The KFF COVID-19 Vaccine Monitor has been tracking intentions to get a COVID-19 vaccine since December 2020, when the initial vaccine first became available. Throughout the past three years, partisanship has continued to play an outsized role in predicting both intentions to get a COVID-19 vaccine as well as other pandemic-related attitudes and behaviors. With the latest COVID-19 vaccine rollout, Republicans are once again among the groups least likely to report having gotten the updated shot. This data note examines how vaccine attitudes and uptake differ between Republicans who sit on different sides of a particular ideological divide within Republican Party – support of the Make America Great Again (MAGA) movement.

The MAGA movement has attracted many Republicans and Republican-leaning independents, with six in ten (58%) saying they support the MAGA movement, representing about one quarter (23%) of all U.S. adults, according to the latest KFF Tracking Poll.

Generally, MAGA supporting Republicans tend to be older and have lower levels of education than Republicans who do not support the MAGA movement, with a larger share of MAGA Republicans being ages 50 and older (58% vs. 41%) and having less than a college degree (81% vs. 53%). MAGA supporting Republicans and Republicans who do not support the MAGA movement look similar across gender, race, and ethnicity.

MAGA Supporters Are Among Groups Least Likely To Get Updated COVID-19 Vaccine

Majorities of adults across partisan groups have reported receiving at least one dose of the COVID-19 vaccine that has been on the market since December 2020, though larger shares of Republicans compared to Democrats and independents remained resistant, with at least a quarter saying they would “definitely not” get a COVID-19 vaccine throughout the three years of KFF COVID-19 Vaccine Monitor surveys.

The November COVID-19 Vaccine Monitor finds that among Republicans and Republican-leaning independents, similar majorities of both those who support the MAGA movement (60%) and those who do not support the MAGA movement (70%) say they have received at least one dose of a COVID-19 vaccine (10 percentage points is within the margin of sampling error).

However, Republicans under 50 years old who support the MAGA movement are particularly resistant to getting a COVID-19 vaccine, with about four in ten saying they have received at least one dose, 20 percentage points lower than their non-MAGA supporting counterparts (39% vs. 59%). Given the increased vulnerability of adults ages 50 and older to the virus, and consistent with our findings that across party lines, older people have been more likely to get the COVID-19 vaccine, large majorities of older MAGA and non-MAGA supporting Republicans ages 50 and older report having gotten a COVID-19 dose.

The newest COVID-19 vaccine recently became available in September of this year, with somewhat muted uptake compared to initial vaccine uptake. As of early November, two in ten adults say they have gotten the updated vaccine including one in three Democrats (32%), 16% of independents, and 12% of Republicans. Among Republicans, alignment with the MAGA movement is a strong predictor of vaccine intentions with supporters of the MAGA movement the fiercest in their opposition to the latest shot.

Among Republicans and Republican-leaning independents, about one in four (26%) of those who do not support MAGA say they have gotten or “probably” or “definitely” will get the latest updated COVID-19 vaccine, compared to about one in six (17%) of those who support MAGA saying they have gotten or plan to get the vaccine.

Most Republicans, regardless of MAGA support, say they will not get the latest updated COVID-19 vaccine with nearly two-thirds (63%) of MAGA Republicans saying they will “definitely not” get the newest vaccine, a slightly larger share than the half of their non-MAGA counterparts (52%) who say the same. The difference between Republican MAGA supporters and non-supporters in the share who have gotten the updated COVID-19 vaccine or say they will persists even after controlling for other demographics of age, gender, community type (such as urban, rural, or suburban communities), education, and household income.

In addition to being among the least likely to be vaccinated against COVID-19 at all, younger MAGA Republicans are among the most adamant that they will “definitely not” get the updated vaccine. Seven in ten (70%) MAGA supporting Republicans under age 50 say they will “definitely not” get the updated shot, compared to 54% of Republicans and leaners in this age group who do not support the MAGA movement (and 34% of the public overall).

MAGA Republicans Are Also Less Likely To Get The Flu Shot Or View Other Vaccines As Safe

As of September, almost six in ten (57%) non-MAGA identifying Republicans said they had already gotten or definitely will get the flu shot this season, compared to 43% of MAGA supporting Republicans. Interestingly, Republicans who do not identify with the MAGA movement are not significantly more likely than MAGA Republicans to say they normally get an annual flu shot. This could suggest that the MAGA impact on vaccine uptake could be a relatively new phenomenon that public health officials may be facing in the years to come.

The differences between Republican MAGA supporters and non-supporters are not only evident in their uptake of vaccines, but also in their assessment of the safety of different types of vaccines. Republicans and Republican-leaning independents who support the MAGA movement are less likely than their non-MAGA counterparts to express confidence in the safety of COVID-19 vaccines (29% vs. 44%), respiratory syncytial virus, or RSV, vaccines (41% vs. 61%), and flu vaccines (53% vs. 74%).

This KFF Health Tracking Poll/COVID-19 Vaccine Monitor was designed and analyzed by public opinion researchers at KFF. The survey was conducted October 31- November 7, 2023, online and by telephone among a nationally representative sample of 1,301 U.S. adults in English (1,222) and in Spanish (79). The sample includes 1,016 adults (n=52 in Spanish) reached through the SSRS Opinion Panel either online (n=991) or over the phone (n=25). The SSRS Opinion Panel is a nationally representative probability-based panel where panel members are recruited randomly in one of two ways: (a) Through invitations mailed to respondents randomly sampled from an Address-Based Sample (ABS) provided by Marketing Systems Groups (MSG) through the U.S. Postal Service’s Computerized Delivery Sequence (CDS); (b) from a dual-frame random digit dial (RDD) sample provided by MSG. For the online panel component, invitations were sent to panel members by email followed by up to three reminder emails.

Another 285 (n=27 in Spanish) interviews were conducted from a random digit dial telephone sample of prepaid cell phone numbers obtained through MSG. Phone numbers used for the prepaid cell phone component were randomly generated from a cell phone sampling frame with disproportionate stratification aimed at reaching Hispanic and non-Hispanic Black respondents. Stratification was based on incidence of the race/ethnicity groups within each frame.

Respondents in the phone samples received a $15 incentive via a check received by mail, and web respondents received a $5 electronic gift card incentive (some harder-to-reach groups received a $10 electronic gift card). In order to ensure data quality, cases were removed if they failed attention check questions in the online version of the questionnaire, or if they had over 30% item non-response, or had a length less than one quarter of the mean length by mode.  Based on this criterion, one case was removed.

The combined cell phone and panel samples were weighted to match the sample’s demographics to the national U.S. adult population based on parameters derived from the Census Bureau’s 2022 Current Population Survey (CPS), 2021 Volunteering and Civic Life Supplement data from the CPS, and the 2023 KFF Benchmarking survey with ABS and prepaid cell phone samples. The demographic variables included in weighting for the general population sample are sex, age, education, race/ethnicity, region, education, civic engagement, internet use, and political party identification by race/ethnicity.  The sample of registered voters was weighted separately to match the U.S. registered voter population using the parameters above plus recalled vote in the 2020 presidential election by county quintiles grouped by Trump vote share. Both weights take into account differences in the probability of selection for each sample type (prepaid cell phone and panel). This includes adjustment for the sample design and geographic stratification of the cell phone sample, within household probability of selection, and the design of the panel-recruitment procedure.

The margin of sampling error including the design effect for the full sample and registered voters is plus or minus 4 percentage points. Numbers of respondents and margins of sampling error for key subgroups are shown in the table below. For results based on other subgroups, the margin of sampling error may be higher. Sample sizes and margins of sampling error for other subgroups are available by request. Sampling error is only one of many potential sources of error and there may be other unmeasured error in this or any other public opinion poll. KFF public opinion and survey research is a charter member of the Transparency Initiative of the American Association for Public Opinion Research.

During the past decade, Medicare Advantage enrollment has increased steadily, with particularly rapid growth among people of color. Today, just over half of all eligible Medicare beneficiaries are enrolled in Medicare Advantage plans, with higher enrollment rates among Black, Hispanic, and Asian and Pacific Islander beneficiaries than among White beneficiaries. As of 2021, 59% of Black Medicare beneficiaries, 67% of Hispanic beneficiaries, and 55% of Asian and Pacific Islander beneficiaries were enrolled in a Medicare Advantage plan as compared with 43% of White beneficiaries.

Despite the relatively high Medicare Advantage enrollment rates among people of color relative to White beneficiaries, little is known about whether there are racial and ethnic disparities in quality of care and health care experiences among Medicare Advantage enrollees.

A previous KFF review of 62 studies compared Medicare Advantage and traditional Medicare on measures of beneficiary experience and quality of care. The prior review identified relatively few studies that examined differences among beneficiaries by race and ethnicity between Medicare Advantage and traditional Medicare, making it difficult to compare the experiences of people of color across the two sources of Medicare coverage.

This review examines differences in measures of quality of care and beneficiary experience between people of color in Medicare Advantage plans and White Medicare Advantage enrollees or the total Medicare Advantage population. The analysis synthesizes findings from 20 identified studies that were published during the 5-year period between January 2018 and April 2023. These 20 studies collectively report on 46 different measures of quality of care and beneficiary experience, but not all studies examined all groups or included all measures. All differences described in this report are statistically significant unless noted otherwise (e.g., for results that are reported as similar). Most of the studies (17 of 20) controlled for differences in enrollee health status and other demographic characteristics in some fashion. (See Methods for additional information about the criteria used to select studies, Appendix Table 1 for a complete list of measures included in these studies, and Appendix Table 2 for more a detailed description of each study.)

While the scope of this review is limited to Medicare Advantage enrollees, the racial and ethnic disparities in quality of care and beneficiary described in this report mirror disparities in health and health care in traditional Medicare, the overall Medicare population, and more broadly, the U.S adult population.

Key Takeaways

Black enrollees: Results are less favorable for Black Medicare Advantage enrollees than White Medicare Advantage enrollees on more than half (24) of the 46 measures examined for this group in 19 studies. Results were more favorable on eight measures, similar on five measures, inconsistent across studies on two measures, and for seven measures, study authors described differences as not practically significant. For example:

• Preventive service use: a higher share of Black Medicare Advantage enrollees than White enrollees received breast cancer screenings, colorectal cancer screenings, and pap smears, but a lower share of Black enrollees received prostate cancer screenings and flu vaccines.
• Hospitalizations: a higher share of Black than White Medicare Advantage enrollees were admitted to the hospital for an ambulatory care sensitive condition – a measure of potentially preventable hospitalizations – and a higher share of Black than White enrollees were readmitted to the hospital within 30 days.
• Mental health: a lower share of Black than White enrollees with depression were treated with antidepressant medication and remained on the medication for at least 12 weeks.
• Experiences with care: a lower share of Black than White Medicare Advantage enrollees reported seeing a specialist in the past year, but similar shares of Black and White enrollees reported having well-coordinated care and getting needed prescription drugs.
• Plan ratings: a lower share of Black than White enrollees were enrolled in higher-rated Medicare Advantage plans.

Hispanic enrollees: Findings are less favorable for Hispanic than White Medicare Advantage enrollees on more than a third (16) of the 42 measures examined for this group in 17 studies. Findings were more favorable on eight measures, similar on five measures, inconsistent across studies on three measures, and for 10 measures, study authors described differences as not practically significant. For example:

• Preventive service use: a higher share of Hispanic than White Medicare Advantage enrollees reported getting screenings for breast cancer, but a lower share received flu vaccines.
• Disease management: a lower share of Hispanic than White Medicare Advantage enrollees received follow-up care after emergency department visits for certain conditions, such as for mental health and a set of multiple high-risk chronic conditions.
• Experiences with care: a lower share of Hispanic than White Medicare Advantage enrollees reported getting appointments and care quickly.
• Hospitalizations: Hispanic and White enrollees had similar rates of hospital readmissions and hospitalizations for ambulatory care sensitive conditions.
• Plan ratings: A lower share of Hispanic than White enrollees were enrolled in higher-rated Medicare Advantage plans.

Asian and Pacific Islander enrollees: Findings are less favorable for Asian and Pacific Islander enrollees than White enrollees on nine of the 36 measures in 13 studies. Findings were more favorable on seven measures, similar on seven measures, inconsistent across studies on three measures, and for 10 measures, study authors described differences as not practically significant. For example:

• Preventive services: a higher share of Asian and Pacific Islander than White enrollees received a flu vaccine, while similar shares received colorectal cancer screenings.
• Disease management: a higher share of Asian and Pacific Islander enrollees than White enrollees received statin therapy as part of their diabetes care, but a lower share of Asian and Pacific Islander enrollees with a new episode of alcohol or other drug dependence initiated treatment for alcohol or other drug dependence.
• Hospitalizations: Asian and Pacific Islander and White enrollees had similar rates of hospitalizations for ambulatory care sensitive conditions.

American Indian and Alaska Native enrollees: Less than half of the studies identified in this review (9 of 20 studies) presented findings for American Indian and Alaska Native Medicare Advantage enrollees, and collectively, they included fewer measures (25) than studies of Black (46), Hispanic (42) or Asian and Pacific Islander (36) Medicare Advantage enrollees. Findings were less favorable for American Indian and Alaska Native enrollees that White Medicare Advantage enrollees on seven measures, more favorable on four measures, similar on 12 measures, inconsistent across studies on one measure, and for one measure, study authors described differences as not practically significant. For example:

• Preventive services: a higher share of American Indian and Alaska Native enrollees than White enrollees received breast cancer screenings, and similar shares received a flu vaccine.
• Disease management: a lower share of American Indian and Alaska Native enrollees than White enrollees had their blood sugar and blood pressure controlled as part of diabetes care.

Gaps in the research and data present challenges in understanding the experiences of specific racial and ethnic groups in Medicare Advantage plans.

• Medicare Advantage insurers do not report data on prior authorization rates and denials by race or ethnicity, or the use of supplemental benefits for the overall Medicare Advantage population or by race or ethnicity.
• None of the studies examine outcomes of care such as mortality rates or hospital-acquired infections.
• None examine the use of post-acute care among Medicare Advantage enrollees by race and ethnicity.
• None of the studies report findings for Native Hawaiians or other Pacific Islanders separately from other groups, and none of the studies compare measures of quality of care and beneficiary experience between people identifying as two or more racial or ethnic groups with White enrollees.
• None of the studies present stratified estimates for all of the racial and ethnic groups listed in current federal minimum standards. Studies also varied in how they identified race and ethnicity, with some using self-identified data and others using imputed race/ethnicity data.
• Few studies stratify race/ethnicity findings among Medicare Advantage enrollees by gender or rural residence.
• None of the studies stratify findings among Medicare Advantage enrollees by race/ethnicity and dual eligibility status, even though people of color comprise a disproportionate share of Medicare Advantage enrollees who are dual-eligible individuals.

With more than half of Black, Hispanic, and Asian and Pacific Islander beneficiaries enrolled in Medicare Advantage plans, the studies in this review provide some insight into how well Medicare Advantage plans are serving people of color relative to White enrollees. However, the relatively small number of studies coupled with gaps in research present challenges for beneficiaries in making coverage decisions and for policymakers in understanding how best to make Medicare Advantage work well generally and for people of color.

Racial and Ethnic Health Equity in Medicare Advantage: Literature Review

Medicare Advantage is now the dominant form of Medicare coverage for Black, Hispanic, and Asian or Pacific Islander beneficiaries, while the share of American Indian or Alaska Native enrollees in these plans has more than doubled within the past decade (Figure 2). Medicare Advantage plans have several features that may attract enrollees, including people of color. These private plans are often available at little or no extra premium (other than the Part B premium), generally include an out-of-pocket limit (unlike traditional Medicare), and typically offer extra benefits such as dental, vision, and hearing services. Given relatively high Medicare Advantage enrollment rates among people of color, questions have emerged from policy makers, consumers, and others in the general community about whether there are racial and ethnic disparities in quality of care and beneficiary experience among Medicare Advantage enrollees.

To inform these questions, this brief summarizes findings from 20 studies published between January 1, 2018 and April 1, 2023 that compare quality of care and beneficiary experiences between people of color in Medicare Advantage and White enrollees, or the total Medicare Advantage population. These studies evaluated 46 different measures of quality of care and enrollee experience in Medicare Advantage. This report also discusses gaps in data and in the literature that contribute to challenges in understanding racial and ethnic disparities in quality of care and beneficiary experience in Medicare Advantage.

How do measures of quality of care and beneficiary experience compare between Black and White Medicare Advantage enrollees?

Nineteen studies examined 46 measures of quality of care and beneficiary experience among non-Hispanic Black enrollees (hereafter referred to as “Black enrollees”), including 17 studies that compared estimates for Black enrollees to White enrollees, and two studies that compared estimates for Black enrollees to the overall Medicare Advantage population, which is comprised of mostly White enrollees. These 19 studies examined hospital readmission rates (1 study), potentially avoidable hospitalizations for ambulatory care sensitive conditions (ACSCs) (2 studies), specific measures of disease management (8 studies), quality of plans (3 studies), measures related to experiences with care (8 studies), and utilization of preventive services (7 studies) (Appendix Table 1, Appendix Table 2). Most studies included multiple measures, and some measures were examined among enrollees with different conditions (e.g., receipt of statin therapy examined separately for enrollees with diabetes and cardiovascular conditions).

