Evidence map›Paper›PMID 42639579›Full record

ArticlePublic health action2026

Digital tools not enough; quality control-linked incentives are needed for tuberculosis active case finding.

H Abdullah, H D Shewade, P Manickam, K Gupta, M S Ambiya, R S Dhakad, K Sharma, S Nag, M Shahnawaz, R Nagar

Abstract read
In one paragraph

Article in Public health action, 2026. 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

10 authors.

H AbdullahKhushi Baby, Jaipur, India.ORCID https://orcid.org/0009-0004-4592-4060
H D ShewadeDivision of Health Systems Research, ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India.
P ManickamDivision of Health Systems Research, ICMR National Institute of Epidemiology (ICMR-NIE), Chennai, India.
K GuptaDepartment of Community Medicine, Geetanjali Institute of Medical Sciences, Jaipur, India.
M S AmbiyaKhushi Baby, Jaipur, India.
R S DhakadKhushi Baby, Jaipur, India.
K SharmaKhushi Baby, Jaipur, India.
S NagKhushi Baby, Jaipur, India.
M ShahnawazKhushi Baby, Jaipur, India.
R NagarKhushi Baby, Jaipur, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLow yield of tuberculosis (TB) active case finding (ACF) in routine health systems is a concern. Following identification of high-risk villages/wards guided by annual digital household survey (captured in Community Health Integrated Platform [CHIP]), the health system utilised the ACF module of CHIP for an ACF cycle (December 2025 to January 2026) in Udaipur district, Rajasthan, India. Despite advocating the recommendations from a recently concluded national-level TB ACF evaluation, sputum collection and transport and quality control-lined incentives were not implemented.

objectivesTo document the reported and estimated (after telephonic data cross-verification by investigators) scale, quality, and yield of ACF, based on a longitudinal descriptive study using secondary data.

resultOf 1.3 million high-risk populations, 70.6% (scale) were reportedly screened. With 44 (0.6% test positivity) reported ACF-detected TB, the number needed to screen (NNS) was 21,082 (quality) and yield was 0.7% (total ACF-detected TB divided by [0.5% × total high-risk population]). The estimated scale was 35% (n = 457,240) and one out of 44 was confirmed to be ACF-detected (estimated NNS = 457,240, estimated yield <0.1%). Similar suboptimal results were observed in 33% villages/wards that were multidimensionally poor as per CHIP and additional 21% villages/wards identified as high-risk through local mapping.

conclusionSuboptimal ACF yield reveals digital tools alone are insufficient.

Indexed as

active case findingcross-verificationdigital health surveytuberculosis

Identifiers

PMID42639579
PMCPMC13502500

What OpenQuestion holds

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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.