Impact of Canada’s Cannabis Act on drug- and alcohol-related collisions in Québec: an interrupted time-series analysis of five major cities
Bibliographic record
Abstract
This study examines the impact of non-medical cannabis laws (NMCLs) on road safety outcomes, specifically focusing on drug- and alcohol-related traffic crashes. Using cannabis sales data as a proxy for consumption trends, the study aims to assess how changes in cannabis availability may influence road safety outcomes, particularly exploring the potential for drugs and alcohol to have distinct yet related influences on impaired driving. An interrupted time-series design was used to assess the impact of NMCLs on daily drug- and alcohol-related traffic crashes, including fatalities and severe injuries (KSI). The analysis covered five cities in the province of Québec—Montréal, Québec, Laval, Longueuil, and Sherbrooke—using data from January 1, 2015; to December 31, 2022. The dependent variables included KSI, drug-related crashes, and alcohol-related crashes, while the independent variables were daily cannabis legal sales (kg) and total legal and estimated illegal cannabis sales. Control variables accounted for temperature, time trends, and the COVID-19 non-pharmaceutical interventions’ index for the province of Québec (QCnPI-Index). To estimate effects, we applied Generalized Linear Models using Negative binomial regression, followed by a random-effects meta-analysis to assess overall effects across cities. Higher total cannabis sales were significantly associated with a 12% increase in drug-related crashes (IRR: 1.12; 95% CI: 1.01–1.27) and a 12% rise in alcohol-related crashes (IRR: 1.12; 95% CI: 1.06–1.18) across all cities combined. In Montréal, cannabis sales were linked to an 87% increase in drug-related crashes (IRR: 1.87; 95% CI: 1.54–2.28) and a 93% increase in alcohol-related crashes (IRR: 1.93; 95% CI: 1.58–2.36). In Longueuil, drug-related crashes rose by 76% (IRR: 1.76; 95% CI: 1.02–3.02) and alcohol-related crashes by 43% (IRR: 1.43; 95% CI: 1.08–1.92). Québec City only showed a 44% increase in alcohol-related crashes (IRR: 1.44; 95% CI: 1.28–1.64). No significant associations were found in Laval or Sherbrooke. The findings suggest that increased cannabis availability, as measured by cannabis sales, is associated with higher rates of both drug- and alcohol-related crashes, particularly in Montréal and Longueuil. These results support the hypothesis that changes in cannabis availability may influence two distinct impaired driving patterns, highlighting the need for region-specific road safety interventions.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".