Stimulant use disorder diagnosis and opioid agonist treatment dispensation following release from prison: a cohort study
Bibliographic record
Abstract
BACKGROUND: Concurrent opioid and stimulant use is on the rise in North America. This increasing trend of use has been observed in the general population, and among people released from prison in British Columbia (BC), who face an elevated risk of overdose post-release. Opioid agonist treatment is an effective treatment for opioid use disorder and reduces risk of overdose mortality. In the context of rising concurrent stimulant use among people with opioid use disorder, this study aims to investigate the impact of stimulant use disorder on opioid agonist treatment dispensation following release from prison in BC. METHODS: 2018 (N = 13,380). Hospital and primary-care administrative health records were used to identify opioid and stimulant use disorder and mental illness. Age, sex, and health region were derived from BC's Client Roster. Incarceration data were retrieved from provincial prison records. Opioid agonist treatment data was retrieved from BC's provincial drug dispensation database. A generalized estimating equation produced estimates for the relationship of stimulant use disorder and opioid agonist treatment dispensation within two days post-release. RESULTS: Cases of release among people with an opioid use disorder were identified (N = 13,380). Approximately 25% (N = 3,328) of releases ended in opioid agonist treatment dispensation within two days post-release. A statistically significant interaction of stimulant use disorder and mental illness was identified. Stratified odds ratios (ORs) found that in the presence of mental illness, stimulant use disorder was associated with lower odds of obtaining OAT [(OR) = 0.73, 95% confidence interval (CI) = 0.64-0.84)] while in the absence of mental illness, this relationship did not hold [OR = 0.89, 95% CI = 0.70-1.13]. CONCLUSIONS: People with mental illness and stimulant use disorder diagnoses have a lower odds of being dispensed agonist treatment post-release compared to people with mental illness alone. There is a critical need to scale up and adapt opioid agonist treatment and ancillary harm reduction, and treatment services to reach people released from prison who have concurrent stimulant use disorder and mental illness diagnoses.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".