Latent Profile Analysis and Predictive Modeling of Psychosocial Risk and Drug Use Among Substance Use Treatment Clients

Adults in substance use treatment present multidimensional psychosocial risk shaped by clinical severity, trauma, psychiatric comorbidity, and structural instability. Standardized treatment models rarely account for this heterogeneity. Person-centered approaches such as Latent Profile Analysis can identify meaningful subgroups, but these profiles require empirical validation to ensure clinical utility. The study analyzed intake and discharge data from 1,983 adults, with 1,008 complete cases used for modeling. Latent Profile Analysis with Gaussian mixture modeling identified subgroups based on trauma exposure, co-occurring disorders, substance use severity, drug type, administration route, housing stability, and education. Competing models were compared using AIC, BIC, and entropy. A Random Forest classifier validated profile distinctiveness, and penalized multinomial logistic regression examined predictors of class membership. Model performance was assessed using accuracy, ROC AUC, Brier Score, Log Loss, and class-specific metrics. A four-class solution offered the strongest empirical and conceptual fit, identifying High-Risk Poly-Drug Users, Low-Use Low-Risk Clients, Moderate-Risk Users with Stable Housing, and High-Use Severe Mental Health cases. Profiles differed sharply in trauma burden, psychiatric comorbidity, drug use intensity, housing conditions, and education. The Random Forest model showed excellent discriminative performance (Accuracy = 0.86, ROC AUC = 0.94) with nearly perfect sensitivity and specificity across classes. Drug use severity, drug type, housing stability, and CODs were the most influential predictors. The combined Latent Profile Analysis and Random Forest framework demonstrated that psychosocial risk in treatment-seeking adults follows reproducible and clinically meaningful patterns. This multimethod validation strengthens the interpretive value of the identified subgroups and aligns with ecological and person-in-environment perspectives that emphasize the interplay of psychological and structural determinants. Psychosocial risk among adults in treatment is heterogeneous and best understood through person-centered, data-driven models. The validated profiles provide a foundation for precision behavioral health strategies that align interventions with subgroup-specific risk constellations. Routine integration of validated classification models can enhance triage, treatment matching, and resource allocation. Future work should examine longitudinal transitions across profiles and their implications for sustained recovery.