Association of Opioid and Stimulant Use Disorder Diagnoses With Fatal and Nonfatal Overdose Among People With a History of Incarceration
Bibliographic record
Abstract
Importance: Studies have suggested a rise in opioid- and stimulant-involved overdoses in recent years in North America. This risk may be acute for individuals who have had contact with the criminal justice system, who are particularly vulnerable to overdose risk. Objective: To examine the association of opioid and/or stimulant use disorder diagnoses with overdose (fatal and nonfatal) among people with histories of incarceration. Design, Setting, and Participants: In this cohort study, population-based health and corrections data were retrieved from the British Columbia Provincial Overdose Cohort, which contains a 20% random sample of residents of British Columbia. The analysis included all people in the 20% random sample who had a history of incarceration between January 1, 2010, and December 31, 2014. Outcomes were derived from 5-years of follow-up data (January 1, 2015, to December 31, 2019). Statistical analysis took place from January 2022 to June 2022. Exposures: Substance use disorder diagnosis type (ie, opioid use disorder, stimulant use disorder, both, or neither), sociodemographic, health, and incarceration characteristics. Main Outcomes and Measures: Hazard ratios (HRs) are reported from an Andersen-Gill model for recurrent nonfatal overdose events and from a Fine and Gray competing risk model for fatal overdose events. Results: The study identified 6816 people (5980 male [87.7%]; 2820 aged <30 years [41.4%]) with histories of incarceration. Of these, 293 (4.3%) had opioid use disorder only, 395 (6.8%) had stimulant use disorder only, and 281 (4.1%) had both diagnoses. During follow-up, 1655 people experienced 4026 overdoses including 3781 (93.9%) nonfatal overdoses, and 245 (6.1%) fatal overdoses. In adjusted analyses, the hazard of both fatal (HR, 2.39; 95% CI, 1.48-3.86) and nonfatal (HR, 2.45; 95% CI, 1.94-3.11) overdose was highest in the group with both opioid and stimulant use disorder diagnoses. Conclusions and Relevance: This cohort study of people with a history of incarceration found an elevated hazard of fatal and nonfatal overdose among people with both opioid and stimulant use disorder diagnoses. This study suggests an urgent need to address the service needs of individuals who have had contact with the criminal justice system and who co-use opioids and stimulants.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".