Spatial clustering of suicide mortality and associated community characteristics in Kanagawa prefecture, Japan, 2011-2017
Spatial clustering of suicide mortality and associated community characteristics in Kanagawa prefecture, Japan, 2011-2017
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DOI：
10.1186/s12888-020-2479-7
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发表时间：
2020-02-18
期刊：
影响因子：
4.4
通讯作者：
Tango, Toshiro
中科院分区：
文献类型：
--
作者：
Yamaoka, Kazue;Suzuki, Masako;Tango, Toshiro
背景日本的自杀死亡率很高，需要早期的干预策略来解决这个问题。准确评估当前自杀死亡率的区域状况将有助于社区干预。神奈川县毗邻东京，是日本人口第二多的县，该县的一些研究已经确定了区域层面上自杀死亡率的空间集聚。本研究利用区域自杀死亡统计的空间数据，研究自杀事件的空间聚集性和随时间的聚集性。方法数据来源于2011年至2017年日本国家生命统计的区域统计数据(神奈川县的58个地区)。以标准化死亡率(SMR)和SMR的经验贝叶斯估计量(EBSMR)为指标。空间簇用Kulldorff的圆形空间扫描统计量、Tango-Takahashi的灵活空间扫描统计量和Tango‘s检验进行检验。线性回归和条件自回归(CAR)模型不仅用于调整协变量，还用于估计区域效应。结果在男性自杀死亡中，失业(50%)与自杀有关最多，而在女性中，健康问题(50%)最常见。FlexScan、SatScan和Tango‘s检验检测到的有意义的空间簇很少，且随方法的不同而略有不同。经协变量调整后，川崎区等部分地区检测到空间聚集性。通过线性回归模型分析，所选有显著意义的变量在性别间存在差异。对于男性，在研究的几年中，检测到了失业、家庭规模和受过高等教育的比例，而对于女性，检测到了在此期间的家庭规模和离婚率。有5个协变量的CAR模型也观察到了这些变量。考虑到男性和女性的空间参数，区域效应更加明显，特别是川崎病区，多年来被检测为自杀死亡的高危区域。还指出了与自杀死亡有关的因素。这些结果将为自杀预防政策的制定提供重要信息。
BackgroundSuicide 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.MethodsData 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.ResultsAmong 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.ConclusionThe 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.