Control of drug-sensitive and drug-resistant tuberculosis in resource-constrained settings [electronic resource]

Tuberculosis (TB) is a complex infectious disease that kills millions of people every year despite effective existing treatments. Even worse, drug-resistant (DR) and multidrug-resistant (MDR) forms of TB are becoming more predominant. How should resource-constrained countries implement policies to combat TB, particularly as new diagnostic technology becomes available? This dissertation focuses on examining questions surrounding drug resistance and technology adoption in the context of the Indian TB epidemic. With one of the largest TB burdens in the world and a limited TB budget, India is an important front in the battle to control TB. In Chapter 2, we examine the relationship between treatment-generated and transmission-generated DR TB, and we evaluate the potential effectiveness of the following two disease control strategies in reducing the prevalence of DR TB: a) improving treatment of non-DR TB; b) shortening the infectious period between the activation of DR TB and initiation of effective DR treatment. We develop a dynamic transmission microsimulation model of TB in India. The model follows individuals by age, sex, TB status, drug resistance status, and treatment status and is calibrated to Indian demographic and epidemiological TB time trends. We find that the proportion of transmission-generated DR TB will continue rise over time. Strategies that disrupt DR TB transmission are projected to provide greater reductions in DR TB prevalence compared with improving non-DR treatment quality. Therefore as transmission-generated DR TB becomes a larger driver of the DR TB epidemic in India, rapid and accurate DR TB diagnosis and treatment will become increasingly effective in reducing DR TB cases compared to non-DR TB treatment improvements. Policies that use new rapid diagnostics may interrupt the transmission pathway, but their effectiveness may be undercut by inaccurate diagnosis and care inaccessibility in India. We evaluate the cost-effectiveness of policies that use rapid diagnostics, transfer patients to clinics that use WHO-approved TB treatment regimens, or combinations of these policies in Chapter 3. We extend the microsimulation model developed in Chapter 2 and additionally evaluate lifetime costs and health outcomes for each of the policies considered. We find that both types of policies (rapid diagnosis and transferring patients) and combination of policies improve health and increase costs relative to the status quo, and all but the rapid diagnosis policies alone would be cost-effective according to WHO thresholds for cost-effectiveness. While combination policies would garner the most health benefits, they would also cost the most, and our results suggest that if budget constraints necessitate implementing one before the other, programs that increase the number of patients going to clinics using WHO-approved TB treatment regimens should be prioritized over rapid diagnosis policies. In Chapter 4, we design a partially observed Markov decision process (POMDP) model for determining when rapid diagnostics should be used for drug sensitivity testing (DST) in first-line TB treatment in India, should it be adopted nationwide. We present structural properties of the model and analytical results. We find that the optimal timing of DST is influenced by availability of TB test results, level of TB transmission, and prevalence of DR TB. We find that India should revise the testing protocol in its first-line national TB treatment program to provide DST during the first month of treatment in areas of average or high DR TB prevalence and transmission. In regions with low DR TB prevalence and transmission, individually tailored testing regimens can reduce cost while maintaining health benefits of treatment. Hundreds of thousands of patients begin first-line TB treatment in India each year. We estimate that using an improved testing protocol for one year could save India up to $2.5 billion by preventing downstream transmission. In this dissertation, we present both practical and methodological contributions in the area of health policy modeling. The methods we develop can be adapted to other diseases and settings and can be useful for informing other policy decisions surrounding the control of infectious disease in resource-constrained settings.

Keywords: Management Science and Engineering.
Advisor: Brandeau Margaret L. primary advisor. Thesis advisor
Goldhaber-Fiebert Jeremy D.
Bendavid Eran