National, regional, and global statistics on alcohol consumption and associated burden of disease 2000–20: a modelling study and comparative risk assessment
Bibliographic record
Abstract
BACKGROUND: Data on alcohol consumption and associated health harms are essential to evaluate progress in achieving global health goals. This study aims to estimate global alcohol consumption from 2000 to 2020, and the global burden of alcohol-attributable harms from 2000 to 2019. METHODS: In this global analysis, adult per capita consumption data estimates were modelled on the basis of sales, survey, and traveller data. Drinking status and past 30-day heavy episodic drinking were estimated through regression analyses of 540 surveys from 174 countries. Alcohol-attributable harms were estimated using a comparative risk assessment methodology by combining alcohol consumption data with corresponding relative risks obtained from meta-analyses and cohort studies. Mortality and morbidity data were obtained from WHO Global Health Estimates. FINDINGS: Globally, average alcohol consumption in 2019 among adults was 5·5 L (95% uncertainty interval 4·9-6·2), which increased from 5·1 L (4·6-5·7) in 2000. From 2019 to 2020 alcohol consumption decreased to 4·9 L (4·3-5·6). In 2019, alcohol consumption was associated with 2·6 (2·3-3·1) million deaths (4·7% of all deaths) and 116·0 million disability-adjusted life-years (DALYs) lost (4·6% of all DALYs lost). In contrast to alcohol consumption, the number of alcohol-attributable deaths decreased by 31·0% and DALYs lost per 100 000 people decreased by 27·4% from 2000 to 2019. INTERPRETATION: Alcohol is attributed to a large burden of disease, which disproportionately affects people in Eastern Europe and in Central and Southern Sub-Saharan Africa, and young people. Accordingly, these regions should implement policies such as alcohol taxation increases, availability reductions, and marketing restrictions to reduce alcohol-related harms. FUNDING: WHO.
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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 | 0.001 | 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".