Evidence map›Paper›PMID 41582958›Full record

ArticleIJTLD open2026

Integrated treatment-decision algorithms for childhood TB: modelling diagnostic performance and costs.

M Gaeddert, D Jaganath, A Civan, H T Nguyen, M Bonnet, E Wobudeya, O Marcy, M De Allegri, C M Denkinger

Abstract read
In one paragraph

Article in IJTLD open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

M GaeddertDepartment of Infectious Diseases and Tropical Medicine, University Hospital Heidelberg, Heidelberg, Germany.
D JaganathDivision of Pediatric Infectious Diseases, University of California San Francisco, San Francisco, CA, USA.
A CivanDepartment of Infectious Diseases and Tropical Medicine, University Hospital Heidelberg, Heidelberg, Germany.
H T NguyenHeidelberg Institute of Global Health, University Hospital Heidelberg and Medical Faculty, Heidelberg University, Heidelberg, Germany.
M BonnetTransVIHMI, Research Institute for Sustainable Development (IRD) UMR 233, National Institute for Health and Medical Research (Inserm) U1175, University of Montpellier, Montpellier, France.
E WobudeyaUganda Tuberculosis Implementation Research Consortium, Walimu, Kampala, Uganda.
O MarcyNational Institute for Health and Medical Research (Inserm) UMR 1219, Research Institute for Sustainable Development (IRD) EMR 271, University of Bordeaux, Bordeaux, France.
M De AllegriHeidelberg Institute of Global Health, University Hospital Heidelberg and Medical Faculty, Heidelberg University, Heidelberg, Germany.
C M DenkingerDepartment of Infectious Diseases and Tropical Medicine, University Hospital Heidelberg, Heidelberg, Germany.

Funding

Rapid Research for Diagnostics Development in TB Network (R2D2 TB Network)U01AI152087 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CATTAMANCHI, ADITHYA, DENKINGER, CLAUDIA MARIA · 2020 to 2024
$19.3M
Host Proteomic Biosignatures for a Urine-based Diagnosis of Pulmonary Tuberculosis in ChildrenK23HL153581 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI JAGANATH, DEVAN · 2020 to 2024
$946k
NHLBI NIH HHS K23 HL153581NIAID NIH HHS U01 AI152087
6 · The paper itself

Abstract

backgroundTo improve childhood TB diagnosis, treatment-decision algorithms (TDAs) with and without chest X-ray (CXR) were developed for children under age 10. We aimed to model diagnostic performance and costs of implementing TDAs in primary health centre (PHC) and district hospital (DH) settings in Uganda.

methodsWe developed decision-tree models following the TDA pathway from evaluation to treatment-decision. We compared six scenarios with combinations of diagnostic testing (stool and respiratory Xpert, urine lipoarabinomannan, and/or CXR) at PHCs and DHs. Outcomes were diagnostic accuracy and cost per correct treatment-decision for a cohort of 10,000 children with presumptive TB using a Monte Carlo simulation from a health system perspective. Costs were reported in 2024 international dollars (I$).

resultsIn all scenarios, TDAs had high sensitivity (80.8%-91.9%) but low specificity (50.9%-60.9%). Total diagnostic and treatment costs for the cohort were I$1,768,958-2,470,298, largely driven by overtreatment of false-positive cases. Diagnostic costs were mostly offset by reducing overtreatment. The cost per treatment-decision was lowest using mobile CXR at PHCs (I$287) and highest with DH referral (I$449).

conclusionThe TDAs have high sensitivity and can be implemented at PHCs with lower costs than DHs. Improving specificity and reducing treatment costs would enable affordable, large-scale implementation.

Indexed as

cost-effectivenessdiagnosismodellingpaediatric TBtuberculosis

Identifiers

PMID41582958
PMCPMC12826592

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.