Projecting the Impact of Diabetes Control on Future Tuberculosis Epidemic in High Tuberculosis Burden Countries: an Epidemic Modeling Analysis = 利用數理模式推估在高結核病疫情國家使用糖尿病控制對於整體結核病疫情的可能影響
Background: Tuberculosis (TB) remains one of the biggest disease burden globally. In order to reach the goal of global TB elimination by 2050, preventive measures that address determinants of TB are likely to be needed in addition to curative interventions. Among the risk factors of TB, diabetes mellitus (DM) has aroused special attention because of the high prevalence of diabetes globally and the strong association between diabetes and TB. DM was noted to be associated with 2 -4 folds of increased risk of TB and 3.89 fold of risk of TB relapse. At the same, the population with DM is increasing due to aging, urbanization, Western diet, obesity, and physical inactivity; the global prevalence of DM is projected to increase from 2.8% in 2000 to 4.4% by 2030. With the increasing trend of DM globally, especially in the low income countries where TB burden is the highest, the current efforts on TB prevention and treatment may be dampened or even reserved. Objective: We aim to use a mathematical model to simulate the effect of DM control on the epidemic of TB in thirteen countries with high TB burden. Methods: We will construct a mathematic model to simulate the dynamic of TB transmission at the country- level and account for the interaction between DM and TB. A compartmental model that describes the natural history of TB (Susceptible-Latent-Infectious-Recovered, SLIR) will be construct for each country and calibrated to the observed diabetes and TB situation. After calibration we will project the impact of five different scenarios of DM control on future epidemiology of TB over the next 25 years. The impact will be evaluated by the number of incident TB cases and TB deaths averted over the study period comparing alternative DM scenarios to the status quo scenario. Anticipated results: Since DM is increasingly prevalent in many high TB burden countries, the results of our analysis will have important contribution to TB control policy in these countries. Furthermore, this modeling framework can be extended to other determinants of TB. Thus, the fundamental work can be used to test other novel strategies in TB prevention and control and to facilitate TB control globally.