Drivers of HIV-1 drug resistance to non-nucleoside reverse-transcriptase inhibitors (NNRTIs) in nine southern African countries: a modelling study.
Gupta, Ravindra K
Kouyos, Roger D
L Althaus, Christian
BMC Infect Dis
Springer Science and Business Media LLC
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Riou, J., Dupont, C., Bertagnolio, S., Gupta, R. K., Kouyos, R. D., Egger, M., & L Althaus, C. (2021). Drivers of HIV-1 drug resistance to non-nucleoside reverse-transcriptase inhibitors (NNRTIs) in nine southern African countries: a modelling study.. BMC Infect Dis, 21 (1. 1042), 1042. https://doi.org/10.1186/s12879-021-06757-6
INTRODUCTION: The rise of HIV-1 drug resistance to non-nucleoside reverse-transcriptase inhibitors (NNRTI) threatens antiretroviral therapy's long-term success (ART). NNRTIs will remain an essential drug for the management of HIV-1 due to safety concerns associated with integrase inhibitors. We fitted a dynamic transmission model to historical data from 2000 to 2018 in nine countries of southern Africa to understand the mechanisms that have shaped the HIV-1 epidemic and the rise of pretreatment NNRTI resistance. METHODS: We included data on HIV-1 prevalence, ART coverage, HIV-related mortality, and survey data on pretreatment NNRTI resistance from nine southern Africa countries from a systematic review, UNAIDS and World Bank. Using a Bayesian hierarchical framework, we developed a dynamic transmission model linking data on the HIV-1 epidemic to survey data on NNRTI drug resistance in each country. We estimated the proportion of resistance attributable to unregulated, off-programme use of ART. We examined each national ART programme's vulnerability to NNRTI resistance by defining a fragility index: the ratio of the rate of NNRTI resistance emergence during first-line ART over the rate of switching to second-line ART. We explored associations between fragility and characteristics of the health system of each country. RESULTS: The model reliably described the dynamics of the HIV-1 epidemic and NNRTI resistance in each country. Predicted levels of resistance in 2018 ranged between 3.3% (95% credible interval 1.9-7.1) in Mozambique and 25.3% (17.9-33.8) in Eswatini. The proportion of pretreatment NNRTI resistance attributable to unregulated antiretroviral use ranged from 6% (2-14) in Eswatini to 64% (26-85) in Mozambique. The fragility index was low in Botswana (0.01; 0.0-0.11) but high in Namibia (0.48; 0.16-10.17), Eswatini (0.64; 0.23-11.8) and South Africa (1.21; 0.83-9.84). The combination of high fragility of ART programmes and high ART coverage levels was associated with a sharp increase in pretreatment NNRTI resistance. CONCLUSIONS: This comparison of nine countries shows that pretreatment NNRTI resistance can be controlled despite high ART coverage levels. This was the case in Botswana, Mozambique, and Zambia, most likely because of better HIV care delivery, including rapid switching to second-line ART of patients failing first-line ART.
Antiretroviral therapy, Epidemiology, HIV drug resistance, Health system science, Modelling, Southern Africa, Bayes Theorem, DNA-Directed RNA Polymerases, Drug Resistance, Viral, HIV Infections, HIV-1, Humans, South Africa
External DOI: https://doi.org/10.1186/s12879-021-06757-6
This record's URL: https://www.repository.cam.ac.uk/handle/1810/331761
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/