| 王薇,刘韫宁,殷鹏,王黎君,刘江美,齐金蕾,由金玲,林琳,周脉耕.不同空间权重矩阵对我国心血管疾病死亡空间自相关分析的影响[J].中华流行病学杂志,2021,42(8):1437-1444 |
| 不同空间权重矩阵对我国心血管疾病死亡空间自相关分析的影响 |
| Influences of using different spatial weight matrices in analyzing spatial autocorrelation of cardiovascular diseases mortality in China |
| 收稿日期:2020-11-02 出版日期:2021-08-19 |
| DOI：10.3760/cma.j.cn112338-20201102-01293 |
| 中文关键词: 心血管疾病 死亡 空间自相关 空间权重矩阵 空间分析 |
| 英文关键词: Cardiovascular disease Mortality Spatial autocorrelation Spatial weight matrix Spatial analysis |
| 基金项目:国家重点研发计划（2018YFC1315301） |
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| 中文摘要: |
| 目的 探索不同空间权重矩阵对我国人群心血管疾病（CVD）死亡空间自相关分析结果的影响及其适用性。方法 使用全国人口死亡信息登记管理系统死因监测数据，构建基于邻接关系的Rook矩阵、Queen矩阵，以及基于距离关系的K最近邻矩阵、距离阈值矩阵，分别进行2018年我国区县水平CVD死亡全局及局部空间自相关分析。结果 使用4类26种空间权重矩阵分析我国CVD死亡全局自相关均有统计学意义，全局Moran's I统计量在一阶Rook矩阵（0.406）、一阶Queen矩阵（0.406）、5个空间单元K最近邻矩阵（0.409）以及距离阈值100 km（0.358）时达到最大。同时，我国CVD死亡呈现局部聚集性分布，不同空间权重矩阵在探测CVD死亡"高-高""低-低""高-低""低-高"空间聚集性方面存在一定差异。结论 我国CVD死亡存在显著全局及局部自相关性。结合不同的空间权重矩阵进行综合分析，有助于深入掌握我国区县水平CVD死亡空间分布特征，为有针对性地CVD早死区域防控、合理配置资源提供依据。 |
| 英文摘要: |
| Objective To explore the potential influences and applicability of different spatial weight matrices used in analyzing spatial autocorrelation of cardiovascular disease (CVD) mortality in China. Methods Using data from the National Cause-of-death Reporting System, we used adjacency-based Rook and Queen contiguity and distance-based K nearest neighbors/distance threshold. We then conducted global and local spatial autocorrelation analysis of CVD mortality at the county level in China, 2018. Results All four categories and 26 types of spatial weight matrices had detected significant global and local spatial autocorrelation of CVD mortality in China. Global Moran's I statistics reached its peak when using first-order Rook (0.406), first-order Queen (0.406), K nearest neighbors including five spatial units (0.409), and distance threshold with 100 kilometers (0.358). Meanwhile, apparent local spatial autocorrelation was found in CVD mortality. Substantial disparities were observed when detecting "High-High clusters", "Low-Low clusters", "High-Low clusters" and "Low-High clusters" of CVD mortality spatial distribution by using different weight matrices.Conclusions Using different spatial weight matrices in analyzing the spatial autocorrelation of CVD mortality, we could understand the spatial distribution characteristics of CVD mortality in-depth at the county level in China. In this way, adequate supports could also be provided on CVD premature death control and rational medical resource allocation regionally. |
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