[Spatiotemporal distribution of newly diagnosed echinococcosis patients in Qinghai Province from 2016 to 2022].

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 spacetime 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.