Wang W, Liu Y N, Yin P, Wang L J, Liu J M, Qi J L, You J L, Lin L, Zhou M G
National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2021 Aug 10;42(8):1437-1444. doi: 10.3760/cma.j.cn112338-20201102-01293.
To explore the potential influences and applicability of different spatial weight matrices used in analyzing spatial autocorrelation of cardiovascular disease (CVD) mortality in China. 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. 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 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. 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.
为探讨不同空间权重矩阵在分析中国心血管疾病（CVD）死亡率空间自相关中的潜在影响和适用性。利用国家死因报告系统的数据，我们使用了基于邻接的Rook和Queen邻接关系以及基于距离的K近邻/距离阈值。然后，我们对2018年中国县级CVD死亡率进行了全局和局部空间自相关分析。所有四类共26种空间权重矩阵均检测到中国CVD死亡率存在显著的全局和局部空间自相关。当使用一阶Rook（0.406）、一阶Queen（0.406）、包含五个空间单元的K近邻（0.409）和100公里距离阈值（0.358）时，全局Moran's统计量达到峰值。同时，在CVD死亡率中发现了明显的局部空间自相关。在使用不同权重矩阵检测CVD死亡率空间分布的“高-高聚类”、“低-低聚类”、“高-低聚类”和“低-高聚类”时，观察到了显著差异。在分析CVD死亡率的空间自相关时使用不同的空间权重矩阵，我们可以深入了解中国县级CVD死亡率的空间分布特征。通过这种方式，还可以为区域内CVD过早死亡控制和合理的医疗资源分配提供充分支持。
