Spatial clustering of suicide mortality and associated community characteristics in Kanagawa prefecture, Japan, 2011–2017

Abstract

Background: Suicide mortality is high in Japan and early interventional strategies to solve that problem are needed. An accurate evaluation of the regional status of current suicide mortality would be useful for community interventions. A few studies in Kanagawa prefecture, located next to Tokyo and with the second largest population in Japan, have identified spatial clusters of suicide mortality at regional levels. This study examined spatial clustering and clustering over time of such events using spatial data from regional statistics on suicide deaths. Methods: Data were obtained from regional statistics (58 regions in Kanagawa prefecture) of the National Vital Statistics of Japan from 2011 to 2017. The standardized mortality ratio (SMR) and Empirical Bayes estimator for the SMR (EBSMR) were used as measures. Spatial clusters were examined by Kulldorff's circular spatial scan statistic, Tango-Takahashi's flexible spatial scan statistic and Tango's test. Linear regression and conditional autoregressive (CAR) models were used not only to adjust for covariates but also to estimate regional effects. The analyses were conducted for each year, inclusive. Results: Among male suicide deaths, being unemployed (50%) was most frequently related to suicide while among female health problem (50%) were frequent. Spatial clusters with significance detected by FlexScan, SatScan and Tango's test were few and varied somewhat according to the method used. Spatial clusters were detected in some regions including Kawasaki ward after adjustment by covariates. By the linear regression models, selected variables with significance were different between the sexes. For males, unemployment, family size, and proportion of higher education were detected for several of the years studied while for females, family size and divorce rate were detected over this period. These variables were also observed by the CAR model with 5 covariates. Regional effects were much clearer by considering the spatial parameter for both males and females and especially, Kawasaki ward was detected as a high risk region in many years. Conclusion: The present results detected some spatial clustering of suicide deaths within certain regions. Factors related to suicide deaths were also indicated. These results would provide important information in policy making for suicide prevention.

The Relationship between the Prevalence of Suicidal Ideation among Older Adults and Individual · Regional Factors

“…In addition, the results of this study are thought to be consistent with a study in Taiwan which found that a higher prevalence of suicidal ideation was associated with the age of 85 years and over, a lower education level, living alone, unemployment, the lack of income, the presence of a disability, poor or very poor perceived health status, and the presence of depression, physical symptoms (heart disease, diabetes, asthma, and osteoporosis), or pain (arthritis, low back pain, sciatica, neck pain, and headache) [25]. Furthermore, the results of this study are also thought to be in line with the findings of previous studies in Japan which reported that factors such as unemployment, the number of family members and a high educational level were factors affecting the suicide rate among men, and factors such as the number of family members and divorce were influencing factors for the suicide rate among women [20].…”

Geographical clusters and social risk factors for suicide in the city of São Paulo, 2006–2015: An ecologic study

“…Several studies have used this procedure to detect spatial clusters of suicide worldwide (Bando, Moreira, et al, 2012; P. Jones et al, 2013; Ngamini Ngui et al, 2014; Yamaoka et al, 2020).…”

Estudo espaço-temporal do suicídio: uma pesquisa bibliográfica sobre técnicas

“…O suicídio é considerado um evento raro, assim o número de eventos ocorridos pode não ser estatisticamente significativo, o que pode gerar uma taxa com alta variância espacial, devido a flutuações nos tamanhos das populações. Para mitigar essa instabilidade, há pesquisadores que realizam a suavização espacial feita por modelos bayesianos na taxa de suicídio [Fontanella et al 2018] [Yamaoka et al 2020.…”

“…Isso é realizado através do agrupamento espacial. Foram mapeadas quatro técnicas utilizadas: modelo de Kulldorff (2005) [Bando et al 2012] [Saman et al 2012] [Fontanella et al 2018] [Sy et al 2019, teste de Moran Local, modelo de varredura espacial flexível de Tango-Takahashi e teste de Tango [Yamaoka et al 2020].…”

“…Técnicas de regressão são comumente utilizadas na análise multivariada. Utilizase desde a regressão logística [Bando et al 2012] [Fontanella et al 2018] até a comparação entre diversas técnicas [Yamaoka et al 2020]. Modelos de regressão espacial têm sido utilizados quando os resíduos do modelo OLS apresentam dependência espacial, o que viola a premissa de independência deste modelo.…”