Outline

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 Lampur02 July 2013

Outline

• Background• ART coverage and risk of HIV acquisition• Population viral load

Global ART scale-up

UNAIDS Report on the Global AIDS Epidemic 2012; WHO Universal Access Report 2013; Aaron Motsoaledi 2012

South Africa has the worldwide

largest absolute number of

patients on ART, >1.6 million by some estimates

Study setting• Adult HIV prevalence

24%

• High levels of unemployment and poverty

• Zulu-speaking population

• Former homeland under apartheid

Africa Centre for Health and Population Studies

The Africa Centre

Bor, Herbst, Newell, and Bärnighausen Science 2013

Adult life expectancy over time

13,060 deaths among 101,286individuals aged 15 years and older, contributinga total of 651,350 person-years of follow-up time

HIV incidence by age and sex 2004-2010

00.010.020.030.040.050.060.070.080.090.1

0 20 40 60 80 100

HIV

Inci

denc

e (p

er Y

ear)

Age (Years)

00.010.020.030.040.050.060.070.080.09

0.1

0 20 40 60 80 100H

IV In

cide

nce

(per

yea

r)

Age (years)

Females Males

Tanser, Bärnighausen, Newell CROI 2011

Spatial clustering of

new HIV infections

Tanser et al CROI. Boston, MA; 2011.

Outline


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 HIV-uninfected subsequently retest

– All individuals geo-located

Tanser, Hosegood, Bärnighausen et al. International Journal of Epidemiology 2007

Tanser, Bärnighausen, Grapsa, Zaidi & Newell Science 2013

ART coverage of all HIV-infected individuals 2004-2011

Adjusted for age, sex, community-level HIV prevalence, urban vs. rural locale, marital status, >1 partner in last 12 months, and household wealth index

<10% 10-20%

20-30%

30-40%

40-50%

>50%0

0.2

0.4

0.6

0.8

1

1.2

Proportion of all HIV positive people receiving ART

Adju

sted

Haz

ard

ratio

p=0.004

P<0.0001p<0.0001

P<0.0001

p=0.171

Survival analysis

> 17,000 HIV-negative individuals followed-up for HIV acquisition over 60,558 person-years1,573 HIV sero-conversionsTime- (and space-) varying demographic, sexual behavior, economic and geographic controls, including HIV prevalence

Population impact of ART coverage on risk of HIV acquisition (2004-2012)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

<20 20-35% 35-50% 50-60% >60%

Adju

sted

haz

ard

ratio

Proportion using condom at last sex


Tanser et al, Science, 2013

Population impact of condom use on risk of HIV acquisition (2005-2011)

Conclusion

• We find continued reductions in risk of acquiring HIV infection with increasing ART coverage in this typical rural sub-Saharan African setting

• However, there is evidence of a “reduction saturation” effect (at coverage of >40%) under treatment guidelines of <350 CD4+ cells/µl

Outline


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

Miller, Powers, Smith et al Lancet Infectious Diseases 2013

CVC as a measure for assessment of HIV

treatment as prevention

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

Objectives1. 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)

01.

0e-0

62.

0e-0

63.

0e-0

6D

ensi

ty

0 2000000 4000000 6000000 8000000 10000000VL Result (copies/ml)

Viral load distribution

Median viral load = 6428 copies per ml

Geometric mean population viral load

Copies /ml

Prevalence of unsuppressed viral load

Prevalence (%)

1.00

1.50

2.00

2.50

3.00

3.50

<10% 10 - 15% 15-20% 20-25% >25%

Adju

sted

haz

ard

ratio

HIV Prevalence

p=0.001

p<0.0001p<0.0001

p=0.034

Adjusted HIV acquisition hazard by prevalence category


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

Documents

Outline