Mapping HIV clustering: a strategy for identifying populations at high risk of HIV infection in sub-Saharan Africa

Abstract

BackgroundThe geographical structure of an epidemic is ultimately a consequence of the drivers of the epidemic and the population susceptible to the infection. The ‘know your epidemic’ concept recognizes this geographical feature as a key element for identifying populations at higher risk of HIV infection where prevention interventions should be targeted. In an effort to clarify specific drivers of HIV transmission and identify priority populations for HIV prevention interventions, we conducted a comprehensive mapping of the spatial distribution of HIV infection across sub-Saharan Africa (SSA).MethodsThe main source of data for our study was the Demographic and Health Survey conducted in 20 countries from SSA. We identified and compared spatial clusters with high and low numbers of HIV infections in each country using Kulldorff spatial scan test. The test locates areas with higher and lower numbers of HIV infections than expected under spatial randomness. For each identified cluster, a likelihood ratio test was computed. A P-value was determined through Monte Carlo simulations to evaluate the statistical significance of each cluster.ResultsOur results suggest stark geographic variations in HIV transmission patterns within and across countries of SSA. About 14% of the population in SSA is located in areas of intense HIV epidemics. Meanwhile, another 16% of the population is located in areas of low HIV prevalence, where some behavioral or biological protective factors appear to have slowed HIV transmission.ConclusionsOur study provides direct evidence for strong geographic clustering of HIV infection across SSA. This striking pattern of heterogeneity at the micro-geographical scale might reflect the fact that most HIV epidemics in the general population in SSA are not far from their epidemic threshold. Our findings identify priority geographic areas for HIV programming, and support the need for spatially targeted interventions in order to maximize the impact on the epidemic in SSA.

Spatial epidemiology of hepatitis C virus infection in Egypt: Analyses and implications

“…We identified spatial clusters of high and low HCV prevalence, as well as spatial clusters of high and low PAT exposure, using a spatial scan statistical analysis, implemented in the SaTScan software . Scan statistics are one of the most widely used statistical methods for cluster detection in epidemiology . Briefly, scan statistical analysis uses circular windows of varying radii that span the study region to identify areas with exposure clustering.…”

“…19,20 Scan statistics are one of the most widely used statistical methods for cluster detection in epidemiology. 21,22 Briefly, scan statistical analysis uses circular windows of varying radii that span the study region to identify areas with exposure clustering. Since we aimed to identify localized clusters, a maximum circular window of 30 Km in radius was used for scanning clusters of HCV exposure and PAT exposure.…”

An Appraisal of Female Sex Work in Nigeria - Implications for Designing and Scaling Up HIV Prevention Programmes

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Spatial and social inequities in HIV testing utilization in the context of rapid scale-up of HIV/AIDS services in rural Mozambique

“…As the innovation diffusion perspective would posit, widening access to HIV/AIDS services should lead to increased utilization of those services. Given the growing availability of spatial data and advances in spatial analysis techniques, as well as worldwide concern about the challenges posed by HIV/AIDS to public health, it is not surprising that GIS is increasingly adopted to support analysis of HIV-related outcomes (Kandwal et al, 2009; Cuadros et al, 2013). For example, Tanser et al (2000) found that geographic access is a vital determinant of HIV heterogeneity in rural South Africa.…”