Jun 19 – 22, 2024
Squamish, BC, Canada
Canada/Pacific timezone
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POPULATION DYNAMICS OF HIV DRUG RESISTANCE DURING TREATMENT SCALE-UP IN UGANDA: A POPULATION-BASED LONGITUDINAL STUDY

Not scheduled
20m
Squamish, BC, Canada

Squamish, BC, Canada

Poster Transmission dynamics & clusters

Speaker

Michael Martin (Johns Hopkins Medicine)

Description

Viral resistance to the efficacy of antiretroviral therapy (ART) has the potential to lead to a resurgence in HIV incidence and death among people living with HIV (PLHIV). To date, there have been no longitudinal, population-based studies of HIV drug resistance trends during ART program expansion in sub-Saharan Africa. We analyzed epidemiological and virological data collected from 93,659 participant visits between 2012 and 2018 collected as part of the open population-based Rakai Community Cohort Study. Consenting participants aged 15-49 years were tested for HIV and completed questionnaires. PLHIV provided samples for viral load quantification and virus deep-sequencing, which was used to predict resistance profiles. The prevalence of class-specific resistance and resistance-conferring substitutions were estimated using robust Poisson regression using inverse probability weighting to account for missing sequence data among some participants. Using deep-sequencing data from 3,709 pre-treatment participant visits, we estimated that between 2012 and 2017 the population prevalence of non-nucleoside reverse transcriptase inhibitor (NNRTI), nucleoside reverse transcriptase inhibitor (NRTI), and protease inhibitor (PI) resistance among all PLHIV decreased significantly (prevalence ratio (PR): 0.38, 95% CI 0.25 – 0.57; 0.20, 95% CI 0.09 – 0.45; 0.19, 95% CI 0.09 – 0.39, respectively). However, among viremic pre-treatment PLHIV, the prevalence of NNRTI resistance increased two-fold (PR = 1.96, 95% CI 1.31-2.94) to 9.8% (7.4% - 13.0%) over the same time period, of which 78.3% was monoresistance. Using data from 417 viremic treatment-experienced PLHIV, we estimated that the 2017 prevalence of NNRTI and NRTI resistances were 47.7% (95% CI 40.9% - 55.5%) and 36.6% (95% CI 30.1% - 44.3%, Figure 1B), respectively, of which 75.6% was NNRTI/NRTI dual-class resistance. In 2017, 10.1% (95% CI 7.7%-13.3%) and 10.2% (95% CI 6.6%-15.6%) of viremic pre-treatment and treatment-experienced PLHIV harbored the compensatory mutation inT97A. The prevalence of inT97A warrants heightened surveillance considering the recent roll-out of dolutegravir-based regimens.

Primary author

Michael Martin (Johns Hopkins Medicine)

Co-authors

Steven James Reynolds (2Rakai Health Sciences Program, Kalisizo, Uganda;3Division of Infectious Disease, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA; 4Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA) Charles Ssuuna (Rakai Health Sciences Program, Kalisizo, Uganda) Brian T. Foley (Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA) Fred Nalugoda (Rakai Health Sciences Program, Kalisizo, Uganda) Thomas C. Quinn (Rakai Health Sciences Program, Kalisizo, Uganda; Division of Infectious Disease, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA; Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA) Steven A. Kemp (Department of Medicine, University of Cambridge, Cambridge, UK) Margaret Nakalanzi (Rakai Health Sciences Program, Kalisizo, Uganda) Edward Nelson Kankaka (Rakai Health Sciences Program, Kalisizo, Uganda) Godfrey Kigozi (Rakai Health Sciences Program, Kalisizo, Uganda) Robert Ssekubugu (Rakai Health Sciences Program, Kalisizo, Uganda) Ravindra K. Gupta (Department of Medicine, University of Cambridge, Cambridge, UK; Africa Health Research Institute, KwaZulu-Natal, South Africa) Lucie Abeler-Dörner (Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK) Joseph Kagaayi (Rakai Health Sciences Program, Kalisizo, Uganda; Makerere University School of Public Health, Kampala, Uganda) Oliver Ratmann (Department of Mathematics, Imperial College London, London, England, United Kingdom) Christophe Fraser (Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Pandemic Sciences Institute, University of Oxford, Oxford, UK) Ronald Moses Galiwango (Rakai Health Sciences Program, Kalisizo, Uganda) David Bonsall (Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Pandemic Sciences Institute, University of Oxford, Oxford, UK) M. Kate Grabowski (Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, USA; Rakai Health Sciences Program, Kalisizo, Uganda; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA)

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