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Exclusion of special populations (older adults; pregnant women, children, and adolescents; individuals of lower socioeconomic status and/or who live in rural communities; people from racial and ethnic minority groups; individuals from sexual or gender minority groups; and individuals with disabilities) in research is a pervasive problem, despite efforts and policy changes by the National Institutes of Health and other organizations. These populations are adversely impacted by social determinants of health (SDOH) that reduce access and ability to participate in biomedical research. In March 2020, the Northwestern University Clinical and Translational Sciences Institute hosted the “Lifespan and Life Course Research: integrating strategies” “Un-Meeting” to discuss barriers and solutions to underrepresentation of special populations in biomedical research. The COVID-19 pandemic highlighted how exclusion of representative populations in research can increase health inequities. We applied findings of this meeting to perform a literature review of barriers and solutions to recruitment and retention of representative populations in research and to discuss how findings are important to research conducted during the ongoing COVID-19 pandemic. We highlight the role of SDOH, review barriers and solutions to underrepresentation, and discuss the importance of a structural competency framework to improve research participation and retention among special populations.
The COVID-19 pandemic presented a challenge to established seed grant funding mechanisms aimed at fostering collaboration in child health research between investigators at the University of Minnesota (UMN) and Children’s Hospitals and Clinics of Minnesota (Children’s MN). We created a “rapid response,” small grant program to catalyze collaborations in child health COVID-19 research. In this paper, we describe the projects funded by this mechanism and metrics of their success.
Using seed funds from the UMN Clinical and Translational Science Institute, the UMN Medical School Department of Pediatrics, and the Children’s Minnesota Research Institute, a rapid response request for applications (RFAs) was issued based on the stipulations that the proposal had to: 1) consist of a clear, synergistic partnership between co-PIs from the academic and community settings; and 2) that the proposal addressed an area of knowledge deficit relevant to child health engendered by the COVID-19 pandemic.
Grant applications submitted in response to this RFA segregated into three categories: family fragility and disruption exacerbated by COVID-19; knowledge gaps about COVID-19 disease in children; and optimizing pediatric care in the setting of COVID-19 pandemic restrictions. A series of virtual workshops presented research results to the pediatric community. Several manuscripts and extramural funding awards underscored the success of the program.
A “rapid response” seed funding mechanism enabled nascent academic-community research partnerships during the COVID-19 pandemic. In the context of the rapidly evolving landscape of COVID-19, flexible seed grant programs can be useful in addressing unmet needs in pediatric health.
Life course research embraces the complexity of health and disease development, tackling the extensive interactions between genetics and environment. This interdisciplinary blueprint, or theoretical framework, offers a structure for research ideas and specifies relationships between related factors. Traditionally, methodological approaches attempt to reduce the complexity of these dynamic interactions and decompose health into component parts, ignoring the complex reciprocal interaction of factors that shape health over time. New methods that match the epistemological foundation of the life course framework are needed to fully explore adaptive, multilevel, and reciprocal interactions between individuals and their environment. The focus of this article is to (1) delineate the differences between lifespan and life course research, (2) articulate the importance of complex systems science as a methodological framework in the life course research toolbox to guide our research questions, (3) raise key questions that can be asked within the clinical and translational science domain utilizing this framework, and (4) provide recommendations for life course research implementation, charting the way forward. Recent advances in computational analytics, computer science, and data collection could be used to approximate, measure, and analyze the intertwining and dynamic nature of genetic and environmental factors involved in health development.
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