Underestimation of alcohol consumption in cohort studies and implications for alcohol's contribution to the global burden of disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Estimated alcohol consumption from national self-report surveys is often only 30-40% of official estimates based on sales or taxation data. Global burden of disease (GBD) estimates for alcohol adjust survey estimates up to 80% of total per capita consumption. This assumes that cohort studies needed to estimate relative risks for disease suffer less from under-reporting than typical national surveys. However, there is limited evidence on which to base that assumption. This paper aims to assess the extent of underestimation of alcohol consumption in cohort studies concerning alcohol and mortality compared with official total consumption estimates. DESIGN: Comparisons of estimated per capita consumption from a comprehensive sample of cohort studies against official estimates by country and year. PARTICIPANTS: A total of 1 876 046 participants in 40 cohort studies from 18 countries on alcohol use and all-cause mortality identified by systematic review. MEASUREMENTS: Alcohol consumption data from the cohort studies were converted into usual grams of ethanol per day and then to total age 15+ per capita consumption. Matched estimates were sourced from the World Health Organization (WHO) Global Health Observatory. FINDINGS: The cohort studies had mean coverages of age 15+ per capita alcohol consumption of 61.71% (ranging from 29.19% for Russia to 96.53% for Japan), after weighting estimates by sample size for within-country estimates and by number of studies per country for the overall estimate. Regional estimates were higher for the United States (66.22%) and lower for western European countries (55.35%). CONCLUSIONS: Underestimation of alcohol consumption in cohort studies is less than in typical population surveys. Because some under-coverage is caused by under-sampling heavier drinkers, the current practice of uplifting survey estimates to 80% of total population consumption in global burden of disease studies appears to be appropriate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".