Abstract
BACKGROUND: Despite advances in TB care, ap-proximately 2.4 million people with TB remain undi-agnosed or unreported each year. A key contributor to these ‘missed millions’ is that major diagnostic gaps persist especially among underserved populations. De-veloping diagnostic algorithms that are accessible and integrated into health care systems where they are intended to be used is essential to meet WHO End TB targets.
METHODS: Start4All a multi-country, cross-sectional diagnostic accuracy study (in Bangladesh, Brazil, Cameroon, Kenya, Malawi, Nigeria, and Viet Nam) aims to assess and model algorithms using combinations of WHO-endorsed, and experimental TB screening and diagnostic tests across the lowest levels of care and the community, compared to a sputum culture reference standard. Evaluated tools include C-reactive protein tests, computer-aided detection–interpreted chest X-rays, pooled Xpert® MTB/RIF Ultra (Xpert Ultra) testing, and a third generation lipoarabinomannan (LF-LAM) assay.
DISCUSSION: Start4All will generate high-quality evidence to guide policy decisions on the adoption and the integration of non-sputum and laboratory independent tools to accelerate early TB detection and close the treatment gap.
CONCLUSION: A feature of Start4All is its implementation within national TB programmes across seven high-burden countries to ensure that findings are directly translatable to policy and operational decision making. Harmonised protocols, standardised microbiological reference testing, and unified data management systems will enable cross-country comparison and strengthen the external validity of results.
| Original language | English |
|---|---|
| Pages (from-to) | 526-533 |
| Number of pages | 8 |
| Journal | IJTLD Open |
| Volume | 3 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 1 Aug 2026 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- ACF
- Bangladesh
- Brazil
- CAD
- Cameroon
- community-based case finding
- facility-based case finding
- Kenya
- Malawi
- Nigeria
- tuberculosis
- Viet Nam
- vulnerable populations
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