Changes in Rates of Hospitalizations due to Cannabis Harms in Ontario, Canada Before the Legalization of Nonmedical Cannabis: Retrospective Population-level Study Between 2003 and 2017
Bibliographic record
Abstract
OBJECTIVES: To assess the burden of hospitalizations due to cannabis harms in Ontario, Canada before Canada's legalization of nonmedical cannabis. METHODS: We conducted a retrospective population-level study that included all individuals living in Ontario between 2003 and 2017. We described patterns of hospitalizations due to cannabis harms in men and women by demographics, socioeconomic factors, and mental health comorbidities. We calculated annual crude rates of hospitalizations due to cannabis harms and assessed time trends using Poisson regression models. RESULTS: There were 39,092 hospitalizations due to cannabis harms among 32,811 unique individuals. Annual hospitalizations due to a cannabis harm increased by 176% between 2003 and 2017 (1712 vs 4730), with increases noted for all age groups and sexes. Rates of hospitalizations due to cannabis harms were greater in young adults, low-income individuals, and those with mental health comorbidities. Overall, the rate of hospitalizations due to cannabis harms increased on average by 7.8% per year (95% CI 7.5-8.0). Women aged 15 to 24 experienced the largest average annual increase (12.2% per year, 95% CI 11.5 to 12.8). CONCLUSIONS: There are distinct patterns of hospitalizations due to cannabis harms in different priority populations. Young women aged 15 to 24 are a key demographic that is disproportionately burdened with a rapid increase in hospitalizations due to cannabis harms. Jurisdictions considering new approaches to cannabis control policy and addiction services should consider the rising burden of harms faced by youth and young adults when planning interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".