Abuse and dependence on prescription opioids in adults: a mixture categorical and dimensional approach to diagnostic classification.
Journal Article (Journal Article)
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.
- Published version (via Digital Object Identifier)
- Pubmed Central version
- Open Access Copy from Duke
- Link to Item
- Wu, L-T; Woody, GE; Yang, C; Pan, J-J; Blazer, DG
- March 2011
Volume / Issue
- 41 / 3
Start / End Page
- 653 - 664
Pubmed Central ID
Electronic International Standard Serial Number (EISSN)
Digital Object Identifier (DOI)