Among the 19 studies included in this review that compared measures for Black and White Medicare Advantage enrollees, results were less favorable for Black Medicare Advantage enrollees on more than half (24) of the measures examined for this group, more favorable on eight measures, similar on five measures, inconsistent across studies on two measures, and for seven measures, study authors described differences as not practically significant. A description of these results follows.

Hospital Readmissions and Potentially Avoidable Hospitalization

• Black Medicare Advantage enrollees had higher rates of hospitalization for ambulatory care sensitive conditions than White enrollees, based on two studies that controlled for differences in enrollees’ health status.¹ , ²  One of these studies also examined hospitalizations for ambulatory care sensitive conditions in different health care markets and found that differences persisted across geographic areas.³ 
• An additional study found that among enrollees in skilled nursing facilities, Black enrollees had higher rates of 30-day hospital readmissions than White enrollees after adjusting for differences in enrollee health status and other demographic characteristics.⁴  The differences were lower, but persisted when comparing Black and White enrollees who had stays at the same skilled nursing facility.

Disease management

• Mental health and substance use care: Compared to both White enrollees and the overall Medicare Advantage population, a lower share of Black enrollees with depression received antidepressant medication management (i.e., treated with antidepressant medication and remained on the medication for at least 12 weeks),^{5 , 6 , 7}  and among those who were hospitalized or who had an emergency department visit for treatment of selected mental health conditions, a lower share of Black enrollees received follow-up care within 30 days of the hospital stay^{8 , 9 , 10}  or emergency department visit.^{11 , 12}  However, among enrollees with a new episode of alcohol or other drug dependence, a higher share of Black enrollees than White enrollees and the overall Medicare Advantage population initiated treatment for alcohol or other drug dependence within 14 days of diagnosis.^{13 , 14} 
• Cardiovascular health: Compared to White enrollees and the overall Medicare Advantage population, a lower share of Black enrollees who were hospitalized and discharged with a diagnosis of acute myocardial infarction (i.e., a heart attack) received beta-blocker treatments;^{15 , 16}  a lower share of Black enrollees with cardiovascular disease adhered to statin therapy;^{17 , 18}  and a lower share of Black enrollees with hypertension had their blood pressure controlled.^{19 , 20 , 21}  Additionally, among enrollees with a history of a cardiac event, a lower share of Black enrollees than White enrollees had their cholesterol adequately controlled.²² 
• Diabetes care: Among enrollees with diabetes, a lower share of Black enrollees than White enrollees and the overall Medicare Advantage population had their blood sugar and blood pressure under control and adhered to statin therapy.^{23 , 24}  A lower share of Black enrollees than White enrollees used diabetes technology (e.g., insulin pump, continuous glucose monitoring).²⁵  Conversely, a higher share of Black enrollees with diabetes than White enrollees and the overall Medicare Advantage population received eye exams.^{26 , 27} 
• Multiple high-risk chronic conditions: Among enrollees with multiple high-risk chronic conditions, a lower share of Black enrollees than White enrollees and the overall Medicare Advantage population received follow-up care within seven days of an emergency department visit.^{28 , 29} 
• Other conditions: Among enrollees with chronic kidney disease, a lower share of Black enrollees than White enrollees were not dispensed a prescription for a potentially harmful medication,³⁰  and a higher share of Black enrollees than White enrollees had their kidney function decline at a faster rate over time.³¹  Conversely, among enrollees with dementia, a higher share of Black enrollees than White enrollees and the overall Medicare Advantage population were not dispensed a prescription for a potentially harmful medication for dementia.^{32 , 33}  A similar share of Black and White women ages 67 to 85 years who suffered a fracture had their osteoporosis adequately managed,³⁴  and a similar share of Black and White enrollees were dispensed a prescription for their rheumatoid arthritis.³⁵ 

Experiences with care

• A lower share of Black enrollees than White enrollees reported having a specialist visit in the past year, having a usual source of care, and having a primary care clinician as the source of regular care.³⁶ 
• A similar share of Black and White enrollees reported well-coordinated care and getting needed prescription drugs.³⁷  Findings related to getting needed care were mixed: one study found that a similar share of Black and White enrollees reported getting needed care,³⁸  while a second study, which did not control for differences in enrollee characteristics, found that a lower share of Black enrollees than White enrollees reported getting needed care.³⁹ 
• A similar share of Black enrollees and the overall Medicare Advantage population reported getting appointments and care quickly and that it was easy to get needed prescription drugs.⁴⁰ 
• A similar share of Black and White enrollees were enrolled in plans with narrow networks (i.e., less than 25% of available providers included in network) of primary care, psychiatry, and mental and behavioral health providers, based on a study that calculated the share of enrollees with various characteristics who were enrolled in plans with networks of different breadths.⁴¹  In a second study, a lower share of Black enrollees with end-stage renal disease were enrolled in plans with narrow networks of dialysis facilities than White enrollees.⁴² 

Utilization of preventive services

• Vaccines: A lower share of Black enrollees than White enrollees and the overall Medicare Advantage population received flu vaccines^{43 , 44 , 45 , 46}  and pneumonia vaccines,⁴⁷  but a higher share of Black enrollees than White enrollees completed the full regimen of COVID-19 vaccines.⁴⁸ 
• Preventive cancer screenings: A lower share of Black enrollees than White enrollees and the overall Medicare Advantage population received prostate cancer screenings⁴⁹  but a higher share of Black enrollees received breast cancer screenings.^{50 , 51 , 52}  A higher share of Black enrollees than White enrollees received colorectal cancer screenings⁵³  and pap smears.⁵⁴  However, a similar share of Black enrollees and the overall Medicare population received colorectal cancer screenings.⁵⁵ 
• Other preventive screenings: A lower share of Black enrollees than White enrollees had their cholesterol levels tested.⁵⁶  A similar share of Black and White enrollees without diabetes had their blood sugar tested to detect diabetes.⁵⁷ 

Quality Ratings of plans

• A lower share of Black enrollees than White enrollees were enrolled in higher-rated plans (i.e., 4-, 4.5- or 5-star plans), a result attributed to limited offerings of higher-rated Medicare plans in counties where Black enrollees reside.⁵⁸ 
• Black enrollees were more likely than White beneficiaries to be enrolled in lower-rated plans based on quality ratings calculated from the subset of measures included in the Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey responses.⁵⁹ 
• A lower share of Black enrollees than White enrollees were in a vertically integrated plan (defined by the study authors as plans that were associated with a hospital health system that provides care), which the study found had higher overall star ratings.⁶⁰ 

How do measures of quality of care and beneficiary experience compare between Hispanic and White Medicare Advantage enrollees?

Seventeen studies examined 42 measures of quality of care and beneficiary experience among Hispanic enrollees, including 15 studies that compared estimates for Hispanic enrollees to White enrollees, and two studies that compared estimates for Hispanic enrollees to the overall Medicare Advantage population. These 17 studies examined hospital readmission rates (1 study), potentially avoidable hospitalizations for ACSCs (1 study), specific measures for disease management (8 studies), quality of plans (3 studies), and measures related to experiences with care (7 studies) (Appendix Table 1, Appendix Table 2). Most studies included multiple measures, and some measures were examined among enrollees with different conditions (e.g., receipt of statin therapy examined separately for enrollees with diabetes and cardiovascular conditions).

Among the 17 studies included in this review that compared measures for Hispanic and White Medicare Advantage enrollees, results were less favorable for Hispanic than White Medicare Advantage enrollees on more than a third (16) of measures examined for this group, more favorable on eight measures, similar on five measures, inconsistent across studies on three measures, and for 10 measures, study authors described differences as not practically significant. A description of these results follows.

Hospital Readmissions and Potentially Avoidable Hospitalization

• Hispanic and White enrollees had similar rates of hospitalization for ambulatory care sensitive conditions after controlling for differences in enrollees’ characteristics, including health status.⁶¹ 
• An additional study found that among enrollees in skilled nursing facilities, Hispanic and White enrollees had similar rates of 30-day hospital readmissions, after adjusting for differences in enrollee characteristics, such as health status.⁶² 

Disease management

• Mental health and substance use care: Compared to White Medicare Advantage enrollees and the overall Medicare Advantage population, a lower share of Hispanic enrollees received antidepressant medication management (among those with depression),^{63 , 64 , 65}  received follow-up care within 30 days of an emergency department visit for treatment of selected mental health conditions,^{66 , 67}  and initiated treatment within 14 days of a new episode of alcohol or other drug dependence.^{68 , 69 , 70}  Among two studies that examined the share of enrollees receiving a follow-up visit within 30 days of hospitalization for treatment of select mental health conditions, one study found that a similar share of Hispanic and White enrollees received this follow-up care,⁷¹  while the other study found that a higher share of Hispanic enrollees than White enrollees received this follow-up care.⁷² 
• Cardiovascular health: Compared to White enrollees and the overall Medicare Advantage population, a lower share of Hispanic enrollees who were hospitalized for a heart attack received beta-blocker treatments,^{73 , 74}  and a lower share of Hispanic enrollees with cardiovascular disease adhered to statin therapy.^{75 , 76}  A similar share of Hispanic and White enrollees with a history of a cardiac event had their cholesterol adequately controlled.⁷⁷  Findings on a measure related to blood pressure control were mixed: one study found that a higher share of Hispanic enrollees with hypertension had their blood pressure under control compared to White enrollees with hypertension,⁷⁸  while the second study found that the rates were similar between Hispanic and White enrollees.⁷⁹ 
• Diabetes care: Among Medicare Advantage enrollees with diabetes, a lower share of Hispanic enrollees than White enrollees and the overall Medicare Advantage population adhered to statin therapy⁸⁰  and used diabetes technology,⁸¹  but a higher share of Hispanic enrollees received eye exams^{82 , 83}  and statin therapy,^{84 , 85}  and had their blood pressure under control.⁸⁶  While a similar share of Hispanic and the overall Medicare Advantage population had their blood sugar controlled,⁸⁷  differences between Hispanic and White enrollees on this measure were mixed: one study found that a lower share of Hispanic enrollees than White enrollees had their blood sugar under control,⁸⁸  while the second study found that the rates were similar between Hispanic and White enrollees.⁸⁹ 
• Multiple high-risk chronic conditions: Among enrollees with multiple high-risk chronic conditions, a lower share of Hispanic enrollees than White enrollees and the overall Medicare Advantage population received follow-up care within seven days of an emergency department visit.^{90 , 91} 
• Other conditions: Among enrollees with chronic kidney disease, a lower share of Hispanic enrollees than White enrollees and the overall Medicare Advantage population were not dispensed a prescription for a potentially harmful medication,^{92 , 93}  and Hispanic enrollees had their kidney function decline at a faster rate over time than White enrollees.⁹⁴  Among enrollees with dementia, a lower share of Hispanic enrollees than White enrollees and the overall Medicare Advantage population were not dispensed a prescription for a potentially harmful medication.^{95 , 96 , 97}  Conversely, among women ages 67 to 85 years in Medicare Advantage plans who suffered a fracture, a higher share of Hispanic enrollees than White enrollees and the overall Medicare Advantage population had their osteoporosis adequately managed.^{98 , 99} 

Experiences with care

• A lower share of Hispanic enrollees than White enrollees and the overall Medicare Advantage population reported getting appointments and care quickly.^{100 , 101}  Additionally, a lower share of Hispanic than White enrollees reported getting needed care,^{102 , 103}  including in one study that did not control for differences in enrollee characteristics.¹⁰⁴ 
• A similar share of Hispanic enrollees and the overall Medicare Advantage population reported that it was easy to get needed prescription drugs.¹⁰⁵ 
• Higher shares of Hispanic enrollees than White enrollees were enrolled in plans with narrow networks (i.e., less than 25% of available providers included in network) of primary care providers,¹⁰⁶  psychiatrists,¹⁰⁷  and dialysis facilities.¹⁰⁸  The study that looked at narrow networks of primary care and psychiatry providers presented bivariate comparisons of enrollees with various characteristics who were enrolled in plans with networks of different breadths,¹⁰⁹  while the study that focused on narrow networks of dialysis facilities presented both bivariate and multivariate comparisons.¹¹⁰ 

Utilization of preventive services

• Vaccines: A lower share of Hispanic enrollees than White enrollees received flu vaccines,^{111 , 112}  including a study that did not present multivariate comparisons.¹¹³  A higher share of Hispanic enrollees than White enrollees received COVID-19 vaccinations.¹¹⁴ 
• Preventive cancer screenings: A higher share of Hispanic enrollees than White enrollees and the overall Medicare population received appropriate screenings for breast cancer^{115 , 116}  and pap smears.¹¹⁷  A similar share of Hispanic and White enrollees were screened for prostate cancer.¹¹⁸ 
• Other preventive screenings: A higher share of Hispanic enrollees than White enrollees had their cholesterol levels tested¹¹⁹  and among enrollees without diabetes, a higher share of Hispanic enrollees than White enrollees had their blood sugar tested to detect diabetes.¹²⁰ 

Quality of plans

• A lower share of Hispanic enrollees than White enrollees were enrolled in 5-star rated plans, but a somewhat higher share of Hispanic than White enrollees were enrolled in 4-, 4.5, or lower (2- to 3.5) star rated plans, after adjusting for availability of plans at the county level.¹²¹ 
• A separate study found that Hispanic enrollees were more likely than White enrollees to be enrolled in lower-rated plans based on quality ratings calculated from the subset of measures included in the Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey responses. ¹²² 
• A third study found similar shares of Hispanic and White beneficiaries were enrolled in vertically integrated Medicare Advantage plans (i.e., plans associated with a hospital health system that provides care), which the study found had higher overall star ratings.¹²³ 

How do measures of quality of care and beneficiary experience compare between Asian and Pacific Islander and White Medicare Advantage enrollees?

Thirteen studies examined 36 measures of quality of care and beneficiary experience among Asian and Pacific Islander enrollees, including 11 studies that compared estimates for Asian and Pacific Islander enrollees to White enrollees and two studies that compared estimates for Asian and Pacific Islander enrollees to the overall Medicare Advantage population. Of the 13 studies, three studies presented estimates for Asian enrollees separately, seven studies presented findings for Asian and Pacific Islander enrollees as a single category, and three studies grouped Asian, Pacific Islander, and Native Hawaiians into a single category (See Appendix Table 1 for results for each group).

These 13 studies examined potentially avoidable hospitalizations for ACSCs (1 study), specific measures of disease management (8 studies), quality of plans (2 studies), measures related to experiences with care (6 studies), and utilization of preventive services (4 studies) (Appendix Table 1, Appendix Table 2). Most studies included multiple measures, and some measures were examined among enrollees with different conditions (e.g., receipt of statin therapy examined separately for enrollees with diabetes and cardiovascular conditions).

Among the 13 studies that compared measures for Asian and Pacific Islander and White Medicare Advantage enrollees, results were less favorable for Asian and Pacific Islander enrollees than White enrollees on nine of the measures examined for this group, more favorable on seven measures, similar on seven measures, inconsistent across studies on three measures, and for 10 measures, study authors described differences as not practically significant. A description of these results follows.

