A Model to Identify Patients at Risk for Prescription Opioid Abuse, Dependence, and Misuse

• Published in: Pain medicine (Malden, Mass.) Vol. 13; no. 9; pp. 1162 - 1173
• Main Authors: Rice, J. Bradford, White, Alan G., Birnbaum, Howard G., Schiller, Matt, Brown, David A., Roland, Carl L.
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.09.2012; Oxford University Press
• Subjects: Abuse; Adolescent; Adult; Analgesics; Analgesics, Opioid - adverse effects; Child; Demography; Dependence; Female; Humans; Male; Middle Aged; Models, Theoretical; Narcotics; Odds Ratio; Opioid-Related Disorders - diagnosis; Opioid-Related Disorders - epidemiology; Opioids; Patients; Risk Assessment; Risk Factors; Young Adult
• ISSN: 1526-2375; 1526-4637
• DOI: 10.1111/j.1526-4637.2012.01450.x

Objective.  The objective of this study was to use administrative claims data to identify and analyze patient characteristics and behavior associated with diagnosed opioid abuse. Design.  Patients, aged 12–64 years, with at least one prescription opioid claim during 2007–2009 (n = 821,916) were selected from a de‐identified administrative claims database of privately insured members (n = 8,316,665). Patients were divided into two mutually exclusive groups: those diagnosed with opioid abuse during 1999–2009 (n = 6,380) and those without a diagnosis for opioid abuse (n =  815,536). A logistic regression model was developed to estimate the association between an opioid abuse diagnosis and patient characteristics, including patient demographics, prescription drug use and filling behavior, comorbidities, medical resource use, and family member characteristics. Sensitivity analyses were conducted on the model's predictive power. Results.  In addition to demographic factors associated with abuse (e.g., male gender), the following were identified as “key characteristics” (i.e., odds ratio [OR] > 2): prior opioid prescriptions (OR = 2.23 for 1–5 prior Rxs; OR = 6.85 for 6+ prior Rxs); at least one prior prescription of buprenorphine (OR = 51.75) or methadone (OR = 2.97); at least one diagnosis of non‐opioid drug abuse (OR = 9.89), mental illness (OR = 2.45), or hepatitis (OR = 2.36); and having a family member diagnosed with opioid abuse (OR = 3.01). Conclusions.  Using medical as well as drug claims data, it is feasible to develop models that could assist payers in identifying patients who exhibit characteristics associated with increased risk for opioid abuse. These models incorporate medical information beyond that available to prescription drug monitoring programs that are reliant on drug claims data and can be an important tool to identify potentially inappropriate opioid use.