Effects of the Minimum Legal Drinking Age on Alcohol-Related Health Service Use in Hospital Settings in Ontario: A Regression–Discontinuity Approach
Bibliographic record
Abstract
OBJECTIVES: We assessed the impact of the minimum legal drinking age (MLDA) on hospital-based treatment for alcohol-related conditions or events in Ontario, Canada. METHODS: We conducted regression-discontinuity analyses to examine MLDA effects with respect to diagnosed alcohol-related conditions. Data were derived from administrative records detailing inpatient and emergency department events in Ontario from April 2002 to March 2007. RESULTS: Relative to youths slightly younger than the MLDA, youths just older than the MLDA exhibited increases in inpatient and emergency department events associated with alcohol-use disorders (10.8%; P = .048), assaults (7.9%; P < .001), and suicides related to alcohol (51.8%; P = .01). Among young men who had recently crossed the MLDA threshold, there was a 2.0% increase (P = .01) in hospitalizations for injuries. CONCLUSIONS: Young adults gaining legal access to alcohol incur increases in hospital-based care for a range of serious alcohol-related conditions. Our regression-discontinuity approach can be used in future studies to assess the effects of the MLDA across different settings, and our estimates can be used to inform cost-benefit analyses across MLDA scenarios.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".