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IJE Advance Access published online on March 4, 2009
International Journal of Epidemiology, doi:10.1093/ije/dyp148
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2009; all rights reserved.
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Localized spatial clustering of HIV infections in a widely disseminated rural South African epidemic

Frank Tanser1,*, Till Bärnighausen1,2, Graham S Cooke1,3 and Marie-Louise Newell1,4

1Africa Centre for Health and Population Studies, University of KwaZulu-Natal, Mtubatuba, South Africa.
2Department of Global Health and Population, Harvard School of Public Health, Boston, USA.
3Imperial College, London, UK.
4Centre for Paediatric Epidemiology and Biostatistics, Institute of Child Health, University College London, UK.

*Corresponding author. Africa Centre for Health and Population Studies, University of KwaZulu-Natal, PO Box 198, Mtubatuba 3935, South Africa. E-mail: tanserf{at}africacentre.ac.za


  Abstract

Background South Africa contains more than one in seven of the world's HIV-positive population. Knowledge of local variation in levels of HIV infection is important for prioritization of areas for intervention. We apply two spatial analytical techniques to investigate the micro-geographical patterns and clustering of HIV infections in a high prevalence, rural population in KwaZulu-Natal, South Africa.

Methods All 12 221 participants who consented to an HIV test in a population under continuous demographical surveillance were linked to their homesteads and geo-located in a geographical information system (accuracy of <2 m). We then used a two-dimensional Gaussian kernel of radius 3 km to produce robust estimates of HIV prevalence that vary across continuous geographical space. We also applied a Kulldorff spatial scan statistic (Bernoulli model) to formally identify clusters of infections (P < 0.05).

Results The results reveal considerable geographical variation in local HIV prevalence (range = 6–36%) within this relatively homogenous population and provide clear empirical evidence for the localized clustering of HIV infections. Three high-risk, overlapping spatial clusters [Relative Risk (RR) = 1.34–1.62] were identified by the Kulldorff statistic along the National Road (P ≤ 0.01), whereas three low risk clusters (RR = 0.2–0.38) were identified elsewhere in the study area (P ≤ 0.017).

Conclusions The findings show the existence of several localized HIV epidemics of varying intensity that are partly contained within geographically defined communities. Despite the overall high prevalence of HIV in many rural South African settings, the results support the need for interventions that target socio-geographic spaces (communities) at greatest risk to supplement measures aimed at the general population.

Keywords HIV seroprevalence, geographical information systems, disease clustering, small-area variation

Accepted 27 January 2009


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