Potentially Avoidable Hospitalization

• Asian and Pacific Islander and White enrollees had similar rates of hospitalization for ambulatory care sensitive conditions, after controlling for differences in enrollee health status.¹²⁴ 

Disease management

• Mental health care: Among Medicare Advantage enrollees with depression, a lower share of Asian and Pacific Islander enrollees than White enrollees and the overall Medicare Advantage population received antidepressant medication management,^{125 , 126 , 127}  and among enrollees with a new episode of alcohol or other drug dependence, a lower share of Asian and Pacific Islander enrollees than both groups initiated treatment for the condition.^{128 , 129} Conversely, a higher share of Asian and Pacific Islander enrollees than White enrollees and the overall Medicare Advantage population were not prescribed opioids at a high dosage for more than 14 days.^{130 , 131}  A similar share of Asian and Pacific Islander enrollees and White enrollees received follow-up care within 30 days of an emergency department visit for select mental health conditions.¹³²  Findings on receipt of follow-up care after hospital stays for mental illness were mixed: one study found that a higher share of Asian and Pacific Islander enrollees than White enrollees received this follow-up care,¹³³  while the second study found that a similar share of Asian and Pacific Islander and White enrollees received this follow-up care.¹³⁴ 

• Diabetes care: A lower share of Asian and Pacific Islander enrollees with diabetes than White enrollees used diabetes technology (either insulin pump or continuous glucose monitoring).¹³⁵  Conversely, among enrollees with diabetes, a higher share of Asian and Pacific Islander enrollees than White enrollees and the overall Medicare Advantage population received statin therapy and eye exams,^{136 , 137}  and a higher share of Asian and Pacific Islander than White enrollees with diabetes had their blood pressure under control.¹³⁸  Two studies that examined blood sugar control for Asian and Pacific Islander enrollees compared to White enrollees had mixed findings: one study found that a lower share of Asian and Pacific Islander enrollees than White enrollees had their blood sugar controlled,¹³⁹  while a second study found that a higher share of Asian and Pacific Islander enrollees than White enrollees had their blood sugar controlled.¹⁴⁰  An additional study found that a higher share of Asian and Pacific Islander enrollees than the share of Medicare Advantage enrollees overall had their blood sugar controlled.¹⁴¹  Among enrollees with diabetes, a similar share of Asian and Pacific Islander enrollees and the overall Medicare Advantage population had their blood pressure under control.¹⁴² 
• Cardiovascular health: Compared to White enrollees and the overall Medicare Advantage population, a similar share of Asian and Pacific Islander enrollees who were hospitalized for a heart attack received beta-blocker treatments,¹⁴³  and a similar share of Asian and Pacific Islander enrollees with a history of a cardiac event had their cholesterol adequately controlled.¹⁴⁴  In the two studies that examined the share of enrollees with hypertension who had their blood pressure under control, differences in the shares of Asian and Pacific Islander and White enrollees on this measure were mixed.^{145 , 146}  An additional study comparing the share of Asian and Pacific Islander enrollees with hypertension to the overall Medicare Advantage population found that a higher share of Asian and Pacific Islander enrollees had their blood pressure controlled than the overall Medicare Advantage population.¹⁴⁷ 
• Other conditions: Compared to White enrollees and the overall Medicare Advantage population, a higher share of Asian and Pacific Islander enrollees with dementia were not dispensed a prescription for a potentially harmful medication,^{148 , 149 , 150}  and a higher share of Asian and Pacific Islander women ages 67 to 85 years who suffered a fracture had their osteoporosis adequately managed.^{151 , 152}  Asian and Pacific Islander and White enrollees with chronic kidney failure experienced similar progression of the disease over time.¹⁵³ 
• Multiple high-risk chronic conditions: Among enrollees with multiple high-risk chronic conditions, a higher share of Asian and Pacific Islander enrollees than the overall Medicare Advantage population received follow-up care within seven days of an emergency department visit.¹⁵⁴ 

Experiences with care

• A lower share of Asian and Pacific Islander enrollees than White enrollees and the overall Medicare Advantage population reported getting needed care, getting appointments and care quickly, having their care well-coordinated, and getting needed prescription drugs.^{155 , 156} 
• A higher share of Asian and Pacific Islander enrollees than White enrollees were enrolled in plans with narrow networks (i.e., less than 25% of available providers included in network) of primary care providers,¹⁵⁷  psychiatrists,¹⁵⁸  mental and behavioral health providers,¹⁵⁹  and dialysis facilities.¹⁶⁰  One of these studies presented bivariate comparisons of enrollees with various characteristics who were enrolled in plans with networks of primary care, psychiatry, and mental health providers of different breadths,¹⁶¹  while the other study, focusing on networks of dialysis facilities, presented both bivariate and multivariate comparisons.¹⁶² 

Utilization of preventive services

• Vaccines: A higher share of Asian and Pacific Islander than White enrollees and the overall Medicare Advantage population received a flu vaccine.^{163 , 164} Preventive cancer screenings: The share of Asian and Pacific Islander enrollees receiving colorectal cancer screenings was similar to the share among White enrollees and the overall Medicare Advantage population.^{165 , 166} 

Quality of plans

• A lower share of Asian and Pacific Islander enrollees than White enrollees were enrolled in 4-, 4.5-, and 5-star rated plans and a higher share of Asian and Pacific Islander than White enrollees were enrolled in plans with ratings of 3.5 stars or below, after adjusting for availability of plans at the county level. ¹⁶⁷ 
• A similar share of Asian and Pacific Islander and White enrollees were enrolled in vertically integrated Medicare Advantage plans.¹⁶⁸ 

How do measures of quality of care and beneficiary experience compare between American Indian and Alaska Native Medicare Advantage enrollees and White enrollees?

Nine studies examined 25 measures of quality of care received by American Indian and Alaska Native enrollees, including seven studies that compared estimates for American Indian and Alaska Native enrollees to White enrollees, and two studies that compared estimates for American Indian and Alaska Native enrollees to the overall Medicare Advantage population. These nine studies examined measures for disease management (5 studies), quality of plans (1 study), experiences with care (7 studies), and utilization of preventive services (7 studies) (Appendix Table 1, Appendix Table 2). Most studies included multiple measures, and some measures were examined among enrollees with different conditions (e.g., receipt of statin therapy examined separately for enrollees with diabetes and cardiovascular conditions). These nine studies did not further disaggregate American Indian and Alaska Native enrollees living in tribal areas, likely due to sample size limitations.

Among the nine studies included in this review that compared American Indian and Alaska Native and White Medicare Advantage enrollees, findings were less favorable for American Indian and Alaska Native enrollees that White Medicare Advantage enrollees on seven measures, more favorable on four measures, similar on 12 measures, inconsistent across studies on one measure, and for one measure, study authors described differences as not practically significant.

Disease management

• Mental health care: A lower share of American Indian and Alaska Native enrollees than White enrollees initiated treatment for alcohol or other drug dependence within 14 days of diagnosis,¹⁶⁹  but a higher share of American Indian and Alaska Native enrollees was not prescribed opioids at a high dosage.¹⁷⁰  A similar share of American Indian and Alaska Native enrollees and White enrollees with depression received antidepressant medication management.¹⁷¹ Compared to the overall Medicare Advantage population, a lower share of American Indian and Alaska Native enrollees received antidepressant medication management, but a higher share of American Indian and Alaska Native enrollees with a new episode of alcohol or other drug dependence initiated treatment for the condition.¹⁷² 

• Cardiovascular health: A lower share of American Indian and Alaska Native enrollees with hypertension than White enrollees with hypertension had their blood pressure under control.¹⁷³  A similar share of American Indian and Alaska Native and White enrollees with cardiovascular disease received and adhered to statin therapy,¹⁷⁴  and a similar share of both groups of enrollees with a prior-year history of a cardiac event had their cholesterol under control.¹⁷⁵  However, compared to the overall Medicare Advantage population, a lower share of American Indian and Alaska Native enrollees with cardiovascular disease received and adhered to statin therapy.¹⁷⁶ 
• Diabetes care: Among enrollees with diabetes, a lower share of American Indian and Alaska Native enrollees than White enrollees and the overall Medicare Advantage population adhered to statin therapy^{177 , 178}  and had their blood sugar controlled.^{179 , 180}  Additionally, a lower share of American Indian and Alaska Native enrollees with diabetes than White enrollees had their blood pressure controlled.¹⁸¹  A similar share of American Indian and Alaska Native and White enrollees with diabetes received statin therapy and blood sugar testing.¹⁸²  Compared to the overall population of Medicare Advantage enrollees with diabetes, a lower share of American Indian and Alaska Native enrollees received statin therapy, and a similar share received eye exams.¹⁸³ 
• Other conditions: Among enrollees with rheumatoid arthritis, a higher share of American Indian and Alaska Native enrollees than White enrollees received therapy for rheumatoid arthritis, and among enrollees with dementia, a higher share of American Indian and Alaska Native enrollees than White enrollees were not dispensed a prescription for a potentially harmful medication.¹⁸⁴  The share of American Indian and Alaska Native enrollees who were not dispensed a prescription for a potentially harmful medication for chronic renal failure was similar to the share among White enrollees¹⁸⁵  and the overall Medicare Advantage population.¹⁸⁶  Compared to the overall population of Medicare Advantage enrollees with dementia, a similar share of American Indian and Alaska Native enrollees with dementia were not dispensed a prescription for a potentially harmful medication.¹⁸⁷ 
• Multiple high-risk chronic conditions: Among enrollees with multiple high-risk chronic conditions, the share of American Indian and Alaska Native enrollees receiving follow-up care within seven days of an emergency department visit was similar to the share among White enrollees¹⁸⁸  and the overall Medicare Advantage population.¹⁸⁹ 

Experiences with care

• Compared to White enrollees and the overall Medicare Advantage population, a similar share of American Indian and Alaska Native enrollees reported getting needed care, getting appointments and care quickly, and having their care coordinated.^{190 , 191}  However, a lower share of American Indian and Alaska Native enrollees than White enrollees reported that it was easy to get needed prescription drugs.¹⁹² 
• Among enrollees with end-stage renal disease, a higher share of American Indian and Alaska Native enrollees than White enrollees were enrolled in plans with narrow networks (e., less than 25% of available providers included in network) of dialysis facilities.¹⁹³  A similar share of American Indian and Alaska Native enrollees and White enrollees were enrolled in plans with narrow networks of primary care, psychiatry, and mental and behavioral health providers, based on a study that calculated the share of enrollees with various characteristics who were enrolled in plans with networks of different breadths.¹⁹⁴ 

Utilization of preventive services

• Vaccines: A similar share of American Indian and Alaska Native and White enrollees received a flu vaccine.¹⁹⁵  This review did not identify studies that compared utilization of other vaccines (e.g., pneumonia and COVID-19 vaccines) among American Indian and Alaska Native enrollees to White enrollees or to the total Medicare Advantage population.
• Preventive cancer screenings: A higher share of American Indian and Alaska Native women ages 50 to 74 in Medicare Advantage plans than White women of the same age in Medicare Advantage plans received breast cancer screening,¹⁹⁶  but a lower share of American Indian and Alaska Native women than women in the overall Medicare Advantage population received this screening.¹⁹⁷ 

Findings related to subgroups of people of color in Medicare Advantage plans

Gender. Only three studies in this review further stratified findings pertaining to Medicare Advantage enrollees by race and ethnicity and gender, with findings that generally mirrored the overall pattern for each racial or ethnic groups.^{198 , 199 , 200}  For example, one study found that compared to White women and men enrolled in Medicare Advantage, a lower share of Asian and Pacific Islander women and men in Medicare Advantage, respectively, reported getting needed care, getting appointments and care quickly, having their care well-coordinated, and getting needed prescription drugs, and a higher share of Asian and Pacific Islander women and men than White women and men in Medicare Advantage reported getting an annual flu vaccine.²⁰¹ 

Rural residence. Just two studies examine measures of quality of care and beneficiary experience among enrollees in rural areas by race and ethnicity, with findings that mirrored the overall pattern in some, but not all measures.^{202 , 203} For example, consistent with the overall pattern, a lower share of Black and Hispanic enrollees in rural areas than White enrollees in rural areas received beta-blocker treatments, but higher shares of Black and Hispanic enrollees in rural areas received breast cancer screenings.²⁰⁴  However, while a lower share of Hispanic enrollees than White enrollees overall reported getting appointments and care quickly, a similar share of Hispanic and White enrollees in rural areas reported getting appointments and care quickly. ²⁰⁵ 

Dual-eligible individuals. This review did not identify any studies that examined how measures of quality of care and beneficiary experience compare between dual-eligible people of color and dual-eligible White beneficiaries. Among dual-eligible individuals, higher shares of people of color are enrolled in Medicare Advantage plans than White beneficiaries: in 2020, a higher share of Black (54%), Hispanic (65%), and Asian/Pacific Islander (48%) dual-eligible individuals were enrolled in Medicare Advantage plans than White (41%) and American Indian or Alaska Native (25%) dual-eligible individuals. A better understanding of how these groups compare on measures of quality of care and beneficiary experiences could help inform an understanding of the extent to which Medicare Advantage plans are meeting the needs of dually-eligible individuals enrolled in these plans.

Discussion

With Medicare Advantage now covering more than half of all Black, Hispanic, and Asian and Pacific Islander Medicare beneficiaries, this review of the literature provides some insight into quality of care and beneficiary experiences for people of color enrolled in Medicare Advantage plans compared to White Medicare Advantage enrollees.

Overall, this review of these studies finds that while higher shares of Black Medicare Advantage enrollees than White enrollees received some preventive services, such as breast cancer screenings, findings were less favorable for Black enrollees than White enrollees on other preventive services, such as receipt of prostate cancer screenings, along with more than half of quality of care and beneficiary experience measures analyzed across the studies, such as higher rates of hospitalization for ambulatory care sensitive conditions. This review of the literature also finds that while higher shares of Hispanic enrollees than White enrollees received preventive services such as breast cancer screenings, findings were less favorable for Hispanic enrollees than White enrollees on more than a third of the measures, such as antidepressant medication management.

While a higher share of Asian and Pacific Islander enrollees than White enrollees received flu vaccines, few differences were found for Asian and Pacific Islander enrollees relative to White enrollees on other measures. Overall, fewer studies focused on American Indian and Alaska Native enrollees, making it difficult to assess the strength of the findings or how broadly they apply for this population. For instance, while a few studies examined receipt of breast cancer screenings among this population, this report did not identify studies that examined, or had sufficient sample size to examine, use of other preventive cancer screenings (e.g., colorectal, prostate, and cervical cancer screenings) among American Indian and Alaska Native enrollees compared with White enrollees or the overall Medicare Advantage population. None of the studies examined outcomes of care such as mortality rates or hospital-acquired infections by race and ethnicity, and very few studies focused on subgroups within each racial or ethnic group, such as gender and rural status. None of the identified studies examined the use of post-acute care or the total number or duration of hospital admissions among Medicare Advantage enrollees by race and ethnicity.

Substantial gaps in Medicare Advantage data hinder researchers’ ability to examine specific areas of interest in the context of Medicare Advantage, such as use of supplemental benefits offered by Medicare Advantage plans or the application of prior authorization and denials, by race and ethnicity. More concerted research efforts and more robust data to understand the experiences of people of color in Medicare Advantage plans would help inform policymakers and beneficiaries as enrollment in these plans continues to climb.

While the scope of this review is limited to Medicare Advantage enrollees, reflecting higher enrollment of Black, Hispanic, and Asian and Pacific Islander enrollees in these plans, the racial and ethnic disparities described in this report mirror disparities in health and health care in traditional Medicare, the overall Medicare population, and more broadly, the U.S adult population. Such disparities are influenced by a multitude of structural factors including systemic racism that accumulate over the course of a lifetime and may contribute to racial disparities in health experiences and outcomes in older ages.

Data and Methods for Comparing Racial/Ethnic Disparities in Medicare Advantage

Study inclusion and exclusion criteria

This literature review summarizes findings from 20 studies published between January 1, 2018 and April 1, 2023. These 20 studies include 18 studies that compare measures of quality of care and beneficiary experience between people of color in Medicare Advantage plans and White enrollees, and two studies that report findings for people of color relative to all Medicare Advantage enrollees rather than just White enrollees. Two of the 20 studies presented estimates for specific subgroups (e.g., Black enrollees in rural areas in Medicare Advantage plans) without presenting overall estimates for a particular racial or ethnic group (e.g., Black enrollees in Medicare Advantage plans overall).

Studies were selected for the review if they included data for at least one year from 2013 or later. Twelve studies used data from a year between 2018 and 2023, while the remaining (8 studies) used data from a year between 2013 and 2017 (Appendix Table 2). The data period is relevant because the Affordable Care Act made substantial changes to how Medicare Advantage plans are paid, which were not fully phased in for several years after that legislation was enacted in 2010, and so their effects may not be fully captured by studies that use older data.

To meet the inclusion criteria, studies also had to include a transparent discussion of methods and data sources, including discussion of limitations. Most studies included in this literature review are articles from peer-reviewed journals, but this review also includes studies published by independent policy and research groups as well as government reports. The brief excludes studies that were fully funded by advocacy or industry groups.

To collect relevant studies, keyword searches were conducted of PubMed, Google Scholar, and other academic search engines, as well as the websites of governmental, research, and policy organizations that publish work related to health care. Additional studies were found using a snowball technique based on bibliographies of previously pulled studies. While the approach was designed to be as comprehensive as possible in including studies that meet the criteria, it is possible that some relevant studies were overlooked.

All differences reported in the text are statistically significant (with p-value less than or equal to 0.05) unless noted otherwise (e.g., for results that are reported as similar). In a few studies, researchers distinguished differences that were statistically significant, but not practically significant due to very small differences in estimates, although the threshold for practical significance varied across studies (Appendix Table 1).

Methods and data used in studies to compare Medicare Advantage enrollees by race and ethnicity

Studies in this review presented stratified estimates for some but not all of the racial and ethnic groups listed in current federal minimum standards. Current federal minimum standards for collecting and presenting data on race and ethnicity, as specified by the Office of Management and Budget (OMB), include the following ethnic groups: 1) not Hispanic or Latino and 2) Hispanic or Latino; and the following racial groups: 1) American Indian and Alaska Native, 2) Asian, 3) Black or African American, 4) Native Hawaiian or Other Pacific Islander, and 5) White. These standards were last updated in 1997, with OMB proposing to update them again in 2023 to reflect the increasing diversity of the U.S population and evolved immigration patterns. A report by the Office of Inspector General (OIG) found that Medicare’s enrollment data are inconsistent with federal data collection standards, limiting researchers’ ability to ensure that their racial/ethnic stratifications are in alignment with federal data collection standards. This report describes results for each of the racial or ethnic groups included in the 20 studies. If a particular racial or ethnic group is not mentioned, it means that estimates for that particular racial or ethnic group for that specific measure were not presented.

