Impact of detection rate and preventive treatment of latent tuberculosis infection on the future burden of tuberculosis among students in shanghai: a predictive study using Markov modeling

Students represent a key demographic for tuberculosis (TB) control in China. This study utilizes a dynamic Markov model to forecast the epidemiological trends of active tuberculosis (ATB) among students in Shanghai through 2035 and evaluate the effectiveness of different control strategies. A Markov model incorporating six distinct states of TB transmission was utilized to simulate disease dynamics within a hypothetical cohort of 100,000 individuals. The model assessed the prevalence of latent tuberculosis infection (LTBI) and ATB, categorizing the cases based on whether they received the recommended treatment regimen. Under the current measures without any additional interventions, the model projected a marginal decline in ATB prevalence. Specifically, ATB prevalence is predicted to decrease to 14.80 per 100,000 by 2035. With an increase in tuberculosis preventive treatment (TPT) coverage to 20%, 50%, or 80%, the reductions in ATB prevalence were modest. When the detection rate of LTBI was fixed at 12% and combined with TPT coverage levels of 20%, 50%, and 80%, the reductions in ATB prevalence were 17.01%, 36.56%, and 50.68% respectively. Increasing the detection rate of LTBI to 35% alongside TPT coverages of 20%, 50%, and 80% led to more pronounced declines in ATB prevalence, at 40.95%, 69.36%, and 80.46% respectively. Under the current TB control measures, the prevalence of ATB among students would result in only marginal decline trajectories through 2035, falling substantially short of achieving the strategic goal of ending TB. Enhanced strategies that simultaneously increase detection rates and coverage of TPT are likely to significantly reduce ATB prevalence in this population.