US adults with unmet mental health treatment needs: Profiling and underlying causes using machine learning techniques

Abstract

According to the Substance Abuse and Mental Health Services Administration (USA), an estimated 9.7 million US adults did not receive mental health treatment in 2016. Based on 2015 and 2016 data reported in the National Survey on Drug Use and Health, hierarchical optimal discriminant analysis reveals that several socio-demographic groups have compelling unmet mental health treatment needs with 94.47% prediction accuracy. Non-parametric hierarchical optimal discriminant analysis was appropriate for this study, especially with little a-priori knowledge about relationships among variables examined. The results are further enhanced by detailed cross-tabulation analyses which show how unmet mental health treatment needs differ among various sociodemographic groups. The underlying reasons are also examined and reported. These results and analyses provide useful information for policymakers and decision makers to offer predictions and take appropriate actions for the purpose of intervention and/or prevention.

Keywords:

• Hierarchical optimal discriminant analysis
• predictive analyses
• radar charts
• unmet mental health treatment needs