The Contribution of Early HIV Infection to HIV Spread in Lilongwe, Malawi:  Implications for
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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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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


Early hiv infection
Early HIV Infection Lilongwe, Malawi: Implications for

  • 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.


Role of ehi in epidemic spread
Role of EHI in Epidemic Spread Lilongwe, Malawi: Implications for

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


Role of ehi model estimates
Role of EHI: Model Estimates Lilongwe, Malawi: Implications for

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.


Role of ehi model estimates1
Role of EHI: Model Estimates Lilongwe, Malawi: Implications for

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.


Study objectives
Study Objectives Lilongwe, Malawi: Implications for

  • 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


Methods
Methods Lilongwe, Malawi: Implications for

  • 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)


Stages of infection
Stages of Infection Lilongwe, Malawi: Implications for

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.


Lilongwe anc prevalence data
Lilongwe ANC Prevalence Data Lilongwe, Malawi: Implications for

ANC data


Lilongwe anc prevalence data1
Lilongwe ANC Prevalence Data Lilongwe, Malawi: Implications for

Best-fitting model estimates

95% credible intervals

ANC data


Predicted contribution of ehi
Predicted Contribution of EHI Lilongwe, Malawi: Implications for

Best fitting model estimates

95% credible intervals

58%

38%

19%


Transmission suppressing intervention
Transmission-suppressing intervention Lilongwe, Malawi: Implications for

  • Assumed generic intervention that ↓↓↓ infectivity in those receiving it

    • e.g., complete viral suppression, effective condom use


Transmission suppressing intervention1
Transmission-suppressing intervention Lilongwe, Malawi: Implications for

EHI

CHI

(Noresidual effect during CHI)

EHI

CHI

(Approximates test-and-treat with annual tests)

EHI

CHI


Ehi only prevention strategy
EHI-only Prevention Strategy Lilongwe, Malawi: Implications for

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


Chi only prevention strategy
CHI-only Prevention Strategy Lilongwe, Malawi: Implications for

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


75 chi coverage 25 ehi coverage
75% CHI coverage, Lilongwe, Malawi: Implications for 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


75 chi coverage 50 ehi coverage
75% CHI coverage, Lilongwe, Malawi: Implications for 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


75 chi coverage 75 ehi coverage
75% CHI coverage, Lilongwe, Malawi: Implications for 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


Limitations
Limitations Lilongwe, Malawi: Implications for

  • 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


Conclusions
Conclusions Lilongwe, Malawi: Implications for

  • 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


Acknowledgments
Acknowledgments Lilongwe, Malawi: Implications for

  • 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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