Burden of opioid toxicity death in the fentanyl-dominant era for people who experience incarceration in Ontario, Canada, 2015–2020: a whole population retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: To describe mortality due to opioid toxicity among people who experienced incarceration in Ontario between 2015 and 2020, during the fentanyl-dominant era. DESIGN: In this retrospective cohort study, we linked Ontario coronial data on opioid toxicity deaths between 2015 and 2020 with correctional data for adults incarcerated in Ontario provincial correctional facilities. SETTING: Ontario, Canada. PARTICIPANTS: Whole population data. MAIN OUTCOMES AND MEASURES: The primary outcome was opioid toxicity death and the exposure was any incarceration in a provincial correctional facility between 2015 and 2020. We calculated crude death rates and age-standardised mortality ratios (SMR). RESULTS: Between 2015 and 2020, 8460 people died from opioid toxicity in Ontario. Of those, 2207 (26.1%) were exposed to incarceration during the study period. Among those exposed to incarceration during the study period (n=1 29 152), 1.7% died from opioid toxicity during this period. Crude opioid toxicity death rates per 10 000 persons years were 43.6 (95% CI=41.8 to 45.5) for those exposed to incarceration and 0.95 (95% CI=0.93 to 0.97) for those not exposed. Compared with those not exposed, the SMR for people exposed to incarceration was 31.2 (95% CI=29.8 to 32.6), and differed by sex, at 28.1 (95% CI=26.7 to 29.5) for males and 77.7 (95% CI=69.6 to 85.9) for females. For those exposed to incarceration who died from opioid toxicity, 10.6% died within 14 days of release and the risk was highest between days 4 and 7 postrelease, at 288.1 per 10 000 person years (95% CI=227.8 to 348.1). CONCLUSIONS: The risk of opioid toxicity death is many times higher for people who experience incarceration compared with others in Ontario. Risk is markedly elevated in the week after release, and women who experience incarceration have a substantially higher SMR than men who experience incarceration. Initiatives to prevent deaths should consider programmes and policies in correctional facilities to address high risk on release.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".