Experiences of healthcare services among racial and ethnic groups in the U.S.

Abstract

Objective

To examine differences in healthcare experiences by race and ethnicity in the U.S.

Methods

Using pooled 2021 and 2023 Medical Expenditure Panel Survey data, we assessed differences in Consumer Assessment of Healthcare Providers and Systems measures between race and ethnicity groups (White, Hispanic, Black, Asian, American Indiana/Alaska Native, and multiple race/ethnicities) with linear and logistic regression. Differences by income and education across race/ethnicity groups were examined in regression analyses stratified by race/ethnicity groups.

Results

Asian adults reported lower overall ratings of services compared to White adults (coefficient = −0.31, 95 % confidence interval: −0.46, −0.17 on a scale from 0 [worst] to 10 [best care]). Compared to White adults, Hispanic, Black, and Asian adults were less likely to report accessing timely care or understanding provider communications. Income and education had the most consistent positive effects for White adults, but effects were greater in magnitude for Hispanic and Black adults.

Conclusions

This study identifies significant differences in experiences of healthcare services in the U.S. across racial and ethnic groups, particularly related to accessing timely care and clear communication with providers. These findings highlight the role of structural inequities as a driver of healthcare disparities among minoritized populations in the U.S.

Highlights

• •Minoritized racial and ethnic groups in the US experience significant health disparities.
• •Asian adults report lower overall satisfaction with healthcare experiences than White adults.
• •Hispanic, Black, and Asian adults report less timely care and worse provider communication.
• •Higher income and education are most consistently protective for White adults.

1. Introduction

The COVID-19 pandemic exposed and exacerbated underlying inequities in the healthcare system related to access to and quality of care for minoritized populations (Nana et al., 2021). In particular, individuals in minoritized racial and ethnic groups experienced disproportionately worse outcomes during the pandemic, largely because of existing disparities (Nana et al., 2021; Lopez III et al., 2021). Historically, minoritized racial and ethnic populations have fared worse than White adults across a range of health and healthcare measures in the U.S. (Artiga et al., 2023) Black, Hispanic, American Indian or Alaska Native, and Asian adults are more likely to report negative experiences in healthcare settings compared to their White counterparts including being treated with disrespect, blamed for health issues, and denied pain treatment; a significant proportion of people attribute these experiences to their race or ethnicity (Artiga et al., 2023; Saha et al., 2003).

These experiences highlight the importance of focusing on racism – both interpersonal and structural – as a key determinant of health outcomes (Bailey et al., 2017). There are multiple avenues through which racism can impact health and one important mechanism is the effect of and interaction between racism and socioeconomic status (Bailey et al., 2017; Williams et al., 2019). A long history of racist policies and practices contributes to lower socioeconomic status among racial and ethnic minority groups in the U.S. which can negatively impact healthcare treatment and outcomes (Bailey et al., 2017; Williams et al., 2019; Abramson et al., 2015; Okunrintemi et al., 2019). Racism can also interact with socioeconomic-related disparities in care, dampening or negating protective effects of higher levels of income and education on health outcomes (Wilson et al., 2017; Figueroa et al., 2016).

Previous studies on income-related health disparities have found that individuals with lower levels of income a consistently report worse healthcare experiences—such as difficulties accessing care, poorer communication, treatment delays, and lower satisfaction compared to those with higher incomes (Okunrintemi et al., 2019). Protective effects of income, though, tend to be greater for high-earning White populations than for their high-earning Black counterparts (Wilson et al., 2017; Assari, 2018).

Higher levels of educational attainment are consistently associated with better health outcomes (Zajacova and Lawrence, 2018). In one study examining the interaction of education and race, greater educational attainment was associated with increased satisfaction with healthcare services for White and Latino adults, but not for Black adults (Assari, 2020). Further, a study of adults in California found increased levels of education among non-White respondents were associated with increased perceptions of discrimination in the healthcare system compared to their counterparts with lower educational attainment (Abramson et al., 2015).

To date, research on the association of either income or education with healthcare experiences across racial and ethnic groups has used data from before the COVID-19 pandemic. This is a key inflection point as many health disparities, including those among minoritized populations and across socioeconomic groups, were exacerbated during this time (Nana et al., 2021; Kim et al., 2020; Mackey et al., 2021; Khanijahani et al., 2021). Thus, pandemic-exacerbated disparities may adversely influence individuals' perceptions of care and their overall access to services. To address these gaps in understanding of current disparities in healthcare experiences, we analyzed a nationally representative survey of U.S. adults to explore differences in 1) patient-reported healthcare experiences and 2) the relationship between socioeconomic status and healthcare experiences across racial/ethnic groups.

2. Methods

2.1. Study design and population

We analyzed data pooled from the 2021 and 2023 Medical Expenditure Panel Survey (MEPS) Household Component. MEPS is a nationally representative survey of civilian, non-institutionalized individuals and families in the U.S. and their doctors, fielded by the Agency for Healthcare Research and Quality (AHRQ) (Medical Expenditure Panel Survey Home, 2024). Survey information is collected for households every five or six months over a two-and-a-half-year period. Data reflect responses to questions about healthcare experiences during the year in which the interview took place (Medical Expenditure Panel Survey Background, 2024). The publicly available household component includes information on individual household members' sociodemographic characteristics, health conditions, health status, use of medical care services, charges and payments, access to care, satisfaction with care, and health insurance coverage.

The study included all adults aged 18 and older who participated in MEPS in 2021 or 2023. Individuals were classified into 6 mutually exclusive race/ethnicity categories based on self-report: (1) White, (2) Hispanic, (3) Black, (4) Asian or Native Hawaiian/Other Pacific Islander (Asian hereafter), (5) American Indian or Alaska Native (AIAN), and (6) Multiple races. All individuals who identified as Hispanic ethnicity were categorized as Hispanic, and all other groups include only non-Hispanic individuals.

