Modelling the epidemiologic impact of achieving UNAIDS fast-track 90-90-90 and 95-95-95 targets in South Africa

Abstract

UNAIDS established fast-track targets of 73% and 86% viral suppression among human immunodeficiency virus (HIV)-positive individuals by 2020 and 2030, respectively. The epidemiologic impact of achieving these goals is unknown. The HIV-Calibrated Dynamic Model, a calibrated agent-based model of HIV transmission, is used to examine scenarios of incremental improvements to the testing and antiretroviral therapy (ART) continuum in South Africa in 2015. The speed of intervention availability is explored, comparing policies for their predicted effects on incidence, prevalence and achievement of fast-track targets in 2020 and 2030. Moderate (30%) improvements in the continuum will not achieve 2020 or 2030 targets and have modest impacts on incidence and prevalence. Improving the continuum by 80% and increasing availability reduces incidence from 2.54 to 0.80 per 100 person-years (−1.73, interquartile range (IQR): −1.42, −2.13) and prevalence from 26.0 to 24.6% (−1.4 percentage points, IQR: −0.88, −1.92) from 2015 to 2030 and achieves fast track targets in 2020 and 2030. Achieving 90-90-90 in South Africa is possible with large improvements to the testing and treatment continuum. The epidemiologic impact of these improvements depends on the balance between survival and transmission benefits of ART with the potential for incidence to remain high.

Health Care Providers’ Challenges to High-Quality HIV Care and Antiretroviral Treatment Retention in Rural South Africa

“…South Africa has the largest HIV epidemic in the world, with an estimated 7.7 million people living with HIV (PLHIV) and an HIV prevalence among adults (15–49 years) of 20.4% (UNAIDS, 2018). South Africa also has the largest HIV care and antiretroviral treatment (ART) program in the world and began implementing universal treatment in 2016 to achieve the UNAIDS fast-track 90-90-90 and 95-95-95 targets by 2020 and 2030, respectively (Abuelezam et al, 2019; Avert, 2019; Johnson et al, 2017; UNAIDS, 2014). To achieve these targets and curb the HIV epidemic, it is imperative to increase the number of people tested, and ensure that PLHIV are linked to care, remain on treatment, and achieve viral suppression.…”

Simulating the End of AIDS in New York: Using Participatory Dynamic Modeling to Improve Implementation of the Ending the Epidemic Initiative

“…90-90-90 and Fast-Track goals in low-and middle-income countries [19][20][21][22][23] and Canada, 24 their conclusions are less directly relevant to the United States and New York State, whose baseline HIV epidemic outcomes were ahead of the national average pre-ETE. The Canadian study projected its targets to be on schedule 23 but did not directly model what it means to end HIV as an epidemic.…”

“…Other dynamic compartmental HIV models 12 similar to our approach have examined related topics, including projecting progress toward the national Ending the Epidemic in America plan nationally and among certain cities 13,14 ; the number of HIV infections averted and the cost effectiveness of HIV prevention interventions for persons who inject drugs 15 ; the cost effectiveness of PrEP among men who have sex with men (MSM) 16 ; requirements needed to achieve goals of the 2015 National HIV/AIDS Strategy; and how achieving the goals would improve racial/ethnic disparities, HIV epidemic outcomes, and economic outcomes. 17,18 Other models examined policies similar to but not directly aligned with the ETE initiative, particularly the UNAIDS 90-90-90 and Fast-Track goals in low-and middle-income countries [19][20][21][22][23] and Canada, 24 as well as Getting to Zero in Illinois. 25 Our study's unique contributions were examining increasing access to housing, PrEP, and ARV, and our stakeholder-focused participatory modeling process.…”

Mixed method estimation of population level HIV viral suppression rate in the Western Cape, South Africa

“…Meeting the '90-90-90' targets requires an approximate population viral suppression rate among all PLWH, including those not in care, of approximately 73%. Most of the published work evaluating the '90-90-90' targets have relied on special surveys [7][8][9][10] or mathematical models [11,12], and when routine data are used, the results typically provide only a snapshot of the progress for a 1-year period [13,14].…”