Alcohol-Attributable Mortality and Years of Potential Life Lost in Chile in 2009
Bibliographic record
Abstract
AIMS: The aim of the study was to estimate mortality and years of potential life lost (YPLL) attributable to alcohol consumption in 2009 in Chile. METHODS: The population considered for this study included those 15 years and over. Exposure to alcohol in the population was estimated by triangulating the records of alcohol per capita consumption in Chile with information from the Eighth National Study of Drugs in the General Population (2008). The effect of alcohol consumption on each cause of death (relative risk) was extracted from previously published meta-analyses. With this information we estimated the alcohol-attributable fraction (AAF) and deaths and YPLL due to alcohol consumption. The confidence intervals for the AAF were estimated with Monte Carlo sampling using the estimated variances of the exposure prevalence and relative effect. RESULTS: The estimated total number of deaths attributable to alcohol consumption was 8753 (95% CI: 6257, 11,584) corresponding to 9.8% (95% CI: 7.01%, 12.98%) of all deaths in Chile in 2009. The total estimated YPLL attributable to alcohol were 195,475 (95% CI: 164,287, 227,726), corresponding to 21.5% (95% CI: 18.1%, 25.0%) of total YPLL for that year in Chile. CONCLUSION: Alcohol consumption is a major risk factor and accounts for nearly one of ten deaths in Chile. These results may be used to guide the design of public health policies and evaluations.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".