Of the 37,675 adults in the pooled 2021–2023 sample, 56.68 % were White, 20.97 % Hispanic, 13.82 % Black, 5.52 % Asian, 0.52 % AIAN, and 2.49 % Multiple races (Appendix B). There were significant differences between racial/ethnic groups on all demographic characteristics (Table 1). Compared to White adults, all other groups generally had lower annual household income and lower levels of educational attainment apart from Asian adults who had a higher proportion with a graduate degree or higher (26.21 %) and income of $100,000 or more (54.08 %) compared to their White counterparts (15.49 % and 43.84 %, respectively).

Table 1.

Weighted Demographic Characteristics of U.S. Adults by Self-Reported Race and Ethnicity, 2021 and 2023.

	White	Hispanic	Black	Asian	American Indian Alaska Native	Multiple Races
Pooled Total = 37,675	N = 21,355 (56.7 %)	N = 7902 (21.0 %)	N = 5207 (13.8 %)	N = 2078 (5.5 %)	N = 196 (0.5 %)	N = 937 (2.5 %)
	Weighted %	Weighted %	Weighted %	Weighted %	Weighted %	Weighted %
Age Group
18–29	16.4	27.2	22.4	20.0	11.9	36.8
30–39	16.2	20.3	19.8	20.5	14.4	17.2
40–49	14.1	19.0	15.9	19.5	25.2	14.9
50–64	25.9	21.2	23.8	22.0	26.2	18.3
65+	27.5	12.3	18.2	18.1	22.4	12.9
Sex
Female	51.1	50.1	54.2	53.3	45.5	47.4
Male	48.9	49.9	45.8	46.8	54.5	52.6
Household Annual Income
< $25,000	12.4	17.2	22.6	12.5	22.9	16.3
$25,000–$49,999	15.6	22.3	22.9	10.5	26.0	17.2
$50,000–$74,999	14.9	19.4	18.6	10.5	16.1	15.7
$75,000–$99,999	13.2	12.4	11.7	12.5	19.0	14.4
$100,000+	43.8	28.7	24.2	54.1	16.0	36.5
Highest Degree Attained
Less than high school	6.2	23.7	11.1	8.9	16.6	12.4
High school or equivalent	42.0	45.3	50.6	25.1	54.4	47.0
Bachelor's degree	23.9	14.4	16.2	29.8	14.5	17.0
Graduate degree or higher	15.5	6.0	10.9	26.2	5.7	9.9
Other/unknown	12.3	10.6	11.2	10.0	8.8	13.6
Employment
Unemployed/ Unknown	37.3	35.1	36.8	35.3	40.0	38.0
Employed	62.7	64.9	63.2	64.8	60.0	62.0
Marital status
Single	24.0	37.4	47.4	26.1	32.8	48.5
Married	56.0	46.4	31.1	63.3	42.8	35.4
Divorced/Separated/Widowed/Unknown	20.1	16.2	21.5	10.6	24.4	16.1
Region
Northeast	18.8	13.7	15.4	20.0	8.2	9.0
Midwest	25.9	9.0	16.6	11.4	17.6	17.6
South	35.3	39.1	59.2	24.2	54.4	44.4
West	20.0	38.3	8.9	44.5	19.8	29.1
Insurance Coverage
Medicare	27.9	11.3	20.3	15.5	21.5	16.6
Medicaid	8.1	17.6	17.9	12.8	10.9	13.2
Private Insurance	62.1	40.8	46.9	64.2	39.4	55.2
Uninsured	4.1	19.6	7.1	3.4	8.4	5.1
Disability Status
No disability	78.8	89.0	81.9	90.9	70.0	78.8
One disability	14.7	7.5	12.4	6.4	18.0	13.8
2+ disabilities	6.5	3.5	5.7	2.7	12.0	7.4

Notes: Bold indicates statistical significance at p < 0.05 compared to the reference group of White adults. Differences between groups were assessed using chi-squared tests. Data from authors' analysis of pooled 2021 and 2023 Medical Expenditure Panel Survey (MEPS).

2.2. Measures

Outcomes were the twelve Consumer Assessment of Healthcare Providers and Systems (CAHPS) measures included in MEPS. The CAHPS measures, developed by the Centers for Medicare and Medicaid Services, focus on various aspects of care quality and outcomes and are specifically designed to reliably assess the experiences of a large sample of patients (Consumer Assessment of Healthcare Providers and Systems (CAHPS) | CMS, 2024). The measures were assessed among all adults (aged 18 and older) who reported receipt of at least one healthcare service in the prior 12 months and refer to all experiences during that period (Medical Expenditure Panel Survey Home, 2024; Consumer Assessment of Healthcare Providers and Systems (CAHPS) | CMS, 2024). CAHPS measures include an overall rating of healthcare services received in the prior 12 months, three measures related to accessing health services (e.g., ability to get care right away), and eight measures related to interactions with healthcare providers (e.g., providers listened to me carefully). Full item wording is included in Appendix A.

Overall rating of healthcare services was measured on a scale from 0 (worst care possible) to 10 (best care possible). Responses to the other eleven items were measured on a 4-point Likert scale (never, sometimes, usually, always) except for “how often did doctors or other health providers give instructions about what to do about a specific illness or condition” which included a binary (yes/no) response. For the main analysis, Likert scale responses were collapsed to dichotomous measures, with responses of “usually” or “always” indicating a positive response and responses of “sometimes” or “never” indicating a negative response.

