Global spatiotemporal analysis of suicide epidemiology and risk factor associations from 2000 to 2019 using Bayesian space time hierarchical modeling.

ABSTRACT: Suicide is a significant global public health issue, with marked disparities in rates between countries. Much of the existing research has concentrated on high-income nations, creating a gap in the understanding of global suicide epidemiology. This study aims to address this gap through a comprehensive spatiotemporal analysis of global suicide trends from 2000 to 2019. Data were collected from the Global Health Observatory, encompassing 183 countries across five regions. Bayesian spatiotemporal modeling and cluster detection techniques were employed to assess variations in suicide rates and identify high-risk clusters, alongside examining associations with various risk factors. The findings indicate diverse global and regional age-standardized suicide trends, with overall rates decreasing

SUBMITTER: Rotejanaprasert C 

PROVIDER: S-EPMC11997172 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature