Faith, policy, and suicide: A Machine learning and spatial analysis approach of religious affiliation and suicide rates in Toronto

Abstract

This research employs spatial analysis to investigate the intricate relationship between religious affiliations and suicide rates in the Greater Golden Horseshoe region of southern Ontario, Canada. As the seventh-leading cause of death in Canada, suicide necessitates targeted prevention strategies, especially in areas with lower suicide rates such as Ontario. To explore spatial patterns of suicide attempts and intentional self-inflicted injuries, individual-level data is collected and generalized to the census tract for rigorous spatial analyses.

Self-Organizing Maps are utilized to build distinct regional clusters based on shared characteristics. These clusters unveil diverse combinations of suicide counts, religious affiliations, income levels, education levels, and immigrant populations, providing comprehensive insights into the multicultural composition of the area.

The study findings reveal specific spatial patterns of suicide attempts associated with particular religious affiliations, shedding new light on the influence of faith and spirituality on mental health outcomes within the context of suicide prevention. The analysis underscores the importance of a nuanced understanding of these factors to guide effective suicide prevention interventions.

Considering the spatial distribution of religious affiliations and suicide rates, this study offers valuable guidance for targeted and culturally sensitive suicide prevention efforts in the Greater Golden Horseshoe region and other similar urban landscapes. The implications of this research extend to the broader mental health field and emphasize the significance of accounting for sociocultural dimensions in devising evidence-based interventions to mitigate suicide effectively.