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Item does not contain fulltext, Purpose: Given the complex association between substance use disorders (SUD), comorbid mental health problems and criminal recidivism in forensic patients, homogenous patient classes can contribute to a refined treatment. This paper aims to construct those classes in forensic patients (N = 286) diagnosed with SUD, unconditionally released between 2004 and 2013 of one of ten Dutch forensic psychiatric centers. Design/methodology/approach: Retrospective data were derived from electronic patient files. Classes were based on the Dutch risk assessment tool, the Historisch Klinisch Toekomst-Revisie (Historical Clinical Future–Revised [HKT-R]) and identified by means of explorative Latent Class Analysis in Latent Gold version 5.1. In a three-step approach, posterior class memberships were related to external variables (i.e. diagnoses, type of drug and type of offence). Findings: Four classes were identified that differ in the risk of recidivism, as well as Axis I and II diagnoses and type of drug consumption. Practical implications: This study informed on the heterogeneity of forensic patients with SUD and identified four homogenous classes that differ in important variables for the treatment approach. Based on these classes, a more refined treatment approach can be developed. Possible treatment approaches are discussed, but future research is needed to provide evidence. Originality/value: This study is the first to identify classes within forensic patients with SUD and, therefore, sets the first step to develop a tailored treatment approach based on characteristics informative for treatment.

In this study, growth trajectories (from admission until unconditional release) of crime-related dynamic risk factors were investigated in a sample of Dutch forensic patients (N = 317), using latent growth curve modeling. After testing the unconditional model, three predictors were added: first-time offender versus recidivist, age, and treatment duration. Postanalyses were chi-square difference tests, t tests, and analyses of variance (ANOVAs) to assess differences in trajectories. Overall, on scale level, a decrease of risk factors over time was found. The predictors showed no significant slope differences although age and treatment duration differed significantly at some time points. The oldest age group performed worse, especially at later time points. Treatment duration effects were found at the second time point. Our results that forensic patients show a decrease in crime-related risk factors may indicate that treatment is effective. This study also found differences in growth rates, indicating the effect of individual differences.