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2021 National Healthcare Quality and Disparities Report [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2021 Dec.

Cover of 2021 National Healthcare Quality and Disparities Report

2021 National Healthcare Quality and Disparities Report [Internet].

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DISPARITIES IN HEALTHCARE

Healthcare delivery is not experienced equitably by all populations. A healthcare disparity is a difference between population groups in the way they access, experience, and receive healthcare. Factors that influence healthcare disparities include social, economic, environmental, and other disadvantages,1, 2 some of which are explored in this report.

Unfortunately, Americans too often do not receive care they need, or they receive care that causes harm. Care can be delivered too late or without full consideration of a patient’s preferences and values. Many times, our healthcare system distributes services inefficiently and unevenly across populations. Some Americans receive worse care than others. These disparities may occur for a variety of reasons, including differences in access to care, social determinants, provider biases, poor provider-patient communication, and poor health literacy.

Research Framework for Health Disparities

The Research Framework in Exhibit 1 was developed by the National Institutes of Health (NIH) National Institute on Minority Health and Health Disparities (NIMHD). This framework is based on an evolving conceptualization of factors relevant to the understanding and promotion of minority health and to the understanding and reduction of health disparities.

The framework serves as a vehicle for encouraging NIH-supported research that addresses the complex and multifaceted nature of minority health and health disparities. This research needs to span different domains of influence (Biological, Behavioral, Physical/Built Environment, Sociocultural Environment, Healthcare System) and different levels of influence (Individual, Interpersonal, Community, Societal) within those domains.

The framework also provides a classification structure that facilitates analysis of the NIMHD minority health and health disparities research portfolios to assess progress, gaps, and opportunities. Examples of factors are provided within each cell of the framework (e.g., Family Microbiome within the Interpersonal-Biological cell). These factors are not intended to be exhaustive. Health disparity populations, as well as other features of this framework, may be adjusted over time.

Matrix showing the intersection of levels of influence (individual, interpersonal, community, and societal) and domains of influence over the lifecourse (biological, behavioral, physical/built environment, sociocultural environment, health care system) -
Individual:
Biological: biological vulnerability and mechanisms
Behavioral: health behaviors, coping strategies
Physical/built environment: personal environment
Sociocultural environment: sociodemographics, limited English, cultural identity, response to discrimination
Health care system: insurance coverage, health literacy, treatment preferences
Interpersonal
Biological: caregiver-child interaction, family microbiome
Behavioral: family functioning, school/work functioning
Physical/built environment: household environment, school/work environment
Sociocultural environment: social networks, family/peer norms, interpersonal discrimination
Health care system: patient-clinician relationship, medical decision making
Community
Biological: community illness exposure, herd immunity
Behavioral: community functioning
Physical/built environment: community environment, community resources
Sociocultural environment: community norms, local structural discrimination
Health care system: availability of services, safety net services
Societal
Biological: sanitation, immunization, pathogen exposure
Behavioral: policies and laws
Physical/built environment: societal structure
Sociocultural environment: social norms, societal structural discrimination
Health care system: quality of care, health care policies
Health outcomes: individual health, family/organizational health, community health, population health

Exhibit 1

NIMHD Research Framework. * Health Disparity Populations: Race/ethnicity, low socioeconomic status, rural, sexual/gender minority. Other Fundamental Characteristics: Sex/gender, disability, geographic region.

Role of Research Framework in the NHQDR

The NHQDR reports on progress and opportunities for improving healthcare quality and reducing healthcare disparities. The NIMHD Minority Health and Health Disparities Research Framework highlights factors ranging from individual biology and behavior to social structure that affect disparities. To successfully reduce disparities, it is necessary to address all these factors.

All Americans should have equitable access to high-quality care. Instead, racial and ethnic minorities and poor people often face more barriers to care and receive poorer quality of care when they can get it.3 In this report, measures were analyzed to assess disparities both by socioeconomic and cultural groups and by settings of care.

An increasing number of healthcare organizations and payers are experimenting with strategies to identify needs and connect patients to resources that address identified needs. The goals are to improve health outcomes, reduce avoidable use of costly health services, and improve health equity.4

Inequitable health outcomes result from inequities in the distribution of or access to resources that promote good health outcomes. Differences refer to outcomes that result from biological risk or other factors that are not a matter of policy or discrimination in access. A difference may become a disparity when some subgroups and not others are given access to resources to manage their differential risk from biology or other factors and the groups without access have poorer outcomes. Thus, differences and disparities may have different determinants requiring different forms of intervention.5

The Disparities in Healthcare section of the 2021 NHQDR examines the best and worst performing quality measures among the measures used in the report. These quality measures are analyzed in this section of the report by race and ethnicity, income, insurance status, and residence location. While these categories are broad, each section begins with key definitions to orient readers and includes analyses showing quality measure performance in the latest data year and analyses showing whether disparities were widening or narrowing over time.

More information on the measures included in this section of the report is available through the NHQDR Data Query Tool (https://datatools.ahrq.gov/nhqdr). The tool also allows readers to stratify NHQDR data by variables such as education, sex, and age, where available.

