Bg. Williams et al., Patterns of infection: using age prevalence data to understand the epidemic of HIV in South Africa, S AFR J SCI, 96(6), 2000, pp. 305-312
South Africa is experiencing an explosive epidemic of HIV/AIDS, with about
one in four women attending ante-natal clinics nationwide being HIV-positiv
e. In order to understand the natural history of the epidemic, to design an
d target interventions to manage it and to evaluate the impact of intervent
ions that are implemented, it is essential to gather information on the pat
terns of infection. In particular it is important to know how these vary wi
th gender, age, migrancy status and between urban and rural settings. Ideal
ly, one should measure age-specific incidence but this is difficult to do.
Many datasets are available, however, on age-specific prevalence of infecti
on and these are used to investigate the risk of infection with age among a
number of different populations. The populations under consideration inclu
de women attending ante-natal clinics, urban and rural populations, migrant
workers and commercial sex workers. Data are also presented from one work-
based survey and from a study of cancer patients at a major hospital in Sow
eto.
We were able to identify four different patterns of infection among a) wome
n attending ante-natal clinics; b) women in the general population; c) men
in the general population; and d) migrant workers. It is interesting that w
e were unable to show differences between urban and rural populations. Furt
hermore, the patterns of infection appear to be fairly constant over time,
although as the epidemic saturates and reaches a steady state this must cha
nge. These data highlight, in particular, the extremely high risk of infect
ion among 15-25-year-old women and among migrant workers of all ages. They
should serve not only to highlight the urgency of the situation and the nee
d to deal with the spread of infection effectively, but should also provide
a basis for detailed epidemiological modelling, which can be used to predi
ct the future course of the epidemic, plan an effective response and evalua
te the impact of interventions.