Additional individual-level measures included age, sex, annual household income (< $25,000, $25,000–$49,999, $50,000–$74,999, $75,000–$99,999, $100,000+), highest degree attained (less than high school, high school or equivalent, bachelor's degree, graduate degree or higher, other/unknown), employment status, marital status, region, insurance coverage, and disability status. Disability status was assessed using measures of functional limitation. Insurance categories, except for the uninsured category, were not mutually exclusive to account for coverage by multiple insurers (e.g., dual enrollees in Medicaid and Medicare).

2.3. Statistical analysis

Descriptive statistics were calculated for individual characteristics across racial/ethnic groups using chi-squared tests. Linear regression was used to examine the relationship between overall ratings of healthcare services (range 0–10) and race/ethnicity. Logistic regression was used to assess relationships between race/ethnicity and all other outcomes. We then conducted stratified analyses to examine differences in the relationships of income and educational attainment with outcomes for each of four race/ethnicity groups: White, Hispanic, Black, and Asian (AIAN and Multiple race groups were excluded from this analysis due to small sample size). Main outcomes for income and education are presented the difference in predicted probability of agreement with the CAHPS measure comparing across levels of income and education. Finally, we conducted sensitivity analyses using ordered logistic regressions to model all outcomes in the full study sample. All models adjusted for individual-level measures described above. All analyses used survey weights to account for the sampling design and obtain nationally representative estimates and were conducted using Stata version 18 (StataCorp, 2023). This analysis was approved by the Rutgers Institutional Review Board.

3. Results

3.1. Healthcare experiences by race/ethnicity

Only Asian adults had a statistically significantly lower adjusted overall rating of healthcare services compared to White adults (coefficient = −0.31, 95 % confidence interval [CI]: −0.46, −0.17) (Table 2). On measures of access to care, Hispanic (odds ratio [OR] = 0.73, 95 % CI: 0.59, 0.90) and Asian (OR = 0.40, 95 % CI: 0.28, 0.58) groups had lower odds of receiving care right away compared to their White counterparts. Hispanic (OR = 0.72, 95 % CI: 0.63, 0.81), Black (OR = 0.81, 95 % CI: 0.70, 0.94), Asian (OR = 0.61, 95 % CI: 0.51, 0.73), and Multiple races (OR = 0.64, 95 % CI: 0.47, 0.88) had lower odds of receiving an appointment as soon they thought it was needed. Compared to White adults, Hispanic (OR = 0.67, 95 % CI: 0.57, 0.79), Black (OR = 0.62, 95 % CI: 0.52, 0.75), and Asian adults (OR = 0.52, 95 % CI: 0.41, 0.66) also had lower odds of finding it easy to see a specialist when needed. Full model results are included in Appendix C.

Table 2.

Adjusted estimates for agreement with Consumer Assessment of Healthcare Providers and Systems measures among U.S. adults by self-reported race/ethnicity, 2021 and 2023.

CAHPS Measures	Hispanic	Black	Asian	American Indian Alaska Native	Multiple Races
	Coef. [95 % CI]	Coef. [95 % CI]	Coef. [95 % CI]	Coef. [95 % CI]	Coef. [95 % CI]
Overall rating of healthcare services	0.09[−0.01,0.20]	0.04[−0.07,0.15]	−0.31[−0.46,-0.17]	−0.32[−0.81,0.17]	−0.16[−0.40,0.08]
Access to care	OR [95 % CI]	OR [95 % CI]	OR [95 % CI]	OR [95 % CI]	OR [95 % CI]
Received care right away	0.73[0.59,0.90]	0.87[0.68,1.11]	0.40[0.28,0.58]	0.61[0.25,1.46]	0.66[0.39,1.11]
Received an appointment as soon as thought it was needed	0.72[0.63,0.81]	0.81[0.70,0.94]	0.61[0.51,0.73]	0.82[0.46,1.46]	0.64[0.47,0.88]
Easy to see a specialist when needed	0.67[0.57,0.79]	0.62[0.52,0.75]	0.52[0.41,0.66]	1.25[0.58,2.68]	0.67[0.43,1.04]
Patient-provider interactions	OR [95 % CI]	OR [95 % CI]	OR [95 % CI]	OR [95 % CI]	OR [95 % CI]
Health providers listened carefully to you	0.96[0.79,1.16]	1.21[0.98,1.49]	1.04[0.76,1.43]	0.78[0.39,1.57]	1.27[0.86,1.87]
Health providers explained things in a way that was easy to understand	0.57[0.47,0.69]	0.73[0.59,0.91]	0.49[0.37,0.65]	0.62[0.30,1.26]	0.89[0.56,1.43]
Health providers showed respect for what you had to say	1.06[0.86,1.31]	1.06[0.85,1.33]	1.00[0.71,1.43]	0.46[0.17,1.24]	0.94[0.62,1.45]
Health providers spent enough time with you	0.90[0.76,1.06]	1.16[0.96,1.40]	0.92[0.72,1.18]	0.49[0.22,1.06]	0.57[0.39,0.83]
Doctors or other health providers gave instructions about what to do for a specific illness or condition	1.15[0.97,1.36]	1.12[0.93,1.35]	1.19[0.91,1.54]	0.70[0.28,1.71]	1.32[0.93,1.87]
The advice given by doctors or other health providers was easy to understand	0.53[0.41,0.69]	0.60[0.44,0.81]	0.42[0.28,0.65]	0.98[0.31,3.09]	1.09[0.56,2.10]
Doctors or other health providers asked you to describe how you are going to follow their instructions	1.97[1.71,2.27]	1.89[1.62,2.20]	1.86[1.51,2.29]	1.47[0.82,2.64]	1.60[1.16,2.20]
Offered help in filling out forms at the office	1.47[1.24,1.73]	0.99[0.82,1.20]	1.78[1.39,2.28]	1.32[0.61,2.86]	1.43[0.97,2.10]

Abbreviations: Coef., coefficient; OR, odds ratio; CAHPS, Consumer Assessment of Healthcare Providers and Systems.

