ArticleJournal of the International AIDS Society2019
Socio-behavioural characteristics and HIV: findings from a graphical modelling analysis of 29 sub-Saharan African countries.
Article in Journal of the International AIDS Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Social, Behavioral, and Cultural factors of HIV in Malawi: Semi-Automated Systematic Review.Journal of medical Internet research · 2020Pooled it
- Causal determinants of postoperative length of stay in cardiac surgery using causal graphical learning.The Journal of thoracic and cardiovascular surgery · 2023Article
- Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa.BMC infectious diseases · 2023Article
- Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences.Science advances · 2022Review
- Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa.BMC medical research methodology · 2021Article
- Between a Rock and a Hard Place: How poverty and lack of agency affect HIV risk behaviors among married women in 25 African countries: A cross-sectional study.Journal of global health · 2021Article
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- Identifying groups of people with similar sociobehavioural characteristics in Malawi to inform HIV interventions: a latent class analysis.Journal of the International AIDS Society · 2020Article
- Socio-behavioural characteristics and HIV: findings from a graphical modelling analysis of 29 sub-Saharan African countries.Journal of the International AIDS Society · 2019Article
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionSocio-behavioural factors may contribute to the wide variance in HIV prevalence between and within sub-Saharan African (SSA) countries. We studied the associations between socio-behavioural variables potentially related to the risk of acquiring HIV.
methodsWe used Bayesian network models to study associations between socio-behavioural variables that may be related to HIV. A Bayesian network consists of nodes representing variables, and edges representing the conditional dependencies between variables. We analysed data from Demographic and Health Surveys conducted in 29 SSA countries between 2010 and 2016. We predefined and dichotomized 12 variables, including factors related to age, literacy, HIV knowledge, HIV testing, domestic violence, sexual activity and women's empowerment. We analysed data on men and women for each country separately and then summarized the results across the countries. We conducted a second analysis including also the individual HIV status in a subset of 23 countries where this information was available. We presented summary graphs showing associations that were present in at least six countries (five in the analysis with HIV status).
resultsWe analysed data from 190,273 men (range across countries 2295 to 17,359) and 420,198 women (6621 to 38,948). The two variables with the highest total number of edges in the summary graphs were literacy and rural/urban location. Literacy was negatively associated with false beliefs about AIDS and, for women, early sexual initiation, in most countries. Literacy was also positively associated with ever being tested for HIV and the belief that women have the right to ask their husband to use condoms if he has a sexually transmitted infection. Rural location was positively associated with false beliefs about HIV and the belief that beating one's wife is justified, and negatively associated with having been tested for HIV. In the analysis including HIV status, being HIV positive was associated with female-headed household, older age and rural location among women, and with no variables among men.
conclusionsLiteracy and urbanity were strongly associated with several factors that are important for HIV acquisition. Since literacy is one of the few variables that can be improved by interventions, this makes it a promising intervention target.
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