ArticleGlobal health action2023
Quality of active case-finding for tuberculosis in India: a national level secondary data analysis.
Article in Global health action, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Digital tools not enough; quality control-linked incentives are needed for tuberculosis active case finding.Public health action · 2026Article
- Does active case finding detect TB early in programme settings? A national-level study in India.Public health action · 2026Article
- Evaluating the Usefulness of Artificial Intelligence-based Chest X-Ray Screening in Improving Tuberculosis Detection Among the High-Risk Tribal Population of Chhattisgarh, India: A Prospective Multi-Centre Study.Open forum infectious diseases · 2026Article
- Global status of policies and practices for systematic TB screening in high-burden countries.IJTLD open · 2025Article
- Article
- Is household contact investigation a missing link for tuberculosis care in Chhattisgarh, India?-Operational research using programmatic data.PLOS global public health · 2025Article
- Active case-finding for TB in India: Assessment of scale and quality benchmarks, time taken and use of rapid molecular diagnostic tests.PLOS global public health · 2025Article
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- Article
- Article
- Active Case-Finding: An Effective Solution for Tuberculosis Detection in Vulnerable Groups - The Romanian Experience.Risk management and healthcare policy · 2024Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
backgroundIndia has been implementing active case-finding (ACF) for TB among marginalised and vulnerable (high-risk) populations since 2017. The effectiveness of ACF cycle(s) is dependent on the use of appropriate screening and diagnostic tools and meeting quality indicators.
objectivesTo determine the number of ACF cycles implemented in 2021 at national, state (
methodsIn this descriptive study, aggregate TB program data for each ACF activity that was extracted was further aggregated against each ACF cycle at the district level in 2021. One ACF cycle was the period identified to cover all the high-risk populations in the district. Three TB ACF quality indicators were calculated: percentage population screened (≥10%), percentage tested among screened (≥4.8%) and percentage diagnosed among tested (≥5%). We also calculated the number needed to screen (NNS) for diagnosing one person with TB (≤1538).
resultsOf 768 TB districts, ACF data for 111 were not available. Of the remaining 657 districts, 642 (98%) implemented one, and 15 implemented two to three ACF cycles. None of the districts or states met all three TB ACF quality indicators' cut-offs. At the national level, for the first ACF cycle, 9.3% of the population were screened, 1% of the screened were tested and 3.7% of the tested were diagnosed. The NNS was 2824: acceptable (≤1538) in institutional facilities and poor for population-based groups. Data were not consistently available to calculate the percentage of i) high-risk population covered, ii) presumptive TB among screened and iii) tested among presumptive.
conclusionIn 2021, India implemented one ACF cycle with sub-optimal ACF quality indicators. Reducing the losses between screening and testing, improving data quality and sensitising stakeholders regarding the importance of meeting all ACF quality indicators are recommended.
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