Notes: Estimates were derived from separate models for each CAHPS measure, using non-Hispanic white as the reference group for all measures. All models adjust for disability, age, sex, income, education, employment, marital status, region, and insurance coverage. Bold indicates statistical significance at p < 0.05. Data from authors' analysis of pooled 2021 and 2023 Medical Expenditure Panel Survey (MEPS).

On measures assessing patient-provider interactions (Table 2), Hispanic (OR = 0.57, 95 % CI: 0.47, 0.69), Black (OR = 0.73, 95 % CI: 0.59, 0.91), and Asian (OR = 0.49, 95 % CI: 0.37, 0.65) groups had lower odds than White adults of reporting health providers explained things in a way that was easy to understand. Adults of multiple races or ethnicities were less likely than White adults to report providers spent enough time with them (OR = 0.57, 95 % CI: 0.39, 0.83). Hispanic (OR = 0.53, 95 % CI: 0.41, 0.69), Black (OR = 0.60, 95 % CI: 0.44, 0.81), and Asian (OR = 0.42, 95 % CI: 0.28, 0.65) adults were also less likely than White adults to report advice given by doctors or other health providers was easy to understand. In contrast, Hispanic (OR = 1.97, 95 % CI: 1.71, 2.27), Black (OR = 1.89, 95 % CI: 1.62, 2.20), Asian (OR = 1.47, 95 % CI 1.51, 2.29), and multiple race/ethnicity (OR = 1.60, 95 % CI: 1.16, 2.20) respondents had higher odds than White respondents of reporting that doctors or other health providers asked them to describe how they were going to follow their instructions. Hispanic (OR = 1.47, 95 % CI: 1.24,1.73) and Asian (OR = 1.78, 95 % CI: 1.39, 2.28) respondents were also more likely than White respondents to report being offered help in filling out forms at the doctor's office.

3.2. Healthcare experiences by income and education within racial/ethnic groups

The most consistent income and education effects were observed among White adults with each increased level of income and education generally associated with greater access to and quality of healthcare services; effects were observed between the highest and lowest levels of income (Table 3) and education (Table 4) on 20 of 22 measures. While consistent, effect sizes were relatively small. The largest difference was observed in reports of receiving care right away for adults with graduate degrees compared to those with less than a high school education (difference in predicted probabilities = 0.15, 95 % CI: 0.09, 0.22).

Table 3.

Difference in predicted probability of agreement with Consumer Assessment of Healthcare Providers and Systems measures among U.S. adults across income levels by race/ethnicity, 2021 and 2023.