Racial and Ethnic Disparities

Researchers, patients, providers, and policymakers have worked to identify, understand, and eliminate the disparities experienced by different racial and ethnic groups across the healthcare system. In 1985, the Department of Health and Human Services published the Report of the Secretary’s Task Force on Black and Minority Health (Heckler Report), which marked the first comprehensive study of racial and minority health by the U.S. government.6 Since then, the Department, along with other stakeholders, has continued this work, including throughout the NHQDR. The growing evidence base shows that patients of different racial and ethnic groups experience quality of care inequitably and disparately.7, 8

Racial and ethnic groups are defined according to Standards for the Classification of Federal Data on Race and Ethnicity, issued by the Office of Management and Budget (available at https://www.gpo.gov/fdsys/granule/FR-1997-10-30/97-28653). The basic racial and ethnic categories for federal statistics and program administrative reporting are defined as follows:

  1. American Indian or Alaska Native (AI/AN). A person having origins in any of the original peoples of North and South America (including Central America) and maintains tribal affiliation or community attachment.
  2. Asian. A person having origins in any of the original peoples of the Far East, Southeast Asia, or Indian subcontinent, including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.
  3. Black or African American. A person having origins in any of the Black racial groups of Africa. Terms such as “Haitian” can be used in addition to “Black or African American.”
  4. Hispanic or Latino. A person of Cuban, Mexican, Puerto Rican, Central or South American, or other Spanish culture or origin, regardless of race. The term “Spanish origin” can be used in addition to “Hispanic or Latino.”
  5. Native Hawaiian/Pacific Islander (NHPI). A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.
  6. White. A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.

This section presents three types of findings related to disparities for each population:

  1. Largest disparities for a single data year, focusing on the most recent data year.
  2. Trends in quality of care (number of measures improving, not changing, and worsening) for the population group.
  3. Comparison with the reference group, focusing on the change in the gap between the two groups (gap is narrowing, widening, and not changing).

Overview of Racial and Ethnic Disparities

Figure 1 displays the number of quality measures for which each racial or ethnic group experienced better, same, or worse quality care compared with White populations in the latest data year. Figure 2 shows the number of quality measures with disparities at baseline that were narrowing (improving), widening (worsening), or not changing.xix

Stacked bar chart showing number of quality measures:
AI/AN (n=108), Better, 12, Same, 53, Worse, 43
Asian (n=173), Better, 50, Same, 75, Worse, 48
Black (n=195), Better, 21, Same, 90, Worse, 84
NHPI (n=81), Better, 15, Same, 43, Worse, 23
Hispanic (n=172), Better, 34, Same, 76, Worse, 62

Figure 1

Number and percentage of quality measures for which members of selected groups experienced better, same, or worse quality of care compared with White people for the most recent data year, 2015, 2017, 2018, or 2019. Key: n = number of measures; AI/AN = (more...)

  • Black populations received worse care than White populations for 43% of quality measures (Figure 1).
  • AI/AN populations received worse care than White populations for 40% of quality measures.
  • Hispanic populations received worse care than non-Hispanic White populations for 36% of quality measures.
  • Asian and NHPI populations received worse care than White populations for about 30% of quality measures but Asian populations also received better care for about 30% of quality measures.
Stacked bar chart showing number of quality measures:
AI/AN (n=40), Improving, 3, Not Changing, 37, Worsening, 0.
Asian (n=41), Improving, 1, Not Changing, 39, Worsening, 1.
Black (n=63), Improving, 2, Not Changing, 60, Worsening, 1.
NHPI (n=20), Improving, 2, Not Changing, 18, Worsening, 0.
Hispanic (n=49), Improving, 2, Not Changing, 47, Worsening, 0.

Figure 2

Number and percentage of quality measures with disparity at baseline for which disparities related to race and ethnicity were improving, not changing, or worsening over time, 2000 through 2015, 2016, 2017, 2018, or 2019. Key: n = number of measures; AI/AN (more...)

  • For all racial and ethnic groups, at least 90% of measures showed no change in disparities (Figure 2).
  • Three measures showed improvement in disparities between AI/AN populations and White populations.
  • Black populations and NHPI populations each had two measures that showed improvement in disparities.
  • Two measures showed improvement between Hispanic populations and non-Hispanic White populations.
  • One measure for Asian populations showed improvement in disparities: People age 13 and over living with HIV who know their HIV status.
  • One measure for Asian populations showed worsening disparities: Home health care patients whose management of oral medications improved.
  • One measure for Black populations showed worsening disparities: Emergency department visits for asthma, ages 2–19.
  • No worsening disparities were observed for AI/AN, Hispanic, or NHPI populations.
  • Fewer quality measures are available for select subpopulations overall.

Disparities for American Indian and Alaska Native Populations

This section presents disparities in quality of care and, new in 2021, access to care for American Indian and Alaska Native (AI/AN) populations. To provide context, findings for other ethnic and racial populations may be included. Additional details on disparities of care for other priority populations are presented in population-specific sections of this report.

Snapshot of Disparities in Access to Care

Stacked bar chart showing number of access measures:
AI/AN vs. White (n=8), Better, 0, Same, 4, Worse, 4.
Asian vs. White (n=14), Better, 2, Same, 8, Worse, 4.
Black vs. White (n=15), Better, 0, Same, 7, Worse, 8.
NHPI vs. White (n=4), Better, 0, Same, 4, Worse, 0.
>1 Race vs. White (n=14), Better, 0, Same, 11, Worse, 3.

