Earlier research has not directly compared dimensional, categorical and hybrid models of opioid abuse and dependence criteria. If opioid abuse and dependence is best represented as a single latent severity trait, then increasing severity may be associated with other clinical outcomes. Alternatively, if a categorical model provides the best representation of the diagnosis and thus identifies more homogenous groups of individuals within the diagnosis, those groups will have more similar opioid dependence symptoms but also potentially different physical and mental health profiles [7, 25, 36]. Both models would provide important clinical information and phenotypes for research that are not available under the current diagnostic system. Therefore the aims of this paper are to: Examine the structure of opioid abuse and dependence criteria within an opioid dependent treatment sample using latent class analysis, factor analysis and factor mixture modeling.Examine the relationship between severity or sub-types of opioid dependence and other clinically relevant variables: other substance dependence diagnoses, mental health, suicide attempts, opioid overdose, and demographic characteristics. Examining the clinical covariates of opioid dependence is one way of externally validating the structure identified in the first part of the analysis (comparing categorical, dimensional and mixture models).
