The importance of age groups in estimates of alcohol‐attributable mortality: impact on trends in Switzerland between 1997 and 2011
Bibliographic record
Abstract
BACKGROUND AND AIMS: Monitoring trends of alcohol-attributable mortality is an integral part of the global strategy to reduce the harmful use of alcohol. However, mortality estimates based on different age ranges come to different conclusions. This study examined the impact of including different age ranges in terms of directions of trends of alcohol-attributable mortality during 14 years in Switzerland. METHOD: Alcohol-attributable mortality was estimated at four time-points between 1997 and 2011 using the Global Burden of Disease 2010 methodology. Estimates were obtained for two age groups: 15-64 years and the total adult population (15 years and older). RESULTS: Alcohol-attributable mortality among 15-64-year-olds decreased [1997: 1334 deaths, confidence interval (CI) = 1237-1432; 2011: 1019 deaths, CI = 964-1073; trend per year odds ratio (OR) = 0.99, P < 0.001]. In contrast, alcohol-attributable mortality among those 65 and older increased in the same time-period (1997: 581 deaths, CI = -196 to 1357; 2011: 1664 deaths, CI = 957-2372; OR = 1.07, P< 0.001), resulting in an overall increase of alcohol-attributable mortality for 15+ year-olds (1997: 1915 deaths, CI = 1133-2697; 2011: 2683, CI = 1973-3393; OR = 1.02, P < 0.001). The main shift in trends was due to changes in the mixture (e.g. hypertension, ischaemic heart disease) of cardiovascular diseases over time among those 65+ years old. CONCLUSIONS: Trends in alcohol-attributable mortality may yield qualitatively different results based on the upper age limit for deaths set for these estimates. Global trends of alcohol-attributable mortality between 1997 and 2011 were influenced heavily by changes in the mixture of deaths across cardiovascular diseases. Trends for alcohol-attributable mortality and cross-country comparisons should be reported separately for 15-64 and 65+ year-olds.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.001 | 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".