Figure 3

Number and percentage of access measures for which members of selected racial groups experienced better, same, or worse access to care compared with White people, 2017–2019. Key: AI/AN = American Indian or Alaska Native, NHPI = Native Hawaiian/Pacific (more...)

  • AI/AN people had worse access to care than White people for 50% of access measures (Figure 3).

Disparities in Quality of Care

American Indian and Alaska Native people experienced worse quality care compared with White people for 40% of all quality measures and 63% of Person-Centered Care measures.

Stacked bar chart showing number of quality measures:
Total (n=110), Better, 20, Same, 48, Worse, 42.
Person-Centered Care (n=19), Better, 1, Same, 6, Worse, 12.
Patient Safety (n=13), Better, 1, Same, 9, Worse, 3.
Care Coordination (n=7), Better, 1, Same, 1, Worse, 5.
Affordable Care (n=1), Better, 0, Same, 1, Worse, 0.
Effective Treatment (n=21), Better, 11, Same, 6, Worse, 4.
Healthy Living (n=45), Better, 4, Same, 23, Worse, 18.

Figure 4

Number and percentage of quality measures for which American Indian and Alaska Native people experienced better, same, or worse quality of care compared with White people for the most recent data year, 2015, 2017, 2018, or 2019. Key: n = number of measures. (more...)

  • Data for the most recent year show that quality care was worse for AI/AN people than for White people for 40% of all quality measures and that quality was better for AI/AN people than for White people for 18% of all quality measures (Figure 4).

Measures with the largest disparities for AI/AN people for the most recent year where data were available include:

  • Hospital patients who received influenza vaccination.
  • Patients with colon cancer who received surgical resection of colon cancer that included at least 12 lymph nodes pathologically examined.
  • New HIV cases per 100,000 population age 13 and over.

Influenza Vaccination

Overall, adjusting for age, Black people had the highest flu-associated hospitalization rates across 10 flu seasons, followed by AI/AN and Hispanic people, with similar trends for intensive care admission rates. Among AI/AN children, rates were 3 to 3.5 times higher for all three severe flu-related outcomes.9

Current clinical guidelines show that people who are 6 months or older should receive an annual flu vaccine, but not all patients can access vaccines or treatment if they become ill. CDC details preventive strategies (https://www.cdc.gov/flu/prevent/index.html) to protect against the flu. Moreover, current research shows that influenza vaccination even provides effective flu protection in patients with chronic obstructive pulmonary disease (COPD).10

Columns showing percentage:
2016 achievable benchmark: 96.6%.
Total, 92
AI/AN, 81.6
Asian, 92.9
Black, 91.6
NHPI, 88.7
White, 92.7

Figure 5

Hospital patients who received influenza vaccination, 2018. Key: AI/AN = American Indian or Alaska Native, NHPI = Native Hawaiian/Pacific Islander. Note: The benchmark calculation takes the average of the top 10% of states with statistically reliable (more...)

  • In 2018, 81.6% of AI/AN hospital patients received influenza vaccinations compared with 92.7% of White patients (Figure 5).
  • The 2016 achievable benchmark was 96.6%.
  • The top 10% of states that contributed to the achievable benchmark were Florida, Indiana, Maine, Utah, and Virginia.

Patients With Colon Cancer

Healthy People 2020 objectives include reducing the colorectal cancer incidence rate to 40 per 100,000 people and the mortality rate to 14.5 per 100,000 people.11 Healthy People 2020 also includes an objective for colorectal cancer screening. The USPSTF expanded the recommended ages for colorectal cancer screening to 45 to 75 years (previously, it was 50 to 75 years). The USPSTF continues to recommend selectively screening adults ages 76 to 85 years for colorectal cancer.12

The American Cancer Society’s newest guidelines recommend that colorectal cancer screenings begin at age 45. The recommended age was lowered from 50 to 45 because colorectal cancer cases are on the rise among young and middle-age people.13

Columns showing percent.
2015 achievable benchmark: 95.5%
Total, 92.9
AI/AN, 83.7
Asian, 94.7
Black, 91.8
NHPI, 95.1
White, 93

Figure 6

Patients with colon cancer who received surgical resection of colon cancer that included at least 12 lymph nodes pathologically examined, 2017. Key: AI/AN = American Indian or Alaska Native, NHPI = Native Hawaiian/Pacific Islander. Note: The benchmark (more...)

  • In 2017, the percentage of patients with colon cancer who received surgical resection of colon cancer that included examination of at least 12 lymph nodes was lower for AI/AN people (83.7%) compared with White people (93%) (Figure 6).
  • The 2015 achievable benchmark was 95.5%.
  • The top 10% of states that contributed to the achievable benchmark were District of Columbia, Maine, Massachusetts, Rhode Island, and Vermont.

New HIV Infections

Recent CDC data show new HIV infections fell 8% from 2015 to 2019, after a period of general stability in new infections in the United States.14 AI/AN people represent about 1.3% of the U.S. population and less than 1% (186) of the HIV diagnoses in 2018 in the United States and dependent areas.15

It is important for everyone to know their HIV status. People who do not know they have HIV cannot take advantage of HIV care and treatment and may unknowingly pass HIV to others.

The United States has 574 federally recognized AI/AN tribes and many different languages. Meaningful engagement with tribal nations is critically important in creating culturally appropriate prevention programs to reduce HIV transmission.

