Alcohol-Attributable Burden of Disease in the Americas in 2000 and 2016.
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to estimate the alcohol-attributable disease burden in the Americas in 2000 and 2016. METHOD: The alcohol-attributable disease burden was estimated using a comparative risk assessment approach. Alcohol exposure and relative risk estimates were obtained from systematic reviews and meta-analyses. Burden of disease estimates were obtained from the World Health Organization's Global Health Estimates. RESULTS: In 2016, 372,000 deaths and 18.9 million disability-adjusted life years (DALYs) lost were because of alcohol use in the Americas. The age-standardized rates (ASRs) of alcohol-attributable deaths ranged from 16.2 to 54.3 deaths per 100,000 in Jamaica and Guyana, respectively. From 2000 to 2016, ASRs decreased by 12.8% for alcohol-attributable deaths and decreased by 10.8% for alcohol-attributable DALYs lost. The decreases in ASRs for alcohol-attributable deaths and alcohol-attributable DALYs lost were less than the relative decreases in the ASRs for all deaths (18.7%) and all DALYs lost (15.7%). ASRs for alcohol-attributable deaths increased in eight countries. CONCLUSIONS: Alcohol continues to be a leading risk factor for the burden of disease in the Americas, with the degree and composition of this burden varying between countries. Despite a general reduction across the region, in many countries the rising alcohol-attributable disease burden constitutes a major public health challenge.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".