Racial Disparities in the Cost of Unplanned Hospitalizations after Breast Reconstruction
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Updated: Aug 12, 2025

Endoscopic Bilateral Nipple-sparing Mastectomy via a Single Axillary Incision with Immediate Pre-pectoral Implant-based Breast Reconstruction
Published on: May 17, 2024
Jacob S Nasser1, Jessica I Billig2, Chang Wang3
1From the George Washington School of Medicine and Health Sciences.
Background:
Given the national attention to disparities in health care, understanding variation provided to minorities becomes increasingly important. This study will examine the effect of race on the rate and cost of unplanned hospitalizations after breast reconstruction procedures.
Methods:
The authors performed an analysis comparing patients undergoing implant-based and autologous breast reconstruction in the Healthcare Cost and Utilization Project. The authors evaluated the rate of unplanned hospitalizations and associated expenditures among patients of different races. Multivariable analyses were performed to determine the association among race and readmissions and health care expenditures.
Results:
The cohort included 17,042 patients. The rate of an unplanned visit was 5%. The rates of readmissions among black patients (6%) and Hispanic patients (7%) in this study are higher compared with white patients (5%). However, after controlling for patient-level characteristics, race was not an independent predictor of an unplanned visit. In our expenditure model, black patients [adjusted cost ratio, 1.35 (95% CI, 1.11 to 1.66)] and Hispanic patients [adjusted cost ratio, 1.34 (95% CI, 1.08 to 1.65)] experienced greater cost for their readmission compared with white patients.
Conclusions:
Although race is not an independent predictor of an unplanned hospital visit after surgery, racial minorities bear a higher cost burden after controlling for insurance status, further stimulating health care disparities. Adjusted payment models may be a strategy to reduce disparities in surgical care. In addition, direct and indirect measures of disparities should be used when examining health care disparities to identify consequences of inequities more robustly.
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