The association between recreational cannabis legalization, commercialization and cannabis‐attributable emergency department visits in Ontario, Canada: an interrupted time–series analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Recreational cannabis was legalized in Canada in October 2018. Initially, the Government of Ontario (Canada's largest province) placed strict limits on the number of cannabis retail stores before later removing these limits. This study measured changes in cannabis-attributable emergency department (ED) visits over time, corresponding to different regulatory periods. DESIGN: Interrupted time-series design using population-level data. Two policy periods were considered; recreational cannabis legalization with strict store restrictions (RCL, 17 months) and legalization with no store restrictions [recreational cannabis commercialization (RCC), 15 months] which coincided with the COVID-19 pandemic. Segmented Poisson regression models were used to examine immediate and gradual effects in each policy period. SETTING: Ontario, Canada. PARTICIPANTS: All individuals aged 15-105 years (n = 13.8 million) between January 2016 and May 2021. MEASUREMENTS: Monthly counts of cannabis-attributable ED visits per capita and per all-cause ED visits in individuals aged 15+ (adults) and 15-24 (young adults) years. FINDINGS: We observed a significant trend of increasing cannabis-attributable ED visits pre-legalization. RCL was associated with a significant immediate increase of 12% [incident rate ratio (IRR) = 1.12, 95% confidence interval (CI) = 1.02-1.23] in rates of cannabis-attributable ED visits followed by significant attenuation of the pre-legalization slope (monthly slope change IRR = 0.98, 95% CI = 0.97-0.99). RCC and COVID-19 were associated with immediate significant increases of 22% (IRR = 1.22, 95% CI = 1.09-1.37) and 17% (IRR = 1.17, 95% CI = 1.00-1.37) in rates of cannabis-attributable visits and the proportion of all-cause ED visits attributable to cannabis, respectively, with insignificant increases in monthly slopes. Similar patterns were observed in young adults. CONCLUSIONS: In Ontario, Canada, cannabis-attributable emergency department visits stopped increasing over time following recreational cannabis legalization with strict retail controls but then increased during a period coinciding with cannabis commercialization and the COVID-19 pandemic.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".