Alcohol Consumption

Bell S, Daskalopoulou M, Rapsomaniki E, George J, Britton A, Bobak M, Casas JP, Dale CE, Denaxas S, Shah AD, Hemingway H

PH9 / 1510 Clinical-Coded Phenotype

  1. Overview

    Phenotype Type
    Lifestyle risk factor
    Sex
    Both
    Valid Event Date Range
    01/01/1999 - 01/07/2016
    Coding System
    Read codes v2
    Data Sources
    Collections
    Phenotype Library
    Tags
    No data
  2. Definition

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    The most recent alcohol consumption record in the five years before entry into the study was used to classify participants drinking behaviour. In light of current debates on the U/J-shaped relationship observed between alcohol consumption and aggregated CVD outcomes10 five drinking categories were defined including: (1) non-drinkers (Read66 codes such as "tee-total" and "non-drinkers"), former drinkers (those with codes for "stopped drinking alcohol"

    and/or "ex-drinker"), occasional drinkers (those with codes for "drinks rarely" and/or "drinks occasionally"), current moderate drinkers (those who had a code for current alcohol consumer and an indicator of whether they drank within daily [32g or 24g of alcohol for men and women respectively]

    and/or weekly [168g of alcohol for men and 112g for women] recommended sensible drinking limits for the UK at the time of observation69) and current heavy drinkers (defined as those who exceeded daily and/or weekly sensible drinking limits). We also utilised data fields with information entered on daily and/or weekly amount of alcohol consumed to define participants as non-drinkers, moderate drinkers (drank within daily and/or weekly guidelines) and heavy drinkers. Weekly alcohol data was available as a continuous variable, so we were able to classify consumption using standard thresholds

    Data on daily alcohol intake was entered using categories of: (1) < 1 UK unit (8 grams of ethanol), (2) 1-2 UK units, (3) 3-6 UK units, (4) 7-9 UK units, and (5) > 9 UK units [Read codes 1362.00-1366.00], for which we defined moderate drinking as anything >1 UK

    unit but less than 3 (women) or 7 (men) UK units. Unfortunately information on binge drinking was only available for a select minority of the cohort (~100 people) therefore a separate category for this drinking behaviour was not defined (but these patients were coded as heavy drinkers). We reclassified non-drinkers as former drinkers if they had any record of drinking recorded in their entire clinical record entered on CPRD prior to study entry (in cases whereby non-drinkers had no record of drinking before entering the study we assumed that they were not former drinkers). This resulted in 19,853 (out of 184,747; 10.7%) non-drinkers being recoded as former drinkers, a further 6,826 (3.7%)

    participants were reclassified through having a positive history of alcohol abuse.

    In the Clinical Practice Research Datalink (CPRD, primary care data) we extracted information on alcohol consumption based on:

    • Clinician-recorded alcohol consumption information
    • Stuctured data elements related to daily/weekly alcohol unit consumption
    • Evidence of alcohol abuse
    • Evidence of alcohol-related harm

    We extracted alcohol unit intake information using the structured data part (entity type 5) of the additional table (units recorded in data2) field. We additionally extract information on intake through clinician-recorded classifications using Read terms (see below).

  3. Implementation

  4. Clinical Code List

  5. Publication

    • Gho JMIH et al. An electronic health records cohort study on heart failure following myocardial infarction in England: incidence and predictors. BMJ Open. 2018 Mar 3;8(3):e018331. doi: 10.1136/bmjopen-2017-018331. PMID: 29502083

      (DOI:10.1136/bmjopen-2017-018331)
    • Steele AJ et al. Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease. PLoS One. 2018 Aug 31;13(8):e0202344. doi: 10.1371/journal.pone.0202344. eCollection 2018. PMID: 30169498

      (DOI:10.1371/journal.pone.0202344)
    • Archangelidi O et al. Clinically recorded heart rate and incidence of 12 coronary, cardiac, cerebrovascular and peripheral arterial diseases in 233,970 men and women: A linked electronic health record study. Eur J Prev Cardiol. 2018 Sep;25(14):1485-1495. doi: 10.1177/2047487318785228. Epub 2018 Jul 2. PMID: 29966429

