Identifying groups of people with similar sociobehavioural characteristics in Malawi to inform HIV interventions: a latent class analysis

Abstract

Introduction: Within many sub-Saharan African countries including Malawi, HIV prevalence varies widely between regions. This variability may be related to the distribution of population groups with specific sociobehavioural characteristics that influence the transmission of HIV and the uptake of prevention. In this study, we intended to identify groups of people in Malawi with similar risk profiles. Methods: We used data from the Demographic and Health Survey in Malawi (2015 to 2016), and stratified the analysis by sex. We considered demographic, socio-behavioural and HIV-related variables. Using Latent Class Analysis (LCA), we identified groups of people sharing common sociobehavioural characteristics. The optimal number of classes (groups) was selected based on the Bayesian information criterion. We compared the proportions of individuals belonging to the different groups across the three regions and 28 districts of Malawi. Results: We found nine groups of women and six groups of men. Most women in the groups with highest risk of being HIV positive were living in female-headed households and were formerly married or in a union. Among men, older men had the highest risk of being HIV positive, followed by young (20 to 25) single men. Generally, low HIV testing uptake correlated with lower risk of having HIV. However, rural adolescent girls had a low probability of being tested (48.7%) despite a relatively high HIV prevalence. Urban districts and the Southern region had a higher percentage of high-prevalence and less tested groups of individuals than other areas. Conclusions: LCA is an efficient method to find groups of people sharing common HIV risk profiles, identify particularly vulnerable sub-populations, and plan targeted interventions focusing on these groups. Tailored support, prevention and HIV testing programmes should focus particularly on female household heads, adolescent girls living in rural areas, older married men and young men who have never been married.

Malaria risk factors in northern Namibia: The importance of occupation, age and mobility in characterizing high-risk populations

“…Latent class analysis (LCA) is a type of mixture modeling that assumes the population consists of unknown sub-populations (latent classes) that differ in their mix of included variables and provides the ability to identify these latent classes. LCA has been used to characterize patterns of multi-risk profiles in relation to HIV and HCV [ 19 – 21 ] and can offer insights to optimize implementation of preventive measures.…”

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

“…So far, studies of HIV risk factors or risk factors for the uptake of interventions against HIV have generally been limited to specific sub-populations ( Sangowawa & Owoaje, 2012 ; Kidman & Anglewicz, 2016 ; Ashaba et al., 2018 ), sub-national regions ( Bailey et al., 2007 ; Gray et al., 2007 ; Eaton et al., 2014 ; Pons-Duran et al., 2016 ) or single countries ( Antelman et al., 2007 ; Gregson et al., 2010 ; Tsai & Venkataramani, 2015 ; Lakew, Benedict & Haile, 2015 ; Kelly, Weiser & Tsai, 2016 ; Smith Fawzi et al., 2016 ; Kim, Skordis-Worrall & Haghparast-Bidgoli, 2016 ; McGillen et al., 2018 ; Merzouki et al., 2020 ). Recent studies included up to 31 SSA countries, but narrowly focused their inquiries to examine, for example, the association between socioeconomic inequalities ( Hajizadeh et al., 2014 ), high-risk sexual behaviour ( Kenyon, Buyze & Schwartz, 2018 ), or HIV-related stigma ( Chan & Tsai, 2015 ; Kelly, Weiser & Tsai, 2016 ) with HIV testing, treatment uptake, ART (antiretroviral treatment) adherence, or HIV prevalence.…”

“…Recent studies have shown that unsupervised learning and clustering analysis allows us to find hidden sub-groups of people with varying drivers and potentially different risk levels of having or acquiring HIV ( Engl, Smittenaar & Sgaier, 2019 ; Merzouki et al., 2020 ). At the country-level, comparing and characterising SSA countries would allow us to test the hypothesis that sociobehavioural heterogeneity might account for spatial variance of HIV epidemic, and inform effective country-specific interventions.…”

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

“…So far, studies of HIV risk factors or risk factors for the uptake of interventions against HIV have generally been limited to specific sub-populations (Sangowawa & Owoaje, 2012;Kidman & Anglewicz, 2016;Ashaba et al, 2018), sub-national regions (Bailey et al, 2007;Gray et al, 2007;Eaton et al, 2014;Pons-Duran et al, 2016) or single countries (Antelman et al, 2007;Gregson et al, 2010;Tsai & Venkataramani, 2015;Lakew, Benedict & Haile, 2015;Kelly, Weiser & Tsai, 2016;Smith Fawzi et al, 2016;Kim et al, 2016;McGillen et al, 2018;Merzouki et al, 2020). Recent studies included up to 31 SSA countries, but narrowly focused their inquiries to examine, for example, the association between socioeconomic inequalities (Hajizadeh et al, 2014), high-risk sexual behaviour (Kenyon, Buyze & Schwartz, 2018), or HIV-related stigma Kelly, Weiser & Tsai, 2016) with HIV testing, treatment uptake, ART (antiretroviral treatment) adherence, or HIV prevalence.…”

“…Recent studies have shown that unsupervised learning and clustering analysis allows us to find hidden sub-groups of people with varying drivers and potentially different risk levels of having or acquiring HIV (Engl, Smittenaar & Sgaier, 2019;Merzouki et al, 2020). At the countrylevel, comparing and characterising SSA countries would allow us to test the hypothesis that sociobehavioural heterogeneity might account for spatial variance of HIV epidemic, and inform effective country-specific interventions.…”