A Tool to Assess Risk of De Novo Opioid Abuse or Dependence - 20/06/16
, Reethi Iyengar, PhD, MBA, MHM b, Amit Bothra, MS b, Dave Tomala, MA b, Geoffrey Cislo, MD a, Brian F. Gage, MD, MSc cAbstract |
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.
Le texte complet de cet article est disponible en PDF.Keywords : Demographic factors, Opioid abuse, Opioid dependence, Pharmacy claims-based factors, Predictive model, Prescription drug monitoring program
Plan
| 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. |
Vol 129 - N° 7
P. 699 - juillet 2016 Retour au numéro
Article précédent
- Real-Time Automated Sampling of Electronic Medical Records Predicts Hospital Mortality
- Hargobind S. Khurana, Robert H. Groves, Michael P. Simons, Mary Martin, Brenda Stoffer, Sherri Kou, Richard Gerkin, Eric Reiman, Sairam Parthasarathy, Hargobind S. Khurana, Robert H. Groves, Michael P. Simons, Mary Martin, Brenda Stoffer, Sherri Kou, Richard Gerkin, Eric Reiman, Sairam Parthasarathy
- Hospital Readmissions Following Physician Call System Change: A Comparison of Concentrated and Distributed Schedules
- Christopher J. Yarnell, Steven Shadowitz, Donald A. Redelmeier
Bienvenue sur EM-consulte, la référence des professionnels de santé.
L’accès au texte intégral de cet article nécessite un abonnement.
Déjà abonné à cette revue ?