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Protective impacts of household-based tuberculosis contact tracing are robust across endemic incidence levels and community contact patterns

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posted on 2021-03-26, 03:13 authored by J Havumaki, T Cohen, C Zhai, Joel MillerJoel Miller, SD Guikema, MC Eisenberg, J Zelner
Copyright: © 2021 Havumaki et al. 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 credited. There is an emerging consensus that achieving global tuberculosis control targets will require more proactive case finding approaches than are currently used in high-incidence settings. Household contact tracing (HHCT), for which households of newly diagnosed cases are actively screened for additional infected individuals is a potentially efficient approach to finding new cases of tuberculosis, however randomized trials assessing the population-level effects of such interventions in settings with sustained community transmission have shown mixed results. One potential explanation for this is that household transmission is responsible for a variable proportion of population-level tuberculosis burden between settings. For example, transmission is more likely to occur in households in settings with a lower tuberculosis burden and where individuals mix preferentially in local areas, compared with settings with higher disease burden and more dispersed mixing. To better understand the relationship between endemic incidence levels, social mixing, and the impact of HHCT, we developed a spatially explicit model of coupled household and community transmission. We found that the impact of HHCT was robust across settings of varied incidence and community contact patterns. In contrast, we found that the effects of community contact tracing interventions were sensitive to community contact patterns. Our results suggest that the protective benefits of HHCT are robust and the benefits of this intervention are likely to be maintained across epidemiological settings.


JH was supported by the NIH National Institute of General Medical Sciences [Grant Number: U01GM110712], TC was supported by NIH National Institute of Allergy and Infectious Disease [R01 AI112438] and JZ was supported by a grant from the Michigan Institute for Computational Science and Discovery (MICDE) at the University of Michigan. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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PLoS Computational Biology





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ARTN e1008713




Public Library of Science (PLOS)



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