CAHPS Measures by Household Income	White	Hispanic	Black	Asian
Access to Care	Difference [95 % CI]	Difference [95 % CI]	Difference [95 % CI]	Difference [95 % CI]
Received care right away
< $25,000	ref.	ref.	ref.	ref.
$25,000–$49,999	0.03[−0.02,0.08]	0.02[−0.06,0.10]	−0.01[−0.10,0.08]	−0.11[−0.37,0.14]
$50,000–$74,999	0.06[0.01,0.11]	0.03[−0.06,0.13]	0.02[−0.08,0.13]	0.01[−0.25,0.27]
$75,000–$99,999	0.05[0.00,0.11]	0.11[0.00,0.22]	0.14[0.04,0.24]	0.17[−0.03,0.37]
$100,000+	0.12[0.08,0.16]	0.05[−0.03,0.13]	0.15[0.06,0.24]	0.03[−0.14,0.20]
Received an appointment as soon as thought it was needed
< $25,000	ref.	ref.	ref.	ref.
$25,000–$49,999	0.02[−0.01,0.06]	−0.01[−0.07,0.04]	−0.03[−0.09,0.03]	−0.06[−0.22,0.09]
$50,000–$74,999	0.03[0.00,0.07]	−0.03[−0.10,0.03]	−0.02[−0.09,0.05]	0.09[−0.05,0.24]
$75,000–$99,999	0.05[0.02,0.09]	0.05[−0.02,0.12]	0.02[−0.06,0.10]	−0.02[−0.18,0.13]
$100,000+	0.05[0.02,0.08]	0.05[−0.01,0.10]	0.02[−0.05,0.08]	0.04[−0.08,0.16]
Easy to see a specialist when needed
< $25,000	ref.	ref.	ref.	ref.
$25,000–$49,999	0.05[0.01,0.09]	−0.03[−0.10,0.05]	0.02[−0.05,0.10]	0.02[−0.20,0.25]
$50,000–$74,999	0.06[0.02,0.10]	−0.05[−0.13,0.03]	0.05[−0.04,0.14]	0.17[−0.01,0.35]
$75,000–$99,999	0.10[0.06,0.13]	0.07[−0.02,0.17]	0.20[0.11,0.29]	0.04[−0.16,0.24]
$100,000+	0.08[0.05,0.11]	0.04[−0.03,0.11]	0.07[−0.02,0.16]	0.09[−0.05,0.24]
Patient-provider interactions	Difference [95 % CI]	Difference [95 % CI]	Difference [95 % CI]	Difference [95 % CI]
Health providers listened carefully to you
< $25,000	ref.	ref.	ref.	ref.
$25,000–$49,999	0.03[0.01,0.06]	0.04[−0.01,0.08]	0.02[−0.02,0.07]	−0.03[−0.13,0.07]
$50,000–$74,999	0.06[0.03,0.09]	0.06[0.02,0.11]	0.06[0.01,0.10]	−0.06[−0.16,0.04]
$75,000–$99,999	0.07[0.05,0.10]	−0.01[−0.08,0.05]	0.06[0.01,0.11]	0.04[−0.03,0.11]
$100,000+	0.08[0.06,0.10]	0.03[−0.02,0.07]	0.06[0.02,0.10]	−0.01[−0.07,0.06]
Health providers explained things in a way that was easy to understand
< $25,000	ref.	ref.	ref.	ref.
$25,000–$49,999	0.03[0.01,0.05]	0.00[−0.05,0.05]	0.05[0.00,0.10]	−0.01[−0.13,0.12]
$50,000–$74,999	0.04[0.02,0.07]	0.03[−0.02,0.08]	0.11[0.06,0.15]	0.02[−0.09,0.14]
$75,000–$99,999	0.06[0.03,0.08]	0.03[−0.03,0.09]	0.08[0.02,0.13]	0.03[−0.09,0.15]
$100,000+	0.05[0.03,0.07]	0.05[0.01,0.10]	0.08[0.03,0.13]	0.07[−0.02,0.16]
Health providers showed respect for what you had to say
< $25,000	ref.	ref.	ref.	ref.
$25,000–$49,999	0.03[0.01,0.06]	0.05[0.00,0.09]	0.07[0.03,0.12]	−0.09[−0.19,0.02]
$50,000–$74,999	0.06[0.03,0.08]	0.07[0.03,0.12]	0.08[0.03,0.13]	−0.05[−0.14,0.04]
$75,000–$99,999	0.06[0.03,0.08]	0.02[−0.04,0.07]	0.11[0.07,0.16]	−0.01[−0.08,0.06]
$100,000+	0.08[0.06,0.10]	0.05[0.01,0.10]	0.11[0.07,0.15]	−0.01[−0.06,0.04]
Health providers spent enough time with you
< $25,000	ref.	ref.	ref.	ref.
$25,000–$49,999	0.02[−0.01,0.05]	0.04[−0.01,0.09]	0.03[−0.02,0.09]	−0.03[−0.16,0.10]
$50,000–$74,999	0.04[0.01,0.07]	0.06[0.01,0.12]	0.08[0.03,0.13]	−0.05[−0.17,0.08]
$75,000–$99,999	0.06[0.03,0.09]	0.02[−0.05,0.08]	0.03[−0.04,0.10]	0.05[−0.06,0.15]
$100,000+	0.05[0.03,0.08]	0.00[−0.06,0.05]	0.09[0.05,0.14]	0.04[−0.05,0.13]
Doctors or other health providers gave instructions about what to do for a specific illness or condition
< $25,000	ref.	ref.	ref.	ref.
$25,000–$49,999	0.001[−0.04,0.04]	0.04[−0.04,0.13]	−0.01[−0.09,0.07]	0.07[−0.14,0.28]
$50,000–$74,999	−0.01[−0.05,0.03]	0.001[−0.09,0.09]	0.005[−0.09,0.08]	0.03[−0.19,0.26]
$75,000–$99,999	0.004[−0.04,0.05]	−0.01[−0.11,0.10]	−0.08[−0.17,0.02]	−0.11[−0.28,0.07]
$100,000+	−0.01[−0.05,0.02]	0.04[−0.05.0.13]	0.09[−0.01,0.19]	−0.07[−0.22,0.08]
The advice given by doctors or other health providers was easy to understand
< $25,000	ref.	ref.	ref.	ref.
$25,000–$49,999	0.02[0.00,0.04]	0.03[−0.03,0.08]	0.04[0.00,0.09]	−0.10[−0.22,0.03]
$50,000–$74,999	0.04[0.02,0.06]	0.02[−0.04,0.08]	0.06[0.00,0.11]	−0.03[−0.11,0.05]
$75,000–$99,999	0.04[0.02,0.06]	0.07[0.02,0.12]	0.07[0.02,0.12]	−0.01[−0.08,0.06]
$100,000+	0.05[0.03,0.06]	0.08[0.03,0.12]	0.07[0.02,0.12]	−0.03[−0.08,0.02]
Doctors or other health providers asked you to describe how you are going to follow their instructions
< $25,000	ref.	ref.	ref.	ref.
$25,000–$49,999	−0.06[−0.10,-0.01]	0.05[−0.04,0.13]	−0.05[−0.13,0.03]	−0.16[−0.36,0.04]
$50,000–$74,999	−0.02[−0.06,0.03]	0.02[−0.06,0.11]	−0.06[−0.15,0.04]	−0.14[−0.36,0.07]
$75,000–$99,999	−0.06[−0.10,-0.01]	0.01[−0.09,0.11]	−0.11[−0.22,0.01]	−0.10[−0.29,0.09]
$100,000+	−0.05[−0.09,-0.02]	−0.07[−0.15,0.01]	−0.12[−0.20,-0.03]	−0.15[−0.30,0.00]
Offered help in filling out forms at the office
< $25,000	ref.	ref.	ref.	ref.
$25,000–$49,999	−0.03[−0.07,0.01]	0.05[−0.04,0.14]	−0.08[−0.16,0.00]	0.16[−0.06,0.38]
$50,000–$74,999	−0.05[−0.09,-0.01]	−0.02[−0.11,0.07]	−0.16[−0.24,-0.08]	0.08[−0.15,0.30]
$75,000–$99,999	−0.04[−0.09,0.00]	−0.03[−0.13,0.08]	−0.18[−0.27,-0.09]	0.21[0.00,0.42]
$100,000+	−0.06[−0.10,-0.03]	−0.06[−0.14,0.03]	−0.15[−0.23,-0.07]	0.03[−0.14,0.19]

Abbreviations: Ref., reference group; CAHPS, Consumer Assessment of Healthcare Providers and Systems.

