Structure of communities in semantic networks of biomedical research on disparities in health and sexism

Introduction: As an initiative to improve the quality of health care, the trend in biomedical research focused on health disparities and sex has increased. Objective: To carry out a characterization of the scientific evidence on health disparity defined as the gap between the distribution of health and the possible gender bias for access to medical services. Materials and methods: We conducted a simultaneous search of two fundamental descriptors in the scientific literature in the Medline PubMed database: healthcare disparities and sexism. Subsequently, a main semantic network was built and some structural subunits (communities) were identified for the analysis of information organization patterns. We used open-source software: Cytoscape to analyze and visualize the semantic network, and MapEquation for community detection, as well as an ad hoc code available in a public access repository. Results: The core network corpus showed that the terms on heart disease were the most common among the descriptors of medical conditions. Patterns of information related to public policies, health services, social determinants, and risk factors were identified from the structural subunits, but with a certain tendency to remain indirectly connected to the nodes of medical conditions. Conclusions: Scientific evidence indicates that gender disparity does matter for the care quality in many diseases, especially those related to the circulatory system. However, there is still a gap between the medical and social factors that give rise to possible disparities by sex.