Gong Tingting, Xiao Lixue, Zeng Xiangye, Ma Xiaoxue, Dai Yifang, Rao Wen, Ren Yulin, Xia Zihao, Hong Feng, Li Jinlan, .
China Tropical Medicine.
Accepted: 2026-08-25
Objective: To analyze the latent infection, case detection, and preventive treatment of student contacts of pulmonary tuberculosis in a high-epidemic county in Guizhou Province, as well as the influencing factors, in order to provide a scientific basis for precise tuberculosis prevention and control among students. Methods: Screening data for 84 index cases of school-associated pulmonary tuberculosis and 8,153 student contacts in the county in 2023 were collected through the Guizhou Provincial CDC Cloud Platform—School Tuberculosis Management System. Descriptive analysis was performed, and group comparisons were conducted using the chi-square test or Fisher's exact test. For latent infection, a generalized estimating equation (GEE) with school ID as the cluster variable and robust standard errors was used for multivariate analysis to control for clustering bias among students within the same school. Due to complete separation in the "primary school" group with zero detected cases, Firth-penalized likelihood logistic regression was employed for the analysis of detected cases. For preventive treatment acceptance, multivariate binary logistic regression was further conducted based on univariate analysis. Results: Among the 8,153 student contacts, the prevalence of latent tuberculosis infection was 6.61% (539/8,153), and the detection rate of active pulmonary tuberculosis was 0.69% (56/8,153). Multivariate analysis using GEE with cluster-robust standard errors revealed that general contact (OR=0.482, 95%CI: 0.336–0.692, P<0.001) and a negative microbiological test result for the index case (OR=0.476, 95%CI: 0.275–0.825, P=0.008) were independent protective factors for latent infection. Age ≥15 years (OR=1.340, 95%CI: 0.913–1.966, P=0.136) and private schools (OR=2.372, 95%CI: 0.949–5.933, P=0.065) did not reach statistical significance after cluster adjustment. Firth regression showed that private schools (OR=47.259, 95%CI: 18.265–152.707, P<0.001) and non-boarding status (OR=6.686, 95%CI: 1.422–28.960, P=0.019) were risk factors for active pulmonary tuberculosis detection, while a negative microbiological test result for the index case (OR=0.317, 95%CI: 0.114–0.718, P=0.004) was a protective factor. The absolute risk difference for case detection between private and public schools was 4.83%. Among latent infection cases, 90.91% (490/539) were eligible for preventive treatment, and the acceptance rate was 75.71% (371/490). Multivariate analysis showed that non-boarding status (OR=0.354, 95%CI: 0.205–0.613, P<0.001), a negative microbiological test result for the index case (OR=0.338, 95%CI: 0.257–0.446, P<0.001), and primary school level (OR=0.410, 95%CI: 0.186–0.903, P=0.027) were independent risk factors for refusing preventive treatment.Conclusion: Enhanced screening should focus on contacts of cases with positive pathogen indicators, with close attention to private schools (absolute risk difference: 4.83%). Health education and medication management for preventive treatment should be strengthened among key populations including non-boarding students and primary school students, in order to effectively block the transmission chain of tuberculosis in schools.