Notes: Differences in predicted probabilities of agreement were derived from separate models for each CAHPS measure, using noted reference groups for all measures. All estimates are adjusted odds ratios with 95 % confidence intervals. Estimates adjusted for age, sex, race/ethnicity, income, education, employment, marital status, region, and insurance coverage. Bold indicates statistical significance at p < 0.05. Data from authors' analysis of pooled 2021 and 2023 Medical Expenditure Panel Survey (MEPS).

Table 4.

Difference in predicted probability of agreement with Consumer Assessment of Healthcare Providers and Systems measures among U.S. adults across education levels by race/ethnicity, 2021 and 2023.

CAHPS Measures by Educational Attainment	White	Hispanic	Black	Asian
Access to Care	Difference [95 % CI]	Difference [95 % CI]	Difference [95 % CI]	Difference [95 % CI]
Received care right away
Less than high school	ref.	ref.	ref.	ref.
High school or equivalent	0.05[−0.01,0.11]	0.09[0.02,0.16]	0.04[−0.05,0.13]	0.02[−0.27,0.32]
Bachelor's degree	0.11[0.05,0.17]	0.16[0.07,0.25]	0.07[−0.04,0.19]	0.26[−0.04,0.56]
Graduate degree or higher	0.15[0.09,0.22]	0.26[0.16,0.37]	0.16[0.03,0.28]	−0.02[−0.32,0.28]
Other/unknown	0.11[0.04,0.17]	0.07[−0.04,0.18]	0.06[−0.06,0.18]	0.22[−0.09,0.53]
Received an appointment as soon as thought it was needed
Less than high school	ref.	ref.	ref.	ref.
High school or equivalent	0.04[0.00,0.08]	0.03[−0.02,0.08]	0.00[−0.08,0.07]	0.05[−0.13,0.23]
Bachelor's degree	0.04[0.00,0.08]	0.10[0.04,0.16]	0.01[−0.08,0.10]	0.03[−0.15,0.20]
Graduate degree or higher	0.08[0.04,0.13]	0.15[0.07,0.23]	0.02[−0.09,0.12]	−0.02[−0.20,0.16]
Other/unknown	0.05[0.01,0.10]	0.03[−0.05,0.10]	−0.02[−0.11,0.08]	0.08[−0.13,0.29]
Easy to see a specialist when needed
Less than high school	ref.	ref.	ref.	ref.
High school or equivalent	0.07[0.01,0.13]	0.14[0.08,0.21]	0.13[0.04,0.22]	−0.07[−0.28,0.13]
Bachelor's degree	0.07[0.01,0.13]	0.19[0.11,0.27]	0.14[0.03,0.25]	−0.10[−0.30,0.10]
Graduate degree or higher	0.09[0.02,0.15]	0.15[0.05,0.25]	0.11[−0.02,0.24]	−0.14[−0.34,0.06]
Other/unknown	0.10[0.04,0.17]	0.17[0.08,0.26]	0.19[0.08,0.30]	0.00[−0.22,0.23]
Patient-provider interactions	Difference [95 % CI]	Difference [95 % CI]	Difference [95 % CI]	Difference [95 % CI]
Health providers listened carefully to you
Less than high school	ref.	ref.	ref.	ref.
High school or equivalent	0.04[0.01,0.08]	0.03[−0.01,0.07]	0.03[−0.02,0.09]	0.00[−0.08,0.08]
Bachelor's degree	0.05[0.01,0.09]	0.01[−0.05,0.06]	0.06[0.00,0.12]	−0.03[−0.12,0.06]
Graduate degree or higher	0.06[0.02,0.10]	0.04[−0.02,0.10]	0.08[0.02,0.14]	−0.03[−0.12,0.05]
Other/unknown	0.04[0.00,0.08]	−0.02[−0.09,0.04]	0.05[−0.01,0.11]	−0.04[−0.15,0.07]
Health providers explained things in a way that was easy to understand
Less than high school	ref.	ref.	ref.	ref.
High school or equivalent	0.03[−0.01,0.06]	0.05[0.01,0.09]	0.05[0.00,0.11]	−0.03[−0.15,0.09]
Bachelor's degree	0.05[0.01,0.08]	0.05[0.00,0.10]	0.09[0.02,0.15]	0.02[−0.10,0.13]
Graduate degree or higher	0.05[0.02,0.08]	0.08[0.02,0.14]	0.08[0.01,0.15]	−0.01[−0.12,0.11]
Other/unknown	0.04[0.01,0.08]	−0.01[−0.07,0.06]	0.08[0.01,0.15]	−0.06[−0.21,0.09]
Providers showed respect for what you had to say
Less than high school	ref.	ref.	ref.	ref.
High school or equivalent	0.03[−0.01,0.06]	0.01[−0.02,0.04]	0.02[−0.03,0.07]	0.04[−0.07,0.14]
Bachelor's degree	0.05[0.01,0.08]	0.00[−0.04,0.04]	0.05[−0.01,0.11]	0.04[−0.06,0.15]
Graduate degree or higher	0.05[0.01,0.09]	0.04[−0.01,0.09]	0.06[0.01,0.12]	0.03[−0.07,0.14]
Other/unknown	0.03[−0.01,0.07]	−0.01[−0.07,0.04]	0.03[−0.03,0.09]	0.01[−0.12,0.13]
Health providers spent enough time with you
Less than high school	ref.	ref.	ref.	ref.
High school or equivalent	0.00[−0.04,0.03]	−0.01[−0.05,0.02]	0.01[−0.05,0.06]	0.04[−0.08,0.17]
Bachelor's degree	0.00[−0.04,0.03]	−0.06[−0.11,0.00]	0.02[−0.05,0.08]	0.02[−0.11,0.14]
Graduate degree or higher	0.01[−0.03,0.04]	−0.02[−0.08,0.05]	0.04[−0.03,0.10]	0.01[−0.11,0.13]
Other/unknown	0.02[−0.02,0.05]	−0.10[−0.17,-0.03]	0.02[−0.05,0.09]	0.01[−0.14,0.16]
Doctors or other health providers gave instructions about what to do for a specific illness or condition
Less than high school	ref.	ref.	ref.	ref.
High school or equivalent	−0.11[−0.17,-0.05]	−0.08[−0.16,-0.01]	−0.09[−0.19,0.01]	−0.23[−0.46.-0.01]
Bachelor's degree	−0.11[−0.18,-0.05]	−0.07[−0.16,0.02]	0.03[−0.09,0.15]	−0.23[−0.44,-0.01]
Graduate degree or higher	−0.12[−0.19,−0.05]	-0.05[−0.17,0.07]	−0.13[−0.27,-0.004]	−0.21[−0.43,0.005]
Other/unknown	−0.13[−0.20,-0.06]	−0.15[−0.25,-0.05]	−0.03[−0.15,0.10]	−0.24[−0.50,0.03]
The advice given by doctors or other health providers was easy to understand
Less than high school	ref.	ref.	ref.	ref.
High school or equivalent	0.04[0.00,0.08]	0.07[0.02,0.12]	0.11[0.03,0.18]	−0.05[−0.15,0.06]
Bachelor's degree	0.06[0.02,0.10]	0.07[0.02,0.13]	0.14[0.07,0.22]	0.00[−0.09,0.09]
Graduate degree or higher	0.07[0.03,0.11]	0.13[0.08,0.18]	0.10[0.02,0.19]	0.03[−0.05,0.12]
Other/unknown	0.06[0.02,0.10]	0.05[−0.01,0.12]	0.13[0.05,0.21]	0.05[−0.03,0.14]
Doctors or other health providers asked you to describe how you are going to follow their instructions
Less than high school	ref.	ref.	ref.	ref.
High school or equivalent	−0.03[−0.09,0.03]	−0.08[−0.15,-0.01]	0.00[−0.10,0.11]	0.00[−0.20,0.21]
Bachelor's degree	−0.14[−0.20,-0.08]	−0.22[−0.30,-0.13]	−0.15[−0.27,-0.02]	0.00[−0.20,0.20]
Graduate degree or higher	−0.15[−0.22,-0.09]	−0.24[−0.35,-0.14]	−0.22[−0.35,−0.09]	-0.09[−0.29,0.11]
Other/unknown	−0.05[−0.12,0.02]	−0.17[−0.27,-0.07]	−0.09[−0.22,0.04]	0.15[−0.08,0.39]
Offered help in filling out forms at the office
Less than high school	ref.	ref.	ref.	ref.
High school or equivalent	−0.08[−0.15,-0.01]	−0.13[−0.22,-0.05]	−0.17[−0.28,-0.07]	0.29[0.14,0.43]
Bachelor's degree	−0.11[−0.18,-0.05]	−0.25[−0.34,-0.16]	−0.27[−0.38,-0.15]	0.23[0.10,0.36]
Graduate degree or higher	−0.13[−0.20,-0.06]	−0.31[−0.41,−0.21]	-0.21[−0.33,-0.09]	0.23[0.11,0.36]
Other/unknown	−0.11[−0.18,-0.04]	−0.25[−0.36,-0.13]	−0.20[−0.33,-0.08]	0.23[0.00,0.46]

