Evidence map›Paper›PMID 40193335›Full record

ArticlePLOS global public health2025

The Accuracy of the Uganda National Tuberculosis and Leprosy Program diagnostic algorithm and the World Health Organisation treatment decision algorithms for childhood tuberculosis: A retrospective analysis.

Peter J Kitonsa, Bernard Kikaire, Peter Wambi, Annet Nalutaaya, Jascent Nakafeero, Gertrude Nanyonga, Emma Kiconco, Deus Atwiine, Robert Castro, Ernest A Oumo and 8 more

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. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
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

18 authors.

Peter J KitonsaUganda Tuberculosis Implementation Research Consortium (U-TIRC), Walimu, Kampala, Uganda.ORCID https://orcid.org/0000-0003-2971-216X
Bernard KikaireDepartment of Pediatrics and Child Health, College of Health Sciences, Makerere University, Kampala, Uganda.
Peter WambiUganda Tuberculosis Implementation Research Consortium (U-TIRC), Walimu, Kampala, Uganda.
Annet NalutaayaUganda Tuberculosis Implementation Research Consortium (U-TIRC), Walimu, Kampala, Uganda.
Jascent NakafeeroUganda Tuberculosis Implementation Research Consortium (U-TIRC), Walimu, Kampala, Uganda.
Gertrude NanyongaUganda Tuberculosis Implementation Research Consortium (U-TIRC), Walimu, Kampala, Uganda.
Emma KiconcoUganda Tuberculosis Implementation Research Consortium (U-TIRC), Walimu, Kampala, Uganda.
Deus AtwiineUganda Tuberculosis Implementation Research Consortium (U-TIRC), Walimu, Kampala, Uganda.
Robert CastroDivision of Pulmonary and Critical Care Medicine, University of California, San Francisco, United States of America.
Ernest A OumoUganda Tuberculosis Implementation Research Consortium (U-TIRC), Walimu, Kampala, Uganda.
Hellen T AanyuDepartment of Pediatrics and Child Health, Mulago National Referral Hospital, Kampala, Uganda.ORCID https://orcid.org/0000-0003-3159-4318
Mary N MudiopeInfectious Diseases Institute, Kampala, Uganda.
Ezekiel MupereDepartment of Pediatrics and Child Health, College of Health Sciences, Makerere University, Kampala, Uganda.ORCID https://orcid.org/0000-0002-8746-9009
Moorine P SekaddeNational TB and Leprosy Programme (NTLP), Ministry of Health, Kampala, Uganda.
Swomitra MohantyDepartments of Chemical and Metallurgical Engineering, University of Utah, Salt Lake City, United States of America.
Adithya CattamanchiUganda Tuberculosis Implementation Research Consortium (U-TIRC), Walimu, Kampala, Uganda.
Eric WobudeyaUganda Tuberculosis Implementation Research Consortium (U-TIRC), Walimu, Kampala, Uganda.
Devan JaganathUganda Tuberculosis Implementation Research Consortium (U-TIRC), Walimu, Kampala, Uganda.

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
Evaluation of a novel breath sensor for rapid, low-cost diagnosis of tuberculosis in childrenR01HL139717 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KATO-MAEDA, MIDORI, MOHANTY, SWOMITRA K · 2018 to 2021
$2.7M
Automated lung sound analysis to improve the clinical diagnosis of pulmonary tuberculosis in childrenR01HL169449 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Devan Jaganath · 2023 to 2026
$2.4M
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 HL153581NHLBI NIH HHS R01 HL139717NHLBI NIH HHS R01 HL169449NIAID NIH HHS U01 AI152087
6 · The paper itself

Abstract

Diagnosing childhood pulmonary tuberculosis (TB) is a challenge. This led the Uganda National Tuberculosis and Leprosy Program (NTLP) to develop a clinical treatment decision algorithm (TDA) for children. However, there is limited data on its accuracy, and how it compares to new World Health Organization (WHO) TB TDAs for children. This study aimed to evaluate and compare the accuracy of the 2017 Uganda NTLP diagnostic algorithm with the 2022 WHO TDAs for TB among children. We analyzed four years of clinical data from children <15 years old in Kampala, Uganda. Children were classified as per National Institutes of Health (NIH) consensus definitions (Confirmed, Unconfirmed or Unlikely TB). We applied the 2017 Uganda NTLP and 2022 WHO algorithms (A with chest x-ray [CXR], B without CXR) to make a decision to treat for TB or not, and calculated the sensitivity, specificity and predictive values in reference to Confirmed vs. Unlikely TB, as well as a microbiological and composite reference standard. Of the 699 children included in this analysis, 64% (451/699) were under 5 years, 53% (373/669) were male, 12% (85/699) were Xpert Ultra positive, 11% (74/669) were HIV positive and 6% had severe acute malnutrition (SAM). The Uganda NTLP algorithm had a sensitivity of 97.9% (95% CI: 96.4-99.4) and specificity of 25.9% (95% CI: 21.2-30.7). If CXR was considered unavailable, sensitivity was 97.9% (95% CI: 96.4-99.4) and specificity 28.1% (95% CI: 23.2-33.0). In comparison, WHO TDAs had similar sensitivity to the Uganda NTLP, but algorithm A was more specific (32.2%, 95% CI: 26.9-37.5) and algorithm B was less specific (15.4%, 95% CI: 11.3-19.5). The WHO TDAs had better specificity than the NTLP algorithm with CXR, and worse specificity without CXR. Further optimization of the algorithms is needed to improve specificity and reduce over-treatment of TB in children.

Identifiers

PMID40193335
PMCPMC11975100

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.