Decentralized TB testing reaches the poorest faster

Tuberculosis remains a major global health problem, with about one-third of cases going undiagnosed or unreported and continuing to fuel transmission. Point-of-care diagnostics can be critical to finding cases quickly, but we know less about how the costs and benefits of these tools are shared across different income groups. To address that gap, a team led by Lelisa Fekadu Assebe used data from the TB-CAPT CORE multi-center, cluster randomized controlled trial to compare decentralized, point-of-care testing with existing hub-and-spoke systems in Mozambique and Tanzania. The trial included a sub-study on patient and provider costs. The specific technologies compared were Truenat MTB Plus and Truenat MTB-RIF Dx deployed at the point of care (the intervention arm) against a hub-and-spoke model using Cepheid Xpert MTB/RIF (‘Xpert‘) or onsite sputum-smear microscopy in the control arm. The study set out to measure whether decentralizing testing improved timely diagnosis and treatment starts, and how any benefits were distributed across socio-economic groups in both countries.

The researchers conducted a benefit incidence analysis (BIA) based strictly on trial data and the accompanying cost sub-study. Their main outcome was diagnosis and TB treatment initiation within 7 days of diagnosis. Costs from both patient and health system perspectives were included, and the team estimated benefits from a health system perspective and a combined societal perspective. They also constructed concentration curves and calculated indices to quantify inequality in how benefits were distributed across wealth quintiles. Results showed the intervention arm led to more rapid treatment starts: 147 people in the Truenat point-of-care group initiated TB treatment within 7 days, compared with 95 in the control group — more than 1.5 times higher overall, with the difference especially notable in Mozambique. Treatment initiation was disproportionately higher among the poorest quintiles (28 patients, 25% in Mozambique; 35 patients, 27% in Tanzania) than among the least poor (20 patients, 18% in Mozambique; 15 patients, 12% in Tanzania). While societal costs of treatment initiation were higher in the intervention arm, the public benefit incidence was slightly skewed toward poorer populations, with a concentration index of –0.0816 (confidence interval: – 0.0829: – 0.0804).

These findings suggest that decentralizing TB diagnostics to the point of care using Truenat MTB Plus and Truenat MTB-RIF Dx can speed up diagnosis and start of treatment, and that the gains are not evenly distributed — they favor poorer people. The study concludes that the investment in decentralized point-of-care testing reached those who need it most, producing a pro-poor distribution of benefits compared with the hub-and-spoke Cepheid Xpert MTB/RIF (‘Xpert‘) or onsite sputum-smear microscopy model. For health planners and funders, this evidence highlights a trade-off: decentralized Truenat testing may carry higher societal costs per treatment initiation, but it appears to improve equity by increasing timely access for the poorest subgroups. The results reinforce the role of point-of-care diagnostics in expanding access to care and suggest that policymakers in settings like Mozambique and Tanzania should consider decentralized testing as a way to reduce diagnostic delays and target resources toward underserved populations.