Data sources used in these studies varied in how they identified race and ethnicity of enrollees. For example, a study that used the Health Outcomes Survey used self-reported data to identify enrollees’ race and ethnicity. The Healthcare Effectiveness Data and Information Set (HEDIS), which was used as a source of data in eight studies, uses an imputed methodology that combines CMS’ administrative data, surname, and residential information to identify enrollees by race or ethnicity. This method is recommended for providing estimates for White, Black, Hispanic, and Asian and Pacific Islander enrollees, but not for American Indian and Alaska Native enrollees. Therefore, some studies were able to present estimates for American Indian and Alaska Native enrollees on CAHPS measures, but not HEDIS measures. Two studies used claims data from a single insurer (UnitedHealth), which uses a proprietary imputation method that cross-references enrollees’ names and zip code to a nationally recognized supplier of consumer marketing data to generate a weighted prediction of race/ethnicity from over 180 ethnicities.

Analyses varied in data sources used to compare measures of quality of care and beneficiary experience by race and ethnicity. Most studies reviewed here (17 out of 20 studies) used nationally representative data sources (Appendix Table 2), such as the Medicare Current Beneficiary Survey and the HEDIS. Two studies used claims data collected from a single health plan (UnitedHealth) that covered enrollees within the plan nationally, and one study used electronic health records data from primary care facilities across 10 states. No studies were identified that used Medicare Advantage encounter data.

Most studies used multivariate regression models to account for differences in beneficiary characteristics, including differences in health status. Of the 20 studies in this review, most (17 studies) used multivariate models to account for differences in the characteristics of enrollees in Medicare Advantage including demographic, socioeconomic, and health risks, though they varied in methodology and transparency (Appendix Table 2). A few studies further controlled for plan and contract-level characteristics, such as county-level market penetration rates, plan type (e.g., HMO versus PPO), integrated health system status, and whether the contract had one or more Special Needs Plans. In the case of studies that presented both bivariate and multivariate estimates, the adjusted estimates are reported in this review. The remaining three studies that did not present findings from multivariate models examined measures of beneficiary experience, rather than quality of care.

The use of more advanced statistical methods varied. Two studies created matched samples (e.g., using propensity score matching to balance samples of enrollees in vertically-integrated plans versus other Medicare Advantage plans) or included inverse probability of treatment weights in the regression model as a further attempt to adjust for differences in the likelihood of certain groups to enroll in certain Medicare Advantage plans over others (i.e., integrated plans versus non-integrated plans) (Appendix Table 2). No studies were identified that used a quasi-experimental design, such as difference-in-differences or an instrumental variable approach, to isolate the effect of race/ethnicity among Medicare Advantage enrollees on outcomes of interest.

Appendix Tables

1. Sungchul Park, Paul Fishman, and Norma B. Coe, “Racial Disparities in Avoidable Hospitalizations in Traditional Medicare and Medicare Advantage,” Medical Care 59 no. 11 (November 2021): 989-996, doi:10.1097/MLR.0000000000001632 ↩︎
2. Sungchul Park, Rachel Werner, and Norma Coe, “Association of Medicare Advantage Star Ratings With Racial and Ethnic Disparities in Hospitalizations for Ambulatory Care Sensitive Conditions,” Medical Care 60 no. 12 (December 2022): 872-879, DOI: 10.1097/MLR.0000000000001770 ↩︎
3. Sungchul Park, Paul Fishman, and Norma B. Coe, “Racial Disparities in Avoidable Hospitalizations in Traditional Medicare and Medicare Advantage,” Medical Care 59 no. 11 (November 2021): 989-996, doi:10.1097/MLR.0000000000001632 ↩︎
4. Maricruz Rivera-Hernandez, Momotazur Rahman, Vincent Mor, and Amal N. Trivedi, “Racial disparities in readmission rates among patients discharged to skilled nursing facilities,” Journal of the American Geriatrics Society 67 no. 8 (August 2019): 1672-1679, https://doi.org/10.1111/jgs.15960 ↩︎
5. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
6. Joshua Breslau et al., “Racial And Ethnic Differences In The Attainment Of Behavioral Health Quality Measures In Medicare Advantage Plans,” Health Affairs 37 no. 10 (October 2018): 1685-1692, https://doi.org/10.1377/hlthaff.2018.0655 ↩︎
7. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
8. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
9. Joshua Breslau et al., “Racial And Ethnic Differences In The Attainment Of Behavioral Health Quality Measures In Medicare Advantage Plans,” Health Affairs 37 no. 10 (October 2018): 1685-1692, https://doi.org/10.1377/hlthaff.2018.0655 ↩︎
10. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
11. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
12. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
13. Joshua Breslau et al., “Racial And Ethnic Differences In The Attainment Of Behavioral Health Quality Measures In Medicare Advantage Plans,” Health Affairs 37 no. 10 (October 2018): 1685-1692, https://doi.org/10.1377/hlthaff.2018.0655 ↩︎
14. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
15. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
16. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
17. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
18. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
19. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
20. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
21. Shayla N. M. Durfey et al, “Neighborhood disadvantage and chronic disease management,” Health Services Research 54 no.S1 (February 2019): 206-216, https://doi.org/10.1111/1475-6773.13092 ↩︎
22. Shayla N. M. Durfey et al, “Neighborhood disadvantage and chronic disease management,” Health Services Research 54 no.S1 (February 2019): 206-216, https://doi.org/10.1111/1475-6773.13092 ↩︎
23. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
24. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
25. Mallika Kommareddi, Kael Wherry, and Robert A Vigersky, “Racial/Ethnic Inequities in Use of Diabetes Technologies Among Medicare Advantage Beneficiaries With Type 1 Diabetes,” Journal of Clinical Endocrinology & Metabolism (January 2023), https://doi.org/10.1210/clinem/dgad046 ↩︎
26. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
27. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
28. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
29. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
30. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
31. Clarissa Diamantidis et al., “Disparities in Chronic Kidney Disease Progression by Medicare Advantage Enrollees,” American Journal of Nephrology 52 no. 12 (January 2022): 949–957, https://doi.org/10.1159/000519758 ↩︎
32. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
33. Joshua Breslau et al., “Racial And Ethnic Differences In The Attainment Of Behavioral Health Quality Measures In Medicare Advantage Plans,” Health Affairs 37 no. 10 (October 2018): 1685-1692, https://doi.org/10.1377/hlthaff.2018.0655 ↩︎
34. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
35. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
36. Kenton J. Johnston et al., “Association of Race and Ethnicity and Medicare Program Type With Ambulatory Care Access and Quality Measures,” JAMA 326, no. 7 (August 2021): 628-636, doi:10.1001/jama.2021.10413 ↩︎
37. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
38. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
39. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
40. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
41. David J. Meyers, Momotazur Rahman, and Amal N. Trivedi, “Narrow Primary Care Networks in Medicare Advantage,” Journal of General Internal Medicine 37 (February 2022): 488-491, https://doi.org/10.1007/s11606-020-06534-2 ↩︎
42. Eunhae Grace Oh, David J. Meyers, Kevin H. Nguyen, and Amal N. Trivedi, “Narrow Dialysis Networks In Medicare Advantage: Exposure By Race, Ethnicity, And Dual Eligibility,” Health Affairs 42, no. 2 (February 2022): 252-260, https://doi.org/10.1377/hlthaff.2022.01044 ↩︎
43. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
44. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
45. Kenton J. Johnston et al., “Association of Race and Ethnicity and Medicare Program Type With Ambulatory Care Access and Quality Measures,” JAMA 326, no. 7 (August 2021): 628-636, doi:10.1001/jama.2021.10413 ↩︎
46. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
47. Kenton J. Johnston et al., “Association of Race and Ethnicity and Medicare Program Type With Ambulatory Care Access and Quality Measures,” JAMA 326, no. 7 (August 2021): 628-636, doi:10.1001/jama.2021.10413 ↩︎
48. Jason Lane et al., “Access to Health Care Improves COVID-19 Vaccination and Mitigates Health Disparities Among Medicare Beneficiaries,” Journal of Racial and Ethnic Health Disparities (September 2022): https://doi.org/10.1007/s40615-022-01343-1 ↩︎
49. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
50. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
51. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
52. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
53. Kenton J. Johnston et al., “Association of Race and Ethnicity and Medicare Program Type With Ambulatory Care Access and Quality Measures,” JAMA 326, no. 7 (August 2021): 628-636, doi:10.1001/jama.2021.10413 ↩︎
54. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
55. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
56. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
57. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
58. Sungchul Park, Rachel M. Werner, and Norma B. Coe, “Racial and ethnic disparities in access to and enrollment in high-quality Medicare Advantage plans,” Health Services Research 58 no.2 (April 2023): 303-313, https://doi.org/10.1111/1475-6773.13977 ↩︎
59. David J. Meyers et al., “Association of Medicare Advantage Star Ratings With Racial, Ethnic, and Socioeconomic Disparities in Quality of Care,” JAMA Health Forum 2 no.6 (June 2021): e210793, doi:10.1001/jamahealthforum.2021.0793 ↩︎
60. Sungchul Park, Brent A. Langellier, and David J. Meyers, “Differences between integrated and non-integrated plans in Medicare Advantage,” Health Services Research (November 2022): 1-9 https://doi.org/10.1111/1475-6773.14101 ↩︎
61. Sungchul Park, Rachel Werner, and Norma Coe, “Association of Medicare Advantage Star Ratings With Racial and Ethnic Disparities in Hospitalizations for Ambulatory Care Sensitive Conditions,” Medical Care 60 no. 12 (December 2022): 872-879, DOI: 10.1097/MLR.0000000000001770 ↩︎
62. Maricruz Rivera-Hernandez, Momotazur Rahman, Vincent Mor, and Amal N. Trivedi, “Racial disparities in readmission rates among patients discharged to skilled nursing facilities,” Journal of the American Geriatrics Society 67 no. 8 (August 2019): 1672-1679, https://doi.org/10.1111/jgs.15960 ↩︎
63. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
64. Joshua Breslau et al., “Racial And Ethnic Differences In The Attainment Of Behavioral Health Quality Measures In Medicare Advantage Plans,” Health Affairs 37 no. 10 (October 2018): 1685-1692 https://doi.org/10.1377/hlthaff.2018.0655 ↩︎
65. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
66. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
67. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
68. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
69. Joshua Breslau et al., “Racial And Ethnic Differences In The Attainment Of Behavioral Health Quality Measures In Medicare Advantage Plans,” Health Affairs 37 no. 10 (October 2018): 1685-1692 https://doi.org/10.1377/hlthaff.2018.0655 ↩︎
70. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
71. Joshua Breslau et al., “Racial And Ethnic Differences In The Attainment Of Behavioral Health Quality Measures In Medicare Advantage Plans,” Health Affairs 37 no. 10 (October 2018): 1685-1692 https://doi.org/10.1377/hlthaff.2018.0655 ↩︎
72. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
73. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
74. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0   ↩︎
75. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
76. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
77. Shayla N. M. Durfey et al, “Neighborhood disadvantage and chronic disease management,” Health Services Research 54 no.S1 (February 2019): 206-216, https://doi.org/10.1111/1475-6773.13092 ↩︎
78. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
79. Shayla N. M. Durfey et al, “Neighborhood disadvantage and chronic disease management,” Health Services Research 54 no.S1 (February 2019): 206-216, https://doi.org/10.1111/1475-6773.13092 ↩︎
80. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
81. Mallika Kommareddi, Kael Wherry, and Robert A Vigersky, “Racial/Ethnic Inequities in Use of Diabetes Technologies Among Medicare Advantage Beneficiaries With Type 1 Diabetes,” Journal of Clinical Endocrinology & Metabolism (January 2023), https://doi.org/10.1210/clinem/dgad046 ↩︎
82. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
83. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
84. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
85. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
86. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0   ↩︎
87. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
88. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
89. Shayla N. M. Durfey et al, “Neighborhood disadvantage and chronic disease management,” Health Services Research 54 no.S1 (February 2019): 206-216, https://doi.org/10.1111/1475-6773.13092 ↩︎
90. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
91. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
92. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
93. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
94. Clarissa Diamantidis et al., “Disparities in Chronic Kidney Disease Progression by Medicare Advantage Enrollees,” American Journal of Nephrology 52 no. 12 (January 2022): 949–957, https://doi.org/10.1159/000519758 ↩︎
95. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
96. Joshua Breslau et al., “Racial And Ethnic Differences In The Attainment Of Behavioral Health Quality Measures In Medicare Advantage Plans,” Health Affairs 37 no. 10 (October 2018): 1685-1692 https://doi.org/10.1377/hlthaff.2018.0655 ↩︎
97. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
98. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
99. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
100. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
101. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
102. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
103. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
104. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
105. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
106. David J. Meyers, Momotazur Rahman, and Amal N. Trivedi, “Narrow Primary Care Networks in Medicare Advantage,” Journal of General Internal Medicine 37 (February 2022): 488-491, https://doi.org/10.1007/s11606-020-06534-2 ↩︎
107. David J. Meyers, Momotazur Rahman, and Amal N. Trivedi, “Narrow Primary Care Networks in Medicare Advantage,” Journal of General Internal Medicine 37 (February 2022): 488-491, https://doi.org/10.1007/s11606-020-06534-2 ↩︎
108. Eunhae Grace Oh, David J. Meyers, Kevin H. Nguyen, and Amal N. Trivedi, “Narrow Dialysis Networks In Medicare Advantage: Exposure By Race, Ethnicity, And Dual Eligibility,” Health Affairs 42, no. 2 (February 2022): 252-260, https://doi.org/10.1377/hlthaff.2022.01044 ↩︎
109. David J. Meyers, Momotazur Rahman, and Amal N. Trivedi, “Narrow Primary Care Networks in Medicare Advantage,” Journal of General Internal Medicine 37 (February 2022): 488-491, https://doi.org/10.1007/s11606-020-06534-2 ↩︎
110. Eunhae Grace Oh, David J. Meyers, Kevin H. Nguyen, and Amal N. Trivedi, “Narrow Dialysis Networks In Medicare Advantage: Exposure By Race, Ethnicity, And Dual Eligibility,” Health Affairs 42, no. 2 (February 2022): 252-260, https://doi.org/10.1377/hlthaff.2022.01044 ↩︎
111. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
112. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
113. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
114. Jason Lane et al., “Access to Health Care Improves COVID-19 Vaccination and Mitigates Health Disparities Among Medicare Beneficiaries,” Journal of Racial and Ethnic Health Disparities (September 2022): https://doi.org/10.1007/s40615-022-01343-1 ↩︎
115. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
116. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
117. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
118. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
119. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
120. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
121. Sungchul Park, Rachel M. Werner, and Norma B. Coe, “Racial and ethnic disparities in access to and enrollment in high-quality Medicare Advantage plans,” Health Services Research 58 no.2 (April 2023): 303-313, https://doi.org/10.1111/1475-6773.13977 ↩︎
122. David J. Meyers et al., “Association of Medicare Advantage Star Ratings With Racial, Ethnic, and Socioeconomic Disparities in Quality of Care,” JAMA Health Forum 2 no.6 (June 2021): e210793, doi:10.1001/jamahealthforum.2021.0793 ↩︎
123. Sungchul Park, Brent A. Langellier, and David J. Meyers, “Differences between integrated and non-integrated plans in Medicare Advantage,” Health Services Research (November 2022): 1-9 https://doi.org/10.1111/1475-6773.14101 ↩︎
124. Sungchul Park, Rachel Werner, and Norma Coe, “Association of Medicare Advantage Star Ratings With Racial and Ethnic Disparities in Hospitalizations for Ambulatory Care Sensitive Conditions,” Medical Care 60 no. 12 (December 2022): 872-879, DOI: 10.1097/MLR.0000000000001770 ↩︎
125. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf   ↩︎
126. Joshua Breslau et al., “Racial And Ethnic Differences In The Attainment Of Behavioral Health Quality Measures In Medicare Advantage Plans,” Health Affairs 37 no. 10 (October 2018): 1685-1692 https://doi.org/10.1377/hlthaff.2018.0655 ↩︎
127. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
128. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
129. Joshua Breslau et al., “Racial And Ethnic Differences In The Attainment Of Behavioral Health Quality Measures In Medicare Advantage Plans,” Health Affairs 37 no. 10 (October 2018): 1685-1692 https://doi.org/10.1377/hlthaff.2018.0655 ↩︎
130. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
131. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
132. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
133. Joshua Breslau et al., “Racial And Ethnic Differences In The Attainment Of Behavioral Health Quality Measures In Medicare Advantage Plans,” Health Affairs 37 no. 10 (October 2018): 1685-1692 https://doi.org/10.1377/hlthaff.2018.0655 ↩︎
134. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
135. Mallika Kommareddi, Kael Wherry, and Robert A Vigersky, “Racial/Ethnic Inequities in Use of Diabetes Technologies Among Medicare Advantage Beneficiaries With Type 1 Diabetes,” Journal of Clinical Endocrinology & Metabolism (January 2023), https://doi.org/10.1210/clinem/dgad046 ↩︎
136. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
137. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
138. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
139. Shayla N. M. Durfey et al, “Neighborhood disadvantage and chronic disease management,” Health Services Research 54 no.S1 (February 2019): 206-216, https://doi.org/10.1111/1475-6773.13092 ↩︎
140. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
141. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
142. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
143. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
144. Shayla N. M. Durfey et al, “Neighborhood disadvantage and chronic disease management,” Health Services Research 54 no.S1 (February 2019): 206-216, https://doi.org/10.1111/1475-6773.13092 ↩︎
145. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
146. Shayla N. M. Durfey et al, “Neighborhood disadvantage and chronic disease management,” Health Services Research 54 no.S1 (February 2019): 206-216, https://doi.org/10.1111/1475-6773.13092 ↩︎
147. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
148. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
149. Joshua Breslau et al., “Racial And Ethnic Differences In The Attainment Of Behavioral Health Quality Measures In Medicare Advantage Plans,” Health Affairs 37 no. 10 (October 2018): 1685-1692 https://doi.org/10.1377/hlthaff.2018.0655 ↩︎
150. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
151. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
152. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
153. Clarissa Diamantidis et al., “Disparities in Chronic Kidney Disease Progression by Medicare Advantage Enrollees,” American Journal of Nephrology 52 no. 12 (January 2022): 949–957, https://doi.org/10.1159/000519758 ↩︎
154. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
155. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
156. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
157. David J. Meyers, Momotazur Rahman, and Amal N. Trivedi, “Narrow Primary Care Networks in Medicare Advantage,” Journal of General Internal Medicine 37 (February 2022): 488-491, https://doi.org/10.1007/s11606-020-06534-2 ↩︎
158. David J. Meyers, Momotazur Rahman, and Amal N. Trivedi, “Narrow Primary Care Networks in Medicare Advantage,” Journal of General Internal Medicine 37 (February 2022): 488-491, https://doi.org/10.1007/s11606-020-06534-2 ↩︎
159. David J. Meyers, Momotazur Rahman, and Amal N. Trivedi, “Narrow Primary Care Networks in Medicare Advantage,” Journal of General Internal Medicine 37 (February 2022): 488-491, https://doi.org/10.1007/s11606-020-06534-2 ↩︎
160. Eunhae Grace Oh, David J. Meyers, Kevin H. Nguyen, and Amal N. Trivedi, “Narrow Dialysis Networks In Medicare Advantage: Exposure By Race, Ethnicity, And Dual Eligibility,” Health Affairs 42, no. 2 (February 2022): 252-260, https://doi.org/10.1377/hlthaff.2022.01044 ↩︎
161. David J. Meyers, Momotazur Rahman, and Amal N. Trivedi, “Narrow Primary Care Networks in Medicare Advantage,” Journal of General Internal Medicine 37 (February 2022): 488-491, https://doi.org/10.1007/s11606-020-06534-2 ↩︎
162. Eunhae Grace Oh, David J. Meyers, Kevin H. Nguyen, and Amal N. Trivedi, “Narrow Dialysis Networks In Medicare Advantage: Exposure By Race, Ethnicity, And Dual Eligibility,” Health Affairs 42, no. 2 (February 2022): 252-260, https://doi.org/10.1377/hlthaff.2022.01044 ↩︎
163. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
164. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
165. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
166. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
167. Sungchul Park, Rachel M. Werner, and Norma B. Coe, “Racial and ethnic disparities in access to and enrollment in high-quality Medicare Advantage plans,” Health Services Research 58 no.2 (April 2023): 303-313, https://doi.org/10.1111/1475-6773.13977 ↩︎
168. David J. Meyers et al., “Association of Medicare Advantage Star Ratings With Racial, Ethnic, and Socioeconomic Disparities in Quality of Care,” JAMA Health Forum 2 no.6 (June 2021): e210793, doi:10.1001/jamahealthforum.2021.0793 ↩︎
169. Steven C. Martino et al., “Disparities In The Quality Of Clinical Care Delivered To American Indian/Alaska Native Medicare Advantage Enrollees,” Health Affairs 41, no. 5 (May 2022): 663:670, https://doi.org/10.1377/hlthaff.2021.01830 ↩︎
170. Steven C. Martino et al., “Disparities In The Quality Of Clinical Care Delivered To American Indian/Alaska Native Medicare Advantage Enrollees,” Health Affairs 41, no. 5 (May 2022): 663:670, https://doi.org/10.1377/hlthaff.2021.01830 ↩︎
171. Steven C. Martino et al., “Disparities In The Quality Of Clinical Care Delivered To American Indian/Alaska Native Medicare Advantage Enrollees,” Health Affairs 41, no. 5 (May 2022): 663:670, https://doi.org/10.1377/hlthaff.2021.01830 ↩︎
172. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
173. Shayla N. M. Durfey et al, “Neighborhood disadvantage and chronic disease management,” Health Services Research 54 no.S1 (February 2019): 206-216, https://doi.org/10.1111/1475-6773.13092 ↩︎
174. Steven C. Martino et al., “Disparities In The Quality Of Clinical Care Delivered To American Indian/Alaska Native Medicare Advantage Enrollees,” Health Affairs 41, no. 5 (May 2022): 663:670, https://doi.org/10.1377/hlthaff.2021.01830 ↩︎
175. Shayla N. M. Durfey et al, “Neighborhood disadvantage and chronic disease management,” Health Services Research 54 no.S1 (February 2019): 206-216, https://doi.org/10.1111/1475-6773.13092 ↩︎
176. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
177. Steven C. Martino et al., “Disparities In The Quality Of Clinical Care Delivered To American Indian/Alaska Native Medicare Advantage Enrollees,” Health Affairs 41, no. 5 (May 2022): 663:670, https://doi.org/10.1377/hlthaff.2021.01830 ↩︎
178. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
179. Shayla N. M. Durfey et al, “Neighborhood disadvantage and chronic disease management,” Health Services Research 54 no.S1 (February 2019): 206-216, https://doi.org/10.1111/1475-6773.13092 ↩︎
180. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
181. Steven C. Martino et al., “Disparities In The Quality Of Clinical Care Delivered To American Indian/Alaska Native Medicare Advantage Enrollees,” Health Affairs 41, no. 5 (May 2022): 663:670, https://doi.org/10.1377/hlthaff.2021.01830 ↩︎
182. Steven C. Martino et al., “Disparities In The Quality Of Clinical Care Delivered To American Indian/Alaska Native Medicare Advantage Enrollees,” Health Affairs 41, no. 5 (May 2022): 663:670, https://doi.org/10.1377/hlthaff.2021.01830 ↩︎
183. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
184. Steven C. Martino et al., “Disparities In The Quality Of Clinical Care Delivered To American Indian/Alaska Native Medicare Advantage Enrollees,” Health Affairs 41, no. 5 (May 2022): 663:670, https://doi.org/10.1377/hlthaff.2021.01830 ↩︎
185. Steven C. Martino et al., “Disparities In The Quality Of Clinical Care Delivered To American Indian/Alaska Native Medicare Advantage Enrollees,” Health Affairs 41, no. 5 (May 2022): 663:670, https://doi.org/10.1377/hlthaff.2021.01830 ↩︎
186. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
187. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
188. Steven C. Martino et al., “Disparities In The Quality Of Clinical Care Delivered To American Indian/Alaska Native Medicare Advantage Enrollees,” Health Affairs 41, no. 5 (May 2022): 663:670, https://doi.org/10.1377/hlthaff.2021.01830 ↩︎
189. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
190. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
191. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
192. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
193. Eunhae Grace Oh, David J. Meyers, Kevin H. Nguyen, and Amal N. Trivedi, “Narrow Dialysis Networks In Medicare Advantage: Exposure By Race, Ethnicity, And Dual Eligibility,” Health Affairs 42, no. 2 (February 2022): 252-260, https://doi.org/10.1377/hlthaff.2022.01044 ↩︎
194. David J. Meyers, Momotazur Rahman, and Amal N. Trivedi, “Narrow Primary Care Networks in Medicare Advantage,” Journal of General Internal Medicine 37 (February 2022): 488-491, https://doi.org/10.1007/s11606-020-06534-2 ↩︎
195. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021),  https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
196. Steven C. Martino et al., “Disparities In The Quality Of Clinical Care Delivered To American Indian/Alaska Native Medicare Advantage Enrollees,” Health Affairs 41, no. 5 (May 2022): 663:670, https://doi.org/10.1377/hlthaff.2021.01830 ↩︎
197. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
198. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
199. Steven C. Martino et al, Disparities in Health Care in Medicare Advantage by Race, Ethnicity, and Sex (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2023), https://www.cms.gov/files/document/disparities-health-care-medicare-advantage-race-ethnicity-and-sex.pdf-0 ↩︎
200. Anuj Gangopadhyaya, Stephen Zuckerman, and Nikhil Rao, “Assessing the Difference in Racial and Ethnic Disparities in Access to and Use of Care Between Traditional Medicare and Medicare Advantage,” Health Services Research (March 2023): 1-10, https://doi.org/10.1111/1475-6773.14150 ↩︎
201. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎
202. Steven C. Martino et al., Rural-Urban Disparities in Health Care in Medicare (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, November 2020), https://www.cms.gov/files/document/omh-rural-urban-report-2020.pdf ↩︎
203. Steven C. Martino et al., Rural-Urban Disparities in Health Care in Medicare (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, November 2022), https://www.cms.gov/files/document/rural-urban-disparities-11-2022.pdf ↩︎
204. Steven C. Martino et al., Rural-Urban Disparities in Health Care in Medicare (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, November 2020), https://www.cms.gov/files/document/omh-rural-urban-report-2020.pdf ↩︎
205. Steven C. Martino et al, Racial, Ethnic, & Gender Disparities in Health Care in Medicare Advantage (Baltimore, MD: Centers for Medicare & Medicaid Services Office of Minority Health, April 2021), https://www.cms.gov/files/document/racial-ethnic-gender-disparities-health-care-medicare-advantage.pdf ↩︎