      (DOI:10.1177/2047487318785228)
    • Koudstaal S et al. Prognostic burden of heart failure recorded in primary care, acute hospital admissions, or both: a population-based linked electronic health record cohort study in 2.1 million people. Eur J Heart Fail. 2017 Sep;19(9):1119-1127. doi: 10.1002/ejhf.709. Epub 2016 Dec 23. PMID: 28008698

      (DOI:10.1002/ejhf.709)
    • Chung SC et al. Time spent at blood pressure target and the risk of death and cardiovascular diseases. PLoS One. 2018 Sep 5;13(9):e0202359. doi: 10.1371/journal.pone.0202359. eCollection 2018. PMID: 30183734

      (DOI:10.1371/journal.pone.0202359)
    • Bell S et al. Association between clinically recorded alcohol consumption and initial presentation of 12 cardiovascular diseases: population based cohort study using linked health records. BMJ. 2017 Mar 22;356:j909. PMID: 28331015

    • Pasea L et al. Personalising the decision for prolonged dual antiplatelet therapy: development, validation and potential impact of prognostic models for cardiovascular events and bleeding in myocardial infarction survivors. Eur Heart J. 2017 Apr 7;38(14):1048-1055. doi: 10.1093/eurheartj/ehw683. PMID: 28329300

      (DOI:10.1093/eurheartj/ehw683)
    • Shah AD et al. Neutrophil Counts and Initial Presentation of 12 Cardiovascular Diseases: A CALIBER Cohort Study. J Am Coll Cardiol. 2017 Mar 7;69(9):1160-1169. doi: 10.1016/j.jacc.2016.12.022. PMID: 28254179

      (DOI:10.1016/j.jacc.2016.12.022)
    • Asaria M et al. Using electronic health records to predict costs and outcomes in stable coronary artery disease. Heart. 2016 May 15;102(10):755-62. doi: 10.1136/heartjnl-2015-308850. Epub 2016 Feb 10. PMID: 26864674

      (DOI:10.1136/heartjnl-2015-308850)
    • Daskalopoulou M et al. Depression as a Risk Factor for the Initial Presentation of Twelve Cardiac, Cerebrovascular, and Peripheral Arterial Diseases: Data Linkage Study of 1.9 Million Women and Men. PLoS One. 2016 Apr 22;11(4):e0153838. doi: 10.1371/journal.pone.0153838. eCollection 2016. PMID: 27105076

      (DOI:10.1371/journal.pone.0153838)
    • Pujades-Rodriguez M et al. Associations between polymyalgia rheumatica and giant cell arteritis and 12 cardiovascular diseases. Heart. 2016 Mar;102(5):383-9. doi: 10.1136/heartjnl-2015-308514. Epub 2016 Jan 19. PMID: 26786818

      (DOI:10.1136/heartjnl-2015-308514)
    • Pujades-Rodriguez M et al. Rheumatoid Arthritis and Incidence of Twelve Initial Presentations of Cardiovascular Disease: A Population Record-Linkage Cohort Study in England. PLoS One. 2016 Mar 15;11(3):e0151245. doi: 10.1371/journal.pone.0151245. eCollection 2016. PMID: 26978266

      (DOI:10.1371/journal.pone.0151245)
    • Shah AD et al. Low eosinophil and low lymphocyte counts and the incidence of 12 cardiovascular diseases: a CALIBER cohort study. Open Heart. 2016 Sep 5;3(2):e000477. doi: 10.1136/openhrt-2016-000477. eCollection 2016. PMID: 27621833

      (DOI:10.1136/openhrt-2016-000477)
    • Timmis A et al. Prolonged dual antiplatelet therapy in stable coronary disease: comparative observational study of benefits and harms in unselected versus trial populations. BMJ. 2016 Jun 22;353:i3163. PMID: 27334486

    • Walker S et al. Long-term healthcare use and costs in patients with stable coronary artery disease: a population-based cohort using linked health records (CALIBER). Eur Heart J Qual Care Clin Outcomes. 2016 Jan 20;2(2):125-140. doi: 10.1093/ehjqcco/qcw003. PMID: 27042338

      (DOI:10.1093/ehjqcco/qcw003)
    • George J et al. How Does Cardiovascular Disease First Present in Women and Men? Incidence of 12 Cardiovascular Diseases in a Contemporary Cohort of 1,937,360 People. Circulation. 2015 Oct 6;132(14):1320-8. doi: 10.1161/CIRCULATIONAHA.114.013797. Epub 2015 Sep 1. PMID: 26330414

