Using unsupervised clustering approaches to identify common mental health profiles and associated mental health-care service-use patterns in Ontario, Canada.
Am J Epidemiol
; 193(7): 976-986, 2024 07 08.
Article
em En
| MEDLINE
| ID: mdl-38576175
ABSTRACT
Mental health is a complex, multidimensional concept that goes beyond clinical diagnoses, including psychological distress, life stress, and well-being. In this study, we aimed 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 was the 2012 Canadian Community Health Survey-Mental Health, linked to administrative health-care data; all Ontario, Canada, adult respondents were included. We used a partitioning around medoids clustering algorithm with Gower's proximity to identify groups with distinct combinations of mental health indicators and described them according to their sociodemographic and service-use characteristics. We identified 4 groups with distinct mental health profiles, including 1 group that met the clinical threshold for a depressive diagnosis, with the remaining 3 groups expressing differences in positive mental health, life stress, and self-rated mental health. The 4 groups had different age, employment, and income profiles and exhibited differential access to mental health-care services. This study represents the first step in identifying complex profiles of mental health at the population level in Ontario. Further research is required to better understand the potential causes and consequences of belonging to each of the mental health profiles identified. This article is part of a Special Collection on Mental Health.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Saúde Mental
/
Serviços de Saúde Mental
Limite:
Adolescent
/
Adult
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Aged
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Female
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Humans
/
Male
/
Middle aged
País/Região como assunto:
America do norte
Idioma:
En
Ano de publicação:
2024