[Spatial distribution characteristics of tuberculosis and its visualization in Qinghai province, 2014-2016].

ABSTRACT

Objective: To analyze the spatial distribution of tuberculosis (TB) and identify the clustering areas in Qinghai province from 2014 to 2016, and provide evidence for the prevention and control of TB. Methods: The data of pulmonary TB cases confirmed by clinical and laboratory diagnosis in Qinghai during this period were collected from National Disease Reporting Information System. The visualization of annual reported incidence, three-dimensional trend analysis and local Getis-Ord G(i)(*) spatial autocorrelation analysis of TB were performed by using software ArcGIS 10.2.2, and global Moran's I spatial autocorrelation analysis were analyzed by using software OpenGeoDa 1.2.0 to describe and analyze the spatial distribution characteristics and high incidence areas of TB in Qinghai from 2014 to 2016. Results: A total of 20 609 pulmonary TB cases were reported in Qinghai during this period. The reported incidences were 101.16/100 000, 123.26/100 000 and 128.70/100 000 respectively, an increasing trend with year was observed (trend χ(2)=187.21, P<0.001). The three-dimensional trend analysis showed that the TB incidence increased from northern area to southern area, and up-arch trend from the east to the west. Global Moran's I spatial autocorrelation analysis showed that annual reported TB incidence in different areas had moderate spatial clustering (Moran's I values were 0.631 3, 0.605 4, and 0.587 3, P<0.001). And local G(i)(*) analysis showed that there were some areas with high TB incidences, such as 10 counties of Yushu and Guoluo prefectures (Gande, Banma and Dari counties, etc., located in the southwest of Qinghai), and some areas with low TB incidences, such as Huangzhong county, Chengdong district and Chengbei district of Xining city and Dachaidan county of Haixi prefecture, and the reported TB incidences in the remaining areas were moderate. Conclusion: The annual reported TB incidence increased year by year in Qinghai from 2014 to 2016. The distribution of TB cases showed obvious spatial clustering, and Yushu and Guoluo prefectures were the key areas in TB prevention and control. In addition, the spatial clustering analysis could provide the important evidence for the development of TB prevention and control measures in Qinghai.