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 Cohen1

1. University of North Carolina at Chapel Hill, 2. Imperial College London, 3. UNC Project Malawi

Early HIV Infection

• HIV transmission risk is ↑ ↑ ↑ ↑ during early HIV infection (EHI).

• Interventions targeting EHI could be very efficient in limiting epidemic spread.

• BUT EHI 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

Useful to elucidate role of EHI

IF BIG EHI ROLE:

Effects of CHI-only interventions may be limited.

IF SMALL EHI ROLE:

EHI detection & interventions may be harder to justify.

SMALL

Role of EHI: Model Estimates

SSA (heterosexual) US (heterosexual/MSM) US (MSM) Europe (MSM)0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Population

Prop

ortio

n ne

w in

fecti

ons d

ue to

EHI

Hollingsworth et al 2008

Hayes & White 2006*

Pinkerton & Abramson 1996**Kretzschmar & Dietz 1998**†

Xiridou et al 2004Jacquez et al 1994

Abu-Raddad & Longini 2008†

Salomon & Hogan

2008*Koopman et al 1997**

Pinkerton 2007

Prabhu et al 2009

* 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 Estimates

SSA (heterosexual) US (heterosexual/MSM) US (MSM) Europe (MSM)0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Population

Prop

ortio

n ne

w in

fecti

ons d

ue to

EHI

Hollingsworth et al 2008

Hayes & White 2006*

Pinkerton & Abramson 1996**Kretzschmar & Dietz 1998**†

Xiridou et al 2004Jacquez et al 1994

Abu-Raddad & Longini 2008†

Salomon & Hogan

2008*Koopman et al 1997**

Pinkerton 2007

Prabhu et al 2009

* 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.

• Difficult to obtain data for informing models• Effects of interventions during EHI unknown

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

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)

Stages of Infection

EHI Asymptomatic Period EarlyAIDS

AIDS→ → →

~ 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

1960 1965 1970 1975 1980 1985 1990 1995 2000 20050

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Year

Adul

t HIV

Pre

vale

nce ANC data

Lilongwe ANC Prevalence Data

1960 1965 1970 1975 1980 1985 1990 1995 2000 20050

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Year

Adul

t HIV

Pre

vale

nce

Best-fitting model estimates95% credible intervalsANC data

Predicted Contribution of EHI

1970 1975 1980 1985 1990 1995 2000 2005 20100

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Year

Prop

ortio

n ne

w ca

ses d

ue to

EHI

38%

19%

58%

Best fitting model estimates95% credible intervals

Transmission-suppressing intervention

• Assumed generic intervention that ↓↓↓ infectivity in those receiving it – e.g., complete viral suppression, effective condom

use

Transmission-suppressing intervention

EHI CHI

EHI CHI

EHI CHI(Approximates test-and-treat with annual tests)

(No residual effect during CHI)

EHI-only Prevention StrategyAssuming transmission is almost completely suppressed in various proportions of EHI cases only (no residual effect):

If suppression in 100% CHI

Transmission suppressed in:25% EHI cases50% EHI cases75% EHI cases100% EHI cases

No intervention

CHI-only Prevention Strategy Assuming transmission is almost completely suppressed in 75% of CHI cases only (beginning to end of CHI):

Transmission suppressed in:75% CHI + 0% EHI cases

No intervention

75% CHI coverage, 25% EHI coverageAssuming transmission is almost completely suppressed in 75% of CHI cases and 25% of EHI cases:

Transmission suppressed in:75% CHI + 0% EHI cases75% CHI + 25% EHI cases

No intervention

75% CHI coverage, 50% EHI coverageAssuming transmission is almost completely suppressed in 75% of CHI cases and 50% of EHI cases:

Transmission suppressed in:75% CHI + 0% EHI cases75% CHI + 50% EHI cases

No intervention

75% CHI coverage, 75% EHI coverageAssuming transmission is almost completely suppressed in 75% of CHI cases and 75% of EHI cases:

Transmission suppressed in:75% CHI + 0% EHI cases75% CHI + 75% EHI cases

No intervention

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

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

Acknowledgments• UNC– Bill Miller– Mike Cohen– Irving Hoffman– Audrey Pettifor

• Imperial College London– Azra Ghani– Christophe Fraser– Tim Hallett– Rebecca Baggaley

• UNC Project Malawi– Gift Kamanga– Robert Jafali– Mina Hosseinipour– David Chilongozi– Francis Martinson

• Funding from– NIH– UNC CFAR