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 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