Alcohol consumption and non‐communicable diseases: epidemiology and policy implications
Bibliographic record
Abstract
AIMS: This paper summarizes the relationships between different patterns of alcohol consumption and various on non-communicable disease (NCD) outcomes and estimates the percentage of NCD burden that is attributable to alcohol. METHODS: A narrative review, based on published meta-analyses of alcohol consumption-disease relations, together with an examination of the Comparative Risk Assessment estimates applied to the latest available revision of Global Burden of Disease study. RESULTS: Alcohol is causally linked (to varying degrees) to eight different cancers, with the risk increasing with the volume consumed. Similarly, alcohol use is related detrimentally to many cardiovascular outcomes, including hypertension, haemorrhagic stroke and atrial fibrillation. For other cardiovascular outcomes the relationship is more complex. Alcohol is furthermore linked to various forms of liver disease (particularly with fatty liver, alcoholic hepatitis and cirrhosis) and pancreatitis. For diabetes the relationship is also complex. Conservatively, of the global NCD-related burden of deaths, net years of life lost (YLL) and net disability adjusted life years (DALYs), 3.4%, 5.0% and 2.4%, respectively, can be attributed to alcohol consumption, with the burden being particularly high for cancer and liver cirrhosis. This burden is especially pronounced in countries of the former Soviet Union. CONCLUSIONS: There is a strong link between alcohol and non-communicable diseases, particularly cancer, cardiovascular disease, liver disease, pancreatitis and diabetes, and these findings support calls by the World Health Organization to implement evidence-based strategies to reduce harmful use of alcohol.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".