Neighbourhood risk factors and spatiotemporal trends for overdoses following cannabis legalization and pandemic restrictions in Toronto, Canada
Bibliographic record
Abstract
Drug & opioid overdoses in Toronto, Canada, have risen substantially in recent years. To explore possible causes, we spatially analyze associations between overdose incidence data over 2019–2022 and select socioeconomic & built-environment variables among Toronto neighbourhoods. Using spatiotemporal analysis, we also assess average area trends and local hotspots before/after two major events: Canada's 2018 legalization of cannabis (2017–2020) and COVID-19 pandemic lockdowns (2019–2022). Previous discussions frame cannabis as a possible alternative to more dangerous drugs, while pandemic lockdowns were likely to reduce mental health and access to care. We find 1) overdose incidence shows positive association with household/neighbourhood instability and percent building coverage, 2) notable overdose increases in Toronto's suburban neighbourhoods, and 3) rising mean-area overdose rates, despite cannabis legalization. Potentially outsized effects of high-potency illicit opioids and pandemic lockdowns may influence these results. Policymakers should monitor post-lockdown overdose trends and explore harm reduction approaches and improved housing options as policy responses to reduce impacts from Toronto's ongoing drug crisis, especially in areas outside the downtown that have rising overdose rates. • Household stability & percent building coverage strongly associated with overdoses. • Overdoses increasing across Toronto following cannabis legalization and pandemic. • Outsized growth in overdoses in more suburban neighbourhoods of Toronto. • Pandemic's social impacts on overdose trends may subside after lifting lockdowns. • Addressing housing crisis may help strengthen households and reduce overdoses.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".