Evidence map›Paper›PMID 42819914›Full record

ArticleOpen forum infectious diseases2026

Bridging Programmatic Gap in Post-TB Treatment Prevention: Applying the COM-B Model and Behavior Change Wheel to Improve the Uptake of TB Preventive Therapy.

Jonathan Izudi, Christine Sekaggya-Wiltshire, Andrew D Kerkhoff, Adithya Cattamanchi

Abstract read
In one paragraph

Article in Open forum infectious diseases, 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

4 authors.

Jonathan IzudiDirectorate of Graduate Training, Research and Innovation, Muni University, Arua, Uganda.ORCID https://orcid.org/0000-0001-9065-0389
Christine Sekaggya-WiltshireDepartment of Research, Makerere University Infectious Diseases Institute, Kampala, Uganda.ORCID https://orcid.org/0000-0001-9247-2950
Andrew D KerkhoffCenter for Tuberculosis, Institute for Global Health Sciences, University of California San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0002-5023-6658
Adithya CattamanchiCenter for Tuberculosis, Institute for Global Health Sciences, University of California San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0002-6553-2601

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Uganda's National Tuberculosis and Leprosy Program (NTP) recommends tuberculosis preventive therapy (TPT) immediately after TB treatment completion (hereafter post-TB treatment TPT or PTT-TPT) for people with retreatment TB, as they are at high risk for recurrent/drug-resistant TB. However, uptake of PTT-TPT is limited. To examine determinants of uptake, identify program-relevant strategies, and improve implementation, we applied a behavioral framework. We used the COM-B model (Capability-Opportunity-Motivation Behavior) to conceptualize the behavioral and health system determinants and the Behavior Change Wheel (BCW) to map the determinants to potential programmatic actions. The COM-B model showed that the low uptake of PTT-TPT reflects interacting limitations in healthcare provider capability, opportunity, and motivation. Based on the BCW, interventions aligned with behavioral drivers could help the NTP and similar settings to integrate PTT-TPT prevention more effectively within routine TB care. In conclusion, addressing the behavioral determinants of PTT-TPT may strengthen uptake.

Indexed as

behavior change wheelCOM-B modelpost-tuberculosis diseasetuberculosis preventive therapyUganda

Identifiers

PMID42819914
PMCPMC13625191

What OpenQuestion holds

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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.