Poverty, including limited access to high-quality housing, directly and indirectly increases the risk of HIV infection and affects the health of people who have and are at risk for HIV infection. Additional structural factors that influence risks of HIV infection in tribal communities are high rates of poverty, lower levels of education, unemployment, and lack of health insurance.

Columns showing rate per 100,000 population:
2015 achievable benchmark: 4.2 per 100,000 population.
Total, 12.9
AI/AN, 10.5
Asian, 4.5
Black, 45.3
>1 Race, 16
NHPI, 13.9
White, 5.3

Figure 7

New HIV cases per 100,000 population age 13 and over, 2019. Key: AI/AN = American Indian or Alaska Native, NHPI = Native Hawaiian/Pacific Islander. Note: The benchmark calculation takes the average of the top 10% of states with statistically reliable (more...)

  • In 2019, the percentage of new HIV cases was higher for AI/AN people (10.5%) compared with White people (5.3%) (Figure 7).
  • The 2015 achievable benchmark was 4.2 per 100,000 population.
  • The top 10% of states that contributed to the achievable benchmark were Idaho, Iowa, Maine, West Virginia, and Wisconsin.
Resource

BESAFE: A Cultural Competency Model for American Indians, Alaska Natives, and Native Hawaiians is a cultural competency guide for healthcare professionals who provide care for American Indian, Alaska Native, and Native Hawaiian patients infected with HIV. It is based on the BESAFE framework, which addresses:

  • Barriers to Care.
  • Ethics.
  • Sensitivity of the Provider.
  • Assessment.
  • Facts.
  • Encounters.

Trends in Quality of Care for American Indian and Alaska Native Populations

Stacked bar chart showing number of quality measures:
Total (n=116), Improving, 53, Not Changing, 55, Worsening, 8.
Person-Centered Care (n=22), Improving, 5, Not Changing, 15, Worsening, 2.
Patient Safety (n=13), Improving, 6, Not Changing, 6, Worsening, 1.
Care Coordination (n=8), Improving, 3, Not Changing, 5, Worsening, 0.
Affordable Care (n=1), Improving, 0, Not Changing, 1, Worsening, 0.
Effective Treatment (n=21), Improving, 11, Not Changing, 6, Worsening, 4.
Healthy Living (n=51), Improving, 28, Not Changing, 22, Worsening, 1.

Figure 8

Number and percentage of all quality measures that were improving, not changing, or worsening over time, total and by priority area, from 2000 to 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, or 2019. Key: n = number of measures. Note: For each (more...)

  • Among the 116 quality measures with data for AI/AN people, 53 (46%) were improving, 55 (47%) were not changing, and 8 (7%) were getting worse from 2000 through 2019 (Figure 8).
  • Effective Treatment (52%) and Healthy Living (55%) showed the most improvement.

Changes in Disparities for American Indian and Alaska Native Populations

Stacked bar chart showing number of quality measures:
Total (n=40), Improving, 3, Not Changing, 37, Worsening, 0.
Person-Centered Care (n=12), Improving, 0, Not Changing, 12, Worsening, 0.
Patient Safety (n=3), Improving, 0, Not Changing, 3, Worsening, 0.
Care Coordination (n=6), Improving, 0, Not Changing, 6, Worsening, 0.
Affordable Care (n=0).
Effective Treatment (n=5), Improving, 1, Not Changing, 4, Worsening, 0.
Healthy Living (n=14), Improving, 2, Not Changing, 12, Worsening, 0.

Figure 9

Number and percentage of quality measures with disparity at baseline for which disparities between AI/AN people and White people were improving, not changing, or worsening over time, total and by priority area, from 2000 to 2010, 2011, 2012, 2013, 2014, (more...)

  • Disparities between AI/AN people and White people did not change for most of the quality measures from 2000 through 2019. Of 40 quality measures with a disparity at baseline, 37 (93%) were not changing (Figure 9).
  • Only three measures showed narrowing disparities:
    • Adjusted incident rates of end stage renal disease (ESRD) due to diabetes per million population.
    • Children ages 2–17 for whom a health provider gave advice within the past 2 years about the amount and kind of exercise, sports, or physically active hobbies they should have.
    • Children ages 2–17 for whom a health provider gave advice within the past 2 years about healthy eating.
  • No Affordable Care measures with data for AI/AN people were available.

End Stage Renal Disease Due to Diabetes

Diabetes is the leading cause of kidney disease in the United States. According to the National Institute of Diabetes and Digestive and Kidney Diseases, White people experience diabetes and kidney disease at a lower rate than other racial and ethnic groups.16, 17

Line graph showing rate per million population.
Key findings are in the text below the chart.

Figure 10

Adjusted incident rates of end stage renal disease due to diabetes per million population, 2001–2018 (lower rates are better). Key: AI/AN = American Indian or Alaska Native.

  • From 2001 to 2018, the disparity between AI/AN people and White people decreased for the adjusted incident rate of ESRD due to diabetes. For AI/AN people, the rate decreased from 526 per million population to 273.1 per million, and for White people, there were no statistically significant changes (from 133.3 per million to 152.2 per million) (Figure 10).
  • Disparities have been persistent, with AI/AN people having higher incident rates of ESRD due to diabetes than White people in all years.

