Evidence map›Paper›PMID 41688116›Full record

ArticleBMJ open2026

Start4All protocol for a Bayesian cost-effectiveness model of tuberculosis screening and diagnosis in seven high burden low-income and middle-income countries.

Amanda McCoy, Tushar Garg, Marc Henrion, Luan Nguyen Quang Vo, Tom Wingfield, Eve Worrall, Start4All: Start Taking Action for TB Diagnosis investigators

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05845112 (Start Taking Action For TB Diagnosis), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05845112 completednot on this map

Start Taking Action For TB Diagnosis (START4ALL)

TypeobservationalSponsorLiverpool School of Tropical MedicineRan2025 to 2025Enrolled14,747ConditionsDiagnosis, TuberculosisArmsClass 1, Point of Care (POC) Quantitative C-Reactive Protein (CRP)., Class 2, POC Qualitative/Semiquantitative CRP, Class 3, Urine Lateral Flow Tests, Class 4, Molecular Diagnostics, Class 5, Portable Chest X-ray Image Acquisition
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Amanda McCoyLiverpool School of Tropical Medicine, Department of Clinical Sciences, Global Health Economics and Financing Unit, Liverpool, UK amanda.mccoy@lstmed.ac.uk.ORCID http://orcid.org/0009-0005-4934-751X
Tushar GargStop TB Partnership, Grand-Saconnex, Switzerland.
Marc HenrionMalawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi.ORCID http://orcid.org/0000-0003-1242-839X
Luan Nguyen Quang VoFriends for International TB Relief, Hanoi, Vietnam.ORCID http://orcid.org/0000-0002-5937-6286
Tom WingfieldCentre for Tuberculosis Research, Liverpool School of Tropical Medicine, Liverpool, UK.
Eve WorrallLiverpool School of Tropical Medicine, Department of Clinical Sciences, Global Health Economics and Financing Unit, Liverpool, UK.
Start4All: Start Taking Action for TB Diagnosis investigators

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionHigh costs of screening and diagnostic tests remain a major barrier to timely tuberculosis (TB) identification in resource-limited settings. Evidence on the cost-effectiveness of scalable screening algorithms is limited. Start4All is a research project aimed at developing and evaluating algorithmic approaches to TB screening and diagnosis, with the goal of optimising technical and allocative efficiency when expanding diagnostic coverage to primary healthcare and community settings. METHODS AND ANALYSIS: Five screening and diagnostic tests will be evaluated: a capillary blood-based assay (C-reactive protein (CRP)), sputum-based rapid molecular tests (PCR; individual and pooled Xpert MTB/RIF Ultra assay (Xpert Ultra, Cepheid®, California, USA)), a lateral-flow urine-based test for lipoarabinomannan (LF-LAM), and digital chest X-rays with artificial intelligence-based computer-aided detection (CXR-CAD). A microbiological reference standard of positive culture using the mycobacteria growth indicator tube will be used to confirm TB disease.We will compare the cost and effectiveness of concurrent and sequential positive serial combinations (screening algorithms) of CRP, CXR-CAD, LF-LAM, individual and pooled Xpert Ultra. Diagnostic performance will be estimated using sensitivity, specificity, predictive values and proportions of positive results, with Bayesian inference used to derive these estimates. The analysis will include adults (15 years and older) only and will be stratified by HIV status and level of care, including facility and community-based case finding. Effectiveness will be assessed based on the number of people with TB detected. Cost analysis will be conducted from the provider perspective, incorporating commodity and implementation costs. A decision tree model will be developed to assess the cost per number of persons with confirmed TB detected across all countries. Probabilistic sensitivity analysis will be conducted to account for uncertainty in model parameters, incorporating willingness-to-pay and willingness-to-accept thresholds. ETHICS AND DISSEMINATION: WHO ethical review committee approval ERC.0003921. Data will be available on reasonable request to the principal investigator of the consortium. TRIAL REGISTRATION NUMBER: NCT05845112.

Indexed as

Mass ScreeningTuberculosisAlgorithmsBayes TheoremCost-Benefit AnalysisCost-Effectiveness AnalysisC-Reactive ProteinDeveloping CountriesHumansLipopolysaccharidesObservational Studies as TopicRapid Diagnostic TestsResearch DesignResource-Limited SettingsC-Reactive ProteinlipoarabinomannanLipopolysaccharidesDecision MakingHEALTH ECONOMICSTuberculosis

Identifiers

PMID41688116
PMCPMC12911692

What OpenQuestion holds

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Registered trials

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.