Prescription-Related Risk Factors for Opioid-Related Overdoses in the Era of Fentanyl Contamination of Illicit Drug Supply: A Retrospective Case-Control Study
Bibliographic record
Abstract
Background: We sought to quantify the association between clinical, physiological, and contextual factors and opioid-related overdose, specifically focusing on current and past use of select prescription medications. Methods: We conducted a case-control study of individuals who experienced a non-fatal opioid-related overdose between January 2015 and November 2016 in British Columbia, Canada. We matched 8,831 cases to 44,155 controls on birth year, sex, and local health area of residence and examined 5-year prescribing history for opioids for pain, medications for opioid use disorder (MOUD), benzodiazepines/z-drugs, and other psychoactive medications. Results: The overall prevalence of prescription opioid drug use was generally low in the study population. Cases had a relatively higher use of selected prescription medications, a higher physical and mental morbidity burden, and were less connected to health services compared with controls. For opioids for pain, current therapy was associated with experiencing an overdose (OR = 8.5, 95%CI: 7.3–10); history of long-term use had a stronger association than history of short-term use (OR = 2.9, 95%CI: 2.6–3.3 vs OR = 1.7, 95%CI: 1.5–1.8, respectively). While persons on MOUD were more likely to overdose compared to persons who were not on therapy (OR = 2.0, 95%CI 1.7–2.4), recent discontinuation of MOUD greatly increased the likelihood of overdose (OR = 25.6, 95%CI 17.5–37.4). Active therapy of benzodiazepines/z-drugs and other sedating medications also significantly increased the likelihood of overdose. Conclusions: While this study supports expansion of efforts to prevent overdoses among individuals actively using opioids for pain and improve retention among those on MOUD, it is also important to address other clinical, physiological, and contextual risk and protective factors to help curb the current overdose crisis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".