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

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 < or = 0.01), whereas three low risk clusters (RR = 0.2-0.38) were identified elsewhere in the study area (P < or = 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.

Figure 1

Location of the study area in South Africa

Figure 2

Map of the study area showing the approximate location (incorporating an intentional random error) of all 12 221 participants coded by HIV status

Figure 3

Geographical variations in HIV prevalence by 5% intervals across the surveillance area (for all women aged 15–49 years and men 15–54 years) obtained by a 3-km standard Gaussian kernel (mean = 21.7%, 95% CI 20.1–22.5). Superimposed on the map are the clusters independently identified by the Kulldorff spatial scan statistic (blue shading, low relative-risk; red, high relative-risk). The National Road can be seen next to KwaMsane Township continuing along the eastern boundary of the surveillance area towards Mozambique

Figure 4

Graph showing the inverse relationship between distance to National Road and the estimated number of HIV-positive residents per square kilometre (for all resident women aged 15–49 years and men 15–54 years). The triangle in the graph indicates the datapoint generated after the exclusion of the population in the township (which is fully contained within 1 km of the National Road) from the analysis