Evidence map›Paper›PMID 38933801›Full record

ArticleIndian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine

Active Case Finding among Adults using an Individual's Vulnerability Score for Pulmonary Tuberculosis in a Rural Village of Goa: A Cross-sectional Study.

Bhargav P Sawant Dessai, Malavika Krishnan, Hemangini K Shah

Abstract read
In one paragraph

Article in Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Bhargav P Sawant DessaiDepartment of Community Medicine, Goa Medical College, Bambolim, Goa, India.
Malavika KrishnanDepartment of Community Medicine, Goa Medical College, Bambolim, Goa, India.
Hemangini K ShahDepartment of Community Medicine, Goa Medical College, Bambolim, Goa, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: "Detect-Treat-Prevent-Build" to achieve tuberculosis (TB)-free India is envisaged in the National Tuberculosis Elimination Program (NTEP). To be able to achieve this, it is important to address the fact that the most vulnerable and hard-to-reach groups need to undertake screening. The present review aimed to examine the vulnerability in connection with TB disparities faced by distinct sub-populations generally viewed as vulnerable and follow these for testing. Materials and Methods: The community-based cross-sectional study was conducted in the field practice area of sub-center Carambolim in a rural area of Goa for 3 months. The households were visited, and data collected via personal interviews were recorded on the questionnaire study tool. Based on the data, the participants' vulnerability mapping was done per the parameters identified. Results: Among 223 households, 528 persons were screened for vulnerability. The 47 highly vulnerable participants were advised sputum CBNAAT, of which 9 (19%) tested positive for pulmonary TB, while of the 86 moderately vulnerable participants, 4 (5%) tested positive for pulmonary TB. Among the 34 with symptoms suggestive of TB, 3 (9%) tested positive for pulmonary TB. Conclusions: The study detected 16 new TB patients from the population and found a higher incidence of pulmonary TB among the vulnerable group with no symptoms of Pulmonary TB. A further state-wide survey is recommended to diagnose such cases.

Indexed as

Active case findingNTEPtuberculosisvulnerability mapping

Identifiers

PMID38933801
PMCPMC11198518

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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.