",
    "datasource_id": 102,
    "source": "HDRUK"
  },
  {
    "id": 1181,
    "name": "Moorfields AMD Dataset 002",
    "description": "The AMD Dataset encompasses all patients who have received at least one injection of either Lucentis (ranibizumab) or Eylea (aflibercept) to treat age-related macular degeneration (AMD) at Moorfields Eye Hospital  - a leading provider of eye health services in the UK and a world-class centre of excellence for ophthalmic research and education. \n\nThese therapies began at Moorfields in 2007, however, the dataset will include any imaging or clinical metadata that is available for these patients prior to that time (for example in patients who were initially monitored for the early or intermediate forms of this disease prior to receiving treatment). Also of note, this dataset will include data from both eyes in each case - for example, it will include data from fellow eyes that are not receiving injections. For these reasons, the dataset will include longitudinal data from a mixture of eyes with both “dry” and “wet” AMD. \n\nClinical metadata includes information regarding:\n- patient demographics\n- visual acuities (predominantly measured with Early Treatment Diabetic Retinopathy Study (ETDRS) charts)\n- diabetic retinopathy grading\n- intravitreal therapies and ocular surgeries\n\nAdditional information is provided in the ‘technical details’ tab. \n\nThe AMD dataset includes eye imaging modalities, such as:\n- Optical coherence tomography (CSO, Heidelberg, Optos, Topcon, Zeiss)\n- Colour fundus photographs (Topcon, Zeiss)\n- Ultra-wide field photographs (Optos, Zeiss)\n- Iris photographs (CSO, Zeiss)\n- Keratoscope topography (CSO)\n- Infrared photographs (Heidelberg, Topcon, Zeiss)\n- Fluorescein angiography (Heidelberg, Optos, Topcon, Zeiss)\n- Indocyanine green angiography (Heidelberg, Optos, Topcon)\n- Fundus autofluorescence (Heidelberg, Optos, Zeiss)\n\nImaging data from CSO is subject to additional approvals.\n\nAs of July 2024, the dataset consisted of 12,641 eyes receiving Lucentis or Eylea for AMD, 220,219 injection episodes, and 2,943,338 ophthalmic images. This is the largest single centre database from patients with AMD and covers more than a decade of follow-up for these patients.",
    "url": "https://healthdatagateway.org/en/dataset/103",
    "uid": "bf392537-82b4-4d71-ace0-e7bea3b167fb",
    "datasource_id": 103,
    "source": "HDRUK"
  },
  {
    "id": 1182,
    "name": "Eye images and retinopathy grades in diabetic eye screening",
    "description": "Background\nDiabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. The National Institute for Health and Care Excellence recommendations are for annual screening using digital retinal photography for all patients with diabetes aged 12 years and over until such time as specialist surveillance or referral to Hospital Eye Services (HES) is required. \n\nBirmingham, Solihull and Black Country DR screening program is a member of the National Health Service (NHS) Diabetic Eye Screening Programme.  This dataset contains routine community annual longitudinal screening patient results of over 200000 patients with screening results per patient ranging from 1 year to 15 years. Key data included in this imaging dataset are:\n• Fundal photographs\n• The national screening diabetic grade category (seven categories from R0M0 to R3M1)\n• Screening Outcome (digital surveillance and time; referral to HES)\n\nGeography\nBirmingham, Solihull and Black Country is set within the West Midlands and has a population of circa 5.9million.  The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData source:  The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic screening scheme in Europe.",
    "url": "https://healthdatagateway.org/en/dataset/92",
    "uid": "3b1bb176-e22f-402a-b863-730d05b22021",
    "datasource_id": 92,
    "source": "HDRUK"
  },
  {
    "id": 1183,
    "name": "Moorfields Eye Dementia Dataset",
    "description": "The Moorfields Eye Dementia Dataset is a large longitudinal multimodal dataset derived from routinely collected data acquired as part of patient care in units under the provisions of Moorfields Eye Hospital NHS Foundation Trust (MEH). This includes sociodemographic and clinical data recorded through the Trust information health systems on all individuals aged 18. The dataset contains individuals who have an image and are at least 40 years of age.  In addition, tabular data on imaging, including imaging characteristics and clinically relevant quantifiable indices (e.g. retinal vascular tortuosity), are provided from select processing algorithms.",
    "url": "https://healthdatagateway.org/en/dataset/93",
    "uid": "a6710c84-0714-412f-a8cb-a6623fb0ded4",
    "datasource_id": 93,
    "source": "HDRUK"
  },
  {
    "id": 1184,
    "name": "UHB Linked Diabetic Eye Disease from National Screening to Hospital Eye Care",
    "description": "Background:\nDiabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. This dataset includes the national screening diabetic grade category (seven categories from R0M0 to R3M1) from the Birmingham, Solihull and Black Country DR screening program (a member of the National Health Service (NHS) Diabetic Eye Screening Programme) and the Queen Elizabeth Hospital, University Hospitals Birmingham NHS Trust ophthalmology treatment and visual outcome data.  \n\nGeography:\nThe West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData sources:  \n1. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 diabetic patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic eye screening scheme in Europe. \n2. The Electronic Health Records from the Ophthalmology clinic at Queen. Elizabeth Hospital, University Hospitals Birmingham NHS Foundation.\n\nScope: \nAll Birmingham, Solihull and Black Country diabetic eye screened participants who have been see in ophthalmology outpatients at University Hospitals Birmingham NHS Foundation from 2006 onwards. Longitudinal and individually linked with their diabetic eye care from primary screening data and secondary hospital eye care including \n• Demographic information (including age, sex and ethnicity)\n• Diabetes status\n• Diabetes type\n• Length of time since diagnosis of diabetes \n• Visual acuity\n• The national screening diabetic screening grade category (seven categories from R0M0 to R3M1)\n• Diabetic eye clinical features\n• Reason for sight and severe sight impairment\n• Ocular treatment including laser treatment and surgical treatment\n• Visual Outcome",
    "url": "https://healthdatagateway.org/en/dataset/94",
    "uid": "7c087b3c-cc04-4d3d-894b-7ea5419fa714",
    "datasource_id": 94,
    "source": "HDRUK"
  },
  {
    "id": 1185,
    "name": "Moorfields DR Dataset 005",
    "description": "The Moorfields DR Dataset encompasses all patients who have been referred via the NHS diabetic eye screening program (DESP) to Moorfields Eye Hospital  - a leading provider of eye health services in the UK and a world-class centre of excellence for ophthalmic research and education.\n\nThe DESP invites all diabetic patients aged 12 years or over to annual primary-care-based screening. Here, two-field fundus photography (one image centred on the macula and a second image centred on the optic disc) is acquired and graded according to the English Screening Programme for Diabetic Retinopathy standards. If criteria were met (R2, R3, R3, M1, or ungradable), patients are referred to hospital eye services and suspended from screening while under secondary care. Urgently referred patients (retinopathy grade R3) are to be seen within 2 weeks, and routinely referred patients within 10 weeks.\n\nThe earliest available screening records are from 2013, however, the dataset will include any imaging or clinical metadata that is available for these patients prior to that time (for example in patients who were initially monitored for the early manifestations of the disease). Also of note, this dataset will include data from both eyes in each case. For these reasons, the dataset will include longitudinal data from a wide range of diabetic eye disease.\n\nClinical metadata includes information regarding:\n- patient demographics\n- visual acuities (predominantly measured with Early Treatment Diabetic Retinopathy Study (ETDRS) charts)\n- diabetic retinopathy grading\n- intravitreal therapies and ocular surgeries\n\nAdditional information is provided in the ‘technical details’ tab.\n\nThe DR dataset includes eye imaging modalities, such as:\n- Optical coherence tomography (CSO, Heidelberg, Optos, Topcon, Zeiss)\n- Colour fundus photographs (Topcon, Zeiss)\n- Ultra-wide field photographs (Optos, Zeiss)\n- Iris photographs (CSO, Zeiss)\n- Keratoscope topography (CSO)\n- Infrared photographs (Heidelberg, Topcon, Zeiss)\n- Fluorescein angiography (Heidelberg, Optos, Topcon, Zeiss)\n- Indocyanine green angiography (Heidelberg, Optos, Topcon)\n- Fundus autofluorescence (Heidelberg, Optos, Zeiss)\n\nImaging data from CSO is subject to additional approvals.\n\nAs of July 2024, the dataset consisted of 91,009 eyes with 445,792 screening readings, and over 5,537,798 ophthalmic images. This is one of the largest single centre databases from patients with DR and covers more than a decade of follow-up for these patients.",
    "url": "https://healthdatagateway.org/en/dataset/95",
    "uid": "9d62e772-8064-4264-bbf3-4f1c34422ccc",
    "datasource_id": 95,
    "source": "HDRUK"
  },
  {
    "id": 1186,
    "name": "Moorfields Ophthalmic Prescription 004",
    "description": "The Moorfields Ophthalmic Prescription Dataset is a longitudinal dataset consisting of routinely collected prescription data from patients who have received and are receiving treatment at Moorfields Eye Hospital - a leading provider of eye health services in the UK and a world-class centre of excellence for ophthalmic research and education.\n\nThere are a total of 4.2M prescriptions from 428,593 patients recorded as of July 2024, with over 1M new prescriptions records entered in the past year.\nThe clinical metadata associated with these prescription records includes details such as the medicine prescribed, the dosage, the laterality, the frequency and method of administration, among others. \n\nAdditional information is provided in the ‘technical details’ tab.",
    "url": "https://healthdatagateway.org/en/dataset/89",
    "uid": "2ec8d8e3-35bc-4304-83e8-3afed4438cd5",
    "datasource_id": 89,
    "source": "HDRUK"
  },
  {
    "id": 1187,
    "name": "Neovascular age related macular degeneration at University Hospitals Birmingham",
    "description": "Background:\nAge-related macular degeneration (AMD) is a degenerative disease of the human retina affecting individuals over the age of 55 years. AMD is the leading cause of blindness in industrialized countries. Worldwide, the number of people with AMD is predicted to increase from 196 million in 2020 to 288 million by 2040.  \n\nThe UHB AMD Dataset is a longitudinal dataset consisting of routinely collected imaging and clinical metadata from patients receiving treatment for age-related macular degeneration (AMD) at UHB, from 2007 to the present. \n\nThis dataset encompasses all patients at UHB who have received at least one injection of either Lucentis (ranibizumab) or Eylea (aflibercept) or avastin. This dataset will include data from both eyes in each case - for example, it will include data from fellow eyes that are not receiving injections. For these reasons, the dataset will include longitudinal data from a mixture of eyes with both “dry” and “wet” AMD. Clinical metadata includes demographic information, visual acuities (predominantly measured with Early Treatment Diabetic Retinopathy Study (ETDRS) charts), treatment, and outcomes.\n\nThis dataset is continuously updating, however, as of October 2021, it consisted of 15063 eyes receiving treatment for AMD. This is a large single centre database from patients with AMD and covers more than a decade of follow-up for these patients.\n\nGeography\nThe Queen Elizabeth Hospital is one of the largest single-site hospitals in the United Kingdom, with 1,215 inpatient beds.   Queen Elizabeth Hospital is part of one of the largest teaching trusts in England (University Hospitals Birmingham).  Set within the West Midlands and it has a catchment population of circa 5.9million.  The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData source:  Ophthalmology department at Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.",
    "url": "https://healthdatagateway.org/en/dataset/90",
    "uid": "89c2c798-2e0f-4235-9cd9-e2347f2dfc50",
    "datasource_id": 90,
    "source": "HDRUK"
  },
  {
    "id": 1188,
    "name": "Glaucoma dataset at University Hospitals Birmingham",
    "description": "Background\nGlaucoma is a worldwide leading cause of irreversible sight loss. Worldwide, an estimated 60 million people have glaucoma. Glaucoma is a condition of increased intraocular pressure in the eye. Because it may be asymptomatic until a relatively late stage, diagnosis is frequently delayed. There are four general categories of glaucoma: primary open-angle and angle-closure, and secondary open and angle-closure glaucoma.\n\nThe UHB glaucoma dataset is a longitudinal dataset consisting of routinely collected clinical metadata from patients receiving treatment for glaucoma at UHB, from 2007 to the present. \n\nThis dataset encompasses all patients at UHB who have received a diagnosis of primary or secondary glaucoma or ocular hypertension. Clinical metadata includes demographic information, visual acuities, central corneal thickness, intraocular pressure, optic nerve head findings, and mean deviation of the Humphrey visual fields.\n\nThis dataset is continuously updating, however, as of 1st October 2021, it consisted of 5065 people This is a large single centre database from patients with glaucoma and covers more than a decade of follow-up for these patients.\n\nGeography\nThe Queen Elizabeth Hospital is one of the largest single-site hospitals in the United Kingdom, with 1,215 inpatient beds. Queen Elizabeth Hospital is part of one of the largest teaching trusts in England (University Hospitals Birmingham).  Set within the West Midlands and it has a catchment population of circa 5.9million.  The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.\n\nData source:  Ophthalmology department at Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.",
    "url": "https://healthdatagateway.org/en/dataset/91",
    "uid": "48c44534-0184-471e-93b8-1385f92c6631",
    "datasource_id": 91,
    "source": "HDRUK"
  },
  {
    "id": 1189,
    "name": "COVID19 in Pregnancy in Scotland (COPS)",
    "description": "COPS linked healthcare records on all pregnancies in Scotland including early pregnancy losses (eg miscarriage, ectopic pregnancy), terminations of pregnancy, live and stillbirths and neonatal health records, with COVID-19 test results and COVID-19 vaccine records. The COPS dataset links together variables from a wide range of source datasets including GP records.",
    "url": "https://healthdatagateway.org/en/dataset/83",
    "uid": "d80bde39-1d9f-43cb-947c-5f6182c786b9",
    "datasource_id": 83,
    "source": "HDRUK"
  },
  {
    "id": 1190,
    "name": "COVID-19 Tests",
    "description": "Contains the results of all PCR / Antigen Tests / LFTs reported to Public Health Scotland by NHS Scotland and UK Government Regional Testing Laboratories and home testing kits",
    "url": "https://healthdatagateway.org/en/dataset/84",
    "uid": "607b17d6-74de-466a-a377-238444707abb",
    "datasource_id": 84,
    "source": "HDRUK"
  },
  {
    "id": 1191,
    "name": "SARS-CoV-2 viral sequencing data (COG-UK data) - Lineage/Variant Data - Scotland",
    "description": "File contains basic public metadata, including sequence_name, location, date, pangolin lineage assignment, version and associated scores, scorpio VOC/VUI constellation call and associated scores, key spike protein mutations calls and a list of all nucleotide mutations found.",
    "url": "https://healthdatagateway.org/en/dataset/72",
    "uid": "69a356d6-599e-4d02-8241-066ce3c297bd",
    "datasource_id": 72,
    "source": "HDRUK"
  },
  {
    "id": 1192,
    "name": "Brain Health - Outpatient Appointments and Attendances - SMR00",
    "description": "The Brain Health Data Pilot (BHDP) project aims to be a shared database (like a library) of information for scientists studying brain health, especially for diseases like dementia, which affects about 900,000 people in the UK. Its main feature is a huge collection of brain images linked to routinely collected health records, both from NHS Scotland, which will help scientists learn more about dementia and other brain diseases. What is special about this database is that it will get better over time – as scientists use it and add their discoveries, it becomes more valuable. \n\nThis is a subset of the Outpatient Appointment and Attendances (SMR00) dataset for use in the Brain Health Data Pilot (BHDP) project. \n\nAn SMR00 is generated for outpatients receiving care in the specialties listed when:\n\n-they attend a medical consultant outpatient clinic;\n-they meet with a consultant or senior member of his/her team outwith an outpatient clinic session (including the patient's home).\n-they attend a clinic run by a nurse or an AHP identified as the Health Care Professional Responsible for Care for that clinic and who has legal and clinical responsibility for that patient.\n\nThe dataset is generally fully complete and ready for analysis three month preceding the current date.  So for example at the end of August, data is available until the end of May.",
    "url": "https://healthdatagateway.org/en/dataset/76",
    "uid": "020de373-396d-4e22-a9c1-63b6e87e6bfa",
    "datasource_id": 76,
    "source": "HDRUK"
  },
  {
    "id": 1193,
    "name": "Scottish Medical Imaging (SMI) Research Dataset",
    "description": "The Scottish Medical Imaging (SMI) Service has created a database of deidentified images (for example CT Scans, MRIs) that can be used by researchers who require access to clinical images and metadata for their approved research. This new national resource will be used to provision de-identified images and associated report data to researchers which, if required, may be linked to other available de-identified datasets",
    "url": "https://healthdatagateway.org/en/dataset/77",
    "uid": "1c49a822-6432-468b-8ba5-6aab534654b9",
    "datasource_id": 77,
    "source": "HDRUK"
  },
  {
    "id": 1194,
    "name": "Brain Health - Prescribing Information System (PIS)",
    "description": "The Brain Health Data Pilot (BHDP) project aims to be a shared database (like a library) of information for scientists studying brain health, especially for diseases like dementia, which affects about 900,000 people in the UK. Its main feature is a huge collection of brain images linked to routinely collected health records, both from NHS Scotland, which will help scientists learn more about dementia and other brain diseases. What is special about this database is that it will get better over time – as scientists use it and add their discoveries, it becomes more valuable. \n\nThis dataset is a subset of the Prescribing Information System (PIS)  data for use with the BHDP project.\n\nThe information is supplied by Practitioner & Counter Fraud Services Division (P&CFS) who is responsible for the processing and pricing of all prescriptions dispensed in Scotland. These data are augmented with information on prescriptions written in Scotland that were dispensed elsewhere in the United Kingdom. GP’s write the vast majority of these prescriptions, with the remainder written by other authorised prescribers such as nurses and dentists. Also included in the dataset are prescriptions written in hospitals that are dispensed in the community. Note that prescriptions dispensed within hospitals are not included. Data includes CHI number, prescriber and dispenser details for community prescribing, costs and drug information. Data on practices (e.g. list size), organisational structures (e.g. practices within Community Health Partnerships (CHPs) and NHS Boards), prescribable items (e.g. manufacturer, formulation code, strength) are also included.\nAround 100 million data items are loaded per annum.",
    "url": "https://healthdatagateway.org/en/dataset/67",
    "uid": "ecf6cd02-c2fe-4bea-9dbc-a34ecaf3de59",
    "datasource_id": 67,
    "source": "HDRUK"
  },
  {
    "id": 1195,
    "name": "SICSAG Daily (Scottish Intensive Care Audit Group)",
    "description": "Each episode will consist of one or more days stay in critical care - a one to many relationship",
    "url": "https://healthdatagateway.org/en/dataset/62",
    "uid": "811a54fb-8b79-442f-8e28-a725a0561a15",
    "datasource_id": 62,
    "source": "HDRUK"
  },
  {
    "id": 1196,
    "name": "Brain Health - General Acute Inpatient and Day Case - Scottish Morbidity Record (SMR01)",
