Alcohol-attributable mortality in France
Bibliographic record
Abstract
BACKGROUND: Alcohol consumption is high in France. AIM: Estimation of alcohol-attributable mortality in France by sex, age and dose, for year 2009. METHOD: We combined survey and sales data to estimate the prevalence of alcohol consumption by age, sex and dose category. For each cause of death, the relative risk of death as a function of dose was obtained from a meta-analysis and combined with prevalence data to obtain the attributable fraction; this fraction multiplied by the number of deaths gave the alcohol-attributable mortality. RESULTS: A total of 36,500 deaths in men are attributable to alcohol in France in 2009 (13% of total mortality) versus 12,500 in women (5% of total mortality). Overall, this includes 15,000 deaths from cancer, 12,000 from circulatory disease, 8000 from digestive system disease, 8000 from external causes and 3000 from mental and behavioural disorder. The alcohol-attributable fractions are 22% and 18% in the population aged 15 to 34 and 35 to 64, respectively, versus 7% among individuals aged 65 or more. Alcohol is detrimental even at a low dose of 13 g per day, causing 1100 deaths. CONCLUSION: With 49 000 deaths in France for the year 2009, the alcohol toll is high, and the effect of alcohol is detrimental even at low dose. Alcohol consumption is responsible for a large proportion of premature deaths. These results stress the importance of public health policies aimed at reducing alcohol consumption in France.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".