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OBJECTIVE: The ability to apply results from a study to a broader population remains a primary objective in translational science. Distinct from intrinsic elements of scientific rigor, the extrinsic concept of generalization requires there be alignment between a study cohort and population in which results are expected to be applied. Widespread efforts have been made to quantify representativeness of a study cohorts. These techniques, however, often consider the study and target cohorts as monolithic collections that can be directly compared. Overlooking known impacts to health from socio-demographic and environmental factors tied to individual's geographical location, and potentially obfuscating misalignment in underrepresented population subgroups. This manuscript introduces several measures to account for geographic information in the measurement of cohort representation. METHODS: Metrics were defined across two themes. First, measures of recruitment, to assess a study cohort is drawn at an expected rate and in an expected geographical pattern with respect to individuals in a reference cohort. Quantifying the coverage and spread across the distinct geographic regions comprising the target population. Second, measures of individual characteristics, to assess if the study cohort accurately reflects the sociodemographic, clinical, and geographic diversity observed across a reference cohort. Employing intra-individual measures of distance and aggregate measures of alignment designed to account for geospatial proximity of individuals. RESULTS: As an empirical demonstration, methods are applied to an active clinical study examining asthma in Black and African American patients at a US Midwestern pediatric hospital. Results illustrate how areas of over- and under-recruitment can be identified and contextualized in light of study recruitment patterns. At an individual-level, highlighting the ability to identify a subset of features for which the study cohort closely resembled the broader population. In addition to an opportunity to dive deeper into misalignments, to identify study cohort members that are in some way distinct from the communities for which they are expected to represent. CONCLUSION: Together, these metrics provide a comprehensive spatial assessment of a study cohort with respect to a broader target population. Such an approach offers researchers a toolset by which to target expected generalization of results derived from a given study.
RESUMO
PURPOSE: Persistent inequities in genomic medicine and research contribute to health disparities. This analysis uses a context-specific and equity-focused strategy to evaluate enrollment patterns for Genomic Answers for Kids (GA4K), a large, metropolitan-wide genomic study on children. METHODS: Electronic health records for 2247 GA4K study participants were used to evaluate the distribution of individuals by demographics (race, ethnicity, and payor type) and location (residential address). Addresses were geocoded to produce point density and 3-digit zip code maps showing local and regional enrollment patterns. Health system reports and census data were used to compare participant characteristics with reference populations at different spatial scales. RESULTS: Racial and ethnic minoritized and populations with low-income were underrepresented in the GA4K study cohort. Geographic variation demonstrates inequity in enrollment and participation among children from historically segregated and socially disadvantaged communities. CONCLUSION: Our findings illustrate inequity in enrollment related to both GA4K study design and structural inequalities, which we suspect may exist for similar US-based studies. Our methods provide a scalable framework for continually evaluating and improving study design to ensure equitable participation in and benefits from genomic research and medicine. The use of high-resolution, place-based data represents a novel and practical means of identifying and characterizing inequities and targeting community engagement.
