1. Peter D Ghys*, Mary Mahy*, Jeff Eaton**, Samir Bhatt**,
applying PHIA data to improve modeling of the HIV epidemic
Peter D Ghys*, Mary Mahy*, Jeff Eaton**, Samir Bhatt**,
Tim Hallett**
*UNAIDS; **UNAIDS Reference Group on Estimates, Modelling and Projections at Imperial College, London
IAS Satellite session. Paris, July 23, 10: :15 h

2. 1. Modelling of national epidemics: EPP and Spectrum

3. Structure of EPP/Spectrum estimation model
Demographic Data
Program Statistics
Epidemic Patterns
Surveillance and Survey Data
Demographic and Epidemic Calculations
Mother-to-child transmission
Child model
Adult model
Prevalence / incidence trend
EPP
CASVR
AEM
Goals
Direct Input
Results
Number HIV+
New Infections
AIDS deaths
Need for ART
Need for PMTCT
Use/influence of PHIA data

4. Modelling of national epidemics: EPP and Spectrum
adults

5. Prevalence data from pregnant women and from national surveys
Surveys inform the trend in HIV prevalence

6. Adult (15‒49) HIV prevalence trends, UNAIDS 2016 and 2017 rounds of estimates, and PHIA 2016 survey
Estimate, 2017 round (UNAIDS)
Range of uncertainty, 2017 round (UNAIDS)
Estimate, 2016 round (UNAIDS)
Range of uncertainty, 2016 round (UNAIDS)
Survey, 2016 (PHIA)
Range of uncertainty, 2016 (PHIA)
Malawi
Zambia
Zimbabwe
2016

7. Adult (15‒49) HIV incidence trends, UNAIDS 2016 and 2017 rounds of estimates, and PHIA 2016 survey, Malawi
Adult (15‒49) HIV incidence (%)
Estimate, 2017 round (UNAIDS)
Range of uncertainty, 2017 round (UNAIDS)
Estimate, 2016 round (UNAIDS)
Range of uncertainty, 2016 round (UNAIDS)
Survey, 2016 (PHIA)
Range of uncertainty, 2016 (PHIA)
2016

8. Antiretroviral therapy coverage among adults living with HIV, 2000–2016*
Malawi
Zambia
Zimbabwe
Antiretroviral therapy coverage (%)
Estimate
Range of uncertainty
2016
*ART coverage from PHIAs estimated by multiplying reported values for first two nineties.
Estimated ART coverage is for 15+ years while PHIA ART coverage is for year olds

9. Overall conclusion from ADULT validation/comparison
PHIA data used directly in EPP: prevalence among year olds in survey year, incidence among year olds in survey year
PHIA data have largely confirmed past estimates of prevalence and incidence in Malawi, Zambia, Zimbabwe
PHIA have largely confirmed the levels of ART coverage based on reported numbers over estimated denominator of all PLHIV.
Overall conclusion from ADULT validation/comparison

10. Future research emerging from comparisons with PHIA data
Surveys suggest larger sex differential in incidence.
2–3x greater for women vs. 1.3–1.5x greater in models informed by historical prevalence data.
Can higher ART uptake in women + VMMC scale-up explain the widening differential?
Not captured in current estimation model.
May be a transient effect? -- incidence reductions in men should (eventually) propagate to women.
Objectives: (1) ensure best model-based epidemic estimates and projections from recent survey data, (2) insights to improve estimates in other countries without direct incidence estimates.

11. Modelling of national epidemics: EPP and Spectrum
children

12. HIV prevalence, Spectrum vs PHIAs
Comparison has led to investigation of model; and improved estimate of breastfeeding-related transmission, especially in children >1 year of age
(Unofficial Zimbabwe estimates)

13. Comparison of 2016 and 2017:
Child estimates

14. Conclusion from validation/comparison
PHIA HIV prevalence among children helped identify discrepancy in prevalence among children, and improve modelling of breastfeeding-related transmission
Conclusion from validation/comparison
Use PHIA data on recent fertility of HIV-positive women and women on ART to inform assumptions about fertility in EPP/Spectrum. This will also help to interpret trends in routine data.
Use data on treatment coverage among children and unmet need.
Future RESEARCH

15. Sub-national epidemiological modelling: GEOSPATIAL MODEL

16. Current GEOSPATIAL MODEL AND results
Model aimed at producing results at 5x5 kms, using:
National or provincial trends over time from EPP/Spectrum
Geo-located data on HIV prevalence among pregnant women (from facilities), local HIV prevalence among general population from DHS and similar surveys, ART (by facility)
Covariates (e.g. population density, distance to roads, etc)
Current GEOSPATIAL MODEL AND results

17. Geospatial model: prevalence, PLHIV, incidence, ART coverage
Bhatt S and Gething P, UNAIDS Reference Group on Estimates, Modelling and Projections, NYC, Nov 2016, draft results 17

18. Future DEVELOPMENT USING PHIA DATA
Include cluster-level PHIA prevalence and HIV incidence (as has been done so far with DHS, HSRC data)
Large sample sizes of PHIA data will improve the precision and granularity of geospatial prevalence estimates
PHIA data have direct measures of ART coverage: to be incorporated in model
PHIA direct measures of VLS: will allow to relate VLS level to incidence estimates and to assess ART service quality
Other survey information, such as where people attend facilities, will help to model service coverage
Future DEVELOPMENT USING PHIA DATA

19. Modelling of the impact
of interventions

20. MONITORING OF THE EPIDEMIC VS MODELLING OF IMPACT OF INTERVENTIONS
Impact modelling used for deriving impact of and other Fast Track targets (UNAIDS, HLM)
(and also targets for GFATM, National Strategic Plans, Global Fund grant applications)
MONITORING OF THE EPIDEMIC VS MODELLING OF IMPACT OF INTERVENTIONS

21. Future RESEARCH (HIV Modelling Consortium)
HIV Modelling Consortium exercise, using PHIA data to ‘validate’ the impact predicted by the intervention models, and examine how incorporating the PHIA data into mathematical models affects model projections and the predicted impact of future interventions:
(1) validate existing model projections about intervention impact
(2) examine how incorporating the PHIA data into mathematical models affects model projections
Compare PHIA estimates to modelled estimates of impact of interventions including , VMMC, condoms.
Understanding who accesses interventions, and who doesn’t, will improve intervention design and modelling the impact of interventions.

22. Conclusion

23. Future results of PHIAs for other countries (Swaziland, Uganda, Lesotho expected in 2017) in addition to results for Malawi, Zambia, Zimbabwe will make for a robust collection of empirical national data. As a body of empirical data, will take on increasing importance, to inform assumptions going into models, and to validate existing results.
Additional data to be released about:
Male circumcision, condom use, PMTCT
Implementation, uptake, and effectiveness of interventions
Confirmation of ART status (current PHIA results are self- reported; reports of direct measurement in process)
Results to be released in timely way, including datasets for use by researchers (e.g. HMC)
Future prospects