Disparities for Asian Populations

This section presents disparities in quality of care and, new in 2021, access to care for Asian populations. To provide context, findings for other ethnic and racial populations may be included. Additional details on disparities of care for other priority populations are presented in population-specific sections of this report.

Snapshot of Disparities in Access to Care

Stacked bar chart showing number of access measures:
Asian vs. White (n=14), Better, 2, Same, 8, Worse, 4.
AI/AN vs. White (n=8), Better, 0, Same, 4, Worse, 4.
Black vs. White (n=15), Better, 0, Same, 7, Worse, 8.
NHPI vs. White (n=4), Better, 0, Same, 4, Worse, 0.
>1 Race vs. White (n=14), Better, 0, Same, 11, Worse, 3.

Figure 11

Number and percentage of access measures for which members of selected racial groups experienced better, same, or worse access to care compared with White people, 2017–2019. Key: AI/AN = American Indian or Alaska Native; NHPI = Native Hawaiian/Pacific (more...)

  • Asian people had worse access to care than White people for 29% of access measures and better access to care for 14% of access measures (Figure 11).

Snapshot of Disparities in Quality of Care

For the most recent year, Asian people experienced worse quality care than White people for 28% of all quality measures.

Stacked bar chart showing number of quality measures:
Total (n=173), Better, 50, Same, 75, Worse, 48.
Person-Centered Care (n=29), Better, 0, Same, 11, Worse, 18.
Patient Safety (n=27), Better, 8, Same, 14, Worse, 5.
Care Coordination (n=21), Better, 16, Same, 1, Worse, 4.
Affordable Care (n=2), Better, 1, Same, 1, Worse, 0.
Effective Treatment (n=34), Better, 10, Same, 18, Worse, 6.
Healthy Living (n=60), Better, 15, Same, 30, Worse, 15.

Figure 12

Number and percentage of quality measures for which Asian people experienced better, same, or worse quality of care compared with White people for the most recent data year, 2015, 2017, 2018, or 2019. Key: n = number of measures. Note: The difference (more...)

  • Data for the most recent year show that quality care was better for Asian people than for White people on 29% of all quality measures, the same for 43%, and worse for 28% (Figure 12).

Largest Disparities

The measures with the largest disparities across all quality domains for Asian people include:

  • Adults with limited English proficiency and usual source of care (USC) whose USC had language assistance.
  • Adults who reported that home health care providers always treated them with courtesy and respect in the last 2 months of care.
  • Adults who reported that home health care providers always treated them as gently as possible in the last 2 months of care.

Providers With Language Assistance

Current research shows that Asian people continue to experience health disparities in several quality areas, including patient-centered care and satisfaction.18 Adults who have limited English proficiency may experience disparities in their care and gaps in communication with their healthcare team.19

According to the Migration Policy Institute, in 2015, an estimated 25.9 million individuals living in the United States reported having limited English proficiency.20 “More than one in four people aged 5 and over with LEP are born in the U.S.”21 Language assistance such as access to translation services, health education materials written in a known language, and other resources are required by law, but not all patients have access to these services at their usual source of care.22

Columns showing percentage:
Total, 88.9, Asian, 68.5, White, 94

Figure 13

Adults with limited English proficiency and a usual source of care (USC) whose USC had language assistance, 2018.

  • In 2018, Asian people with limited English proficiency and a usual source of care were less likely than White people to have a USC with language assistance (68.5% compared with 94.0%) (Figure 13).

The Limited English Proficiency website23 offers a repository of resources collated by the Department of Justice to support improved communication with patients. AHRQ has also established a Limited English Proficiency module as part of its TeamSTEPPS® training that shows the importance of language assistance services in keeping patients safe and avoiding adverse events.24

Treatment by Home Health Care Providers

Home health care providers are committed to delivering high-quality and compassionate care and services to patients in a respectful manner that supports each patient’s dignity. Home health performance is examined through several types of quality measures that look at areas such as efficiency, patient safety, and patient-centered care. Evaluation of patient experience of care is conducted with the Consumer Assessment of Healthcare Providers and Systems Home Health Care Survey.25

Columns showing percentage:
2015 achievable benchmark: 95%
Total, 93.7
AI/AN, 91.3
Asian, 85.5
Black, 93
>1 Race, 89.3
NHPI, 90.7
White 94.4

Figure 14

Adults who reported that home health care providers always treated them with courtesy and respect in the last 2 months of care, 2019. Key: AI/AN = American Indian or Alaska Native, NHPI = Native Hawaiian/Pacific Islander. Note: The benchmark calculation (more...)

  • In 2019, the percentage of adults who reported that home health providers always treated them with courtesy and respect in the last 2 months was lower for Asian people (85.5%) compared with White people (94.4%) (Figure 14).
  • The 2015 achievable benchmark was 95.0%.
  • The top 10% of states that contributed to the achievable benchmark were Alabama, Louisiana, Mississippi, Rhode Island, South Carolina, and West Virginia. Guam was not included in the benchmark but its percentage was in the benchmark range.
Columns showing percentage:
2015 achievable benchmark: 92.5%
Total, 90.3
AI/AN, 87.6
Asian, 80.6
Black, 89.2
>1 Race, 85.1
NHPI, 86.5
White, 91.1

Figure 15

Adults who reported that home health care providers always treated them as gently as possible in the last 2 months of care, 2019. Key: AI/AN = American Indian or Alaska Native, NHPI = Native Hawaiian/Pacific Islander. Note: The benchmark calculation takes (more...)

