Understanding racial differences in health-related quality of life in a population-based cohort of breast cancer survivors

Abstract

Purpose Although racial disparities in health-related quality of life (HRQOL) among women with breast cancer (BC) are well documented, less is known about HRQOL changes over time among women of different races. Our objective was to assess racial differences in HRQOL during active treatment and survivorship phases of BC care. Methods We used data from the third phase of the Carolina Breast Cancer Study (CBCS-III). CBCS-III enrolled 3,000 women in North Carolina aged 20-74 years diagnosed with BC between 2008 and 2013. HRQOL assessments occurred 5- and 25-months post-diagnosis, representing distinct phases of care. HRQOL measures included the Functional Assessment of Cancer Therapy for BC and Functional Assessment of Chronic Illness Therapy for Spiritual Well-Being. Analysis of covariance models were employed to assess racial differences in changes in HRQOL. Results The cohort included 2,142 Non-Hispanic White (n=1,105) and Black women (n=1,037) who completed both HRQOL assessments. During active treatment, Whites reported physical and functional scores 2-2.5 points higher than Blacks (p<0.0001). Spiritual HRQOL was 2.1 points higher for Blacks (p<0.0001). During survivorship, differences persisted. After adjusting for demographic, socioeconomic, tumor and treatment characteristics, physical and functional HRQOL gaps narrowed, but spiritual HRQOL gaps widened. Conclusions Racial differences in physical and functional HRQOL during active treatment and survivorship may be largely mediated by socioeconomic factors. However, our results suggest that among Black women, spiritual HRQOL is well supported throughout the BC care continuum. These results inform opportunities for improving the quality and equity of supportive services for women with BC.

Health-related quality of life in Black breast cancer survivors with and without triple-negative breast cancer (TNBC)

“…[26–30] Our findings that the TNBC-group scored 8.4 points lower on HRQOL compared to those in the non-TNBC group exceeds the established clinically significant difference of 7-to-8 points for the FACT-B total score. [31] In subsequent analyses, we identified commonalities and differences in individual and systemic variables associated with HRQOL.…”

“…While a recent 2016 study included a large sample of Black BC patients diagnoses <age 50 (n=480) from the Carolina Breast Cancer Study, their analysis of HRQOL included few patient reported psychological, socioecological, health care system, or cultural factors [30]. Thus, the current fills a notable gap in the literature, and is among the first to examine HRQOL among Black TNBC and non-TNBC survivors [33].…”

Examining health-related quality of life patterns in women with breast cancer

“…At 25-months, Black race was actually associated with membership in the poorest HRQOL profile, but no other socioeconomic factors were associated with poorest HRQOL profile membership. Understanding which patient-level characteristics might be most associated with poor HRQOL LP membership at different phases of the BC care continuum helps inform HRQOL management strategies, which can vary over time [31]. For example, if clinicians are aware that particular characteristics are associated with worse HRQOL patterns at specific BC continuum phases, they might be better equipped to provide the necessary support for patients.…”

“…Self-reported smoking status, medical-record confirmed comorbid conditions (e.g., diabetes, chronic obstructive pulmonary disease, obesity, hypertension, heart disease), tumor stage and grade, surgery type, and receipt of radiation, chemotherapy, and Herceptin were included in analyses [31]. …”

“…The highest HRQOL LP served as the reference category. In this approach, we adjusted for smoking status, comorbid conditions, treatment, and tumor characteristics, which could influence HRQOL at a single time point and HRQOL changes over time [31,43]. Variables presented in Table 1 were potential covariates for adjusted models.…”