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

PeerJ

article

Aziza Merzouki¹  Janne Estill¹  Erol Orel¹  Kali Tal³  Olivia Keiser¹ 

[1] Institute of Global Health, University of Geneva;Institute of Mathematical Statistics and Actuarial Science, University of Bern;Institute of Primary Health Care ,(BIHAM), University of Bern

关键词: HIV incidence;    Sociobehavioural characteristics;    Unsupervised machine learning;    Dimensionality reduction;    Hierarchical clustering;    Principal component analysis;   

DOI  :  10.7717/peerj.10660

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

IntroductionHIV 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.MethodsWe 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.ResultsThe 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)).ConclusionsOur 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.

【 授权许可】

CC BY