Name Alcohol Consumption
Type Lifestyle Risk Factor
Group Lifestyle Risk Factor
Data Sources
Clinical Terminologies Read Version 2
Codelists caliber_alcohol_consumption_7aXDiYMtSR6aYEkZKPBdvm_Read2.csv caliber_alcohol_daily_intake_7aXDiYMtSR6aYEkZKPBdvm_Read2.csv caliber_alcohol_harm_7aXDiYMtSR6aYEkZKPBdvm_Read2.csv
Valid Event Data Range 01/01/1999 - 01/07/2016
Sex Female/Male
Authors Bell S, Daskalopoulou M, Rapsomaniki E, George J, Britton A, Bobak M, Casas JP, Dale CE, Denaxas S, Shah AD, Hemingway H
Agreement Date 23 Nov 2012
Version (UUID) Revision 1 (7aXDiYMtSR6aYEkZKPBdvm)

Primary Care

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

Alcohol consumption information

Read code Read term CALIBER category
1361 Teetotaller Non drinker
1361.11 Non drinker alcohol Non drinker
1361.12 Non-drinker alcohol Non drinker
136M.00 Current non drinker Non drinker
1367 Stopped drinking alcohol Ex drinker
136A.00 Ex-trivial drinker (<1u/day) Ex drinker
136B.00 Ex-light drinker - (1-2u/day) Ex drinker
136C.00 Ex-moderate drinker - (3-6u/d) Ex drinker
136D.00 Ex-heavy drinker - (7-9u/day) Ex drinker
136E.00 Ex-very heavy drinker-(>9u/d) Ex drinker
1362.11 Drinks rarely Occasional drinker (less than once per week)
1362.12 Drinks occasionally Occasional drinker (less than once per week)
136F.00 Spirit drinker Current drinker
136G.00 Beer drinker Current drinker
136H.00 Drinks beer and spirits Current drinker
136I.00 Drinks wine Current drinker
136J.00 Social drinker Current drinker
136L.00 Alcohol intake within recommended sensible limits Current drinker
136N.00 Light drinker Current drinker
136O.00 Moderate drinker Current drinker
1D19.00 Pain in lymph nodes after alcohol consumption Current drinker
2577 O/E - breath - alcohol smell Current drinker
2577.11 O/E - alcoholic breath Current drinker
E250.11 Drunkenness NOS Current drinker
E250.12 Hangover (alcohol) Current drinker
E250.13 Inebriety NOS Current drinker
E250.14 Intoxication - alcohol Current drinker
R103.00 [D]Alcohol blood level excessive Current drinker
U81..00 [X]Evid of alcohol involv determind by level of intoxication Current drinker
136K.00 Alcohol intake above recommended sensible limits Excess drinker
136P.00 Heavy drinker Excess drinker
136Q.00 Very heavy drinker Excess drinker
136S.00 Hazardous alcohol use Excess drinker
136T.00 Harmful alcohol use Excess drinker
136W.00 Alcohol misuse Excess drinker
13ZY.00 Disqualified from driving due to excess alcohol Excess drinker
E23..12 Alcohol problem drinking Excess drinker
E250.00 Nondependent alcohol abuse Excess drinker
E250000 Nondependent alcohol abuse, unspecified Excess drinker
E250100 Nondependent alcohol abuse, continuous Excess drinker
E250300 Nondependent alcohol abuse in remission Excess drinker
E250z00 Nondependent alcohol abuse NOS Excess drinker
ZV11311 [V]Problems related to lifestyle alcohol use Excess drinker
136R.00 Binge drinker Binge drinker
E250200 Nondependent alcohol abuse, episodic Binge drinker
136..00 Alcohol consumption Drinker status not specified
1368 Alcohol consumption unknown Drinker status not specified
136V.00 Alcohol units per week Drinker status not specified
136X.00 Alcohol units consumed on heaviest drinking day Drinker status not specified
136Z.00 Alcohol consumption NOS Drinker status not specified
9k19.00 Alcohol assesment declined - enhanced services admin Drinker status not specified
9k19.11 Alcohol assessment declined Drinker status not specified
Z786200 Drinking practice Drinker status not specified
ZV4KC00 [V] Alcohol use Drinker status not specified

Daily/weekly alcohol intake

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).