Key Takeaways

• New federal funding of $3 billion provided to health departments from the American Rescue Plan is designed to build and bolster what has been a depleted public health workforce in the United States.
• The funding, distributed through the CDC’s Public Health Infrastructure Grants (PHIG) program, has been provided to 107 different jurisdictions, including state, county, city, and territorial health departments.
• By region, health departments in the South have received the largest share of funding (39%), followed by those in the West (23%), Midwest (20%) and Northeast (17%).
• State health departments have received the greatest share of workforce funding (70.6%) followed by counties (17.1%), cities (10.4%), and territories (1.9%).
• Among states, Florida received the largest amount of workforce support ($141.4 million), followed by Texas ($140.8 million), and New York ($107.8 million). Together the top ten funded states accounted for 47% of all state funding and 33% of funding overall.
• Jurisdictions report that they plan to use PHIG workforce funds to hire or retain more than 6,000 staff over the five-year grant period, with most planned hires/retentions in Texas, Florida, and Missouri by the end of the grant.
• The size, scope, and flexibility of this funding is unprecedented, as it exceeds any funding provided for the workforce thus far, reaches many more jurisdictions, and extends over a five-year period.
• Data on outcomes and impacts will take longer to materialize, and challenges remain, including the remaining gap between estimated workforce needs and planned staff hires and retentions, and questions about workforce sustainability after the five-year grant period ends. 

Introduction

COVID-19 highlighted and exacerbated existing weaknesses in the U.S. public health system, particularly in the public health workforce, which had already been depleted. Public health departments at all levels – including states, counties, large cities, and territories – faced numerous challenges in mounting a response to the pandemic due to a lack of staff, limited funding, and outdated data and communication capabilities, among other issues, and concerns remain about preparedness for future pandemics. In response, Congress provided $7.7 billion through the American Rescue Plan to help shore up the public health workforce, which included funds to be directed to public health departments. In turn, the Centers for Disease Control and Prevention used some of these funds to create a new, five-year, $3 billion Public Health Infrastructure Grants (PHIG) program to help public health departments recruit, retain, and train workers, including to better prepare for future pandemics (the PHIG also includes an additional $140 million for “foundational capabilities” and $200 million for “data modernization”). PHIG launched in 2022 with the $3 billion in workforce funds awarded to health departments in the first year of the grant. The PHIG program is significant for several reasons:

• It is a “non-categorical and cross-cutting” grant mechanism, in contrast to most federal public health funding which is generally targeted to specific diseases, activities, or populations;
• It provides funding for a multi-year period – in this case, five years – as opposed to most public health funding which is typically provided annually (or at most over a two- to three-year period, as was done with other ARPA funding);
• The number of local jurisdictions eligible for funding is significantly greater than other CDC funding mechanisms, including others funded through ARPA; and
• The amount of funding made available for public health jurisdictions to expand their workforce exceeds any prior grant mechanism; for example, the next largest amount was $2 billion in ARPA funds¹  awarded in 2021 for a two-year period primarily to address COVID (although those funds could be used for preparedness).

Using newly available data from the CDC, this issue brief analyzes the distribution of PHIG workforce funding by jurisdiction, as well as jurisdictional plans for enhancing the workforce (data on foundational capacities and data modernization are not included), to provide an initial snapshot of how this new funding will be used. Data are as of September 30, 2023.

Overview of PHIG Workforce Funding

As described in the grant announcement, PHIG workforce funds are intended to “reinforce and expand the public health workforce by hiring, retaining, supporting, and training the workforce and by strengthening relevant workforce planning, systems, processes, and policies.” Jurisdictions can use these funds to fill vacancies and create new positions, as well as invest in worker well-being and engagement, along with other activities. Key characteristics and requirements of the grant include:

• As many as 111 jurisdictions, including state² , county, city, and territorial health departments, are eligible to receive PHIG funds. Counties serving a population of 2,000,000 or more and cities serving a population 400,000 or more are eligible as direct recipients.
• A funding formula based on the size of the population served by a jurisdiction as well as an adjustment factor for “community vulnerability”, using the U.S. Census’s Community Resilience Estimates, is used to determine allocations. The base funding amount for all awards is set at $2.5 million and the ceiling at $150 million.
• Jurisdictions are expected to focus their workforce efforts on six key activities and have some flexibility to choose among these (see Table 1). While encouraged to include all six activities in their plans, they are required to include #1 (recruit and hire new public health staff) and #6 (strengthen support for implementation). Under #6, recipients must hire a Workforce Director to help oversee implementation and at least one staff member to conduct evaluation.

Findings

PHIG workforce funding has been awarded to 107 public health departments across the country, with most channeled to state jurisdictions, and the largest share to the South, largely reflecting the size of a jurisdiction’s population.

• The 107 health department recipients include those in all 50 states and Washington D.C., 27 counties, 21 cities, and eight territories (see Figure 1).
• State health departments have received the largest share of funding (70.6%), followed by counties (17.1%), cities (10.4%), and territories (1.9%) (see Figure 2).
  • The top ten funded states accounted for 47% of all state funding and 33% of funding overall. Among states, Florida received the largest amount ($141.4 million), followed by Texas ($140.8 million), and New York ($107.8 million) (see Figure 3).
  • The top ten funded counties accounted for 61% of all county funding and 10% of funding overall. Among counties, Los Angeles County, California received the largest amount ($79.8 million), followed by Maricopa County, Arizona ($38.0 million), and Miami-Dade County, Florida ($27.8 million).
  • The top ten funded cities accounted for 71% of all city funding and 7% of funding overall. Among cities, New York City received the largest amount ($84.6 million), followed by Chicago ($27.1 million), and Houston ($23.3 million).
  • The eight territories received 2% of funding overall. Among territories, Puerto Rico received the largest amount ($34.9 million), followed by Guam ($4.3 million), and the U.S. Virgin Islands ($3.6 million).
• By region, health departments in the South have received the largest share of funding (39%), followed by the West (23%), Midwest (20%) and Northeast (17%) (see Figure 4).

Funding per capita for all recipients combined was $8.95, with significant variation in some cases. For example, per capita funding for territories was almost twice that of states. This partly reflects the fact that there is a base funding amount for all jurisdictions, regardless of population size.³ 

• Per capita funding was $8.95 overall, though it ranged among all jurisdictions from a low of $7.49 for the state of Utah to a high of $125.69 for the territory of Palau.
• Comparing across jurisdiction types, states had the lowest per capita funding ($8.57), followed by counties ($9.10) and cities ($11.00). Territories, which generally have the lowest population size of any jurisdiction, received $15.91 per capita.
• Within states, funding per capita ranged from $7.49 in Utah to $11.75 in Wyoming. In counties, it ranged from $8.03 in King County, Washington to $11.87 in Douglas County, Nebraska. Among city recipients per capita funding amounts ranged from $9.43 in Columbus to $13.88 in Long Beach, and in territories it ranged from $11.27 in Puerto Rico to $125.69 in Palau (see Figure 5).

Most workforce funding is being channeled to the two required workforce activities under the grant – “strengthening grant implementation” and “recruitment and hiring” – although there is variation across jurisdictions.

• More than a third ($1.1 billion or 36%) of funding is to “Strengthen support for implementation of this grant”, which includes hiring a Workforce Director and evaluation staff. The next largest category is recruitment and hiring of staff ($613 million or 20%) followed by staff retention ($342 million or 11%). The remaining 33% is divided among four other activities (see Figure 6).
• There is some variation by jurisdiction level. For example, states were more likely to use funds for strengthening grant implementation (38%) than counties (28%), and counties were more likely to use funding for recruitment and hiring (23%) than territories (11%) (see Figure 7).
• Only seven jurisdictions did not channel most workforce funding to the two required categories. For example, in two jurisdictions (Washington and Fulton County, Georgia), “Support and sustain the public health workforce” was the top funded category and in two jurisdictions (Austin and Dallas County, Texas), “Retain public health staff” was the top category.

