Abstract:

Objective　To investigate the spatiotemporal distribution characteristics and potential influencing factors of newly diagnosed echinococcosis cases in Qinghai Province from 2016 to 2022, so as to provide insights into the formulation of the echinococcosis control strategy in Qinghai Province. Methods　The number of individuals screened for echinococcosis, number of newly diagnosed echinococcosis cases, number of registered dogs and number of stray dogs were captured from the annual reports of echinococcosis control program in Qinghai Province from 2016 to 2022, and the detection of newly diagnosed echinococcosis cases was calculated. The number of populations, precipitation, temperature, wind speed, sunshine hours, average altitude, number of year⁃end cattle stock, number of year⁃end sheep stock, gross domestic product (GDP) per capita, and number of village health centers in each county (district) of Qinghai Province were captured from the Qinghai Provincial Statistical Yearbook, and county⁃level electronic maps in Qinghai Province were downloaded from the National Platform for Common Geospatial Information Services. The software ArcGIS 10.8 was used to map the distribution of newly diagnosed echinococcosis cases in Qinghai Province, and the spatial autocorrelation analysis of newly diagnosed echinococcosis cases was performed. In addition, the space⁃time scan analyses of number of individuals screened for echinococcosis, number of newly diagnosed echinococcosis cases and geographical coordinates in Qinghai Province were performed with the software SaTScan 10.1.2, and the spatial stratified heterogeneity of the detection of newly diagnosed echinococcosis cases was investigated with the software GeoDetector. Results　A total of 6 569 426 residents were screened for echinococcosis in Qinghai Province from 2016 to 2022, and 5 924 newly diagnosed echinococcosis cases were found. The detection of newly diagnosed echinococcosis cases appeared a tendency towards a decline over years from 2016 to 2022 ([χ2] = 11.107, P < 0.01), with the highest detection in Guoluo Tibetan Autonomous Prefecture in 2017 (82.12/105). There were spatial clusters in the detection of newly diagnosed echinococcosis cases in Qinghai Province from 2016 to 2018 (Moran's I = 0.34 to 0.65, all Z values > 1.96, all P values < 0.05), and the distribution of newly diagnosed echinococcosis cases appeared random distribution from 2019 to 2022 (Moran's I = -0.09 to 0.04, all Z values < 1.96, all P values > 0.05). Local spatial autocorrelation analysis showed high⁃high clusters and low⁃low clusters in the detection of new diagnosed echinococcosis cases in Qinghai Province from 2016 to 2022, and space⁃time scan analysis showed that the first most likely cluster areas of newly diagnosed echinococcosis cases in Qinghai Province from 2016 to 2022 were mainly distributed in Yushu Tibetan Autonomous Prefecture and Guoluo Tibetan Autonomous Prefecture. GeoDetector⁃based analysis of the driving factors for the spatial stratified heterogeneity of detection of newly diagnosed echinococcosis cases in Qinghai Province showed that average altitude, number of village health centers, number of cattle and sheep stock, GDP per capita, annual average sunshine hours, and annual average temperature had a strong explanatory power for the spatial distribution of newly diagnosed echinococcosis cases, with q values of 0.630, 0.610, 0.600, 0.590, 0.588, 0.537 and 0.526, respectively. Conclusions　The detection of newly diagnosed echinococcosis cases appeared a tendency towards a decline in Qinghai Province over years from 2016 to 2022, showing spatial clustering. Targeted control measures are required in cluster areas of newly diagnosed echinococcosis cases for further control of the disease. 

Key words: Echinococcosis, Newly diagnosed case, Spatial autocorrelation, Spatial clustering, Spatial stratified heterogeneity, Qinghai Province 

摘要： 目的　分析2016—2022年青海省新发现棘球蚴病病例时空分布特征及潜在影响因素，为制定青海省棘球蚴病防治策略提供参考依据。方法　自2016—2022年青海省棘球蚴病防治项目年报表获取棘球蚴病筛查人数、新发现棘球蚴病病例数、登记管理犬数、流浪犬数等数据，计算新发现棘球蚴病病例检出率。检索《青海省统计年鉴》，获取青海省各县（市、区）人口数、降水量、温度、风速、日照时数、平均海拔、年末牛存栏数、年末羊存栏数、人均国内生产总值（gross domestic product，GDP）、村卫生室数等数据。于国家地理信息公共服务平台下载青海省县级电子地图，采用ArcGIS 10.8软件绘制青海省新发现棘球蚴病病例分布电子地图，并进行空间自相关分析。采用SaTScan 10.1.2软件对青海省棘球蚴病筛查人数、新发现棘球蚴病病例数、地理坐标进行时空扫描分析，采用地理探测器（GeoDetector）分析新发现棘球蚴病病例检出率空间分层异质性。结果　2016—2022年，青海省累计筛查棘球蚴病6 569 426人，新发现棘球蚴病病例5 924例，各年新发现棘球蚴病病例检出率呈逐年下降趋势（[χ2] = 11.107，P < 0.01），其中2017年果洛藏族自治州新发现棘球蚴病病例检出率最高（82.12/10万）。2016—2018年，青海省新发现棘球蚴病病例检出率存在空间聚集性（Moran's I = 0.34 ~ 0.65，Z均> 1.96，P均< 0.05）；2019—2022年呈随机分布（Moran's I = -0.09 ~ 0.04，Z均< 1.96，P均> 0.05）。局部空间自相关分析发现，2016—2022年青海省新发现棘球蚴病病例检出率“高⁃高”聚集区和“低⁃低”聚集区均呈聚集趋势。时空扫描分析发现，2016—2022年青海省新发现棘球蚴病病例一级聚集区主要分布于玉树藏族自治州和果洛藏族自治州。基于地理探测器的青海省新发现棘球蚴病病例检出率空间分层异质性驱动因子分析发现，平均海拔、村卫生室数、牛羊存栏数、人均GDP、年均日照时数和年均温度对其空间分布的解释力较强，q值分别为0.630、0.610、0.600、0.590、0.588、0.537和0.526。结论　2016—2022年青海省新发现棘球蚴病病例检出率逐年下降，呈一定空间聚集性分布。应在青海省新发现棘球蚴病病例聚集区开展针对性防控，以进一步控制该病流行。

关键词: 棘球蚴病, 新发现病例, 空间自相关, 空间聚集性, 空间分层异质性, 青海省