Download population viral load

The population impact of ART scale-up
on the risk of new HIV infection in rural
KwaZulu-Natal, South Africa
Frank Tanser
Presentation at IAS, Kuala Lampur
02 July 2013
Outline
• Background
• ART coverage and risk of HIV acquisition
• Population viral load
Global ART scale-up
South Africa has the
worldwide largest
absolute number of
patients on ART,
>1.6 million by some
estimates
UNAIDS Report on the Global AIDS Epidemic 2012; WHO Universal Access Report 2013;
Aaron Motsoaledi 2012
Study setting
• Adult HIV prevalence
24%
• High levels of
unemployment and
poverty
• Zulu-speaking
population
• Former homeland
under apartheid
The Africa Centre
Africa Centre for Health and Population Studies
Adult life expectancy in years
Adult life expectancy over time
60
13,060 deaths
among 101,286
individuals aged
15 years and
older,
contributing
a total of
651,350 personyears of followup time
55
50
45
2000
2002
2004
2006
2008
2010
Year
Bor, Herbst, Newell, and Bärnighausen Science 2013
2012
HIV incidence by age and sex
2004-2010
Males
0.1
0.1
0.09
0.09
0.08
0.08
HIV Incidence (per year)
HIV Incidence (per Year)
Females
0.07
0.06
0.05
0.04
0.03
0.02
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.01
0
0
0
20
40
60
80
100
Age (Years)
Tanser, Bärnighausen, Newell CROI 2011
0
20
40
60
Age (years)
80
100
Spatial
clustering of
new HIV
infections
Tanser et al CROI. Boston, MA; 2011.
Outline
• Background
• ART coverage and risk of HIV acquisition
• Population viral load
Population-based HIV surveillance
• Since 2003: Population-based
HIV surveillance
– Longitudinal, dynamic cohort
– Entire adult population living in a
contiguous geographical area of 438
km2 under surveillance
– Annual rounds
– 75% of those observed to be HIVuninfected subsequently retest
– All individuals geo-located
Tanser, Hosegood, Bärnighausen et al. International Journal of Epidemiology 2007
ART coverage of all HIV-infected individuals
2004-2011
Tanser, Bärnighausen, Grapsa, Zaidi & Newell Science 2013
Population impact of ART coverage on risk of
Survival analysis
HIV acquisition (2004-2012)
> 17,000 HIVnegative
individuals
followed-up for
HIV acquisition
over 60,558
person-years
Adjusted Hazard ratio
1.2
1
0.8
p=0.171
1,573 HIV seroconversions
0.6
P<0.0001
p<0.0001
0.4
P<0.0001 p=0.004
0.2
0
<10%
10-20%
20-30%
30-40%
40-50%
Proportion of all HIV positive people receiving ART
>50%
Time- (and
space-) varying
demographic,
sexual behavior,
economic and
geographic
controls,
including HIV
prevalence
Adjusted for age, sex, community-level HIV prevalence, urban vs. rural locale,
marital status, >1 partner in last 12 months, and household wealth index
Population impact of condom use on risk of HIV
acquisition (2005-2011)
1.60
Adjusted hazard ratio
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
<20
20-35%
35-50%
50-60%
>60%
Proportion using condom at last sex
Adjusted for age, sex, community-level HIV prevalence, urban vs. rural locale,
marital status, >1 partner in last 12 months, and household wealth index
Tanser et al, Science, 2013
Conclusion
• We find continued reductions in risk of
acquiring HIV infection with increasing
ART coverage in this typical rural subSaharan African setting
• However, there is evidence of a “reduction
saturation” effect (at coverage of >40%)
under treatment guidelines of <350 CD4+
cells/µl
Outline
• Background
• ART coverage and risk of HIV acquisition
• Population viral load
Population/Community viral
load
• Proposed as:
– an aggregate measure of the
potential for ongoing HIV
transmission within a community
– as a surveillance measure for
monitoring uptake and
effectiveness of antiretroviral
therapy.
Figure 1
CVC as a measure for assessment
of HIV treatment as prevention
Miller, Powers, Smith et al Lancet Infectious Diseases 2013
CVC as a measure for assessment
of HIV treatment as prevention
“ The issue of selection bias [in community
viral load] could be addressed with a
population-based survey in a clearly defined
target population of all people in a
community, including those with and
without known HIV infection”
Miller, Powers, Smith et al Lancet Infectious Diseases 2013
Objectives
1. Asses whether viral loads in this
population are randomly distributed in
space or whether high or low viral loads
tend to cluster in certain areas
2. Assess the degree to which different
population viral load summary measures
highlight known areas of high incidence
3. (Establish the degree to which different
population viral load summary measures
predict future risk of new HIV infection in
uninfected individuals)
Methods
• All 2,456 individuals testing positive in the
population-based HIV testing conducted in
2011
• Total number DBS specimens tested was
2,420 (36 specimens excluded due to
being insufficient for testing).
• Of these, 30% (726) were below the
detectable limit of 1550 copies/ml (Viljoen
et al, JAIDS 2010)
2.0e-06
Median viral
load = 6428
copies per ml
0
1.0e-06
Density
3.0e-06
Viral load distribution
0
2000000
4000000
6000000
VL Result (copies/ml)
8000000
10000000
Geometric mean population
viral load
Copies /ml
Prevalence of unsuppressed
viral load
Prevalence (%)
Adjusted HIV acquisition hazard by
prevalence category
3.50
Adjusted hazard ratio
3.00
2.50
p<0.0001
p<0.0001
2.00
p=0.001
p=0.034
1.50
1.00
<10%
10 - 15%
15-20%
20-25%
>25%
HIV Prevalence
Adjusted for age, sex, community-level HIV prevalence, urban vs. rural locale,
marital status, >1 partner in last 12 months, and household wealth index
Population prevalence of
detectable virus (PPDV)
Prevalence(%)
Conclusion
• To measure transmission potential of a
community, any viral load summary index
must take into account the size of the
uninfected population
• Population prevalence of detectable virus
(PPDV) successfully identified known areas
of continued high HIV incidence
• We propose the PPDV as a simple
community-level index of transmission
potential
Acknowledgements
• The members of the community in Hlabisa sub-district
who gave their time to this research
• Staff of the Africa Centre for Health and Population
Studies and the Hlabisa HIV Care and Treatment
Programme
• Colleagues at Africa Centre (Marie-Louise Newell, Till
Bärnighausen, Tulio de Oliveira,Kobus Herbst, Colin
Newell, Jacob Bor, Jaffer Zaidi and many more)
• Funding
− Wellcome Trust (Africa Centre for Health and Population
Studies)
− National Institutes of Health grants R01 HD058482-01 and
1R01MH083539-01