Peer-reviewed article
Potential health impacts and costs of active case finding guided by Mycobacterium tuberculosis immunoreactivity survey results in Blantyre, Malawi: A mathematical modeling study
Sun Kim, Melike Hazal Can, Hannah M. Rickman, Mphatso Dennis Phiri, Marriott Nliwasa et al. ·
Summary
Written by AI, not yet AICS's view · claude-sonnet-5-5This modeling study looks at how tuberculosis (TB) case finding could be targeted across 33 urban neighborhoods in Blantyre, Malawi. It uses survey data on TB infection in children under 5 years to estimate the local annual risk of TB infection and guide where to search. It compares passive case finding only, untargeted active case finding, and targeted active case finding. Targeted searching was estimated to cost less per DALY averted than untargeted searching. In the main analysis, both approaches cost more than the available cost-effectiveness thresholds for Malawi. Children appear only as the source of the survey data. The health and cost results are for the whole population with TB, not for children's well-being.
Key points
- Compared with passive case finding only, untargeted active case finding was estimated to improve life expectancy by 3.3 years (95% CrI 1.7 to 5.4) for individuals with TB disease, but at high cost.
- Targeted active case finding covering half of the study population was estimated to identify 80% of all individuals with TB.
- Targeted active case finding cost US$400 per DALY averted (95% CrI 20 to 1,000), against US$700 (95% CrI 100 to 1,500) for untargeted.
- In the main analysis, these cost-effectiveness ratios exceeded the available thresholds for Malawi. Cost-effectiveness improved if TB prevalence was higher or transmission reduction was greater.
- The authors say low-cost ways to collect the immunoreactivity survey data are needed for wider adoption.
Limitations
This is a simulation, not a trial of an intervention in real children or communities, and results depend on uncertain assumptions about how infection risk relates to TB prevalence. The abstract gives no outcomes specific to children.
Abstract
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.