Opioid Agonist Treatment and Risk of Mortality During an Opioid Overdose Public Health Emergency: A Population-Based Retrospective Cohort Study
Bibliographic record
Abstract
IntroductionOpioid agonist treatment (OAT) is a safe and effective treatment for opioid use disorder (OUD). However, people commonly stop and start OAT and their risk of death is high immediately after stopping. The prevalence of illicitly manufactured fentanyl and other highly potent synthetic opioids have increased in the illicit drug supply globally. Yet, there is limited evidence examining the relationship between OAT and mortality when these contaminants are widely available in the illicit drug supply. Objectives and ApproachWe aimed to compare the risk of mortality on and off OAT in a setting with a high prevalence of illicitly manufactured fentanyl and other potent synthetic opioids in the illicit drug supply. We linked five health administrative datasets in British Columbia, Canada, creating a cohort of 55,347 people with OUD who received OAT during a 23-year period (1996 to 2018). We compared the risk of mortality on and off treatment over time, and according to time since starting or stopping treatment and by medication type. Results7,030 of 55,347 (12.7%) OAT recipients died during follow-up. All-cause SMR was substantially lower on OAT (4.6 [4.4 to 4.8]) compared to off OAT (9.7 [9.5 to 10.0]). In a period of increasing prevalence of fentanyl, the relative risk of mortality off OAT was 2.1 [1.8 to 2.4] times higher than on OAT prior to the introduction of fentanyl, and increased to 3.4 [2.8 to 4.3] at the end of the study period (65% increase in relative risk). Conclusion / ImplicationsThe protective effect of OAT on mortality increased as fentanyl and other synthetic opioids became common in the illicit drug supply, while the risk of mortality remained high off OAT. As fentanyl becomes more widespread globally, these findings highlight the importance of interventions that improve retention on opioid agonist treatment and prevent recipients from stopping treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".