Factors Associated With Survival Disparities Between Non-Hispanic Black and White Patients With Uterine Cancer

Abstract

ImportanceDisparities in survival exist between non-Hispanic Black (hereafter, Black) and non-Hispanic White (hereafter, White) patients with uterine cancer.ObjectiveTo investigate factors associated with racial disparities in survival between Black and White patients with uterine cancer.Design, Setting, and PatientsThis cohort study used data from the National Cancer Database on 274 838 Black and White patients who received a diagnosis of uterine cancer from January 1, 2004, to December 31, 2017, with follow-up through December 2020. Statistical analysis was performed in July 2022.Main Outcomes and MeasuresOverall survival by self-reported race and evaluation of explanatory study factors associated with hazard ratio (HR) reduction for Black vs White patients. A propensity scoring approach was applied sequentially to balance racial differences in demographic characteristics, comorbidity score, neighborhood income, insurance status, histologic subtype, disease stage, and treatment.ResultsThe study included 32 230 Black female patients (mean [SD] age at diagnosis, 63.8 [10.0] years) and 242 608 White female patients (mean [SD] age at diagnosis, 63.5 [10.5] years) and had a median follow-up of 74.0 months (range, 43.5-113.8 months). Black patients were more likely than White patients to have low income (44.1% vs 14.0%), be uninsured (5.7% vs 2.6%), present with nonendometrioid histologic characteristics (46.1% vs 21.6%), have an advanced disease stage (34.1% vs 19.8%), receive first-line chemotherapy (33.8% vs 18.2%), and have worse 5-year survival (58.6% vs 78.5%). Among patients who received a diagnosis at younger than 65 years of age, the HR for death for Black vs White patients was 2.43 (95% CI, 2.34-2.52) in a baseline demographic-adjusted model and 1.29 (95% CI, 1.23-1.35) after balancing other factors. Comorbidity score, neighborhood income, insurance status, histologic subtype, disease stage, treatment, and unexplained factors accounted for 0.8%, 7.2%, 11.5%, 53.1%, 5.8%, 1.2%, and 20.4%, respectively, of the excess relative risk (ERR) among the younger Black vs White patients. Among patients 65 years or older, the HR for death for Black vs White patients was 1.87 (95% CI, 1.81-1.93) in the baseline model and 1.14 (95% CI, 1.09-1.19) after balancing other factors. Comorbidity score, neighborhood income, insurance status, histologic subtype, disease stage, treatment, and unexplained factors accounted for 3.0%, 7.5%, 0.0%, 56.2%, 10.6%, 6.9%, and 15.8%, respectively, of the ERR among Black vs White patients aged 65 years or older.Conclusions and RelevanceThis study suggests that histologic subtype was the dominant factor associated with racial survival disparity among patients with uterine cancer, while insurance status represented the main modifiable factor for women younger than 65 years. Additional studies of interactions between biology and social determinants of health are merited.

Diabetes and the social, biologic, and behavioral determinants of endometrial cancer in the United States

“…This can due to the mental, social, and financial support provided by their spouses [ 32 , 44 – 46 ]. Awareness of the symptoms and risk factors for this disease, and its early diagnosis, are usually coupled with higher levels of education and financial stability that enable women to lead a healthier lifestyle and have better access to preventive measures and quality of care [ 30 , 47 – 49 ].…”

Role of Age and Competing Risk of Death in the Racial Disparity of Kidney Failure Incidence after Onset of CKD

“…To quantify the contribution of each factor set, we calculated the percentage of excess relative risk (hazard) of incident KFRT (and decreased relative risk of pre-KFRT death) in Black versus White veterans that was explained by that factor set. Excess relative risk was defined as HR minus 1.0, and decreased relative risk was defined as 1.0 minus HR, both denoted by D. We then calculated the explainable percentage using the formula (D base 2D base1factor )/ D base 3100%, 5,26,27 where D base and D base1factor are the values of D from the base model and each of the above models 2-8. In addition, we calculated the proportional reduction in the Wald chi-square statistic associated with the HR in each of the models 2-8 from the base model.…”

Disparities in Gynecologic Cancers

“…Although adherence to quality standards provides potential strategies for intervention and mitigation by providers and health systems, several studies have shown that although quality treatment or other socioeconomic variables may improve survival, these factors alone do account for the persistent and growing disparities among minority women 93–96 . For example, in a large study using the National Cancer Database data of more than 270,000 Black and White women, survival disparity was noted (5-year survival, 58.6% vs. 78.5%) 96 . Estimated excess relative risk for survival was most attributable to histology in all women (~50%) and unexplained factors with slight differences in the proportional distribution by age (≤65 years old vs. older).…”