Changing spatial patterns and increasing rurality of HIV prevalence in the Democratic Republic of the Congo between 2007 and 2013

Abstract

The Democratic Republic of the Congo (DRC) has one of the lowest HIV prevalence in sub-Saharan Africa, estimated at 1.1% [0.9-1.3] of adults aged 15-49 in 2013 (UNAIDS). Within the 2 million km2 country, however, there exists spatial variation in HIV prevalence, with the highest HIV prevalence observed in the large cities of Kinshasa and Lubumbashi. Globally, HIV is an increasingly rural disease, diffusing outwards from urban centers of high HIV prevalence to places where HIV was previously absent or present at very low levels. Utilizing data collected during Demographic and Health Surveillance (DHS) in 2007 and 2013 in the DRC, we sought to update the map of HIV prevalence in the DRC as well as to explore whether HIV in the DRC is an increasingly rural disease or remains confined to urban areas. Bayesian kriging and regression indicate that HIV prevalence in rural areas of the DRC is higher in 2013 than in 2007 and that increased distance to an urban area is no longer protective against HIV as it was in 2007. These findings suggest that HIV education, testing and prevention efforts need to diffuse from urban to rural areas just as HIV is doing.

Geographic Information Systems, spatial analysis, and HIV in Africa: A scoping review

“…More complex methods, such as kriging or Bayesian hierarchical modeling with a spatial component, used statistical models to predict HIV prevalence [24, 27, 32, 34–37, 39–41]. Software use varied by method, with IDW and kriging maps typically created using ArcGIS [38], kernel density smoothing done in BayesX [18] or Idrisi [23], and Bayesian modeling done in R [35, 36], WinBUGS [39, 40], or OpenBUGS [27] (see S1 Table).…”

“…Detailed smoothed maps also enabled visual comparisons of the spatial patterns of HIV over time periods [17, 29, 34, 35], or between various subgroups of the population, such as men and women or those living in sero-discordant partnerships and those not [19, 28, 36, 39]. GIS software also helped validate HIV prevalence estimates from antenatal care surveillance against population-based survey estimates, which are more comprehensive, but more resource-intensive, by comparing data between the two sources at similar locations [43, 44].…”

“…For example, GIS technology allowed for the characterization of a defined area around a study participant’s community, such as the risk of schistosomiasis [57] or the density of refugee camps within a 25km radius [19]. Similarly, GIS tools were used to measure distances between a household’s location and the nearest city [19, 35], major road [19, 58], body of water or refugee camp [19]. One study used geo-referenced mobile phone data from a cell service provider in Côte d’Ivoire to develop a predictive model of area HIV prevalence based on residents' travel and communication patterns [42].…”

Spatial characteristics and the epidemiology of human infections with avian influenza A(H7N9) virus in five waves from 2013 to 2017 in Zhejiang Province, China

“…Ordinary kriging interpolation was widely used to estimate the extent and geographical distribution for infectious diseases such as HIV, West Nile Disease and Dengue fever [ 25 – 27 ]. Kriging is an effective supplement for regular case surveillance, which depends on the medical personnel report.…”

Temporal stability of HIV prevalence in high-burden areas regardless of declines in national HIV prevalence in Malawi and Zimbabwe

“…Despite substantial reductions in the national HIV prevalence for several sub-Saharan Africa (SSA) countries, recent studies have identified important disparities in the decline of HIV prevalence at subnational levels [1][2][3] . Results from these studies suggest that the observed declines in HIV prevalence reflect aggregate measures at the national level, obscuring local variations in the spatio-temporal dynamics of the HIV epidemic.…”