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| Abstract #45 - E-Posters English | ||||
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Session: 50.103: E-Posters English (Poster) on Sunday in Chaired by Authors: Presenting Author: Dr Olive Shisana - Human Sciences Research Council, South Africa |
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| Additional Authors: Dr. Jordi Casabona, Sra Cristina Sanclemente, Dra. Anna Esteve, Dra. Victoria Gonzalez, Grupo HIVITS TS, | ||||
| Aim: To investigate the social and behavioral determinants of spatial clustering of HIV in South Africa, and to investigate the association between HIV prevention services and clustering of HIV infections. | ||||
| Method / Issue: The study uses data from the 2008 national population-based household survey conducted using a multistage stratified second-generation surveillance survey design. The ?District Municipality? in South Africa is used as the spatial mapping unit in the study. A Geographically Weighted Regression (GWR) model is used to assess spatial nonstationarity in relationship between local patterns of HIV prevalence and the determinants within districts. | ||||
| Results / Comments: The study groups districts in South Africa through spatial mapping into three HIV zones: endemic (green belt: <=10% prevalence), epidemic (orange belt: 11-19%) and hyper-epidemic (red belt: =>20%). Data indicates that Districts with high HIV prevalence have a homogenous population with a relatively high proportion of poor single Black African women living in informal settlements that are likely to have multiple sexual partners, and have older sexual partners. These determinants explain approximately half of the variance of HIV clustering. | ||||
| Discussion: This study shows that the distribution of HIV infections is clustered in certain districts. What distinguishes the hyper-epidemic districts from others is that they have a very homogeneous population defined by the following characteristics: Black African origin, unfavorable sex ratio (high proportion of females), low socioeconomic status, being single or low marriage rates, multiple sexual partners and intergenerational sex. All these factors account for, approximately, half of the spatial variation in HIV infection clustering. The impacts of these variables vary across districts. Notably, intergenerational sex compounds the risk of acquiring HIV infection for females. | ||||
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