Abstract
AbstractBackgroundWithin 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.MethodsWe used data from the Demographic and Health Survey in Malawi from 2016, and stratified the analysis by sex. We considered demographic, socio-behavioural and HIV-related variables. Using Latent Class Analysis (LCA), we identified clusters of people sharing common sociobehavioural characteristics. The optimal number of clusters was selected based on the Bayesian information criterion. We compared the proportions of individuals belonging to the different clusters across the three regions and 28 districts of Malawi.ResultsWe found nine clusters of women and six clusters of men. Most women in the clusters with highest risk of being HIV infected 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 infected, followed by young (20-25) 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 Southern region had a higher percentage of high-prevalence and less tested clusters of individuals than other areas.ConclusionsLCA is an efficient method to find clusters of people sharing common HIV risk profiles, identify particularly vulnerable population groups, 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.FundingThe project was funded by the Swiss National Science Foundation (grant no 163878).
Publisher
Cold Spring Harbor Laboratory
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