Interest and enrollment in clinical trials by race and ethnicity, rurality, and insurance status in patients with ovarian cancer. Issue 28 (1st October 2022)

• Record Type: Journal Article
• Authors: Wall, Jaclyn; Caston, Nicole E.; Rocque, Gabrielle Betty; Arend, Rebecca Christian; Sussell, Jesse; Liang, Margaret Irene
• Is Part Of: Journal of clinical oncology. Volume 40:Issue 28(2022)Supplement
• Page Start: 83
• Page End: 83
• Publication Date: 2022-10-01
• Subjects: 130-4679-2614-11166 -- 613-135-244-3335 -- 298-145-222 -- 130-273-455 -- 283-252-190 -- 130-4679-7524 -- 329-334-533; 6 -- 3 -- 3 -- 3 -- 2 -- 2 -- 2; Oncology -- Periodicals; Cancer -- Periodicals; Oncology; Medical Oncology; Cancérologie -- Périodiques; Cancer -- Périodiques; Cancérologie; Cancer; Oncology; Oncologia; Càncer; Periodicals; 616.994
• DOI: 10.1200/JCO.2022.40.28_suppl.083
• Languages: English
• ISSNs: 0732-183X

Abstract

Abstract : 83 Background: Enrollment in Phase III clinical trials in gynecologic cancer patients has decreased by approximately 90% since 2011. Efforts to increase enrollment are needed. Previous research showed that not all eligible patients are approached, and those who are do not consistently enroll. Data regarding enrollment by patient characteristics, such as race/ethnicity, rurality, and insurance status is limited. We aimed to identify how these characteristics affect enrollment in ovarian cancer patients. Methods: We conducted retrospective chart review for patients with incident ovarian cancer presenting to the University of Alabama at Birmingham from 1/2017-3/2020. We abstracted patient race, ethnicity, Rural-Urban Commuting Area (RUCA; rural vs urban) and Area Deprivation Index (ADI; most vs least disadvantaged) based on Census tract codes, insurance status, eligibility for available trials, and trial participation from medical records. Patient interest in participation was abstracted from a patient-reported outcomes database. We calculated descriptive statistics and estimated enrollment as a multivariate function of age, race, ethnicity, insurance, RUCA and ADI using binomial logistic regression. We reported associations as odds ratios with 95% confidence intervals. Results: Of 156 patients, 25% were Black, Indigenous, or Persons of Color (BIPOC). 19% lived in a rural area. Mean age was 62 (SD 11.7). Most (95%) patients were insured; 49% Medicare, 40% private insurance, and 6% Medicaid. 126 (81%) were eligible for a trial during their treatment course. Of 102 patients who completed the question on clinical trial interest, 58% were interested; 42% were not. Ultimately, 36% of the 102 enrolled in a trial including 47% of those initially interested and 21% of those not. 39% of white patients (n = 117) initially expressed interest in a trial compared to 33% of BIPOC (n = 39); 48% of white patients ultimately enrolled vs 23% BIPOC. Of patients living in urban vs rural areas with known interest, patients in urban areas had higher interest (44% vs 10%) and higher enrollment (44% vs 31%). Among insurance types, interest and enrollment differed (Medicare (n = 76) 33% and 1%, Private (n = 63) 46% and 46%, Medicaid (n = 9) 33% and 22%, no insurance (n = 8) 25% and 36%). In our adjusted analysis, BIPOC patients had lower odds of enrolling onto clinical trials compared to white patients (OR 0.32, 95% CI 0.13-0.76). Additionally, as age increased by 1 year, odds of enrollment decreased (OR 0.96, 95% CI 0.92-0.99). Conclusions: BIPOC identity and older age were associated with lower rates of clinical trial enrollment. Comprehensive eligibility screening and early introduction could improve enrollment, particularly among BIPOC and older patients. These efforts have potential to improve enrollment as a greater percentage of patients ultimately enrolled on trial than initially expressed interest.