Evaluating the effects of Toronto’s supervised consumption sites on local crime
Bibliographic record
Abstract
Abstract Importance Beginning in August 2017, nine overdose prevention sites and supervised consumption sites (OPS/SCS) began operating in Toronto, Canada. Following years of community pushback that these sites increased local crime and disorder, they were closed in March 2025. Objective To evaluate the population-level effects of OPS/SCS on crime and disorder. Design, Setting, Participants This two-part ecological study used Toronto Police Service data to compare crime incidence before and after OPS/SCS implementation using interrupted time series analyses with and without controls. We restricted analysis to crimes that occurred within city boundaries between 1 January 2014 and 30 June 2024. Main Outcomes and Measures We used monthly event counts of all assaults, auto thefts, break and enters, robberies, thefts over $5000, bicycle thefts, thefts from motor vehicle and mental health apprehensions as our eight outcomes. We compared incidence within 100m, 200m, and 400m of the geolocation of each OPS/SCS before and after implementation; and repeated analysis using treated and synthetic control neighbourhoods. We pooled estimates for population-level effects. Results Within 400m (approximately a quarter mile), we observed level effects for break and enters (48.87%, 95% CI: 26.15, 75.68%) and bicycle thefts (-19.46%, 95% CI: -30.36, -6.86%) immediately post-implementation. Meanwhile, monthly trends for assaults (-0.5%, 95% CI: -0.92, -0.07%), break and enters (-1.11%, 95% CI: -1.60, -0.61%), robberies (-1.36, 95% CI: -1.96, -0.75%), thefts over $5000 (-1.47%, 95% CI: -2.92, 0.01%), bicycle thefts (-1.54%, 95% CI: -2.44, -0.62%), and thefts from vehicles (-1.59%, 95% CI: -2.58, -0.60%) declined. Pooled neighbourhood analyses showed level-effects for break and enters (18.83%, 95% CI: 1.70, 38.84%) and mental health apprehensions (10.20%, 95% CI: 1.55, 19.60%) post-implementation; and monthly changes in trends for break and enters (-0.46%, 95% CI: - 0.93, 0.01%), auto theft (-0.66%, 95% CI: -1.31, 0.00%), thefts over $5000 (-1.02%, 95% CI: -2.05, 0.03%), and bicycle thefts (-1.07%, 95% CI: -1.58, -0.57%). Site- and neighbourhood-specific results revealed some communities were impacted while others were not. Conclusions and Relevance The effects of Toronto’s OPS/SCS on crime were generally neutral. Break and enters increased immediately post-implementation, but declined with time. Data Sharing Agreement This study used publicly available data provided by Toronto Police Services. For more information, please visit the Toronto Police Service Public Safety Data Portal at: https://data.torontopolice.on.ca/ . For Statistics Canada census tract profiles visit: https://www12.statcan.gc.ca/census-recensement/2021/dp-pd/prof/index.cfm?Lang=E . Key points Question: What was the effect of overdose prevention sites and supervised consumption sites (OPS/SCS) on crime in Toronto? Findings: Pooled site- and neighbourhood-level random effects models found counts of break and enters increased, but trends in outcomes declined per month. Trends in assaults, robberies, thefts over $5000, bicycle thefts, and thefts from motor vehicles also decreased. Site- and neighbourhood-specific interrupted time series showed some communities were impacted negatively while others were not. Meaning: The effects of Toronto’s OPS/SCS on crime were generally neutral to positive; break and enters increased immediately post-implementation, but declined with time.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".