• Li Wang, Patrick J Hong, Wenjun Jiang, Yasir Rehman, Brian Y Hong, Rachel J Couban, Chunming Wang, Corey J Hayes, David N Juurlink, and Jason W Busse.
• Department of Anesthesia (L. Wang, Busse); The Michael G. DeGroote Institute for Pain Research and Care (L. Wang, Rehman, Couban, Busse); Department of Health Research Methods, Evidence & Impact (L. Wang, Rehman, Busse), McMaster University, Hamilton, Ont.; Department of Anesthesiology and Pain Medicine (P.J. Hong), University of Toronto, Toronto, Ont.; Faculty of Health Science (Jiang), McMaster University, Hamilton, Ont.; Division of Plastic Surgery, Department of Surgery (B.Y. Hong), University of Toronto, Toronto, Ont.; Guangdong Science and Technology Library (C. Wang), Institute of Information, Guangdong Academy of Sciences, Guangzhou, China; Department of Biomedical Informatics (Hayes), College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Ark.; Center for Mental Healthcare and Outcomes Research (Hayes), Central Arkansas Veterans Healthcare System, North Little Rock, Ark.; Sunnybrook Health Sciences Centre (Juurlink); Institute for Clinical Evaluative Sciences (Juurlink); Institute of Health Policy, Management, and Evaluation (Juurlink), University of Toronto, Toronto, Ont. wangli1@mcmaster.ca.
• CMAJ. 2023 Oct 23; 195 (41): E1399E1411E1399-E1411.

BackgroundHigher doses of opioids, mental health comorbidities, co-prescription of sedatives, lower socioeconomic status and a history of opioid overdose have been reported as risk factors for opioid overdose; however, the magnitude of these associations and their credibility are unclear. We sought to identify predictors of fatal and nonfatal overdose from prescription opioids.MethodsWe systematically searched MEDLINE, Embase, CINAHL, PsycINFO and Web of Science up to Oct. 30, 2022, for observational studies that explored predictors of opioid overdose after their prescription for chronic pain. We performed random-effects meta-analyses for all predictors reported by 2 or more studies using odds ratios (ORs) and 95% confidence intervals (CIs).ResultsTwenty-eight studies (23 963 716 patients) reported the association of 103 predictors with fatal or nonfatal opioid overdose. Moderate- to high-certainty evidence supported large relative associations with history of overdose (OR 5.85, 95% CI 3.78-9.04), higher opioid dose (OR 2.57, 95% CI 2.08-3.18 per 90-mg increment), 3 or more prescribers (OR 4.68, 95% CI 3.57-6.12), 4 or more dispensing pharmacies (OR 4.92, 95% CI 4.35-5.57), prescription of fentanyl (OR 2.80, 95% CI 2.30-3.41), current substance use disorder (OR 2.62, 95% CI 2.09-3.27), any mental health diagnosis (OR 2.12, 95% CI 1.73-2.61), depression (OR 2.22, 95% CI 1.57-3.14), bipolar disorder (OR 2.07, 95% CI 1.77-2.41) or pancreatitis (OR 2.00, 95% CI 1.52-2.64), with absolute risks among patients with the predictor ranging from 2-6 per 1000 for fatal overdose and 4-12 per 1000 for nonfatal overdose.InterpretationWe identified 10 predictors that were strongly associated with opioid overdose. Awareness of these predictors may facilitate shared decision-making regarding prescribing opioids for chronic pain and inform harm-reduction strategies SYSTEMATIC REVIEW REGISTRATION: Open Science Framework (https://osf.io/vznxj/).© 2023 CMA Impact Inc. or its licensors.

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