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Determining the risk-factors for molecular clustering of drug-resistant tuberculosis in South Africa

BACKGROUND: Drug-resistant tuberculosis (DR-TB) epidemic is driven mainly by the effect of ongoing transmission. In high-burden settings such as South Africa (SA), considerable demographic and geographic heterogeneity in DR-TB transmission exists. Thus, a better understanding of risk-factors for clu...

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Autores principales: Said, Halima, Kachingwe, Elizabeth, Gardee, Yasmin, Bhyat, Zaheda, Ratabane, John, Erasmus, Linda, Lebaka, Tiisetso, van der Meulen, Minty, Gwala, Thabisile, Omar, Shaheed, Ismail, Farzana, Ismail, Nazir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668341/
https://www.ncbi.nlm.nih.gov/pubmed/38001453
http://dx.doi.org/10.1186/s12889-023-17234-x
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author Said, Halima
Kachingwe, Elizabeth
Gardee, Yasmin
Bhyat, Zaheda
Ratabane, John
Erasmus, Linda
Lebaka, Tiisetso
van der Meulen, Minty
Gwala, Thabisile
Omar, Shaheed
Ismail, Farzana
Ismail, Nazir
author_facet Said, Halima
Kachingwe, Elizabeth
Gardee, Yasmin
Bhyat, Zaheda
Ratabane, John
Erasmus, Linda
Lebaka, Tiisetso
van der Meulen, Minty
Gwala, Thabisile
Omar, Shaheed
Ismail, Farzana
Ismail, Nazir
author_sort Said, Halima
collection PubMed
description BACKGROUND: Drug-resistant tuberculosis (DR-TB) epidemic is driven mainly by the effect of ongoing transmission. In high-burden settings such as South Africa (SA), considerable demographic and geographic heterogeneity in DR-TB transmission exists. Thus, a better understanding of risk-factors for clustering can help to prioritise resources to specifically targeted high-risk groups as well as areas that contribute disproportionately to transmission. METHODS: The study analyzed potential risk-factors for recent transmission in SA, using data collected from a sentinel molecular surveillance of DR-TB, by comparing demographic, clinical and epidemiologic characteristics with clustering and cluster sizes. A genotypic cluster was defined as two or more patients having identical patterns by the two genotyping methods used. Clustering was used as a proxy for recent transmission. Descriptive statistics and multinomial logistic regression were used. RESULT: The study identified 277 clusters, with cluster size ranging between 2 and 259 cases. The majority (81.6%) of the clusters were small (2–5 cases) with few large (11–25 cases) and very large (≥ 26 cases) clusters identified mainly in Western Cape (WC), Eastern Cape (EC) and Mpumalanga (MP). In a multivariable model, patients in clusters including 11–25 and ≥ 26 individuals were more likely to be infected by Beijing family, have XDR-TB, living in Nelson Mandela Metro in EC or Umgungunglovo in Kwa-Zulu Natal (KZN) provinces, and having history of imprisonment. Individuals belonging in a small genotypic cluster were more likely to infected with Rifampicin resistant TB (RR-TB) and more likely to reside in Frances Baard in Northern Cape (NC). CONCLUSION: Sociodemographic, clinical and bacterial risk-factors influenced rate of Mycobacterium tuberculosis (M. tuberculosis) genotypic clustering. Hence, high-risk groups and hotspot areas for clustering in EC, WC, KZN and MP should be prioritized for targeted intervention to prevent ongoing DR-TB transmission.
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spelling pubmed-106683412023-11-24 Determining the risk-factors for molecular clustering of drug-resistant tuberculosis in South Africa Said, Halima Kachingwe, Elizabeth Gardee, Yasmin Bhyat, Zaheda Ratabane, John Erasmus, Linda Lebaka, Tiisetso van der Meulen, Minty Gwala, Thabisile Omar, Shaheed Ismail, Farzana Ismail, Nazir BMC Public Health Research BACKGROUND: Drug-resistant tuberculosis (DR-TB) epidemic is driven mainly by the effect of ongoing transmission. In high-burden settings such as South Africa (SA), considerable demographic and geographic heterogeneity in DR-TB transmission exists. Thus, a better understanding of risk-factors for clustering can help to prioritise resources to specifically targeted high-risk groups as well as areas that contribute disproportionately to transmission. METHODS: The study analyzed potential risk-factors for recent transmission in SA, using data collected from a sentinel molecular surveillance of DR-TB, by comparing demographic, clinical and epidemiologic characteristics with clustering and cluster sizes. A genotypic cluster was defined as two or more patients having identical patterns by the two genotyping methods used. Clustering was used as a proxy for recent transmission. Descriptive statistics and multinomial logistic regression were used. RESULT: The study identified 277 clusters, with cluster size ranging between 2 and 259 cases. The majority (81.6%) of the clusters were small (2–5 cases) with few large (11–25 cases) and very large (≥ 26 cases) clusters identified mainly in Western Cape (WC), Eastern Cape (EC) and Mpumalanga (MP). In a multivariable model, patients in clusters including 11–25 and ≥ 26 individuals were more likely to be infected by Beijing family, have XDR-TB, living in Nelson Mandela Metro in EC or Umgungunglovo in Kwa-Zulu Natal (KZN) provinces, and having history of imprisonment. Individuals belonging in a small genotypic cluster were more likely to infected with Rifampicin resistant TB (RR-TB) and more likely to reside in Frances Baard in Northern Cape (NC). CONCLUSION: Sociodemographic, clinical and bacterial risk-factors influenced rate of Mycobacterium tuberculosis (M. tuberculosis) genotypic clustering. Hence, high-risk groups and hotspot areas for clustering in EC, WC, KZN and MP should be prioritized for targeted intervention to prevent ongoing DR-TB transmission. BioMed Central 2023-11-24 /pmc/articles/PMC10668341/ /pubmed/38001453 http://dx.doi.org/10.1186/s12889-023-17234-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Said, Halima
Kachingwe, Elizabeth
Gardee, Yasmin
Bhyat, Zaheda
Ratabane, John
Erasmus, Linda
Lebaka, Tiisetso
van der Meulen, Minty
Gwala, Thabisile
Omar, Shaheed
Ismail, Farzana
Ismail, Nazir
Determining the risk-factors for molecular clustering of drug-resistant tuberculosis in South Africa
title Determining the risk-factors for molecular clustering of drug-resistant tuberculosis in South Africa
title_full Determining the risk-factors for molecular clustering of drug-resistant tuberculosis in South Africa
title_fullStr Determining the risk-factors for molecular clustering of drug-resistant tuberculosis in South Africa
title_full_unstemmed Determining the risk-factors for molecular clustering of drug-resistant tuberculosis in South Africa
title_short Determining the risk-factors for molecular clustering of drug-resistant tuberculosis in South Africa
title_sort determining the risk-factors for molecular clustering of drug-resistant tuberculosis in south africa
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10668341/
https://www.ncbi.nlm.nih.gov/pubmed/38001453
http://dx.doi.org/10.1186/s12889-023-17234-x
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