Department of Public Health and Preventive Medicine, Changzhi Medical College, Changzhi 046000, China.
State Key Laboratory for Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Sci Total Environ. 2021 Jul 10;777:146145. doi: 10.1016/j.scitotenv.2021.146145. Epub 2021 Feb 28.
To analyze the spatiotemporal dynamic distribution and detect the related meteorological factors of scarlet fever from an ecological perspective, which could provide scientific information for effective prevention and control of this disease.
The data on scarlet fever cases in mainland China were downloaded from the Data Center of the China Public Health Science, while monthly meteorological data were extracted from the official website of the National Bureau of Statistics. Global Moran's I, local Getis-Ord G hotspot statistics, and Kulldorff's retrospective space-time scan statistical analysis were used to detect the spatial and spatiotemporal clusters of scarlet fever across all settings. A spatial panel data model was conducted to estimate the impact of meteorological factors on scarlet fever incidence.
Scarlet fever in China had obvious spatial, temporal, and spatiotemporal clustering, high-incidence spatial clusters were located mainly in the north and northeast of China. Nine spatiotemporal clusters were identified. A spatial lag fixed effects panel data model was the best fit for regression analysis. After adjusting for spatial individual effects and spatial autocorrelation (ρ = 0.5623), scarlet fever incidence was positively associated with a one-month lag of average temperature, precipitation, and total sunshine hours (all P-values < 0.05). Each 10 °C, 2 cm, and 10 h increase in temperature, precipitation, and sunshine hours, respectively, was associated with a 6.41% increment and 1.04% and 1.41% decrement in scarlet fever incidence, respectively.
The incidence of scarlet fever in China showed an upward trend in recent years. It had obvious spatiotemporal clustering, with the high-risk areas mainly concentrated in the north and northeast of China. Areas with high temperature and with low precipitation and sunshine hours tended to have a higher scarlet fever incidence, and we should pay more attention to prevention and control in these places.
从生态角度分析猩红热的时空动态分布，并探测相关气象因素，为该病的有效防控提供科学信息。
从中国公共卫生科学数据中心下载中国大陆猩红热病例数据，从国家统计局官方网站提取逐月气象数据。采用全局 Moran's I 指数、局部 Getis-Ord G 热点统计量和 Kulldorff 回顾性时空扫描统计分析方法，检测猩红热全人群时空聚集性。采用空间面板数据模型估计气象因素对猩红热发病率的影响。
中国猩红热存在明显的时空聚集性，高发空间聚集区主要位于中国北方和东北地区。共发现 9 个时空聚集区。空间滞后固定效应面板数据模型是回归分析的最佳拟合模型。在调整空间个体效应和空间自相关（ρ=0.5623）后，猩红热发病率与平均气温、降水量和总日照时数的滞后 1 个月呈正相关（均 P 值<0.05）。气温、降水量和日照时数每增加 10°C、2 cm 和 10 h，猩红热发病率分别增加 6.41%、1.04%和 1.41%。
近年来中国猩红热发病率呈上升趋势，具有明显的时空聚集性，高风险地区主要集中在中国北方和东北地区。高温、低降水和低日照时数地区猩红热发病率较高，应重点关注这些地区的防控工作。