    "description": "The Brain Health Data Pilot (BHDP) project aims to be a shared database (like a library) of information for scientists studying brain health, especially for diseases like dementia, which affects about 900,000 people in the UK. Its main feature is a huge collection of brain images linked to routinely collected health records, both from NHS Scotland, which will help scientists learn more about dementia and other brain diseases. What is special about this database is that it will get better over time – as scientists use it and add their discoveries, it becomes more valuable. \n\nThis is a subset of the General Acute Inpatient and Day Case - Scottish Morbidity Record  (SMR01) dataset for use in the Brain Health Data Pilot (BHDP) project. \n\nThe dataset contains patient identifiers such as name, date of birth, Community Health Index number, NHS number, postcode and ethnicity and episode management data. Of particular interest to researchers would be variables such as where the episode took place, admission type (includes patient injury classifications such as self-inflicted or home accident), waiting times, patients condition (as classified under ICD-10), operations, and discharge location. A wide variety of geographical data is also included in the dataset including Scottish Index of Multiple Deprivation and Carstairs measures, census output area, NHS Board, Electoral Ward and Parliamentary constituency.",
    "url": "https://healthdatagateway.org/en/dataset/63",
    "uid": "8db9420f-01c1-4cfc-b205-8f1831a113eb",
    "datasource_id": 63,
    "source": "HDRUK"
  },
  {
    "id": 1197,
    "name": "Brain Health - Scottish Cancer Registry (SMR06)",
    "description": "The Brain Health Data Pilot (BHDP) project aims to be a shared database (like a library) of information for scientists studying brain health, especially for diseases like dementia, which affects about 900,000 people in the UK. Its main feature is a huge collection of brain images linked to routinely collected health records, both from NHS Scotland, which will help scientists learn more about dementia and other brain diseases. What is special about this database is that it will get better over time – as scientists use it and add their discoveries, it becomes more valuable. \n\nThis is a subset of the Scottish Cancer Registry (SMR06) dataset for use in the Brain Health Data Pilot (BHDP) project. \n\nThe registry began in 1958 collecting personal, demographic and diagnosis information (such as site, histology, behaviour, histological confirmation and hospital of diagnosis) from cancer patients. In 1997, a new electronic cancer recording system was launched and at this point the registry was extended to include extra information on tumour stage (for breast, cervical and colorectal cancer), tumour grade and treatment information. A wide variety of geographical data is also included in the dataset including Scottish Index of Multiple Deprivation and Carstairs measures, census output area, NHS Board, Electoral Ward and Parliamentary constituency.",
    "url": "https://healthdatagateway.org/en/dataset/66",
    "uid": "c59245d4-d2df-4273-8fca-9c00d52f82fc",
    "datasource_id": 66,
    "source": "HDRUK"
  },
  {
    "id": 1198,
    "name": "RAPID (Hospital Stay Level Data)",
    "description": "The data collection known as 'System Watch' was developed in 2001 to meet the requirements of the System Watch product, which monitors and predicts emergency admissions and bed occupancy in mainland NHS Boards. The data collection is now known as 'RAPID'. The data collected are management information and have very limited validation.",
    "url": "https://healthdatagateway.org/en/dataset/58",
    "uid": "1fc57971-5af5-499e-a500-4ffbb087f0c4",
    "datasource_id": 58,
    "source": "HDRUK"
  },
  {
    "id": 1199,
    "name": "SICSAG Episodes (Scottish Intensive Care Audit Group)",
    "description": "Each episode will consist of one or more days stay in critical care - a one to many relationship.",
    "url": "https://healthdatagateway.org/en/dataset/59",
    "uid": "6f8182d2-2993-400d-829d-b52cdb324bf3",
    "datasource_id": 59,
    "source": "HDRUK"
  },
  {
    "id": 1200,
    "name": "Brain Health - Mental Health Inpatient and Day Case - Scottish Morbidity Record (SMR04)",
    "description": "The Brain Health Data Pilot (BHDP) project aims to be a shared database (like a library) of information for scientists studying brain health, especially for diseases like dementia, which affects about 900,000 people in the UK. Its main feature is a huge collection of brain images linked to routinely collected health records, both from NHS Scotland, which will help scientists learn more about dementia and other brain diseases. What is special about this database is that it will get better over time – as scientists use it and add their discoveries, it becomes more valuable. \n\nThis is a subset of the Mental Health Inpatient and Day Case - Scottish Morbidity Record (SMR04) dataset for use in the Brain Health Data Pilot (BHDP) project. \n\nThe dataset contains a wide variety of information such as patient characteristics, mental health diagnosis, length of stay, destination on discharge, whether they are admitted under Mental Health Legislation and any previous psychiatric care. Patient identifiers such as name, date of birth, Community Health Index number, NHS number, and postcode are included together with a wide variety of geographical measures. This includes the Scottish Index of Multiple Deprivation and Carstairs measures, census output area, NHS Board, Electoral Ward and Parliamentary constituency.",
    "url": "https://healthdatagateway.org/en/dataset/60",
    "uid": "0c69005f-6420-47f5-8b95-609c7316ecea",
    "datasource_id": 60,
    "source": "HDRUK"
  },
  {
    "id": 1201,
    "name": "Brain Health - Scotland Accident and Emergency",
    "description": "The Brain Health Data Pilot (BHDP) project aims to be a shared database (like a library) of information for scientists studying brain health, especially for diseases like dementia, which affects about 900,000 people in the UK. Its main feature is a huge collection of brain images linked to routinely collected health records, both from NHS Scotland, which will help scientists learn more about dementia and other brain diseases. What is special about this database is that it will get better over time – as scientists use it and add their discoveries, it becomes more valuable. \n\nThis dataset is a subset of the Scottish Accident and Emergency data for use with the BHDP project.\n\nAccident and Emergency Statistics. The A&E datamart was established in June 2007 to monitor the compliance of each NHS Board against the 4 hour wait standard. In July 2010 the A&E data mart was extended further to collect items such as diagnosis, several injury fields and an alcohol involved flag, which will be used to identify whether the patient’s alcohol consumption was a factor in the attendance. The collection of the new fields has been driven by a variety of SG policy decisions and interest from a number of organisations. Although there is now the facility to submit these additional fields, they are still under development and ISD are working with the NHS Boards to support data collection and quality. There are two types of data submitted to the A&E datamart: episode and aggregate level data. All hospitals with Emergency Departments submit episode level data containing a detailed record for each patient attendance. Some smaller sites with minor injury units or community hospitals only submit aggregate files containing monthly summary attendance and compliance figures only. This is because they do not have the information systems and support to enable collection of detailed patient based information. Sites that submit episode level data account for around 94% of all attendances at A&E.",
    "url": "https://healthdatagateway.org/en/dataset/51",
    "uid": "f74bacde-b28c-41b6-b489-98f8dac5a893",
    "datasource_id": 51,
    "source": "HDRUK"
  },
  {
    "id": 1202,
    "name": "Scottish Covid-19 Vaccination Data",
    "description": "This dataset contains COVID-19 Vaccination events in Scotland since December 2020. This includes information such as eligibility cohort, date of vaccination and vaccination product.",
    "url": "https://healthdatagateway.org/en/dataset/53",
    "uid": "bcfd3b49-f7f0-489e-bc1b-bcce0bd261f2",
    "datasource_id": 53,
    "source": "HDRUK"
  },
  {
    "id": 1203,
    "name": "Brain Health - National Records of Scotland (NRS) - Deaths Data",
    "description": "This is a subset of National Records of Scotland (NRS) - Deaths dataset for use in the Brain Health Data Pilot (BHDP) project. \n\nThe Brain Health Data Pilot (BHDP) project aims to be a shared database (like a library) of information for scientists studying brain health, especially for diseases like dementia, which affects about 900,000 people in the UK. Its main feature is a huge collection of brain images linked to routinely collected health records, both from NHS Scotland, which will help scientists learn more about dementia and other brain diseases. What is special about this database is that it will get better over time – as scientists use it and add their discoveries, it becomes more valuable.",
    "url": "https://healthdatagateway.org/en/dataset/54",
    "uid": "0058b492-05a2-473a-8673-4dc3d64f340f",
    "datasource_id": 54,
    "source": "HDRUK"
  },
  {
    "id": 1204,
    "name": "National Cancer TRE",
    "description": "NHS Digital’s TRE service for England provides approved researchers with access to essential linked, de-identified health data to quickly answer COVID-19 related research questions. The TRE service provides researchers with support on data access requests, provision of data using the secure data platform the Data Processing Service (DPS) and help with analysis work. Approved research projects will help to guide national decision making and recommend potential interventions to reduce the severity of COVID-19 outcomes.\nNHSD are delivering the TRE service in partnership with HDRUK.  The shared objective is to provide rapid, safe and trustworthy access to data in a transparent way that accelerates the pace of quality research.",
    "url": "https://healthdatagateway.org/en/dataset/31",
    "uid": "b5439bc5-8141-4a13-a024-a6db2003b40e",
    "datasource_id": 31,
    "source": "HDRUK"
  },
  {
    "id": 1205,
    "name": "South West Primary Care Dataset",
    "description": "",
    "url": "https://healthdatagateway.org/en/dataset/29",
    "uid": "a47a8c77-3ced-43a0-acf0-3997c4eb5a29",
    "datasource_id": 29,
    "source": "HDRUK"
  },
  {
    "id": 1206,
    "name": "Leeds-IQVIA Collaboration",
    "description": "This uses the data within PPM+, the EHR for the Leeds Cancer Centre and Leeds Teaching Hospitals. It includes all patients diagnosed with cancer since 1990, all\nchemotherapy since 1993, all radiotherapy since 1994. It integrates all sources of electronic data including in-patient admissions, out-patient events, all radiology\nreports, all pathology reports, all blood tests, all microbiology, all letters/annotations generated digitally. It collects detailed diagnosis information on patients\nreferred with a suspicion of/diagnosis of cancer and generates the outputs which support national cancer registration, cancer outcomes services dataset, SACT\n(chemo), RTDS (radiotherapy), cancer waiting times. It contains data on >300,000 cancer patients which reflect Leeds activity as a local hospital for the population\nof 850,000, the regional cancer centre for 2.7M and the specialist referral centre for rare cancers (paediatric, soft-tissue sarcoma) for the 5.4M from Yorkshire &\nHumber. This dataset has been used to examine diagnosis to death care of cohorts of cancer patients with lung, breast, colorectal, ovary, bladder cancer including\nall recurrence events and hence can describe time to progression, progression-free and overall survival from first diagnosis, 1st recurrence, 2nd recurrence.",
    "url": "https://healthdatagateway.org/en/dataset/25",
    "uid": "00b897c7-d50d-486a-af1b-bd94821b58f7",
    "datasource_id": 25,
    "source": "HDRUK"
  },
  {
    "id": 1207,
    "name": "Real Time Data Network (RTDN)",
    "description": "This data set has been designed to support the aim to improve NHS cancer patient outcomes by sharing real-time data about UK cancer services during the COVID-19 pandemic and afterwards.  This will be achieved by creating a network of UK-wide hospitals large and representative enough to allow national and local data analysis, enabling insights that are not possible from current national datasets.  All hospitals share an agreed minimum dataset at minimum quality on a weekly basis (Level 1) with the option to share deeper datasets if that fits their digital maturity, patient choice, information governance and organisational priorities (Level 2 and 3).  All data will be kept and shared under appropriate and agreed information governance and legal contracts, and via Trusted Research Environment(s).",
    "url": "https://healthdatagateway.org/en/dataset/26",
    "uid": "616c4488-65a5-4608-a187-0c6bc78b359a",
    "datasource_id": 26,
    "source": "HDRUK"
  },
  {
    "id": 1208,
    "name": "PHM and Cancer Outcomes in the WYH Cancer Alliance",
    "description": "Improving cancer outcomes are an explicit priority for the WH&H CA.  The high-level outcomes of one-year survival, emergency presentation, stage at diagnosis, and patient experience have been endorsed by the alliance board and the system leadership of the Health and Care Partnership (ICS). The aims are to use data on outcomes to drive policy, service development and improvement and the use of granular data by tumour site to understand the impact of clinical decisions on patient outcomes and actively engage with clinicians through the Optimum Pathway Groups.\n\nThe Yorkshire and Humber LHCRE has developed and funded a secure, cloud-based environment to support the analysis of clinical data from across the region.  The LHCRE has prioritised cancer as one of its two key domains with a specific focus on population health analyses that can support the development and delivery of improved pathway of care. This capability will be used to develop a real time analysis of cancer data and its outcomes. \n\nThe work will be carried out in partnership with the NHS to ensure that the products of this capability links directly with the ability to influence decisions on resources used in WY&H CA to improve cancer outcomes.\n\nThe inputs required for this are the real-time (?7 days) data on cancer waiting times data (CWT), cancer outcomes and services dataset (COSD) and the Systemic Anticancer Treatment dataset (SACT) submissions and Radio Therapy (RT) Care by provider. These datasets are already captured by hospitals across the region and hence no new data collection is proposed at this time. These datasets will continue to be submitted to support national analyses supporting audit and registration. \n\nThe outputs will be developed as a suite of dashboards which will allow each provider and the alliance as a whole to monitor trends and changes in CWT, emergency presentation and stage at diagnosis by cancer type, and a rolling 3 and 12-month survival analysis.\n\nThe data will be handled within the Google Cloud Platform (GCP) in the following containers that are located in data centres in London, UK:",
    "url": "https://healthdatagateway.org/en/dataset/27",
    "uid": "bc839e9c-e303-4c3c-9c2c-9975602376e2",
    "datasource_id": 27,
    "source": "HDRUK"
  },
  {
    "id": 1209,
    "name": "Northern Ireland Regional Maternity system (NIMATS)",
    "description": "What is NIMATS?\nThe Northern Ireland Maternity System (NIMATs) contains a range of demographic and clinical information on mothers and infants.  It captures data relating to the current complete maternity process, but also contains details about the mother&amp;rsquo;s past medical and obstetric history.  It is a key source for data on birth numbers, interventions, maternal risk factors, birth weights, maternal smoking, BMI and breastfeeding on discharge. \n\nWhere is it used?\nNIMATs is available in all five Health Trust areas across Northern Ireland, within each hospital providing maternity services (11 hospitals in total).  Access to NIMATS is also available to midwives/clerical staff in various community clinics across NI to allow for booking appointments to be recorded.\n\nWhere does it get its data from?\nThe main source of data for NIMATS (excluding data input) is the Patient Administration System (PAS).  PAS provides mainly demographic details which will have been recorded when the mother attended for her booking appointment and also data recorded on admission to hospital for delivery.  Some laboratory results data is also provided by the NI Blood Transfusion Service.",
    "url": "https://healthdatagateway.org/en/dataset/19",
    "uid": "be7efa4f-e500-4a54-8157-6c4442a7e8a8",
    "datasource_id": 19,
    "source": "HDRUK"
  },
  {
    "id": 1210,
    "name": "Enhanced Prescribing Database",
    "description": "The Enhanced Prescribing Database (EPD) covers drugs prescribed in primary care and dispensed by community pharmacies. Prescriptions are submitted to the Business Services Organisation (BSO) by community pharmacies for payment. The EPD includes prescriptions issued by all types of prescribers including doctors and nurses, and all those issued and dispensed by pharmacists, dispensing doctors and appliance suppliers. Only prescriptions that are subsequently dispensed and processed by BSO for payment are included in the EPD. Patient information is not captured for all prescriptions. Only prescription forms with barcodes that were successfully read have patient Health and Care Numbers (HCN) recorded. Consequently datasets containing prescription information linked to patient HCN will not give a count of the total number of items prescribed during the time period. Rather it contains all relevant items prescribed during the time period for which the patient details were known.",
    "url": "https://healthdatagateway.org/en/dataset/20",
    "uid": "2843087f-23a4-4cfe-8e72-53f2b9562cc3",
    "datasource_id": 20,
    "source": "HDRUK"
  },
  {
    "id": 1211,
    "name": "Emergency Department (Symphony)",
    "description": "Information on attendances at emergency care departments in 3 of the 5 Health &amp;amp;amp;amp; Social Care Trusts in Northern Ireland - see Emergency Department (eEMS) for the other 2 Trusts.",
    "url": "https://healthdatagateway.org/en/dataset/21",
    "uid": "8002b4d7-ff3b-4402-b805-a6a0022836fd",
    "datasource_id": 21,
    "source": "HDRUK"
  },
  {
    "id": 1212,
    "name": "COVID-19 Vaccination",
    "description": "This dataset is extracted from the HSCNI Vaccine Management System, this system was introduced for management of COVID-19 vaccine bookings across Health and Social Care Northern Ireland. Data covers all bookings including GP Practice and HSC Trust bookings. Initially vaccines had been booked on an array of different systems however all historic data has been migrated to the Vaccine Management System. The data shows the key information about the vaccine record for each patient.",
    "url": "https://healthdatagateway.org/en/dataset/22",
    "uid": "b0049073-3335-433d-bdb9-de22547698f8",
    "datasource_id": 22,
    "source": "HDRUK"
  },
  {
    "id": 1213,
    "name": "COVID antigen testing - Pillar 1",
    "description": "This dataset is extracted from the Laboratory Information Systems in Northern Ireland hospitals on a daily basis. It contains details of COVID-19 antigen tests carried out in each of the hospital laboratories, including those processed on behalf of primary care, social care and community settings.\n\nhttps://www.gov.uk/government/publications/coronavirus-covid-19-testing-data-methodology/covid-19-testing-data-methodology-note",
    "url": "https://healthdatagateway.org/en/dataset/11",
    "uid": "3abc20ce-3dc6-4715-9de0-56aa97a6f5ec",
    "datasource_id": 11,
    "source": "HDRUK"
  },
  {
    "id": 1214,
    "name": "Patient Medical Card Registration (NI)",
    "description": "In order to access primary care services in Northern Ireland, patients need to register with a GP practice.  Registrations can be divided into different types: first registrations, transfers from other parts of the UK, migrant registrations and service related registrations.  Individual registrations will be deducted from the index of registered patients for a number of reasons including notification of death, emigration, returning to their home country, moving to Great Britain etc.  There may be a lag between a patient presenting themselves at a GP Practice and completion of registration.  This lag may be greater for patients who have to provide additional documentation as proof of entitlement to services.  Similarly for deductions, there may be a lag in removing individuals from the index of registered patients.\n\nGiven the sensitive nature of the data, this dataset is primarily used to identify patient populations and facilitate linkage to other datasets.  Some variables may be provided in aggregated format, for example age may be replaced with age band and postcode replaced with higher level geographical classifications.\n\nGP Cypher codes and Practice numbers will  not be provided.",
    "url": "https://healthdatagateway.org/en/dataset/12",
    "uid": "4e5ba567-c452-4979-8e2e-865f7df20252",
    "datasource_id": 12,
    "source": "HDRUK"
  },
  {
    "id": 1215,
    "name": "SARS-CoV-2 viral sequencing data (COG-UK data) - Lineage/Variant Data - NI",
