Overdose mortality incidence and supervised consumption services in Toronto, Canada: an ecological study and spatial analysis
Bibliographic record
Abstract
BACKGROUND: Supervised consumption services (SCS) prevent overdose deaths onsite; however, less is known about their effect on population-level overdose mortality. We aimed to characterise overdose mortality in Toronto, ON, Canada, and to establish the spatial association between SCS locations and overdose mortality events. METHODS: For this ecological study and spatial analysis, we compared crude overdose mortality rates before and after the implementation of nine SCS in Toronto in 2017. Data were obtained from the Office of the Chief Coroner of Ontario on cases of accidental death within the City of Toronto for which the cause of death involved the use of an opiate, synthetic or semi-synthetic opioid, or other psychoactive substance. We assessed overdose incident data for global spatial autocorrelation and local clustering, then used geographically weighted regression to model the association between SCS proximity and overdose mortality incidence in 2018 and 2019. FINDINGS: We included 787 overdose mortality events in Toronto between May 1, 2017, and Dec 31, 2019. The overdose mortality rate decreased significantly in neighbourhoods that implemented SCS (8·10 deaths per 100 000 people for May 1-July 31, 2017, vs 2·70 deaths per 100 000 people for May 1-July 31, 2019; p=0·037), but not in other neighbourhoods. In a geographically weighted regression analysis that adjusted for the availability of substance-use-related services and overdose-related sociodemographic factors by neighbourhood, the strongest local regression coefficients of the association between SCS and overdose mortality location ranged from -0·60 to -0·64 per mile in 2018 and from -1·68 to -1·96 per mile in 2019, suggesting an inverse association. INTERPRETATION: We found that the period during which SCS were implemented in Toronto was associated with a reduced overdose mortality in surrounding neighbourhoods. The magnitude of this inverse association increased from 2018 to 2019, equalling approximately two overdose fatalities per 100 000 people averted in the square mile surrounding SCS in 2019. Policy makers should consider implementing and sustaining SCS across neighbourhoods where overdose mortality is high. FUNDING: The Canadian Institutes of Health Research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".