Sociobehavioural characteristics and HIV incidence in 29 sub-Saharan African countries: Unsupervised machine learning analysis using the Demographic and Health Surveys

Abstract Background 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. Methods We used unsupervised machine learning to analyse data from the Demographic and Health Surveys of 29 SSA countries completed after 2010. We preselected 48 demographic, socio-economic, behavioural and HIV-related attributes to describe each country. We used Principle 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. We selected the number of clusters using the Silhouette Index and compared the distribution of HIV incidence and sociobehavioural variables within each cluster. Findings The most important characteristics, which explained 69% of 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)). Interpretation Our findings suggest that sociobehavioural factors play a key role in determining the course of the HIV epidemic, and that similar models may eventually be used to design and predict the effects of targeted country-specific interventions to impede HIV transmission. Funding This project was funded by the Swiss National Science Foundation (grant no 163878).