    "description": "File contains basic public metadata, including sequence_name, location, date, pangolin lineage assignment, version and associated scores, scorpio VOC/VUI constellation call and associated scores, key spike protein mutations calls, and a list of all nucleotide mutations found.",
    "url": "https://healthdatagateway.org/en/dataset/13",
    "uid": "3d2d9c5e-6111-4583-a6db-07affd628f1e",
    "datasource_id": 13,
    "source": "HDRUK"
  },
  {
    "id": 1216,
    "name": "Mortality (Death registration)",
    "description": "The Northern Ireland Mortality Dataset contains information on deaths registered in Northern Ireland (NI). The data is collected from death certificates and information provided by GPs and other Health and Social Care Northern Ireland (HSCNI) providers. The data is provided with a three month delay from the date of registration and the information will relate to the latest certificate. This means the cause of death for an individual may be updated in cases where the death undergoes  further investigation, for example by a coroner. The data can be linked to other health-related datasets using patient Health and Care Number (HCN). HCN is not usually supplied along with death certificate data. This is added after matching to medical records, meaning that not all records will have a HCN to allow linkage to other health datasets.",
    "url": "https://healthdatagateway.org/en/dataset/14",
    "uid": "59fb436a-f8c3-4d4a-bc76-d4a02904b078",
    "datasource_id": 14,
    "source": "HDRUK"
  },
  {
    "id": 1217,
    "name": "COVID antigen testing - Pillar 2",
    "description": "Pillar 2 data is processed by NHS Digital and extracts for NI residents are sent to the NI Public Health Agency.\n\nhttps://www.gov.uk/government/publications/coronavirus-covid-19-testing-data-methodology/covid-19-testing-data-methodology-note",
    "url": "https://healthdatagateway.org/en/dataset/15",
    "uid": "c8f2ee14-2831-445d-a207-2f7f932f4735",
    "datasource_id": 15,
    "source": "HDRUK"
  },
  {
    "id": 1218,
    "name": "Admissions and Discharges",
    "description": "The Hospital Inpatient System (HIS) dataset is made up of data items relating to admitted patient care delivered by NHS hospitals in Northern Ireland, generated by the patient administration systems within each hospital.",
    "url": "https://healthdatagateway.org/en/dataset/16",
    "uid": "1d5b9205-dae4-4b0e-a3d3-04707875edf8",
    "datasource_id": 16,
    "source": "HDRUK"
  },
  {
    "id": 1219,
    "name": "Dental Payment System",
    "description": "The dataset is extracted from the Family Practitioner Services dental payment system and relates to HSCNI General Dental Services activity. This payment system began operation in December 2014, with the first associated payment being made in January 2015. Data covers patient details, patient registrations, treatments, dentist contracts, dental premises and payment details. Seven years of historical data was migrated over, where possible, to this system in December 2014, however new tables were not always in the same format as old, so historical data is not always comparable. This information does not include any dental treatments delivered by Trust based dental services or any treatments carried out privately by health service dentists.",
    "url": "https://healthdatagateway.org/en/dataset/17",
    "uid": "76683b1d-ce9d-41b7-8ca2-45c79a4d797a",
    "datasource_id": 17,
    "source": "HDRUK"
  },
  {
    "id": 1220,
    "name": "Emergency Department (eEMS)",
    "description": "Information on attendances at emergency care departments in 2 Trusts of the 5 Health &amp; Social Care Trusts in Northern Ireland - see Emergency Department (Symphony) for the other 3 Trusts.",
    "url": "https://healthdatagateway.org/en/dataset/18",
    "uid": "2ba72371-1527-4d8d-94e2-f795e45b9f77",
    "datasource_id": 18,
    "source": "HDRUK"
  },
  {
    "id": 1221,
    "name": "Omega 3 Cohort",
    "description": "Prebiotics are compounds in food that benefit health via affecting the gut microbiome. Omega-3 fatty acids have been associated with differences in gut microbiome composition and are widely accepted to have health benefits, although recent large trials have been inconclusive. We carried out a 6-week dietary intervention comparing the effects of daily supplementation with 500 mg of omega-3 versus 20 g of a well-characterized prebiotic, inulin. Inulin supplementation resulted in large increases in Bifidobacterium and Lachnospiraceae. In contrast, omega-3 supplementation resulted in significant increases in Coprococcus spp. and Bacteroides spp, and significant decreases in the fatty-liver associated Collinsella spp. On the other hand, similar to the results with inulin supplementation which resulted in significant increases in butyrate, iso-valerate, and iso-butyrate (p < .004), omega-3 supplementation resulted in significant increases in iso-butyrate and isovalerate (p < .002) and nearly significant increases in butyrate (p < .053). Coprococcus, which was significantly increased post-supplementation with omega-3, was found to be positively associated with iso-butyric acid (Beta (SE) = 0.69 (0.02), P = 1.4 x 10-3) and negatively associated with triglyceride-rich lipoproteins such as VLDL (Beta (SE) = -0.381 (0.01), P = .001) and VLDL-TG (Beta (SE) = -0.372 (0.04), P = .001) after adjusting for confounders. Dietary omega-3 alters gut microbiome composition and some of its cardiovascular effects appear to be potentially mediated by its effect on gut microbial fermentation products indicating that it may be a prebiotic nutrient.",
    "url": "https://healthdatagateway.org/en/dataset/710",
    "uid": "a1bcea4c-89dd-4cdb-978c-43db676ed73c",
    "datasource_id": 710,
    "source": "HDRUK"
  },
  {
    "id": 1222,
    "name": "Barts Imaging Dataset",
    "description": "Barts Health NHS Imaging Metadata and Report Dataset",
    "url": "https://healthdatagateway.org/en/dataset/1491",
    "uid": null,
    "datasource_id": 1491,
    "source": "HDRUK"
  },
  {
    "id": 1223,
    "name": "Knee Pain and related health In the Community (KPIC)",
    "description": "The Knee Pain and related health In the Community (KPIC) was initiated in 2014 as a prospective community-based cohort of 9500 people aged 40 years or over within the East Midlands region (UK). KPIC has, to date, completed 4 waves (0, 1, 3, 6 years) of questionnaire surveys, wave 6 administered within the context of the COVID-19 pandemic. Available data describe:\n\n- demographic characteristics\n- knee pain; pain severity, quality and distribution\n- risk factors for knee pain and osteoarthritis (OA) (age, body mass index, knee alignment, nodal OA, index: ring finger length (2D4D ratio)\n- quality of life (SF12)\n- mental health (Hospital Anxiety and Depression Scale).\n\nClinical assessment/phenotyping of a subsample of 400 wave-0 participants plus 115 incidence knee pain cases was undertaken the first 3 waves. These baseline cases were purposively sampled from the KPIC population to comprise 3 groups: early knee pain (≤3 years), established knee pain or no knee pain. Available data include:\n\n- knee radiographs (standing semi-flexed and 300 skyline views)\n- knee ultrasound (synovial effusion, hypertrophy, and power Doppler)\n- quantitative sensory testing (pressure pain detection thresholds and temporal summation)\n- muscle strength (quadriceps, hip abductor, and hand-grip)\n- measured balance\n- gait analysis (GAITrite)\n- blood and urine samples for biomarkers.",
    "url": "https://healthdatagateway.org/en/dataset/709",
    "uid": "11dee486-22d2-4cf4-a955-512249e61645",
    "datasource_id": 709,
    "source": "HDRUK"
  },
  {
    "id": 1224,
    "name": "MFT Demographics Dataset",
    "description": "A basic demographics dataset for MFT linkable patent population. Derived from source EPRs.",
    "url": "https://healthdatagateway.org/en/dataset/1495",
    "uid": null,
    "datasource_id": 1495,
    "source": "HDRUK"
  },
  {
    "id": 1225,
    "name": "Prediction outcomes for postpartum haemorrhage : an external validation in EHR cohort study",
    "description": "The project aims to evaluate the performance of a recently developed risk prediction model for postpartum haemorrhage at onset of labour, using data collected during routinely delivered care. By analysing electronic health record (EHR) data from a cohort of pregnant women, the study will externally validate the performance of the risk prediction model in a local population and for local care practices.  These findings will support decisions on local implementation of the model and contribute to research on the transportability of the model into alternative contexts.",
    "url": "https://healthdatagateway.org/en/dataset/1494",
    "uid": null,
    "datasource_id": 1494,
    "source": "HDRUK"
  },
  {
    "id": 1226,
    "name": "MFT BMI Dataset",
    "description": "This dataset pulls height, weight and BMI measurements from various source data",
    "url": "https://healthdatagateway.org/en/dataset/1493",
    "uid": null,
    "datasource_id": 1493,
    "source": "HDRUK"
  },
  {
    "id": 1227,
    "name": "MFT Ethnicity Dataset",
    "description": "This is a dataset of linkable patients who have been mapped to an ethnicity category",
    "url": "https://healthdatagateway.org/en/dataset/1492",
    "uid": null,
    "datasource_id": 1492,
    "source": "HDRUK"
  },
  {
    "id": 1228,
    "name": "Pregnancy outcomes in women who have undergone induction of labour: electronic health record cohort",
    "description": "This study aims to describe outcomes in maternity services to identify changes that may be occurring in maternity services over the past years, such as rates, complications and characteristics of induced pregnancies compared to similar, non-induced pregnancies, to contribute to evidence about the safety or lack of safety of inductions, inform women about likely outcomes of inductions, and identify trends in practice following changes in guidelines.   \nElectronic Health Records (EHRs) will be used from several maternity services in West Midlands that see around 20,000 pregnancies each year. These records are routinely collected by healthcare workers and contain valuable information on pregnancy care. Pregnancies will be looked at where induction of labour was recorded and compare their risk factors with pregnancies without induction, and also use data about age, ethnicity, smoking and weight.",
    "url": "https://healthdatagateway.org/en/dataset/1490",
    "uid": null,
    "datasource_id": 1490,
    "source": "HDRUK"
  },
  {
    "id": 1229,
    "name": "Cancer Waiting Times (CWT) - OUH NHS FT",
    "description": "The National Cancer Waiting Times Monitoring Data Set (known as Cancer Waiting Times or CWT) requires the submission of data to monitor NHS providers&rsquo; compliance with the government&rsquo;s operational standards for ensuring that cancer services (diagnosis and treatment) are delivered to patients in a timely manner.\n\nIn particular the data is used to:\n\n- monitor timed pathways of care for cancer patients\n- manage pathways of care for cancer patients \n- performance manage elective services for cancer patients \n- report against the requirements of the NHS Operating Framework for cancer  waiting times \n- support the right of patients to access cancer services within the NHS Constitution \n- produce national, official and local statistics for cancer patients \n- support investment planning for cancer services",
    "url": "https://healthdatagateway.org/en/dataset/1260",
    "uid": null,
    "datasource_id": 1260,
    "source": "HDRUK"
  },
  {
    "id": 1230,
    "name": "Electronic Patient Record - OUH NHS FT",
    "description": "EPR patient demographics\nEPR emergency department attendances\nEPR emergency department escalations\nEPR bed stays\nEPR waiting lists - inpatient and outpatient\nEPR referral to treatment\nCapacity management information\nVital signs, procedures, problems, clinical scores, risk factors\nPrescribing, administration\nEPR inpatient spells and episodes, ward movements, diagnoses\nEPR outpatient attendances\nEPR free text in PowerForms\nEPR attachments (clinical letters etc)",
    "url": "https://healthdatagateway.org/en/dataset/1229",
    "uid": null,
    "datasource_id": 1229,
    "source": "HDRUK"
  },
  {
    "id": 1231,
    "name": "MFT Blood Pressure Dataset",
    "description": "This dataset pulls blood pressure measurements in from various source data. It is sporadically updated with new data sources.",
    "url": "https://healthdatagateway.org/en/dataset/1497",
    "uid": null,
    "datasource_id": 1497,
    "source": "HDRUK"
  },
  {
    "id": 1232,
    "name": "MFT Deaths Dataset",
    "description": "Dataset of deaths of subjects who are registered as patients of Manchester University NHS Foundation Trust",
    "url": "https://healthdatagateway.org/en/dataset/1496",
    "uid": null,
    "datasource_id": 1496,
    "source": "HDRUK"
  },
  {
    "id": 1233,
    "name": "Assess determinants and understand strategies which may delay/halt aortic stenosis progression",
    "description": "Aortic Stenosis is a condition which affects a valve in the heart by causing it to become stiff and narrowed, it develops over a number of years and affects mainly only older adults. The narrowing is caused by calcium build up on the valve making it stiff and difficult to open. The rate at which the valve narrows varies between people and is affected by the presence of other conditions and a protein in the blood called lipoprotein a.  Currently the only treatment is valve replacement. However, for a score/tool to be developed which can categorise people according to their risk of rapidly progressing from mild to moderate to severe disease. Knowing who the rapid progressing are will allow for risk factor management and using a drug which lowers Lp(a) to slow disease progression avoiding the need for valve replacement.",
    "url": "https://healthdatagateway.org/en/dataset/1500",
    "uid": null,
    "datasource_id": 1500,
    "source": "HDRUK"
  },
  {
    "id": 1234,
    "name": "Feasibility testing of digital platform to capture PROs for CAR-T precision cellular therapies",
    "description": "The field of precision cellular therapy is forecast to undergo rapid expansion in the coming decade and there is an urgent need for evidence-based tools, co-produced with patients, to better support the growing number of individuals with haematologic malignancies who will receive these potentially curative treatments. The novelty of Chimeric Antigen Receptor T-cell (CAR-T) therapies, the unique and potentially severe toxicities associated with these treatments, and emerging evidence of long-term and late-effects of treatment mean effective symptom monitoring is key to maximising treatment benefit and ensuring patient safety. Development of predictive models that include ePRO data have been developed for other cancers and therapies; however, these systems may not be appropriate for monitoring the distinctive symptom profiles associated with CAR-T cellular therapy, providing the warrant for this research.",
    "url": "https://healthdatagateway.org/en/dataset/1498",
    "uid": null,
    "datasource_id": 1498,
    "source": "HDRUK"
  },
  {
    "id": 1235,
    "name": "OMOP CDM Dataset: Hospitalized Asthma Exacerbation Cohort",
    "description": "Asthma remains a significant clinical and public health challenge for the NHS, affecting millions and contributing to substantial morbidity, healthcare utilisation, and economic burden. Effective management and improved outcomes rely on comprehensive, high-quality data that enable detailed patient phenotyping, longitudinal tracking, and intervention evaluation. This dataset aims to support outcome improvement in asthma care by providing a rich, standardised resource capturing the complexity and diversity of asthma presentations and treatments within a large UK cohort. \n\nProduced for the NIHR Respiratory TRC and Respiratory Catalyst, this OMOP CDM dataset covers 11,834 admissions from 6,502 asthma patients, with demographics, clinical measures, medications, and outcomes for interoperable research. It provides highly granular, interoperable data essential for advanced observational research. The data encompasses demographics, clinical observations and measurements, medication usage, including route of administration, and outcomes such as death and readmission. This comprehensive dataset supports precision medicine approaches and healthcare quality initiatives to improve asthma management. \n\nGeography: The West Midlands (WM) has a population of 6 million &amp;amp;amp;amp; includes a diverse ethnic &amp;amp;amp;amp; socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services &amp;amp;amp;amp; specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds &amp;amp;amp;amp; &amp;amp;amp;gt; 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary &amp;amp;amp;amp; secondary care record (Your Care Connected) &amp;amp;amp;amp; a patient portal &amp;amp;amp;ldquo;My Health&amp;amp;amp;rdquo;. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation &amp;amp;amp;amp; refinement; A.I. support.  Data partner support for ETL (extract, transform &amp;amp;amp;amp; load) processes.  Bespoke and &amp;amp;amp;ldquo;off the shelf&amp;amp;amp;rdquo; Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient &amp;amp;amp;amp; end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and &amp;amp;amp;ldquo;fast screen&amp;amp;amp;rdquo; services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1499",
    "uid": null,
    "datasource_id": 1499,
    "source": "HDRUK"
  },
  {
    "id": 1236,
    "name": "Dataset &amp; Biosample for Gateway Demonstration Only",
    "description": "asfa sfasgaGASGASGASG AS GAS",
    "url": "https://healthdatagateway.org/en/dataset/912",
    "uid": null,
    "datasource_id": 912,
    "source": "HDRUK"
  },
  {
    "id": 1237,
    "name": "Chemotherapy clinical information OUH NHS FT",
    "description": "Chemotherapy prescribing and administration, held ARIA Medonc",
    "url": "https://healthdatagateway.org/en/dataset/1261",
    "uid": null,
    "datasource_id": 1261,
    "source": "HDRUK"
  },
  {
    "id": 1238,
    "name": "Social Care (Source)",
    "description": "The Source Social Care (SourceSC) dataset provides an extract of data on social care clients and the services they receive. Information is held on every client/service user regardless of their age (please note that some exclusions apply, see exclusions section below) who has had an assessment or review of their needs and who as a result of this assessment received/used the following support or services which have been active at any time during the collection period: \n1)\tsocial worker/support worker services (community care, mental health, substance misuse, children with disabilities) (provided or funded by your local authority)\n2)\tcommunity alarm\n3)\tother telecare service\n4)\treablement\n5)\thome care (personal and non-personal care)\n6)\thousing support\n7)\tcare home\n8)\tself-directed support\n9)\tcarer\n10)\tlearning disability services: if a person with a learning disability and/or autism spectrum diagnosis is known to the local authority they should be included within this return regardless of the services they receive (if any).\n\nExclusions:\nThe SourceSC does not hold records for client/service users who: \n1)\thave been assessed but do not require a care plan or social care service\n2)\tchild protection social work\n3)\tlooked after children social work\n4)\tadoption and fostering social work\n5)\tresidential child care social work\n6)\tcriminal justice social work",
    "url": "https://healthdatagateway.org/en/dataset/1504",
    "uid": null,
    "datasource_id": 1504,
    "source": "HDRUK"
  },
  {
    "id": 1239,
    "name": "MFT Legacy Inpatients Dataset",
    "description": "The dataset includes both patient level demographic, and inpatient data. It includes data on diagnosis, admission type and department for each episode of care for the patients.",
    "url": "https://healthdatagateway.org/en/dataset/1507",
    "uid": null,
    "datasource_id": 1507,
    "source": "HDRUK"
  },
  {
    "id": 1240,
    "name": "MFT HIVE Inpatient Dataset",
    "description": "The dataset includes both patient level demographic, and inpatient data. It includes data on diagnosis,  admission type and  department  for each episode of care for the patients.",
    "url": "https://healthdatagateway.org/en/dataset/1103",
    "uid": null,
    "datasource_id": 1103,
    "source": "HDRUK"
  },
  {
    "id": 1241,
    "name": "MFT HIVE Radiology Dataset",
    "description": "The dataset includes the radiology examination of patients across different specialities such as Accident &amp;amp; Emergency, General Medicine, Paediatrics, Trauma &amp;amp; Orthopaedics e.t.c. The data includes, inpatient/outpatient and radiology report data. Data has been sourced from Manchester Foundation Trust systems.",
    "url": "https://healthdatagateway.org/en/dataset/1509",
    "uid": null,
    "datasource_id": 1509,
    "source": "HDRUK"
  },
  {
    "id": 1242,
    "name": "MFT Legacy Outpatient Dataset",
    "description": "The dataset includes both patient level demographic, and outpatient data. It includes data on diagnosis, speciality and appointment for each episode of care for the patients.",
