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Primary Submission Category: Health equity
Is “everyday discrimination” a distinct exposure compared to healthcare discrimination?
Authors: Michael Green, Heather Farmer, January Cornelius, Zaire Cullins, Tomi Akinyemiju, Michael Green,
Presenting Author: Andrea Thoumi*
Background
Patients face different types of unfair treatment in many places. Everyday discrimination encompasses routine experiences of unfair treatment in various contexts, and the Everyday Discrimination Scale (EDS) is a well-known instrument to assess perceptions of these experiences.
Objective
Evaluate if longitudinal response patterns for a single-item healthcare measure differs from multi-item patterns of EDS.
Methods
Data from 19,848 non-Hispanic Black, non-Hispanic White, and Hispanic adults (mean age 65; 59% female) in the Health and Retirement Study (2008-2022). Everyday discrimination was assessed with a 5-item EDS, healthcare discrimination was assessed by a single item asking how often participants faced “poorer treatment from doctors or hospitals,” with responses ranging from “never” to “every day.” Group-based trajectory models identified longitudinal patterns of discrimination for up to 14 years. Agreement statistics (Cohen’s Kappa) and cross-classification are used to determine how often participants fell into matching versus divergent response patterns across each measure.
Results
We identified 3 patterns of responses for both measures. For the EDS, trajectories were categorized as: Low (40.0%), Medium (54.2%), and High (5.8%). In contrast, the single healthcare item exhibited a floor effect, with trajectories categorized as: Low (71.3%), Medium (27.9%), and High (0.9%). Trajectory assignments between the two measures showed poor agreement (Kappa=0.292). Among trajectory groups, 60.2% were concordant and 37.4% diverged, exhibiting a higher EDS pattern than the healthcare discrimination pattern. Only 2.5% of the sample showed the reverse pattern.
Conclusions
Our findings indicate that adults report varying patterns of bias over time, attributed to different sources. This divergence in patterns indicates the importance of measuring discrimination in relevant contexts for older adults, such as healthcare, in addition to general everyday discrimination.