ArticleEpidemics2022
Spatially targeted digital chest radiography to reduce tuberculosis in high-burden settings: A study of adaptive decision making.
Article in Epidemics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 3 citations in OpenAlex.
- Pre-screening village tuberculosis notification rates and the yield of interferon-gamma release assay testing in a rural health-examination campaign in Xinjiang, China.Frontiers in public health · 2026Article
- Adaptive bandit algorithms increase efficiency of mobile tuberculosis screening programs.Scientific reports · 2025Article
- Integrating testing volume into bandit algorithms for infectious disease surveillance.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundSpatially-targeted approaches to screen for tuberculosis (TB) could accelerate TB control in high-burden populations. We aimed to estimate gains in case-finding yield under an adaptive decision-making approach for spatially-targeted, mobile digital chest radiography (dCXR)-based screening in communities with varying levels of TB prevalence.
methodsWe used a Monte-Carlo simulation model to simulate a spatially-targeted screening intervention in 24 communities with TB prevalence estimates derived from a large community-randomized trial. We implemented a Thompson sampling algorithm to allocate screening units based on Bayesian probabilities of local TB prevalence that are continuously updated during weekly screening rounds. Four mobile units for dCXR-based screening and subsequent Xpert Ultra-based testing were allocated among the communities during a 52-week period. We estimated the yield of bacteriologically-confirmed TB per 1000 screenings comparing scenarios of spatially-targeted and untargeted resource allocation.
resultsWe estimated that under the untargeted scenario, an expected 666 (95% uncertainty interval 522-825) TB cases would be detected over one year, equivalent to 8.9 (7.5-10.3) per 1000 individuals screened. Allocating the screening units to the communities with the highest (prior-year) cases notification rates resulted in an expected 760 (617-926) TB cases detected, 10.1 (8.6-11.8) per 1000 screened. Adaptive, spatially-targeted screening resulted in an expected 1241 (995-1502) TB cases detected, 16.5 (14.5-18.7) per 1000 screened. Numbers of dCXR-based screenings needed to detect one additional TB case declined during the first 12-14 weeks as a result of Bayesian learning.
conclusionWe introduce a spatially-targeted screening strategy that could reduce the number of screenings necessary to detect additional TB in high-burden settings and thus improve the efficiency of screening interventions. Empirical trials are needed to determine whether this approach could be successfully implemented.
Indexed as
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.