1. 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
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 – – done in a specific way – use ref tracker to bring youth up to X %
Stopping dropout – no drug resistance.

2. “If you reach 90-90-90, you end up with 73 percent of people with H. I
“If you reach , 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 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

3. 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

4. 90-90-90 leaves 27% of HIV positive individuals unsuppressed
The impact of 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 goals can and should be implemented, we were concerned that they might not be enough to end the epidemic over the next years 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.

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

6. The HIV epidemic in eSwatini
PHIA, 2016

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

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

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

10. Scenario a: Maintain current ART coverage

11. Scenario a: Maintain current ART coverage

12. Scenario b: Age-targeted scale-up

13. EMOD Mathematical model calibrated to eSwatini epidemic|

14. ART scale-up consistent with incidence declines

15. 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

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

17. Male incidence by age 3/1000 infections per year

18. 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 targets to be age and gender-specific
Modeling/Data: Improve models to incorporate heterogeneity in ART coverage by demographic / risk indicators
Recommendations

19. 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

21. 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):

22. 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, e (2012).

23. 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)

24. Model Results: Incidence

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

26. Modeling the expansion of ART guidelines Eligibility = immediate, CD4 350, CD Coverage = % year retention = %

27. Swaziland on track to achieve 90-90-90

29. Results: Modeled HIV prevalence by age and gender

30. eSwatini HIV prevalence by age and sex 2016
PHIA, 2016

31. 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 …”
Grobler et al., Progress of UNAIDS 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).

32. 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).

33. 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

34. 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.

35. 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

36. 40-50% decline in incidence between 2010-2016
Ages 15-49