Patterns of infection: using age prevalence data to understand the epidemic of HIV in South Africa

Citation
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
Citations number
14
Categorie Soggetti
Multidisciplinary,Multidisciplinary
Journal title
SOUTH AFRICAN JOURNAL OF SCIENCE
ISSN journal
00382353 → ACNP
Volume
96
Issue
6
Year of publication
2000
Pages
305 - 312
Database
ISI
SICI code
0038-2353(200006)96:6<305:POIUAP>2.0.ZU;2-O
Abstract
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.