Evaluating the effects of Toronto’s supervised consumption sites on local crime
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».