Making Population-level,
HIV prevalence estimates
in South Africa
Should we use the
NM/HSRC data or ASSA
projections?
Antenatal HIV prevalence data
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ANC data is widely used in
developing countries to monitor the
HIV epidemic
Wealthier countries use more
sophisticated surveillance tools
ANC data useful for monitoring
trends over time – not so useful for
making population level estimates
Int J Epidemiol. 2002 Apr;31(2) :449-55. How well
do antenatal clinic (ANC) attendees represent the
general population? A comparison of HIV
prevalence from ANC sentinel surveillance sites
with a population-based survey of women aged
15-49 in Cambodia.Saphonn V, et al
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Although ANC data can be used to
estimate trends over time, it should
be realized that ANC data may
overestimate the actual prevalence
in the younger age group in rural
areas in Cambodia.
AIDS. 1998 Jul 9;12(10):1227-34. Studying
dynamics of the HIV epidemic: populationbased data compared with sentinel
surveillance in Zambia. Fylkesnes K
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ANC-based data might draw a rather
distorted picture of current dynamics
of the HIV epidemic.
AIDS. 2002 Mar 8;16(4):661-5. Comparison
of HIV prevalences in community-based and
antenatal clinic surveys inrural Mwanza,
Tanzania.Changalucha J
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In this rural population, the HIV
prevalence in ANC attenders
underestimated the prevalence
among women in the general
population.
East Afr Med J. 1996 May;73(5):298-302.
Sentinel surveillance and cross sectional
survey on HIV infection prevalence:
acomparative study.Kwesigabo G,
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Seroprevalence among antenatal
clinic attenders was significantly
lower than that of females from the
general population sample (p
=0.016).
AIDS. 2003 Feb 14;17(3):399-405. Evaluating two
adjustment methods to extrapolate HIV prevalence
from pregnantwomen to the general female
population in sub-Saharan Africa.Fabiani M,
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The non-adjusted HIV prevalence
among ANC attendees
underestimates the prevalence
among the general female population
by 8.0% in Chelston in 1998 and by
between 20.7% and 31.9% in all
other cases.
AIDS. 1993 Apr;7(4):567-72. Sentinel surveillance
for HIV-1 infection: how representative are blood
donors,outpatients with fever, anaemia, or sexually
transmitted diseases, and antenatalclinic attenders
in Mwanza Region, Tanzania? Borgdorff M,
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Patients with fever and antenatal
clinic attenders may reflect
trends,but data from patients with
fever markedly overestimate, and
data from antenatal clinic attenders
underestimate, population HIV-1
prevalence.
Population used for the ANC HIV survey
46,600,000
23,800,000
1,100,000
Population of
SA
Women (51.1%) Births in 2002
825,000
Access Public
ANC Services
(1.8% of
Population)
Antenatal data much less useful for
making population level estimates
about the prevalence of HIV
because of the serial extrapolations
based on assumptions, often
backed by sparse data, make the
final estimates subject to great
potential error.
Data source for ANC surveys
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Prevalence measured is among
pregnant women attending a small
proportion of public sector
antenatal clinics for 6 to 8 weeks a
year.
From the sample to all pregnant
women
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Extrapolated to all pregnant women
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Assumptions are made about:
• women not using ANC services,
• prevalence among private sector
users.
From pregnant women to all
women
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Extrapolated to all women
assumptions made about HIV
prevalence among:
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contraceptive users,
infertile,
non-sexually active,
elderly and
young women
From pregnant women to all
women
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HIV/AIDS reduces fertility and so the HIV
prevalence among the over 25 year old
pregnant women is probably an underestimate
of the true HIV prevalence.
With increased access to HIV VCT, more HIV
infected women know their status and hence
are likely to tend to prevent pregnancy which
results in an underestimate of the true HIV
prevalence.
Pregnant women under 20 are not truly
representative of this age group because not all
women in this age group are sexually active.
This probably results in the antenatal survey
overestimating HIV prevalence
From women to men & children
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Extrapolated to men and children
assumptions about:
• the male to female prevalence ratios
and
• adult to children ratios
Specific weaknesses in using
South African antenatal data
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In some clinics the nurses obtain consent
from the patients which may result in biased
results if a substantial proportion refuse. No
data on the refusal rate.
Certain provinces do not follow the protocol.
For example in KZN the clinics were hand
selected instead of being randomly selected
using the “probability proportional to size
methods”. This has resulted in all 35 clinics
being on main roads
There is limited quality control or validation
of the South African survey and the data has
never been released to the public making it
impossible to double check the analysis