Legal Recreational Cannabis Sales and Opioid-Related Mortality in the 5 Years Following Cannabis Legalization in Canada: A Granger Causality Analysis
Bibliographic record
Abstract
Objective: Little is known about the population-level impact of recreational cannabis legalization on trends in opioid-related mortality. Increased access to cannabis due to legalization has been hypothesized to reduce opioid-related deaths because of the potential opioid-sparing effects of cannabis. The objective of this study was to examine the relations between national retail sales of recreational (non-medical) cannabis and opioid overdose deaths in the 5 years following legalization in Canada. Method: Using time-series data, we applied Granger causality methods to evaluate the association between trends in legal recreational cannabis sales and opioid-related deaths over time. Both sales and opioid deaths grew over time, with the latter exhibiting significant increases following the onset of the COVID-19 pandemic. Results: We found no support for the hypothesis that increasing post-legalization sales Granger caused changes in opioid-related deaths in British Columbia, Ontario, or at the national level. Conclusions: These findings suggest that increases in legal recreational cannabis sales following legalization were not meaningfully associated with changes in opioid-related mortality. Further examination with longer follow-up periods will be needed as the legal cannabis market becomes more entrenched in Canada, but these findings converge with previous work suggesting legalization is not related to opioid overdose mortality and further undermine that hypothesized link as a basis for legalization in other jurisdictions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".