  • In 2019, 80.6% of Asian adults reported that home health providers always treated them as gently as possible compared with 91.1% of White adults (Figure 15).
  • The 2015 achievable benchmark was 92.5%.
  • The top 10% of states that contributed to the achievable benchmark were Alabama, Kentucky, Louisiana, Mississippi, and West Virginia.

Trends in Quality of Care for Asian People

Stacked bar chart showing number of quality measures:
Total (n=120), Improving, 70, Not Changing, 45, Worsening, 5.
Person-Centered Care (n=26), Improving, 16, Not Changing, 10, Worsening, 0.
Patient Safety (n=14), Improving, 8, Not Changing, 5, Worsening, 1.
Care Coordination (n=8), Improving, 4, Not Changing, 3, Worsening, 1.
Affordable Care (n=2), Improving, 0, Not Changing, 2, Worsening, 0.
Effective Treatment (n=17), Improving, 7, Not Changing, 9, Worsening, 1.
Healthy Living (n=53), Improving, 35, Not Changing, 16, Worsening, 2.

Figure 16

Number and percentage of all quality measures that were improving, not changing, or worsening over time, total and by priority area, from 2000 to 2019. Key: n = number of measures. Note: For each measure with at least four data points over time, the estimates (more...)

  • Across the 120 measures of healthcare quality tracked in the report for Asian populations, 58% were improving, 38% were not changing, and 4% were getting worse from 2000 to 2019 (Figure 16).xx
  • Affordable Care (no measures) and Effective Treatment (41% of measures) showed the least improvement.
  • Healthy Living (66%) and Person-Centered Care (62%) showed the most improvement.

Changes in Disparities for Asian People

Stacked bar chart showing number of quality measures:
Total (n=41), Improving, 1, Not Changing, 39, Worsening, 1.
Person-Centered Care (n=19), Improving, 0, Not Changing, 19, Worsening, 0.
Patient Safety (n=1), Improving, 0, Not Changing, 0, Worsening, 1.
Care Coordination (n=3), Improving, 0, Not Changing, 3, Worsening, 0.
Affordable Care (n=0).
Effective Treatment (n=3), Improving, 1, Not Changing, 2, Worsening, 0.
Healthy Living (n=15), Improving, 0, Not Changing, 15, Worsening, 0.

Figure 17

Number and percentage of quality measures with disparity at baseline for which disparities between Asian people and White people were improving, not changing, or worsening over time, total and by priority area, from 2000 to 2019. Key: n = number of measures. (more...)

  • From 2000 through 2019, disparities in quality of care between Asian people and White people remained the same for most measures. Of 41 quality measures with a disparity at baseline, disparities were not changing for 39 (95%) (Figure 17).
  • One measure showed narrowing disparities: People age 13 and over living with HIV who know their HIV status.
  • One measure showed a widening disparity: Home health patients whose management of oral medications improved.
  • No Affordable Care measures with data for Asian people were available.

Knowledge of HIV Status

HIV and other related stigmas hinder patients from getting tested, which may delay treatment and affect a patient’s health and quality of life.26 According to CDC, people ages 13–24 are less likely to know their HIV status.27 Accurate estimates of new HIV infection rates are crucial for preventing the spread of the disease.

Line graph showing percentage.
2015 achievable benchmark: 90.2%.
Key findings are in the text below the chart.

Figure 18

People age 13 and over living with HIV who know their HIV status, 2010–2019. Key: AI/AN = American Indian or Alaska Native, NHPI = Native Hawaiian/Pacific Islander. Note: The benchmark calculation takes the average of the top 10% of states with (more...)

  • Data from 2010 to 2019 show that the disparity between Asian people and White people is narrowing as the percentage of Asian people (68.1% to 86.6%) who know their HIV status increased at a faster rate compared with White people (85.8% to 89.2%) (Figure 18).
  • The 2015 achievable benchmark was 90.2%. At the current rate of increase, overall, the benchmark could be achieved in 2 years.
  • The top 10% of states that contributed to the achievable benchmark were Connecticut, District of Columbia, Massachusetts, New Hampshire, and New York. Puerto Rico was not included in the benchmark but its percentage was in the benchmark range.

Oral Medication Management

The ability to perform daily activities, such as taking medications correctly, is important to the health status and quality of life of people living in the community. Taking too much or too little can keep the drugs from working properly and may cause unintended harm, including death. The home health team can help teach patients ways to organize medications and to take them properly. If patients get better at taking medications correctly, it means the home health team is doing a good job teaching patients how to take their drugs and about the possible harm if they do not follow these instructions.

Specific items that should be discussed include all the prescriptions and other medications the patient takes, allergic or other adverse reactions to drugs experienced in the past, and actions to take if a medication is not working. This measure shows how often the home health team helped patients get better at taking their medications correctly (including prescription medications, over-the-counter medications, vitamins, and herbal supplements). Only medications the patient takes by mouth are considered.

Line graph showing percentage.
2015 achievable benchmark: 66.2%.
Key findings are in the text below the chart.

