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

Data

We used aggregated data from the most recent Demographic and Health Surveys (DHS) of 29 SSA countries completed between 2010 and 2017, that were available up to July 2018 (Table S1). DHS typically gathers nationally representative (USAID, 2020b) data on health (including HIV-related data) and population (including social, behavioural, geographic and economic data) every 5 years. These data are publicly accessible at the individual-, district- and country-level. We used data aggregated at the national-level.

We attempted to include in our analysis all variables possibly influencing the risks of HIV acquisition among population aged 15–49 years that were available for the 29 SSA countries through the StatCompiler tool (USAID, 2020a). We only included variables that varied significantly across the region, and did not strongly correlate with other variables. We finally pre-selected the following variables: age (under 25 vs older); rurality (rural vs urban); religion (Christian, Muslim, Folk/Popular religions, unaffiliated, others); marital status (married or in union vs widowed/divorced/other), number of wives for men (1, ≥2) or co-wives for women (0, 1, ≥2); literacy (literate vs illiterate); media access (with access to newspaper, television and radio at least once a week vs without such access); employment (worked in the last 12 months and currently working vs others); wealth (Gini coefficient); age at first sexual intercourse (first sexual intercourse by age 15 vs older); general fertility (number of births to women of reproductive age in the last 3 years); contraception use (using any method of contraception vs not using any); condom use (belief that a woman is justified in asking condom use if she knows her husband has a Sexually Transmitted Infection (STI) vs belief that she is not justified); number of sexual partners in lifetime; unprotected higher risk sex (men who had sex with a non-marital, non-cohabiting partner in the last 12 months and did not use condom during last sexual intercourse vs not); paid sex (men who ever paid for sexual intercourse vs never paid for sex); unprotected paid sex (men who used condom during the last paid sexual intercourse in the last 12 months vs did not use condom); gender-based violence (wife beating justified for at least one specific reason vs not justified for any reason); married women participation to decision making (yes vs no); gender of household head (female vs male); comprehensive correct knowledge about AIDS (yes vs no); HIV testing (ever receiving an HIV test vs never tested); male circumcision (yes vs no); ART coverage (i.e., percentage of people on antiretroviral treatment among those living with HIV); and accepting attitudes toward people living with HIV/AIDS (would buy fresh vegetables from a shopkeeper with AIDS vs would not); see Table 1 for a complete summary of the variables.

We represented each country using 48 dimensions. Each dimension corresponded to an attribute in Table 1, such as the percentage of women married or in union, the mean number of sexual partners in a lifetime for men, the percentage of Christian populations and the Gini coefficient in this country. Data were represented as percentages; the mean number of sexual partners in lifetime was scaled using min-max normalisation. Most of these country-level data were exported from the DHS with the StatCompiler tool, except for data on religion that we obtained from Pew-Templeton Global Religious Futures Project (Pew-Templeton Global Religious Futures Project, 2019), and ART coverage that we obtained from UNAIDS’ AIDSinfo ( UNAIDS, 2019). We used the latest (2018) UNAIDS estimates of national HIV incidence for the year 2016 ( UNAIDS, 2019; UNAIDS, 2018a).

Ethics approval

The study was conducted using aggregated data that are publicly accessible through StatCompiler (for DHS data), Pew-Templeton Global Religious Futures Project, and UNAIDS’ AIDSinfo. No ethics approval was needed from our side.

Analysis

We used Principal Component Analysis (PCA) (Hastie, Tibshirani & Friedman, 2009; James et al., 2013) to reduce the dimensionality of data from 48 to two dimensions (2D) so we could visualise sociobehavioural similarity between SSA countries. PCA allows to extract directions, called principal components (PCs), along which the variation of data is maximal. The first two PCs, which explain the most variance, represent the axes of the 2D-space used for visualisation. Countries closest to each other when projected on the 2D-space correspond to similar countries in terms of demographic, socioeconomic and behavioural characteristics. PCs consist of a linear combination of the initial 48 dimensions and can therefore be interpreted in terms of the original sociobehavioural variables (STHDA, 2020). We can then identify the variables among those we included in the analysis, that explain most of the sociobehavioural heterogeneity across SSA.

We identified groups of similar SSA countries in terms of sociobehavioural characteristics using hierarchical clustering. Pairwise country dissimilarity was calculated using the Euclidian distance (Equation S1). These distances were used by the hierarchical clustering algorithm to create a dendrogram with 29 terminal nodes representing the countries to be grouped. Cutting the dendrogram at a certain height produces clusters of similar countries. The number of clusters depends on the height at which the tree is cut. We measured the quality of the clustering results using the Silhouette Index, and selected the optimal number of clusters such that it maximised this index (Equation S4).

We compared the HIV incidence of countries between the identified clusters, and visualised the distribution of HIV incidence within each cluster using box plots. We also identified the sociobehavioural variables that characterized the resulting clusters, and visualized the distribution of these variables within each cluster using density plots.

We used the open source R language, version 3.5.1 for our analysis. Code and country-level data are available on GitLab (https://gitlab.com/AzizaM/dhs_ssa_countries_clustering), in two separate folders “code” and “data_countries”, respectively.

