Congregate air sampling for population-based detection of tuberculosis

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Project Summary Tuberculosis is one of the leading causes of morbidity and mortality worldwide. Conventional approaches to studying tuberculosis transmission, and to early case detection, have been based on individual-level screening. Because of the long, subclinical phase of tuberculosis, during which infectious individuals have many casual contacts, understanding where transmission occurs in community settings has been a vexing challenge. Social network and molecular epidemiologic studies indicate that over 80% of transmission cannot be linked to close contacts or household members. Additionally, this prolonged period of transmission prior to healthcare-seeking makes tuberculosis difficult to control. Active case finding approaches, which involve screening individuals prior to care-seeking, are resource-intensive due to the high number-needed- to-test and have not been sustainable in most high burden countries. Here, we propose a fundamentally new approach to tuberculosis detection that would overcome these limitations: identifying M. tuberculosis in the shared air of congregate settings. Using custom-built air sampling devices and highly sensitive and specific molecular diagnostic techniques, we will rigorously investigate this approach with three fundamental, real- world applications. The first is to evaluate congregate air sampling as a group screening method for early case detection of tuberculosis, and to identify optimal sampling approaches through empirical data collection and model-based analysis. The second application is to identify high-risk environments for tuberculosis transmission, through systematic air sampling in public settings throughout a highly endemic South African township. The third is to efficiently generate population-level tuberculosis prevalence estimates by combining air sampling and social mixing data, and applying statistical inferential models based upon pooled diagnostic sampling. This project will generate critical data on this new population-based approach to M. tuberculosis detection, and will develop an epidemiologic and statistical framework with which to translate this into a tool for surveillance and early case detection.