High Rate of Fatal Overdose After Release from Prison In BC, Canada: A Data Linkage Study
Bibliographic record
Abstract
IntroductionThe province of BC, Canada is in the grips of a sustained overdose epidemic. People released from prison are at increased risk of fatal drug overdose, but the impact of the overdose epidemic on mortality after release from prison in BC is poorly understood. Few studies have been able to examine risk factors for overdose death in this population. Objectives and ApproachWe aimed to (a) measure risk of overdose-related and all-cause death in different time periods after release from prison; and (b) identify risk factors for overdose-related and all-cause death. In a random 20% sample of the population of BC, Canada, we identified those released from prison 2015-2017 and examined linked health and correctional records for this cohort. ResultsOf 6106 persons released from prison 2015-2017, 77 (1.3%) died from any cause and 48 (0.8%) died from overdose 2015-2017. The incidence of all-cause death was 16.1 (95%CI 13.7-18.8) per 1000 person years, and the incidence of overdose death was 11.2 (95%CI 9.2-13.5) per 1000 person years. Risk factors for overdose death included a history of 3 or more incarcerations (HR=3.00, 95%CI 1.67-5.39), co-occurring substance use disorder and mental illness (HR=4.73, 95%CI 2.94-7.62), chronic physical morbidity (HR=3.10, 95%CI 1.97-4.88), and being dispensed benzodiazepines (HR=3.31, 95%CI 2.27-4.84) or opioids for pain (HR=6.77, 95%CI 3.86-11.89). The incidence of fatal overdose was significantly higher in the first two weeks post-release than at any other time during follow-up. ConclusionPeople released from prison in BC are at markedly increased risk of preventable death, mainly due to overdose. As such, people transitioning from prison to the community should be a key target population for overdose prevention efforts. To be maximally effective, these efforts must go beyond provision of methadone and naloxone on release, to consider physical and mental health comorbidities, and psychosocial disadvantage.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".