Citation

Ciesielski T, Iyengar R, Bothra A, Tomala D, Cislo G, Gage BF. Am. J. Med. 2016; 129(7): 699-705.e4.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.amjmed.2016.02.014

PMID

unavailable

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 between October 2009 and September 2013. Patients with at least one opioid prescription claim during the index period (index claim) were identified. We ascertained risk factors using data from 12 months prior to index claim (pre-period) and captured abuse or dependency diagnosis using data from 12 months post index claim (post-period). We included continuously eligible (pre- and post-period) commercially insured patients aged 18 or older. We excluded patients with cancer, residence in a long-term care facility, or previous diagnosis of opioid abuse or dependence (identified by International Classification of Diseases-9th revision (ICD-9) code or buprenorphine/naloxone claim in the pre-period). The outcome was a diagnosis of opioid abuse (ICD9 code 304.0x) or dependence (305.5).
Results
The final sample consisted of 694,851 patients. Opioid abuse or dependence was observed in 2,067 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], non-opioid 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 likelihood of opioid abuse or dependence.

Language: en