Spatiotemporal characteristics of hemorrhagic fever with renal syndrome in China from 2004 to 2020 / 中华地方病学杂志

ABSTRACT

Objective:To analyze the spatiotemporal characteristics and spatial aggregation of the incidence of hemorrhagic fever with renal syndrome (HFRS) in China from 2004 to 2020, and to provide a scientific basis for prevention and control of HFRS.Methods:The epidemic information of HFRS in China from 2004 to 2020 was collected from the Public Health Science Data Center, the China Health Statistics Yearbook, and the National Statutory Infectious Disease Epidemic Profile Report. The Joinpoint model was used to analyze the annual average incidence rate change trend, ArcGIS 10.5 software was used for spatial visualization analysis, and global spatial autocorrelation, local spatial autocorrelation and spatiotemporal scan analysis were applied to detect hot spots and aggregation areas.Results:From 2004 to 2020, a total of 208 441 cases of HFRS were reported in China, with an average annual incidence rate of 0.91/100 000. Joinpoint model analysis showed that the average annual incidence rate of HFRS in China showed a decreasing trend from 2004 to 2020. In the provinces with high incidence, the disease was mostly distributed with multimodal distribution in spring, autumn and winter, especially in autumn and winter. The results of global spatial autocorrelation analysis showed that the global Moran's I of HFRS incidence rate in China from 2004 to 2019 were all positive. Except 2012 and 2020, the random distribution pattern was not excluded, other years showed spatial clustering ( Z > 1.65, P < 0.05). The results of phased local spatial autocorrelation analysis indicated that Heilongjiang, Jilin and Liaoning provinces were high-high aggregation regions. A total of five aggregation regions were detected in the month-by-month spatiotemporal scan analysis, and the differences of each aggregation region were statistically significant ( P < 0.001). Conclusions:From 2004 to 2020, the overall incidence of HFRS in China shows a downward trend, and the incidence rate has obvious spatial aggregation. High-risk areas still exist, and it is necessary to focus on and take targeted prevention and control measures.