Localized spatial clustering of HIV infections in a widely disseminated rural South African epidemic
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 pat...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
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1 August 2009
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International journal of epidemiology
Year: 2009, Jahrgang: 38, Heft: 4, Pages: 1008-1016 |
| ISSN: | 1464-3685 |
| DOI: | 10.1093/ije/dyp148 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1093/ije/dyp148 Verlag, kostenfrei, Volltext: https://academic.oup.com/ije/article/38/4/1008/849465/Localized-spatial-clustering-of-HIV-infections-in 
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| Verfasserangaben: | Frank Tanser, Till Bärnighausen, Graham S. Cooke, Marie-Louise Newell |
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| 520 | |a 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. | ||
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