Assessing the spatial nonstationarity in relationship between local patterns of HIV infections and the covariates in South Africa: a geographically weighted regression analysis

SOURCE: Spatial and Spatio-temporal Epidemiology
OUTPUT TYPE: Journal Article
PUBLICATION YEAR: 2015
TITLE AUTHOR(S): N.Wabiri, O.Shisana, K.Zuma, J.Freeman
KEYWORDS: HIV/AIDS, HIV/AIDS PREVALENCE, HOUSEHOLD SURVEYS
DEPARTMENT: Human and Social Capabilities (HSC), Impact Centre (IC), GE Support Service (DED:CE), GE Support Service (BDE), Impact Centre (PRESS), Impact Centre (CC), GE Support Service (DCEO:OE), GE Support Service (HR), GE Support Service (IT), GE Support Service (LS), GE Support Service (IS), GE Support Service (RT), GE Support Service (IC), GE Support Service (SD), GE Support Service (PTA Caf), GE Support Service (OPS : Fac), GE Support Service (OPS: Build), GE Support Service (DCEO: Man), GE Support Service (Protect)
Print: HSRC Library: shelf number 8955

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Abstract

Beyond the structural drivers such as distance from the road, rural/urban divide or demographic profiles, not much is known about the spatial relationship between HIV and social covariates. Spatial relations between social covariates and HIV infection of persons above 15 years were explored and mapped using geographically weighted regression model using data from a national HIV household survey conducted in 2008 and comprising 23 369 individuals from approximately 1000 enumeration areas that were randomly selected from the national census. The maps show spatial non-stationarity in relationship between local patterns of HIV prevalence and the social covariates across South Africa. The high prevalence districts have very homogeneous population defined by the following characteristics: Black origin, unfavorable sex ratio (high proportion of females), low socioeconomic status, being single or low marriage rates, multiple sexual partners and intergenerational sex. Markedly, intergenerational sex compounds the risk of acquiring HIV infection for females in poor districts. Identification of key social drivers of HIV and how they vary from location to location can help to effectively guide and focus intervention programs to areas of particular need.