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Explaining age disparities in tuberculosis burden in Taiwan: a modelling study

BACKGROUND: Tuberculosis (TB) burden shows wide disparities across ages in Taiwan. In 2016, the age-specific notification rate in those older than 65 years old was about 100 times as much as in those younger than 15 years old (185.0 vs 1.6 per 100,000 population). Similar patterns are observed in ot...

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Autores principales: Fu, Han, Lin, Hsien-Ho, Hallett, Timothy B., Arinaminpathy, Nimalan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7057673/
https://www.ncbi.nlm.nih.gov/pubmed/32131756
http://dx.doi.org/10.1186/s12879-020-4914-2
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author Fu, Han
Lin, Hsien-Ho
Hallett, Timothy B.
Arinaminpathy, Nimalan
author_facet Fu, Han
Lin, Hsien-Ho
Hallett, Timothy B.
Arinaminpathy, Nimalan
author_sort Fu, Han
collection PubMed
description BACKGROUND: Tuberculosis (TB) burden shows wide disparities across ages in Taiwan. In 2016, the age-specific notification rate in those older than 65 years old was about 100 times as much as in those younger than 15 years old (185.0 vs 1.6 per 100,000 population). Similar patterns are observed in other intermediate TB burden settings. However, driving mechanisms for such age disparities are not clear and may have importance for TB control efforts. METHODS: We hypothesised three mechanisms for the age disparity in TB burden: (i) older age groups bear a higher risk of TB progression due to immune senescence, (ii) elderly cases acquired TB infection during a past period of high transmission, which has since rapidly declined and thus contributes to little recent infections, and (iii) assortative mixing by age allows elders to maintain a higher risk of TB infection, while limiting spillover transmission to younger age groups. We developed a series of dynamic compartmental models to incorporate these mechanisms, individually and in combination. The models were calibrated to the TB notification rates in Taiwan over 1997–2016 and evaluated by goodness-of-fit to the age disparities and the temporal trend in the TB burden, as well as the deviance information criterion (DIC). According to the model performance, we compared contributions of the hypothesised mechanisms. RESULTS: The ‘full’ model including all the three hypothesised mechanisms best captured the age disparities and temporal trend of the TB notification rates. However, dropping individual mechanisms from the full model in turn, we found that excluding the mechanism of assortative mixing yielded the least change in goodness-of-fit. In terms of their influence on the TB dynamics, the major contribution of the ‘immune senescence’ and ‘assortative mixing’ mechanisms was to create disparate burden among age groups, while the ‘declining transmission’ mechanism served to capture the temporal trend of notification rates. CONCLUSIONS: In settings such as Taiwan, the current TB burden in the elderly may be impacted more by prevention of active disease following latent infection, than by case-finding for blocking transmission. Further studies on these mechanisms are needed to disentangle their impacts on the TB epidemic and develop corresponding control strategies.
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spelling pubmed-70576732020-03-10 Explaining age disparities in tuberculosis burden in Taiwan: a modelling study Fu, Han Lin, Hsien-Ho Hallett, Timothy B. Arinaminpathy, Nimalan BMC Infect Dis Research Article BACKGROUND: Tuberculosis (TB) burden shows wide disparities across ages in Taiwan. In 2016, the age-specific notification rate in those older than 65 years old was about 100 times as much as in those younger than 15 years old (185.0 vs 1.6 per 100,000 population). Similar patterns are observed in other intermediate TB burden settings. However, driving mechanisms for such age disparities are not clear and may have importance for TB control efforts. METHODS: We hypothesised three mechanisms for the age disparity in TB burden: (i) older age groups bear a higher risk of TB progression due to immune senescence, (ii) elderly cases acquired TB infection during a past period of high transmission, which has since rapidly declined and thus contributes to little recent infections, and (iii) assortative mixing by age allows elders to maintain a higher risk of TB infection, while limiting spillover transmission to younger age groups. We developed a series of dynamic compartmental models to incorporate these mechanisms, individually and in combination. The models were calibrated to the TB notification rates in Taiwan over 1997–2016 and evaluated by goodness-of-fit to the age disparities and the temporal trend in the TB burden, as well as the deviance information criterion (DIC). According to the model performance, we compared contributions of the hypothesised mechanisms. RESULTS: The ‘full’ model including all the three hypothesised mechanisms best captured the age disparities and temporal trend of the TB notification rates. However, dropping individual mechanisms from the full model in turn, we found that excluding the mechanism of assortative mixing yielded the least change in goodness-of-fit. In terms of their influence on the TB dynamics, the major contribution of the ‘immune senescence’ and ‘assortative mixing’ mechanisms was to create disparate burden among age groups, while the ‘declining transmission’ mechanism served to capture the temporal trend of notification rates. CONCLUSIONS: In settings such as Taiwan, the current TB burden in the elderly may be impacted more by prevention of active disease following latent infection, than by case-finding for blocking transmission. Further studies on these mechanisms are needed to disentangle their impacts on the TB epidemic and develop corresponding control strategies. BioMed Central 2020-03-04 /pmc/articles/PMC7057673/ /pubmed/32131756 http://dx.doi.org/10.1186/s12879-020-4914-2 Text en © The Author(s). 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research Article
Fu, Han
Lin, Hsien-Ho
Hallett, Timothy B.
Arinaminpathy, Nimalan
Explaining age disparities in tuberculosis burden in Taiwan: a modelling study
title Explaining age disparities in tuberculosis burden in Taiwan: a modelling study
title_full Explaining age disparities in tuberculosis burden in Taiwan: a modelling study
title_fullStr Explaining age disparities in tuberculosis burden in Taiwan: a modelling study
title_full_unstemmed Explaining age disparities in tuberculosis burden in Taiwan: a modelling study
title_short Explaining age disparities in tuberculosis burden in Taiwan: a modelling study
title_sort explaining age disparities in tuberculosis burden in taiwan: a modelling study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7057673/
https://www.ncbi.nlm.nih.gov/pubmed/32131756
http://dx.doi.org/10.1186/s12879-020-4914-2
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