Comparing drug-resistant tuberculosis empirical treatment choices

Tuberculosis (TB) remains a major global health challenge, with the growing threat of drug resistance posing a significant barrier to effective treatment and disease control. Limited access, inadequate infrastructure, and long delays in obtaining drug susceptibility test (DST) results hinders our ability to tailor treatment regimens to individual patient resistance strain profiles.

This necessitates other approaches, such as empirical treatment, in the absence of complete clinical information. Population-level data could guide clinical decision-making at the national level by using resistance prevalence and other population data. Modelling studies using population-level data could help estimate the long-term impact of empirical treatment regimens on resistance accumulation.

This could include future pan-TB regimens to treat both drug-susceptible and drug-resistant TB without prior DST. Such regimens have the potential to substantially improve patient outcomes, but this benefit must be balanced against the impact on the lifespan of new drugs. The research will focus on three key objectives:

1. Assess the cost-effectiveness of new empirical treatment regimens for drug-resistant TB under various resistance scenarios. 2. Evaluate how widespread use of empirical treatment affects resistance accumulation to novel drugs. 3. Estimate the impact of different strategies on prolonging the lifespan of treatment approaches.

The outcomes of this project will include an evidence-based framework for empirical TB treatment decision-making, predictive models for resistance accumulation under different treatment strategies, and policy recommendations for optimising TB treatment approaches. Alignment with MRC Strategy and Core Skills:

1. Applying mathematics, statistics and computational skills (health economics and predictive modelling) to address complex TB treatment challenges. 2. Integrating insights from epidemiology and microbiology literature to inform model development and interpretation.

3. Effectively communicating results to diverse audiences and translating research findings into evidence-based recommendations for TB treatment guidelines. Keywords: Tuberculosis, Drug-resistance, Empirical treatment, Modelling, Health economics, Health decision science