Using unsupervised clustering approaches to identify common mental health profiles and associated mental healthcare service use patterns in Ontario, Canada.

Abstract

Mental health is a complex, multidimensional concept that goes beyond clinical diagnoses, including psychological distress, life stress and well-being. This study aims to use unsupervised clustering approaches to identify multidimensional mental health profiles that exist in the population, and their associated service use patterns. The data source for this study is the 2012 Canadian Community Health Survey- Mental Health linked to administrative healthcare data holdings, included were all Ontario adult respondents. We used a Partioning Around Medoids clustering algorithm with Gower's proximity to identify groups with distinct combinations of mental health indicators and described them by their sociodemographic and service use characteristics. We identified four groups with distinct mental health profiles, including one group who met the clinical threshold for a depressive diagnosis, with the remaining three groups expressing differences in positive mental health, life stress and self-rated mental health. The four groups had different age, employment and income profiles and exhibited differential access to mental healthcare services. This study represents the first step in identifying complex profiles of mental health at the population level in Ontario, Canada. Further research is required to better understand the potential causes and consequences of belonging to each of the mental health profiles identified.