    "url": "https://healthdatagateway.org/en/dataset/1508",
    "uid": null,
    "datasource_id": 1508,
    "source": "HDRUK"
  },
  {
    "id": 1243,
    "name": "MFT HIVE Outpatient Dataset",
    "description": "The dataset includes both patient level demographic, and outpatient data. It includes  data on diagnosis, speciality and  appointment  for each episode of care for the patients.",
    "url": "https://healthdatagateway.org/en/dataset/1105",
    "uid": null,
    "datasource_id": 1105,
    "source": "HDRUK"
  },
  {
    "id": 1244,
    "name": "MFT Legacy Radiology Dataset",
    "description": "The dataset includes the radiology examination of patients across different specialities such as Accident &amp;amp; Emergency, General Medicine, Paediatrics, Trauma &amp;amp; Orthopaedics e.t.c. The data includes both patient level demographic, inpatient/outpatient and radiology report data. Data has been sourced from Manchester Foundation Trust systems, and is patient level.",
    "url": "https://healthdatagateway.org/en/dataset/1102",
    "uid": null,
    "datasource_id": 1102,
    "source": "HDRUK"
  },
  {
    "id": 1245,
    "name": "Emergency Medical Retrieval and Transfer Service (EMRT)",
    "description": "This is the dataset for the The Emergency Medical Retrieval and Transfer Service (EMRTS) Cymru. Which is a pre-hospital critical care service in Wales. \n\nThe Emergency Medical Retrieval and Transfer Service (EMRTS) Cymru is a service for Wales that provides Consultant and Critical Care Practitioner-delivered pre-hospital critical care across Wales. It was launched on the 27 April 2015 and is a partnership between Welsh Air Ambulance Charitable Trust, Welsh Government and NHS Wales.\n\nThe service was commissioned &amp;amp;amp;lsquo;to provide advanced decision making and critical care for life or limb threatening emergencies that require transfer for time critical specialist treatment at an appropriate facility.&amp;amp;amp;rsquo; \n\nEMRTS Cymru is a clinically led service, commissioned by the Emergency Ambulance Services Committee, and is hosted by Swansea Bay University Health Board.\n\nEMRTS Cymru has been developed to bring specific benefits to Wales, specifically: \n\nReductions in geographical inequity for patients with critical care needs.\n\nHealth gains by improving clinical outcomes.\n\nImproved clinical and skills sustainability &amp;amp;amp;ndash; improving the clinical skills, recruitment, and retention in key acute care areas. \n\nThere is also a service provision for the enhancement of neonatal and maternal pre-hospital critical care (both for home deliveries and deliveries in free-standing midwifery-led units). \n\nThe service provides a highly trained critical care team comprising consultants (from an emergency medicine, anaesthesia, and intensive care background) and critical care practitioners (who are advanced-trained former paramedics and nurses). The service has two main areas of activity: \n\nPre-hospital critical care for all age groups (i.e., interventions/decisions that are outside standard paramedic practice).\n\nUndertaking time-critical, life or limb-threatening adult and paediatric transfers from peripheral centres for patients requiring specialist intervention at the receiving hospital.",
    "url": "https://healthdatagateway.org/en/dataset/344",
    "uid": "3afb0566-0f39-4786-a08c-cd3800fa1649",
    "datasource_id": 344,
    "source": "HDRUK"
  },
  {
    "id": 1246,
    "name": "North West London Acute Patient level Data (NWL Acute PLD)",
    "description": "SLAM is comprehensive transactional commissioning toolkit, showing CCGs and providers including planned and actual reports to support NHS commissioning (tariff, non-tariff & block contracts). Acute PLD shows A&E, OP & IP data for NWL contracted providers.",
    "url": "https://healthdatagateway.org/en/dataset/1541",
    "uid": null,
    "datasource_id": 1541,
    "source": "HDRUK"
  },
  {
    "id": 1247,
    "name": "Weekly SUS Inpatient Dataset",
    "description": "Initially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver.\nThis same data can also be processed and used for non-clinical purposes such as research and planning health services. Because these uses are not to do with direct patient care they are called 'secondary uses'. This is the SUS data set.\nSUS data covers all NHS Clinical Commissioning Groups (CCGs) in England including:\n1. private patients treated in NHS hospitals\n2. patients resident outside of England\n3. care delivered by treatment centres (including those in the independent sector) funded by the NHS\n\nEach SUS record contains a wide range of information about an individual patient admitted to an NHS hospital including:\n1. clinical information about diagnoses and operations\n2. patient information such as age group gender and ethnicity\n3. administrative information such as dates and methods of admission and discharge\n4. geographical information such as where patients are treated and the area where they live\n\nNHS Digital apply a strict statistical disclosure control in accordance with the NHS Digital protocol to all published SUS data. This suppresses small numbers to stop people identifying themselves and others to ensure that patient confidentiality is maintained.\n\nWho SUS is for\nSUS provides data for the purpose of healthcare analysis to the NHS government and others including:\n\nThe Secondary Users Service (SUS) database is made up of many data items relating to A&E care\ndelivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data Set (CDS) and are generated by the patient administration systems within each hospital.\n1. national bodies and regulators such as the Department of Health NHS England Public Health England NHS Improvement and the CQC\n2. local Clinical Commissioning Groups (CCGs)\n3. provider organisations\n4. government departments\n5. researchers and commercial healthcare bodies\n6. National Institute for Clinical Excellence (NICE)\n7. patients service users and carers\n8. the media\n\nUses of the statistics\nThe statistics are known to be used for:\n1. national policy making\n2. benchmarking performance against other hospital providers or CCGs\n3. academic research\n4. analysing service usage and planning change\n5. providing advice to ministers and answering a wide range of parliamentary questions\n6. national and local press articles\n7. international comparison",
    "url": "https://healthdatagateway.org/en/dataset/1540",
    "uid": null,
    "datasource_id": 1540,
    "source": "HDRUK"
  },
  {
    "id": 1248,
    "name": "Weekly SUS ECDS Dataset",
    "description": "Initially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver.\nThis same data can also be processed and used for non-clinical purposes such as research and planning health services. Because these uses are not to do with direct patient care they are called 'secondary uses'. This is the SUS data set.\nSUS data covers all NHS Clinical Commissioning Groups (CCGs) in England including:\n1. private patients treated in NHS hospitals\n2. patients resident outside of England\n3. care delivered by treatment centres (including those in the independent sector) funded by the NHS\n\nEach SUS record contains a wide range of information about an individual patient admitted to an NHS hospital including:\n1. clinical information about diagnoses and operations\n2. patient information such as age group gender and ethnicity\n3. administrative information such as dates and methods of admission and discharge\n4. geographical information such as where patients are treated and the area where they live\n\nNHS Digital apply a strict statistical disclosure control in accordance with the NHS Digital protocol to all published SUS data. This suppresses small numbers to stop people identifying themselves and others to ensure that patient confidentiality is maintained.\n\nWho SUS is for\nSUS provides data for the purpose of healthcare analysis to the NHS government and others including:\n\nThe Secondary Users Service (SUS) database is made up of many data items relating to A&E care\ndelivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data Set (CDS) and are generated by the patient administration systems within each hospital.\n1. national bodies and regulators such as the Department of Health NHS England Public Health England NHS Improvement and the CQC\n2. local Clinical Commissioning Groups (CCGs)\n3. provider organisations\n4. government departments\n5. researchers and commercial healthcare bodies\n6. National Institute for Clinical Excellence (NICE)\n7. patients service users and carers\n8. the media\n\nUses of the statistics\nThe statistics are known to be used for:\n1. national policy making\n2. benchmarking performance against other hospital providers or CCGs\n3. academic research\n4. analysing service usage and planning change\n5. providing advice to ministers and answering a wide range of parliamentary questions\n6. national and local press articles\n7. international comparison",
    "url": "https://healthdatagateway.org/en/dataset/1539",
    "uid": null,
    "datasource_id": 1539,
    "source": "HDRUK"
  },
  {
    "id": 1249,
    "name": "North West London population data (NWL POP)",
    "description": "NHAIS is responsible for providing critical national systems and providing a large range of products and services underpinning vital operations in the NHS.\n\nThese data-intense services include payments to GPs and managing Patient Registration records.",
    "url": "https://healthdatagateway.org/en/dataset/1538",
    "uid": null,
    "datasource_id": 1538,
    "source": "HDRUK"
  },
  {
    "id": 1250,
    "name": "North West London Mental Health Data (NWL MH)",
    "description": "The MHSDS brings together information captured on clinical systems as part of patient care. It covers:\n\nadult and children's mental health\nlearning disabilities or autism spectrum disorder\nChildren and Young People Improving Access to Psychological Therapies (CYP-IAPT) services\nearly intervention care pathway\nThe MHSDS covers not only services provided in hospitals but also outpatient clinics and in the community, where the majority of people in contact with these services are treated.",
    "url": "https://healthdatagateway.org/en/dataset/1536",
    "uid": null,
    "datasource_id": 1536,
    "source": "HDRUK"
  },
  {
    "id": 1251,
    "name": "North West London Integrated Care Record (NWL ICR)",
    "description": "This dataset is a merged dataset of the existing datasets already published. This dataset contains all the columns made available within the individual datasets but as one large amalgamated dataset. This provides a longitudinal linked dataset of NWL patients to support pathway analysis, population health analysis and research analytics that requires data from various care settings.",
    "url": "https://healthdatagateway.org/en/dataset/1535",
    "uid": null,
    "datasource_id": 1535,
    "source": "HDRUK"
  },
  {
    "id": 1252,
    "name": "National Waiting List Open Pathways",
    "description": "Restoration of elective activity is one of the highest priorities for NHS England and NHS Improvement following the impact of the Covid-19 pandemic. Understanding the composition of the waiting list is critical to managing restoration within North West London.\n\nData will be collected via data submissions made by each individual provider of NHS Acute healthcare services in North West London. This dataset includes data from Imperial College Healthcare NHS Trust, Chelsea and Westminster NHS Foundation Trust, London North West Healthcare NHS Trust and The Hillingdon Hospital NHS Trust. Data will be processed under an Information Sharing Agreement between North West London CCG and each organisation. Data submissions will be processed and used for the following purposes:\n1. Developing a visual display of the waiting list composition (Elective Waiting List Data Dashboard).\n2. Developing a data quality improvement programme with providers.\n\nAll open (incomplete) RTT & not current RTT pathways as at 23:59 on the Sunday of the reporting period.",
    "url": "https://healthdatagateway.org/en/dataset/1534",
    "uid": null,
    "datasource_id": 1534,
    "source": "HDRUK"
  },
  {
    "id": 1253,
    "name": "North West London Admitted Patient Care Data (NWL APC)",
    "description": "The Secondary Users Service (SUS) database is made up of many data items relating to admitted patient care delivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data Set (CDS), and are generated by the patient administration systems within each hospital.  When a patient or service user is treated or cared for, information is collected which supports their treatment. This information is also useful to commissioners and providers of NHS-funded care for 'secondary' purposes - purposes other than direct or 'primary' clinical care - such as:  Healthcare planning Commissioning of services National Tariff reimbursement Development of national policy SUS is a secure data warehouse that stores this patient-level information in line with national standards and applies complex derivations which support national tariff policy and secondary analysis.   Access to SUS is managed using Role-Based Access Control (RBAC) which grants appropriate access levels to identifiable or de-identified data based on the users job role.",
    "url": "https://healthdatagateway.org/en/dataset/1533",
    "uid": null,
    "datasource_id": 1533,
    "source": "HDRUK"
  },
  {
    "id": 1254,
    "name": "North West London High Cost Drugs Data (NWL HCD)",
    "description": "The purpose of the Drugs Patient Level Contract Monitoring (DrPLCM) is to enable the interchange, in a uniform format, of monthly patient level drug contract monitoring data between commissioners and providers of healthcare. This will ensure that contract monitoring and reporting is consistent and comparable across all commissioning organisations and their footprints.",
    "url": "https://healthdatagateway.org/en/dataset/1532",
    "uid": null,
    "datasource_id": 1532,
    "source": "HDRUK"
  },
  {
    "id": 1255,
    "name": "North West London Primary Care Events Data (NWL PCE)",
    "description": "Organisations we collect information for include:\n\nNHS England - information is used to collect GP payments, based on achievements under the Quality and Outcomes Framework (QOF) and the delivery of quality services\nother government departments - for information about certain medical conditions and GP activity\nuniversities and other organisations - for academic research and services such as screening programmes\nThe systems that we use to collect data and information include:\n \n\nGeneral Practice Extraction Service (GPES) - used to collect information and data\nCalculating Quality Reporting Service (CQRS) - to record practice participation and to process and display information\nGP clinical systems - to record information at practice level\nWe are responsible for producing and maintaining the extract specification (business rules) to enable the extraction of these services.\n\nInteraction between a patient and the practice they are registered to\n\nAll Appointments data\n\nAll clinical diagnostics data\n\nAll administrative activity related to patients communication with practice\n\nAll test results related to patients\n\nAll screening information related to patients",
    "url": "https://healthdatagateway.org/en/dataset/1531",
    "uid": null,
    "datasource_id": 1531,
    "source": "HDRUK"
  },
  {
    "id": 1256,
    "name": "National Waiting List Diagnostics",
    "description": "Restoration of elective activity is one of the highest priorities for NHS England and NHS Improvement following the impact of the Covid-19 pandemic. Understanding the composition of the waiting list is critical to managing restoration within North West London.\n\nData will be collected via data submissions made by each individual provider of NHS Acute healthcare services in North West London. This dataset includes data from Imperial College Healthcare NHS Trust, Chelsea and Westminster NHS Foundation Trust, London North West Healthcare NHS Trust and The Hillingdon Hospital NHS Trust. Data will be processed under an Information Sharing Agreement between North West London CCG and each organisation. Data submissions will be processed and used for the following purposes:\n1. Developing a visual display of the waiting list composition (Elective Waiting List Data Dashboard).\n2. Developing a data quality improvement programme with providers.\n\nAll open (incomplete) and planned diagnostic waits for modalities in scope DM01 before 23:59 on Sunday of the reporting period.",
    "url": "https://healthdatagateway.org/en/dataset/1530",
    "uid": null,
    "datasource_id": 1530,
    "source": "HDRUK"
  },
  {
    "id": 1257,
    "name": "National Waiting List Clock Stops",
    "description": "Restoration of elective activity is one of the highest priorities for NHS England and NHS Improvement following the impact of the Covid-19 pandemic. Understanding the composition of the waiting list is critical to managing restoration within North West London.\n\nData will be collected via data submissions made by each individual provider of NHS Acute healthcare services in North West London. This dataset includes data from Imperial College Healthcare NHS Trust, Chelsea and Westminster NHS Foundation Trust, London North West Healthcare NHS Trust and The Hillingdon Hospital NHS Trust. Data will be processed under an Information Sharing Agreement between North West London CCG and each organisation. Data submissions will be processed and used for the following purposes:\n\n1. Developing a visual display of the waiting list composition (Elective Waiting List Data Dashboard).\n2. Developing a data quality improvement programme with providers.\n\nAll RTT pathways with a clock stop date after 23:59 on Sunday 4th April 2021 and before 23:59 on the Sunday of the reporting period and not recorded to date (in a previous submission).",
    "url": "https://healthdatagateway.org/en/dataset/1529",
    "uid": null,
    "datasource_id": 1529,
    "source": "HDRUK"
  },
  {
    "id": 1258,
    "name": "Air Pollution Exposure Estimates",
    "description": "Modelled concentrations (µg/m3) of annual average nitrogen dioxide (NO2) and particulate matter with diameter <2.5µm (PM2.5) were linked to postcode centroids in Greater London. Air pollution exposure estimates (i.e. concentrations) were derived using models developed for the year 2015 by overlaying the x,y location of postcode centroids with NO2 and PM2.5 maps (25m x 25m resolution) within a geographic information system (GIS). Following analysis of changes in air pollution measurements over time, using routine monitoring data from the DEFRA-run Automatic Urban and Rural Network (AURN), we made adjustments to the 2015 modelled concentrations to estimate 2010 to 2019 exposures using a method know as ‘differencing’ (Gulliver et al. 2013).",
    "url": "https://healthdatagateway.org/en/dataset/1528",
    "uid": null,
    "datasource_id": 1528,
    "source": "HDRUK"
  },
  {
    "id": 1259,
    "name": "North West London Outpatient Care Data (NWL OP)",
    "description": "When a patient or service user is treated or cared for, information is collected which supports their treatment. This information is also useful to commissioners and providers of NHS-funded care for 'secondary' purposes - purposes other than direct or 'primary' clinical care - such as:  Healthcare planning Commissioning of services National Tariff reimbursement Development of national policy SUS is a secure data warehouse that stores this patient-level information in line with national standards and applies complex derivations which support national tariff policy and secondary analysis.   Access to SUS is managed using Role-Based Access Control (RBAC) which grants appropriate access levels to identifiable or de-identified data based on the users job role.  The Secondary Users Service (SUS) database is made up of many data items relating to outpatient care delivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data Set (CDS), and are generated by the patient administration systems within each hospital.",
    "url": "https://healthdatagateway.org/en/dataset/1527",
    "uid": null,
    "datasource_id": 1527,
    "source": "HDRUK"
  },
  {
    "id": 1260,
    "name": "North West London Primary Care Prescriptions Data (NWL PCP)",
    "description": "Organisations we collect information for include:\n\nNHS England - information is used to collect GP payments, based on achievements under the Quality and Outcomes Framework (QOF) and the delivery of quality services\nother government departments - for information about certain medical conditions and GP activity\nuniversities and other organisations - for academic research and services such as screening programmes\nThe systems that we use to collect data and information include:\n \n\nGeneral Practice Extraction Service (GPES) - used to collect information and data\nCalculating Quality Reporting Service (CQRS) - to record practice participation and to process and display information\nGP clinical systems - to record information at practice level\nWe are responsible for producing and maintaining the extract specification (business rules) to enable the extraction of these services.",
