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[Spatial analysis of echinococcosis in pastoral area of Qinghai province, 2019].

作者信息

Zhang T T, Ma X, Lei W, Liu Y Y, Li B, Ma B C, Liu S

机构信息

Department of Public Health, Faculty of Medicine, Qinghai University, Xining 810001, China.

Qinghai Institute for Endemic Disease Prevention and Control, Xining 810000, China.

出版信息

Zhonghua Liu Xing Bing Xue Za Zhi. 2022 May 10;43(5):709-715. doi: 10.3760/cma.j.cn112338-20211210-00966.

Abstract

To understand the spatial characteristics of echinococcosis and associated factors in the pastoral area of Qinghai province, and provide evidence for the effective prevention and control of echinococcosis. The number of echinococcosis cases in the pastoral areas of Qinghai in 2019 was collected to perform spatial epidemiological analysis. The thematic map of the distribution of echinococcosis cases was generated with software ArcGIS 10.8 for visual analysis and spatial autocorrelation analysis. The spatial autocorrelation and spatial scanning analysis were performed to estimate the clustering of echinococcosis with software SaTScan 9.5. Software GeoDa 1.14 and ArcGIS 10.8 were used to establish spatial lag model and geographical weighted regression model to analyze the related factors of echinococcosis epidemic. In 2019, the echinococcosis surveillance covered 64 741 people in the pastoral area of Qinghai, and 829 echinococcosis cases were found, with a prevalence rate of 1.28%. The distribution of the cases had spatial correlation (Moran's =0.41, <0.001). The most possible clustering areas indicated by spatial scanning analysis included Banma, Jiuzhi, Dari and Gande counties of Guoluo Tibetan Autonomous Prefecture (=460.77, =9.20, <0.001). The prevalence of echinococcosis in the pastoral areas was positively associated with the total annual precipitation (=0.13, =0.036), and negatively associated with population density (=-1.36, =0.019) and doctors/nurse ratio (=-25.60, =0.026). The distribution of echinococcosis cases in the pastoral areas of Qinghai in 2019 had spatial correlation, and the prevalence was affected by total annual precipitation, population density, and doctors/nurse ratio.

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