Jurisdictions report that they plan to hire or retain more than 6,000 staff over the five-year grant period, with the largest share expected to support organizational competencies.⁴ 

• In year 1, jurisdictions plan to hire or retain 3,862 staff, rising to 6,152 by year 5.
• State jurisdictions plan to hire the greatest number of staff (2,471 staff in year 1 and 3,822 by year 5), followed by counties (800 and 1,207), cities (377 and 754), and territories (214 and 369). The average number of staff to be hired/retained across all jurisdictions is 36.4 in year 1 and 58.0 in year 5, though this varies by health department level.
• The jurisdictions planning to hire/retain the most staff in year 1 are Texas (433), Los Angeles County (189), and Florida (186); by year 5, it is Texas, Florida, and Missouri.
• When asked for expected hiring/retention numbers across ten defined public health program areas, jurisdictions reported the largest share of expected hires/retained staff by year 5 in “Organizational competencies” (38% of all staff hires and retentions), followed by “Assessment and surveillance” (14%) and “Other” (14%). The program areas with the smallest shares of planned hires/retained staff are “Maternal, child, and family health” (2%), “Chronic disease and injury prevention (3%), and “Environmental public health” (4%). There is some variation by health department level. For example, by year 5, 42% of staff hired/retained by states are expected in organizational competencies compared to 31% by territories in that category. Territories plan for a greater share of staff in assessment and surveillance (25%) compared to other jurisdiction types (see Figure 8).

Discussion

New federal funding of $3 billion provided to health departments from the American Rescue Plan is designed to build and bolster what has been a depleted public health workforce in the United States. The size, scope, and flexibility of the funding is unprecedented, as it exceeds any funding provided for the workforce thus far, reaches many more jurisdictions, and extends over a five-year period. This analysis of initial data from the CDC provides an early look at how funding has been distributed across the country and what jurisdictions plan to do with this support. As shown here, states have received the largest share of funding, (70.6%), followed by counties (17.1%), cities (10.4%), and territories (1.9%), reflecting the size of the populations they serve. Jurisdictions plan to use funds to hire or retain more than 6,000 staff by the end of the five-year period, with states leading the way in this area. Most funding will be channeled to the two required areas of the grant, strengthening support for implementation through hiring key personnel and recruitment and hiring of staff, followed by staff retention more generally. Across all these areas there is notable variation among recipients, which appears to reflect the design of the PHIG in allowing flexibility to address the unique needs of different jurisdictions.

Still, while these data provide an early snapshot of how this funding will be used, data on outcomes and impacts will take a much longer time to materialize. Moreover, despite the size of the grant and multi-year nature of the funding, there are challenges that remain. First, the planned number of staff hires and retentions represent only a fraction of estimates of the need for a public health workforce in the U.S.  Additionally, while funding is provided for a five-year period, longer than most jurisdictional grants, there are questions about the sustainability of hiring beyond this period, with jurisdictions already expressing concerns about the future. More broadly, the extent to which this funding can help plug the very deep hole, even temporarily, in the public health workforce that would be required to adequately prepare and respond to future threats remains to be seen. Monitoring the impact of these investments, including how enduring they may be, will be important for assessing the strength of the nation’s public health infrastructure and its ability to handle new challenges going forward.

1. At least 25% of these awards were required to be used school-based health programs. ↩︎
2. The grant also requires that no less than 40% of workforce funds provided to state health departments be distributed to local health departments that have not received direct funding from the grant. ↩︎
3. Population served estimates consider counties within states and cities within states and counties, so population is not counted more than once. ↩︎
4. Data on staff to be hired/retained based on workforce and foundational capabilities funds. Jurisdictions potentially will have additional hires via data modernization funds, but that data is not yet available. ↩︎

There have been increased attention and calls to address racism in the U.S. in recent years, particularly in the wake of the initial wave of the COVID-19 pandemic and the growth in recognition of the harms caused by systemic racism following the police killings of George Floyd and Breonna Taylor. As a result of historic and ongoing policies often rooted in discriminatory practices, there are stark differences in access to resources, opportunities, and power by race and ethnicity in the U.S., including access to safe housing and neighborhoods, economic and educational opportunities, and health care. Racism and discrimination at multiple levels, intentional or not, result in differences in experiences across many aspects of everyday life as well as in health care settings, which can negatively impact individuals’ health and well-being. Moreover, reflecting the intersectional nature of people’s identities, some individuals experience the combined impacts of racism and discrimination based on other factors such as gender or sexual orientation.

KFF’s 2023 Racism, Discrimination, and Health Survey is a major effort to document the extent and implications of racism and discrimination, particularly with respect to people’s interactions with the health care system. This large, nationally representative survey based on responses from over 6,000 adults provides new data on individuals’ experiences with racism and discrimination and the impacts of these experiences, both broadly and within racial and ethnic groups. It documents racial and ethnic differences in social and economic circumstances, interactions with the police, experiences with unfair treatment in daily life and while seeking health care, and the impacts of such experiences on health and well-being. Moreover, it examines how these inequities and experiences vary within racial and ethnic groups by factors such as income, gender, skin tone, age and LGBT identity where data allow. Future publications will delve deeper into results for specific populations and additional topics. This survey is part of a broader body of work that builds on KFF’s commitment to amplifying the voices of marginalized populations, including the recently released 2023 KFF/LA Times Survey of Immigrants, which provides insight into experiences of immigrants by different factors including immigration status. Having comprehensive and nuanced data to understand individuals’ experiences may inform and direct efforts to address disparities and advance equity.

This report is broadly divided into three sections. The first examines how social and economic circumstances and feelings of safety for Hispanic, Black, Asian, and American Indian and Alaska Native (AIAN) people in the U.S. differ from White individuals in ways that reflect underlying structural inequities. The second section examines experiences with interpersonal racism and discrimination in daily activities and impacts of these experiences on well-being and stress. The third section delves deeper into experiences with racism and discrimination in health care settings.

Acknowledgements:

KFF would like to thank the following individuals and organizations for their invaluable inputs, insights, and suggestions throughout the planning and dissemination of this survey project:

Mayra Alvarez, MHA, The Children’s PartnershipUché Blackstock, MD, Advancing Health EquityKimberly Chang, MD, MPH, Asian Health ServicesJuliet Choi, JD and Mary Smith, JD, Asian and Pacific Islander Health ForumGail Christopher, DN, National Collaborative for Health EquityCarmen Green, MPH, Reproductive Health ImpactDaniel Dawes, JD, Institute of Global Health Equity, Meharry Medical CollegeAdolph P. Falcón, MPP, National Alliance for Hispanic HealthSharlene Kemler, The Loveland FoundationPedro Martinez, MPH, UnidosUSAletha Maybank, MD, MPH, and Fernando De Maio, PhD, American Medical AssociationMeredith Raimondi, National Council of Urban Indian HealthA.C. Locklear, JD, National Indian Health Board

Key Takeaways

Reflecting ongoing residential segregation patterns rooted in contemporary and historic policies, Hispanic, Black, Asian, and AIAN adults feel less safe in their homes and neighborhoods and experience higher rates of police mistreatment compared to White adults. Hispanic, Black, Asian, and American Indian and Alaska Native (AIAN) adults are significantly less likely than White adults to say they feel “very safe” in their neighborhoods and in their homes, and about one in ten in each of these groups say they or a family member was a victim of violence in the past year, about twice the share of White adults who say so. About one in six AIAN adults and one in ten Black and Hispanic adults say they or a family member living with them have been threatened or mistreated by the police in the past year compared with 4% of White adults. Black and Hispanic adults who have self-reported darker skin tones report even higher rates of police mistreatment. Hispanic, Black, and AIAN adults also report disproportionate challenges with finances and employment due to underlying structural inequities, which are reflected in their daily worries and concerns. Among adults overall and across most racial and ethnic groups, having a strong network of local support is associated with increased feelings of safety in their homes and neighborhoods and reduced frequency of worries and concerns about meeting their family’s basic needs and health.

At least half of AIAN, Black, and Hispanic adults and about four in ten Asian adults say they have experienced at least one type of discrimination in daily life at least a few times in the past year, and they are more likely to say these experiences were due to their race or ethnicity compared to their White counterparts. These experiences include receiving poorer service than others at restaurants or stores, people acting as if they are afraid of them, people acting as if they are not smart, being threatened or harassed, or being criticized for speaking a language other than English. Overall, four in ten Black adults and about three in ten Hispanic, Asian, and AIAN adults say they have experienced at least one of these acts of discrimination in the past year and that their race or ethnicity was a reason for these experiences compared with just 6% of White adults.

Highlighting the impacts of racism and discrimination on well-being and health, people who report experiences with discrimination in daily life are more likely than those who rarely or never experience discrimination to report adverse effects from worry or stress as well as regular feelings of loneliness, anxiety and depression. For example, those who experienced discrimination in their everyday lives are at least twice as likely as those who report rarely or never experiencing discrimination to say that in the past 30 days worry or stress has led to sleep problems (65% vs. 35%); poor appetite or overeating (52% vs. 20%) frequent headaches or stomachaches (41% vs. 15%); difficulty controlling their temper (34% vs. 11%); worsening of chronic conditions (19% vs. 9%); or an increase in their alcohol or drug use (19% vs. 6%). Similarly, those who have experienced discrimination are more likely than those who haven’t had these experiences to say they always or often felt anxious, lonely, or depressed in the past year. These patterns are similar across racial and ethnic groups and persist even when controlling for other factors like age, income, gender, and LGBT identity.

Black and AIAN adults report facing particularly high rates of discrimination, and among Black adults, those with self-reported darker skin tones are more likely to report discrimination experiences than those with lighter skin tones. AIAN and Black adults are the most likely to report an experience with discrimination in daily life asked about in the survey, with over half of both groups saying they experienced at least one of these types of discrimination at least a few times a year and about three in ten reporting experiencing two or more of these types of discrimination at least a few times a year. Among Black adults, those who say their skin color is “very dark,” “dark,” (62%) or “medium” (55%) are more likely to report an experience with discrimination compared with 42% of Black adults who say their skin color is “very light” or “light.”

Specific discrimination experiences also vary by gender, with Black men being the most likely to say people act as if they are afraid of them and Hispanic women most likely to say they are treated as if they are not smart. About a quarter (27%) of Black men say people acted afraid of them in the past year, as do 17% of Hispanic men and 17% of Black women, compared with about one in ten among Hispanic women and White and Asian adults of either gender. Conversely, the share who say they were treated as if they are not smart is higher among Hispanic women (37%) than Hispanic men (27%). Hispanic women are also more likely than Hispanic men to say they received poorer service than others in stores or restaurants at least a few times in the past year (29% vs. 21%). Discrimination experiences also vary by LGBT identity and age. About two-thirds (65%) of LGBT adults say they experienced at least one form of discrimination measured in the survey in the past year compared to four in ten (40%) non-LGBT adults, although there are no significant differences by race and ethnicity among LGBT adults. Similarly, over half (54%) of adults ages 18-49 report these experiences compared to three in ten (29%) of those ages 50 and over.

Experiences with unfair treatment extend into health care, with Black, Hispanic, AIAN, and Asian adults reporting higher levels of unfair treatment when seeking health care than their White counterparts, and Black women report even higher rates of unfair treatment. About one in five Black adults (18%) and about one in ten Hispanic (11%), Asian (10%) and AIAN (12%) adults say they have been treated unfairly or with disrespect by a health care provider in the past three years because of their race or ethnic background compared with 3% of White adults. Among Black adults, women are more likely than men to say they were treated unfairly by a health care provider because of their racial or ethnic background (21% vs. 13%).

Reflecting experiences with unfair treatment, large shares of Black, AIAN, Hispanic, and Asian adults say they prepare for possible insults or feel they must be very careful about their appearance to be treated fairly during health care visits. Six in ten (60%) Black adults, about half of AIAN (52%) and Hispanic (51%) adults, and four in ten (42%) Asian adults say they prepare for possible insults from providers or staff and/or feel they must be very careful about their appearance to be treated fairly during health care visits at least some of the time compared with one in three (33%) White adults.

Having providers with a shared background matters, as Black, Hispanic, and Asian adults who have more health care visits with providers who share their racial and ethnic background report more frequent positive and respectful interactions. Reflecting limited racial and ethnic diversity of the health care workforce, most Hispanic, Black, Asian, and AIAN adults say that fewer than half of their health care visits in the past three years were with a provider who shared their racial or ethnic background. However, the survey shows how provider racial and ethnic concordance can make a difference in patient interactions. For example, Black adults who had at least half of recent visits with a provider who shares their background are more likely than those who have fewer of these visits to say that their doctor explained things in a way they could understand (90% vs. 85%), involved them in decision-making about their care (84% vs. 73%), understood and respected their cultural values or beliefs (84% vs. 76%), or asked them about social and economic factors (39% vs. 24%) during recent visits.

A third of adults overall report at least one of several negative experiences with a health care provider in the past three years, and many Black, Hispanic, Asian, and AIAN adults say they were treated this way because of their race or ethnicity. These negative experiences include a provider assuming something about them without asking, suggesting they were personally to blame for a health problem, ignoring a direct request or question, or refusing to prescribe pain medication they thought they needed. About a quarter (24%) of Black adults and one in five (19%) AIAN adults say they experienced at least one of these negative experiences and that their race or ethnicity was a reason why they were treated this way, as do 15% of Hispanic and 11% of Asian adults, compared with just 4% of White adults.  Notably, 22% of Black adults who were pregnant or gave birth in the past ten years say they were refused pain medication they thought they needed, roughly twice the share of White adults with a pregnancy or birth experience (10%).

Negative experiences with health care providers as well as language access challenges have consequences for health and health care use. Among adults who used health care in the past three years, one in four (25%) say they had a negative experience (including being treated unfairly or with disrespect, a negative provider interaction, or difficulty with language access), and it led to worse health, being less likely to seek care, and/or switching providers. AIAN and Black adults are more likely than White adults to say they had a negative experience and it contributed to at least one of these consequences.

Implications

The survey reveals that, in wake of the initial COVID-19 pandemic and amid ongoing economic challenges and political division within the U.S., people’s experiences in their everyday lives and in health care settings often vary starkly by race and ethnicity, highlighting the ongoing impacts of racism and discrimination within the health care system and more broadly. The survey shows that many challenges are shared across all adults, including White adults, but that Hispanic, Black, Asian, and AIAN adults face disproportionate challenges and higher rates of unfair treatment due to their race and ethnicity, which have implications for health and well-being. The survey data identify areas for increased attention, resources, and initiatives to address these challenges and disparities, such as mechanisms to improve social and economic circumstances and provide safer communities as well as to address ongoing bias and discrimination, particularly in health care. The survey results also highlight factors that mitigate some of these challenges, including having strong local support networks and more health care visits with providers who have a shared racial and ethnic background. They also illustrate opportunities to increase respectful and positive provider interactions that can support high-quality and culturally competent care. Addressing the challenges identified in the survey is important not only from an equity standpoint but also for improving the nation’s overall health and economic prosperity.

Section 1: Racial and Ethnic Differences in Social and Economic Factors, Safety, and Police Interactions

Historic and contemporary policies contribute to ongoing structural inequities in access to opportunities and resources, shaping where people live, their education and employment, and other factors that influence their daily lives, experiences, and interactions with systems and institutions. For example, historic housing policies, including discriminatory practices such as redlining, have ongoing impacts today, including residential segregation of Black people into urban areas with fewer resources and educational opportunities and higher rates of poverty, violence and crime. The survey shows racial and ethnic differences in finances and employment, feelings of safety and exposure to violence, and interactions with the police that reflect these types of underlying inequities.

Finances and Employment

Black, AIAN, Hispanic, and Asian adults face increased challenges across an array of social and economic factors relative to White adults, which reflect underlying structural inequities, including access to employment and educational opportunities. About three in ten AIAN (29%) and Black (28%) adults, one quarter (24%) of Hispanic adults, and one in five (20%) Asian adults say they or a family member experienced problems getting or keeping a job in the past year compared with 15% of White adults (Figure 1). Just under half of AIAN (48%) and Black (45%) adults say they or a family member had a problem paying for food, housing, transportation, or other necessities in the past year, nearly twice the share of White adults (27%) who report these issues. Similarly, about one in five Black (22%) and AIAN (22%) adults and one in seven Hispanic adults (15%) say they have difficulty affording their bills each month, larger than the share of White adults who say this (11%). In contrast, paying for health care is a common challenge across racial and ethnic groups. At least one in five adults across racial and ethnic groups say they or a family member living with them had a problem paying for health care in the past 12 months. Hispanic adults are more likely than White adults to report problems affording health care in the past year (27% vs. 23%), reflecting that they have a higher rate of being uninsured.

Differences in finances and employment may also reflect bias and discrimination, with Black and AIAN adults more likely than White adults to report unfair treatment in the workplace and in housing. About four in ten Black (42%) and AIAN (42%) adults say they have ever been paid less than other people doing the same job compared with a third (33%) of White adults (Figure 2). Black adults are also more likely than White adults to say they have ever been fired, denied a job, or denied a promotion for unfair reasons (27% vs. 20%), and Black and AIAN adults are more likely than White adults to say they have ever been evicted or denied housing (14% and 13% vs. 5%).

