The impact of recreational cannabis retailer allocation on emergency department visits: A natural experiment utilizing lottery design
Bibliographic record
Abstract
BACKGROUND: In October 2018, Canada legalized recreational cannabis, with Ontario distributing retailer licenses through a lottery system in 2019. This study investigates the impact of recreational cannabis retailer allocation on emergency department (ED) visits related to cannabis, alcohol, and opioids. METHOD: A longitudinal study of 278 communities in Ontario (proxied by Forward Sortation Areas, FSAs) was conducted using health administrative data from ICES for all Ontario residents covered by public health insurance. The cohort included 11,156,100 adults aged 18 and above, monitored quarterly from January 2016 to March 2023. The allocation of cannabis retailers through a randomized lottery system provided a natural experiment. Staggered difference-in-differences proposed by Callaway and Sant'Anna (CSDID) models, weighted by the inverse probability of retailer allocation, were used to estimate the impact of cannabis store openings on ED visits, comparing FSAs with and without retailers. RESULTS: No significant effects were found in cannabis-, alcohol-, or opioid-related ED visits following the allocation of cannabis retailers. Sensitivity analyses, including alternate diagnostic codes, co-use of cannabis and other substances, and cannabis use without other substances, corroborated our main findings. The null results may be due to online cannabis sales preceding retail store openings, geographic distribution minimizing access disparities, and potential spillover effects. CONCLUSION: The allocation of recreational cannabis retailer licenses did not significantly impact acute care use. Continuous monitoring, comprehensive sales tracking, and integrated substance use prevention strategies are recommended for future policy considerations.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".