Figure 19

Home health care patients whose management of oral medications improved, 2013–2018. Key: AI/AN = American Indian or Alaska Native, NHPI = Native Hawaiian/Pacific Islander.

  • From 2013 to 2018, the percentage of home health care patients whose management of oral medications improved increased for both Asian and White populations. The percentage for White people, however, improved faster than for Asian people, so the disparity between the groups increased (Figure 19).
  • The 2015 achievable benchmark was 66.2%. At the current rate of increase, the benchmark could be achieved by Asian people in 2 years; White people have already achieved the benchmark.
  • The top 10% of states that contributed to the achievable benchmark were Delaware, Mississippi, New Jersey, North Dakota, and South Carolina.

Disparities for Black Populations

This section presents disparities in quality of care and, new in 2021, access to care for Black populations. To provide context, findings for other ethnic and racial populations may be included. Additional details on disparities of care for other priority populations are presented in population-specific sections of this report.

Snapshot of Disparities in Access to Care

Stacked bar chart showing number of access measures:
Black vs. White (n=15), Better, 0, Same, 7, Worse, 8.
AI/AN vs. White (n=8), Better, 0, Same, 4, Worse, 4.
Asian vs. White (n=14), Better, 2, Same, 8, Worse, 4.
NHPI vs. White (n=4), Better, 0, Same, 4, Worse, 0.
>1 Race vs. White (n=14), Better, 0, Same, 11, Worse, 3.

Figure 20

Number and percentage of access measures for which members of selected racial groups experienced better, same, or worse access to care compared with White people, 2017–2019. Key: AI/AN = American Indian or Alaska Native; NHPI = Native Hawaiian/Pacific (more...)

  • Black people had worse access to care than White people for 53% of access measures (Figure 20).

Disparities in Quality of Care

In 2019, Black people were more than 8 times as likely as White people to have new HIV cases.

Stacked bar chart showing number of quality measures:
Total (n=195), Better, 21, Same, 90, Worse, 84.
Person-Centered Care (n=27), Better, 3, Same, 17, Worse, 7.
Patient Safety (n=29), Better, 5, Same, 13, Worse, 11.
Care Coordination (n=22), Better, 1, Same, 5, Worse, 16.
Affordable Care (n=2), Better, 1, Same, 1, Worse, 0.
Effective Treatment (n=43), Better, 5, Same, 20, Worse, 18.
Healthy Living (n=72), Better, 6, Same, 34, Worse, 32.

Figure 21

Number and percentage of quality measures for which Black people experienced better, same, or worse quality of care compared with White people for the most recent data year, 2015, 2017, 2018, or 2019. Key: n = number of measures. Note: The difference (more...)

  • Data for the most recent year show that quality of care was better for Black people than for White people on only 11% of all quality measures and that quality was better for White people than for Black people on 43% of all quality measures (Figure 21).
  • For Patient Safety, quality was better for Black people than for White people for 17% of the measures and better for White people than for Black people for 38% of the measures.

Largest Disparities

The measures with the largest disparities for Black people include:

  • New HIV cases per 100,000 population age 13 and over.
  • HIV infection deaths per 100,000 population.
  • Hospital admissions for hypertension per 100,000 population, adults age 18 and over.

New HIV Cases

According to CDC research, in 2018, Black people accounted for 13% of the nation’s population but represented 42% of all new HIV cases. Most of these cases affect Black male adolescents and adults.28 The Office of Minority Health reports that in 2019, African Americans were 8.1 times more likely to be diagnosed with HIV infection compared with the White population.29

Columns showing rate per 100,000 population:
2015 achievable benchmark: 4.2 per 100,000 population.
Total, 12.9
Hispanic, 20
Black, 45.3
White, 5.3
Non-Hispanic White, 5.9

Figure 22

New HIV cases per 100,000 population age 13 and over, 2019 (lower rates are better). Note: Black and White are non-Hispanic. Hispanic includes all races. The benchmark calculation takes the average of the top 10% of states with statistically reliable (more...)

  • In 2019, Black people reported 45.3 new HIV cases per 100,000 population for people age 13 and over compared with 5.3 per 100,000 cases for White people (Figure 22).
  • The 2015 achievable benchmark was 4.2 per 100,000 population.
  • The top 10% of states that contributed to the achievable benchmark were Idaho, Iowa, Maine, West Virginia, and Wisconsin.
Resources

The Department of Health and Human Services has committed to “reducing new infections by 75 percent in the next five years and by 90 percent in the next ten years.”30 The Department’s website www.hiv.gov also outlines key resources for patients, provides data, and details programs supporting a federal response to the epidemic in the United States.

Deaths From HIV Infection

HIV mortality disproportionately affects some racial and ethnic groups more than others. According to CDC data, in 2019, HIV was the sixth leading cause of death for Black men ages 25–34 and seventh for Black women ages 35–44.31

Columns showing rate per 100,000 population:
2015 achievable benchmark: 0.8 per 100,000 population.
Total, 1.5
AI/AN, 1.1
Black, 6.2
White, 0.9

Figure 23

HIV infection deaths per 100,000 population, 2018 (lower rates are better). Key: AI/AN = American Indian or Alaska Native. Note: The benchmark calculation takes the average of the top 10% of states with statistically reliable data. U.S. territories are (more...)

