Social psychiatry and psychiatric epidemiology · 2026
Geospatial clustering of suicide, mental health, sociodemographic and workforce features in Australia.
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Khan Jahidur Rahman, Hu Nan, Hodgins Michael, Sawrikar Vilas, Bakar K Shuvo, Lingam Raghu
Аннотация
PURPOSE: Identifying geographic clusters with distinct profiles may support evidence-based policy and planning. This study aimed to identify clusters based on suicide rates, proportion of people reporting a mental health condition, sociodemographic characteristics, and mental health workforce features.
METHODS: Area-level data were obtained for 294 Statistical Area Level 3 (SA3) regions across Australia using routine administrative and census data. Hierarchical clustering of principal components identified clusters, and spatial join-count analysis assessed their spatial concentration.
RESULTS: Our analysis found three clusters. Cluster 1 (N = 136; 46.2%) was characterised by a higher suicide rate and a high proportion of people self-reporting a mental health condition. This cluster was mainly in regional and remote areas with an older population, lower household income and educational attainment, and lower mental health workforce density. Cluster 2 (N = 146; 49.7%) had the lowest suicide rates and the lowest proportion of people reporting a mental health condition; these areas were predominantly metropolitan and characterised by a younger population, fewer Indigenous people, higher income and educational attainment, and higher mental health workforce density. Cluster 3 (N = 12; 4.1%) had the highest suicide rate yet the lowest proportion of people reporting a mental health condition. This final cluster was entirely regional and remote, had the highest Indigenous population proportion, lowest educational attainment, and lowest mental health workforce density. All clusters exhibited significant spatial clustering.
CONCLUSION: This study highlights geographic disparities in mental health and suicide-related profiles across Australia. Prioritising community-level interventions that reduce socioeconomic disparities and improve access to mental health care is crucial in regional and remote areas.
METHODS: Area-level data were obtained for 294 Statistical Area Level 3 (SA3) regions across Australia using routine administrative and census data. Hierarchical clustering of principal components identified clusters, and spatial join-count analysis assessed their spatial concentration.
RESULTS: Our analysis found three clusters. Cluster 1 (N = 136; 46.2%) was characterised by a higher suicide rate and a high proportion of people self-reporting a mental health condition. This cluster was mainly in regional and remote areas with an older population, lower household income and educational attainment, and lower mental health workforce density. Cluster 2 (N = 146; 49.7%) had the lowest suicide rates and the lowest proportion of people reporting a mental health condition; these areas were predominantly metropolitan and characterised by a younger population, fewer Indigenous people, higher income and educational attainment, and higher mental health workforce density. Cluster 3 (N = 12; 4.1%) had the highest suicide rate yet the lowest proportion of people reporting a mental health condition. This final cluster was entirely regional and remote, had the highest Indigenous population proportion, lowest educational attainment, and lowest mental health workforce density. All clusters exhibited significant spatial clustering.
CONCLUSION: This study highlights geographic disparities in mental health and suicide-related profiles across Australia. Prioritising community-level interventions that reduce socioeconomic disparities and improve access to mental health care is crucial in regional and remote areas.