Read code Read term CALIBER category
1362 Trivial drinker - <1u/day Less than 1 unit/day
1363 Light drinker - 1-2u/day 1-2 units/day
1364 Moderate drinker - 3-6u/day 3-6 units/day
1365 Heavy drinker - 7-9u/day 7-9 units/day
1366 Very heavy drinker - >9u/day More than 9 units/day

Alcohol abuse

Read code Read term CALIBER category
C150500 Alcohol-induced pseudo-Cushing's syndrome Endocrine system
1B1c.00 Alcohol induced hallucinations Mental Health
E01..00 Alcoholic psychoses Mental Health
E011.00 Alcohol amnestic syndrome Mental Health
E011000 Korsakov's alcoholic psychosis Mental Health
E011100 Korsakov's alcoholic psychosis with peripheral neuritis Mental Health
E011200 Wernicke-Korsakov syndrome Mental Health
E011z00 Alcohol amnestic syndrome NOS Mental Health
E012.00 Other alcoholic dementia Mental Health
E012.11 Alcoholic dementia NOS Mental Health
E015.00 Alcoholic paranoia Mental Health
E01y.00 Other alcoholic psychosis Mental Health
E01yz00 Other alcoholic psychosis NOS Mental Health
E01z.00 Alcoholic psychosis NOS Mental Health
Eu10.00 [X]Mental and behavioural disorders due to use of alcohol Mental Health
Eu10000 [X]Mental & behav dis due to use alcohol: acute intoxication Mental Health
Eu10100 [X]Mental and behav dis due to use of alcohol: harmful use Mental Health
Eu10500 [X]Mental & behav dis due to use alcohol: psychotic disorder Mental Health
Eu10511 [X]Alcoholic hallucinosis Mental Health
Eu10512 [X]Alcoholic jealousy Mental Health
Eu10513 [X]Alcoholic paranoia Mental Health
Eu10514 [X]Alcoholic psychosis NOS Mental Health
Eu10600 [X]Mental and behav dis due to use alcohol: amnesic syndrome Mental Health
Eu10611 [X]Korsakov's psychosis, alcohol induced Mental Health
Eu10700 [X]Men & behav dis due alcoh: resid & late-onset psychot dis Mental Health
Eu10711 [X]Alcoholic dementia NOS Mental Health
Eu10y00 [X]Men & behav dis due to use alcohol: oth men & behav dis Mental Health
Eu10z00 [X]Ment & behav dis due use alcohol: unsp ment & behav dis Mental Health
E012000 Chronic alcoholic brain syndrome Nervous system
Eu10712 [X]Chronic alcoholic brain syndrome Nervous system
F11x000 Cerebral degeneration due to alcoholism Nervous system
F11x011 Alcoholic encephalopathy Nervous system
F144000 Cerebellar ataxia due to alcoholism Nervous system
F25B.00 Alcohol-induced epilepsy Nervous system
F375.00 Alcoholic polyneuropathy Nervous system
F394100 Alcoholic myopathy Circulatory system
G555.00 Alcoholic cardiomyopathy Circulatory system
G852300 Oesophageal varices in alcoholic cirrhosis of the liver Digestive system
J153.00 Alcoholic gastritis Digestive system
J610.00 Alcoholic fatty liver Digestive system
J611.00 Acute alcoholic hepatitis Digestive system
J612.00 Alcoholic cirrhosis of liver Digestive system
J612000 Alcoholic fibrosis and sclerosis of liver Digestive system
J613.00 Alcoholic liver damage unspecified Digestive system
J613000 Alcoholic hepatic failure Digestive system
J617.00 Alcoholic hepatitis Digestive system
J617000 Chronic alcoholic hepatitis Digestive system
J671000 Alcohol-induced chronic pancreatitis Digestive system


CALIBER Alcohol consumption Phenotype flowchart
Flow chart diagram illustrating the CALIBER phenotype algorithm for alcohol consumption

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.


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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