      (DOI:10.1161/CIRCULATIONAHA.114.013797)
    • Morley KI et al. Defining disease phenotypes using national linked electronic health records: a case study of atrial fibrillation. PLoS One. 2014 Nov 4;9(11):e110900. doi: 10.1371/journal.pone.0110900. eCollection 2014. PMID: 25369203

      (DOI:10.1371/journal.pone.0110900)
    • Pujades-Rodriguez M et al. Heterogeneous associations between smoking and a wide range of initial presentations of cardiovascular disease in 1937360 people in England: lifetime risks and implications for risk prediction. Int J Epidemiol. 2015 Feb;44(1):129-41. doi: 10.1093/ije/dyu218. Epub 2014 Nov 20. PMID: 25416721

      (DOI:10.1093/ije/dyu218)
    • Pujades-Rodriguez M et al. Socioeconomic deprivation and the incidence of 12 cardiovascular diseases in 1.9 million women and men: implications for risk prediction and prevention. PLoS One. 2014 Aug 21;9(8):e104671. doi: 10.1371/journal.pone.0104671. eCollection 2014. PMID: 25144739

      (DOI:10.1371/journal.pone.0104671)
    • Rapsomaniki E et al. Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1.25 million people. Lancet. 2014 May 31;383(9932):1899-911. doi: 10.1016/S0140-6736(14)60685-1. PMID: 24881994

      (DOI:10.1016/S0140-6736(14)60685-1)
    • Shah AD et al. Type 2 diabetes and incidence of cardiovascular diseases: a cohort study in 1.9 million people. Lancet Diabetes Endocrinol. 2015 Feb;3(2):105-13. doi: 10.1016/S2213-8587(14)70219-0. Epub 2014 Nov 11. PMID: 25466521

      (DOI:10.1016/S2213-8587(14)70219-0)
    • Rapsomaniki E et al. Prognostic models for stable coronary artery disease based on electronic health record cohort of 102 023 patients. Eur Heart J. 2014 Apr;35(13):844-52. doi: 10.1093/eurheartj/eht533. Epub 2013 Dec 17. PMID: 24353280

      (DOI:10.1093/eurheartj/eht533)

    Citation Example

    Bell S, Daskalopoulou M, Rapsomaniki E, George J, Britton A, Bobak M, Casas JP, Dale CE, Denaxas S, Shah AD, Hemingway H. PH9 / 1510 - Alcohol Consumption. Phenotype Library [Online]. 19 October 2021. Available from: http://phenotypes.healthdatagateway.org/phenotypes/PH9/version/1510/detail/. [Accessed 18 June 2024]

  6. API

    To Export Phenotype Details:

    FormatAPI
    JSON site_root/api/v1/phenotypes/PH9/version/1510/detail/?format=json
    R Package

    # Download here

    library(ConceptLibraryClient)


    # Connect to API

    client = ConceptLibraryClient::Connection$new(public=TRUE)


    # Get details of phenotype

    phenotype_details = client$phenotypes$get_detail(
     'PH9',
     version_id=1510
    )

    Py Package

    # Download here

    from pyconceptlibraryclient import Client


    # Connect to API

    client = Client(public=True)


    # Get codelist of phenotype

    phenotype_codelist = client.phenotypes.get_detail(
     'PH9',
     version_id=1510
    )

    To Export Phenotype Code List:

    FormatAPI
    JSON site_root/api/v1/phenotypes/PH9/version/1510/export/codes/?format=json
    CSV site_root/phenotypes/PH9/version/1510/export/codes/
    R Package

    # Download here

    library(ConceptLibraryClient)


    # Connect to API

    client = ConceptLibraryClient::Connection$new(public=TRUE)


    # Get codelist of phenotype

    phenotype_codelist = client$phenotypes$get_codelist(
     'PH9',
     version_id=1510
    )

    Py Package

    # Download here

    from pyconceptlibraryclient import Client


    # Connect to API

    client = Client(public=True)


    # Get codelist of phenotype

    phenotype_codelist = client.phenotypes.get_codelist(
     'PH9',
     version_id=1510
    )

  7. Version History

    Version IDNameOwnerPublish date
    1510 Alcohol Consumption ieuan.scanlon2021-10-19currently shown