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

AIDS Research and Therapy(2020)

引用 10|浏览42
暂无评分
摘要
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.
更多
查看译文
关键词
Antiretroviral therapy,Acquired drug resistance,Predictors,HIV drug resistance testing,Low Middle Income Countries (LMICs),Resource-limited settings
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要