A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients

Hsien-Yen Chang; Noa Krawczyk; Kristin E Schneider; Lindsey Ferris; Matthew Eisenberg; Tom M Richards; B Casey Lyons; Kate Jackson; Jonathan P Weiner; Brendan Saloner

doi:10.1016/j.drugalcdep.2019.04.016

A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients

Drug Alcohol Depend. 2019 Aug 1:201:127-133. doi: 10.1016/j.drugalcdep.2019.04.016. Epub 2019 Jun 7.

Authors

Affiliations

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.

Keywords: Buprenorphine; Opioid analgesics; Opioid overdose; Opioid use disorder; Predictive risk model; Prescription drug monitoring programs.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Adolescent
Adult
Aged
Aged, 80 and over
Analgesics, Opioid / therapeutic use*
Benzodiazepines / poisoning
Buprenorphine / therapeutic use*
Cohort Studies
Drug Overdose / epidemiology*
Drug Prescriptions / statistics & numerical data
Female
Forecasting
Humans
Male
Maryland / epidemiology
Medicaid
Medicare
Middle Aged
Models, Statistical
Opiate Alkaloids / poisoning*
Opiate Substitution Treatment
Retrospective Studies
Risk Factors
Sex Factors
Socioeconomic Factors
United States
Young Adult

Substances

Analgesics, Opioid
Opiate Alkaloids
Benzodiazepines
Buprenorphine

Grants and funding

F31 DA047021/DA/NIDA NIH HHS/United States