Safety, Exposure to Violence, and Police Interactions

Reflecting ongoing residential segregation patterns rooted in historic and contemporary policies, Black, Hispanic, Asian, and AIAN adults say they feel less safe in their homes and neighborhoods compared to White adults. Large majorities of adults across racial and ethnic groups report feeling at least somewhat safe in their neighborhoods. However, Black, Hispanic, Asian, and AIAN adults are less likely than White adults to say they feel “very safe.” Roughly half of each group say they feel very safe in their neighborhood compared to about two-thirds of White adults (Figure 3). Similarly, vast majorities of adults across racial and ethnic groups say they feel at least somewhat safe in their homes, but the shares of Black, Hispanic, and AIAN adults (61% each) and Asian adults (55%) who report feeling very safe in their home are lower than the share of White adults who say this (73%).

Having a strong network of local support is associated with increased feelings of safety. Specifically, adults who say they have “a lot” or a “fair amount” of family members or friends living near them who they can ask for help or support are more likely to say they feel “very safe” in their home or neighborhood than those who have “just a few” or no family members or friends nearby that they can ask for support. For example, among Asian adults, about six in ten (62%) of those with “a lot” or a “fair amount” of family and friends nearby say they feel very safe in their home compared with about half (47%) of those with “just a few” or no close by family and friends available for support (Figure 4). Among adults overall and across most racial and ethnic groups, this relationship between having a local support network and feelings of safety remains significant even after controlling for other demographic characteristics including education, income, gender, LGBT identity, and age.¹ 

While few adults overall say they or a family member have been a victim of violence, the shares are higher among Hispanic, AIAN, Black, and Asian adults as well as those living in urban areas. About one in ten Hispanic (12%), AIAN (11%), Black (9%), and Asian adults (9%) say they or a family member in their household was a victim of an act of violence such as a robbery, carjacking, or shooting in the past year, about twice the share of White adults (5%) who report the same (Figure 5). Among all adults, those living in urban areas are more likely than those who live in suburban and rural communities to say they or a family member have been a victim of violence in the past year, with about one in ten Hispanic (13%), Black (12%), and White (9%) adults living in urban areas reporting this. Among those living in rural communities, however, Black adults are significantly more likely than White adults to say they or someone in their family have been a victim of violence in the past year (9% vs. 2%).

Beyond differences in safety and experiences with violence, Black, AIAN, and Hispanic adults are more likely than White adults to say they or a family member experienced recent mistreatment by the police, particularly Black and Hispanic adults with self-reported darker skin tones. About one in six AIAN adults (17%) and about one in ten Black (11%) and Hispanic (8%) adults say they or a family member living with them have been threatened or mistreated by the police in the past year compared with 4% of White adults (Figure 6). For Black and Hispanic adults, the shares reporting recent police mistreatment are larger among those with self-described darker skin tones compared to those with lighter skin tones. For example, 12% of Black adults with self-described “very dark,” “dark,” or “medium” skin tones say they or a family member living with them have been threatened or mistreated by police compared to 7% of those with lighter skin tones. Similar differences occur among Hispanic adults between those with darker vs. lighter skin tones (10% vs. 5%).

Daily Worries and Concerns

People’s frequent daily worries and concerns reflect these racial and ethnic differences in finances and employment, safety, and police interactions. About one in five AIAN (23%), Black (21%), and Hispanic (18%) adults say that in the past 30 days they experienced worry or stress related to providing for their family’s basic needs either “every day” or “almost every day” compared to fewer White adults (13%) (Figure 7). Similarly, larger shares of AIAN, Black, and Hispanic adults compared to White adults say they experienced daily or near-daily worry about experiences with racism and discrimination and the possibility of someone in their family being a victim of gun or police violence. AIAN, Black, and Hispanic adults are also more likely to say they frequently experienced worry or stress related to their health in the past 30 days compared to their White counterparts. Having a strong local support network, as measured by having at least a “fair amount” of friends and family living nearby who you can ask for help or support, mitigates the frequency of worries and concerns about providing for basic needs and health among adults overall and across most racial and ethnic groups.² 

Section 2: Experiences with Discrimination in Daily Life and Their Impacts on Wellbeing and Stress

Black, AIAN, Hispanic, and Asian adults are more likely to report certain experiences with discrimination in daily life compared with their White counterparts, with the greatest frequency reported among Black and AIAN adults. For example, about one-third of Black adults (35%) and about a quarter of AIAN (28%), Hispanic (25%), and Asian adults (25%) say that they received poorer service than other people at restaurants or stores at least a few times in the past year, all higher than the share of White adults who say the same (16%) (Figure 8). Similarly, about four in ten AIAN adults (42%) and one-third (33%) of both Black and Hispanic adults say that people have acted as if they think they are not smart at least a few times in the past year, higher than the one-quarter (26%) of White adults who say so. In addition, Asian (17%) and Black adults (16%) are somewhat more likely than White adults (13%) to say they were threatened or harassed at least a few times in the past year. Further, about one in five Black (21%) and AIAN adults (19%) as well as 13% of Hispanic adults say people acted as if they were afraid of them at least a few times in the past year, compared to 9% of White adults. Among those who completed the survey in a language other than English, one-quarter (24%) say they were criticized for speaking another language in public in the past year, including 28% of Hispanic adults who responded in Spanish. Cumulatively, at least half of AIAN (58%), Black (54%), and Hispanic adults (50%) say they have experienced one of these forms of discrimination at least a few times in the past year, as do four in ten Asian adults (42%). About four in ten (38%) White adults also say they have experienced at least one of these types of discrimination in the past year.³ 

Black, AIAN, Hispanic, and Asian adults are more likely than White adults to report experiencing more than one of these types of discrimination. Three in ten Black (31%) and AIAN adults (28%) and about one in four Hispanic (26%) and Asian adults (25%) say they experienced at least two of these types of discrimination at least a few times in the past year, all higher than the share of White adults who say so (18%) (Figure 9).

When discrimination in daily life occurs, Black, Hispanic, AIAN, and Asian adults are far more likely than White adults to say their race or ethnicity was a factor in these experiences. Among those who say they experienced at least one form of discrimination measured in the survey, most Black, Hispanic, AIAN, and Asian adults say their race or ethnicity was a major or minor reason they were treated this way, compared to a much smaller share of White adults. Overall, four in ten Black adults (40%) and about three in ten Hispanic (30%), AIAN (30%), and Asian adults (28%) say they experienced at least one of these acts of discrimination in the past year and say their race or ethnicity was at least a minor reason for these experiences. By contrast, only 6% of White adults report this (Figure 10).

Across racial and ethnic groups, reports of experiences with discrimination in daily life are particularly high among younger adults and LGBT adults. There is a strong relationship between age and reports of discrimination in daily life, with a majority of adults ages 18-29 (62%) and about half of 30-49 year-olds (49%) reporting such experiences compared to smaller shares of those ages 50-64 (36%) and 65 and over (22%) (Figure 11). This pattern is consistent across racial and ethnic groups. Similarly, about two-thirds (65%) of LGBT adults say they experienced at least one form of discrimination measured in the survey at least a few times in the past year compared to four in ten non-LGBT adults (40%). Among LGBT adults, there are no differences by race and ethnicity, with about two-thirds of Hispanic (69%), Black (64%), and White (64%) LGBT adults reporting discrimination experiences in the past year.

A somewhat larger share of women compared to men report at least one of these discrimination experiences, but this overall pattern masks some differences in individual measures. For example, a larger share of women compared to men say they were treated as if they were not smart in the past year (33% vs. 23%), while men are more likely than women to say people acted as if they were afraid of them (14% vs. 9%).

The combination of race, ethnicity, and gender also highlights disproportionate discrimination for certain groups. For example, 27% of Black men say people acted afraid of them in the past year, as do 17% of Hispanic men and 17% of Black women. For Hispanic women and White and Asian adults of either gender, these shares are about one in ten. Conversely, the share who say they were treated as if they are not smart is higher among Hispanic women (37%) than Hispanic men (27%). Hispanic women are also more likely than Hispanic men to say they received poorer services than others in stores or restaurants at least a few times in the past year (29% vs. 21%) (Figure 12).

Black adults who self-describe as having darker skin color report more experiences of discrimination in their everyday lives compared to those with lighter skin color. Most Black adults who say their skin color is “very dark” or “dark” (62%) or “medium” (55%) report at least one of these experiences of discrimination, compared with 42% of Black adults who say their skin color is “very light” or “light.” For example, four in ten Black adults who say their skin color is “very dark” or “dark” (42%) say they have received poorer service at restaurants or stores in the past year, compared with about a quarter of those who say their skin color is “very light” or “light” (27%). Black adults with self-reported darker skin color are also more likely to say people acted as if they were afraid of them in the past year compared with those with lighter skin color (25% vs. 18%) (Figure 13).

Among Black adults, those with higher educational attainment report more experiences with discrimination compared to their counterparts with lower educational attainment. Black adults with a four-year college degree are more likely to report experiences of discrimination in their everyday lives compared to those without college education (59% vs. 52% respectively) (Figure 14). Specifically, about four in ten Black adults with a college degree (42%) say they received poorer service at restaurants or stores at least a few times in the past year compared with one-third of Black adults without a college degree (33%). Black adults with a college degree are also more likely to report people acting as if they are afraid of them compared to those without a four-year degree (28% vs. 19%). These findings are consistent with previous research, and may reflect increased exposure to perceived discrimination among those with higher incomes and education levels as well as having greater awareness of racism and therefore greater ability to identify it in different aspects of life.

Relationship Between Discrimination Experiences and Well-being

Racism is an underlying driver of health disparities and repeated and ongoing exposure to perceived experiences of racism and discrimination can increase risks for poor health outcomes. Research has shown that the exposure to racism and discrimination can lead to negative mental health outcomes and certain negative impacts on physical health, including depression, anxiety, and hypertension. Studies also show that perceived discrimination can negatively impact healthy behaviors by increasing smoking and alcohol use and lowering adherence to medical guidance and preventative screenings.

Among all U.S. adults and across racial and ethnic groups, those who report experiences with discrimination in daily life are more likely than others to report adverse effects from worry or stress such as appetite and sleep issues, increased substance use, and worsening of chronic health conditions. Adults who report experiences with at least one type of discrimination in daily life as measured in the survey are more likely than adults who “rarely” or “never” experienced such discrimination to report certain adverse effects of worry or stress. For example, those who experienced discrimination in their everyday lives are more likely than others to say that in the past 30 days worry or stress has led to sleep problems (65% vs. 35%); poor appetite or overeating (52% vs. 20%) frequent headaches or stomachaches (41% vs. 15%); difficulty controlling their temper (34% vs. 11%); worsening of chronic conditions (19% vs. 9%); or an increase in their alcohol or drug use (19% vs. 6%) (Figure 15). Overall, eight in ten (79%) adults who experienced discrimination in the past year say they have had at least one of these adverse effects of worry and stress, compared to about half (47%) of adults who say they rarely or never had these experiences in the past year. These patterns are similar across racial and ethnic groups. While other underlying factors beyond discrimination may contribute to these differences, the relationship between adverse effects of stress and experiences with discrimination remains significant even after controlling for other demographic characteristics including education, income, gender, LGBT identity, and age.⁴ 

Adults who report discrimination experiences in daily life are more likely than those who say they rarely or never experience discrimination to report always or often feeling lonely, depressed, or anxious in the past 12 months. Among those with discrimination experiences, four in ten (40%) say they “always” or “often” felt anxious in the past year, compared to 14% of adults who rarely or never experience such discrimination. Those with discrimination experiences in their daily life are more than three times as likely as others to say they always or often felt lonely (26% vs. 7%) or depressed (25% vs. 7%) in the past year (Figure 16). These patterns are similar across racial and ethnic groups and persist even after controlling for other demographic characteristics including education, income, gender and age.⁵ 

Section 3: Experiences in Health Care Settings

Reflecting underlying structural inequities in the U.S., there are ongoing racial and ethnic disparities in health and health care. AIAN, Hispanic, and Black people have higher uninsured rates compared to their White counterparts and face other increased barriers to accessing care. Among adults in the survey, Hispanic (20%), AIAN (14%), and Black (10%) adults are more likely to report being uninsured compared to White (6%) adults. However, beyond differences in the ability to access care, the survey highlights differences in experiences within the health care system, including interactions with providers, experiences with unfair treatment, and the consequences of these experiences.

Provider Interactions

Among those who used health care within the past three years, most adults across racial and ethnic groups report having positive and respectful interactions with their health care providers most of the time. Health care that is respectful and responsive to individual preferences, needs, and values is an important component of health care quality and equitable health care. Among those who used health care in the past three years, large shares say that their health care providers explained things in a way they could understand (89%), respected their cultural values and beliefs (84%), involved them in decision-making about their care (81%), and spent enough time with them (76%) most of the time or every time during visits. Across racial and ethnic groups, at least two-thirds say their provider did each of these things at least most of the time.

However, Hispanic, Black, Asian, and AIAN adults report having these positive and respectful interactions with health care providers less often than White adults. For example, AIAN (18%), Asian (18%), and Hispanic (16%) adults are about twice as likely as White adults (8%) to say their health care providers explained things in a way they could understand just some of the time, rarely, or never in the past three years (Figure 17). Similarly, about one in four AIAN adults (24%) and about one in five Black (21%), Asian (21%) and Hispanic (19%) adults say their health care providers understood and respected their cultural beliefs just some of the time, rarely, or never compared with about one in ten White adults (12%). These groups also are more likely than their White counterparts to say their providers did not frequently involve them in decision-making about their care during their visits in the past three years. Many of these racial and ethnic differences persist among adults with higher incomes and those with health coverage. Among Hispanic adults there also are some differences by English proficiency. For example, Hispanic adults who have limited English proficiency are about twice as likely as those who are English proficient to say they their providers rarely or never involved them in decision-making about their care in the past three years (16% vs. 9%).

Few adults across racial and ethnic groups say a health care provider frequently asked them about their work, housing situation, or access to food or transportation in the last three years. While health coverage and access to health care shape health, social and economic factors, such as employment, housing, food access, and transportation also play a major role. Research indicates that screening for social and economic risks can positively impact health, and there are growing efforts among some providers focused on serving low-income populations to screen for social risks and needs. However, just over one in four (27%) adults who used health care in the last three years say a health care provider asked about their work, housing situation, or access to food or transportation at least most of the time during visits (Figure 18). Overall, and among Hispanic and Asian adults, lower income adults are more likely than higher income adults to say a provider asked about these factors most of the time or every time, but majorities of lower income adults still say this happens just some of the time, rarely, or never.

Reflecting limited racial and ethnic diversity of the health care workforce, Black, Hispanic, AIAN, and Asian adults are less likely than White adults to say most of their recent health care visits were with a provider who shares their racial and ethnic background. At least half of Black (62%), Hispanic (56%), AIAN (56%) and Asian (53%) adults who used health care in the past three years say that fewer than half of their visits were with a provider who shared their racial and ethnic background (Figure 19). In contrast, about three-quarters (73%) of White adults say that half or more of their visits were with a provider who shares their racial and ethnic background.

Black, Hispanic and Asian adults who have at least half of their visits with providers who share their racial or ethnic background report having more frequent positive and respectful interactions with providers. For example, among those who used health care in the past three years, Black adults who had at least half of recent visits with a provider who shares their background are more likely than those who have fewer of these visits to say that their doctor explained things in a way they could understand (90% vs. 85%), involved them in decision making about their care (84% vs. 73%), understood or respected their cultural values or beliefs (84% vs. 76%), or asked them about social and economic factors (39% vs. 24%) at least most the time (Figure 20). Patterns are similar for Hispanic adults. Asian adults who had half or more visits with a provider who shared their racial or ethnic background are more likely than those who had fewer such visits to say their provider understood and respected their cultural values and beliefs every time or most of the time.

Despite these differences, few Black, Hispanic, Asian, and AIAN adults say they think they either would or do receive better care from health care providers who share their racial and ethnic background. About a quarter of Black (27%), Hispanic (26%), and Asian adults (24%) and about one in five AIAN adults (19%) say they think they would receive better care from doctors who share their racial or ethnic background, while majorities in each of these groups say they don’t think the race or ethnicity of their provider makes much difference in the quality of care they receive. Other research has shown an association between patients and providers having shared racial and ethnic backgrounds and improved communication but mixed impacts of this patient-provider concordance on patient experiences and health outcomes. However, other recent research suggests racial concordance may contribute to improved health care use and health outcomes including lower emergency department use, reductions in racial disparities in mortality for Black infants, and increased visits for preventative care and treatment.

