Latent Profile Analysis of Psychosocial Risk and Drug Use in Substance Use Treatment Clients

Background: Substance use treatment clients present with diverse psychosocial risk factors and substance use patterns, yet many treatment models apply uniform approaches. Latent Profile Analysis (LPA) allows for identification of unobserved subgroups with shared characteristics to inform more tailored interventions.

Objective: This study used LPA to identify latent subgroups based on trauma exposure, mental health comorbidity, primary drug use characteristics, housing stability, and education among individuals in substance use treatment.

Methods: Data from 1,158 adults receiving substance use treatment were analyzed using Latent Profile Analysis via Gaussian Mixture Modeling in Python, integrated with STATA v18. Indicators included trauma history, co-occurring mental health disorders, primary drug type, days of use, route of administration, education, and housing status. Models with 2 to 5 classes were evaluated using AIC, BIC, and conceptual interpretability to determine the optimal solution.

Findings: To identify the optimal number of latent profiles, multiple model fit indices were evaluated, including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), where lower values indicate better fit. A marked improvement in model fit was observed between the 2-class and 4-class solutions. The 4-class solution demonstrated a substantial decrease in both AIC (2811.66) and BIC (3514.61) compared to the 3-class solution (AIC = 10267.67, BIC = 10793.65). While the 5-class model slightly reduced AIC (2652.99), it resulted in an increased BIC (3532.91), suggesting potential overfitting. Based on statistical fit and interpretability, the 4-class solution was selected as the optimal model.

LPA uncovered four clinically distinct subgroups of clients, revealing substantial heterogeneity that is often masked by average treatment effects:

1. High-Risk Poly-Drug Users (n = 323): High trauma, severe drug use (e.g., injection), co-occurring disorders, and housing instability.

2. Low-Use, Low-Risk Clients (n = 226): Minimal drug use, low trauma, high housing and educational stability (suited for low-intensity services).

3. Moderate-Risk Users with Stable Housing (n = 417): High trauma with lower drug frequency and safer use practices (candidates for integrated mental health and harm reduction).

4. High-Use, Severe Mental Health Group (n = 42): Highest psychiatric comorbidity and drug use, implying need for intensive, multidisciplinary care.

Conclusions and Implications: The LPA identified four distinct subgroups with unique patterns of trauma exposure, co-occurring mental health disorders, substance use, and psychosocial functioning. The selected 4-class model demonstrated strong statistical fit alongside meaningful clinical interpretability. Subgroup identification through LPA offers a data-driven framework for triaging services and developing individualized care plans. Integrating these profiles into treatment planning may enhance intervention precision, reduce premature dropout, and improve client outcomes. Embedding trauma-informed, person-centered strategies across public health and behavioral health systems is essential for effectively supporting individuals with complex and intersecting needs.