Clusters of sub-Saharan African countries based on sociobehavioural characteristics and associated HIV incidence

Abstract

Introduction HIV incidence varies widely between sub-Saharan African (SSA) countries. This variation coincides with a substantial sociobehavioural heterogeneity, which complicates the design of effective interventions. In this study, we investigated how sociobehavioural heterogeneity in sub-Saharan Africa could account for the variance of HIV incidence between countries. Methods We analysed aggregated data, at the national-level, from the most recent Demographic and Health Surveys of 29 SSA countries (2010–2017), which included 594,644 persons (183,310 men and 411,334 women). We preselected 48 demographic, socio-economic, behavioural and HIV-related attributes to describe each country. We used Principal Component Analysis to visualize sociobehavioural similarity between countries, and to identify the variables that accounted for most sociobehavioural variance in SSA. We used hierarchical clustering to identify groups of countries with similar sociobehavioural profiles, and we compared the distribution of HIV incidence (estimates from UNAIDS) and sociobehavioural variables within each cluster. Results The most important characteristics, which explained 69% of sociobehavioural variance across SSA among the variables we assessed were: religion; male circumcision; number of sexual partners; literacy; uptake of HIV testing; women’s empowerment; accepting attitude toward people living with HIV/AIDS; rurality; ART coverage; and, knowledge about AIDS. Our model revealed three groups of countries, each with characteristic sociobehavioural profiles. HIV incidence was mostly similar within each cluster and different between clusters (median (IQR); 0.5/1000 (0.6/1000), 1.8/1000 (1.3/1000) and 5.0/1000 (4.2/1000)). Conclusions Our findings suggest that the combination of sociobehavioural factors play a key role in determining the course of the HIV epidemic, and that similar techniques can help to predict the effects of behavioural change on the HIV epidemic and to design targeted interventions to impede HIV transmission in SSA.

Changes of sociobehavioural characteristics and HIV in 29 Sub-Saharan African countries, 2000-2018

“…We reduced the dimensionality of each survey using Principal Component Analysis (PCA) (23) (See details in Supplementary Material). To capture the trends in our dataset, and to facilitate comparison with previous results(11), we computed the rotation matrix using the most recent surveys collected since 2015. This rotation matrix was then used to project all other surveys onto the dimensionally reduced PCA space.…”

“…As in a previous publication (11), we retained only variables that varied significantly across regions and did not strongly correlate with other variables (Table S1 in Supplementary Material). The data were represented as percentages, with mean number of sexual partners in lifetime scaled using min-max normalization.…”

“…To capture sociobehavioural heterogeneity across SSA, we used a consensus clustering method to identify groups of similar surveys in terms of sociobehavioural characteristics. We identified an optimum number of three clusters by maximizing the Silhouette score (11). The consensus clustering method aggregated results from three hierarchical clusterings and three k-means clusterings (24), each run on a different number of PCs.…”

“…These studies often used descriptive statistics and regression analyses. In a previous study (11), Merzouki et al assessed sociobehavioural heterogeneity across 29 SSA countries based on 48 demographic, socio-economic, behavioural and HIV-related attributes and their relation to HIV incidence using clustering techniques, but the study was cross-sectional only.…”

Comparative clustering and visualization of socioeconomic and health indicators: A case of 47 counties in Kenya

“…Recent research has shown a direct connection between socioeconomic and health indicators, and cluster analyzes have been shown to reveal patterns of hidden regional economic development [15,16,17,18]. By comparing and characterizing counties, we can test the theory that socioeconomic and health heterogeneity may account for the spatial diversity of regions.…”