Fatal overdoses after release from prison in British Columbia: a retrospective data linkage study
Bibliographic record
Abstract
BACKGROUND: People recently released from prison are at increased risk of preventable death; however, the impact of the current overdose epidemic on this population is unknown. We aimed to document the incidence and identify risk factors for fatal overdose after release from provincial prisons in British Columbia. METHODS: We conducted a retrospective, population-based, open cohort study of adults released from prisons in BC, using linked administrative data. Within a random 20% sample of the BC population, we linked provincial health and correctional records from 2010 to 2017 for people aged 23 years or older as of Jan. 1, 2015, who were released from provincial prisons at least once from 2015 to 2017. We identified exposures that occurred from 2010 to 2017 and deaths from 2015 to 2017. We calculated the piecewise incidence of overdose-related and all-cause deaths after release from prison. We used multivariable, mixed-effects Cox regression to identify predictors of all-cause death and death from overdose. RESULTS: Among 6106 adults released from prison from 2015 to 2017 and followed in the community for a median of 1.6 (interquartile range 0.9-2.3) years, 154 (2.5%) died, 108 (1.8%) from overdose. The incidence of all-cause death was 16.1 (95% confidence interval [CI] 13.7-18.8) per 1000 person-years. The incidence of overdose deaths was 11.2 (95% CI 9.2-13.5) per 1000 person-years, but 38.8 (95% CI 3.2-22.6) in the first 2 weeks after release from prison. After adjustment for covariates, the hazard of overdose death was 4 times higher among those who had been dispensed opioids for pain. INTERPRETATION: People released from prisons in BC are at markedly increased risk of overdose death. Overdose prevention must go beyond provision of opioid agonist treatment and naloxone on release to address systemic social and health inequities that increase the risk of premature death.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".