Evidence map›Paper›PMID 39718911›Full record

ArticleLung India : official organ of Indian Chest Society2025

Evaluation of the Drug-Resistant Tuberculosis (DR-TB) management component under the National Tuberculosis Elimination Program (NTEP) in Kerala, India, 2021-22.

Raman Swathy Vaman, Madhanraj Kalyanasundaram, Malu Mohan, Narayana Pradeepa, Manoj V Murhekar

Abstract read
In one paragraph

Article in Lung India : official organ of Indian Chest Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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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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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. IJID regions · 2025
    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

5 authors.

Raman Swathy VamanAssistant Director of Health Services, District Hospital, Kanhangad, Kasaragod, Kerala, India.
Madhanraj KalyanasundaramScientist E, ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India.
Malu MohanPublic Health Researcher, Women's Institute for Social and Health Science Studies (WISHS), Kerala, India.
Narayana PradeepaJunior Consultant-Respiratory Medicine, District TB Centre, Kasaragod, Kerala, India.
Manoj V MurhekarScientist G and Director, ICMR-National Institute of Epidemiology, Chennai, Tamil Nadu, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveWe evaluated the DR-TB component of the National Tuberculosis Elimination Program (NTEP) in a high-burden district in Kerala to identify the programmatic gaps, if any, in screening, diagnosis, treatment, and follow-up of notified DR-TB patients.

methodsA mixed-methods design was used, and the evaluation was performed in two steps. In the first step, we reviewed the program documents and conducted stakeholder interviews to develop a detailed description of the program design and developed a logical framework to evaluate program performance. Consequently, in the next step, we conducted programmatic data reviews, facility surveys, and in-depth interviews with key stakeholders to identify the programmatic gaps in implementation, guided by the logic framework.

resultsOf the 494 microbiologically confirmed TB patients during 2021-22, 342 (69%) were tested for drug sensitivity, and 30 DR-TB patients were identified. There was no separate district DR-TB treatment center with airborne infection control facilities, and only 16% (66/422) of the various categories of staff were trained in recent guidelines. Only 30% (9/30) of DR-TB patients were provided with any psychological assessment. The favorable treatment outcome was 80% Interviews revealed poor readiness and motivation from the private sector for screening, contextual barriers in human resource availability, transportation, and financial barriers to the beneficiary despite providing financial benefits.

conclusionPrioritizing the establishment of a district DR-TB treatment center and sputum transport mechanism, posting a clinical psychologist dedicated to counseling patients on therapy, and training all categories of staff on DR-TB management guidelines will significantly contribute to improving program outcomes.

Identifiers

PMID39718911
PMCPMC11789963

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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.