Elsevier

Drug and Alcohol Dependence

Volume 138, 1 May 2014, Pages 202-208
Drug and Alcohol Dependence

Factors predicting development of opioid use disorders among individuals who receive an initial opioid prescription: Mathematical modeling using a database of commercially-insured individuals

https://doi.org/10.1016/j.drugalcdep.2014.02.701Get rights and content

Abstract

Background

Prescription drug abuse in the United States and elsewhere in the world is increasing at an alarming rate with non-medical opioid use, in particular, increasing to epidemic proportions over the past two decades. It is imperative to identify individuals most likely to develop opioid abuse or dependence to inform large-scale, targeted prevention efforts.

Methods

The present investigation utilized a large commercial insurance claims database to identify demographic, mental health, physical health, and healthcare service utilization variables that differentiate persons who receive an opioid abuse or dependence diagnosis within two years of filling an opioid prescription (OUDs) from those who do not receive such a diagnosis within the same time frame (non-OUDs).

Results

When compared to non-OUDs, OUDs were more likely to: (1) be male (59.9% vs. 44.2% for non-OUDs) and younger (M = 37.9 vs. 47.7); (2) have a prescription history of more opioids (1.7 vs. 1.2), and more days supply of opioids (M = 272.5, vs. M = 33.2; (3) have prescriptions filled at more pharmacies (M = 3.3 per year vs. M = 1.3); (4) have greater rates of psychiatric disorders; (5) utilize more medical and psychiatric services; and (6) be prescribed more concomitant medications. A predictive model incorporating these findings was 79.5% concordant with actual OUDs in the data set.

Conclusions

Understanding correlates of OUD development can help to predict risk and inform prevention efforts.

Introduction

North America comprises the world's largest drug market and evidences the highest drug-related mortality rate in the world (International Narcotics Control Board, 2012). Within the United States the problem of prescription drug misuse and opioid misuse (broadly defined as using the medication in a manner different than prescribed) in particular, has reached epidemic proportions. Pain relievers were the most commonly misused drug in the psychotherapeutics category from 2002 to 2011 (Substance Abuse and Mental Health Services Administration (SAMHSA), 2012) and from 2004 to 2011, the number of medical emergencies involving opioids increased by 183% (SAMHSA, 2013).

Abuse of prescription drugs is a significant public health problem, associated with high costs both to the health care system and to the individuals who use them. From an economic perspective, it is estimated that opioid misusers’ medical care costs are eight times greater than those of non-misusers (White et al., 2005). Mortality due to prescription drug use is a significant cause of death in the United States, accounting for 36% of all poisoning deaths in 2007, a number that tripled from 1999 to 2007 (Warner et al., 2011). It is estimated that 0.04% of individuals receiving a prescription opioid have a fatal overdose, with the odds of mortality higher among those receiving an opioid for pain (Bohnert et al., 2011).

Identifying patients who misuse these substances is often difficult, since clinicians must disentangle legitimate pain management needs from possible abuse. When opioid abuse or dependence develops, patients’ medical treatment is complicated by tolerance, withdrawal, or potential overdose. Little is known about factors that may place individuals at risk for the development of prescription drug use disorders. As a recent editorial indicates, these individuals may differ significantly from those who are typically studied in substance use disorder research; specifically, many at risk for opioid use disorders may not have a history of illicit drug use prior to developing a problem with opioids (Darke, 2011). Since the rates of prescribing opioids, state by state, are linked to mortality due to overdose, it is clear that a prescription of an opioid places individuals at risk for eventual misuse (Paulozzi et al., 2011).

Researchers have attempted to identify factors that may predict later drug abuse and dependence. Earlier age of nonmedical use of prescription drugs, earlier initiation of alcohol use, family history of alcoholism, and polydrug abuse are predictive of greater risk for developing prescription drug abuse or dependence (McCabe et al., 2007). Previous research has found that there are particular demographic variables that place individuals at higher risk for the development of a diagnosis of opioid abuse and dependence. Specifically, individuals who are younger (Edlund et al., 2007, Edlund et al., 2010) and male (Edlund et al., 2007) were more likely to develop abuse and dependence. Additionally, receiving a larger number of days’ supply of prescription opioids was a predictor of an opioid use disorder diagnosis (Edlund et al., 2007), as was having a higher average daily dose (Edlund et al., 2010).

