Multiple exposures, reinfection and risk of progression to active tuberculosis

A recent study reported on a tuberculosis (TB) outbreak in a largely Inuit village. Among newly infected individuals, exposure to additional active cases was associated with an increasing probability of developing active disease within a year. Using binomial risk models, we evaluated two potential m...

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Main Authors: Sarah F. Ackley, Robyn S. Lee, Lee Worden, Erin Zwick, Travis C. Porco, Marcel A. Behr, Caitlin S. Pepperell
Format: Article
Language:English
Published: The Royal Society 2019-03-01
Series:Royal Society Open Science
Subjects:
Online Access:https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.180999
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author Sarah F. Ackley
Robyn S. Lee
Lee Worden
Erin Zwick
Travis C. Porco
Marcel A. Behr
Caitlin S. Pepperell
author_facet Sarah F. Ackley
Robyn S. Lee
Lee Worden
Erin Zwick
Travis C. Porco
Marcel A. Behr
Caitlin S. Pepperell
author_sort Sarah F. Ackley
collection DOAJ
description A recent study reported on a tuberculosis (TB) outbreak in a largely Inuit village. Among newly infected individuals, exposure to additional active cases was associated with an increasing probability of developing active disease within a year. Using binomial risk models, we evaluated two potential mechanisms by which multiple infections during the first year following initial infection could account for increasing disease risk with increasing exposures. In the reinfection model, each infectious contact confers an independent risk of an infection, and infections contribute independently to active disease. In the threshold model, disease risk follows a sigmoidal function with small numbers of infectious contacts conferring a low risk of active disease and large numbers of contacts conferring a high risk. To determine the dynamic impact of reinfection during the early phase of infection, we performed simulations from a modified Reed–Frost model of TB dynamics following spread from an initial number of cases. We parametrized this model with the maximum-likelihood estimates from the reinfection and threshold models in addition to the observed distribution of exposures among new infections. We find that both models can plausibly account for the observed increase in disease risk with increasing infectious contacts, but the threshold model confers a better fit than a nested model without a threshold (p = 0.04). Our simulations indicate that multiple exposures to infectious individuals during this critical time period can lead to dramatic increases in outbreak size. In order to decrease TB burden in high-prevalence settings, it may be necessary to implement measures aimed at preventing repeated exposures, in addition to preventing primary infection.
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spelling doaj.art-bd69e3f5e9fe4207ac046740539b91db2022-12-22T00:16:10ZengThe Royal SocietyRoyal Society Open Science2054-57032019-03-016310.1098/rsos.180999180999Multiple exposures, reinfection and risk of progression to active tuberculosisSarah F. AckleyRobyn S. LeeLee WordenErin ZwickTravis C. PorcoMarcel A. BehrCaitlin S. PepperellA recent study reported on a tuberculosis (TB) outbreak in a largely Inuit village. Among newly infected individuals, exposure to additional active cases was associated with an increasing probability of developing active disease within a year. Using binomial risk models, we evaluated two potential mechanisms by which multiple infections during the first year following initial infection could account for increasing disease risk with increasing exposures. In the reinfection model, each infectious contact confers an independent risk of an infection, and infections contribute independently to active disease. In the threshold model, disease risk follows a sigmoidal function with small numbers of infectious contacts conferring a low risk of active disease and large numbers of contacts conferring a high risk. To determine the dynamic impact of reinfection during the early phase of infection, we performed simulations from a modified Reed–Frost model of TB dynamics following spread from an initial number of cases. We parametrized this model with the maximum-likelihood estimates from the reinfection and threshold models in addition to the observed distribution of exposures among new infections. We find that both models can plausibly account for the observed increase in disease risk with increasing infectious contacts, but the threshold model confers a better fit than a nested model without a threshold (p = 0.04). Our simulations indicate that multiple exposures to infectious individuals during this critical time period can lead to dramatic increases in outbreak size. In order to decrease TB burden in high-prevalence settings, it may be necessary to implement measures aimed at preventing repeated exposures, in addition to preventing primary infection.https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.180999tuberculosisdisease progressioncase/control studyepidemiologycanadacontact tracing
spellingShingle Sarah F. Ackley
Robyn S. Lee
Lee Worden
Erin Zwick
Travis C. Porco
Marcel A. Behr
Caitlin S. Pepperell
Multiple exposures, reinfection and risk of progression to active tuberculosis
Royal Society Open Science
tuberculosis
disease progression
case/control study
epidemiology
canada
contact tracing
title Multiple exposures, reinfection and risk of progression to active tuberculosis
title_full Multiple exposures, reinfection and risk of progression to active tuberculosis
title_fullStr Multiple exposures, reinfection and risk of progression to active tuberculosis
title_full_unstemmed Multiple exposures, reinfection and risk of progression to active tuberculosis
title_short Multiple exposures, reinfection and risk of progression to active tuberculosis
title_sort multiple exposures reinfection and risk of progression to active tuberculosis
topic tuberculosis
disease progression
case/control study
epidemiology
canada
contact tracing
url https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.180999
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