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Epidemiologic and viral predictors of antiretroviral drug resistance among persons living with HIV in a large treatment program in Nigeria

BACKGROUND: Expanded access to combination antiretroviral therapy (cART) throughout sub-Saharan Africa over the last decade has remarkably improved the prognosis of persons living with HIV (PLWH). However, some PLWH experience virologic rebound after a period of viral suppression, usually followed b...

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Autores principales: Ekong, Ernest, Ndembi, Nicaise, Okonkwo, Prosper, Dakum, Patrick, Idoko, John, Banigbe, Bolanle, Okuma, James, Agaba, Patricia, Blattner, William, Adebamowo, Clement, Charurat, Manhattan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7027291/
https://www.ncbi.nlm.nih.gov/pubmed/32066473
http://dx.doi.org/10.1186/s12981-020-0261-z
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author Ekong, Ernest
Ndembi, Nicaise
Okonkwo, Prosper
Dakum, Patrick
Idoko, John
Banigbe, Bolanle
Okuma, James
Agaba, Patricia
Blattner, William
Adebamowo, Clement
Charurat, Manhattan
author_facet Ekong, Ernest
Ndembi, Nicaise
Okonkwo, Prosper
Dakum, Patrick
Idoko, John
Banigbe, Bolanle
Okuma, James
Agaba, Patricia
Blattner, William
Adebamowo, Clement
Charurat, Manhattan
author_sort Ekong, Ernest
collection PubMed
description BACKGROUND: Expanded access to combination antiretroviral therapy (cART) throughout sub-Saharan Africa over the last decade has remarkably improved the prognosis of persons living with HIV (PLWH). However, some PLWH experience virologic rebound after a period of viral suppression, usually followed by selection of drug resistant virus. Determining factors associated with drug resistance can inform patient management and healthcare policies, particularly in resource-limited settings where drug resistance testing is not routine. METHODS: A case–control study was conducted using data captured from an electronic medical record in a large treatment program in Nigeria. Cases PLWH receiving cART who developed acquired drug resistance (ADR) and controls were those without ADR between 2004 and 2011. Each case was matched to up to 2 controls by sex, age, and education. Logistic regression was used estimate odds ratios (ORs) and 95% confidence intervals (CIs) for factors associated with ADR. RESULTS: We evaluated 159 cases with ADR and 299 controls without ADR. In a multivariate model, factors associated with ADR included older age (OR = 2.35 [age 30–40 years 95% CI 1.29, 4.27], age 41 + years OR = 2.31 [95% CI 1.11, 4.84], compared to age 17–30), higher education level (secondary OR 2.14 [95% CI 1.1.11–4.13]), compared to primary and tertiary), non-adherence to care (OR = 2.48 [95% CI 1.50–4.00]), longer treatment duration (OR = 1.80 [95% CI 1.37–2.35]), lower CD4 count((OR = 0.95 [95% CI 0.95–0.97]) and higher viral load (OR = 1.97 [95% CI 1.44–2.54]). CONCLUSIONS: Understanding these predictors may guide programs in developing interventions to identify patients at risk of developing ADR and implementing prevention strategies.
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spelling pubmed-70272912020-02-24 Epidemiologic and viral predictors of antiretroviral drug resistance among persons living with HIV in a large treatment program in Nigeria Ekong, Ernest Ndembi, Nicaise Okonkwo, Prosper Dakum, Patrick Idoko, John Banigbe, Bolanle Okuma, James Agaba, Patricia Blattner, William Adebamowo, Clement Charurat, Manhattan AIDS Res Ther Research BACKGROUND: Expanded access to combination antiretroviral therapy (cART) throughout sub-Saharan Africa over the last decade has remarkably improved the prognosis of persons living with HIV (PLWH). However, some PLWH experience virologic rebound after a period of viral suppression, usually followed by selection of drug resistant virus. Determining factors associated with drug resistance can inform patient management and healthcare policies, particularly in resource-limited settings where drug resistance testing is not routine. METHODS: A case–control study was conducted using data captured from an electronic medical record in a large treatment program in Nigeria. Cases PLWH receiving cART who developed acquired drug resistance (ADR) and controls were those without ADR between 2004 and 2011. Each case was matched to up to 2 controls by sex, age, and education. Logistic regression was used estimate odds ratios (ORs) and 95% confidence intervals (CIs) for factors associated with ADR. RESULTS: We evaluated 159 cases with ADR and 299 controls without ADR. In a multivariate model, factors associated with ADR included older age (OR = 2.35 [age 30–40 years 95% CI 1.29, 4.27], age 41 + years OR = 2.31 [95% CI 1.11, 4.84], compared to age 17–30), higher education level (secondary OR 2.14 [95% CI 1.1.11–4.13]), compared to primary and tertiary), non-adherence to care (OR = 2.48 [95% CI 1.50–4.00]), longer treatment duration (OR = 1.80 [95% CI 1.37–2.35]), lower CD4 count((OR = 0.95 [95% CI 0.95–0.97]) and higher viral load (OR = 1.97 [95% CI 1.44–2.54]). CONCLUSIONS: Understanding these predictors may guide programs in developing interventions to identify patients at risk of developing ADR and implementing prevention strategies. BioMed Central 2020-02-17 /pmc/articles/PMC7027291/ /pubmed/32066473 http://dx.doi.org/10.1186/s12981-020-0261-z 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
Ekong, Ernest
Ndembi, Nicaise
Okonkwo, Prosper
Dakum, Patrick
Idoko, John
Banigbe, Bolanle
Okuma, James
Agaba, Patricia
Blattner, William
Adebamowo, Clement
Charurat, Manhattan
Epidemiologic and viral predictors of antiretroviral drug resistance among persons living with HIV in a large treatment program in Nigeria
title Epidemiologic and viral predictors of antiretroviral drug resistance among persons living with HIV in a large treatment program in Nigeria
title_full Epidemiologic and viral predictors of antiretroviral drug resistance among persons living with HIV in a large treatment program in Nigeria
title_fullStr Epidemiologic and viral predictors of antiretroviral drug resistance among persons living with HIV in a large treatment program in Nigeria
title_full_unstemmed Epidemiologic and viral predictors of antiretroviral drug resistance among persons living with HIV in a large treatment program in Nigeria
title_short Epidemiologic and viral predictors of antiretroviral drug resistance among persons living with HIV in a large treatment program in Nigeria
title_sort epidemiologic and viral predictors of antiretroviral drug resistance among persons living with hiv in a large treatment program in nigeria
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7027291/
https://www.ncbi.nlm.nih.gov/pubmed/32066473
http://dx.doi.org/10.1186/s12981-020-0261-z
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