| 张甜甜,马霄,雷雯,刘玉英,李斌,马冰村,刘寿.2019年青海省牧业区棘球蚴病时空分布特征[J].Chinese journal of Epidemiology,2022,43(5):709-715 |
| 2019年青海省牧业区棘球蚴病时空分布特征 |
| Spatial analysis of echinococcosis in pastoral area of Qinghai province, 2019 |
| Received:December 10, 2021 |
| DOI：10.3760/cma.j.cn112338-20211210-00966 |
| KeyWord: 棘球蚴病 空间特征 空间滞后模型 地理加权回归模型 |
| English Key Word: Echinococcosis Spatial characteristics Spatial lag model Geographical weighted regression |
| FundProject:国家自然科学基金(81860606);青海省自然科学基金(2019-ZJ-906) |
| Author Name | Affiliation | E-mail | | Zhang Tiantian | Department of Public Health, Faculty of Medicine, Qinghai University, Xining 810001, China | | | Ma Xiao | Qinghai Institute for Endemic Disease Prevention and Control, Xining 810000, China | | | Lei Wen | Qinghai Institute for Endemic Disease Prevention and Control, Xining 810000, China | | | Liu Yuying | Department of Public Health, Faculty of Medicine, Qinghai University, Xining 810001, China | | | Li Bin | Department of Public Health, Faculty of Medicine, Qinghai University, Xining 810001, China | | | Ma Bingcun | Department of Public Health, Faculty of Medicine, Qinghai University, Xining 810001, China | | | Liu Shou | Department of Public Health, Faculty of Medicine, Qinghai University, Xining 810001, China | liushou2004@aliyun.com |
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| Abstract: |
| 目的 分析青海省牧业区棘球蚴病空间分布特征及相关因素，为有效防控棘球蚴病提供参考。方法 收集2019年青海省牧业区棘球蚴病监测点数据，采用ArcGIS 10.8软件绘制棘球蚴病例分布地图进行可视化分析和空间自相关分析；采用SaTScan 9.5软件进行空间扫描和棘球蚴病例的聚集性分析；采用GeoDa 1.14软件和ArcGIS 10.8软件建立空间滞后模型和地理加权回归模型，分析棘球蚴病流行的相关因素。结果 2019年青海省牧业区棘球蚴病监测点共监测64 741人，病例829例，患病率为1.28%，病例分布具有空间相关性（Moran'sI=0.41，P<0.001）。空间扫描分析一类聚集区有果洛藏族自治州班玛县、久治县、达日县和甘德县（LLR=460.77，RR=9.20，P<0.001）。牧业区棘球蚴病的流行与年降水总量呈正相关（β=0.13，P=0.036），与人口密度（β=-1.36，P=0.019）和医护比（β=-25.60，P=0.026）呈负相关。结论 青海省牧业区棘球蚴病例空间聚集特征明显，年降水总量增加，其流行风险增加，人口密度和医护比增加，其流行风险降低。 |
| English Abstract: |
| 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). Conclusion 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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