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BackgroundLarge geographical variations in the intensity of the HIV epidemic in sub-Saharan Africa call for geographically targeted resource allocation where burdens are greatest. However, data available for mapping the geographic variability of HIV prevalence and detecting HIV 'hotspots' is scarce, and population-based surveillance data are not always available. Here, we evaluated the viability of using clinic-based HIV prevalence data to measure the spatial variability of HIV in South Africa and Tanzania.MethodsPopulation-based and clinic-based HIV data from a small HIV hyper-endemic rural community in South Africa as well as for the country of Tanzania were used to map smoothed HIV prevalence using kernel interpolation techniques. Spatial variables were included in clinic-based models using co-kriging methods to assess whether cofactors improve clinic-based spatial HIV prevalence predictions. Clinic- and population-based smoothed prevalence maps were compared using partial rank correlation coefficients and residual local indicators of spatial autocorrelation.ResultsRoutinely-collected clinic-based data captured most of the geographical heterogeneity described by population-based data but failed to detect some pockets of high prevalence. Analyses indicated that clinic-based data could accurately predict the spatial location of so-called HIV 'hotspots' in > 50% of the high HIV burden areas.ConclusionClinic-based data can be used to accurately map the broad spatial structure of HIV prevalence and to identify most of the areas where the burden of the infection is concentrated (HIV 'hotspots'). Where population-based data are not available, HIV data collected from health facilities may provide a second-best option to generate valid spatial prevalence estimates for geographical targeting and resource allocation.

Original publication





International journal of health geographics

Publication Date





Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH, 45221, USA.


Humans, HIV Infections, Health Surveys, Health Facilities, Tanzania, South Africa, Epidemics, Spatial Analysis, Data Analysis