title: A Novel Tool Improves Existing Estimates of Recent Tuberculosis Transmission in Settings of Sparse Data Collection.
abstract: In any setting, a proportion of incident active tuberculosis (TB) reflects recent transmission ("recent transmission proportion"), whereas the remainder represents reactivation. Appropriately estimating the recent transmission proportion has important implications for local TB control, but existing approaches have known biases, especially where data are incomplete. We constructed a stochastic individual-based model of a TB epidemic and designed a set of simulations (derivation set) to develop two regression-based tools for estimating the recent transmission proportion from five inputs: underlying TB incidence, sampling coverage, study duration, clustered proportion of observed cases, and proportion of observed clusters in the sample. We tested these tools on a set of unrelated simulations (validation set), and compared their performance against that of the traditional 'n-1' approach. In the validation set, the regression tools reduced the absolute estimation bias (difference between estimated and true recent transmission proportion) in the 'n-1' technique by a median [interquartile range] of 60% [9%, 82%] and 69% [30%, 87%]. The bias in the 'n-1' model was highly sensitive to underlying levels of study coverage and duration, and substantially underestimated the recent transmission proportion in settings of incomplete data coverage. By contrast, the regression models' performance was more consistent across different epidemiological settings and study characteristics. We provide one of these regression models as a user-friendly, web-based tool. Novel tools can improve our ability to estimate the recent TB transmission proportion from data that are observable (or estimable) by public health practitioners with limited available molecular data.
authors.affiliation: Department of Epidemiology, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, United States of America.
authors.forename: Parastu
authors.initials: P
authors.lastname: Kasaie
authors.affiliation: Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States of America.
authors.forename: Barun
authors.initials: B
authors.lastname: Mathema
authors.affiliation: Department of Operations, Business Analytics, and Information Systems, University of Cincinnati, Cincinnati, OH, United States of America.
authors.forename: W David
authors.initials: WD
authors.lastname: Kelton
authors.affiliation: Department of Epidemiology, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, United States of America.
authors.forename: Andrew S
authors.initials: AS
authors.lastname: Azman
authors.affiliation: Department of Epidemiology, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, United States of America.
authors.forename: Jeff
authors.initials: J
authors.lastname: Pennington
authors.affiliation: Department of Epidemiology, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, United States of America.
authors.forename: David W
authors.initials: DW
authors.lastname: Dowdy
mesh_terms: D003625:Data Collection; D006801:Humans; D015994:Incidence; D013269:Stochastic Processes; D014376:Tuberculosis / Q000453:epidemiology / Q000635:transmission*