Incidence and risk factors for non-fatal overdose among a cohort of recently incarcerated illicit drug users
Bibliographic record
Abstract
Background Release from prison is associated with a markedly increased risk of both fatal and non-fatal drug overdose, yet the risk factors for overdose in recently released prisoners are poorly understood. The aim of this study was to identify risk and protective factors for non-fatal overdose (NFOD) among a cohort of illicit drug users in Vancouver, Canada, according to recent incarceration. Methods Prospective cohort of 2515 community-recruited illicit drug users in Vancouver, Canada, followed from 1996 to 2010. We examined factors associated with NFOD in the past six months separately among those who did and did not also report incarceration in the last six months. Results One third of participants (n=829, 33.0%) reported at least one recent NFOD. Among those recently incarcerated, risk factors independently and positively associated with NFOD included daily use of heroin, benzodiazepines, cocaine or methamphetamine, binge drug use, public injecting and previous NFOD. Older age, methadone maintenance treatment and HIV seropositivity were protective against NFOD. A similar set of risk factors was identified among those who had not been incarcerated recently. Conclusions Among this cohort, and irrespective of recent incarceration, NFOD was associated with a range of modifiable risk factors including more frequent and riskier patterns of drug use. Not all ex-prisoners are at equal risk of overdose and there remains an urgent need to develop and implement evidence-based preventive interventions, targeting those with modifiable risk factors in this high risk group.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".