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Early HIV Infection

The Contribution of Early HIV Infection to HIV Spread in Lilongwe, Malawi: Implications for Transmission Prevention Strategies. Kimberly Powers, 1 Azra Ghani, 2 William Miller, 1 Irving Hoffman, 1 Audrey Pettifor, 1 Gift Kamanga, 3 Francis Martinson, 3 Myron Cohen 1.

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Early HIV Infection

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  1. The Contribution of Early HIV Infection to HIV Spread in Lilongwe, Malawi: Implications for Transmission Prevention Strategies Kimberly Powers,1Azra Ghani,2 William Miller,1 Irving Hoffman,1 Audrey Pettifor,1 Gift Kamanga,3 Francis Martinson,3 Myron Cohen1 1. University of North Carolina at Chapel Hill, 2. Imperial College London, 3. UNC Project Malawi

  2. Early HIV Infection • HIV transmission risk is ↑ ↑ ↑ ↑ during early HIV infection (EHI). • Interventions targeting EHI could be very efficient in limiting epidemic spread. • BUTEHI is brief and case detection is difficult. • EHI contribution to epidemic spread varies and has implications for prevention strategies.

  3. Role of EHI in Epidemic Spread IF BIG EHI ROLE: Effects of CHI-only interventions may be limited. EHI detection & interventions may be harder to justify. IF SMALL EHI ROLE: SMALL Useful to elucidate role of EHI

  4. Role of EHI: Model Estimates Pinkerton & Abramson 1996** Kretzschmar & Dietz 1998**† Hayes & White 2006* Salomon & Hogan 2008* Koopman et al 1997** Jacquez et al 1994 Xiridou et al 2004 Pinkerton 2007 Prabhu et al 2009 Abu-Raddad & Longini 2008† Hollingsworth et al 2008 * Range of estimates reflects the proportion of all transmissions during an individual’s entire infectious period that occur during EHI. The extent to which this proportion corresponds with the proportion of all transmissions that occur during EHI at the population level will depend on the epidemic phase and the distribution of sexual contact patterns. ** Transmission probabilities were drawn from the population category shown, but the reported estimates result from a range of hypothetical sexual behavior parameters that do not necessarily reflect a specific population. † The range of estimates shown was extracted from the endemic-phase portion of graphs showing the time-course of the proportion due to EHI.

  5. Role of EHI: Model Estimates Pinkerton & Abramson 1996** Kretzschmar & Dietz 1998**† • Difficult to obtain data for informing models • Effects of interventions during EHI unknown Hayes & White 2006* Salomon & Hogan 2008* Koopman et al 1997** Jacquez et al 1994 Xiridou et al 2004 Pinkerton 2007 Prabhu et al 2009 Abu-Raddad & Longini 2008† Hollingsworth et al 2008 * Range of estimates reflects the proportion of all transmissions during an individual’s entire infectious period that occur during EHI. The extent to which this proportion corresponds with the proportion of all transmissions that occur during EHI at the population level will depend on the epidemic phase and the distribution of sexual contact patterns. ** Transmission probabilities were drawn from the population category shown, but the reported estimates result from a range of hypothetical sexual behavior parameters that do not necessarily reflect a specific population. † The range of estimates shown was extracted from the endemic-phase portion of graphs showing the time-course of the proportion due to EHI.

  6. Study Objectives • Based on data from our ongoing work in Lilongwe, Malawi: • Estimate the proportion of HIV transmissions attributable to index cases with EHI • Predict the reduction in HIV prevalence achievable through detection and interventions during EHI

  7. Methods • Data-driven, deterministic model, with: • Heterosexual transmission within & outside steady pairs • Multiple infection stages • Two risk groups • Sexual behavior parameters from detailed study of partnership patterns at Lilongwe STI Clinic • Bayesian melding procedure to fit model to observed HIV prevalence (ANC data)

  8. Stages of Infection EarlyAIDS AIDS EHI Asymptomatic Period → → → ~ 1 to ~6 months* Average EHI transmission probability 26 times as high as during asymptomatic period* Changing transmission probabilities within EHI based on longitudinal viral load data from Lilongwe** * Hollingsworth et al, JID 2008. **Pilcher et al, AIDS 2007.

  9. Lilongwe ANC Prevalence Data ANC data

  10. Lilongwe ANC Prevalence Data Best-fitting model estimates 95% credible intervals ANC data

  11. Predicted Contribution of EHI Best fitting model estimates 95% credible intervals 58% 38% 19%

  12. Transmission-suppressing intervention • Assumed generic intervention that ↓↓↓ infectivity in those receiving it • e.g., complete viral suppression, effective condom use

  13. Transmission-suppressing intervention EHI CHI (Noresidual effect during CHI) EHI CHI (Approximates test-and-treat with annual tests) EHI CHI

  14. EHI-only Prevention Strategy Assuming transmission is almost completely suppressed in various proportions of EHI cases only (no residual effect): No intervention Transmission suppressed in: 25% EHI cases 50% EHI cases 75% EHI cases 100% EHI cases If suppression in 100% CHI

  15. CHI-only Prevention Strategy Assuming transmission is almost completely suppressed in 75% of CHI cases only (beginning to end of CHI): No intervention Transmission suppressed in: 75% CHI + 0% EHI cases

  16. 75% CHI coverage, 25% EHI coverage Assuming transmission is almost completely suppressed in 75% of CHI cases and 25% of EHI cases: No intervention Transmission suppressed in: 75% CHI + 0% EHI cases 75% CHI + 25% EHI cases

  17. 75% CHI coverage, 50% EHI coverage Assuming transmission is almost completely suppressed in 75% of CHI cases and 50% of EHI cases: No intervention Transmission suppressed in: 75% CHI + 0% EHI cases 75% CHI + 50% EHI cases

  18. 75% CHI coverage, 75% EHI coverage Assuming transmission is almost completely suppressed in 75% of CHI cases and 75% of EHI cases: No intervention Transmission suppressed in: 75% CHI + 0% EHI cases 75% CHI + 75% EHI cases

  19. Limitations • Models are simplified representations of reality. • Model was based on data from setting of interest • Model allowed transmission within and outside pairs • Model included multiple risk groups & infection stages • Uncertainties surround input parameter values. • Model fit to ANC data to identify most likely input values • Sensitivity analyses around predicted EHI contribution

  20. Conclusions • EHI plays an important role in the HIV epidemic of Lilongwe, Malawi. • A perfect intervention with 100% coverage throughout ALL of CHI may eliminate HIV. Anything less will require strategies during EHI. • It is time to determine: • The best ways to identify EHI cases • The optimal prevention strategies during EHI

  21. Acknowledgments • UNC Project Malawi • Gift Kamanga • Robert Jafali • Mina Hosseinipour • David Chilongozi • Francis Martinson • Funding from • NIH • UNC CFAR • UNC • Bill Miller • Mike Cohen • Irving Hoffman • Audrey Pettifor • Imperial College London • AzraGhani • Christophe Fraser • Tim Hallett • Rebecca Baggaley

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