Epidemiological Characteristics and Spatiotemporal Clustering of Pulmonary Tuberculosis Among Students in Southwest China From 2016 to 2022: Analysis of Population-Based Surveillance Data

Abstract

Background: Pulmonary tuberculosis (PTB), as a respiratory infectious disease, poses significant risks of covert transmission and dissemination. The high aggregation and close contact among students in Chinese schools exacerbate the transmission risk of PTB outbreaks.

Objective: This study investigated the epidemiological characteristics, geographic distribution, and spatiotemporal evolution of student PTB in Chongqing, Southwest China, aiming to delineate the incidence risks and clustering patterns of PTB among students.

Methods: PTB case data from students monitored and reported in the Tuberculosis Information Management System within the China Information System for Disease Control and Prevention were used for this study. Descriptive analyses were conducted to characterize the epidemiological features of student PTB. Spatial trend surface analysis, global and local spatial autocorrelation analyses, and disease rate mapping were performed using ArcGIS 10.3. SaTScan 9.6 software was used to identify spatiotemporal clusters of PTB cases.

Results: From 2016 to 2022, a total of 9920 student TB cases were reported in Chongqing, Southwest China, with an average incidence rate of 24.89/100,000. The incidence of student TB showed an initial increase followed by a decline, yet it remained relatively high. High school students (age: 13-18 years; 6649/9920, 67.03%) and college students (age: ≥19 years; 2921/9920, 29.45%) accounted for the majority of student PTB cases. Patient identification primarily relied on passive detection, with a high proportion of delayed diagnosis and positive etiological results. COVID-19 prevention measures have had some impact on reducing incidence levels, but the primary factor appears to be the implementation of screening measures, which facilitated earlier case detection. Global spatial autocorrelation analysis indicated Moran I values of >0 for all years except 2018, ranging from 0.1908 to 0.4645 (all P values were <.05), suggesting strong positive spatial clustering of student PTB cases across Chongqing. Local spatial autocorrelation identified 7 high-high clusters, 13 low-low clusters, 5 high-low clusters, and 4 low-high clusters. High-high clusters were predominantly located in the southeast and northeast parts of Chongqing, consistent with spatial trend surface analysis and spatiotemporal clustering results. Spatiotemporal scan analysis revealed 4 statistically significant spatiotemporal clusters, with the most likely cluster in the southeast (relative risk [RR]=2.87, log likelihood ratio [LLR]=574.29, P<.001) and a secondary cluster in the northeast (RR=1.99, LLR=234.67, P<.001), indicating higher reported student TB cases and elevated risks of epidemic spread within these regions.

Conclusions: Future efforts should comprehensively enhance prevention and control measures in high-risk areas of PTB in Chongqing to mitigate the incidence risk among students. Additionally, implementing proactive screening strategies and enhancing screening measures are crucial for early identification of student patients to prevent PTB outbreaks in schools.

Keywords: Southwest China; epidemiology; pulmonary tuberculosis; spatial autocorrelation analysis; spatiotemporal clusters; student PTB; visualizing incidence map.

Figure 1.. The geographical location and administrative division of Chongqing at the county level in China.

Figure 2.. Epidemiological characteristics of pulmonary tuberculosis (PTB) among students in Chongqing, Southwest China, 2016‐2022. (A) The annual case number of PTB among students across different genders and the annual incidence of total cases. (B) The grade/age distribution. (C) Ways of discovery. (D) Interval between first symptoms occurrence to hospital visit. (E) Etiological results. (F) Treatment outcomes.

Figure 3.. The monthly distribution of pulmonary tuberculosis among students in Chongqing, Southwest China, 2016‐2022.

Figure 4.. The spatial distribution of pulmonary tuberculosis among students in Chongqing, Southwest China, 2016‐2022.

Figure 5.. Spatial 3D trend surface analysis of pulmonary tuberculosis among students in Chongqing, Southwest China, 2016‐2022.

Figure 6.. Annual local spatial autocorrelation of pulmonary tuberculosis among students in Chongqing, Southwest China, 2016‐2022.

Figure 7.. Spatiotemporal clusters of student pulmonary tuberculosis cases in Chongqing, Southwest China, 2016‐2022.