Reduced risk of overdose among clients of a safer opioid supply program in Southwestern Ontario: A pre-post observational cohort study
Bibliographic record
Abstract
INTRODUCTION: North America faces an ongoing overdose crisis driven by a volatile and toxic drug supply comprised primarily of fentanyl, fentanyl analogues and other adulterants. Safer opioid supply (SOS) programs prescribe pharmaceutical opioids to individuals at high risk of overdose mortality. This study evaluated changes in non-fatal overdose prevalence in the 6 months after SOS program initiation among SOS participants in Kitchener-Waterloo, Canada. METHODS: We analyzed data from a pre-post observational cohort of clients enrolled in a SOS program between July 2021 and October 2023. Baseline surveys were completed upon program entry, with follow-up surveys after 6 months. We compared non-fatal overdose prevalence between baseline and follow-up using McNemar's test and calculated adjusted odds ratios (aOR) using generalized estimating equation (GEE) models, controlling for potential confounders including homelessness, hospitalization, daily fentanyl use, and incarceration. RESULTS: Among 100 participants completing follow-up (out of 162 who completed a baseline survey), overdose prevalence decreased significantly from 60 % (95 % Confidence Interval (CI): 50-69) at baseline to 15 % (95 % CI: 9-23) at follow-up (p < 0.001). Overdose incidence rates declined from 48.5 to 3.3 per 100 person-months. After adjusting for confounders, participants had 83 % lower odds of overdose during follow-up (aOR 0.17, 95 % CI: 0.08-0.38). DISCUSSION: Participants in a SOS program experienced significant reductions in non-fatal overdose during the 6 months following program initiation. SOS clients are a high-risk population with elevated overdose rates at baseline; these results support expanding safer supply programs as part of a comprehensive set of strategies to address the overdose crisis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".