Experiences with Unfair Treatment by Health Care Providers

About one in five (18%) Black adults and roughly one in ten AIAN (12%), Hispanic (11%), and Asian (10%) adults who received health care in the past three years report being treated unfairly or with disrespect by a health care provider because of their racial or ethnic background. These shares are higher than the 3% of White adults who report this. Among Black adults, women are more likely than men to say they were treated unfairly by a health care provider because of their racial or ethnic background (21% vs. 13%). AIAN (26%) and Black (18%) adults also are more likely than White adults (13%) to say they have been treated unfairly or with disrespect by a health care provider in the past three years due to some other factor, such as their gender, health insurance status, or ability to pay for care (Figure 21). Overall, roughly three in ten (29%) AIAN adults and one in four (24%) Black adults say they were treated unfairly or with disrespect by a health care provider in the past three years for any reason compared with 14% of White adults. Additionally, LGBT adults are about twice as likely as non-LGBT adults to say they experienced unfair treatment by health care provider for any reason in the past three years (33% vs. 15%). Among LGBT adults, similar shares of Black (33%), White (33%), and Hispanic adults (26%) report these experiences.

Reports of unfair treatment by health care providers due to race and ethnicity persist among Black, Hispanic, and Asian adults with higher incomes, who have health coverage, or who have a usual source of care. Overall and among Hispanic adults, those with lower incomes are more likely than those with higher incomes to report unfair or disrespectful treatment by a provider in the past three years. However, even among those with higher incomes (annual household incomes of $90,000 or more), 15% of Black adults report being treated unfairly or with disrespect by a health care provider because of their race or ethnic background, as do higher shares of Asian (8%) and Hispanic (5%) adults compared with White adults (1%) (Figure 22). Among adults with health coverage, 18% of Black adults and one in ten Asian (11%) and Hispanic (10%) adults say they have been treated unfairly by a health care provider in the past three years because of their race or ethnicity compared with 3% of White adults. Similarly, among adults with a usual source of care, one in five Black adults (18%) and about one in ten Hispanic (9%), and Asian (9%) adults report being treated unfairly or with disrespect by a health care provider due to their race or ethnic background compared with just 3% of White adults.

Reflecting these experiences with unfair treatment, large shares of Black, AIAN, Hispanic and Asian adults say that they prepare for possible insults from a provider or staff or feel they need to be very careful about their appearance to be treated fairly during health care visits. Vigilant behaviors, such as preparing for insults or considering one’s appearance, are sometimes adopted by people who experience discrimination as a means of protection from the threat of possible discrimination and to reduce exposure. Research has shown that heightened vigilance is associated with poor physical and mental health outcomes, including hypertension, sleep difficulties, and depression. Over half (55%) of Black adults, about half of AIAN (49%) and Hispanic (47%) adults, and about four in ten (39%) Asian adults say they feel they must be very careful about their appearance at least some of the time to be treated fairly when receiving health care (Figure 23), including one in five (21%) Black adults who say they feel they have to be careful “every time.” Each of these groups is more likely than White adults to report being vigilant about their appearance during health care visits at least some of the time, although notably about three in ten (29%) White adults say they take these actions. About three in ten (29%) Black adults and roughly a quarter of AIAN (26%) and Hispanic (23%) adults say they try to prepare for possible insults during health care visits, all higher than the share of White adults who say this (16%). About one in five Asian adults (19%) also report preparing for insults. Together, six in ten (60%) Black adults, about half of AIAN (52%) and Hispanic (51%) adults, and about four in ten (42%) Asian adults say they engage in at least one of these practices at least some of the time during health care visits compared with one in three (33%) White adults. These shares are also particularly high among LGBT adults across racial and ethnic groups, with at least six in ten Black (63%), Hispanic (61%), and White (60%), LGBT adults saying they take either of these steps.

Among higher income adults and those with health coverage, Black, Hispanic, and Asian adults remain more likely than White adults to say they prepare for insults during health care visits or feel they need to be very careful about their appearance to be treated fairly at least some of the time. While higher income adults generally are less likely than those with lower incomes to say they take these steps, among those with higher incomes ($90,000 or more annually), about half of Black adults (51%) and about four in ten Hispanic (41%) and Asian (38%) adults say they take either of these steps at least some of the time during health care visits, higher than the share of White adults (22%) who say this. Similarly, among adults with coverage, Black (59%), Hispanic (51%), and Asian (42%) adults are more likely than their White (34%) counterparts to say they take either of these steps at least some of the time, including 47% of insured Black adults who say they feel they have to be very careful about their appearance (Figure 24).

Negative Experiences When Receiving Health Care

A third of adults who received health care in the past three years report at least one of several negative experiences with a health care provider, including a provider assuming something about them without asking, suggesting they were personally to blame for a health problem, ignoring a direct request or question, or refusing to prescribe pain medication they thought they needed. While many of these negative experiences are shared across racial and ethnic groups, including White adults, AIAN adults are more likely than their White counterparts to say a provider assumed something without asking (29% vs. 19%), ignored a direct request or question (29% vs. 15%), and refused to prescribe pain medication they thought they needed (19% vs. 9%) (Figure 25). In addition, Black adults are more likely than White adults to say a provider ignored a direct request or question (19% vs. 15%) or refused them pain medication they thought they needed (15% vs. 9%). These differences persist among Black adults with health coverage but are not significant after controlling for income.

AIAN, Black, Hispanic, and Asian adults are more likely than White adults to say they had at least one of these negative experiences with a health care provider due to their race and ethnicity. Among adults who received health care in the past three years, about a quarter (24%) of Black adults and one in five (19%) AIAN adults say they experienced at least one of these negative experiences and that their race or ethnicity was a major or minor reason why they were treated this way, as do 15% of Hispanic and 11% of Asian adults. Just 4% of White adults who received care report a negative experience due to their race or ethnicity.

White and Asian women are more likely than their male counterparts to report at least one of these negative experiences with health care providers, but there are no significant differences between Hispanic and Black women and men. Among adults overall, the largest differences by gender include women being more likely than men to say a provider assumed something without asking and a provider ignoring a direct request or question (Figure 26). Additionally, 22% of Black adults who were pregnant or gave birth in the past ten years say they were refused pain medication they thought they needed, roughly twice the share of White adults with a pregnancy or birth experience (10%).

Among adults with limited English proficiency, about half (48%) say that difficulty speaking or reading English made it difficult to complete at least one of several activities related to using health care in the past three years. These activities include filling out forms at a doctor’s office (34%), communicating with staff at a doctor’s office (33%), understanding instructions from a health care provider (30%), filling a prescription or knowing how to use it (27%), or scheduling a medical appointment (25%).

One in four adults who used health care in the past three years report that they had one or more of these negative experiences with a health care provider and/or a language access challenge and that it resulted in worse health, them being less likely to seek care, and/or them switching providers. About four in ten (39%) AIAN adults and three in ten (30%) Black adults say they had a negative experience with one of these consequences compared with about one in four (24%) White adults (Figure 27). The shares of Hispanic and Asian adults reporting a negative experience with at least one of these consequences are similar to that of White adults.

Implications

The survey reveals that, in wake of the initial COVID-19 pandemic and amid ongoing economic challenges and political division within the U.S., people’s experiences in their everyday lives and in health care settings often vary starkly by race and ethnicity, highlighting the ongoing impacts of racism and discrimination within the health care system and more broadly. The survey shows that many challenges are shared across all adults, including White adults, but that Hispanic, Black, Asian, and AIAN adults face disproportionate challenges and higher rates of unfair treatment due to their race and ethnicity, which have implications for health and well-being. The survey data identify areas for increased attention, resources, and initiatives to address these challenges and disparities, such as mechanisms to improve social and economic circumstances and provide safer communities as well as to address ongoing bias and discrimination, particularly in health care. The survey results also highlight factors that mitigate some of these challenges, including having strong local support networks and more health care visits with providers who have a shared racial and ethnic background. They also illustrate opportunities to increase respectful and positive provider interactions that can support high-quality and culturally competent care. Addressing the challenges identified in the survey is important not only from an equity standpoint but also for improving the nation’s overall health and economic prosperity.

The Survey on Racism, Discrimination, and Health was designed and analyzed by researchers at KFF. The survey was conducted June 6 – August 14, 2023, online and by telephone among a nationally representative sample of 6,292 U.S. adults in English (5,706), Spanish (520), Chinese (37), Korean (16), and Vietnamese (13).

The sample includes 5,073 adults who were reached through an address-based sample (ABS) and completed the survey online (4,529) or over the phone (544). An additional 1,219 adults were reached through a random digit dial telephone (RDD) sample of prepaid (pay-as-you-go) cell phone numbers. Marketing Systems Groups (MSG) provided both the ABS and RDD sample. All fieldwork was managed by SSRS of Glen Mills, PA; sampling design and weighting was done in collaboration with KFF.

Sampling strategy:

The project was designed to reach a large sample of Black adults, Hispanic adults, and Asian adults. To accomplish this, the sampling strategy included increased efforts to reach geographic areas with larger shares of the population having less than a college education and larger shares of households with a Hispanic, Black, and/or Asian resident within the ABS sample, and geographic areas with larger shares of Hispanic and non-Hispanic Black adults within the RDD sample.

The ABS was divided into areas (strata) based on the share of households with a Hispanic, Black, and/or Asian resident, as well as the share of the population with a college degree within each Census block group. To increase the likelihood of reaching the populations of interest, strata with higher incidence of Hispanic, Black, and Asian households, and with lower educational attainment, were oversampled in the ABS design. The RDD sample of prepaid (pay-as-you-go) cell phone numbers was disproportionately stratified to reach Hispanic and non-Hispanic Black respondents based on incidence of these populations at the county level.

Incentives:

Respondents received a $10 incentive for their participation, with interviews completed by phone receiving a mailed check and web respondents receiving a $10 electronic gift card incentive to their choice of six companies, a Visa gift card, or a CharityChoice donation.

Community and expert input:

Input from organizations and individuals that directly serve or have expertise in issues facing historically underserved or marginalized populations helped shape the questionnaire and reporting. These community representatives were offered a modest honorarium for their time and effort to provide input, attend meetings, and offer their expertise on dissemination of findings.

Translation:

After the content of the questionnaire was largely finalized, SSRS conducted a telephone pretest in English and adjustments were made to the questionnaire. Following the English pretest, Cetra Language Solutions translated the survey instrument from English into the four languages outlined above and checked the CATI and web programming to ensure translations were properly overlayed. Additionally, phone interviewing supervisors fluent in each language reviewed the final programmed survey to ensure all translations were accurate and reflected the same meaning as the English version of the survey.

Data quality check:

A series of data quality checks were run on the final data. The online questionnaire included two questions designed to establish that respondents were paying attention and cases were monitored for data quality including item non-response, mean length, and straight lining. Cases were removed from the data if they failed two or more of these quality checks. Based on this criterion, 4 cases were removed.

Weighting:

The combined cell phone and ABS samples were weighted to match the sample’s demographics to the national U.S. adult population using data from the Census Bureau’s 2021 Current Population Survey (CPS). The combined sample was divided into five groups based on race or ethnicity (White alone, non-Hispanic; Hispanic; Black alone, non-Hispanic; Asian alone, non-Hispanic; and other race or multi-racial, non-Hispanic) and each group was weighted separately. Within each group, the weighting parameters included sex, age, education, nativity, citizenship, census region, urbanicity, and household tenure. For the Hispanic and Asian groups, English language proficiency and country of origin were also included in the weighting adjustment. The general population weight combines the five groups and weights them proportionally to their population size.

A separate weight was created for the American Indian and Alaska Native (AIAN) sample using data from the Census Bureau’s 2022 American Community Survey (ACS). The weighting parameters for this group included sex, education, race and ethnicity, region, nativity, and citizenship. For more information on the AIAN sample including some limitations, adjustments made to make the sample more representative, and considerations for data interpretation, see Appendix 2.

All weights also take into account differences in the probability of selection for each sample type (ABS and prepaid cell phone). This includes adjustment for the sample design and geographic stratification of the samples, and within household probability of selection.

The margin of sampling error including the design effect for the full sample is plus or minus 2 percentage points. Numbers of respondents and margins of sampling error for key subgroups are shown in the table below. Appendix 1 provides more detail on how race and ethnicity was measured in this survey and the coding of the analysis groups. For results based on other subgroups, the margin of sampling error may be higher. All tests of statistical significance account for the design effect due to weighting. Dependent t-tests were used to test for statistical significance across the overlapping groups.

Sample sizes and margins of sampling error for other subgroups are available by request. Sampling error is only one of many potential sources of error and there may be other unmeasured error in this or any other public opinion poll. KFF public opinion and survey research is a charter member of the Transparency Initiative of the American Association for Public Opinion Research.

Appendix 1

Racial and ethnic groups included in this report are defined using a two-question format. The initial question asks respondents if they are of Latino or Hispanic origin or descent. The second question asks respondents to select as many racial identity groups as apply from a list that includes eight response options: White, Black or African-American, Asian, American Indian, Alaska Native, Native Hawaiian, Pacific Islander, and “some other race” (with a text box for respondents to provide details). The wording for these questions is similar to the standard two-question measure used by the U.S. Census Bureau, other government organizations, and some survey research organizations. Using the two-question format and “select all that apply” for racial identity allows respondents to self-identify into multiple categories that better reflect their racial and/or ethnic identity or identities.

The table below provides some breakdown on the racial and ethnic identities for the Hispanic, Black, Asian, and American Indian and Alaska Native (AIAN) groups. It includes both the unweighted number of interviews and the weighted proportion within each group, including the share who selected only one race (single race), the share who selected more than one race (multiracial), and the share who selected Hispanic ethnicity within each of these groups. About nine in ten Hispanic adults identify as Hispanic and a single race, and at least eight in ten Black and Asian adults identify as a single race and non-Hispanic. By contrast, most AIAN adults identify as multiracial and about one-third identify as Hispanic.

Appendix 2: Reporting on the Experiences of American Indian and Alaska Native Adults

Sample and Population Represented: The KFF Survey on Racism, Discrimination, and Health was designed to include large samples of adults identifying as Black or African American, Latino or Hispanic, and Asian, with the goal of reporting results specifically for these populations. More details on the sampling strategy are available in the project Methodology.

In addition to these planned larger samples, the sample design also yielded 267 interviews with individuals identifying as American Indian (n=263) and/or Alaska Native (n=6). The American Indian and Alaska Native (AIAN) sample includes individuals who identified AIAN as their only racial identity as well as those who selected AIAN and at least one other race, as well as those who identified as having Hispanic and non-Hispanic ethnicity (see Appendix 1 for more details on how racial and ethnic groups were categorized in this analysis).

Limitations and Data Quality Considerations: Given ongoing concerns about data erasure and invisibility of smaller populations, including Indigenous people, KFF has decided to include results for the AIAN population in this report despite some limitations. The following describes the limitations of the sample, adjustments made to make the sample more representative, and considerations for data interpretation.

Because the survey was not explicitly designed to include a representative sample of AIAN people, the research falls short of some recommended best practices for surveying this population. These include advance outreach to Tribal organizations, face-to-face interviews for some groups, and geographic oversampling of federally recognized Indian reservations and other Tribal lands. The small size of the AIAN sample also does not allow for reporting on more detailed groups (such as within the AIAN population by age, geography, and other demographics).

To increase representativeness of the results, the AIAN sample was weighted to the most current demographic data available for this population: the U.S. Census Bureau’s 2022 American Community Survey (ACS). The weighting parameters include education, age by gender, region, nativity (U.S.-born vs. foreign-born), race and ethnicity, and U.S. citizenship.

Researchers also took other steps to assess the representativeness of the AIAN sample. While Tribal lands were not explicitly included in the sampling strategy, the sample was analyzed to assess whether individuals living on federally recognized Indian reservations and other Tribal lands could have responded to the survey using the Census tracts and zip codes of survey respondents. Overall, 19% of respondents in the AIAN sample live in geographic areas where Tribal lands are located, including 25% of those who identify as AIAN alone. In addition, researchers compared this AIAN sample to other federally available data across a series of benchmarks not included in the weighting such as insurance status, insurance type, income, and the use of English in the household.

Despite these efforts, we suggest using caution when interpreting the results of the AIAN sample in the report. The data included may not be reflective of the entirety of experiences of the AIAN adult population. It may particularly fall short of capturing the experiences of those living on federally recognized Indian reservations and other Tribal lands. In addition, the Census data used for weighting is imperfect and may overrepresent the experiences of multiracial AIAN individuals, which may lead to a similar imbalance in the survey results.

KFF is committed to improving our future efforts and working to more fully represent and reflect the diversity of experiences among Indigenous people in our survey and other work.

1. To examine the relationship between variables of interest throughout the report, a series of logistic regression models were conducted to test whether these relationships hold after controlling for demographics. The demographic variables used across models include educational attainment, income, age, gender, LGBT identity, and region. Race and ethnicity were also included as control variables in the overall model. To test the same relationships within racial and ethnic groups, separate logistic regressions were conducted among Hispanic adults, Black adults, Asian adults, and White adults, controlling for the same demographics listed. ↩︎
2. Based on regression analysis as described above. ↩︎
3. The survey questionnaire was designed by KFF researchers, drawing on previous research. Several items measuring discrimination in everyday life were informed by the Everyday Discrimination Scale. For more information see: https://scholar.harvard.edu/davidrwilliams/node/32397 ↩︎
4. Based on regression analysis as described above. ↩︎
5. Based on regression analysis as described above. ↩︎