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Stochastic agent-based modeling of tuberculosis in Canadian Indigenous communities
BACKGROUND: In Canada, active tuberculosis (TB) disease rates remain disproportionately higher among the Indigenous population, especially among the Inuit in the north. We used mathematical modeling to evaluate how interventions might enhance existing TB control efforts in a region of Nunavut. METHO...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5237134/ https://www.ncbi.nlm.nih.gov/pubmed/28086846 http://dx.doi.org/10.1186/s12889-016-3996-7 |
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author | Tuite, Ashleigh R. Gallant, Victor Randell, Elaine Bourgeois, Annie-Claude Greer, Amy L. |
author_facet | Tuite, Ashleigh R. Gallant, Victor Randell, Elaine Bourgeois, Annie-Claude Greer, Amy L. |
author_sort | Tuite, Ashleigh R. |
collection | PubMed |
description | BACKGROUND: In Canada, active tuberculosis (TB) disease rates remain disproportionately higher among the Indigenous population, especially among the Inuit in the north. We used mathematical modeling to evaluate how interventions might enhance existing TB control efforts in a region of Nunavut. METHODS: We developed a stochastic, agent-based model of TB transmission that captured the unique household and community structure. Evaluated interventions included: (i) rapid treatment of active cases; (ii) rapid contact tracing; (iii) expanded screening programs for latent TB infection (LTBI); and (iv) reduced household density. The outcomes of interest were incident TB infections and total diagnosed active TB disease over a 10- year time period. RESULTS: Model-projected incidence in the absence of additional interventions was highly variable (range: 33–369 cases) over 10 years. Compared to the ‘no additional intervention’ scenario, reducing the time between onset of active TB disease and initiation of treatment reduced both the number of new TB infections (47% reduction, relative risk of TB = 0.53) and diagnoses of active TB disease (19% reduction, relative risk of TB = 0.81). Expanding general population screening was also projected to reduce the burden of TB, although these findings were sensitive to assumptions around the relative amount of transmission occurring outside of households. Other potential interventions examined in the model (school-based screening, rapid contact tracing, and reduced household density) were found to have limited effectiveness. CONCLUSIONS: In a region of northern Canada experiencing a significant TB burden, more rapid treatment initiation in active TB cases was the most impactful intervention evaluated. Mathematical modeling can provide guidance for allocation of limited resources in a way that minimizes disease transmission and protects population health. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12889-016-3996-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5237134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-52371342017-01-18 Stochastic agent-based modeling of tuberculosis in Canadian Indigenous communities Tuite, Ashleigh R. Gallant, Victor Randell, Elaine Bourgeois, Annie-Claude Greer, Amy L. BMC Public Health Research Article BACKGROUND: In Canada, active tuberculosis (TB) disease rates remain disproportionately higher among the Indigenous population, especially among the Inuit in the north. We used mathematical modeling to evaluate how interventions might enhance existing TB control efforts in a region of Nunavut. METHODS: We developed a stochastic, agent-based model of TB transmission that captured the unique household and community structure. Evaluated interventions included: (i) rapid treatment of active cases; (ii) rapid contact tracing; (iii) expanded screening programs for latent TB infection (LTBI); and (iv) reduced household density. The outcomes of interest were incident TB infections and total diagnosed active TB disease over a 10- year time period. RESULTS: Model-projected incidence in the absence of additional interventions was highly variable (range: 33–369 cases) over 10 years. Compared to the ‘no additional intervention’ scenario, reducing the time between onset of active TB disease and initiation of treatment reduced both the number of new TB infections (47% reduction, relative risk of TB = 0.53) and diagnoses of active TB disease (19% reduction, relative risk of TB = 0.81). Expanding general population screening was also projected to reduce the burden of TB, although these findings were sensitive to assumptions around the relative amount of transmission occurring outside of households. Other potential interventions examined in the model (school-based screening, rapid contact tracing, and reduced household density) were found to have limited effectiveness. CONCLUSIONS: In a region of northern Canada experiencing a significant TB burden, more rapid treatment initiation in active TB cases was the most impactful intervention evaluated. Mathematical modeling can provide guidance for allocation of limited resources in a way that minimizes disease transmission and protects population health. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12889-016-3996-7) contains supplementary material, which is available to authorized users. BioMed Central 2017-01-13 /pmc/articles/PMC5237134/ /pubmed/28086846 http://dx.doi.org/10.1186/s12889-016-3996-7 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Tuite, Ashleigh R. Gallant, Victor Randell, Elaine Bourgeois, Annie-Claude Greer, Amy L. Stochastic agent-based modeling of tuberculosis in Canadian Indigenous communities |
title | Stochastic agent-based modeling of tuberculosis in Canadian Indigenous communities |
title_full | Stochastic agent-based modeling of tuberculosis in Canadian Indigenous communities |
title_fullStr | Stochastic agent-based modeling of tuberculosis in Canadian Indigenous communities |
title_full_unstemmed | Stochastic agent-based modeling of tuberculosis in Canadian Indigenous communities |
title_short | Stochastic agent-based modeling of tuberculosis in Canadian Indigenous communities |
title_sort | stochastic agent-based modeling of tuberculosis in canadian indigenous communities |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5237134/ https://www.ncbi.nlm.nih.gov/pubmed/28086846 http://dx.doi.org/10.1186/s12889-016-3996-7 |
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