We will evaluate the value-for-money of new TB screening algorithms and the impact of the detection campaigns on households across the socio-economic spectrum. Data collection from the study will be used to inform models of disease outcomes and costs.
Cost analysis. We will first assess the differential health system costs of implementing each of the screening algorithms under study in the trial as well as treating the additional TB cases detected by each algorithm compared to the standard of care.
Health impact analysis. Then we will assess the long-term health impact of improved screening across different socioeconomic groups within a simulation-modelling framework. Given that the most vulnerable households are most likely to be missed by standard screening protocols, we hypothesize that the proposed platforms may particularly benefit the poorest socioeconomic groups. Because the health impact of the higher detection rates can only be captured on a longer time-horizon than that of the trial, we will use a hybrid probability tree-Markov model to estimate the long-term population level health impact of improved early detection of TB. The model will be stratified by HIV status and TB drug resistance – two factors with important implications on both outcomes and costs of TB treatment.
Cost-effectiveness analysis. Both screening platforms will increase health systems costs in the short run, so a detailed comparison of costs and benefits is essential for both platforms. We will therefore compare direct health care costs for diagnosis and treatment with health outcomes, yielding incremental cost-effectiveness ratios (ICERs). ICERs directly quantify value-for-money of each trial intervention as compared to the reference scenario, which will be current standard of care.
Equity impact of earlier TB diagnosis. Tuberculosis often causes catastrophic economic effects on both the individual suffering the disease and their households, and hence, its amelioration is closely tied with the Sustainable Development Goals and other anti-poverty initiatives. One of our primary hypotheses is that the earlier detection and treatment of TB cases will substantially lower the financial burden of TB to patients and their families, and that these differences will be particularly important for the poorest strata in each setting. Therefore, we will assess the impact on out-of-pocket expenditures averted and financial risk protection provided across the socioeconomic spectrum.