Abstract:

Objective: To analyze the spatial clustering characteristics of tuberculosis among students in Hu’nan Province from 2011 to 2020, and to provide a reference for the prevention and control of tuberculosis in schools. Methods: Medical record data of student tuberculosis patients registered from January 1, 2011 to December 31, 2020 was download from the “Tuberculosis Management Information System” which was an subsystem of the “Chinese Disease Prevention and Control Information System”. There were 17397 cases of student tuberculosis patients, and the student population data during the same period was obtained from the “Hu’nan Statistical Yearbook”. ArcGIS 10.7 was used for global and local spatial autocorrelation analysis, and SaTScan 9.5 was used for spatiotemporal aggregation analysis, and results were visualized with ArcGIS 10.7. Results: From 2011 to 2020, the registered incidence rate of tuberculosis among students in Hu’nan Province showed a downward-up-down trend. The average annual registered incidence rate was 14.04/100000 (17397/123882600). From 2011 to 2020, the registered incidence rate were 13.67/100000 (1581/11561800), 12.00/100000 (1375/11454400), and 11.92/100000 (1374/11525900), 10.74/100000 (1269/11813300), 10.16/100000 (1238/12181200), 9.22/100000 (1154/12512600), 12.23/100000 (1566/12808700), 21.97/100000 (2864/13037100), 18.97/100000 (2532/13344,800), 17.91/100000 (2444/13642800) respectively. From 2011 to 2020, the results of the global autocorrelation analysis of the registration incidence of tuberculosis among students in Hu’nan Province showed that there was spatial clustering for total (Moran I=0.316, P<0.05), and spatial clustering in 2013 (Moran I=0.379, P=0.045),2015 (Moran I=0.353,P=0.041),2019 (Moran I=0.472, P=0.007) and 2020 (Moran I=0.434, P=0.014). The results of the local spatial autocorrelation analysis showed three aggregation modes (high-high, low-low, low-high), and the high-value clustering area (high-high clustering) mainly concentrated in Zhangjiajie City. The results of spatio-temporal scanning proved spatio-temporal clustering existed with 2 spatio-temporal clustering areas. The first-level clustering area was the northwestern region of Hu’nan Province, including Zhangjiajie, Xiangxi and Changde City. The clustering time was during 2017—2020, LLR=622.22, RR=2.53, P<0.001. The secondary clustering area included 5 cities, namely Chenzhou, Hengyang, Yongzhou, Shaoyang and Zhuzhou. Conclusion: From 2011 to 2020, the registered incidence of tuberculosis among students was clustered in time and space at the city or state level. The “high-high” clustering area was mainly in the northwest of Hu’nan Province, with Zhangjiajie City as the center, and radiating to the surrounding cities (Xiangxi and Changde).

Key words: Tuberculosis, Students, Space-time clustering