# Developing a public-facing tool to monitor inclusion of special populations in clinical research

## TL;DR

This paper describes a public tool to monitor how well clinical trials include diverse populations, revealing disparities in participation.

## Contribution

The novel contribution is a public-facing snapshot tool for assessing clinical trial inclusivity using demographic benchmarks.

## Key findings

- Females were overrepresented in clinical trials compared to geographic benchmarks.
- Black/African Americans were underrepresented at the county level but overrepresented at state and national levels.
- Hispanic/Latino and Asian groups were underrepresented in clinical trial enrollment.

## Abstract

Clinical trials have provided evidence for determining treatment effectiveness. However, clinical trial participants have been underrepresented by diverse and special population groups (e.g., younger and older adults, different races/ethnicities), contributing to disparities in our understanding of diseases and treatments in all those affected. Addressing these disparities in clinical trial participation would be critical to achieving health equity in the USA and beyond. To assess enrollment inclusivity in clinical research at a large academic medical center in the southeast, we used administrative information to develop a snapshot of clinical research participation by age, sex, race, ethnicity, and rurality that was accessible to the public. We compared research enrollment statistics with relevant geographic benchmarks (county, state, and national) from the 2020 US Census. Comparisons revealed 1) over-participation by females relative to county, state, and national benchmarks; 2) under-representation of Black/African Americans relative to county, but higher relative to state and national, levels; and 3) underrepresentation of Hispanic/Latino and Asian groups. The ISP Snapshot has promoted accountability and transparency in this institution’s efforts toward health equity. The process has highlighted the need to update and standardize use of outdated categories (e.g., binary gender, rural status) that limit accurate reporting.