Background and objective: Tuberculosis (TB) remains a major public health challenge. China accounts for more than 10% of the global TB burden, and effective modelling of TB trends remains limited.
Methods: We used data drawn primarily from two Chinese nation-wide cross-sectional epidemiological surveys combined with data from China's National Disease Reporting Network to construct an eight-state Markov model that simulates TB prevalence. By adjusting the relevant parameters, we evaluated which characteristics have the greatest bearing upon prevalence and efficacy of the response measures.
Results: If current trends continue, the prevalence of TB in China will enter an 8-year period of decline from approximately 390 to 200 cases per 100 000 population and stabilize at 163 cases per 100 000 population, which is a figure well above the World Health Organization (WHO) goal of eliminating TB by 2050. We find that the proportion of notified cases in the population, the rate of progression from latent to active and the overall treatment success rate are the chief factors affecting disease progression.
Conclusion: We suggest a 90-90-90 strategy, wherein the proportion of notified cases in the population reaches 90%, the risk of progression from latent to active is decreased by 90% compared with the current level and the overall treatment success rate is increased to 90%. This strategy could reduce TB prevalence to less than 10 cases per 100 000 population within 5 years and to 1.77 cases per 100 000 population within 50 years.
Keywords: Markov chains; prevalence; prevention and control; proportion of notified cases; tuberculosis.
© 2017 Asian Pacific Society of Respirology.