In addition to demographic and other markers, behaviorally-based criteria have been successfully used to identify problematic cases of prescription drug misuse (Smith et al., 2010). In a recent study, clinical expert raters identified key indicators of misuse, including interpersonal problems, arrest history, multiple opioid use, use for no identifiable reason, and comorbid other substance misuse, and used these indicators along with known indicators of misuse to improve accuracy in identifying misuse. This study indicates that multiple sources of data, particularly those regarding different domains of functioning, may best identify those at risk for opioid abuse and dependence.

Previous studies have also linked problematic use of prescription drugs and mental health diagnoses. Nonmedical use of opioids has been associated with panic, depressive, social phobic or agoraphobic symptoms, and the overall number of psychiatric symptoms endorsed (Becker et al., 2007a, Becker et al., 2007b). Development of opioid abuse and dependence has also been associated with non-opioid substance use and mental health disorders (Edlund et al., 2007, Edlund et al., 2010). Recent prospective research has indicated that non-medical use of prescription medications, including opioids, places individuals at risk for unipolar depressive, bipolar, and anxiety disorders (Schepis and Hakes, 2011). The converse relationship may also be true: other mental health conditions may predispose individuals to misuse opioids. In a recent review of the known factors predicting opioid misuse, the authors caution that although many mental health diagnoses may be risk factors for opioid misuse, these conditions are likely to be concealed due to stigma, and some individuals may choose to take prescription opioids to treat undiagnosed co-occurring disorders rather than the appropriate psychiatric medication (Pergolizzi et al., 2012).

This study seeks to identify demographic and healthcare related variables that predict the development of opioid abuse or dependence, utilizing data obtained from the Thomson Reuters MarketScan Commercial Claims and Encounters (CCAE) database, which contains information about commercially insured and Medicare eligible patients. The use of a large sample, physician-diagnosed disorders, and comprehensive demographic and health care utilization data enable detailed analysis of individuals at risk for the development for opioid abuse or dependence. First, individuals diagnosed with opioid use disorders will be compared with those who are not given opioid use diagnoses on a variety of domains. Second, the use of mathematical modeling techniques will aid in identifying people who are at risk for the development of opioid abuse or dependence.

Section snippets

Methods and analytic strategy

Patients within the CCAE database who had at least one opioid prescription claim between January 1, 2000 and December 31, 2008 were identified. Patients were included if they maintained continuous insurance eligibility for 6 months prior to, and 2 years beyond, this initial prescription claim (N = 2,841,793). Individuals who subsequently received an ICD-9 CM diagnosis (304.0× or 305.5×) of opioid abuse or dependence were classified as those with opioid use disorders, hereafter referred to as

Bivariate comparisons between OUDs and non-OUDs

For the first series of analyses, bivariate comparisons of OUDs and non-OUDs were completed on variables related to demographics, medical service utilization, co-occurring conditions, and concomitant medication usage. The exact variables of interest were chosen by a team of researchers with expertise in pharmacoeconomics, public health, substance misuse, and mental health.

Table 1 presents demographic metrics for OUDs and non-OUDs. As expected, OUDs were more likely than non-OUDs to be younger

Discussion

The detection of opioid misuse is an important step in addressing the public health problems of prescription drug abuse, dependence, diversion, and overdose. Although previous studies have identified some of the factors that place individuals at greater risk for misuse of opioids, this investigation benefits from a comprehensive database that has illuminated more differences between those who develop opioid use disorders and those who receive an initial prescription but do not develop a

Role of funding source

This research was supported by a grant from the National Institute on Drug Abuse (R03 DA023237-01A1) awarded to Bryan N. Cochran and Jean Carter. Additional support for Annesa Flentje was provided by National Institute on Drug Abuse (T32 DA007250 and P50 DA009253). The funding sources had no involvement in the study design, analysis, report writing, or other aspects of the project.

Contributors

Author Bryan Cochran was involved in study design, writing of the report, and data interpretation. Annesa Flentje and Nicholas Heck were involved in data interpretation, background literature review, and manuscript writing. Author Jill Van Den Bos led a data analysis team at Milliman that included Dan Perlman and Jorge Torres; these authors all conducted the relevant analyses and provided feedback on the manuscript. As a consultant on the project, author Robert Valuck assisted in the

Conflict of interest

All authors declare that they have no conflicts of interest.

Acknowledgements

The authors wish to thank Cathy Murphy-Barron for her helpful comments on a previous draft of the manuscript. Brandon Stewart, at the University of Montana, assisted in developing the tables and verifying the data contained in the manuscript. We would also like to thank Heather Woodward at the University of Colorado, Denver, for converting drug dosages to their morphine equivalents for analyses.

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