Opioid Agonist Treatment and Risk of Mortality During an Opioid Overdose Public Health Emergency: A Population-Based Retrospective Cohort Study
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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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it