ReviewTuberculosis research and treatment2022
Yield and Coverage of Active Case Finding Interventions for Tuberculosis Control:A Systematic Review and Meta-analysis.
Review in Tuberculosis research and treatment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- Article
- The yield of tuberculosis contact investigation in São Paulo, Brazil: a community-based cross-sectional study.Epidemiology and infection · 2025Article
- Neutrophil proteins as potential biomarkers for a sputum-based tuberculosis screening test.Frontiers in immunology · 2025Article
- Impact of the use of the ultra-portable digital x-ray with CAD4TB for active case finding for tuberculosis in Nigeria.Frontiers in digital health · 2025Article
- Article
- Geographical targeting of active case finding for tuberculosis in Pakistan using hotspots identified by artificial intelligence software (SPOT-TB): study protocol for a pragmatic stepped wedge cluster randomised control trial.BMJ open respiratory research · 2024Article
- Drug-Resistant Tuberculosis Case-Finding Strategies: Scoping Review.JMIR public health and surveillance · 2024Article
- Community tuberculosis screening, testing and care, Uganda.Bulletin of the World Health Organization · 2024Article
- Gendered gaps to tuberculosis prevention and care in Kenya: a political economy analysis study.BMJ open · 2024Article
- Article
- Delays in Treatment Initiation and Treatment Outcomes in Patients with Tuberculosis in the Kyrgyz Republic: Are There Differences between Migrants and Non-Migrants?Tropical medicine and infectious disease · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Active case finding (ACF) for tuberculosis (TB) is a key strategy to reduce diagnostic delays, expedite treatment, and prevent transmission. Objective: Our objective was to identify the populations, settings, screening and diagnostic approaches that optimize coverage (proportion of those targeted who were screened) and yield (proportion of those screened who had active TB) in ACF programs. Methods: We performed a comprehensive search to identify studies published from 1980-2016 that reported the coverage and yield of different ACF approaches. For each outcome, we conducted meta-analyses of single proportions to produce estimates across studies, followed by meta-regression to identify predictors. Conclusion: ACF activities can be implemented successfully in various populations and settings. Screening yield was highest in internally-displaced person and healthcare settings, and among PLWH and children. In high-prevalence settings, ACF approaches that screen individuals with laboratory tests regardless of symptoms have higher yield than approaches focused on symptomatic individuals.
Identifiers
What OpenQuestion holds
Registered trials
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.