Analysis and optimization of equitable US cancer clinical trial center access by travel time.

Abstract

6524 Background: Racially minoritized and socioeconomically disadvantaged populations are underrepresented in cancer clinical trials. Data-driven, quantitative approaches are required to address this inequity. Here, we systematically analyze the geographical distribution of self-identified racial and socioeconomic demographics within commuting distance to cancer clinical trial centers and all hospitals U.S.-wide. Methods: We calculate self-identified racial and socioeconomic demographics of populations living within 30-, 60-, and 120-minute one-way driving commute times from U.S. cancer clinical trial centers and all U.S. hospitals. Data sources include the U.S. 2020 census (for race and socioeconomic data), OpenStreetMaps (for calculation of commuting times), NCT trial registry (for cancer clinical trial quantification), NCI Designated Cancer Centers List, Nature Index of Cancer Research Health Institutions and National Homeland Infrastructure Foundation-Level Data (for U.S.-wide hospital and cancer clinical trial center identification). Data analysis was conducted in R, and Ggplot2 used for all data visualizations. Results: Analysis of clinical trial numbers between 2012–2022 identified 78 high-volume U.S. cancer trial centers. Compared to U.S. national averages, these centers are in areas with socioeconomically more affluent populations (+27.6K USD (95% CI +23.1 to 32.8K) median income unpaired mean difference; -0.073 (95% CI -0.063 to -0.084) deprivation index unpaired mean difference), with higher proportions of affluent self-identified White individuals (+10.1% unpaired mean difference (95% CI +6.8 to +13.7%)). The top 10th percentile of all U.S. hospitals (N=7,623) show a range of absolute sum difference from 2.4% to 35% from equal racial representation of White, Black/African American and Asian/Mixed/Other groups. For each U.S. city above 500,000 inhabitants, we compile map overlays that display all prospective hospitals within commutable distance to racially diverse and socioeconomically disadvantaged populations. These maps may inform the design of future clinical trials or investigations in enrollment and retention strategies for clinical trials. Conclusions: Our analysis identifies biases in the sociodemographics of populations living within commuting distance to U.S.-based cancer trial sites and enables the determination of more equitably commutable prospective satellite hospital sites that could be mobilized for enhanced racial and socioeconomic representation in clinical trials. Other recruitment barriers still need to be tackled to ensure racial and socioeconomic demographics within the geographical vicinity of a clinical site can translate to equitable trial participant representation.