The associations of supervised consumption services with the rates of opioid‐related mortality and morbidity outcomes at the public health unit level in Ontario (Canada): A controlled interrupted time‐series analysis
Bibliographic record
Abstract
INTRODUCTION: This study aimed to assess the impact of the implementation of legally sanctioned supervised consumption sites (SCS) in the Canadian province of Ontario on opioid-related deaths, emergency department (ED) visits and hospitalisations at the public health unit (PHU) level. METHODS: Monthly rates per 100,000 population of opioid-related deaths, ED visits and hospitalisations for PHUs in Ontario between December 2013 and March 2022 were collected. Aggregated and individual analyses of PHUs with one or more SCS were conducted, with PHUs that instituted an SCS being matched to control units that did not. Autoregressive integrated moving average models were used to estimate the impact of SCS implementation on opioid-related deaths, ED visits and hospitalisations. RESULTS: Twenty-one legally sanctioned SCS were implemented across nine PHUs in Ontario during the study period. Interrupted time series analyses showed no statistically significant changes in opioid-related death rates in aggregated analyses of intervention PHUs (increase of 0.02 deaths/100,000 population/month; p = 0.27). Control PHUs saw a significant increase of 0.38 deaths/100,000 population/month; p < 0.001. No statistically significant changes were observed in the rates of opioid-related ED visits in intervention PHUs (decrease of 0.61 visits/100,000 population/month; p = 0.39) or controls (increase of 0.403 visits; p = 0.76). No statistically significant changes to the rates of opioid-related hospitalisations were observed in intervention PHUs (0 hospitalisations/100,000 population/month; p = 0.98) or controls (decrease of 0.05 hospitalisations; p = 0.95). DISCUSSION AND CONCLUSIONS: This study did not find significant mortality or morbidity effects associated with SCS availability at the population level in Ontario. In the context of a highly toxic drug supply, additional interventions will be required to reduce opioid-related harms.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".