Latent Classes of Baseline Mental Health and Substance Use Symptoms Predict 3-Month Health and Well-Being Outcomes Among Drug Court Participants

Abstract

Drug treatment courts (DTCs) serve diverse populations with substance use and co-occurring mental health conditions. These conditions may impact recovery capital (RC) and quality of life (QoL); however, limited research has examined how patterns of substance use and mental health symptoms influence these outcomes in DTC settings. Two waves of data were used from DTC participants (N = 165). Latent class analysis (LCA) classified participants based on substance use and mental health symptoms. Four unique classes of DTC participants emerged. These classes were then used to predict RC and QoL. Class membership was able to significantly predict QoL but not RC over time. This study is among the first to use LCA to determine DTC subgroups and predict QoL outcomes. Results further establish the variability of DTC participants’ profiles and emphasize the need for tailored treatment approaches.

Keywords

substance use mental health latent class analysis longitudinal community corrections

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