OBJECTIVES: The older population are at a high risk for suicide. This study sought to learn more about the characteristics of suicide in the oldest-old and to use a cluster analysis to determine if oldest-old suicide victims assort into clinically meaningful subgroups. METHODS: Data were collected from a coroner's chart review of suicide victims in Toronto from 1998 to 2011. We compared two age groups (65-79 year olds, n = 335, and 80+ year olds, n = 191) and then conducted a hierarchical agglomerative cluster analysis using Ward's method to identify distinct clusters in the 80+ group. RESULTS: The younger and older age groups differed according to marital status, living circumstances and pattern of stressors. The cluster analysis identified three distinct clusters in the 80+ group. Cluster 1 was the largest (n = 124) and included people who were either married or widowed who had significantly more depression and somewhat more medical health stressors. In contrast, cluster 2 (n = 50) comprised people who were almost all single and living alone with significantly less identified depression and slightly fewer medical health stressors. All members of cluster 3 (n = 17) lived in a retirement residence or nursing home, and this group had the highest rates of depression, dementia, other mental illness and past suicide attempts. CONCLUSIONS: This is the first study to use the cluster analysis technique to identify meaningful subgroups among suicide victims in the oldest-old. The results reveal different patterns of suicide in the older population that may be relevant for clinical care.
OBJECTIVES: The older population are at a high risk for suicide. This study sought to learn more about the characteristics of suicide in the oldest-old and to use a cluster analysis to determine if oldest-old suicide victims assort into clinically meaningful subgroups. METHODS: Data were collected from a coroner's chart review of suicide victims in Toronto from 1998 to 2011. We compared two age groups (65-79 year olds, n = 335, and 80+ year olds, n = 191) and then conducted a hierarchical agglomerative cluster analysis using Ward's method to identify distinct clusters in the 80+ group. RESULTS: The younger and older age groups differed according to marital status, living circumstances and pattern of stressors. The cluster analysis identified three distinct clusters in the 80+ group. Cluster 1 was the largest (n = 124) and included people who were either married or widowed who had significantly more depression and somewhat more medical health stressors. In contrast, cluster 2 (n = 50) comprised people who were almost all single and living alone with significantly less identified depression and slightly fewer medical health stressors. All members of cluster 3 (n = 17) lived in a retirement residence or nursing home, and this group had the highest rates of depression, dementia, other mental illness and past suicide attempts. CONCLUSIONS: This is the first study to use the cluster analysis technique to identify meaningful subgroups among suicide victims in the oldest-old. The results reveal different patterns of suicide in the older population that may be relevant for clinical care.
Authors: Marie Dorow; Janine Stein; Alexander Pabst; Siegfried Weyerer; Jochen Werle; Wolfgang Maier; Lisa Miebach; Martin Scherer; Anne Stark; Birgitt Wiese; Lilia Moor; Jens-Oliver Bock; Hans-Helmut König; Steffi G Riedel-Heller Journal: Int J Methods Psychiatr Res Date: 2017-09-25 Impact factor: 4.035
Authors: Malcolm P Forbes; Adrienne O'Neil; Melissa Lane; Bruno Agustini; Nick Myles; Michael Berk Journal: Drugs Aging Date: 2021-04-29 Impact factor: 3.923
Authors: Scott J Fitzpatrick; Bronwyn K Brew; Donna M Y Read; Kerry J Inder; Alan Hayes; David Perkins Journal: Int J Environ Res Public Health Date: 2019-08-16 Impact factor: 3.390