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Estimating tuberculosis drug resistance amplification rates in high-burden settings

BACKGROUND: Antimicrobial resistance develops following the accrual of mutations in the bacterial genome, and may variably impact organism fitness and hence, transmission risk. Classical representation of tuberculosis (TB) dynamics using a single or two strain (DS/MDR-TB) model typically does not ca...

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Autores principales: Karmakar, Malancha, Ragonnet, Romain, Ascher, David B., Trauer, James M., Denholm, Justin T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785585/
https://www.ncbi.nlm.nih.gov/pubmed/35073862
http://dx.doi.org/10.1186/s12879-022-07067-1
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author Karmakar, Malancha
Ragonnet, Romain
Ascher, David B.
Trauer, James M.
Denholm, Justin T.
author_facet Karmakar, Malancha
Ragonnet, Romain
Ascher, David B.
Trauer, James M.
Denholm, Justin T.
author_sort Karmakar, Malancha
collection PubMed
description BACKGROUND: Antimicrobial resistance develops following the accrual of mutations in the bacterial genome, and may variably impact organism fitness and hence, transmission risk. Classical representation of tuberculosis (TB) dynamics using a single or two strain (DS/MDR-TB) model typically does not capture elements of this important aspect of TB epidemiology. To understand and estimate the likelihood of resistance spreading in high drug-resistant TB incidence settings, we used epidemiological data to develop a mathematical model of Mycobacterium tuberculosis (Mtb) transmission. METHODS: A four-strain (drug-susceptible (DS), isoniazid mono-resistant (INH-R), rifampicin mono-resistant (RIF-R) and multidrug-resistant (MDR)) compartmental deterministic Mtb transmission model was developed to explore the progression from DS- to MDR-TB in The Philippines and Viet Nam. The models were calibrated using data from national tuberculosis prevalence (NTP) surveys and drug resistance surveys (DRS). An adaptive Metropolis algorithm was used to estimate the risks of drug resistance amplification among unsuccessfully treated individuals. RESULTS: The estimated proportion of INH-R amplification among failing treatments was 0.84 (95% CI 0.79–0.89) for The Philippines and 0.77 (95% CI 0.71–0.84) for Viet Nam. The proportion of RIF-R amplification among failing treatments was 0.05 (95% CI 0.04–0.07) for The Philippines and 0.011 (95% CI 0.010–0.012) for Viet Nam. CONCLUSION: The risk of resistance amplification due to treatment failure for INH was dramatically higher than RIF. We observed RIF-R strains were more likely to be transmitted than acquired through amplification, while both mechanisms of acquisition were important contributors in the case of INH-R. These findings highlight the complexity of drug resistance dynamics in high-incidence settings, and emphasize the importance of prioritizing testing algorithms which allow for early detection of INH-R. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07067-1.
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spelling pubmed-87855852022-01-24 Estimating tuberculosis drug resistance amplification rates in high-burden settings Karmakar, Malancha Ragonnet, Romain Ascher, David B. Trauer, James M. Denholm, Justin T. BMC Infect Dis Research BACKGROUND: Antimicrobial resistance develops following the accrual of mutations in the bacterial genome, and may variably impact organism fitness and hence, transmission risk. Classical representation of tuberculosis (TB) dynamics using a single or two strain (DS/MDR-TB) model typically does not capture elements of this important aspect of TB epidemiology. To understand and estimate the likelihood of resistance spreading in high drug-resistant TB incidence settings, we used epidemiological data to develop a mathematical model of Mycobacterium tuberculosis (Mtb) transmission. METHODS: A four-strain (drug-susceptible (DS), isoniazid mono-resistant (INH-R), rifampicin mono-resistant (RIF-R) and multidrug-resistant (MDR)) compartmental deterministic Mtb transmission model was developed to explore the progression from DS- to MDR-TB in The Philippines and Viet Nam. The models were calibrated using data from national tuberculosis prevalence (NTP) surveys and drug resistance surveys (DRS). An adaptive Metropolis algorithm was used to estimate the risks of drug resistance amplification among unsuccessfully treated individuals. RESULTS: The estimated proportion of INH-R amplification among failing treatments was 0.84 (95% CI 0.79–0.89) for The Philippines and 0.77 (95% CI 0.71–0.84) for Viet Nam. The proportion of RIF-R amplification among failing treatments was 0.05 (95% CI 0.04–0.07) for The Philippines and 0.011 (95% CI 0.010–0.012) for Viet Nam. CONCLUSION: The risk of resistance amplification due to treatment failure for INH was dramatically higher than RIF. We observed RIF-R strains were more likely to be transmitted than acquired through amplification, while both mechanisms of acquisition were important contributors in the case of INH-R. These findings highlight the complexity of drug resistance dynamics in high-incidence settings, and emphasize the importance of prioritizing testing algorithms which allow for early detection of INH-R. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-022-07067-1. BioMed Central 2022-01-24 /pmc/articles/PMC8785585/ /pubmed/35073862 http://dx.doi.org/10.1186/s12879-022-07067-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Karmakar, Malancha
Ragonnet, Romain
Ascher, David B.
Trauer, James M.
Denholm, Justin T.
Estimating tuberculosis drug resistance amplification rates in high-burden settings
title Estimating tuberculosis drug resistance amplification rates in high-burden settings
title_full Estimating tuberculosis drug resistance amplification rates in high-burden settings
title_fullStr Estimating tuberculosis drug resistance amplification rates in high-burden settings
title_full_unstemmed Estimating tuberculosis drug resistance amplification rates in high-burden settings
title_short Estimating tuberculosis drug resistance amplification rates in high-burden settings
title_sort estimating tuberculosis drug resistance amplification rates in high-burden settings
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8785585/
https://www.ncbi.nlm.nih.gov/pubmed/35073862
http://dx.doi.org/10.1186/s12879-022-07067-1
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