Scaling digital TB screening could prevent millions in Asia

Tuberculosis remains a major public health problem in Asia, with about two-thirds of the global TB burden concentrated in ten high-burden countries. Social vulnerabilities such as multi-dimensional poverty, malnutrition, comorbidities, health inequity and barriers to healthcare access continue to fuel transmission. The abstract highlights persistent problems in diagnosis: many people have asymptomatic or sub-clinical TB that goes undetected, services often rely on passive case-finding, and widespread dependence on smear microscopy delays diagnosis and treatment. To explore solutions, researchers led by Sandip Mandal built a mathematical model to estimate the epidemiological impact and cost of scaling up TB screening and diagnostics across these ten countries. They focused on active case-finding approaches, including community-based screening that uses highly sensitive screening tools and newer rapid diagnostics, reasoning that earlier detection and treatment can break chains of transmission. The modelling study aimed to compare different ways of delivering these services and to quantify the investments needed and the likely public health benefits over a decade.

The team evaluated multiple intervention scenarios and compared four service delivery models: 1) digital ultraportable chest x-ray (UPCXR) & Xpert/Truenat in community, 2) digital UPCXR in community and Xpert/Truenat at health facilities, 3) digital UPCXR in community and near point of care (nPOC) at health facilities, and 4) nPOC in community & Xpert/Truenat at health facilities. Their analysis showed that strengthening health facility capacity — including enhanced TB screening, expanded molecular diagnostics, reduced loss to follow-up, private sector standard of care, increased treatment coverage and quality of active disease treatment, reduced post-treatment relapse, scale-up of TB preventive treatment (TPT), and provision of nutritional support to 80% of TB patients and their household contacts — can significantly reduce TB incidence and mortality. However, the study found that community-wide mass screening remains essential for elimination targets. Targeted screening of vulnerable populations was more cost-effective than untargeted approaches. The modelling also noted that AI-enabled digital UPCXR–based screening combined with Xpert/Truenat testing at the community level showed the greatest epidemiological impact potential, while the most cost-efficient model was Digital UPCXR in the community combined with nPOC testing at health facilities. Finally, they estimated that an investment of USD 12.7 billion over five years for community-level digital UPCXR and molecular diagnostics could avert an additional 9.8 million TB cases and 1.9 million deaths across the ten countries over ten years.

The findings underline a clear trade-off between maximum health impact and cost-efficiency, and point to practical choices for policymakers. Community-based, AI-assisted screening paired with rapid molecular tests like Xpert/Truenat offers the largest potential to cut transmission and save lives, but combining digital UPCXR screening in communities with nPOC testing at health facilities may provide the best value for money. The study also emphasizes that facility improvements — from better diagnostics and treatment follow-through to wider provision of TB preventive treatment (TPT) and nutritional support for 80% of patients and contacts — are critical complements to community screening. Targeting screening toward vulnerable groups improves cost-effectiveness and helps focus limited resources where they will do the most good. Yet even with these measures, the authors note that reaching the End TB goals will likely require a new, effective TB vaccine with high coverage. The modelling gives health leaders a clearer picture of the investments and program designs that could avert millions of cases and deaths, informing decisions about where to allocate funds and how to balance reach, speed, and cost.