Impacts of drinking-age laws on mortality in Canada, 1980–2009
Bibliographic record
Abstract
BACKGROUND: Given the recent international debates about the effectiveness and appropriate age setpoints for legislated minimum legal drinking ages (MLDAs), the current study estimates the impact of Canadian MLDAs on mortality among young adults. Currently, the MLDA is 18 years in Alberta, Manitoba and Québec, and 19 years in the rest of Canada. METHODS: Using a regression-discontinuity approach, we estimated the impacts of the MLDAs on mortality from 1980 to 2009 among 16- to 22-year-olds in Canada. RESULTS: In provinces with an MLDA of 18 years, young men slightly older than the MLDA had significant and abrupt increases in all-cause mortality (14.2%, p=0.002), primarily due to deaths from a broad class of injuries [excluding motor vehicle accidents (MVAs)] (16.2%, p=0.008), as well as fatalities due to MVAs (12.7%, p=0.038). In provinces/territories with an MLDA of 19 years, significant jumps appeared immediately after the MLDA among males in all-cause mortality (7.2%, p=0.003), including injuries from external causes (10.4%, p<0.001) and MVAs (15.3%, p<0.001). Among females, there were some increases in mortality following the MLDA, but these jumps were statistically non-significant. CONCLUSIONS: Canadian drinking-age legislation has a powerful impact on youth mortality. Given that removal of MLDA restrictions was associated with sharp upturns in fatalities among young men, the MLDA likely reduces population-level mortality among male youth under the constraints of drinking-age legislation. Alcohol-control policies should target the transition across the MLDA as a pronounced period of mortality risk, especially among males.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".