1. Boston University Slideshow Title Goes Here District Prevalence of Unsuppressed HIV in South African Women: Monitoring Programme Performance and Progress Towards 90-90-90 Jacob Bor 1,2, Alana Brennan 1,2, Matthew Fox 1,2, Mhairi Maskew 2, Wendy Stevens 3, Sergio Carmona 3, Bill MacLeod 1,2 1 Department of Global Health, Boston University School of Public Health, USA, 2 Health Economics and Epidemiology Research Office, University of Witwatersrand, South Africa, 3 National Health Laboratory Service, South Africa and Department of Molecular Medicine and Haematology, University of the Witwatersrand Funded by NIAID R01 AI115979, NIMH

2. Boston University Slideshow Title Goes Here Monitoring progress to 90-90-90  How do we measure success?  How do we identify areas of unmet need?

3. Boston University Slideshow Title Goes Here Monitoring progress to 90-90-90  How do we measure success?  How do we identify areas of unmet need?  Traditional approach: the conditional cascade of care

4. Boston University Slideshow Title Goes Here Conditional Cascade of Care 90% tested 90% ART | tested 90% VL<1000 | tested, ART

5. Boston University Slideshow Title Goes Here Monitoring progress to 90-90-90  How do we measure success?  How do we identify areas of unmet need?  Traditional approach: the conditional cascade of care  Problem 1: selection bias implies moving targets  Patients not yet tested likely have lower demand for lifelong ART  As countries scale up “treat all”, testing and linkage will increase, but ART initiation and viral suppression – conditional on testing – may fall  Comparisons over time and and space may be misleading  The potential for selection bias is quite real…

6. Boston University Slideshow Title Goes Here Lower ART uptake at higher CD4 counts Source: Chiu et al. (poster at AIDS 2016), Africa Centre for Population Health Proportion starting ART in 6 months

7. Boston University Slideshow Title Goes Here Monitoring progress to 90-90-90  How do we measure success?  How do we identify areas of unmet need?  Traditional approach: the conditional cascade of care  Problem 1: selection bias implies moving targets  Problem 2: cascade does not capture prevention  The denominator of the cascade is the HIV-infected population  But we have techniques to reduce % of population that is HIV-infected: Condoms, PreP, MMC, Secondary Schooling, TasP

8. Boston University Slideshow Title Goes Here Monitoring progress to 90-90-90  How do we measure success?  How do we identify areas of unmet need?  Traditional approach: the conditional cascade of care  Problem 1: selection bias implies moving targets  Problem 2: cascade does not capture prevention  Problem 3: facility- not population-based perspective  Cascade designed based on what can be monitored at facility level.  But our ultimate goals – reduction of incidence, morbidity, and mortality – are population-based goals.

9. Boston University Slideshow Title Goes Here Monitoring progress to 90-90-90  How do we measure success?  How do we identify areas of unmet need?  Traditional approach: the conditional cascade of care  Problem 1: selection bias implies moving targets  Problem 2: cascade does not capture prevention  Problem 3: facility- not population-based perspective  A public health measurement challenge  How can we best measure programme performance and unmet need when population health impact has a significant lag?

10. Boston University Slideshow Title Goes Here Inverting the Cascade of Care Total Population HIV+, suppressed Life expectancy ~HIV-negatives HIV transmission close to zero HIV+, unsuppressed High risk for HIV transmission Morbidity, mortality if treatment is substantially delayed Incidence

11. Boston University Slideshow Title Goes Here Population-based key indicators 1. Population prevalence of unsuppressed HIV  # HIV-infected and VL>1000 / # total population  Unmet need for HIV treatment and prevention services  Key causal determinant of HIV morbidity and mortality  Key causal determinant of HIV incidence (direct/indirect) 2. Percent suppressed in HIV-infected population  # HIV-infected and VL<1000 / # HIV-infected  Programme performance for the treatment programme  “Effective coverage”

12. Boston University Slideshow Title Goes Here Objective 1.Measure programme performance and unmet need for HIV services at a population level using routine programme and surveillance data 2.Describe geographic variability in programme performance and unmet need in South Africa

13. Boston University Slideshow Title Goes Here Data sources  District level, n=52  N, Number of women ages 15-49  South African Census, 2011  p, HIV prevalence in pregnant women  Antenatal Surveillance HIV Prevalence estimates, pooled 2011/2012  r, Ratio of prevalence in pregnant vs. all women, 15-49  HSRC National Survey 2012 (national estimate)  A, Number of patients receiving ART  District Health Information System 2012  v, Proportion of patients virally suppressed  National Health Laboratory Services, 2012  De-duplicated through novel record linkage, NHLS National HIV Cohort

14. Boston University Slideshow Title Goes Here Methods  # HIV-infected = N*p/r  # HIV-infected, suppressed = A*v  Unsuppressed HIV prevalence = (N*p/r – A*v) / N  Percent suppressed in HIV-infected = (A*v) / (N*p/r)  Assumptions  Ratio r is constant across districts, apart from demographics  No patients are virally suppressed without treatment  Patients who are not virally monitored have same probability of viral suppression as patients who are virally monitored.

15. Boston University Slideshow Title Goes Here Population viral suppression Substantial heterogeneity in population viral suppression

16. Boston University Slideshow Title Goes Here Population viral suppression

17. Boston University Slideshow Title Goes Here Population viral suppression Viral suppression highest in high- prevalence districts.

18. Boston University Slideshow Title Goes Here Population viral suppression Viral suppression highest in high- prevalence districts. Many districts far from 90-90-90 target.

19. Boston University Slideshow Title Goes Here Unsuppressed HIV prevalence

20. Boston University Slideshow Title Goes Here Unsuppressed HIV prevalence ART scale-up has led to a reduction in the proportion of the population at risk for transmitting HIV.

21. Boston University Slideshow Title Goes Here Unsuppressed HIV prevalence vs. total HIV prevalence

22. Boston University Slideshow Title Goes Here Unsuppressed HIV prevalence vs. total HIV prevalence Success of ART programme

23. Boston University Slideshow Title Goes Here Unsuppressed HIV prevalence vs. total HIV prevalence Unmet need for HIV services

24. Boston University Slideshow Title Goes Here Unsuppressed HIV prevalence vs. total HIV prevalence Unmet need for HIV services Hardest to reach Highest transmission risk Performance on ART unknown

25. Boston University Slideshow Title Goes Here Ranking Districts with the highest HIV prevalence are not the districts with the highest unsuppressed HIV prevalence.

26. Boston University Slideshow Title Goes Here Districts with the highest HIV prevalence are not the districts with the highest unsuppressed HIV prevalence. Ranking

27. Boston University Slideshow Title Goes Here Districts with the highest HIV prevalence are not the districts with the highest unsuppressed HIV prevalence. Ranking

28. Boston University Slideshow Title Goes Here Conclusions  Population viral suppression and unsuppressed HIV prevalence are easily-interpretable measures of program performance and unmet need at the population level.  Can be estimated in routine program and surveillance data, including NHLS National Patient Cohort.  Large variability in population viral suppression presents opportunity to learn from high-performing districts.  Districts with greatest unmet need were not those with highest HIV prevalence, suggesting successful targeting but also a need for renewed focus on districts with high prevalence of unsuppressed HIV.

29. Boston University Slideshow Title Goes Here Thank you and Acknowledgments  Patients, care providers, and NHLS staff  Sue Candy and colleagues at the NHLS CDW  Research colleagues at BUSPH and HE 2 RO  National Department of Health  Funders  NIH (NIAID, NIMH)  PEPFAR  USAID