Abbreviations: Ref., reference group; CAHPS, Consumer Assessment of Healthcare Providers and Systems.

Notes: Differences in predicted probabilities of agreement were derived from separate models for each CAHPS measure, using noted reference groups for all measures. All estimates are adjusted odds ratios with 95 % confidence intervals. Estimates adjusted for age, sex, race/ethnicity, income, education, employment, marital status, region, and insurance coverage. Bold indicates statistical significance at p < 0.05. Data from authors' analysis of pooled 2021 and 2023 Medical Expenditure Panel Survey (MEPS).

Among Hispanic adults, higher levels of income and education had positive associations with five of eight patient-provider interaction measures. Higher levels of education, though not income, were also positively associated with all three measures of access to care. The greatest effects were observed related to educational attainment; effects were stronger for Hispanic respondents compared to their White counterparts. For example, compared to the difference in predicted probability for Whites (noted above), Hispanic respondents with a graduate degree had a 0.26 percentage-point increase in the predicted probability of reporting receiving care right away (95 % CI: 0.16, 0.37) compared to those with less than a high school degree.

Higher levels of income and education were both positively associated with two of three access to care measures and seven of eight measures related to patient-provider interactions for Black adults. Differences in responses between the highest and lowest levels of income and education were similar in magnitude to differences observed in White respondents. The greatest effect observed for Black respondents was on being asked how they will follow the health providers' instructions with those with bachelors (difference in predicted probabilities = −0.15, 95 % CI: −0.27, −0.02) and graduate degrees (difference in predicted probabilities = −0.22, 95 % CI: −0.35, −0.09) less likely to be asked compared to those with less than a high school degree.

For Asian respondents, there were few effects of income and education on access to or quality of care. Unlike all other groups, Asian adults were more likely to report being offered help filling out forms with increasing levels of education and income. High school and bachelors degrees (vs. less than heigh school) were associated with decreased probability of being given specific instructions by providers. Annual income above $100,000 was associated with a decreased probability of being asked how respondents will follow the providers' instructions (difference in predicted probabilities = −0.15, 95 % CI: −0.30, −0.004) compared to the lowest income group.

3.3. Sensitivity analyses

In sensitivity analyses with ordered logistic regression models, overall ratings of healthcare and access to care were generally consistent with main models (Appendix D). On measures related to patient-provider interactions, Hispanic and Black adults reported similar or better experiences compared to White adults. Reponses from Asian and multiple race/ethnicity respondents were largely consistent with main findings.

