Analysis of the current status and influencing factors of latent tuberculosis infection among new students in Taishan District in 2025
Bibliographic Data
Objective: To analyze the current status and influencing factors of latent tuberculosis infection among new students in Taishan District in 2025, providing data support for the formulation of tuberculosis prevention and control strategies in schools. Methods: A total of 38,863 new students in 2025 were selected as the study subjects and underwent standardized PPD skin tests. Statistical analysis was performed using SPSS software, with group comparisons conducted via chi-square tests, and a multivariate logistic regression model employed to analyze influencing factors. Results: In 2025, 38,863 new students in Taishan District underwent valid screening, with an overall screening rate of 92.70%. PPD test results showed that 30,438 students (78.32%) were negative, 2,243 (5.77%) were strongly positive, and the total number of latent tuberculosis infection cases was 5,395, with a latent infection rate of 13.88%. Multivariate analysis revealed that male gender (OR = 0.880, 95%CI: 0.829-0.935), junior high school (OR = 0.331, 95%CI: 0.230-0.477), senior high school (OR = 0.471, 95%CI: 0.404-0.550),vocational school (OR = 0.531, 95%CI: 0.457-0.618); junior college (OR = 0.690, 95%CI: 0.644-0.739); new students aged 17-18 years (OR = 0.787, 95%CI: 0.737-0.841), and students with no history of tuberculosis exposure (OR = 0.096, 95%CI: 0.079-0.116) had a relatively lower risk of latent infection. Conclusion: The prevalence of latent tuberculosis infection and the rate of strongly positive results among new students in Taishan District in 2025 were relatively high. Female students, undergraduate students, those aged 19 years and older, and students with a history of tuberculosis exposure were identified as high-risk groups for latent infection. Screening of incoming new students should be strengthened, and individuals with latent infection should undergo dynamic monitoring and preventive interventions to reduce the risk of tuberculosis occurrence on campus
Active tuberculosis · Latent class model · Latent tuberculosis · Logistic regression · Multivariate analysis · Multivariate statistics · Statistical analysis · Tuberculosis · Diagnosis and treatment of tuberculosis · Infectious Diseases and Tuberculosis · Tuberculosis Research and Epidemiology
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