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[Preprint]. 2025 May 2:rs.3.rs-6507515.
doi: 10.21203/rs.3.rs-6507515/v1.

Factors Associated with Perceived Discrimination in Healthcare Among Middle-Aged and Older Adults

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Factors Associated with Perceived Discrimination in Healthcare Among Middle-Aged and Older Adults

Michael D Green et al. Res Sq. .

Update in

Abstract

Background: Discrimination in healthcare settings impedes quality care, leading to poorer health outcomes.

Objective: To examine racial differences in perceived discrimination in healthcare settings across age among middle-aged and older adults and identify factors associated with these experiences.

Design: Longitudinal cohort data from the Health and Retirement Study collected between 2008 and 2020.

Participants: The sample included 17,478 United States adults aged 50 and older who had at least one doctor visit or hospitalization in the prior two years.

Main measures: Self-reported perceived discrimination in healthcare settings, measured using an item from the Everyday Discrimination Scale and categorized as "never" versus "ever" experienced discrimination. Generalized linear mixed models were used to identify factors associated with experiencing discrimination. Assessed factors included sociodemographic (age, gender, marital status, education, wealth, insurance status, employment) and clinical characteristics (depressive symptoms, difficulty with activities of daily living [ADLs], number of doctor visits, hospitalizations, body mass index [BMI], and comorbidities).

Results: Black adults were significantly more likely to experience discrimination in healthcare settings than White adults, and these disparities were most pronounced at younger ages. Factors associated with higher odds of reporting discrimination included Black race, male gender, not being married, being uninsured, higher educational attainment, depressive symptoms, difficulty with ADLs, history of arthritis, and higher BMI. In race-stratified analyses, unemployment was associated with higher odds of reporting discrimination among Black adults. Among White adults, being unmarried and uninsured were significant factors associated with discrimination.

Conclusions and relevance: Black adults reported higher rates of perceived discrimination in healthcare settings than White adults, especially during middle adulthood. Multiple sociodemographic and clinical factors were associated with these experiences. These findings underscore the need to address discrimination in healthcare to improve patient-provider relationships among middle-aged and older adults.

Keywords: Aging; Health Equity; Healthcare Discrimination; Healthcare Services; Perceived Discrimination; Quality Improvement; Racial Disparities.

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Conflict of interest statement

Conflicts of Interest: N/A

Figures

Figure 1
Figure 1. Predicted Probabilities of Reporting Discrimination in Healthcare Settings Among U.S. Middle-Aged and Older Adults, Health and Retirement Study (2008–2020)
Note: Plots are based on mixed models with indicators for Black race (P<0.001), interactions with time (race*age; P=0.013), Hispanic ethnicity (P=0.713), mortality over the study period (P<0.001), male gender (P=0.002). Shaded areas represent 95% confidence intervals.
Figure 2
Figure 2. Adjusted Odds Ratios (95% Confidence Intervals) for the Factors Associated with Reporting Discrimination in Healthcare Settings Among U.S. Middle-aged and Older Adults, Health and Retirement Study (2008–2020)
Note: Statistically significant values (P < 0.05) are bolded in black. Wealth variable log transformed. Time constant mortality to account for attrition, and an interaction between Black race and age were components of the model.
Figure 3
Figure 3. Adjusted Odds Ratios (95% Confidence Intervals) for the Factors Associated with Reporting Discrimination in Healthcare Settings Among Black and White U.S. Middle-aged and Older Adults, Health and Retirement Study (2008–2020)
Note: Statistically significant values (P < 0.05) are bolded in black. Wealth variable log transformed. Time-constant mortality was a component of each model to account for attrition.

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