%0 Journal Article
%T Geoadditive models to assess spatial variation of HIV infections among women in Local communities of Durban, South Africa
%A Handan Wand
%A Claire Whitaker
%A Gita Ramjee
%J International Journal of Health Geographics
%D 2011
%I BioMed Central
%R 10.1186/1476-072x-10-28
%X We used geoadditive models to assess nonlinear geographical variation in HIV prevalence while simultaneously controlling for important demographic and sexual risk factors. A total of 3,469 women who were screened for a Phase-III randomized trial were included in the current analysis.We found significant spatial patterns that could not be explained by demographic and sexual risk behaviors. In particular, the epidemic was determined to be much worse 44 km south of Durban after controlling for all demographic and sexual risk behaviors.The study revealed significant geographic variability in HIV infection in the eThekwini Metropolitan Municipality in KwaZulu-Natal, South Africa.South Africa is home to 5.7 million people living with HIV - the largest epidemic in the world [1], accounting for some 17% of the global HIV positive population. The reasons for the high rates of infection in South Africa remain uncertain, but socio-economic, cultural and historical factors are known to be important determinants of the spread of HIV infection [2-4]. HIV infection is not homogenously distributed throughout the South African population; population sub-groups are particularly vulnerable. The most vulnerable are young women between the ages of 25 and 29 years, 33% of whom are living with HIV [5], while the province of KwaZulu-Natal (on the eastern seaboard) has the highest HIV prevalence of the country's nine provinces at 26% (15-49 year age group) [5]. Other studies have demonstrated geographical variation in prevalence at the sub-provincial and even sub-district level [6,7].Prevalence of HIV infection in South Africa has previously been reported as a national or provincial average [5]. Linking individual behavioral survey records with disease prevalence at community level has not previously been possible because of methodological challenges in existent methods. However, fine-scale geographical display and analysis of the data gathered during large-scale clinical trials could assis
%U http://www.ij-healthgeographics.com/content/10/1/28