Abstract:

Objective To analyze the spatial-temporal characteristics of tuberculosis (TB) affected students at the township level in Guangzhou from 2014 to 2018 and provide evidence for the prevention and control of TB in schools. Methods The information of TB patients who were students was collected by “China Disease Prevention and Control Information System and Infective Diseases Management Information System”. We obtained data of 2755 cases in Guangzhou City with date of onset from January 1, 2014 to December 31, 2018. Global and local spatial auto-correlation analysis and Kulldorff’s Scan Statistics were applied to map the spatial distribution and detect the space-time cluster of those cases during 2014-2018. Result The TB incidence rates among students in Guangzhou City from 2014 to 2018 were 17.14/100000, 16.42/100000, 15.64/100000, 16.57/100000 and 17.22/100000 respectively which presented a trend of decreasing at the beginning and then rising. There were two peaks of monthly incidence during the study period: March to April and September in year 2014 to 2016; December to January and September in year 2017 to 2018. Global spatial auto-correlation analysis found that the incidence among students in Guangzhou from 2014 to 2015 showed a random distribution at the street/township level (Moran I: -0.004 and 0.023, Z: 0.098 and 1.238, respectively). From 2016 to 2018, it showed that there were obvious clustering (Moran I: 0.059, 0.172, 0.088; Z: 2.954, 6.706, 3.565; P: 0.012, 0.001, 0.005). Local indicators of spatial association analysis found 7 high-high clusters as follows: Xiaoguwei and Xinzao in Panyu District, Pazhou and Guanzhou in Haizhu District, Yuangang and Changxing in Tianhe District. Spatiotemporal scan showed that the primary cluster was located in Panyu district, including 2 streets/townships: Xiaoguwei and Xinzao, during March 2016 to July 2018 (log likelihood ratio=360.04, relative risk=29.28, and P=0.000). Conclusion Incidence rates of TB among students displayed spatial clusters at the township level in Guangzhou during 2016 to 2018, with high risk areas relatively concentrated in Xiaoguwei and Yuexiu District with high density of colleges and universities.

Key words: Tuberculosis, pulmonary, Students, Incidence, Epidemiologic studies, Spatial-time clustering, Small-area analysis, Guangzhou