Alcohol-attributable mortality in Switzerland in 2011 – age-specific causes of death and impact of heavy versus non-heavy drinking
Bibliographic record
Abstract
BACKGROUND: Alcohol use causes high burden of disease and injury globally. Switzerland has a high consumption of alcohol, almost twice the global average. Alcohol-attributable deaths and years of life lost in Switzerland were estimated by age and sex for the year 2011. Additionally, the impact of heavy drinking (40+grams/day for women and 60+g/day for men) was estimated. METHODS: Alcohol consumption estimates were based on the Addiction Monitoring in Switzerland study and were adjusted to per capita consumption based on sales data. Mortality data were taken from the Swiss mortality register. Methodology of the Comparative Risk Assessment for alcohol was used to estimate alcohol-attributable fractions. RESULTS: Alcohol use caused 1,600 (95% CI: 1,472 - 1,728) net deaths (1,768 deaths caused, 168 deaths prevented) among 15 to 74 year olds, corresponding to 8.7% of all deaths (men: 1,181 deaths; women: 419 deaths). Overall, 42,627 years of life (9.7%, 95% CI: 40,245 - 45,008) were lost due to alcohol. Main causes of alcohol-attributable mortality were injuries at younger ages (15-34 years), with increasing age digestive diseases (mainly liver cirrhosis) and cancers (particularly breast cancers among women). The majority (62%) of all alcohol-attributable deaths was caused by chronic heavy drinking (men: 67%; women: 48 %). CONCLUSION: Alcohol is a major cause of premature mortality in Switzerland. Its impact, among young people mainly via injuries, among men mainly through heavy drinking, calls for a mix of preventive actions targeting chronic heavy drinking, binge drinking and mean consumption.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".