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Assessing how racial identity correlates with time to surgery initiation among early-stage breast cancer patients.

Year: 2025
Type: conference-abstract
Abstract: 184 Background: This study seeks to assess racial disparities pertaining to the timing of surgical intervention for early-stage breast cancer patients across recent years. Methods: Patients (n = 27,952) with early-stage breast cancer, identifying as Black or White, who received surgery as their initial treatment and were diagnosed between January 2019 and June 2023, were extracted from the Q-Centrix Clinical Data Warehouse. The Q-Centrix Clinical Data Warehouse is a proprietary database of de-identified clinical data produced through expert-driven human abstraction. The subset sourced from the Clinical Data Warehouse includes information from 73 hospitals, health systems, and cancer centers across the country. Time to surgery was split into 2 groups: < 90 days post diagnosis and > = 90 days post diagnosis. Logistic regression was employed to evaluate the likelihood of delayed treatment (> = 90 days post-diagnosis) among Black patients relative to White patients across breast cancer stages 0, 1, and 2. A p-value cutoff of < 0.05 was considered significant. Results: The sample encompassed 23,928 patients identifying as white and 4,024 patients identifying as black. Regression models indicated that the odds of stage 0 black breast cancer patients receiving surgery 90 or more days post diagnosis are 1.86 times higher compared to white patients diagnosed with the same stage, 2.02 times higher in stage 1 black patients, and 1.88 times higher in stage 2 black patients (p-value < .001). Conclusions: The odds ratios of black breast cancer patients receiving surgery within an optimal time frame relative to white patients suggests that a disparity in care related to time to surgery exists across early-stage breast cancer patients. Further analyses are required to determine how this disparity differs across regions and rural or urban locations.
Authors: Bethany Zigler, Victor Wang, Vontyna Smith
Language: English
Subfield: Oncology
Field: Medicine
Open Access status: bronze
OpenAlex ID: W4414893333
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