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Drug Alcohol Depend. 2014 May 1;138:202-8. doi: 10.1016/j.drugalcdep.2014.02.701. Epub 2014 Mar 12.

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

Author information

1
Department of Psychology, Skaggs Building Room 143, The University of Montana, Missoula, MT 59812, United States. Electronic address: bryan.cochran@umontana.edu.
2
University of California, San Francisco, San Francisco General Hospital, 1001 Potrero Avenue, Suite 7 M, San Francisco, CA 94110, United States.
3
Department of Psychology, Skaggs Building Room 143, The University of Montana, Missoula, MT 59812, United States.
4
Milliman, Inc., 1400 Wewatta St, Suite 300, Denver, CO 80202, United States.
5
University of Colorado, Skaggs School of Pharmacy and Pharmaceutical Sciences, Mail Stop C238, 12850 E. Montview Blvd. V20-1201, Aurora, CO 80045, United States.
6
Department of Pharmacy Practice, 32 Campus Drive, The University of Montana, Missoula, MT 59812, United States.

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.

KEYWORDS:

Health claims database; Opioid dependence; Opioid use disorder; Prescription drug misuse

PMID:
24679839
PMCID:
PMC4046908
DOI:
10.1016/j.drugalcdep.2014.02.701
[Indexed for MEDLINE]
Free PMC Article

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