    "url": "https://healthdatagateway.org/en/dataset/1526",
    "uid": null,
    "datasource_id": 1526,
    "source": "HDRUK"
  },
  {
    "id": 1261,
    "name": "Weekly SUS Outpatients Dataset",
    "description": "Initially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver.\nThis same data can also be processed and used for non-clinical purposes such as research and planning health services. Because these uses are not to do with direct patient care they are called 'secondary uses'. This is the SUS data set.\nSUS data covers all NHS Clinical Commissioning Groups (CCGs) in England including:\n1. private patients treated in NHS hospitals\n2. patients resident outside of England\n3. care delivered by treatment centres (including those in the independent sector) funded by the NHS\n\nEach SUS record contains a wide range of information about an individual patient admitted to an NHS hospital including:\n1. clinical information about diagnoses and operations\n2. patient information such as age group gender and ethnicity\n3. administrative information such as dates and methods of admission and discharge\n4. geographical information such as where patients are treated and the area where they live\n\nNHS Digital apply a strict statistical disclosure control in accordance with the NHS Digital protocol to all published SUS data. This suppresses small numbers to stop people identifying themselves and others to ensure that patient confidentiality is maintained.\n\nWho SUS is for\nSUS provides data for the purpose of healthcare analysis to the NHS government and others including:\n\nThe Secondary Users Service (SUS) database is made up of many data items relating to A&E care\ndelivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data Set (CDS) and are generated by the patient administration systems within each hospital.\n1. national bodies and regulators such as the Department of Health NHS England Public Health England NHS Improvement and the CQC\n2. local Clinical Commissioning Groups (CCGs)\n3. provider organisations\n4. government departments\n5. researchers and commercial healthcare bodies\n6. National Institute for Clinical Excellence (NICE)\n7. patients service users and carers\n8. the media\n\nUses of the statistics\nThe statistics are known to be used for:\n1. national policy making\n2. benchmarking performance against other hospital providers or CCGs\n3. academic research\n4. analysing service usage and planning change\n5. providing advice to ministers and answering a wide range of parliamentary questions\n6. national and local press articles\n7. international comparison",
    "url": "https://healthdatagateway.org/en/dataset/1525",
    "uid": null,
    "datasource_id": 1525,
    "source": "HDRUK"
  },
  {
    "id": 1262,
    "name": "North West London Accident and Emergency Data (NWL A&E)",
    "description": "Initially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver. \nThis same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the SUS data set.\nSUS data covers all NHS Clinical Commissioning Groups (CCGs) in England, including:\n•\tprivate patients treated in NHS hospitals\n•\tpatients resident outside of England\n•\tcare delivered by treatment centres (including those in the independent sector) funded by the NHS\nEach SUS record contains a wide range of information about an individual patient admitted to an NHS hospital, including:\n•\tclinical information about diagnoses and operations\n•\tpatient information, such as age group, gender and ethnicity\n•\tadministrative information, such as dates and methods of admission and discharge\n•\tgeographical information such as where patients are treated and the area where they live\nNHS Digital apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published SUS data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.\n\nWho SUS is for\nSUS provides data for the purpose of healthcare analysis to the NHS, government and others including:\n\nThe Secondary Users Service (SUS) database is made up of many data items relating to A&E care\ndelivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data\nSet (CDS), and are generated by the patient administration systems within each hospital.\n•\tnational bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC\n•\tlocal Clinical Commissioning Groups (CCGs)\n•\tprovider organisations\n•\tgovernment departments\n•\tresearchers and commercial healthcare bodies\n•\tNational Institute for Clinical Excellence (NICE)\n•\tpatients, service users and carers\n•\tthe media\n\nUses of the statistics\nThe statistics are known to be used for:\n•\tnational policy making\n•\tbenchmarking performance against other hospital providers or CCGs  \n•\tacademic research\n•\tanalysing service usage and planning change\n•\tproviding advice to ministers and answering a wide range of parliamentary questions\n•\tnational and local press articles\n•\tinternational comparison\nMore information can be found at \nhttps://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/hospital-episode-statistics\nhttps://digital.nhs.uk/data-and-information/publications/statistical/hospital-accident--emergency-activity\"",
    "url": "https://healthdatagateway.org/en/dataset/1524",
    "uid": null,
    "datasource_id": 1524,
    "source": "HDRUK"
  },
  {
    "id": 1263,
    "name": "North West London Coordinate My Care (NWL CMC)",
    "description": "List of patients with CMC care plan (end of life care plan).",
    "url": "https://healthdatagateway.org/en/dataset/1523",
    "uid": null,
    "datasource_id": 1523,
    "source": "HDRUK"
  },
  {
    "id": 1264,
    "name": "North West London Pathology (NWL PATH)",
    "description": "Pathology results from NWL Pathology and Doctors Labs in regards to COVID-19 Tests.",
    "url": "https://healthdatagateway.org/en/dataset/1522",
    "uid": null,
    "datasource_id": 1522,
    "source": "HDRUK"
  },
  {
    "id": 1265,
    "name": "National Waiting List Clock Starts",
    "description": "Restoration of elective activity is one of the highest priorities for NHS England and NHS Improvement following the impact of the Covid-19 pandemic. Understanding the composition of the waiting list is critical to managing restoration within North West London.\n\nData will be collected via data submissions made by each individual provider of NHS Acute healthcare services in North West London. This dataset includes data from Imperial College Healthcare NHS Trust, Chelsea and Westminster NHS Foundation Trust, London North West Healthcare NHS Trust and The Hillingdon Hospital NHS Trust. Data will be processed under an Information Sharing Agreement between North West London CCG and each organisation. Data submissions will be processed and used for the following purposes:\n\n1. Developing a visual display of the waiting list composition (Elective Waiting List Data Dashboard).\n2. Developing a data quality improvement programme with providers.\n\nAll RTT pathways with a clock start date after 23:59 on Sunday 4th April 2021 and before 23:59 on the Sunday of the reporting period and not recorded to date (in a previous submission).",
    "url": "https://healthdatagateway.org/en/dataset/1521",
    "uid": null,
    "datasource_id": 1521,
    "source": "HDRUK"
  },
  {
    "id": 1266,
    "name": "North West London Patient Index (NWL PI)",
    "description": "When a patient or service user is treated or cared for, information is collected which supports their treatment. This information is also useful to commissioners and providers of NHS-funded care for 'secondary' purposes - purposes other than direct or 'primary' clinical care - such as:\r\n\r\n- Healthcare planning\r\n- Commissioning of services\r\n- National Tariff reimbursement\r\n- Development of national policy\r\n\r\nSUS is a secure data warehouse that stores this patient-level information in line with national standards and applies complex derivations which support national tariff policy and secondary analysis. \r\n\r\nAccess to SUS is managed using Role-Based Access Control (RBAC) which grants appropriate access levels to identifiable, anonymised or pseudonymised data based on the users job role.",
    "url": "https://healthdatagateway.org/en/dataset/1520",
    "uid": null,
    "datasource_id": 1520,
    "source": "HDRUK"
  },
  {
    "id": 1267,
    "name": "North West London Adult Social Care Data (NWL ASC)",
    "description": "The Adult Social Care Outcomes Framework (ASCOF) measures how well care and support services achieve the outcomes that matter most to people. The measures are grouped into four domains which are typically reviewed in terms of movement over time. These domains are:\n\nenhancing quality of life for people with care and support needs\ndelaying and reducing the need for care and support\nensuring that people have a positive experience of care and support\nsafeguarding adults whose circumstances make them vulnerable and protecting from avoidable harm\nThe ASCOF aims to give an indication of the strengths and weaknesses of social care in delivering better outcomes for people who use services. This report will be of interest to:\n\ncentral government - for policy development and monitoring, and for parliamentary questions and Prime Minister's Questions\nCouncils with Adult Social Services Responsibilities (CASSRs) - for measuring local performance and for benchmarking against other CASSRs\ncharities\nacademics\nthe general public.",
    "url": "https://healthdatagateway.org/en/dataset/1519",
    "uid": null,
    "datasource_id": 1519,
    "source": "HDRUK"
  },
  {
    "id": 1268,
    "name": "Children and Young People Children Looked After (CYP CLA)",
    "description": "Local Authority &amp;amp;lsquo;Children Looked After&amp;amp;rsquo; (CLA) data is linked with health data that is held in the NWL&amp;amp;rsquo;s Whole Systems Integrated Care (WSIC) platform, to help better understand the needs of children within a Local Authority.\nBi-borough and Harrow are piloting this data sharing, with IG already in place and approved to enable the data to be shared directly to WSIC.\nCombining this data will help improve developmental, safeguarding, and wellbeing outcomes for children and young people, by providing analysis which gives a more informed understanding of children and young people. Analysis of the impacts of the wider determinants of health would also be possible, helping NHS Practitioners, DCSs, and their teams to identify and prioritise opportunities for development and improvement of the service offer, to best support the most vulnerable children and young people and their families.\nSpecific use cases could include:\n&amp;amp;bull;Understanding the health needs of children in contact with children&amp;amp;rsquo;s services, by analysing specific health events experienced across cohorts, such as, long-term conditions e.g., Mental Health, and A&amp;amp;amp;E visits.\n&amp;amp;bull;Geographically mapping the level of need within a Local Authority, using the combined data to inform the level of need, using data points such as the proportion of children: in contact with children&amp;amp;rsquo;s services, with mental health conditions, visiting A&amp;amp;amp;E, with care plans.",
    "url": "https://healthdatagateway.org/en/dataset/1515",
    "uid": null,
    "datasource_id": 1515,
    "source": "HDRUK"
  },
  {
    "id": 1269,
    "name": "East of England SDE Secondary Care EPR dataset",
    "description": "This data has been assembled to support health and care research using pseudonymized NHS patient data in East of England Secure Data Environment. The dataset contains inpatient, outpatient and emergency care records from secondary care trusts in the east of England.  This covers demographic information, diagnoses, events, prescribing information and lab test results for patients from 2015 to today. Currently the dataset contains data from Cambridge University Hospitals foundation trust and will be extended over time.",
    "url": "https://healthdatagateway.org/en/dataset/1506",
    "uid": null,
    "datasource_id": 1506,
    "source": "HDRUK"
  },
  {
    "id": 1270,
    "name": "MFT Legacy Referral Dataset",
    "description": "The dataset contains patient referrals, reason for referrals and related treatment functions.",
    "url": "https://healthdatagateway.org/en/dataset/1543",
    "uid": null,
    "datasource_id": 1543,
    "source": "HDRUK"
  },
  {
    "id": 1271,
    "name": "MFT HIVE Referral Dataset",
    "description": "The dataset contains patient  referrals, reason for referrals and related treatment functions.",
    "url": "https://healthdatagateway.org/en/dataset/1542",
    "uid": null,
    "datasource_id": 1542,
    "source": "HDRUK"
  },
  {
    "id": 1272,
    "name": "North West London COVID-19 Patient Level Situation Report (NWL COVID19 PLD SITREP)",
    "description": "The Daily Situation Report collects data on:\nthe number of urgent operations cancelled, including those cancelled for the 2nd time or more, throughout the month\ncritical care capacity, including adult, paediatric and neonatal available and occupied critical care beds",
    "url": "https://healthdatagateway.org/en/dataset/1537",
    "uid": null,
    "datasource_id": 1537,
    "source": "HDRUK"
  },
  {
    "id": 1273,
    "name": "Dataset and Biosample for Gateway Demonstration Only",
    "description": "asfa sfasgaGASGASGASG AS GAS",
    "url": "https://healthdatagateway.org/en/dataset/912",
    "uid": null,
    "datasource_id": 912,
    "source": "HDRUK"
  },
  {
    "id": 1274,
    "name": "MFT HIVE Acute Pharmacy Dataset",
    "description": "The dataset includes pharmacy prescription of patients and detailed data on the prescription,  therapeutic class, pharmaceutical class, and prescription strength. It also contains data on medication discontinuity.",
    "url": "https://healthdatagateway.org/en/dataset/1104",
    "uid": null,
    "datasource_id": 1104,
    "source": "HDRUK"
  },
  {
    "id": 1275,
    "name": "Testing 1397",
    "description": "wibble",
    "url": "https://healthdatagateway.org/en/dataset/1546",
    "uid": null,
    "datasource_id": 1546,
    "source": "HDRUK"
  },
  {
    "id": 1276,
    "name": "North West London Pathology: Virology",
    "description": "Pathology culture results from NWL Pathology in regards to Virology Tests.",
    "url": "https://healthdatagateway.org/en/dataset/1551",
    "uid": null,
    "datasource_id": 1551,
    "source": "HDRUK"
  },
  {
    "id": 1277,
    "name": "North West London Pathology: Microbiology",
    "description": "Pathology culture results from NWL Pathology in regards to Microbiology Tests.",
    "url": "https://healthdatagateway.org/en/dataset/1552",
    "uid": null,
    "datasource_id": 1552,
    "source": "HDRUK"
  },
  {
    "id": 1278,
    "name": "RIO",
    "description": "Window study of the PARP inhibitor rucaparib in patients with primary triple negative or BRCA1/2 related breast cancer (RIO)",
    "url": "https://healthdatagateway.org/en/dataset/1553",
    "uid": null,
    "datasource_id": 1553,
    "source": "HDRUK"
  },
  {
    "id": 1279,
    "name": "North West London Lung Function Screening Data",
    "description": "Update of National Lung Health Screening Programme",
    "url": "https://healthdatagateway.org/en/dataset/1555",
    "uid": null,
    "datasource_id": 1555,
    "source": "HDRUK"
  },
  {
    "id": 1280,
    "name": "North West London Breast Screening Data",
    "description": "Update of National Breast Screening Programme",
    "url": "https://healthdatagateway.org/en/dataset/1554",
    "uid": null,
    "datasource_id": 1554,
    "source": "HDRUK"
  },
  {
    "id": 1281,
    "name": "North West London Community Data (NWL COM)",
    "description": "Providers of publicly-funded community services are legally mandated to collect and submit community health data, as set out by the Health and Social Care Act 2012. \n\nThe Community Services Data Set (CSDS) expands the scope of the  Children and Young People's Health Services Data Set (CYPHS) data set, by removing the 0-18 age restriction. The CSDS supersedes the CYPHS data set, to allow adult community data to be submitted.\n\nThe structure and content of the CSDS remains the same as the CYPHS data set. The Community Information Data Set (CIDS) has been retired, to remove the need for a separate local collection and reduce burden on providers.\n\nReports from the CSDS are available to download from the Community Services Data Set reports webpage.",
    "url": "https://healthdatagateway.org/en/dataset/1556",
    "uid": null,
    "datasource_id": 1556,
    "source": "HDRUK"
  },
  {
    "id": 1282,
    "name": "Our Future Health Imputed Genotype Data",
    "description": "Our Future Health is a prospective, observational cohort study of the general adult population of the United Kingdom (UK). The programme aims to support a wide range of observational health research. We gather personal, health and lifestyle information from each participant through a self-completed baseline health questionnaire and at an in-person clinic visit. We will further link this data to other health-related data sets. Participants have also given consent for us to recontact them, for example to invite them to take part in further or repeat data collections, or other embedded studies such as clinical trials.  \n\nThe Our Future Health programme is currently open to all adults (18 years and older) living in the UK. In July 2022, we started recruiting participants in England and will continue to expand across the rest of the UK. The data we&amp;rsquo;ve gathered so far (September 2025) includes imputed genotype data on 159,587,100 variants and 550,000 participants \n\nThese data were obtained using a custom Illumina Infinium Excalibur beadchip array, designed by Our Future Health in collaboration with Illumina. The array includes variants related to a wide range of health phenotypes, blood typing, pharmacogenetics, selected copy number variants, clinically relevant variants, and a &amp;ldquo;backbone&amp;rdquo; of variants to support imputation.\n\nA separate, linked dataset is available that provides participant baseline demographic information and responses to our baseline health questionnaire. Clinical measurements data is also available from participants.\nAdditionally linked NHS England data that provides clinical information on participants is also available.  \n\nThe data is stored in the Our Future Health Trusted Research Environment. We de-identify all participant data we gather before it&amp;rsquo;s available for use. All researchers will need to become registered researchers at Our Future Health and have an approved research study before they&amp;#039;re given access to the data.\n\nWe aim to collect a variety of data types from up to 5 million adult participants from across the UK. We hope to make more data types available on a quarterly basis.",
    "url": "https://healthdatagateway.org/en/dataset/1557",
    "uid": null,
    "datasource_id": 1557,
    "source": "HDRUK"
  },
  {
    "id": 1283,
    "name": "NIHR PSRC AMU Dataset for Medical Same Day Emergency Care Patients and Pathways",
    "description": "Medical Same Day Emergency Care (SMDEC) services review almost half of all unplanned attendances requiring medical review. Activity has risen sharply, with monthly MSDEC admissions increasing by up to 160 percent in the past year. Patients present with a wide range of conditions and follow varied diagnostic pathways. Many require further investigations such as CT or MRI or assessment by specialty services which can lead to long waits or return visits.  There are currently no agreed criteria for MSDEC patient selection. \n\nTo support research into these pathways, the NIHR PSRC and PIONEER have created a dataset of MSDEC activity. The dataset includes detailed demographics, symptoms, comorbidities, vital signs, investigations, imaging requests, specialty input, treatments, ward movement, discharge decisions, readmissions and mortality. It provides a comprehensive view of how patients move through MSDEC and the resources they use. The dataset enables analysis of variation, delays and predictors of outcomes in Same Day Emergency Care. \n\nGeography: The West Midlands has a population of 6 million &amp;amp;amp; includes a diverse ethnic &amp;amp;amp; socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services &amp;amp;amp; specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds &amp;amp;amp; &amp;amp;gt; 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary &amp;amp;amp; secondary care record (Your Care Connected) &amp;amp;amp; a patient portal &amp;amp;ldquo;My Health&amp;amp;rdquo;. \n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. \n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. \n\nAvailable supplementary support: Analytics, model build, validation &amp;amp;amp; refinement; A.I. support.  Data partner support for ETL (extract, transform &amp;amp;amp; load) processes.  Bespoke and &amp;amp;ldquo;off the shelf&amp;amp;rdquo; Trusted Research Environment build and run.  Consultancy with clinical, patient &amp;amp;amp; end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and &amp;amp;ldquo;fast screen&amp;amp;rdquo; services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1559",