4. Discussion

In a national sample of U.S. adults, individuals from minoritized racial and ethnic groups were generally less likely than White adults to report ease of access to care or understanding provider communications. On other aspects of patient-provider interactions, including feeling providers respected what they had to say or spent enough time with them, minoritized groups reported similar experiences to their White counterparts. These findings highlight the need for continued efforts to mitigate structural barriers to care for minoritized patient populations in addition to interventions targeted at interpersonal racism or bias.

Although only Asian respondents reported lower overall ratings of healthcare services compared to White respondents, Hispanic, Black, and Asian individuals were more likely than White individuals to report specific barriers to access, including difficulties securing timely appointments, seeing specialists, and receiving clear explanations from providers. However, in main analyses, no significant differences were found in whether providers listened carefully, showed respect, or spent enough time with patients. This may highlight the persistent effects of structural barriers to care for minoritized populations, such as living in a medically underserved area (Fishman et al., 2018; Caraballo et al., 2022). Alternatively, it is possible only individuals with the greatest resources were able to navigate these access barriers, and these individuals may also be better positioned to engage with providers and experience more positive patient-provider interactions.

While higher income and education levels were generally associated with better healthcare experiences, exceptions existed where these advantages did not extend to minority racial and ethnic groups. A system that prioritizes wealthier or more educated individuals is inherently flawed, and provider behaviors—such as listening more attentively or showing greater respect—should not be contingent on these factors. For example, pregnant people of minoritized race or ethnicity often report negative healthcare experiences across age, education, and income including discrimination and disrespect, and communication barriers that reduce trust, decision-making power, and satisfaction while increasing missed appointments (Goh et al., 2024; Dahlem et al., 2015). These factors then play a role in disparate health outcomes with Black women with a college degree facing higher pregnancy-related mortality than less-educated White women, and high-income Black women having the same postpartum mortality risk as the poorest White women (Hill et al., 2024). These findings underscore how income and education partially contribute to racial health disparities, with benefits disproportionately favoring White patients, exposing systemic inequities in healthcare.

Measures with no observed differences across racial/ethnic groups, such as providers spending enough time with patients, could reflect broader trends in healthcare that are generally associated with negative patient experiences, such as shorter physician visits, which are influenced by low provider availability (Zhang et al., 2020). On other measures, including being asked how they would follow instructions and being offered assistance filling out forms, Hispanic, Black, and Asian respondents were more likely than White adults to report these experiences. Particularly for Hispanic and Asian adults, this may be reflective of appropriate service for people who potentially face language barriers to care.

Taken together, these findings highlight structural biases within the healthcare system and emphasize the need to address minoritized patients' ability to consistently access culturally appropriate care. Some interventions, such as clinical decision support tools, may help reduce any interpersonal biases contributing to the observed disparities (Byrne and Tanesini, 2015). Language concordance tools, for example, have been shown to improve patient experiences by increasing comfort, satisfaction, and primary care utilization while reducing unnecessary hospital and emergency visits, highlighting their potential to enhance care quality and reduce disparities for patients with limited English proficiency (Chandrashekar et al., 2022; Lopez Vera et al., 2023). Other interventions, including expanding access to care through insurance coverage (e.g., Medicaid expansion), community health programs, and mobile clinics, target structural barriers for minority groups that contribute to health disparities including difficult accessing care as observed in our findings (Caraballo et al., 2022). Addressing socioeconomic obstacles with financial assistance programs and patient navigation services can further mitigate disparities linked to income and education. Additionally, improving data collection on healthcare disparities will enable more informed policy decisions and accountability. Finally, increasing diversity among healthcare providers can foster more equitable, culturally responsive care, ensuring all patients receive high-quality treatment.

This study is subject to several limitations. First, this was an analysis of cross-sectional data, and causal interpretations are not possible. Second, the MEPS sample is limited to a non-institutionalized, civilian population, so it is not representative of individuals living in non-community settings (e.g., incarcerated, homeless) who may disproportionately comprise racial/ethnic minorities and who may have worse experiences with healthcare. Further, CAHPS measures ask people to rate experiences across the entire last year without differentiating between types of providers or healthcare services. Thus, information about experiences with specific providers or service types was not available and responses may be subject to recall bias. Sample sizes of race/ethnicity groups vary across CAHPS measures due to different response rates, and some of the observed variation in outcome measures could potentially be attributed to differences in response tendencies across racial and ethnic groups (Martino et al., 2023; Mayer et al., 2016). In primary analyses, we collapsed responses into binary outcomes which may mask differences in gradations of patient experiences. We include sensitivity analyses using the full range of responses to mitigate this concern.

5. Conclusion

In a national sample, Black, Hispanic, and Asian adults reported significant gaps in accessing timely care and clear communication with providers compared to their White counterparts. While there were some protective effects of higher socioeconomic status, this was most consistent for White adults, indicating that socioeconomic advantage does not necessarily lead to better health care experiences across racial/ethnic groups. These findings highlight the need to address structural inequities as a key driver of disparities in healthcare and to improve experiences across populations.

CRediT authorship contribution statement

Sofia Bonsignore: Writing – original draft, Formal analysis. Hillary Samples: Writing – review & editing, Supervision. Elizabeth M. Stone: Writing – review & editing, Supervision, Methodology, Conceptualization.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Samples has received consulting fees from The Pew Charitable Trusts outside this work. The other authors have no conflicts of interest to declare.

Appendix A. Supplementary data

Supplementary material

Data availability

Data used for this reseach is publically available from the Agency for Healthcare Research and Quality from https://meps.ahrq.gov/mepsweb/