ABSTRACT
BACKGROUND: Spatially-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. METHODS: We 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. RESULTS: We 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. CONCLUSION: We 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.
