Mapping the spatial variability of HIV infection in Sub-Saharan Africa: Effective information for localized HIV prevention and control

Abstract

Under the premise that in a resource-constrained environment such as Sub-Saharan Africa it is not possible to do everything, to everyone, everywhere, detailed geographical knowledge about the HIV epidemic becomes essential to tailor programmatic responses to specific local needs. However, the design and evaluation of national HIV programs often rely on aggregated national level data. Against this background, here we proposed a model to produce high-resolution maps of intranational estimates of HIV prevalence in Kenya, Malawi, Mozambique and Tanzania based on spatial variables. The HIV prevalence maps generated highlight the stark spatial disparities in the epidemic within a country, and localize areas where both the burden and drivers of the HIV epidemic are concentrated. Under an era focused on optimal allocation of evidence-based interventions for populations at greatest risk in areas of greatest HIV burden, as proposed by the Joint United Nations Programme on HIV/AIDS (UNAIDS) and the United States President's Emergency Plan for AIDS Relief (PEPFAR), such maps provide essential information that strategically targets geographic areas and populations where resources can achieve the greatest impact.

Figure 1

The overall study area (top right panel) and Demographic and Health Survey (DHS) sample locations (blue dots) for each country included in the study. Maps were created using ArcGIS® software by Esri version 10.3 (http://www.esri.com/).

Figure 2

High resolution maps for HIV prevalence in (A) Kenya; (B) Malawi; (C) Mozambique; and (D) Tanzania. Maps were created using ArcGIS® software by Esri version 10.3 (http://www.esri.com/).

Figure 3

Areas with high HIV prevalence (≥80^th percentile) in (A) Kenya; (B) Malawi; (C) Mozambique; and (D) Tanzania. Maps were created using ArcGIS® software by Esri version 10.3 (http://www.esri.com/).

Figure 4

Density distribution of HIV prevalence in Kenya, Malawi, Mozambique and Tanzania for the entire country (left panel; A, C, E, G) and areas with high HIV prevalence (≥80^th percentile) in the same country (right panel; B, D, F, H).

Figure 5

Kriging interpolation of residuals from multivariable logistic regression models for data from (A) Malawi; (B) Mozambique; and (C) Tanzania. Maps were created using ArcGIS® software by Esri version 10.3 (http://www.esri.com/).