Reasons for Perceived Everyday Discrimination, Quality of Life, and Psychosocial Health of Breast Cancer Survivors: A Cross-Sectional Cluster Analysis

Abstract

IntroductionDiscrimination exacerbates disparities among breast cancer survivors (BCS), yet how different reasons for experiencing perceived discrimination (e.g., race, age) influence health remains understudied. We explored the association between self-reported discrimination, psychosocial health, and quality of life (QOL), identified clusters based on reasons for perceived discrimination, and examined differences in QOL and psychosocial outcomes between these clusters.MethodsIn this cross-sectional study, we examined correlations between reasons for perceived discrimination (Everyday Discrimination Scale; EDS), QOL domains (cognitive, physical, social, emotional, and functional QOL measured with FACT-G), social dysfunction (Social Difficulties Inventory), and a psychological distress composite score (included measures of stress [Perceived Stress Scale], anxiety [PROMIS Anxiety], and depression [PROMIS Depression]), among 174 breast cancer survivors (stage 0-IV; ≥21 years). We used k-modes clustering to identify discrimination groups. Differences in demographics, clinical characteristics, and outcomes across clusters were assessed using Chi-square, analysis of variance, covariance, or non-parametric tests, followed by post hoc analyses.ResultsOverall, experiences of discrimination were associated with poorer QOL and psychosocial health (|0.306|<r<|0.452|, P < 0.001). Six distinct clusters emerged based on reasons for perceived discrimination from the EDS. Compared to Cluster 4 (no discrimination), participants in Cluster 1 (discrimination due to gender, age, and physical characteristics) had lower cognitive and physical QOL (4.3 < mean difference [MD]< 5.0, P < 0.001). Participants in Cluster 3 (discrimination due to physical characteristics) had poorer functional QOL, greater social disfunction, and higher psychological distress composite scores (0.3<MD <9.4, P < 0.001) than Cluster 4. Differences between Clusters 2 (discrimination due to gender) and 5 (discrimination due to gender, race/ethnicity) with all other Clusters were not statistically significant (P > 0.05).ConclusionQOL and psychosocial health scores varied between clusters based on reasons for perceived discrimination. Future interventions to improve QOL for breast cancer survivors should consider addressing stigma related to gender, physical appearance, and other forms of discrimination.

Keywords: discrimination; machine learning; psycho-oncology; quality of life; social phenotype.

Figure 1.

Bar Plot Displaying the Frequency of Discrimination Reasons Before Clustering. Note. The Y-Axis Represents the Frequencies and the X-Axis the Discrimination Reasons. The Numbers on Top of Each Bar are the Total of Participants Referring That Factor as One of the Reasons for Perceived Discrimination. Participants Could Select One or More Reasons. SES, Socioeconomic Status

Figure 2.

Elbow, Gap, and Silhouette Graphs to Determine the Number of Clusters. Note. From Left to Right, Elbow, Gap, and Silhouette Graphs. Substantial Drops in the Elbow Statistic Suggest Significant Reduction in Within-Cluster Sum of Squares. For the Gap and Silhouette Graphs, Higher Points Indicate Better Cluster Separation and Cohesion, Respectively