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May 9, 2026Journal of Clinical and Translational Science0 citationsOpen Access

509 Understanding community engagement with research through a curated medical record-based database: A cross-sectional, single site study

CTChuan Ching TsaiRush UniversityLFLeping FangJiangsu Province HospitalDSDavid SedilloRush University

Key Result

Higher-participating communities had significantly lower Area Deprivation Index (39.9 vs 61.9), lower Social Vulnerability Index (0.51 vs 0.80), and higher income compared to lower-participating areas.

Key Points

  • The aim is to characterize research participants using electronic health record data and assess the influence of community-level factors on participation rates.
  • Retrospective, cross-sectional study of patients with ICD-10 research encounter codes from 2016 to 2024
  • Data organized in the CAPriCORN Common Data Model, linked to Census data for neighborhood factor analysis
  • Demographics and socioeconomic factors compared using Chi-square and t-tests
  • Out of 1,069,226 patients, 11,937 had research visits, resulting in a participation prevalence of 1.12%
  • Higher participation prevalence found outside Chicago (1.21%) than within (1.03%)
  • Communities with higher participation had significantly lower Area Deprivation Index (39.9 vs 61.9) and lower Social Vulnerability Index (0.51 vs 0.80)

Study Design

Type

Cross-Sectional (n=1,069,226)

Multicenter

No

Structured PICO

P
Population
1,069,226 patients seen from 2016 to 2024 at a single health system (Rush), of which 11,937 had research visits (ICD-10 code Z00.6).
O
Outcome
Research participation prevalence, calculated by geographic area and linked to Census data for neighborhood factor analysis

Research participation is significantly associated with community-level socioeconomic factors, with higher participation observed in areas with lower deprivation and higher education and income.

Main Result

Absolute Event Rate: 39.9% vs 61.9%

Limitations

  • single-center data
  • required entry of EHR codes

Abstract

Objectives/Goals: Since clinical trial participation is a key translational research initiative, the objectiveis to characterize research participants within a health system using electronic health record (EHR) data and evaluate how community-level factors influence research participation rates. Methods/Study Population: Retrospective, cross-sectional study of patients with ICD-10 research encounter codes (Z00. 6) from 2016 to 2024 using Rush EHR data organized in the CAPriCORN Common Data Model. Research participation prevalence was the main outcome, calculated by geographic area (3-digit zip codes, Chicago community areas) and linked to Census data for neighborhood factor analysis. Variables include Area Deprivation Index (ADI), Social Vulnerability Index (SVI), education, occupation, and income. The Chi-square test compared demographics and t-tests compared mean SDOH variables between high- and low-participation community areas. Results/Anticipated Results: Of the 1, 069, 226 patients seen from 2016 to 2024, 11, 937 patients had research visits. Of these patients, 45. 4% lived in Chicago, 65 years or above is the largest age group (29%), 62% female, 58% White, 82% non-Hispanic, and 9% non-English preferred language. Overall, the research participation prevalence was 1. 12%, higher outside of Chicago (1. 21%) than within (1. 03%). Within Chicago community areas, prevalence ranged from 0. 34% to 2. 8% (mean: 1. 03%, SD: 0. 44%). Higher-participating communities (above median) had significantly lower ADI (39. 9 vs 61. 9), lower SVI (0. 51 vs 0. 80), lower unemployment (8. 0% vs 15. 3%), higher bachelor’s degree attainment (43. 6% vs 19. 6%), higher income (69, 859 vs 36, 376), and lower uninsured rates (7. 4% vs 11. 5%). Discussion/Significance of Impact: This work is the first local exploration of community factors and research participation using curated EHR data. Although limited by single-center data and required entry of EHR codes, it is a step into understanding factors that may influence research participation. Local estimates could be improved by utilizing data from all CAPriCORN sites.

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Cite This Study

Tsai et al. (2026) conducted a cross-sectional in Research participation (n=1,069,226). High-participating communities vs. Low-participating communities was evaluated on Area Deprivation Index (ADI). Higher-participating communities had significantly lower Area Deprivation Index (39.9 vs 61.9), lower Social Vulnerability Index (0.51 vs 0.80), and higher income compared to lower-participating areas.

synapsesocial.com/papers/69fed16ab9154b0b82878bf7https://doi.org/10.1017/cts.2026.10634
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