Estimating the harms and costs of cannabis-attributable collisions in the Canadian provinces
Bibliographic record
Abstract
INTRODUCTION: In 2012, 10% of Canadians used cannabis and just under half of those who use cannabis were estimated to have driven under the influence of cannabis. Substantial evidence has accumulated to indicate that driving after cannabis use increases collision risk significantly; however, little is known about the extent and costs associated with cannabis-related traffic collisions. This study quantifies the costs of cannabis-related traffic collisions in the Canadian provinces. METHODS: Province and age specific cannabis-attributable fractions (CAFs) were calculated for traffic collisions of varying severity. The CAFs were applied to traffic collision data in order to estimate the total number of persons involved in cannabis-attributable fatal, injury and property damage only collisions. Social cost values, based on willingness-to-pay and direct costs, were applied to estimate the costs associated with cannabis-related traffic collisions. The 95% confidence intervals were calculated using Monte Carlo methodology. RESULTS: Cannabis-attributable traffic collisions were estimated to have caused 75 deaths (95% CI: 0-213), 4407 injuries (95% CI: 20-11,549) and 7794 people (95% CI: 3107-13,086) were involved in property damage only collisions in Canada in 2012, totalling $1,094,972,062 (95% CI: 37,069,392-2,934,108,175) with costs being highest among younger people. DISCUSSION: The cannabis-attributable driving harms and costs are substantial. The harm and cost of cannabis-related collisions is an important factor to consider as Canada looks to legalize and regulate the sale of cannabis. This analysis provides evidence to help inform Canadian policy to reduce the human and economic costs of drug-impaired driving.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".