Modeling the effects of differential ART scale-up by age and gender in eSwatini Adam Akullian, PhD Postdoctoral Research Scientist | Institute for Disease Modeling Affiliate Assistant Professor | Department of Global Health, University of Washington aakullian@idmod.org akullian@uw.edu How many people living with HIV over time? To 2050 and how many are on tx. Number of life years gained over time relative to No ART. Starting in 2020 – 95-95-95 – done in a specific way – use ref tracker to bring youth up to X % Stopping dropout – no drug resistance.

“If you reach 90-90-90, you end up with 73 percent of people with H. I “If you reach 90-90-90, you end up with 73 percent of people with H.I.V. being noncontagious. That 73 percent is the tipping point, at which the epidemic starts to burn out.” Mathematical models have shown that the 90-90-90 goals can theoretically reduce HIV transmission to the point where the epidemic burns out. People waiting to be tested for H.I.V. in Harare, Zimbabwe, in 2012. CreditTsvangirayi Mukwazhi/Associated Press

What do previous models say about the impact of UTT? Hontelez et al., 2013: 90% ART Coverage of 15+ by 2019 results in reduction in incidence to below 1/1000 pyar. Granich et al., 2009: 90% ART coverage by 2016 results in reduction in incidence to below 1/1000 pyar

90-90-90 leaves 27% of HIV positive individuals unsuppressed The impact of 90-90-90 on the HIV epidemic may depend on the risk profile of the missing 27% This is where our work as epidemiologists and mathematical modelers comes in. While the 90-90-90 goals can and should be implemented, we were concerned that they might not be enough to end the epidemic over the next 10-15 years. 90-90-90 leaves 27% of people living with HIV unsuppressed. What if that 27% are disproportionately responsible for transmitting HIV? We published a commentary last month to challenge the scientific community to better understand who those 27% are, and to update our models to better reflect the uncertainty around that group’s contribution to the epidemic.

Goal: Explicitly model the impact of age- and sex- specific differences in ART scale-up towards 90-90-90 in eSwatini

The HIV epidemic in eSwatini PHIA, 2016

Viral load suppression and incidence (2011-2016) HIV incidence (%) PHIA, 2016

Differences in viral load suppression by age and sex PHIA, 2016

Modeling Scenarios ART scale-up to >81% by 2020 and >90% by 2030 with: (a) Maintain current coverage (non age-targeted 90-90-90) (b) Age-targeted campaigns

Scenario a: Maintain current ART coverage

Scenario a: Maintain current ART coverage

Scenario b: Age-targeted scale-up

EMOD Mathematical model calibrated to eSwatini epidemic https://github.com/InstituteForDiseaseModeling/EMOD | www.idmod.org/idmdoc

ART scale-up consistent with incidence declines

Results: incidence reductions by scenario (2016-2050) Men (> 15 years) Women (> 15 years) Age targeting No targeting Additional reduction of 2/1000 infections per year Additional reduction of 3/1000 infections per year

Largest reductions in younger women 5/1000 infections per year 7/1000 infections per year 4/1000 infections per year

Male incidence by age 3/1000 infections per year

Conclusions Recommendations Dramatic ART scale-up in eSwatini  reduced incidence Disparities in ART coverage by age and gender Closing age-gaps in ART coverage can further reduce incidence. Policy: Update 90-90-90 targets to be age and gender-specific Modeling/Data: Improve models to incorporate heterogeneity in ART coverage by demographic / risk indicators Recommendations

Acknowledgements Bill and Melinda Gates Foundation Global Implementing Partners Eswatini MoH Africa Health Research Institute Michelle Morrison Geoff Garnet Emilio Emini Institute for Disease Modelling Anna Bershteyn Britta Jewell Clark Kirkman Dan Bridenbecker

Powers, K. A., et al. (2014). "Impact of early-stage HIV transmission on treatment as prevention." Proc Natl Acad Sci U S A 111(45): 15867-15868.

Model-Based Effects of Universal Test and Treat UTT at 90% ART coverage reduces incidence by 60-80% by 2020 Linear increase in incidence reduction with ART scale-up Percent reduction in incidence Systematic Comparison of Mathematical Models of the Potential Impact of Antiretroviral Therapy on HIV Incidence in South Africa. PLoS Medicine 9, e1001245 (2012).

Gender-disparities in transmission driven by age-gaps in partnerships, differential ART coverage, and VMMC Average Time from infection to transmission is short Men (25-34) Women (25-34) aging Women (15-24) Average time from infection to transmission is long de Oliveira, T., et al. Lancet HIV (2017), Akullian et al AIDS (2017)

Model Results: Incidence

Not all age-groups have reached 90-90-90 levels

Modeling the expansion of ART guidelines Eligibility = immediate, CD4 350, CD4 200 Coverage = 50 - 100% 3-year retention = 75 - 100%

Swaziland on track to achieve 90-90-90

Results: Modeled HIV prevalence by age and gender

eSwatini HIV prevalence by age and sex 2016 PHIA, 2016

Viral load suppression by age/sex (KwaZulu-Natal, 2016) Data on who is suppressing are beginning to emerge. The age and sex profile of viral load suppression is now well understood. Women tend to suppress at higher levels than men, especially at younger ages. Part of this is attributed to differences in health seeking behaviors. Women often have more opportunities to access healthcare through ANC visits or are more concerned about their health status. Men tend to delay treatment for a variety of reasons. Data from Grobler et al 2017 “Progression of UNAIDS 90-90-90…” Grobler et al., Progress of UNAIDS 90-90-90 targets in a district in KwaZulu-Natal, South Africa, with high HIV burden, in the HIPSS study: a household-based complex multilevel community survey. The lancet. HIV 4, e505-e513 (2017).

HIV risk depends on partner’s age Women’s report P < 0.01 15,435 pyo 874 seroconverstions Partners < 35 Akullian, A., et al. (2017). "Sexual partnership age-pairings and risk of HIV acquisition in rural South Africa: a population-based cohort study." AIDS (London, England).

EMOD: Model Structure Intrahost Network Interventions Transmission HIV Person STI Person Sexual behavior PreventionProgram HIV Infection infectivity, AIDS prognosis Pair-Forming Algorithm Treatment Program Heterosexual MSM CSW Opportunistic infections Immune State (CD4 etc.) Marital Informal Transitory Transmission Relationship With Interactions Relationship Without Interactions Drug resistance Transmission by Relationship Adherence Profile Transmission by Interaction Single Interaction ARV Regimen

Pair formation algorithm Informal relationship type Age-gaps in sexual partnerships: seeing beyond ‘sugar daddies’, M.Q. Ott, T. Barnighausen, F. Tanser, M.N. Lurie. and M.L. Newell, AIDS, v25, n6, p861, 2011.

Effect of age-targeting: 2016-2030 (age 15-49) No targeting 43% decline 28% decline 48% decline 30% decline 46% decline 29% decline IRD = 0.16/100py IRD = 0.28/100py IRD = 0.22/100py

40-50% decline in incidence between 2010-2016 Ages 15-49