    "uid": null,
    "datasource_id": 1559,
    "source": "HDRUK"
  },
  {
    "id": 1284,
    "name": "Unplanned General Medical Attendances and Patient Flow Data",
    "description": "This PIONEER-curated dataset comprises 1,335,540 Acute Medical Emergency Department admissions recorded between 2020 and 2024. The data is structured to facilitate the analysis of medical emergency services and patient outcomes, being particularly suited for modelling the flow of acute medicine patients through the Emergency Department (ED). The underlying data infrastructure is designed for flexibility, allowing both the time period and the included fields to be expanded or refined in line with project requirements.\n\nThe dataset features a high level of detail, including core demographics and the pre-calculated Charlson Comorbidity Index (CCI). Furthermore, it features precise patient flow logs detailing movements between the ED and the Acute Medical Unit (AMU). The available data is enriched by comprehensive records of drug administrations (including dose and route) and associated medical imaging reports.\n\nGeography: The West Midlands (WM) has a population of 6 million &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp; includes a diverse ethnic &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp; socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp; specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp; &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;gt; 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp; secondary care record (Your Care Connected) &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp; a patient portal &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;ldquo;My Health&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;rdquo;.\n\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\n\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\n\nAvailable supplementary support: Analytics, model build, validation &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp; refinement; A.I. support.  Data partner support for ETL (extract, transform &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp; load) processes.  Bespoke and &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;ldquo;off the shelf&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;rdquo; Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp; end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;ldquo;fast screen&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;rdquo; services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1558",
    "uid": null,
    "datasource_id": 1558,
    "source": "HDRUK"
  },
  {
    "id": 1285,
    "name": "DELIVER-MS Biorepository",
    "description": "A biorepository of samples from individuals recruited to the DELIVER-MS clinical trial. \nPatients were followed in the DELIVER-MS study for a total of 3 years.  Those in the randomized part of the study were told which initial DMT approach their neurologist would take, but their neurologist and the patient together were free to decide which medication within that approach to choose.  Once a person with MS was enrolled in the study and commenced their first DMT, any subsequent changes that were required to the DMT were made at the discretion of their treating neurologist, in just the same way as in routine clinical settings. The aims of the study are to:\ni)Determine whether starting on a highly effective treatment is more effective than an escalation approach in slowing brain volume loss over 36 months\nii) It will also assess which approach is more effective at improving patient reported outcomes (PRO) and clinical measures, and the safety and tolerability of each approach.\n\nBiorepository Remit:\nBiomarkers that are predictive of a) long-term disability in MS and b) responses to disease-modifying therapy (whether a broad therapy response or a response to a specific therapy) are needed for people with MS. While new data suggest that several potential biomarkers may portend a worse long-term prognosis for MS, the interpretation of these results may be limited by the fact that the data were generated from either observational, non-randomized cohorts or from clinical trials in which patients who were chosen on the basis of a certain degree of recent disease activity. Hence, there is a need to evaluate such novel biomarkers in a prospective, less biased study in order to assess its true clinical applicability and generalizability. The DELIVER-MS trial, supported by PCORI and the NMSS, presents a unique opportunity for collection of biospecimens. The specific features making the biorepository include pragmatic design, broad inclusion criteria, of essentially newly-diagnosed, treatment-na&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;iuml;ve patients, and their goals of long term follow-up. The collection of samples from study participants will be an optional activity.\n\nBio-samples were collected at 2 time points between Baseline and 36 month. Serum, Plasma and PBMCs isolated at both time points and DNA at a single time point only. Samples were split into aliquots and stored at -80 degrees for long term storage.",
    "url": "https://healthdatagateway.org/en/dataset/1547",
    "uid": null,
    "datasource_id": 1547,
    "source": "HDRUK"
  },
  {
    "id": 1286,
    "name": "A HDRUK dataset of the risk and Severity of Hospitalised Asthma Exacerbations.",
    "description": "Asthma is common disease and can be life threatening. Exacerbations of asthma are one of the leading causes of unplanned hospitalisation globally and represent a substantial healthcare burden.  Exacerbations are also associated with a decline in lung function. Because of this, researchers, policy makers and clinicians have focused treatment plans on preventing and managing exacerbations.  Despite this, outcomes remain poor.\nDrivers of frequent exacerbations include poor compliance with therapy, a lack of asthma exacerbation plans and inadequate treatment. There are also some biological drivers of frequent exacerbations such as small airways disease and specific types of airways inflammation.\nThis granular dataset contains 48,042 patients admitted with Asthma Exacerbation curated by PIONEER. The data includes demographics, Vital signs (e.g. oxygen saturation, heart rate, respiratory rate, peak flow rate), Comorbidities, assessments (e.g. smoking), lab sample results (e.g. eosinophil counts and inflammatory markers), Imaging, lung function tests, medications and outcomes such as intensive care, outpatient admissions, mortality and readmissions.\n\nGeography: The West Midlands has a population of 6 million &amp;amp;amp; includes a diverse ethnic &amp;amp;amp; socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services &amp;amp;amp; specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds &amp;amp;amp; &amp;amp;gt; 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary &amp;amp;amp; secondary care record (Your Care Connected) &amp;amp;amp; a patient portal &amp;amp;ldquo;My Health&amp;amp;rdquo;.\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\nAvailable supplementary support: Analytics, model build, validation &amp;amp;amp; refinement; A.I. support.  Data partner support for ETL (extract, transform &amp;amp;amp; load) processes.  Bespoke and &amp;amp;ldquo;off the shelf&amp;amp;rdquo; Trusted Research Environment build and run.  Consultancy with clinical, patient &amp;amp;amp; end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and &amp;amp;ldquo;fast screen&amp;amp;rdquo; services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1561",
    "uid": null,
    "datasource_id": 1561,
    "source": "HDRUK"
  },
  {
    "id": 1287,
    "name": "NIHR PSRC: A dataset comparing Acute COPD In-patient and Virtual Ward Admissions",
    "description": "There is increasing interest in care pathways for acute exacerbations of disease that are safe and avoid hospital admission. A virtual ward is a system where people who may otherwise be admitted receive hospital-led care in their home with observations and reviews completed remotely by a specialist team. A virtual ward for COPD exacerbations has been recommended by NHS England, supported by early evaluation reports and several small studies.\nTo support the evaluation of this service, the NIHR PSRC Acute Theme and PIONEER have developed a highly granular dataset of 41,419 acute admissions with Acute Exacerbation of COPD. The dataset includes demography, comorbidities, symptoms, serial physiology, assessments, diagnostic codes, imaging, lung function tests, Breeze spirometry, DECAF scores, prescriptions, ward locations and outcomes including mortality, readmissions and follow up. It also contains detailed reviews of the acute care delivered. The dataset covers admissions from 2019 to May 2024 and can be expanded to other timelines if required.\nGeography: The West Midlands has a population of 6 million &amp;amp;amp; includes a diverse ethnic &amp;amp;amp; socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services &amp;amp;amp; specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds &amp;amp;amp; &amp;amp;gt; 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary &amp;amp;amp; secondary care record (Your Care Connected) &amp;amp;amp; a patient portal &amp;amp;ldquo;My Health&amp;amp;rdquo;.\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\nAvailable supplementary support: Analytics, model build, validation &amp;amp;amp; refinement; A.I. support.  Data partner support for ETL (extract, transform &amp;amp;amp; load) processes.  Bespoke and &amp;amp;ldquo;off the shelf&amp;amp;rdquo; Trusted Research Environment build and run.  Consultancy with clinical, patient &amp;amp;amp; end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and &amp;amp;ldquo;fast screen&amp;amp;rdquo; services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1560",
    "uid": null,
    "datasource_id": 1560,
    "source": "HDRUK"
  },
  {
    "id": 1288,
    "name": "NIHR PSRC Dataset for Older Medical Same Day Emergency Care Patients",
    "description": "Medical Same Day Emergency Care (SMDEC) services review almost half of all unplanned attendances requiring medical review. Activity has risen sharply, with monthly MSDEC admissions increasing by up to 160 percent in the past year. Patients present with a wide range of conditions and follow varied diagnostic pathways. Many require further investigations such as CT or MRI or assessment by specialty services which can lead to long waits or return visits.  There are currently no agreed criteria for MSDEC patient selection.\nTo support research into these pathways, the NIHR PSRC and PIONEER have created a dataset of MSDEC activity. There are 5,081 patients included who are aged 65+ years and older. The dataset includes detailed demographics, symptoms, comorbidities, vital signs, investigations, imaging requests, specialty input, treatments, ward movement, discharge decisions, readmissions and mortality. It provides a comprehensive view of how patients move through MSDEC and the resources they use. The dataset enables analysis of variation, delays and predictors of outcomes in Same Day Emergency Care.\nGeography: The West Midlands has a population of 6 million &amp;amp;amp; includes a diverse ethnic &amp;amp;amp; socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services &amp;amp;amp; specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds &amp;amp;amp; &amp;amp;gt; 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary &amp;amp;amp; secondary care record (Your Care Connected) &amp;amp;amp; a patient portal &amp;amp;ldquo;My Health&amp;amp;rdquo;.\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\nAvailable supplementary support: Analytics, model build, validation &amp;amp;amp; refinement; A.I. support.  Data partner support for ETL (extract, transform &amp;amp;amp; load) processes.  Bespoke and &amp;amp;ldquo;off the shelf&amp;amp;rdquo; Trusted Research Environment build and run.  Consultancy with clinical, patient &amp;amp;amp; end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and &amp;amp;ldquo;fast screen&amp;amp;rdquo; services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1564",
    "uid": null,
    "datasource_id": 1564,
    "source": "HDRUK"
  },
  {
    "id": 1289,
    "name": "Unplanned General Medical Attendances And Patient Flow Data For Older Adults",
    "description": "This PIONEER-curated dataset comprises 390K Acute Medical Emergency Department admissions recorded between 2020 and 2024 for patients aged 65+. The data is structured to facilitate the analysis of medical emergency services and patient outcomes, being particularly suited for modelling the flow of acute medicine patients through the Emergency Department (ED). The underlying data infrastructure is designed for flexibility, allowing both the time period and the included fields to be expanded or refined in line with project requirements.\nThe dataset features a high level of detail, including core demographics and the pre-calculated Charlson Comorbidity Index (CCI). Furthermore, it features precise patient flow logs detailing movements between the ED and the Acute Medical Unit (AMU). The available data is enriched by comprehensive records of drug administrations (including dose and route) and associated medical imaging reports.\nGeography: The West Midlands (WM) has a population of 6 million &amp;amp;amp; includes a diverse ethnic &amp;amp;amp; socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services &amp;amp;amp; specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds &amp;amp;amp; &amp;amp;gt; 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary &amp;amp;amp; secondary care record (Your Care Connected) &amp;amp;amp; a patient portal &amp;amp;ldquo;My Health&amp;amp;rdquo;.\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\nAvailable supplementary support: Analytics, model build, validation &amp;amp;amp; refinement; A.I. support.  Data partner support for ETL (extract, transform &amp;amp;amp; load) processes.  Bespoke and &amp;amp;ldquo;off the shelf&amp;amp;rdquo; Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient &amp;amp;amp; end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and &amp;amp;ldquo;fast screen&amp;amp;rdquo; services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1563",
    "uid": null,
    "datasource_id": 1563,
    "source": "HDRUK"
  },
  {
    "id": 1290,
    "name": "NIHR PSRC: A dataset comparing Acute COPD In-patient and Virtual Ward Admissions for Older Adults",
    "description": "There is increasing interest in care pathways for acute exacerbations of disease that are safe and avoid hospital admission. A virtual ward is a system where people who may otherwise be admitted receive hospital-led care in their home with observations and reviews completed remotely by a specialist team. A virtual ward for COPD exacerbations has been recommended by NHS England, supported by early evaluation reports and several small studies.\nTo support the evaluation of this service, the NIHR PSRC Acute Theme and PIONEER have developed a highly granular dataset of  31,804 acute admissions with Acute Exacerbation of COPD. The dataset includes demography, comorbidities, symptoms, serial physiology, assessments, diagnostic codes, imaging, lung function tests, Breeze spirometry, DECAF scores, prescriptions, ward locations and outcomes including mortality, readmissions and follow up. It also contains detailed reviews of the acute care delivered. The dataset covers admissions from 2019 to May 2024 and can be expanded to other timelines if required.\nGeography: The West Midlands has a population of 6 million &amp;amp;amp; includes a diverse ethnic &amp;amp;amp; socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services &amp;amp;amp; specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds &amp;amp;amp; &amp;amp;gt; 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary &amp;amp;amp; secondary care record (Your Care Connected) &amp;amp;amp; a patient portal &amp;amp;ldquo;My Health&amp;amp;rdquo;.\nData set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.\nAvailable supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.\nAvailable supplementary support: Analytics, model build, validation &amp;amp;amp; refinement; A.I. support.  Data partner support for ETL (extract, transform &amp;amp;amp; load) processes.  Bespoke and &amp;amp;ldquo;off the shelf&amp;amp;rdquo; Trusted Research Environment build and run.  Consultancy with clinical, patient &amp;amp;amp; end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and &amp;amp;ldquo;fast screen&amp;amp;rdquo; services to assess population size.",
    "url": "https://healthdatagateway.org/en/dataset/1562",
    "uid": null,
    "datasource_id": 1562,
    "source": "HDRUK"
  },
  {
    "id": 1291,
    "name": "All Wales Renal Dataset (AWRD)",
    "description": "The All Wales Renal Database contains details of patients who have been identified by or received renal services\nwithin the NHS, with associated demographic, administrative and clinical diagnoses and treatment details.",
    "url": "https://healthdatagateway.org/en/dataset/1565",
    "uid": null,
    "datasource_id": 1565,
    "source": "HDRUK"
  },
  {
    "id": 1292,
    "name": "PlasmaMATCH - The UK plasma based molecular profiling of advanced breast cancer to inform therapeuti",
    "description": "PlasmaMATCH is a multiple parallel cohort, open-label, multi-centre phase IIa clinical trial aiming to provide proof of principle efficacy for designated targeted therapies in patients with advanced breast cancer where the targetable mutation is identified through ctDNA screening.\n\nClinical data and sample collection",
    "url": "https://healthdatagateway.org/en/dataset/1570",
    "uid": null,
    "datasource_id": 1570,
    "source": "HDRUK"
  },
  {
    "id": 1293,
    "name": "PHOENIX - Pre-surgical window of opportunity and post-surgical biomarker study of DNA damage respons",
    "description": "WOP, open-label, multi-centre, phase IIa trial comprising multiple non-comparative treatment cohorts with patient allocation via minimisation or allocation according to HRD and germline BRCA1/2 mutation status.\nThe trial consists of two parts: a post-neoadjuvant treatment preoperative WOP component (PART 1); and a post-operative component (PART 2).\n\nCollection of samples and clinical data.",
    "url": "https://healthdatagateway.org/en/dataset/1569",
    "uid": null,
    "datasource_id": 1569,
    "source": "HDRUK"
  },
  {
    "id": 1294,
    "name": "UNIRAD (UK) - Sample collection",
    "description": "UNIRAD: Randomised, double-blind, multicentre phase III trial evaluating\nthe safety and benefit of adding everolimus to adjuvant hormone\ntherapy in women with high risk of relapse, ER+ and HER2-\nprimary breast cancer who remain free of disease\n\nCollection of UK samples and data across the following diseases: Malignant tumour of breast",
    "url": "https://healthdatagateway.org/en/dataset/1568",
    "uid": null,
    "datasource_id": 1568,
    "source": "HDRUK"
  },
  {
    "id": 1295,
    "name": "IMPORT-HIGH - A phase III randomised trial testing dose-escalated simultaneous integrated boost radi",
    "description": "IMPORT HIGH is a phase 3, non-inferiority, open-label, randomised controlled trial that recruited women after breast-conserving surgery for pT1&amp;ndash;3pN0&amp;ndash;3aM0 invasive carcinoma from radiotherapy and referral centres in the UK. Clinical data and blood samples are available.",
    "url": "https://healthdatagateway.org/en/dataset/1567",
    "uid": null,
    "datasource_id": 1567,
    "source": "HDRUK"
  },
  {
    "id": 1296,
    "name": "POUT - A phase III randomised trial of peri-operative chemotherapy versus surveillance in upper trac",
    "description": "Clinical data and samples",
    "url": "https://healthdatagateway.org/en/dataset/1566",
    "uid": null,
    "datasource_id": 1566,
    "source": "HDRUK"
  },
  {
    "id": 1297,
    "name": "TNT - A phase III RCT of carboplatin vs docetaxel for patients with metastatic or recurrent locally",
    "description": "TNT is a phase 3, parallel group, open-label randomized controlled trial.  376 participants were allocated (in a 1:1 ratio) to groups that received six cycles of carboplatin (AUC 6), day 1 of a 3-weekly cycle, or six cycles of docetaxel (100&amp;thinsp;mg/m2), day 1 of a 3-weekly cycle.  Subjects were offered six cycles of the alternative (crossover) treatment upon progression or where allocated treatment was discontinued due to toxicity (pre-progression crossover).\n\nClinical data and sample collection.",
    "url": "https://healthdatagateway.org/en/dataset/1572",
    "uid": null,
    "datasource_id": 1572,
    "source": "HDRUK"
  },
  {
    "id": 1298,
    "name": "CASPS - A Phase II Trial of Cediranib in the Treatment of Patients with Alveolar Soft Part Sarcoma",
    "description": "CASPS is a multicentre, double-blind, placebo-controlled, randomised, phase 2 trial.  48 participants were randomly assigned (2:1) to either cediranib (30 mg orally, once daily) or matching placebo tablets for 24 weeks. Participants were unblinded at week 24 or sooner if they had progression; those on placebo crossed over to cediranib and all participants continued on treatment until progression or death.\n\nClinical data and sample collection.",
