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dc.contributor.authorEkong, E.
dc.contributor.authorNdembi, N.
dc.contributor.authorDakum, P.
dc.contributor.authorBlattner, W.
dc.contributor.authorAdebamowo, C.
dc.contributor.authorCharurat, M.
dc.date.accessioned2020-03-03T19:54:55Z
dc.date.available2020-03-03T19:54:55Z
dc.date.issued2020
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85079648026&doi=10.1186%2fs12981-020-0261-z&partnerID=40&md5=51ce0461d47e34ec05e461f9b81df51c
dc.identifier.urihttp://hdl.handle.net/10713/12126
dc.description.abstractBackground: 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. Copyright 2020 The Author(s).en_US
dc.description.urihttps://doi.org/10.1186/s12981-020-0261-zen_US
dc.language.isoen_USen_US
dc.publisherBioMed Central Ltd.en_US
dc.relation.ispartofAIDS Research and Therapy
dc.subjectAcquired drug resistanceen_US
dc.subjectAntiretroviral therapyen_US
dc.subjectHIV drug resistance testingen_US
dc.subjectLow Middle Income Countries (LMICs)en_US
dc.subjectPredictorsen_US
dc.subjectResource-limited settingsen_US
dc.titleEpidemiologic and viral predictors of antiretroviral drug resistance among persons living with HIV in a large treatment program in Nigeriaen_US
dc.typeArticleen_US
dc.identifier.doi10.1186/s12981-020-0261-z
dc.identifier.pmid32066473


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