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 c...
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Nature Portfolio
2025-04-01
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| Online Access: | https://doi.org/10.1038/s41598-025-97064-6 |
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| author | Chawarat Rotejanaprasert Papin Thanutchapat Chiraphat Phoncharoenwirot Ornrakorn Mekchaiporn Peerut Chienwichai Richard J. Maude |
| author_facet | Chawarat Rotejanaprasert Papin Thanutchapat Chiraphat Phoncharoenwirot Ornrakorn Mekchaiporn Peerut Chienwichai Richard J. Maude |
| author_sort | Chawarat Rotejanaprasert |
| collection | DOAJ |
| description | 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 from an average of 12.97 deaths per 100,000 population in 2000 to 9.93 deaths per 100,000 in 2019. Significant regional variations were noted, particularly in Europe, Asia, and Africa, where high-risk clusters were identified. Additionally, age and sex-specific trends revealed consistently higher rates among males, although these rates have been declining over time. Spatial maps illustrated hotspots of elevated suicide rates, which can inform targeted intervention strategies. Risk factor analysis further revealed associations with socioeconomic and health indicators. The results underscore the necessity for tailored prevention strategies and highlight the importance of international collaboration and surveillance systems in addressing the complexities of global suicide epidemiology. This study contributes valuable insights into suicide patterns and offers implications for mental health policies worldwide. |
| format | Article |
| id | doaj-art-1fad185fbee7451fa015f9d2dc3e525e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-1fad185fbee7451fa015f9d2dc3e525e2025-08-20T02:17:47ZengNature PortfolioScientific Reports2045-23222025-04-0115111010.1038/s41598-025-97064-6Global spatiotemporal analysis of suicide epidemiology and risk factor associations from 2000 to 2019 using Bayesian space time hierarchical modelingChawarat Rotejanaprasert0Papin Thanutchapat1Chiraphat Phoncharoenwirot2Ornrakorn Mekchaiporn3Peerut Chienwichai4Richard J. Maude5Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol UniversityPrincess Srisavangavadhana College of Medicine, Chulabhorn Royal AcademyPrincess Srisavangavadhana College of Medicine, Chulabhorn Royal AcademyPrincess Srisavangavadhana College of Medicine, Chulabhorn Royal AcademyPrincess Srisavangavadhana College of Medicine, Chulabhorn Royal AcademyMahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol UniversityAbstract 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 from an average of 12.97 deaths per 100,000 population in 2000 to 9.93 deaths per 100,000 in 2019. Significant regional variations were noted, particularly in Europe, Asia, and Africa, where high-risk clusters were identified. Additionally, age and sex-specific trends revealed consistently higher rates among males, although these rates have been declining over time. Spatial maps illustrated hotspots of elevated suicide rates, which can inform targeted intervention strategies. Risk factor analysis further revealed associations with socioeconomic and health indicators. The results underscore the necessity for tailored prevention strategies and highlight the importance of international collaboration and surveillance systems in addressing the complexities of global suicide epidemiology. This study contributes valuable insights into suicide patterns and offers implications for mental health policies worldwide.https://doi.org/10.1038/s41598-025-97064-6SuicideGlobalSpatiotemporalBayesianMental healthHealth policy |
| spellingShingle | Chawarat Rotejanaprasert Papin Thanutchapat Chiraphat Phoncharoenwirot Ornrakorn Mekchaiporn Peerut Chienwichai Richard J. Maude Global spatiotemporal analysis of suicide epidemiology and risk factor associations from 2000 to 2019 using Bayesian space time hierarchical modeling Scientific Reports Suicide Global Spatiotemporal Bayesian Mental health Health policy |
| title | Global spatiotemporal analysis of suicide epidemiology and risk factor associations from 2000 to 2019 using Bayesian space time hierarchical modeling |
| title_full | Global spatiotemporal analysis of suicide epidemiology and risk factor associations from 2000 to 2019 using Bayesian space time hierarchical modeling |
| title_fullStr | Global spatiotemporal analysis of suicide epidemiology and risk factor associations from 2000 to 2019 using Bayesian space time hierarchical modeling |
| title_full_unstemmed | Global spatiotemporal analysis of suicide epidemiology and risk factor associations from 2000 to 2019 using Bayesian space time hierarchical modeling |
| title_short | Global spatiotemporal analysis of suicide epidemiology and risk factor associations from 2000 to 2019 using Bayesian space time hierarchical modeling |
| title_sort | global spatiotemporal analysis of suicide epidemiology and risk factor associations from 2000 to 2019 using bayesian space time hierarchical modeling |
| topic | Suicide Global Spatiotemporal Bayesian Mental health Health policy |
| url | https://doi.org/10.1038/s41598-025-97064-6 |
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