    "url": "https://healthdatagateway.org/en/dataset/1571",
    "uid": null,
    "datasource_id": 1571,
    "source": "HDRUK"
  },
  {
    "id": 1299,
    "name": "TARN - Trauma Audit and Research Network &amp;ndash; UHS data",
    "description": "The Trauma Audit and Research Network (TARN) was an established national clinical audit for trauma care across England, Wales, Northern Ireland and the Republic of Ireland and supports 218 trauma receiving trusts (170 in England) by providing each trauma unit with case mix adjusted outcome analysis, performance of key process measures and comparisons of trauma care. \nTARN data was collected until 2024 until it was replaced by the National Major Trauma Registry (NMTR).  \nThe current dataset only covers UHS held TARN data.",
    "url": "https://healthdatagateway.org/en/dataset/1502",
    "uid": null,
    "datasource_id": 1502,
    "source": "HDRUK"
  },
  {
    "id": 1300,
    "name": "Care and Repair Cymru (CARC)",
    "description": "CARC contains client and service level information of all services provided by Care and Repair Cymru from 2009 to\n2023, including programme and funding type and amount, service type and service start and end dates. This\ndataset also contains Hospital to Healthier Homes programme data which runs from 2019 to 2023.",
    "url": "https://healthdatagateway.org/en/dataset/1573",
    "uid": null,
    "datasource_id": 1573,
    "source": "HDRUK"
  },
  {
    "id": 1301,
    "name": "Impact of personalised care and continuity of carer on disparities in maternity outcomes",
    "description": "This study measures how different maternity care plans and provision of care by the same midwife or team have reduced differences in pregnancy results among underprivileged populations in the West Midlands.  It investigates other factors that might explain the differences in outcomes among women from similar backgrounds who received comparable maternity care.",
    "url": "https://healthdatagateway.org/en/dataset/1574",
    "uid": null,
    "datasource_id": 1574,
    "source": "HDRUK"
  },
  {
    "id": 1302,
    "name": "Improving care for adults with peripheral nerve injury in the West Midlands",
    "description": "A peripheral nerve injury is associated with trauma and commonly affects the arm.  This study will help to understand more clearly what types of psychological consequences patients experience after peripheral nerve injury and the impact adults with nerve injury have on current NHS rehabilitation services. Many fail to fully recover and live with paralysis, loss of sensation, chronic pain with added psychological consequences. This study will help to examine in detail demographics of those who can get support with development of a new digitally delivered psychologically informed rehabilitation.",
    "url": "https://healthdatagateway.org/en/dataset/1575",
    "uid": null,
    "datasource_id": 1575,
    "source": "HDRUK"
  },
  {
    "id": 1303,
    "name": "STATS-19 - Police Road Traffic Collision data",
    "description": "Statistics on road safety in Great Britain are mostly based on personal-injury collisions reported to the police in the STATS19 data collection. Most of the statistics published are accredited official statistics. Road casualty statistics were assessed by the UK Statistics Authority and confirmed as accredited official statistics in July 2009 and again in 2013 with a further compliance check in 2019.",
    "url": "https://healthdatagateway.org/en/dataset/1579",
    "uid": null,
    "datasource_id": 1579,
    "source": "HDRUK"
  },
  {
    "id": 1304,
    "name": "RAIDS - Road Accident In Depth Studies",
    "description": "Brings together different types of investigation from earlier studies into a single programme combining existing data with new in a common and comprehensive database. The two types of investigation covered are 1) a crash scene investigation done at the time of the collision while all emergency services are still present, 2) backward looking investigations following hospitalisation(s).",
    "url": "https://healthdatagateway.org/en/dataset/1578",
    "uid": null,
    "datasource_id": 1578,
    "source": "HDRUK"
  },
  {
    "id": 1305,
    "name": "National ECDS - Emergency Care Data Set",
    "description": "A data warehouse containing records of all patients recorded in the Nationally collected ECDS data filtered for patients coded as being involved in a Road Traffic Collision.",
    "url": "https://healthdatagateway.org/en/dataset/1577",
    "uid": null,
    "datasource_id": 1577,
    "source": "HDRUK"
  },
  {
    "id": 1306,
    "name": "National HES - Hospital Episode Statistics",
    "description": "A data warehouse containing records of all patients recorded in the Nationally collected HES data filtered for patients coded as being involved in a Road Traffic Collision.",
    "url": "https://healthdatagateway.org/en/dataset/1576",
    "uid": null,
    "datasource_id": 1576,
    "source": "HDRUK"
  },
  {
    "id": 1307,
    "name": "SWAST - South Western Ambulance Service Trust",
    "description": "Data collected by the South Western Ambulance Service Trust, including ambulance, air ambulance and dispatch control",
    "url": "https://healthdatagateway.org/en/dataset/1503",
    "uid": null,
    "datasource_id": 1503,
    "source": "HDRUK"
  },
  {
    "id": 1308,
    "name": "Genomics England - Cancer",
    "description": "Cancer data are presented for either the patient level cancer diagnosis or 'disease type' or the tumour specific sample details of participants in the Cancer arm of the 100,000 Genomes Project.Data Relating to Cancer Participants:cancer_participant_disease: For each cancer participant in the 100,000 Genomes Project, this table includes data about their cancer disease type and subtype.cancer_participant_tumour: For each cancer participant's tumour in the 100,000 Genomes Project, this table contains data that characterises the tumour, e.g. staging and grading; morphology and location; recurrence at time of enrolment; and the basis of diagnosis.cancer_participant_tumour_ metastatic_site: For each cancer participant in the 100,000 Genomes Project, this table contains the site of their metastatic disease in the body (if applicable) at diagnosis.cancer_care_plan: For a proportion of cancer participants in the 100,000 Genomes Project, this table contains information from their NHS cancer care plan on their treatment and care intent, in particular outcomes of MDT meetings and coded connected data (e.g. diagnoses from scans).cancer_surgery: For a proportion of cancer participants in the 100,000 Genomes Project, this table contains details of what surgical procedures were had, as well as the specific location of the intervention.cancer_risk_factor_general: For a proportion of cancer participants in the 100,000 Genomes Project, this table contains data on general cancer risk factors, namely smoking status, height, weight and alcohol consumption. This table was compiled with input from GeCIP members.cancer_risk_factor_cancer_specific: For a proportion of cancer participants in the 100,000 Genomes Project, this table contains data on specific risk factors related to particular cancer types. This table was compiled with input from GeCIP members.cancer_invest_imaging: For a proportion of cancer participants in the 100,000 Genomes Project, this table contains: coded data on imaging investigations characterising the scan, its modality, anatomical site and outcome; as well as the outcome of the imaging report in free text form.Data derived from or relating to tumour samples:cancer_invest_sample_pathology: For a proportion of cancer participants in the 100,000 Genomes Project, this table contains full pathology reports and other related data on and from their tumour samples around diagnosis and characterisation of the cancer. Please note that much of this information is also found in the clinic_sample and cancer_participant_tumour tables.cancer_specific_pathology: For a proportion tumours from cancer participants in the 100,000 Genomes Project, this table contains pathology data specific to that participant’s cancer type. This may provide additional data to the cancer_invest_sample_pathology and cancer_participant_tumour tables.cancer_systemic_anti_cancer_therapy: For a proportion tumours from cancer participants in the 100,000 Genome",
    "url": "https://healthdatagateway.org/en/dataset/1592",
    "uid": null,
    "datasource_id": 1592,
    "source": "HDRUK"
  },
  {
    "id": 1309,
    "name": "Genomics England - Quick View",
    "description": "Quickviews bring together data from several LabKey tables for convenient access, including:rare_disease_analysis Data for all rare disease participants including: sex, ethnicity, disease recruited for and relationship to proband; latest genome build, QC status of latest genome, path to latest genomes and whether tiering data are available; as well as family selection quality checks for rare disease genomes on GRCh38, reporting abnormalities of the sex chromosomes, family relatedness, Mendelian inconsistencies and reported vs genetic sex summary checks. Please note that only sex checks are unpacked into individual data fields; a final status is shown in the \"genetic vs reported results\" column.cancer_analysis Data for all cancer participants whose genomes have been through Genomics England bioinformatics interpretation and passed quality checks, including: sex, ethnicity, disease recruited for and diagnosis; tumour ID, build of latest genome, QC status of latest genome and path to latest genomes; as well file paths to the genomes. This table includes information derived from laboratory_sample and cancer_participant_tumour.",
    "url": "https://healthdatagateway.org/en/dataset/1591",
    "uid": null,
    "datasource_id": 1591,
    "source": "HDRUK"
  },
  {
    "id": 1310,
    "name": "Genomics England - Secondary Data - NHSE",
    "description": "Secondary data tables are the corpus of curated data we receive from national data warehouses for all eligible participants not belonging in a data restricting cohort and not registered in Northern Ireland, Wales or Scotland. They are mostly longitudinal in nature and agnostic to the recruited disease. Data at the point of release captures all activity contained in the period covered within each of the datasets up to the latest quarter published by NHSE and end of calendar year for PHE/NCRAS.\r\n\r\nPlease Note: The linking files MH_bridge and DID_bridge will no longer be provided as part of the main programme release. Participant id is already been included in all tables making these files redundant.\r\n\r\n- HES: Hospital Episode Statistics containing details of all commissioned activity during admissions, outpatient appointments and A&E attendances.\r\n- DID: Metadata (demographics, modalities, ordering entity and dates) on diagnostic imaging tests collated from local radiology information systems.\r\n- MORTALITY/CANCER_REGISTRY: Office of National Statistics registry data for cancer registrations and deaths inside and outside hospitals. Issue of death certificates and cancer network registrations are a requirement for an entry to these manifests.\r\n- COVID: Data on covid test results for 100K participants. Pre Data Release V14 this data was found in the frequent release folder. For more information please see Clinical and phenotype data Secondary Data - COVID.\r\n- MHMDS: Data on patients receiving care in NHS specialist mental health services. Reporting care period for this dataset is up to March '14.\r\n- MHLDDS: Data on patients receiving care in NHS specialist mental health services. Reporting care period for this dataset is from March '14 to March '16.\r\n- MHSDS: Data on patients receiving care in NHS specialist mental health services. Reporting care period for this dataset us from March '16 to March '19.",
    "url": "https://healthdatagateway.org/en/dataset/1590",
    "uid": null,
    "datasource_id": 1590,
    "source": "HDRUK"
  },
  {
    "id": 1311,
    "name": "Genomics England - Secondary Data - MHSDS",
    "description": "Mental Health Datasets contain historic data on patients receiving care in NHS specialist mental health services.",
    "url": "https://healthdatagateway.org/en/dataset/1589",
    "uid": null,
    "datasource_id": 1589,
    "source": "HDRUK"
  },
  {
    "id": 1312,
    "name": "Genomics England - Secondary Data - Cancer Specific Curated Datasets - Pilot",
    "description": "Genomics England are striving to improve the clinical data provided for its researchers. We understand the value of accurate and granular clinical data, especially in the context of cancer.\r\n\r\nIn order to deliver this, we are planning a series of pilot datasets, aiming to incorporate additional clinical data provided by Public Health England cancer registry (NCRAS). Genomics England will aim to deliver cancer specific datasets, with the initial focus being on providing a broad pathological understanding. This will aim to incorporate data points such as molecular mutations and resection margins in pathology reports. The focus will then incorporate radiological imaging reports and finally focus on live/ up-to-date clinical data. In addition, we are also including the date each participant was last seen alive (data provided up to October 2020) and dates and causes of death to aid with outcomes.\r\n\r\nIt must be stressed that this work is a development process, and we are working in unison with NCRAS to progress this. Whilst we do not possess the extensive experience and resource of Public Health England, we are developing a natural language based algorithm for focused data extraction. NCRAS have a dedicated team to curating clinical data and the gold standard remains the NCRAS curated tables. However, for this dataset to improve and move forward, Genomics England are keen for feedback and for you to highlight areas for improvement.\r\n\r\nYou will note subtle differences to the structure of the table compared to the curated NCRAS tables and thus additional data dictionaries have been provided. Genomics England hopes to continue developing this uncurated live dataset with feedback and look forward to hearing your thoughts. Please reach out to us with related thoughts and suggestions via the Genomics England Service Desk, including \"cancer_specific_datasets_pilot\" in the title of your enquiry.\r\n\r\nWith the addition of the new pathology_reports dataset introduced in v16, the aml_path_reports and testes_path_reports datasets have been deprecated in v17.",
    "url": "https://healthdatagateway.org/en/dataset/1588",
    "uid": null,
    "datasource_id": 1588,
    "source": "HDRUK"
  },
  {
    "id": 1313,
    "name": "Genomics England - Research Community Provided Data",
    "description": "Data provided by or on behalf of the wider research community. This can include data resources from both GeCIP and Discovery Forum members. Researchers can reach out to Genomics England via the Service Desk for more information or if they wish to share their data with the wider community.",
    "url": "https://healthdatagateway.org/en/dataset/1586",
    "uid": null,
    "datasource_id": 1586,
    "source": "HDRUK"
  },
  {
    "id": 1314,
    "name": "Genomics England - Rare Disease",
    "description": "Rare Disease data are presented at the level of Rare Disease families (families of probands), Rare Disease pedigrees, and participants. Participants are individuals who have consented to be part of the project with the expectation that a sample of their DNA will be obtained and their genome sequenced. Pedigree members are extended members of the proband’s family, this includes participants as well a small amounts of deidentified data recorded to allow a full picture of the proband’s extended family. This additional information is extracted from the proband’s medical record.",
    "url": "https://healthdatagateway.org/en/dataset/1584",
    "uid": null,
    "datasource_id": 1584,
    "source": "HDRUK"
  },
  {
    "id": 1315,
    "name": "Genomics England - Transcriptomics",
    "description": "The Genomics England 100kGP Transcriptomics Pilot and Extension comprises RNA-sequencing of a subset of rare disease probands from the 100,000 Genomes Project who did not receive a genetic diagnosis through the Genomics England Interpretation Pipeline (7840 samples from 7829 probands: 5546 samples in the initial Pilot project, 2294 samples in the Extension). We prioritised probands who were found to carry variants of unknown significance.\r\n\r\nPriorities were based on:\r\n\r\n - Variants highlighted through Splice AI\r\n - Autosomal recessive disorders with only a single pathogenic variant identified\r\n - GMC-selected VUS AND contribution to phenotype partial / unknown AND variant type likely to affect RNA processing\r\n - Based on outcome questionnaire and a call to clinicians\r\n - VUS with a high Exomiser score AND variant likely to results in detectable abnormal RNA processing\r\n - Disorder category ranking by Genomics England on the basis of likely monogenic cause (ranks 1-5) for participants from 1.1 AND no diagnosis in outcome questionnaire\r\n - Call to GMCs / clinicians to propose cases based on strong phenotype for a monogenic disorder with no lead from WGS\r\n - Review whether RNA sample is available or requirement for fresh RNA sample",
    "url": "https://healthdatagateway.org/en/dataset/1585",
    "uid": null,
    "datasource_id": 1585,
    "source": "HDRUK"
  },
  {
    "id": 1316,
    "name": "Genomics England - Long Read Sequencing",
    "description": "Contains tables related to long-reads sequencing data for 100,000 Genomes Project participants.\r\n\r\n- lrs_laboratory_sample: Data describing the characteristics and processing methods (DNA to library preparation) of samples from participants in the 100,000 Genomes Project for which long-reads sequencing has been carried out.\r\n- lrs_sequencing_data: This table includes data describing long-read sequencing of a subset of 100,000 Genomes Project participants and associated output, including paths to raw and BAM files.\r\n- cancer_ont_cohorts: Table listing participant ids, sample data, file paths and sequencing statistics for Oxford Nanopore cancer cohorts available in the Research Environment, along with corresponding matched germline and Illumina short reads files where available\r\n- rare_disease_pacbio_pilot: This is a dataset of 91 rare disease samples from the 100k genome project re-sequenced with Pacific Biosciences (PacBio) as an example dataset to to demonstrate the utility of their HiFi technology.\r\n- Rare_disease_ont_cohorts: Genomics England have sequenced 315 rare disease participants with ONT and have generated structural variant calls.",
    "url": "https://healthdatagateway.org/en/dataset/1583",
    "uid": null,
    "datasource_id": 1583,
    "source": "HDRUK"
  },
  {
    "id": 1317,
    "name": "Genomics England - Bioinformatics",
    "description": "To identify and enrol participants for the 100,000 Genomes Project we have created NHS Genomic Medicine Centres (GMCs). Each centre includes several NHS Trusts and hospitals. GMCs recruit and consent patients. They then provide DNA samples and clinical information for analysis.\r\n\r\nIllumina, a biotechnology company, have been commissioned to sequence the DNA of participants. They return the whole genome sequences to Genomics England. We have created a secure, monitored, infrastructure to store the genome sequences and clinical data. The data is analysed within this infrastructure and any important findings, like a diagnosis, are passed back to the patient’s doctor.\r\n\r\nTo help make sure that the project brings benefits for people who take part, we have created the Genomics England Clinical Interpretation Partnership (GeCIP). GeCIP brings together funders, researchers, NHS teams and trainees. They will analyse the data – to help ensure benefits for patients and an increased understanding of genomics. The data will also be used for medical and scientific research. This could be research into diagnosing, understanding or treating disease.\r\n\r\nTo learn more about how we work you can read the 100,000 Genomes Project protocol. It has details of the development, delivery and operation of the project. It also sets out the patient and clinical benefit, scientific and transformational objectives, the implementation strategy and the ethical and governance frameworks.",
    "url": "https://healthdatagateway.org/en/dataset/1582",
    "uid": null,
    "datasource_id": 1582,
    "source": "HDRUK"
  },
  {
    "id": 1318,
    "name": "Genomics England - Common",
    "description": "Data views that are common to both the rare disease and the cancer domains. This data pertains to sample handling, genome sequencing, and participant data.\r\n\r\nData Relating to Participants:\r\n\r\n- participant: Data on each individual participant in the 100,000 Genomes Project, e.g. personal information (such as relatives or self-reported ethnicity); points of contact with the Project (e.g. handling Genomic Medicine Centre or Trust); and a record of the status of their clinical review.\r\n- death_details: Data on participant deaths submitted by GMCs, likely less complete than the data collected by ONS and NHSE.\r\n\r\nData Relating to Samples:\r\n\r\n- clinic_sample:\tData describing the taking and handling of participant samples at the Genomic Medicine Centres, i.e. in the clinic, as well as the type of samples obtained. Because of the complexities of handling and managing tumour tissues samples in a clinical setting, there are many fields that are cancer-specific.\r\n- clinic_sample_quality_check_result: Data describing the quality control of obtaining and handling participant samples at the Genomic Medicine Centres, i.e. in the clinic.\r\n- laboratory_sample: Data describing the handling of samples at the biorepository and in preparation for sequencing, as well as the type of sample.\r\n- plated_sample: Data describing the handling and QC of samples at Illumina (the sequencing provider).\r\n- laboratory_sample_omics_availability: Availability of samples collected from participants in the 100,000 Genomes Project for the purpose of omics research. Data includes: Participant ID, Sample Type (e.g. Serum, RNA Blood), the number of aliquots of that sample type for that participant, and the availability status - whether the sample has already been used for a research project. Research proposals for the use of these samples can be submitted, via the GECIP team, to the Scientific Advisory Committee and Access Review Committee.",
    "url": "https://healthdatagateway.org/en/dataset/1581",
    "uid": null,
    "datasource_id": 1581,
    "source": "HDRUK"
  },
  {
    "id": 1319,
    "name": "Genomics England - Secondary Data - PHE/NCRAS",
    "description": "av_patient: Patient information - demographics and death details.\r\nav_tumour: Tumour catalogue and characterisation for all patients with registerable tumour. Table's anon_tumour_id is used to link treatment tables also available in NCRAS. One row per tumour, per participant at the point of registration of that cancer/tumour with NCRAS.\r\nav_treatment: Tumour linked catalogue of treatments and sites that provided them for all patients with registerable tumour.\r\nav_imd: The Income Deprivation Domain (IMD table) measures the proportion of the population experiencing deprivation relating to low income. The definition of low income used includes both those people that are out-of-work and those that are in work but who have low earnings.\r\nav_rtd: Routes to Diagnosis: cancer registration data are combined with Administrative Hospital Episode Statistics data, Cancer Waiting Times daca and data from the cancer screening programmes. Using these datasets cancers registered in England which were diagnosed in 2006 to 2016 are categorised into one of eight Routes to Diagnosis. The methodology is described in detail in the British Journal of Cancer article 'Routes to Diagnosis for cancer - Determining the patient journey using multiple routine datasets'.\r\nCwt: The National Cancer Waiting Times Monitoring Data Set supports the continued management and monitoring of waiting times.\r\nsact: Systemic Anti-Cancer Therapy (chemotherapy detail) data for cancer participants from NHSE covering regimens between 04/2012 and 08/2022. One row per chemotherapy cycle, per tumour (SACT-specific anon_tumour_id), per participant.\r\nrtds: The Radiotherapy Data Set (RTDS) standard (SCCI0111) is an existing standard that has required all NHS Acute Trust providers of radiotherapy services in England to collect and submit standardised data monthly against a nationally defined data set since 2009. Data is available from 01/04/2009. The data is linked at a patient level and can be linked to the latest available av_patient table.\r\nncras_did: The Diagnostic Imaging Dataset (DID) is a central collection of detailed information about diagnostic imaging tests carried out on NHS patients, extracted from local radiology information systems and submitted monthly. The DID captures information about referral source, details of the test (type of test and body site), demographic information such as GP registered practice, patient postcode, ethnicity, gender and date of birth, plus data items about different events (date of imaging request, date of imaging, date of reporting, which allows calculation of time intervals.\r\nlucada_2013: The National Lung Cancer Audit (LUCADA) looks at the care delivered during referral, diagnosis, treatment and outcomes for people diagnosed with lung cancer and mesothelioma. The data items in the LUCADA dataset are not to be confused with the data items identified as Lung Cancer in the National Cancer dataset. \r\nlucada_2014: As above. Different schema to lucada_2013.",
    "url": "https://healthdatagateway.org/en/dataset/1587",
    "uid": null,
    "datasource_id": 1587,
    "source": "HDRUK"
  },
  {
    "id": 1320,
    "name": "CPRD Aurum Source file",
    "description": "The source data are provided to enable researchers to ascertain which patients are eligible for linkage to each dataset and to clarify the coverage periods for each data source. The linkage eligibility file only includes patients from practices that have consented to take part in the linkage process. The file contains flags to indicate whether the patient is eligible for each individual linked data source. Some patients will not be eligible for any of the linked data sources, whereas others may be eligible for some/all of them. These data are provided so that researcher can determine the appropriate population to include in their study. The linkage coverage file indicates the start and end of coverage for each individual linked data source.",
    "url": "https://healthdatagateway.org/en/dataset/1600",
    "uid": null,
    "datasource_id": 1600,
    "source": "HDRUK"
  },
  {
    "id": 1321,
    "name": "Childcare Offer for Wales (COCD)",
    "description": "The Childcare Offer for Wales (the Offer) is a Welsh Government-funded programme that provides up to 30 hours of combined nursery education and childcare a week, for up to 48 weeks of the year, for children aged 3 and 4 years of eligible parents. \nAll local authorities in Wales are required to provide a minimum of 10 hours per week of nursery education for children aged 3 and 4 years, which can be delivered either through a local authority maintained setting (typically a school) or in a non-maintained early education provider (typically a private nursery, funded by the local authority). The Offer funds additional hours of childcare to ensure that eligible parents in each area can access 30 hours in total. \nFunded childcare is provided by childcare services. To deliver funded hours of childcare through the Offer, a childcare provider must be registered with Care Inspectorate Wales (CIW). Services are either childminders, who provide care in their own home, or children day care providers, who provide care in centres or other facilities.\nParents make applications to access the Offer through an online service hosted on the Welsh Government website. As part of this application, parents must demonstrate that they meet the eligibility criteria. To be eligible for the Offer, each parent must live in Wales, have a child aged 3 or 4 years old, have a gross income of 100,000 pounds or less per yea.\nAll parents also must be either, employed and earning at least, on average, the equivalent to 16 hours a week at National Minimum Wage or Living Wage, on Statutory Pay and Leave (Sick, Maternity, Paternity, Parental, \nBereavement or Adoption Leave); or enrolled on a further or higher education course that is at least 10 weeks in length\nThe Offer was launched in seven pilot areas in 2017. Rollout expanded to more areas in September 2018. For the purposes of reporting, the Offer is considered to run alongside the school year, beginning in September and ending in August. \nChildren can begin to access funded childcare in the term after their third birthday, and their access ends in the September of the school year in which they will turn five. \nThe Offer has been the subject of government social research and analysis. This has included published evaluations of the first five years of the programme, including the temporary Coronavirus Childcare Assistance Scheme (C-CAS)which operated in place of the Offer during 2020 as a result of the COVID-19 pandemic. \nThe present report addresses the delivery of the Offer in the academic year 2023-24. Where possible, data relating to this year has been presented alongside comparable data from previous years.",
    "url": "https://healthdatagateway.org/en/dataset/1599",
    "uid": null,
    "datasource_id": 1599,
    "source": "HDRUK"
  },
  {
    "id": 1322,
    "name": "National Mortality - Road Traffic Collisions",
    "description": "A data warehouse containing records of all patients recorded in the Nationally collected Mortality data filtered for patients coded as being involved in a Road Traffic Collision and managed by PRANA.",
    "url": "https://healthdatagateway.org/en/dataset/1595",
    "uid": null,
    "datasource_id": 1595,
    "source": "HDRUK"
  },
  {
    "id": 1323,
    "name": "Born in Wales OMOP Common Data Model",
    "description": "The BiW OMOP CDM database contains longitudinal routinely-collected health records (EHR data) from UK primary and secondary care practices. The data has been transformed into a common format (data model) using an open community data standard and structure from the OHDSI standardised vocabularies.",
    "url": "https://healthdatagateway.org/en/dataset/1580",
    "uid": null,
    "datasource_id": 1580,
    "source": "HDRUK"
  },
  {
    "id": 1324,
    "name": "Synthetic Data - Monoclonal Gammopathy of Undetermined Significance dataset",
    "description": "MGUS Dataset is used to generate a synthetic version of this dataset.",
    "url": "https://healthdatagateway.org/en/dataset/1204",
    "uid": null,
    "datasource_id": 1204,
    "source": "HDRUK"
  },
  {
    "id": 1325,
    "name": "Synthetic Data - Early Warning Score Dataset",
    "description": "EWS Dataset is used to generate a synthetic version of this dataset.",
    "url": "https://healthdatagateway.org/en/dataset/1203",
    "uid": null,
    "datasource_id": 1203,
    "source": "HDRUK"
  },
  {
    "id": 1326,
    "name": "Wrightington, Wigan and Leigh NHS Trust OMOP Dataset",
    "description": "The Wrightington, Wigan and Leigh NHS Foundation Trust (WWL) OMOP database is a longitudinal secondary care dataset derived from routinely collected electronic health record (EHR) data for patients treated at WWL. The dataset has been mapped to the OMOP Common Data Model (CDM), enabling interoperability with other OMOP aligned data assets and supporting privacy preserving, multi centre observational research.\nThe database captures a broad range of clinical information spanning key secondary care activity, including patient demographics, diagnoses, procedures, medications, laboratory measurements, and outcomes. This provides a rich foundation for research across multiple clinical domains, including diagnostics, elective and emergency care pathways, long term conditions, and service evaluation within a district general hospital setting.  With internationally recognised orthopaedic services, largescale realworld dermatology pathways, and data structured in the OMOP Common Data Model, WWL is positioned as a trusted partner for academic, industry, and health system research. This makes it an invaluable resource for researchers focusing on these clinical areas, as well as those interested in broader secondary care settings.\n\nBy conforming to the OMOP CDM, the WWL OMOP database supports the use of standardised OHDSI analytical tools and federated analytics approaches. This allows researchers to conduct reproducible analyses and participate in collaborative studies across institutions.\nThe WWL OMOP database is intended to support a wide range of use cases, including:\n- Secondary care outcomes research\n- Pathway and waiting list analyses\n- Population health and health inequalities research\n- Service evaluation and quality improvement\n- Participation in regional, national, and international federated studies\n\nOverall, the WWL OMOP database represents a growing and strategically important research asset. Its alignment to international data standards, combined with high quality routinely collected clinical data, positions WWL to contribute meaningfully to collaborative research initiatives and to generate insights that can directly inform improving patient outcomes.",
    "url": "https://healthdatagateway.org/en/dataset/1604",
    "uid": null,
    "datasource_id": 1604,
    "source": "HDRUK"
  },
  {
    "id": 1327,
    "name": "Treatment and clinical pathways of patients with Idiopathic or Progressive Pulmonary Fibrosis",
    "description": "The goal of this study is to understand current standard of treatment in a real-world setting for IPF and PPF.  Additionally, the study will characterise the patient populations by assessing the demographic and baseline clinical characteristics, diagnosis pathways, natural history of the conditions under current treatment patterns, and clinical outcomes.  Moreover, the risk factors for disease progression will be explored, with particular interest in the relationship between the forced vital capacity (FVC) decline and mortality.",
    "url": "https://healthdatagateway.org/en/dataset/1165",
    "uid": null,
    "datasource_id": 1165,
    "source": "HDRUK"
  },
  {
    "id": 1328,
    "name": "Eastern England SDE Secondary Care EPR dataset",
    "description": "This data has been assembled to support health and care research using pseudonymized NHS patient data in the Eastern England Secure Data Environment. The dataset contains inpatient, outpatient and emergency care records from secondary care trusts.  This covers demographic information, diagnoses, events, prescribing information and lab test results for patients from 2015 to today. Currently the dataset contains data from Cambridge University Hospitals foundation trust. Over time, the dataset will be extended to include additional secondary care trusts from across Eastern England.",
    "url": "https://healthdatagateway.org/en/dataset/1506",
    "uid": null,
    "datasource_id": 1506,
    "source": "HDRUK"
  },
  {
    "id": 1329,
    "name": "Geographical variation in prescription of hormone therapies in prostate cancer treatment",
    "description": "Prostate cancer is the most common cancer among men in the UK. Understanding use of different prostate cancer treatments can help improve care for patients, make better use of NHS resources, and highlight any gaps or inconsistencies in treatment. \n\nClinicians may choose different treatments due to a variety of reasons, such as personal experience, patient preferences, or how they interpret medical guidelines. These differences can lead to inconsistent treatment across the country. There is a lack of evidence on about how much variation there is in prescribing these hormone therapies. \n\nThe study seeks to identifying where and why these differences occur in order to work towards making sure all patients get the best possible care. The study will start by looking at how hormone treatments (especially a type called androgen deprivation therapy (ADT)) are prescribed in different parts of England, based on NHS Trust areas.",
    "url": "https://healthdatagateway.org/en/dataset/1154",
    "uid": null,
    "datasource_id": 1154,
    "source": "HDRUK"
  },
  {
    "id": 1330,
    "name": "Wrightington, Wigan and Leigh Teaching Hospitals NHS Trust OMOP Dataset",
    "description": "The Wrightington, Wigan and Leigh NHS Foundation Trust (WWL) OMOP database comprises longitudinal secondary care data derived from routinely collected electronic health records for patients across the Wigan borough and surrounding areas. Alignment to the OMOP Common Data Model (CDM) enables interoperability with other OMOP standardised datasets and facilitates privacy preserving, multi centre observational research.\nThe database captures a wide range of secondary care activity, including patient demographics, diagnoses, procedures, and outcomes. This provides a robust foundation for research across diagnostics, elective and emergency care pathways, long term conditions, population health, and service evaluation within a district general hospital setting. Alignment to OMOP standards enables use of OHDSI analytical tools and participation in federated analytics, supporting reproducible and collaborative research across institutions.\nWrightington Hospital is home to WWL&amp;rsquo;s internationally recognised Orthopaedic and Musculoskeletal Centre of Excellence, established through Professor Sir John Charnley&amp;rsquo;s pioneering work on modern hip replacement in 1962. With approximately 1,600 hip and 1,000 knee replacements performed annually and specialist-led complex surgery, the Trust holds high quality longitudinal data well suited to implant survivorship, device surveillance, pathway evaluation, health economics, and national benchmarking.\nOverall, the WWL OMOP database represents a growing and strategically important research asset. .Its combination of high quality routinely collected clinical data, internationally recognised clinical services, and alignment to global data standards, positions WWL to contribute meaningfully to collaborative research initiatives and to generate insights that can directly inform improving patient outcomes.",
    "url": "https://healthdatagateway.org/en/dataset/1604",
    "uid": null,
    "datasource_id": 1604,
    "source": "HDRUK"
  },
  {
    "id": 1331,
    "name": "Demonstration only",
    "description": "Description for demonstration dataset.",
    "url": "https://healthdatagateway.org/en/dataset/1221",
    "uid": null,
    "datasource_id": 1221,
    "source": "HDRUK"
  },
  {
    "id": 1332,
    "name": "Octopus Clinical Trial Biorepository",
    "description": "A biorepository of samples from individuals recruited to the OCTOPUS clinical trial for progressive-MS. Samples (serum, plasma, DNA) were collected longitudinally every 6 months (W0, W26, W52, W78), alongside MRI and measurements of disability progression.\n\nBiomarkers that are predictive of a) long-term disability in MS and b) responses to medication/ therapy (whether a broad therapy response or a response to a specific medication) are needed for people with MS. While new data suggest that several potential biomarkers may portend a worse long-term prognosis for MS, the interpretation of these results may be limited by the fact that the data were generated from either observational, non-randomised cohorts or from clinical trials, in which people with MS were chosen based on a certain degree of recent disease activity. Hence, there is a need to evaluate such novel biomarkers in a prospective, less biased study to assess its true clinical applicability and generalisability. The OCTOPUS trial presents a unique opportunity for collection of biospecimens.",
    "url": "https://healthdatagateway.org/en/dataset/1608",
    "uid": null,
    "datasource_id": 1608,
    "source": "HDRUK"
  },
  {
    "id": 1333,
    "name": "The Human Genotype-Phenotype Map",
    "description": "The Human Genotype-Phenotype Map (GPMap) is an integrated discovery engine designed to bridge the gap between GWAS discovery and functional follow-up. While standard browsers identify genes in proximity to lead SNPs, the GPMap uses rigorous fine-mapping and colocalization to identify causal links between thousands of complex traits and molecular layers (eQTL, pQTL, sQTL, and methQTL). \n\nCore Capabilities   \nCausal Locus Resolution: Transition from nearest gene heuristics to empirical evidence. By scanning Colocalization Groups (CGs), you can identify the specific phenotypes and molecular mechanisms sharing a genetic architecture at a single locus.\nSystemic Pleiotropy &amp; Comorbidity: Instantly visualize pleiotropic neighbors. The GPMap allows you to deconvolve whether a variant affects multiple traits independently (horizontal pleiotropy) or acts through a molecular mediator like a protein (vertical pleiotropy).\n\nPrecision MR Instruments: Streamline Mendelian Randomization by selecting instruments backed by high colocalization posterior probabilities (H4&gt;0.8). This minimizes LD-contamination and ensures your IVs are functionally relevant. \n  \nUser-Led Extensibility: Beyond our library of 4,500+ traits, you can upload your own GWAS summary statistics. The platform will automatically run fine-mapping and colocalization against our entire multi-omic database to identify supported mechanisms for your novel hits.",
    "url": "https://healthdatagateway.org/en/dataset/1611",
    "uid": null,
    "datasource_id": 1611,
    "source": "HDRUK"
  },
  {
    "id": 1334,
    "name": "North West London Pathology: Chemistry",
    "description": "Pathology results from NWL Pathology in regards to Chemistry Tests.",
    "url": "https://healthdatagateway.org/en/dataset/1614",
    "uid": null,
    "datasource_id": 1614,
    "source": "HDRUK"
  },
  {
    "id": 1335,
    "name": "North West London Pathology: Haematology",
    "description": "Pathology results from NWL Pathology in regards to Haematology Tests.",
    "url": "https://healthdatagateway.org/en/dataset/1613",
    "uid": null,
    "datasource_id": 1613,
    "source": "HDRUK"
  }
]