Association between legalization of recreational cannabis and fatal motor vehicle collisions in the United States: an ecologic study
Bibliographic record
Abstract
BACKGROUND: With the recent legalization of recreational cannabis in Canada, cannabis-impaired driving is an important public safety concern. Our aim was to examine the association between recreational cannabis legalization and fatal motor vehicle collisions using data from the United States, which present a timely natural experiment of cannabis legalization. METHODS: We conducted an ecologic study using the number of fatal motor vehicle collisions and the associated number of deaths for US jurisdictions with legalized recreational cannabis (2007-2018) retrieved from the US Fatality Analysis Reporting System. We examined jurisdiction-specific rates of fatal motor vehicle collisions and associated deaths before and after recreational cannabis legalization using Poisson regression and meta-analyzed estimates across jurisdictions using DerSimonian and Laird random-effects models. RESULTS: After adjustment for calendar year, legalization was associated with increases in rates of fatal motor vehicle collisions (incidence rate ratio [IRR] 1.15, 95% confidence interval [CI] 1.06-1.26) and associated deaths (IRR 1.16, 95% CI 1.06-1.27). Differences between the first 12 months after legalization relative to subsequent months were inconclusive for rates of fatal motor vehicle collisions (IRR 0.92, 95% CI 0.84-1.02) and associated deaths (IRR 0.92, 95% CI 0.84-1.01). INTERPRETATION: Recreational cannabis legalization in the US was associated with a relative increased risk of fatal motor vehicle collisions of 15% and a relative increase in associated deaths of 16%, with no conclusive difference between the first and subsequent years after legalization. These findings raise concern that there could be a similar increase in fatal motor vehicle collisions and associated deaths in Canada following recreational cannabis legalization.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".