To understand the epidemiological characteristics of student TB patients in the province and analyze their spatial aggregation, 2018-2023, and to further optimize the TB prevention and control measures in schools in the province, as well as to provide scientific reference bases for the relevant departments to formulate practical and effective TB prevention and control policies in schools.

From the “Infectious Disease Reporting Information Management System”, the case data of tuberculosis patients whose current address was within the counties (cities and districts) under the jurisdiction of Guizhou Province and whose occupations were students from January 1, 2018 to December 31, 2023 were derived according to the date of onset, and the epidemiological characteristics and spatial aggregation of tuberculosis among students were analyzed by using Excel 2010, SPSS 26 0 and Arc GIS 10 7 software, SPSS 26.0 and Arc GIS 10.7 software to organize and analyze the epidemiological characteristics and spatial aggregation of students' tuberculosis.

There were 23 699 cases of tuberculosis among students in Guizhou Province from 2018 to 2023, with an average annual registration rate of 40.67/100 000, showing a decreasing trend (trend χ²=1 244, P<0.001). The registration time was mainly concentrated in March-April and August-September each year; the regional distribution was mainly concentrated in Bijie City, Qiandongnan Prefecture, and Zunyi City; the age was mainly 15-19 years old (60.15%); the source of the patients was the largest proportion of referrals (40.08%), and the diagnostic results were mainly pathogenetically negative (65.80%), and the overall delayed rate of consultation for student TB patients during 2018-2023 was 49.01%.The global Moran's I values of student TB registration rates in Guizhou Province during 2018-2023 were all >0, ranging from 0.135 to 0.221, and all were statistical significance (P<0.05). The results of local autocorrelation analysis showed that there were different types of aggregation in the province's aggregation zones in each year, and the number of “high-high” aggregation zones in 2018-2023 were: 3, 2, 5, 4, 3, 4, and the number of “low-low” aggregation zones were: 3, 2, 5, 4, 3, and 4, respectively. The number of “high-low” agglomerations in 2018-2023 are: 3, 2, 5, 4, 3, 4, the number of “low-low” agglomerations in 2018-2023 are: 18, 15, 11, 9, 4, 5, the number of “high-low” agglomerations in 2018-2023 are: 0, 0, 1, 1, 1, 2, 4, the number of “low-high” agglomerations in 2018-2023 are: 1, 1, 2, 1, 1, 1, 1.

Guizhou Province, the student tuberculosis patient registration rate is a year-on-year decline in the trend of three inter-distribution is obvious, and the student tuberculosis patient registration non-uniform distribution between areas, and there is obvious spatial aggregation. It is recommended to strengthen active detection in schools, to strengthen health education, and to increase the prevention and control of tuberculosis in schools in areas of aggregation.