Artificial Neural Network Analysis Examining Substance Use Problems Co-Occurring with Anxiety and Depressive Disorders Among Adults Receiving Mental Health Treatment

Abstract

Objective: The co-occurrence of anxiety disorders, depressive disorders, and substance use problems was examined. Methods: The Mental Health Client-Level Data dataset was used to conduct logistic regression models and an artificial neural network analysis. Logistic regression analyses were conducted among adults with anxiety (n = 547,473) or depressive disorders (n = 1,610,601) as their primary diagnosis who received treatment in a community mental health center. The artificial neural network analysis was conducted with the entire sample (N = 2,158,074). Results: Approximately 30% of the sample had co-occurring high-risk substance use or substance use disorder. Characteristics including region of treatment receipt, age, education, gender, race and ethnicity, and the presence of co-occurring anxiety and depressive disorders were associated with the co-occurring high-risk substance use or a substance use disorder. Conclusions: Findings from this study highlight the importance of mental health facilities to screen for and provide integrated treatment for co-occurring disorders.

Keywords:

• Adult
• anxiety
• community mental health center
• depression
• substance use disorder

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethical statement

The University of North Carolina at Chapel Hill Institutional Review Board reviewed this research and deemed this study Not Human Subjects Research (IRB#: 22-1833; PI: Ware, O. D.).

Additional information

Funding

The authors did not receive any funding associated with the work featured in this article.