Clinical research study
A Tool to Assess Risk of De Novo Opioid Abuse or Dependence

https://doi.org/10.1016/j.amjmed.2016.02.014Get rights and content

Abstract

Background

Determining risk factors for opioid abuse or dependence will help clinicians practice informed prescribing and may help mitigate opioid abuse or dependence. The purpose of this study is to identify variables predicting opioid abuse or dependence.

Methods

A retrospective cohort study using de-identified integrated pharmacy and medical claims was performed between October 2009 and September 2013. Patients with at least 1 opioid prescription claim during the index period (index claim) were identified. We ascertained risk factors using data from 12 months before the index claim (pre-period) and captured abuse or dependency diagnosis using data from 12 months after the index claim (postperiod). We included continuously eligible (pre- and postperiod) commercially insured patients aged 18 years or older. We excluded patients with cancer, residence in a long-term care facility, or a previous diagnosis of opioid abuse or dependence (identified by International Classification of Diseases 9th revision code or buprenorphine/naloxone claim in the pre-period). The outcome was a diagnosis of opioid abuse (International Classification of Diseases 9th revision code 304.0x) or dependence (305.5).

Results

The final sample consisted of 694,851 patients. Opioid abuse or dependence was observed in 2067 patients (0.3%). Several factors predicted opioid abuse or dependence: younger age (per decade [older] odds ratio [OR], 0.68); being a chronic opioid user (OR, 4.39); history of mental illness (OR, 3.45); nonopioid substance abuse (OR, 2.82); alcohol abuse (OR, 2.37); high morphine equivalent dose per day user (OR, 1.98); tobacco use (OR, 1.80); obtaining opioids from multiple prescribers (OR, 1.71); residing in the South (OR, 1.65), West (OR, 1.49), or Midwest (OR, 1.24); using multiple pharmacies (OR, 1.59); male gender (OR, 1.43); and increased 30-day adjusted opioid prescriptions (OR, 1.05).

Conclusions

Readily available demographic, clinical, behavioral, pharmacy, and geographic information can be used to predict the likelihood of opioid abuse or dependence.

Section snippets

Materials and Methods

We used de-identified (in accordance with Health Insurance Portability and Accountability Act requirements) pharmacy and medical claims data from a pharmacy benefit manager (Express Scripts) from October 1, 2009, to September 30, 2013. These data include health insurance claims (inpatient/outpatient medical and outpatient pharmacy) and enrollment data from large employers and health plans across the United States. This study included patients aged 18 years or older as of the index opioid claim

Results

The derivation cohort included 694,851 patients, of whom 2067 (0.3%) were opioid abusers/dependents. They were significantly younger (Table 2). There were more chronic opioid users (55.8% vs 10.4%) in the group that developed abuse or dependence.

Clinical factors significantly varied between the 2 groups of patients. Opioid abusers/dependents had a higher proportion of mental illness (52.1% vs 14.9%) and nonopioid substance abuse (4.1% vs 0.2%), and nondependent alcohol abuse (4.0% vs 0.5%)

Discussion

This study identified 12 patient characteristics that predict increased risk of de novo abuse or dependence in opioid users. The strongest predictors were chronic use, mental illness, nonopioid substance use, alcohol abuse, high morphine equivalent dose per day, younger age, and male gender. These effects were in the direction as hypothesized. In this study, the relationships between the distance from patient to prescriber and being a chronic immediate-release user to the odds of developing

Conclusions

In light of the opioid abuse epidemic, the findings of this study warrant updating tools that estimate the risk for abuse or dependence. We recommend incorporating factors found in a prescription drug monitoring program into a patient's risk analysis. We found that risk factors for a patient being diagnosed with opioid abuse or dependence are younger age; being a chronic opioid user; histories of mental illness, nonopioid substance abuse, and alcohol abuse; being a high morphine equivalent dose

Acknowledgments

H. M. Dinesh, MS, Genpact, contributed to data collection. From Express Scripts, Craig Reno, BS, MBA, provided clinical expertise on the analysis, Ria Westergaard, PharmD, provided clinical expertise on the analysis. In addition to the authors, Ruth Martinez, RPh, contributed to writing and editing the manuscript.

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    Funding: TC receives support from an unrestricted grant from the Foundation for Barnes-Jewish Hospital. RI and AB receive salary support from Express Scripts, an independent pharmacy benefits manager. DT also received salary support from Express Scripts at the time the study was conducted. BFG receives support from Washington University Institute of Clinical and Translational Sciences Grant UL1 TR000448 from the National Institutes of Health.

    Conflict of Interest: None.

    Authorship: All authors had access to the data and played a role in writing this manuscript.

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