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Modeling ‘test and treat’ for HIV in South Africa

Modeling ‘test and treat’ for HIV in South Africa. Jan AC Hontelez 1,2,3 , Mark N Lurie 4 , Till Bärnighausen 3,5 , Roel Bakker 1 Rob Baltussen 2 , Frank Tanser 3 , Timothy B Hallett 6 , Marie-Louise Newell 3 , and Sake J de Vlas 1

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Modeling ‘test and treat’ for HIV in South Africa

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  1. Modeling ‘test and treat’ for HIV in South Africa Jan AC Hontelez1,2,3, Mark N Lurie4, Till Bärnighausen3,5, Roel Bakker1 Rob Baltussen2, Frank Tanser3, Timothy B Hallett6, Marie-Louise Newell3, and Sake J de Vlas1 1 Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands 2 Nijmegen International Center for Health System Analysis and Education (NICHE), Department of Primary and Community Care, Radboud University Nijmegen Medical Centre, Netherlands 3 Africa Centre for Health and Population Studies, University of KwaZulu-Natal, Mtubatuba, South Africa 4 Department of Epidemiology and the International Health Institute, Warren Alpert Medical School, Brown University, Providence, RI, USA 5 Department of Global Health and Population, Harvard School of Public Health, Boston, USA 6 Imperial College, London, UK Contact: j.hontelez@erasmusmc.nl

  2. Introduction Universal test and treat (UTT) suggested to drive HIV into an elimination phase (incidence < 1 / 1,000 person-years) in South Africa Many mathematical models predict impact of UTT, wide range of different results Eaton et al: Models agree on potential of ART to reduce incidence, but disagree on the amount of reduction and overall impact Objective: Examine the impact of different model structures and parameterizations on predicting the impact of UTT in South Africa using a highly controlled experiment

  3. Model A Age structured population (Model B1) Heterogeneity in transmission by HIV stage (Model B2) ModelB Heterogeneity in sexual behaviour (Model C1) HIV prevention interventions and STI co-factors (Model C2) Up-to-date ART effectiveness assumptions (Model C3) Model C Current ART scale-up in South Africa (at ≤350 cells/µL) Model D Study outline Universal HIV testing and immediate treatment for all, at 90% coverage, starting in 2012

  4. Model A Age structured population (Model B1) Heterogeneity in transmission by HIV stage (Model B2) Model B Heterogeneity in sexual behaviour (Model C1) HIV prevention interventions and STI co-factors (Model C2) Up-to-date ART effectiveness assumptions (Model C3) Model C Current ART scale-up in South Africa (at ≤350 cells/µL) Model D Model A

  5. Model A

  6. Overall effectiveness

  7. Model A Age structured population (Model B1) Heterogeneity in transmission by HIV stage (Model B2) ModelB Heterogeneity in sexual behaviour (Model C1) HIV prevention interventions and STI co-factors (Model C2) Up-to-date ART effectiveness assumptions (Model C3) Model C Current ART scale-up in South Africa (at ≤350 cells/µL) Model D

  8. Model B

  9. Overall effectiveness

  10. Model A Age structured population (Model B1) Heterogeneity in transmission by HIV stage (Model B2) ModelB Heterogeneity in sexual behaviour (Model C1) HIV prevention interventions and STI co-factors (Model C2) Up-to-date ART effectiveness assumptions (Model C3) Model C Current ART scale-up in South Africa (at ≤350 cells/µL) Model D

  11. Model C

  12. Overall effectiveness

  13. Model A Age structured population (Model B1) Heterogeneity in transmission by HIV stage (Model B2) ModelB Heterogeneity in sexual behaviour (Model C1) HIV prevention interventions and STI co-factors (Model C2) Up-to-date ART effectiveness assumptions (Model C3) Model C Current ART scale-up in South Africa (at ≤350 cells/µL) Model D

  14. Model D

  15. Overall effectiveness

  16. Conclusions and implications • We confirm results by Granich and colleagues that the HIV epidemic in South Africa can be driven into an elimination phase through expanded access to ART • Models differ substantially in predicted time till 0.1% incidence is achieved and impact of the intervention Most important components in driving differences between models: • Sexual networks • Prevalence density function versus prevention interventions • ART scale-up • Heterogeneity in HIV transmission

  17. Conclusions and implications (2) • Predicted effectiveness of UTT declines as important underlying dynamics of the epidemic are taking into account Important implications for future modeling studies on the impact of treatment as prevention • Current treatment roll-out may already have such a substantial impact that the epidemic will reach the 0.1% incidence threshold if current scale-up is maintained and universal access achieved

  18. Conclusions and implications (3) Universal test and treat intervention is still cost effective, yet assumptions on programmatic effectiveness are rather optimistic. Detailed incremental cost-effectiveness analyses with more realistic assumptions on programmatic effectiveness of treatment as prevention are needed Detailed cost-effectiveness analyses to inform policy makers and guidelines should be performed with models that allow for sufficient levels of detail in modeling the underlying epidemic

  19. Acknowledgements Co-authors Mark N Lurie Till Bärnighausen Roel Bakker Rob Baltussen Frank Tanser Timothy B Hallett Marie-Louise Newell Sake J de Vlas Funders: Bill & Melinda Gates Foundation through grants from the HIV modelling consortium NIH

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