Evidence map›Paper›PMID 42746607›Full record

ArticleIJTLD open2026

The impact and cost of using mobile digital X-ray units to detect pulmonary TB in South Africa.

L Coetzee, D Evans, A Fononda, H Hausler, L Booyens, J Steingo, K Hirasen, L Jamieson, M Kubjane, G Meyer-Rath

Abstract read
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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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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

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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

10 authors.

L CoetzeeHealth Economics and Epidemiology Research Office, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
D EvansHealth Economics and Epidemiology Research Office, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
A FonondaTB HIV Care, Cape Town, South Africa.
H HauslerTB HIV Care, Cape Town, South Africa.
L BooyensTB HIV Care, Cape Town, South Africa.
J SteingoTB HIV Care, Cape Town, South Africa.
K HirasenHealth Economics and Epidemiology Research Office, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
L JamiesonHealth Economics and Epidemiology Research Office, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
M KubjaneHealth Economics and Epidemiology Research Office, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
G Meyer-RathHealth Economics and Epidemiology Research Office, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.

Funding

Bill & Melinda Gates Foundation INV-019496
6 · The paper itself

Abstract

backgroundDigital chest X-ray (dCXR) enables early TB detection where symptoms screening is inadequate. We evaluated the impact and cost of three dCXR delivery models: long-term community-based mobile vans (LT-vans), long-term facility-based containers (LT-containers), and short-term community-based mobile vans (ST-vans), implemented by a non-governmental organisation (NGO) across two South African provinces.

methodsBetween 2020 and 2022, nine dCXR units were deployed in four high-TB-burden districts for 1-19 months. Screening yield and TB test positivity were collected from electronic databases. Costs were estimated from the provider perspective using micro-costing methods, with both economic and financial costs reported for NGO-led and routine care implementation.

resultsOverall, 34,110 individuals were screened, 8,605 (25.2%) had abnormal dCXR results, and 1,372 TB cases were diagnosed (4.0% positivity). The cost per person screened with dCXR ranged from $39 to $240. LT-vans had the highest TB positivity (10.1%), followed by LT-containers (3.7%) and ST-vans (1.7%). The lowest cost per TB case identified was with LT-vans ($384 economic; $375 financial) under routine care implementation, and the highest with ST-vans ($14,157 economic; $8,741 financial) under NGO-led implementation.

conclusionLong-term mobile vans were the most cost-effective model, yielding the highest TB rates, while short-term vans offered high volumes but at higher costs.

Indexed as

cost-effectivenessdigital chest X-ray (dCXR)economicsscreeningtuberculosis

Identifiers

PMID42746607
PMCPMC13577202

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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.