[Spatial analysis of echinococcosis in pastoral area of Qinghai province, 2019]. 2022

T T Zhang, and X Ma, and W Lei, and Y Y Liu, and B Li, and B C Ma, and S Liu
Department of Public Health, Faculty of Medicine, Qinghai University, Xining 810001, China.

Objective: 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. Methods: 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. Results: 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 I=0.41, P<0.001). The most possible clustering areas indicated by spatial scanning analysis included Banma, Jiuzhi, Dari and Gande counties of Guoluo Tibetan Autonomous Prefecture (LLR=460.77, RR=9.20, P<0.001). The prevalence of echinococcosis in the pastoral areas was positively associated with the total annual precipitation (β=0.13, P=0.036), and negatively associated with population density (β=-1.36, P=0.019) and doctors/nurse ratio (β=-25.60, P=0.026). Conclusions: 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.

UI MeSH Term Description Entries
D002681 China A country spanning from central Asia to the Pacific Ocean. Inner Mongolia,Manchuria,People's Republic of China,Sinkiang,Mainland China
D004443 Echinococcosis An infection caused by the infestation of the larval form of tapeworms of the genus Echinococcus. The liver, lungs, and kidney are the most common areas of infestation. Cystic Echinococcosis,Cysts, Hydatid,Hydatid Cyst,Hydatidosis,Echinococcus Granulosus Infection,Echinococcus Infection,Hydatid Disease,Cyst, Hydatid,Cystic Echinococcoses,Echinococcoses,Echinococcoses, Cystic,Echinococcosis, Cystic,Echinococcus Granulosus Infections,Echinococcus Infections,Granulosus Infection, Echinococcus,Granulosus Infections, Echinococcus,Hydatid Cysts,Hydatid Diseases,Hydatidoses,Infection, Echinococcus,Infection, Echinococcus Granulosus,Infections, Echinococcus Granulosus
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D015995 Prevalence The total number of cases of a given disease in a specified population at a designated time. It is differentiated from INCIDENCE, which refers to the number of new cases in the population at a given time. Period Prevalence,Point Prevalence,Period Prevalences,Point Prevalences,Prevalence, Period,Prevalence, Point,Prevalences
D016000 Cluster Analysis A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both. Clustering,Analyses, Cluster,Analysis, Cluster,Cluster Analyses,Clusterings
D058872 Epidemics Sudden outbreaks of a disease in a country or region not previously recognized in that area, or a rapid increase in the number of new cases of a previous existing endemic disease. Epidemics can also refer to outbreaks of disease in animal or plant populations.
D062206 Spatial Analysis Investigative techniques which measure the topological, geometric, and or geographic properties of the entities studied. Kernel Density Estimation,Kriging,Spacial Analysis,Spatial Autocorrelation,Spatial Dependency,Spatial Interpolation,Analyses, Spacial,Analyses, Spatial,Analysis, Spacial,Analysis, Spatial,Autocorrelation, Spatial,Autocorrelations, Spatial,Density Estimation, Kernel,Density Estimations, Kernel,Dependencies, Spatial,Dependency, Spatial,Estimation, Kernel Density,Estimations, Kernel Density,Interpolation, Spatial,Interpolations, Spatial,Kernel Density Estimations,Krigings,Spacial Analyses,Spatial Analyses,Spatial Autocorrelations,Spatial Dependencies,Spatial Interpolations

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