Data_Sheet_1_Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis.ZIP

Health equity is a rather complex issue. Social context and economical disparities, are known to be determining factors. Cultural and educational constrains however, are also important contributors to the establishment and development of health inequities. As an important starting point for a comprehensive discussion, a detailed analysis of the literature corpus is thus desirable: we need to recognize what has been done, under what circumstances, even what possible sources of bias exist in our current discussion on this relevant issue. By finding these trends and biases we will be better equipped to modulate them and find avenues that may lead us to a more integrated view of health inequity, potentially enhancing our capabilities to intervene to ameliorate it. In this study, we characterized at a large scale, the social and cultural determinants most frequently reported in current global research of health inequity and the interrelationships among them in different populations under diverse contexts. We used a data/literature mining approach to the current literature followed by a semantic network analysis of the interrelationships discovered. The analyzed structured corpus consisted in circa 950 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor from 2014 to 2021. Further analyses involved systematic searches in the LILACS and DOAJ databases, as additional sources. The use of data analytics techniques allowed us to find a number of non-trivial connections, pointed out to existing biases and under-represented issues and let us discuss what are the most relevant concepts that are (and are not) being discussed in the context of Health Equity and Culture.

健康公平性问题错综复杂。众所周知，社会背景与经济差异是其决定性因素。然而，文化及教育限制亦为健康不公平性形成与发展的关键贡献者。作为全面讨论的重要起点，对文献资料库的细致分析尤为必要：我们需识别已有哪些成果，在何种情境下完成，甚至当前关于该议题的讨论中可能存在的偏见来源。通过发现这些趋势与偏见，我们将更具备调节之能力，并探寻可能导向对健康不公平性更为整合视角的途径，从而增强我们干预以改善之的能力。在本研究中，我们对大规模报道的健康不公平性社会与文化决定因素进行了特征化，以及在不同背景下不同人群中这些因素间的相互关系。我们采用数据/文献挖掘方法对现有文献进行了分析，随后对发现的相互关系进行了语义网络分析。所分析的有序语料库包括约950篇文章，这些文章通过2014年至2021年间的医学主题词（MeSH）内容描述进行了分类。进一步的分析涉及对LILACS和DOAJ数据库的系统检索，作为额外来源。数据分析技术的应用使我们发现了一系列非平凡的联系，指出了现有偏见和代表性不足的问题，并让我们讨论在健康公平性与文化背景下，哪些最相关的概念正在被（以及未被）讨论。