Potential health impacts and costs of active case finding guided by Mycobacterium tuberculosis immunoreactivity survey results in Blantyre, Malawi: A mathematical modeling study

Background Active case finding (ACF) for tuberculosis (TB) can reduce transmission, yet efficient targeting requires high-quality surveillance data. We investigated the potential costs and impact of targeted ACF, guided by local estimates of the annual risk of TB infection (ARTI) derived from Mycobacterium tuberculosis ( Mtb ) immunoreactivity survey data in children aged <5 years. Methods and findings Using mathematical models parameterized with local data, we compared three case-finding approaches across 33 urban neighborhoods in Blantyre, Malawi: passive case finding (PCF) only; PCF with untargeted ACF; and PCF with ARTI-guided targeted ACF. Health outcomes (life expectancy, disability-adjusted life years [DALYs]) and costs were estimated using a Markov microsimulation model. Costs were assessed from health system and societal perspectives. Results were calculated for different assumptions about the relationship between ARTI and true TB prevalence. Compared to PCF-only, untargeted ACF was estimated to improve life expectancy by 3.3 years (95% credible interval (CrI) [1.7, 5.4]) for individuals with TB disease, but at high cost. Targeted ACF covering half of the study population was estimated to identify 80% of all individuals with TB and achieved a lower cost per DALY averted (US$400, 95% CrI [20,1,000]) than untargeted ACF (US$700, 95% CrI [100, 1,500]). In the main analysis, these cost-effectiveness ratios exceeded available cost-effectiveness thresholds for Malawi, although cost-effectiveness improved under assumptions of higher TB prevalence or greater transmission reduction. The findings are based on mathematical modeling and are subject to several limitations, including assumptions regarding the relationship between ARTI and TB prevalence, which remains uncertain. Conclusions Targeting ACF using Mtb immunoreactivity survey data could substantially improve health impact and cost-effectiveness compared to untargeted approaches. However, low-cost approaches for collecting these data are needed for wider adoption.