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Abuse and dependence on prescription opioids in adults: a mixture categorical and dimensional approach to diagnostic classification

Published online by Cambridge University Press:  12 May 2010

L.-T. Wu ,
G. E. Woody ,
C. Yang ,
J.-J. Pan  and
D. G. Blazer
L.-T. Wu*
Affiliation:
Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Duke Clinical Research Institute, Durham, NC, USA
G. E. Woody
Affiliation:
Department of Psychiatry, School of Medicine, University of Pennsylvania and Treatment Research Institute, Philadelphia, PA, USA
C. Yang
Affiliation:
Social Science Research Institute, Duke University, Durham, NC, USA
J.-J. Pan
Affiliation:
Veterans Health Administration, Washington, DC, USA
D. G. Blazer
Affiliation:
Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Duke Clinical Research Institute, Durham, NC, USA
*
*Address for correspondence: L.-T. Wu, Sc.D., Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Duke Clinical Research Institute, Duke University Medical Center, Durham, NC27710, USA. (Email: litzy.wu@duke.edu)
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Abstract

Background

For the emerging DSM-V, it has been recommended that dimensional and categorical methods be used simultaneously in diagnostic classification; however, little is known about this combined approach for abuse and dependence.

Method

Using data (n=37 708) from the 2007 National Survey on Drug Use and Health (NSDUH), DSM-IV criteria for prescription opioid abuse and dependence among non-prescribed opioid users (n=3037) were examined using factor analysis (FA), latent class analysis (LCA, categorical), item response theory (IRT, dimensional), and factor mixture (hybrid) approaches.

Results

A two-class factor mixture model (FMM) combining features of categorical latent classes and dimensional IRT estimates empirically fitted more parsimoniously to abuse and dependence criteria data than models from FA, LCA and IRT procedures respectively. This mixture model included a severely affected group (7%) with a comparatively moderate to high probability (0.32−0.88) of endorsing all abuse and dependence criteria items, and a less severely affected group (93%) with a low probability (0.003−0.16) of endorsing all criteria. The two empirically defined groups differed significantly in the pattern of non-prescribed opioid use, co-morbid major depression, and substance abuse treatment use.

Conclusions

A factor mixture model integrating categorical and dimensional features of classification fits better to DSM-IV criteria for prescription opioid abuse and dependence in adults than a categorical or dimensional approach. Research is needed to examine the utility of this mixture classification for substance use disorders and treatment response.

Keywords

DSM-IVfactor mixture modelitem response theorylatent class analysesnosologyprescription opioid use disorders
Type
Original Articles
Information
Psychological Medicine , Volume 41 , Issue 3 , March 2011 , pp. 653 - 664
Copyright
Copyright © Cambridge University Press 2010

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