[Spatiotemporal distribution of etiologically positive pulmonary tuberculosis in Shaanxi Province, 2015-2023]

Abstract

Objective: To understand the spatiotemporal distribution of pulmonary tuberculosis (TB) in Shaanxi Province from 2015 to 2023, and provide reference for the prevention and control of pulmonary TB in Shaanxi. Methods: The registration data of etiologically positive pulmonary TB cases in Shaanxi from 2015 to 2023 were collected from the tuberculosis subsystem of Chinese Disease Control and Prevention Information System. Descriptive method was used to analyze the basic characteristics of the etiologically positive pulmonary TB cases. Linear trend χ² test was used to analyze trends in registration rate and pathogen positive rate. Software SPSS 25.0 was used for statistical analysis. Software ArcGIS 10.8 was used for global spatial autocorrelation and hotspot analysis to explore spatial clustering of the etiologically positive pulmonary TB cases. Software SaTScan 10.0 was used for spatiotemporal scan statistics, and software ArcGIS 10.8 was used to visualize the spatiotemporal clustering. Results: A total of 64 148 cases of etiologically positive pulmonary TB were registered in Shaanxi from 2015 to 2023, with an average annual registration rate of 18.33/100 000. The registration rate and pathgen positive rate all showed upward trends from 2015 to 2023, and the differences were significant (the trend χ²=4 555.18 and 19 330.43, both P<0.001). Global spatial autocorrelation and hotspot analysis showed that the registration rate of etiologically positive pulmonary TB in Shaanxi from 2017 to 2023 showed a spatial clustering. The hotspots were mainly in Zhenba and Xixiang counties of Hanzhong, six counties (districts) of Ankang, and Yanchuan and Yanchang counties of Yan'an. The coldspots were mainly in parts of the Guanzhong area, including Baoji, Xi'an, and Xianyang. A total of 4 spatiotemporal clustering areas were explored by spatiotemporal scanning analysis (all P<0.001), in which the first-level clustering areas covered 17 counties (districts), mainly Zhenping, Ziyang, Zhenba, in southern Shaanxi from 2019 to 2022, the second-level clustering areas covered 6 counties (districts), mainly Yanchuan, Yanchang, Qingjian, in northern Shaanxi from 2018 to 2021, the third-level clustering areas covered 14 counties (districts), mainly Yanta, Chang'an, Jingyang, in Guanzhong area from 2018 to 2019, and the fourth-level clustering areas covered 10 counties (districts) from 2019 to 2021. Conclusions: The registration rate of labortory confirmed pulmonary TB cases in Shaanxi showed an upward trend, with obvious differences in spatiotemporal clustering distribution. The clustering areas were mainly in southern Shaanxi, such as Zhenba, Zhenping, Hanbin, Langao, Pingli, Xunyang, Ziyang counties, and northern Shaanxi, such as Yanchuan and Yanchang counties, as well as in capital city, Xi'an and the adjacent Guanzhong area. It is necessary to develope targeted measures according to local conditions for the improvement of pulmonary TB prevention and control strategies in Shaanxi.