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.

Geospatial mapping to identify feasible HIV prevention and treatment strategies that target specific settings

“…The stark contrast among the conclusions drawn by Coburn et al (1,6), the current treatment allocation strategy implemented in Lesotho (4,10), and the findings of Cuadros et al (11) underscore the importance of gathering highly sensitive local data to understand the geographic characteristics of an epidemic for the development of optimal disease elimination strategies. It is important for programs and countries (e.g., ministries of health) to consider strategies that are appropriate for their specific population and available resources.…”

“…In direct contrast to this argument is a publication by Cuadros et al (11), who argue that directing prevention and treatment resources to areas with a relatively small population density, but high HIV prevalence, could in fact be more cost-effective and have greater impact compared to always allocating resources to high population density areas. The results in this study did not demonstrate a consistent association between population density and HIV prevalence; instead, the findings do suggest that in areas with a smaller population density, such as Mozambique and Tanzania, geographical variation within high burden areas was much smaller compared to the variation observed in areas with a greater population density, such as Kenya.…”

How is the United States’s international HIV prevention programme evolving?

“…While the country data transmitted to Ambassador Birx proves that a fast-track strategy to eradicate the disease is feasible within five years, PEPFAR must continue to work within a budget that, through efficient targeting, provides steady, but sure and constant progress towards that goal. Recent research identifies populations at risk (Jordan, Edwards 2016, Onovo and and provides high-resolution maps of estimates of HIV prevalence in Sub-Saharan Africa which help to strategically target "geographic areas and populations where resources can achieve the greatest impact" (Cuadros et al 2017). Ambassador Birx herself has proven to be extremely determined to not only ferociously defend the PEPFAR budget, but also to use data and technology and innovation to wring every last bit of value and salvation from the resources she is given.…”

Geospatial modelling in guiding health program strategies in resource-limited settings—the way forward

“…Only through a deep understanding of the local environment can modelers effectively address the needs and constraints that are specific to the local context in their final model. While directing prevention and treatment efforts to locations with high population density and high prevalence should have greater impact and be more cost-effective, the implication of these findings on program design and resource allocation needs to be further explored and debated (7).…”

“…However, few have included assumptions and parameters that would address the dynamics of geospatial characteristics of an epidemic. Geospatial models have only gained attention in recent years and have since been adopted in various infectious disease settings (3,(5)(6)(7)(8).…”