Social, Behavioral, and Cultural factors of HIV in Malawi: Semi-Automated Systematic Review.

Thiabaud, Amaury; Triulzi, Isotta; Orel, Erol; Tal, Kali; Keiser, Olivia (2020). Social, Behavioral, and Cultural factors of HIV in Malawi: Semi-Automated Systematic Review. Journal of medical internet research, 22(8), e18747. Centre of Global eHealth Innovation 10.2196/18747

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Demographic and sociobehavioral factors are strong drivers of HIV infection rates in sub-Saharan Africa. These factors are often studied in qualitative research but ignored in quantitative analyses. However, they provide in-depth insight into the local behavior and may help to improve HIV prevention.


To obtain a comprehensive overview of the sociobehavioral factors influencing HIV prevalence and incidence in Malawi, we systematically reviewed the literature using a newly programmed tool for automatizing part of the systematic review process.


Due to the choice of broad search terms ("HIV AND Malawi"), our preliminary search revealed many thousands of articles. We, therefore, developed a Python tool to automatically extract, process, and categorize open-access articles published from January 1, 1987 to October 1, 2019 in the PubMed, PubMed Central, JSTOR, Paperity, and arXiV databases. We then used a topic modelling algorithm to classify and identify publications of interest.


Our tool extracted 22,709 unique articles; 16,942 could be further processed. After topic modelling, 519 of these were clustered into relevant topics, of which 20 were kept after manual screening. We retrieved 7 more publications after examining the references so that 27 publications were finally included in the review. Reducing the 16,942 articles to 519 potentially relevant articles using the software took 5 days. Several factors contributing to the risk of HIV infection were identified, including religion, gender and relationship dynamics, beliefs, and sociobehavioral attitudes.


Our software does not replace traditional systematic reviews, but it returns useful results to broad queries of open-access literature in under a week, without a priori knowledge. This produces a "seed dataset" of relevance that could be further developed. It identified known factors and factors that may be specific to Malawi. In the future, we aim to expand the tool by adding more social science databases and applying it to other sub-Saharan African countries.

Item Type:

Journal Article (Review Article)


04 Faculty of Medicine > Medical Education > Institute of General Practice and Primary Care (BIHAM)

UniBE Contributor:

Tal, Kali


600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services




Centre of Global eHealth Innovation


[4] Swiss National Science Foundation




Andrea Flükiger-Flückiger

Date Deposited:

26 Aug 2020 17:13

Last Modified:

01 Sep 2020 20:57

Publisher DOI:


PubMed ID:


Uncontrolled Keywords:

HIV/AIDS Malawi machine learning risk factors text mining topic modelling




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