Despite tuberculosis (TB) being a preventable infectious disease, one million children fall sick with TB every year. Children are a uniquely vulnerable population because they are difficult to diagnose using common microbiological tests and, once exposed and infected, they have a high risk of progression to TB disease and death. Because children progress more quickly to disease than adults, they can be considered as a sentinel TB event? cautioning that recent exposure to an infectious, untreated person has occurred and signaling for thorough contact tracing to commence. Identifying infectious individuals in a community is the first of multiple steps to breaking the cycle of TB transmission. The goal of this project is to define the potential role of spatial data analyses of childhood TB in identifying geographic areas with high TB transmission in children and adults. This project proposes to make children the cornerstone of TB surveillance, which is a novel recommendation considering children have been largely neglected in TB epidemiology research and current interventions designed to target children at high risk for TB are largely underutilized.
The first aim of this proposal is to characterize spatial distributions of TB among children exposed at home to TB. Additionally, spatial associations with known patient- and population-level risk factors will be explored. Geographic distributions, spatial clustering, and transmission patterns over time? of child TB cases compared to adult TB cases? may identify priority locations in which to target activities to find infectious TB cases.
The second aim i s to explore the application of spatial data and predictive modelling of childhood TB to elucidate patterns of community-wide TB transmission. This will allow a characterization of the predictive abilities of varying signals of recent TB transmission in children, such as a case of TB infection or disease, in predicting TB cases in a specified geographic locale and timeframe. This proposal builds upon the candidate?s demonstrated commitment to a career in global health research, with a focus on pediatric TB epidemiology. Through mentored training and research, this proposal will allow the candidate to develop expertise in pediatric TB epidemiology and geospatial methodology. The overarching goal of this F32 proposal is to inform the development of a K01 application that validates the application of predictive geospatial techniques in TB epidemiology using childhood TB data as analytic signals. The candidate has defined a detailed career development plan and assembled an experienced team of mentors, led by Dr. Mercedes Becerra, a globally recognized expert in pediatric TB epidemiology.
Active-case finding efforts are essential to detecting missing cases of tuberculosis and identifying individuals who may benefit from prophylactic treatment. Utilizing known spatial patterns of tuberculosis distribution and considering cases of childhood tuberculosis as sentinel events for transmission can inform novel, efficient, and targeted case-finding strategies. The proposed research will lay the groundwork for targeted interventions that can promptly identify tuberculosis transmission, reduce the number of undetected tuberculosis cases, and, ultimately, drive down tuberculosis rates globally.
