Diversifying Recruitment Registries: Considering Neighborhood Health Metrics

Abstract

BACKGROUND: Disparities in clinical research participation perpetuate broader health disparities. Recruitment registries are novel tools to address known challenges in accrual to clinical research. Registries may accelerate accrual, but the utility of these tools to improve generalizability is unclear. Objective: To examine the diversity of a local on-line recruitment registry using the Area Deprivation Index (ADI), a publicly available metric of neighborhood disadvantage. Design: Retrospective analysis. Setting: Data were collected in the University of California Irvine Consent-to-Contact Registry. Participants: We categorized N=2,837 registry participants based on the ADI decile (collapsed into quintiles) using a state-based rankings. Measurements: We examined the proportion of enrollees per ADI quintile and quantified the demographics of these groups. We assessed willingness to participate in studies involving unique research procedures among the ADI groups. Results: Although registry enrollees represented the full spectrum of the ADI, they disproportionately represented less disadvantaged neighborhoods (lowest to highest quintiles: 42%, 30%, 15%, 6%, 7%). Compared to participants from less disadvantaged neighborhoods, participants from more disadvantaged neighborhoods were more often female, of non-white race, and Hispanic ethnicity. Despite demographic differences, ADI groups were observed to have similar willingness to participate in research studies. Conclusions: People from more disadvantaged neighborhoods may be underrepresented in recruitment registries, increasing the risk that they will be underrepresented when using these tools to facilitate prospective recruitment to clinical research. Once enrolled in registries, participants from more disadvantaged neighborhoods may be equally willing to participate in research. Efforts to increase representation of participants from disadvantaged neighborhoods in registries could be an important first step toward increasing the generalizability of clinical research.