Start4All: protocol for evaluating TB screening and diagnostic tests and test algorithms in the community and lower care levels in seven high-burden countries.

Start4All: protocol for evaluating TB screening and diagnostic tests and test algorithms in the community and lower care levels in seven high-burden countries.

Publication date: Aug 01, 2026

Despite advances in TB care, approximately 2. 4 million people with TB remain undiagnosed or unreported each year. A key contributor to these ‘missed millions’ is that major diagnostic gaps persist especially among underserved populations. Developing 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. 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(R) MTB/RIF Ultra (Xpert Ultra) testing, and a third generation lipoarabinomannan (LF-LAM) assay. 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. 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.

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Concepts Keywords
Bangladesh ACF
Brazil Bangladesh
Laboratory Brazil
Start4all CAD
Underserved Cameroon
community-based case finding
facility-based case finding
Kenya
Malawi
Nigeria
tuberculosis
Viet Nam
vulnerable populations

Semantics

Type Source Name
disease MESH Nam
disease MESH LAM
disease MESH ACF
disease MESH tuberculosis
pathway KEGG Tuberculosis

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