Visualizing the SSA countries: Geographical and sociobehavioural similarities

Using PCA, we found that the first principal component (PC) explained 49.5% and the second 19.5% of the total sociobehavioural variance across SSA among the 48 variables we considered (Fig. 1). The original sociobehavioural variables that contributed most to these PCs were religion (12.6% for Muslim and 12.1% for Christian populations), male circumcision (9.4%), number of sexual partners (7.8% for men and 3.4% for women), literacy (6.1% for women and 3.2% for men), HIV testing (5.5% for men and 5.4% for women), women’s participation in decision making (3.8%), an accepting attitude towards those living with HIV/AIDS (3.6% for women and 3.2% for men), rurality (3.0% for women and 2.7% for men), ART coverage (2.5%), and women’s knowledge about AIDS (2.5%) (Fig. 1B and Fig. S1).

Projecting the 29 SSA countries in two dimensions produced a roughly V-shaped scatterplot (Fig. 1A). Figure 1B shows how the 48 original sociobehavioural variables vary over the 2D-space. At the end of the V-shape’s left branch, Eastern and Southern African countries, such as Namibia, Zimbabwe, Malawi, Zambia and Uganda, lied next to each other. In these countries, fewer men are circumcised, but the percentage of literate people who had accepting attitudes toward people living with HIV/AIDS (PLWHA) was higher and so was the uptake of HIV testing. Knowledge about AIDS and ART coverage were also high. The end of the right branch, in the upper right quadrant, included countries from the Sahel region, like Senegal, Burkina Faso, Mali, Niger and Chad, where the percentage of Muslims is higher and people have fewer sexual partners. The lower tip of the V-shape included countries from West and Central Africa, like Liberia, Ghana, Côte d’Ivoire, Democratic Republic of the Congo, and Gabon, where people have more sexual partners, more men are circumcised, and the population is less rural.

Clustering the SSA countries and analysis of the associated HIV incidence

The hierarchical clustering of the 29 SSA countries produced a dendrogram (Fig. 2A). Cluster compactness and separation were optimal (maximum silhouette index = 0.3) when we cut the dendrogram at a height that separated countries into three groups (Fig. 2B).

The countries of the first cluster, in yellow, had the lowest HIV incidence (median of 0.5/1000 population) (Fig. 3). This cluster included countries from the Sahel Region, where the population was mostly rural (median of 71.1% for men) and Muslim (median of 86.2%). On the one hand, countries of this cluster were characterized by many factors that could account for low HIV incidence and prevalence. Countries were characterized by high proportions of circumcised men (median of 95.0%), high percentages of women who were married or lived in union (median of 70.6%), late sexual initiation for men (median of 1.9% of men who had their first sexual intercourse by the age of 15), low numbers of sexual partners (median of 3.5 partners for men), low percentages of unprotected higher-risk sex (median of 9.7% for men) and low percentages of men having ever paid for sex (median of 3.9%). Polygyny (Reniers & Tfaily, 2012; Eaton et al., 2014), an institutionalized form of sexual concurrency, was also frequent in this region (median of 22.3%). On the other hand, this cluster was also characterized by frequent belief that wife beating is justified (median of 61.2% for women), and low levels of literacy (median of 29.0% for women). Participation of married women in decision making (median of 18.5%), contraceptive prevalence (median of 13.9%), and knowledge about AIDS (median of 23.7% for women) was also low. These countries had low percentages of people ever tested for HIV (median of 19.2% for men; 36.6% for women), low ART coverage (median of 38.0%) and low levels of acceptance of PLWHA (Median of 47.4% for men); see Figs. 4 and 5.

The countries of the second cluster, coloured in orange, included countries from West and Central Africa. These countries had a rather low HIV incidence (median of 1.8/1000 population), though Mozambique was a remarkable outlier, with a high HIV incidence (9.8/1000 population) (Fig. 3). Like the first cluster, these countries had a high percentage of circumcised men (median of 97.0%, except in Mozambique where only 48.4% of men were circumcised). However, these countries were also characterized by the lowest proportions of rural populations (median of 49.0% for men), the highest numbers of sexual partners (median of 10.1 for men), early sexual initiation (median of 12.0% of men who had their first sexual intercourse by the age of 15), and more frequent unprotected high-risk sex (median of 24.3% for men) and paid sexual intercourse (median of 9.5% for men). HIV testing uptake (median of 25.8% for men and 48.6% for women), knowledge about AIDS (median of 23.6% for women), and ART coverage (median of 31.0%) were all low.

The third cluster, in red, included Southern and East African countries. These countries had high HIV incidence (median of 5.0/1000 population), except two countries that had a lower HIV incidence: Rwanda (1.1/1000 population) and Burundi (0.5/1000) (Fig. 3). Countries belonging to the third cluster were characterized by the lowest percentage of circumcised men (median of 27.9%). But they were also the ones with the highest uptake of HIV testing (median of 65.2% for men; 83.3% for women) and ART (median of 61.0%), and the highest percentage with knowledge about AIDS (median of 54.6% for women) and accepting attitudes towards PLWHA (median of 84.4% for men). This cluster was also characterized by the highest percentage of literacy (median of 80.2% for women), high use of contraceptives (median of 42.6%), low percentages of unprotected high-risk sex (median of 9.8% for men) and higher percentages of married women participating in decision making (median of 67.7%) and women-headed households (median of 31.0%). Rwanda and Burundi had the lowest HIV incidence and were characterized by a lower number of sexual partners (Rwanda, 2.6; Burundi, 2.1) vs a median of 6.3 partners for men in the other countries of the third cluster. They also had larger per capita rural populations (Rwanda, 80.4%; Burundi, 89.4%) vs a median of 61.3% for women in the other countries of the same cluster.