Hsien-Yen Chang1, Noa Krawczyk2, Kristin E Schneider3, Lindsey Ferris4, Matthew Eisenberg5, Tom M Richards6, B Casey Lyons7, Kate Jackson8, Jonathan P Weiner9, Brendan Saloner10. 1. Johns Hopkins Bloomberg School of Public Health, Department of Health Policy and Management, Baltimore, MD, USA; Johns Hopkins Center for Population Health Information Technology, Baltimore, MD, USA; Johns Hopkins Center for Drug Safety and Effectiveness, Baltimore, MD, USA. Electronic address: hchang24@jhmi.edu. 2. Johns Hopkins Bloomberg School of Public Health, Department of Mental Health, Baltimore, MD, USA. Electronic address: noa.krawczyk@jhu.edu. 3. Johns Hopkins Bloomberg School of Public Health, Department of Mental Health, Baltimore, MD, USA. Electronic address: kschne18@jhmi.edu. 4. Johns Hopkins Bloomberg School of Public Health, Department of Health Policy and Management, Baltimore, MD, USA; The Chesapeake Regional Information System for our Patients, Baltimore, MD, USA. Electronic address: lferris1@jhu.edu. 5. Johns Hopkins Bloomberg School of Public Health, Department of Health Policy and Management, Baltimore, MD, USA. Electronic address: eisenberg@jhu.edu. 6. Johns Hopkins Bloomberg School of Public Health, Department of Health Policy and Management, Baltimore, MD, USA; Johns Hopkins Center for Population Health Information Technology, Baltimore, MD, USA. Electronic address: tom.richards@jhu.edu. 7. Maryland Department of Health, Public Health Services, Office of PDMP and Overdose Prevention Applied Data Programs, Baltimore, MD, USA. Electronic address: brianna.lyons@maryland.gov. 8. Maryland Department of Health, Public Health Services, Office of PDMP and Overdose Prevention Applied Data Programs, Baltimore, MD, USA. Electronic address: kate.jackson@maryland.gov. 9. Johns Hopkins Bloomberg School of Public Health, Department of Health Policy and Management, Baltimore, MD, USA; Johns Hopkins Center for Population Health Information Technology, Baltimore, MD, USA. Electronic address: jweiner1@jhu.edu. 10. Johns Hopkins Bloomberg School of Public Health, Department of Health Policy and Management, Baltimore, MD, USA. Electronic address: bsaloner@jhu.edu.
Abstract
BACKGROUND: Predicting which individuals who are prescribed buprenorphine for opioid use disorder are most likely to experience an overdose can help target interventions to prevent relapse and subsequent consequences. METHODS: We used Maryland prescription drug monitoring data from 2015 to identify risk factors for nonfatal opioid overdoses that were identified in hospital discharge records in 2016. We developed a predictive risk model for prospective nonfatal opioid overdoses among buprenorphine patients (N = 25,487). We estimated a series of models that included demographics plus opioid, buprenorphine and benzodiazepine prescription variables. We applied logistic regression to generate performance measures. RESULTS: About 3.24% of the study cohort had ≥1 nonfatal opioid overdoses. In the model with all predictors, odds of nonfatal overdoses among buprenorphine patients were higher among males (OR = 1.39, 95% CI:1.21-1.62) and those with more buprenorphine pharmacies (OR = 1.19, 95% CI:1.11-1.28), 1+ buprenorphine prescription paid by Medicaid (OR = 1.21, 95% CI:1.02-1.48), Medicare (OR = 1.93, 95% CI:1.63-2.43), or a commercial plan (OR = 1.98, 95% CI:1.30-2.89), 1+ opioid prescription paid by Medicare (OR = 1.30, 95% CI:1.03-1.68), and more benzodiazepine prescriptions (OR = 1.04, 95% CI:1.02-1.05). The odds were lower among those with longer days of buprenorphine (OR = 0.64, 95% CI:0.60-0.69) or opioid (OR = 0.79, 95% CI:0.65-0.95) supply. The model had moderate predictive ability (c-statistic = 0.69). CONCLUSIONS: Several modifiable risk factors, such as length of buprenorphine treatment, may be targets for interventions to improve clinical care and reduce harms. This model could be practically implemented with common prescription-related information and allow payers and clinical systems to better target overdose risk reduction interventions, such as naloxone distribution.
BACKGROUND: Predicting which individuals who are prescribed buprenorphine for opioid use disorder are most likely to experience an overdose can help target interventions to prevent relapse and subsequent consequences. METHODS: We used Maryland prescription drug monitoring data from 2015 to identify risk factors for nonfatal opioid overdoses that were identified in hospital discharge records in 2016. We developed a predictive risk model for prospective nonfatal opioid overdoses among buprenorphinepatients (N = 25,487). We estimated a series of models that included demographics plus opioid, buprenorphine and benzodiazepine prescription variables. We applied logistic regression to generate performance measures. RESULTS: About 3.24% of the study cohort had ≥1 nonfatal opioid overdoses. In the model with all predictors, odds of nonfatal overdoses among buprenorphinepatients were higher among males (OR = 1.39, 95% CI:1.21-1.62) and those with more buprenorphine pharmacies (OR = 1.19, 95% CI:1.11-1.28), 1+ buprenorphine prescription paid by Medicaid (OR = 1.21, 95% CI:1.02-1.48), Medicare (OR = 1.93, 95% CI:1.63-2.43), or a commercial plan (OR = 1.98, 95% CI:1.30-2.89), 1+ opioid prescription paid by Medicare (OR = 1.30, 95% CI:1.03-1.68), and more benzodiazepine prescriptions (OR = 1.04, 95% CI:1.02-1.05). The odds were lower among those with longer days of buprenorphine (OR = 0.64, 95% CI:0.60-0.69) or opioid (OR = 0.79, 95% CI:0.65-0.95) supply. The model had moderate predictive ability (c-statistic = 0.69). CONCLUSIONS: Several modifiable risk factors, such as length of buprenorphine treatment, may be targets for interventions to improve clinical care and reduce harms. This model could be practically implemented with common prescription-related information and allow payers and clinical systems to better target overdose risk reduction interventions, such as naloxone distribution.
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