Opportunities for life course research through the integration of data across Clinical and Translational Research Institutes

Abstract

IntroductionEarly life exposures affect health and disease across the life course and potentially across multiple generations. The Clinical and Translational Research Institutes (CTSIs) offer an opportunity to utilize and link existing databases to conduct lifespan research.MethodsA survey with Lifespan Domain Taskforce expert input was created and distributed to lead lifespan researchers at each of the 64 CTSIs. The survey requested information regarding institutional databases related to early life exposure, child-maternal health, or lifespan research.ResultsOf 64 CTSI, 88% provided information on a total of 130 databases. Approximately 59% (n=76/130) had an associated biorepository. Longitudinal data were available for 72% (n=93/130) of reported databases. Many of the biorepositories (n=44/76; 68%) have standard operating procedures that can be shared with other researchers.ConclusionsThe majority of CTSI databases and biorepositories focusing on child-maternal health and lifespan research could be leveraged for lifespan research, increased generalizability and enhanced multi-institutional research in the United States.

Barriers to and solutions for representative inclusion across the lifespan and in life course research: The need for structural competency highlighted by the COVID-19 pandemic

“…To fully characterize the long-term impact of the COVID-19 pandemic, longitudinal and intergenerational investigations are needed. This requires combining diverse data sources to incorporate complex health, genetic, environmental, and experiential data [116].…”

Improving child health through Big Data and data science

“…89 Life research is an example of integration of multiple data types from diverse sources (e.g., institutional data warehouses and research repositories) to capture the complexity of health trajectories. 14,90,91 Incorporation of geocoded data and environmental factors as well as patient-reported measures such as social well-being into the electronic health record represent concrete strategies for greater inclusion and more accurate representation of populations currently underrepresented in research and permit analysis of the impact of social determinants of health on disease pathogenesis and response to therapies. 91 A significant note of caution must be raised about racial bias in the use of physiology and electronic health record-based Big Data.…”

“…14,90,91 Incorporation of geocoded data and environmental factors as well as patient-reported measures such as social well-being into the electronic health record represent concrete strategies for greater inclusion and more accurate representation of populations currently underrepresented in research and permit analysis of the impact of social determinants of health on disease pathogenesis and response to therapies. 91 A significant note of caution must be raised about racial bias in the use of physiology and electronic health record-based Big Data. Two sources of error may contribute to data-related bias.…”