Temporal-spatial distribution of tuberculosis in China, 2004-2016
10.3760/cma.j.cn112338-20190614-00441
- VernacularTitle:2004-2016年我国结核病流行的时空特征分析
- Author:
Yesheng WANG
1
;
Jianmei WANG
;
Weibing WANG
Author Information
1. 复旦大学公共卫生学院流行病学教研室 公共卫生安全教育部重点实验室，上海 200032
- Keywords:
Tuberculosis;
Geographic information system;
Spatial autocorrelation analysis;
Spatial-temporal analysis
- From:
Chinese Journal of Epidemiology
2020;41(4):526-531
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the spatial distribution pattern and the cluster spots of tuberculosis (TB) patients in China from 2004 to 2016, so as to provide evidence for prevention and control of the disease.Methods:Using ArcGIS 10.0 software as a platform for data management and presentation, a TB spatial analysis database from 2004 to 2016 was established, and spatial autocorrelation analysis was performed based on the TB epidemics. SaTScan 9.6 software was used for spatiotemporal scanning analysis.Results:From 2004 to 2016, a total of 13 157 794 cases of pulmonary tuberculosis were registered in China, with the mean annual registered incidence rate as 75.90/100 000 (range: 27.95/100 000-180.82/100 000). Through Global spatial autocorrelation studies, the results showed that the distribution of TB incidence was somehow clustered. The result of local Moran’s I autocorrelation analysis showed that Xinjiang, Tibet, Guizhou, Guangxi, Hainan provinces were high-high cluster areas, and Beijing, Hebei, Tianjin, Shandong, Jiangsu, and Shanghai provinces were low-low cluster areas. Result from the Getis-Ord General G spatial autocorrelation analysis showed the existence of fifteen "hot spot" regions, of which three "positive hot spots" were Xinjiang, Tibet, and Hainan provinces, and twelve "negative hot spots" were Beijing, Tianjin, Liaoning, Inner Mongolia, Hebei, Shandong, Jiangsu, Anhui, Shanghai, Shanxi, Henan, Jilin provinces. Using the SaTScan 9.6 software, results from the Phased spatial-temporal analysis identified twelve cluster areas, with statistical significances ( P<0.05) among them. Conclusions:From 2004 to 2016, tuberculosis epidemics showed an annual downward trend in China. The average annual rates of notification among provinces were not randomly distributed, showing the existence of obvious spatial aggregation. Numbers of areas with clustering nature that noticed through the temporal and spatial scanning technics had gradually decreased. At the same time, progress had been made in TB control programs, despite the existence of high-risk areas. Development of more strict and targeted prevention and control measures are called for.
