全国慢型克山病空间分布特征分析  

Spatial distribution characteristics analysis of chronic Keshan disease in China

作　　者：韩晓敏[1] 王铜[2] 郭中影 侯杰[1] 段雅妮 王亚楠[1] 周慧慧[1] 梁虹 张晓 李海蓉[3] 杨林生[3] Han Xiaomin;Wang Tong;Guo Zhongying;Hou Jie;Duan Yani;Wang Ya＇nan;Zhou Huihui;Liang Hong;Zhang Xiao;Li Hairong;Yang Linsheng(Institute of Keshan Disease, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin 150081, China （Han XM, Guo ZY, Hou J, Duan YN, Wang YN, Zhou HH, Liang H, Zhang X;Division of Health Education, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin 150081, China （Wang T;Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China （Li HR, Yang LS)

机构地区：[1]哈尔滨医科大学中国疾病预防控制中心地方病控制中心克山病防治研究所,哈尔滨150081 [2]哈尔滨医科大学健康教育处,哈尔滨150081 [3]中国科学院地理科学与资源研究所,北京100101

出　　处：《中华地方病学杂志》2018年第4期301-305,共5页Chinese Journal of Endemiology

基　　金：国家自然科学基金（81372938、81773368）

摘　　要：目的 探索全国慢型克山病空间分布聚集性及其影响因素，为克山病重点防控提供依据。方法应用非概率抽样的方法，结合病例搜索和重点调查收集2013、2014年全国慢型克山病检出率数据、病情影响因素数据，建立空间数据库，采用ArcGIS 9.0软件对全国慢型克山病检出率进行空间全局自相关（Moran＇s I）、局部自相关（Moran＇s Ii）、局部热点（Getis-Ord Gi）和反距离加权插值分析，并通过空间回归分析探索慢型克山病病情影响因素。结果 全局自相关分析Moran＇s I = 0.03，Z = 2.72，P 〈 0.01，即克山病检出率全局存在聚集性；局部Moran＇s Ii自相关结果显示，克山病检出率局部存在高检出率聚集区域，高-高聚集区主要集中在甘肃、内蒙古、山西的病区县；高-低聚集区主要位于黑龙江、吉林、山东的病区县；低-高聚集区主要位于黑龙江的病区县。Getis-Ord Gi自相关结果显示，克山病热点区域主要位于内蒙古、黑龙江、甘肃、山东、山西、云南的病区县。反距离加权插值结果显示，甘肃、内蒙古的病区县检出率较高，黑龙江、吉林、辽宁、山西、山东、陕西、云南仅有个别病区县检出率较高，其余省份的病区县检出率均处于较低水平。空间回归分析结果显示，全国慢型克山病检出率空间分布与农村人均纯收入、年平均气温呈空间负相关（Z = - 2.808、- 2.747，P均 ＜ 0.05）。结论 慢型克山病全局存在空间聚集性，局部存在聚集区域，主要位于甘肃、内蒙古的病区县，慢型克山病的空间分布可能受农村人均纯收入和年平均气温水平影响。Objective To explore the spatial distribution clustering and influencing factors of chronic Keshan disease in China, and to provide evidence for prevention and control of Keshan disease. Methods Using non-probability sampling methods, combined with case search and key surveys, data on national detection rate of chronic Keshan disease, on disease influencing factors in 2013 - 2014 were collected; a spatial database was established, and ArcGIS 9.0 software was used to perform global Moran＇s I, local Moran＇s Ii, local Getis-Ord Gi and inverse distance weighted interpolation analysis for the detection rate of national chronic Keshan disease. Spatial regression was used to analyze the influencing factors of chronic Keshan disease. Results Global autocorrelation analysis showed that Moran＇s I = 0.03, Z = 2.72, P 〈 0.01, indicating that there was aggregation in the detection rate of Keshan disease. The results of local Moran＇s Ii showed that there were local high-detection rate clusters in the wards of Keshan disease, and the high-high aggregation areas were mainly concentrated in the wards of Gansu, Inner Mongolia, and Shanxi; the high-low aggregation areas were mainly located in the wards of Heilongjiang, Jilin, Shandong; the low-high aggregation area were mainly located in the wards of Heilongjiang. Getis-Ord Gi autocorrelation results showed that Keshan disease hotspots were mainly located in the wards of Inner Mongolia, Heilongjiang, Gansu, Shandong, Shanxi and Yunnan; the results of reverse distance weighted interpolation showed that the detection rates of the counties in Gansu and Inner Mongolia were higher than that in Heilongjiang, Jilin, Liaoning, Shanxi, Shandong, Shaanxi and Yunnan, the detection rate of wards in other provinces was at a lower level. Spatial regression analysis showed that the spatial distribution of chronic Keshan disease was negatively related to rural per capita net income and annual average temperature in the ward （Z = - 2.808, - 2.747, P 〈 0.05）. Conclusions Global chron

关 键 词：克山病 空间自相关 反距离加权插值 空间回归分析 

分 类 号：R542.3[医药卫生—心血管疾病]