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A Novel Tool Improves Existing Estimates of Recent Tuberculosis Transmission in Settings of Sparse Data Collection

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...

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Autores principales: Kasaie, Parastu, Mathema, Barun, Kelton, W. David, Azman, Andrew S., Pennington, Jeff, Dowdy, David W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4683006/
https://www.ncbi.nlm.nih.gov/pubmed/26679499
http://dx.doi.org/10.1371/journal.pone.0144137
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author Kasaie, Parastu
Mathema, Barun
Kelton, W. David
Azman, Andrew S.
Pennington, Jeff
Dowdy, David W.
author_facet Kasaie, Parastu
Mathema, Barun
Kelton, W. David
Azman, Andrew S.
Pennington, Jeff
Dowdy, David W.
author_sort Kasaie, Parastu
collection PubMed
description 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.
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spelling pubmed-46830062015-12-31 A Novel Tool Improves Existing Estimates of Recent Tuberculosis Transmission in Settings of Sparse Data Collection Kasaie, Parastu Mathema, Barun Kelton, W. David Azman, Andrew S. Pennington, Jeff Dowdy, David W. PLoS One Research Article 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. Public Library of Science 2015-12-17 /pmc/articles/PMC4683006/ /pubmed/26679499 http://dx.doi.org/10.1371/journal.pone.0144137 Text en © 2015 Kasaie et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Kasaie, Parastu
Mathema, Barun
Kelton, W. David
Azman, Andrew S.
Pennington, Jeff
Dowdy, David W.
A Novel Tool Improves Existing Estimates of Recent Tuberculosis Transmission in Settings of Sparse Data Collection
title A Novel Tool Improves Existing Estimates of Recent Tuberculosis Transmission in Settings of Sparse Data Collection
title_full A Novel Tool Improves Existing Estimates of Recent Tuberculosis Transmission in Settings of Sparse Data Collection
title_fullStr A Novel Tool Improves Existing Estimates of Recent Tuberculosis Transmission in Settings of Sparse Data Collection
title_full_unstemmed A Novel Tool Improves Existing Estimates of Recent Tuberculosis Transmission in Settings of Sparse Data Collection
title_short A Novel Tool Improves Existing Estimates of Recent Tuberculosis Transmission in Settings of Sparse Data Collection
title_sort novel tool improves existing estimates of recent tuberculosis transmission in settings of sparse data collection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4683006/
https://www.ncbi.nlm.nih.gov/pubmed/26679499
http://dx.doi.org/10.1371/journal.pone.0144137
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