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 - 12:15 h

1. Modelling of national epidemics: EPP and Spectrum

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

Modelling of national epidemics: EPP and Spectrum adults

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

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

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

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 15-64 year olds

Overall conclusion from ADULT validation/comparison PHIA data used directly in EPP: prevalence among 15-49 year olds in survey year, incidence among 15-49 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

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.

Modelling of national epidemics: EPP and Spectrum children

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)

Comparison of 2016 and 2017: Child estimates

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

Sub-national epidemiological modelling: GEOSPATIAL MODEL

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

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

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

Modelling of the impact of interventions

MONITORING OF THE EPIDEMIC VS MODELLING OF IMPACT OF INTERVENTIONS Impact modelling used for deriving impact of 90-90-90 and other Fast Track targets (UNAIDS, 2016 HLM) (and also targets for GFATM, National Strategic Plans, Global Fund grant applications) MONITORING OF THE EPIDEMIC VS MODELLING OF IMPACT OF INTERVENTIONS

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 909090, VMMC, condoms. Understanding who accesses interventions, and who doesn’t, will improve intervention design and modelling the impact of interventions.

Conclusion

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