Readily available demographic, clinical, behavioral, pharmacy, and geographic information can be used to predict the likelihood of opioid abuse or dependence.
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.
The United States has seen a dramatic increase in opioid prescriptions in the past decade with a concomitant increase in abuse of opioid medications. There has been a tripling in the rate of opioid-related overdose deaths from 2000 to 2014, with more than 28,000 deaths in 2014. This epidemic creates a dilemma for prescribers who seek to provide adequate pain relief while minimizing risks of abuse and dependence. Abuse is defined as the intentional self-administration of a medication for a nonmedical reason, whereas dependence is a maladaptive pattern of substance use.
Guidelines exist for using opioids in noncancer pain, but prescribers face challenging situations when prescribing opioids and need tools to aid their decisions. Prescription drug monitoring programs can help reveal aberrant behavior. Forty-nine states have enacted these programs; however, monitoring alone does not prevent abuse. Currently, there are limited tools that help predict which patients may develop opioid abuse or dependence. The Opioid Risk Tool identifies at-risk patients on the basis of medical, family, and social history. However, the Opioid Risk Tool does not combine patient and prescription drug monitoring program information to assess risk. Clinicians need to know how risk factors ascertained at the time of prescribing opioids predict subsequent abuse or dependence.
To read this article in its entirety please visit our website.
-Thomas Ciesielski, MD, Reethi Iyengar, PhD, MBA, MHM, Amit Bothra, MS, Dave Tomala, MA, Geoffrey Cislo, MD, Brian F. Gage, MD, MSc
For related reading follow this link.
This article originally appeared in the July 2016 issue of The American Journal of Medicine.
