Title: HIV drug resistance in sub-Saharan Africa: public health questions and the potential role of real-world data and mathematical modelling
Authors: de waal R, Lessells R, Hauser A, Kouyos R, Davies M-A, Egger M, Wandeler G, for ieDeA-Southern Africa.
Journal: Journal of Virus Eradication,4(Supplement 2): :55–58 (2018)
The prevalence of pretreatment resistance to non-nucleoside reverse transcriptase inhibitors (NNRTis) is >10% in many low-income countries. As a consequence, several sub-Saharan African countries have implemented, or are considering the introduction of, non-NNRTi-based first-line antiretroviral therapy (ART) for treatment-nai?ve and treatment-experienced patients. This is occurring at a time when ART programmes are expanding, in response to the world Health Organization guidelines, which recommend ART initiation regardless of CD4 cell count. Both those developments raise important questions regarding their potential impact on Hiv drug resistance and the impact of Hiv drug resistance on clinical outcomes. Those issues are particularly relevant to sub-Saharan Africa, where standardised ART regimens are used and where viral load monitoring and resistance testing are often not done routinely. it is therefore essential to forecast the impact of the implementation of universal ART, and the introduction of drugs such as dolutegravir to first-line regimens, on Hiv drug resistance in order to inform future policies and to help ensure sustainable positive long-term outcomes. we discuss important public health considerations regarding Hiv drug resistance, and describe how mathematical modelling, combined with real-world data from the four African Regions of the international epidemiology Databases to evaluate AiDS consortium, could provide an early warning system for Hiv drug resistance in sub-Saharan Africa.
Citation: de waal R, Lessells R, Hauser A, Kouyos R, Davies M-A, Egger M, Wandeler G, for ieDeA-Southern Africa. HIV drug resistance in sub-Saharan Africa: public health questions and the potential role of real-world data and mathematical modelling Journal of Virus Eradication,4(Supplement 2): :55–58 (2018).