Evidence map›Paper›PMID 41248188›Full record

ArticlePLOS global public health2025

Evaluating the impact of decentralized testing for tuberculosis in Ghana: A simulation model.

Parastu Kasaie, Lad Dombrowski, Jeffrey Pennington, Ernest Kenu, Yaw Adusi-Poku, Bernard Wadie, David W Dowdy, Lee F Schroeder

Abstract read
In one paragraph

Article in PLOS global public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

8 authors.

Parastu KasaieDepartment of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States of America.
Lad DombrowskiDepartment of Pathology, University of Michigan, Ann Arbor, Michigan, United States of America.
Jeffrey PenningtonDepartment of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States of America.
Ernest KenuSchool of Public Health, University of Ghana, Accra, Ghana.
Yaw Adusi-PokuNational Tuberculosis Programme, Ghana Health Service, Accra, Ghana.
Bernard WadieNational Tuberculosis Programme, Ghana Health Service, Accra, Ghana.
David W DowdyDepartment of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States of America.
Lee F SchroederDepartment of Pathology, University of Michigan, Ann Arbor, Michigan, United States of America.ORCID https://orcid.org/0000-0002-2787-3608

Funding

Understanding and Improving the Effectiveness of Public Health Laboratory Networks for Infectious Diseases in GhanaR01AI136977 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DOWDY, DAVID WESLEY, SCHROEDER, LEE FREDERICK · 2019 to 2023
$3.4M
Projecting the Epidemiologic and Economic Impact of HIV Prevention Efforts in the Context of Other Sexually Transmitted InfectionsK01AI138853 · NIAID · JOHNS HOPKINS UNIVERSITY · PI KASAIE, PARASTU · 2019 to 2023
$591k
NIAID NIH HHS K01 AI138853NIAID NIH HHS R01 AI136977
6 · The paper itself

Abstract

Poor geographical accessibility to tuberculosis (TB) diagnostic services in Ghana is a key barrier to timely diagnosis. Decentralizing molecular testing for TB could improve TB case notification among underserved populations and enhance treatment initiation while avoiding unnecessary treatments. This study aims to project the epidemiological impact of decentralized TB testing at the country level. We developed an individual-based simulation of the TB diagnostic system and treatment cascade in Ghana, calibrated to key TB care targets using data from the District Health Information Management System (DHIMSII) from 2019-2022. We assessed the effects of expanding molecular (Xpert MTB/RIF) testing to (1) all 218 district hospitals and (2) all 703 modeled facilities reporting TB notifications, comparing these scenarios to the current diagnostic network. Under the baseline scenario, 26% of samples remained untested, with over 30% of diagnoses made through clinical judgment alone, and 35% with false positive diagnoses. Decentralizing Xpert to district-level facilities could reduce the proportion of untested samples to 20%, lowering false positive diagnosis to 32%. While projected TB notifications and treatment rates in 2030 remained similar, this shift could result in 51 additional treatment initiations for individuals with undiagnosed TB, prevent 36 unnecessary treatments, and save 43 lives per million people over an eight-year period by 2030. Full Xpert decentralization could eliminate untested samples by enabling on site testing at all facilities, reduce false positive diagnosis to 25%, and lead to 220 additional treatment initiations, 140 fewer unnecessary treatments, and 180 lives saved per million relative to baseline from 2023-2030. Decentralizing Xpert testing offers significant benefits, improving diagnostic accuracy, reducing untested samples, and enhancing TB treatment outcomes - but wide-scale implementation is required to realize full benefits at a country level.

Identifiers

PMID41248188
PMCPMC12622812

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.