Ineligible, Unaware, or Uninterested? Associations Between Underrepresented Patient Populations and Retention in the Pathway to Cancer Clinical Trial Enrollment. Issue 11 (30th November 2022) - Caston, Nicole E.; Lalor, Fallon; Wall, Jaclyn; Sussell, Jesse; Patel, Shilpen; Williams, Courtney P.; Azuero, Andres; Arend, Rebecca; Liang, Margaret I.; Rocque, Gabrielle B.
Abstract : PURPOSE: Cancer clinical trials can benefit current and future patients; however, Black patients, rural residents, and patients living in disadvantaged areas are often underrepresented. Using an adapted version of Unger and colleagues' model of the process of clinical trial enrollment, we evaluated the relationship between underrepresented patient populations and trial end points. METHODS: This retrospective study included 512 patients with breast or ovarian cancer who were prescribed a therapeutic drug at the University of Alabama at Birmingham from January 2017 to February 2020. Patient eligibility was assessed using open clinical trials. We estimated odds ratios and 95% CIs using logistic regression models to examine the relationship between underrepresented patient populations and trial enrollment end points: eligibility, interest, offer, enrollment, and declining enrollment. RESULTS: Of the patients in our sample, 27% were Black, 18% were rural residents, and 19% lived in higher disadvantaged neighborhoods. In adjusted models, each comparison group had similar odds of being eligible for a clinical trial. Black versus White patients had 0.40 times the odds of interest in clinical trials and 0.56 times the odds of enrollment. Patients living in areas of higher versus lower disadvantage had 0.46 times the odds of enrolling and 3.40 times the odds of declining enrollment when offered. CONCLUSION: Eligibility did not drive clinical trial enrollment disparities in our sample; however, retention in the clinical trial enrollment process appears to vary by group. Additional work is needed to understand how interventions can be tailored to each population's specific needs.