Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis

Abstract

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.

Unraveling non‐participation in genomic research: A complex interplay of barriers, facilitators, and sociocultural factors

“…Student's t ‐tests were also performed for group differences on the total barriers and facilitators scores, genomic knowledge scale, and the distrust index (interpersonal and societal distrust subscales) to determine their inclusion in the logistic regressions. We did not compare groups on race/ethnicity, since a narrow focus on race and ethnicity is likely to obscure the underlying factors that may be driving the reasons for non‐participation of populations experiencing health disparities in research or clinical services (Martínez‐García et al, 2022; White et al, 2020). MANOVAs were computed in an exploratory manner, for group differences on the individual items in the barriers survey.…”

How debunking biases in research and development decisions could lead to more equitable healthcare?

“…In contrast, health inequities are reflected in differences in length of life; quality of life; rates of disease, disability, and death; severity of disease; and access to treatment. Social determinants, cultural issues, and economic disparities are important determining factors of health inequities 18,19 . In addition, the impact of cognitive biases on health equity can be observed in several ways, most notably: Understanding and prioritizing unmet medical needs to steer biomedical and pharmaceutical R&D efforts; Evidence generation including the design and conduct of clinical trials; Interpretation of experimental data in the context of human health and disease followed by decisions like go/no‐go or regulatory approval; Defining treatment guidelines and pathways based on available evidence for a given disease; Planning individual therapy approaches for patients accounting for both clinical evidence and patient's health data. …”

“…Social determinants, cultural issues, and economic disparities are important determining factors of health inequities. 18,19 In addition, the impact of cognitive biases on health equity can be observed in several ways, most notably:…”

Multiple modes of data sharing can facilitate secondary use of sensitive health data for research

“…The community-level impact of genomic studies with San participants in Southern Africa clearly illustrates these risks 15 16. Responsible data governance, sharing, analysis and reporting are particularly important to support the inclusion of underrepresented populations in health research, in order to ensure that innovations and new therapeutic approaches are equitable and effective for all populations groups; and equitable and appropriate sharing of data from under-represented groups can contribute to addressing the existing bias in health research 17–19…”

“…15 16 Responsible data governance, sharing, analysis and reporting are particularly important to support the inclusion of underrepresented populations in health research, in order to ensure that innovations and new therapeutic approaches are equitable and effective for all populations groups; and equitable and appropriate sharing of data from under-represented groups can contribute to addressing the existing bias in health research. [17][18][19] Fortunately, together with the growing availability of granular and identifying datasets and a concomitant growing recognition of the need to protect the interests of individuals, communities and researchers, there has been rapid growth in the development of data governance and ethical data use to address these challenges. Traditionally, Open data sharing has been viewed as a unidirectional process whereby researchers who collect and generate data pass them onward for secondary use, either directly or via centralised repositories.…”