Evidence map›Paper›PMID 42445215›Full record

ArticleIJTLD open2026

Nurse case management to improve multidrug-resistant TB: a cluster-randomised trial.

J E Farley, K Lowensen, C Budhathoki, A Leonard, K McNabb, C M Weizer, L Krotee, A J Bergman, N N Ndjeka

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.

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

9 authors.

J E FarleyJohns Hopkins University School of Nursing, Baltimore, MD, USA.
K LowensenJohns Hopkins University School of Nursing, Baltimore, MD, USA.
C BudhathokiJohns Hopkins University School of Nursing, Baltimore, MD, USA.
A LeonardJohns Hopkins University School of Nursing, Baltimore, MD, USA.
K McNabbJohns Hopkins Center for Infectious Disease and Nursing Innovation (CIDNI), Baltimore, MD, USA.
C M WeizerJohns Hopkins Center for Infectious Disease and Nursing Innovation (CIDNI), Baltimore, MD, USA.
L KroteeJohns Hopkins Center for Infectious Disease and Nursing Innovation (CIDNI), Baltimore, MD, USA.
A J BergmanJohns Hopkins Center for Infectious Disease and Nursing Innovation (CIDNI), Baltimore, MD, USA.
N N NdjekaTB Control and Management, Department of Health, Pretoria, South Africa.

Funding

A Nurse Case Management Intervention to Improve MDR-TB/HIV Coinfection OutcomesR01AI104488 · NIAID · JOHNS HOPKINS UNIVERSITY · PI FARLEY, JASON EDWARD · 2014 to 2019
$3.2M
NIAID NIH HHS R01 AI104488
6 · The paper itself

Abstract

backgroundNurse case management (NCM) models have improved outcomes in chronic disease and HIV, but evidence in multidrug-resistant TB (MDR-TB) is limited. We therefore evaluated whether NCM could improve MDR-TB outcomes.

methodsThis pragmatic, cluster-randomised trial was conducted at 10 district hospitals in KwaZulu-Natal and Eastern Cape, South Africa.

resultsBetween 13 November 2014 and 5 September 2019, 2,844 were enrolled. A total of 2,134 were analysed (1,093 NCM, 1,041 usual care). Among 1,236 men (57.9%) and 898 women (42.1%), mean age was 37.4 years (standard deviation 12.2). Treatment success occurred in 706 (64.5%) of NCM group and 645 (61.9%) in usual care (

conclusionNCM did not improve MDR-TB treatment success. Yet, NCM was associated with reduced odds of treatment failure. The intervention alone was insufficient to overcome barriers associated with loss to follow-up, or delayed presentation.

Indexed as

chronic care modelMDR-TB outcomesnursingSouth Africatreatmenttuberculosis

Identifiers

PMID42445215
PMCPMC13362299

What OpenQuestion 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 OpenQuestion graph.