  • In 2018, Black people had 6.2 HIV infection deaths per 100,000 population compared with 0.9 per 100,000 cases for White people (Figure 23). These cases represent mortality for which HIV was the primary cause of death.
  • The 2015 achievable benchmark was 0.8 per 100,000 population.
  • The top 10% of states that contributed to the achievable benchmark were Kansas, Kentucky, Minnesota, Missouri, Ohio, and Washington.
Resources

Federal efforts to reduce mortality include promotion of treatment therapies, such as antiretroviral therapy, pre-exposure prophylaxis, and postexposure prophylaxis.32 Several HHS agencies provide a federal response to the HIV epidemic in the United States, including the Health Resources and Services Administration (HRSA) HIV/AIDS Bureau, which administers the Ryan White HIV/AIDS Program (RWHAP). This is the largest federal program focused exclusively on providing HIV care and treatment to patients with inadequate or no insurance. Through RWHAP’s partnerships, nearly 568,000 people receive care annually.33

Federal efforts to prevent HIV infections include the High-Impact Prevention (HIP) program. HIP is a public health approach to disease prevention in which cost-effective, proven, and scalable interventions are targeted to specific populations based on disease burden. It provides a strategy for using data to maximize the impact of available resources and interventions. The primary goals of HIP are to prevent the largest number of new infections, save life-years, and reduce disparities among populations. In this approach to disease prevention, resources are aligned with disease burden in geographic areas and within populations.34

Hospital Admissions for Hypertension

Hypertension affects nearly half of all U.S. adults and is responsible for substantial burden of morbidity, mortality, and financial costs on the healthcare system.35 The cumulative incidence of hypertension by age 55 years was substantially higher for Black men and women compared with White men and women. Based on the 2017 American College of Cardiology/American Heart Association blood pressure guideline definition,36 75.5% of Black men and 75.7% of Black women developed hypertension compared with 54.5% of White men and 40.0% of White women by age 55 years.37

Columns showing rate per 100,000 population:
Total, 69.3
Hispanic, 69.3
API, 32.7
Black, 212.9
White, 38.4

Figure 24

Hospital admissions for hypertension per 100,000 population, adults age 18 and over, 2018 (lower rates are better). Key: API = Asian/Pacific Islander. Note: API, Black, and White are non-Hispanic. Hispanic includes all races.

  • In 2018, the rate of hospital admissions for hypertension was 212.9 per 100,000 population for Black adults compared with 38.4 per 100,000 cases for White adults (Figure 24).
Resources

CDC’s current effort to reduce prevalence and improve control is Hypertension Control Champions. The Million Hearts® Hypertension Control Champions are clinicians, practices, and health systems that have successfully completed the Million Hearts® Hypertension Control Challenge. The Challenge is an opportunity for clinicians, practices, and health systems to demonstrate excellence in hypertension control. Hypertension Control Champions must reach 80% control rates among their hypertensive patients.

Trends in Quality of Care for Black People

Stacked bar chart showing number of quality measures:
Total (n=152), Improving, 74, Not Changing, 68, Worsening, 10.
Person-Centered Care (n=26), Improving, 10, Not Changing, 16, Worsening, 0.
Patient Safety (n=19), Improving, 8, Not Changing, 10, Worsening, 1.
Care Coordination (n=9), Improving, 4, Not Changing, 2, Worsening, 3.
Affordable Care (n=2), Improving, 0, Not Changing, 2, Worsening, 0.
Effective Treatment (n=33), Improving, 14, Not Changing, 15, Worsening, 4.
Healthy Living (n=63), Improving, 38, Not Changing, 23, Worsening, 2.

Figure 25

Number and percentage of all quality measures that were improving, not changing, or worsening over time, total and by priority area, from 2000 to 2019. Key: n = number of measures. Note: For each measure with at least four data points over time, the estimates (more...)

  • Across the 152 measures of healthcare quality tracked in the report for Black people, 49% showed improvement, 45% remained unchanged, and 7% were getting worse from 2000 to 2019 (Figure 25).xxi
  • Healthy Living (60% of measures), Care Coordination (44% of measures), Effective Treatment (42% of measures), and Patient Safety (42% of measures) showed more improvement than other priority areas.

Changes in Disparities for Black People

Stacked bar chart showing number of quality measures:
Total (n=63), Improving, 2, Not Changing, 60, Worsening, 1.
Person-Centered Care (n=7), Improving, 0, Not Changing, 7, Worsening, 0.
Patient Safety (n=3), Improving, 0, Not Changing, 3, Worsening, 0.
Care Coordination (n=6), Improving, 0, Not Changing, 5, Worsening, 1.
Affordable Care (n=0).
Effective Treatment (n=17), Improving, 2, Not Changing, 15, Worsening, 0.
Healthy Living (n=30), Improving, 0, Not Changing, 30, Worsening, 0.

Figure 26

Number and percentage of quality measures with disparity at baseline for which disparities between Black people and White people were improving, not changing, or worsening over time, total and by priority area, 2000–2019. Key: n = number of measures. (more...)

  • From 2000 to 2019, disparities between Black people and White people were narrowing in only 3% of measures of quality of care experienced (Figure 26).
  • Of 63 quality measures for which Black people experienced worse care than White people at baseline, only 2 showed narrowing disparities:
    • Adjusted incident rates of end stage renal disease (ESRD) due to diabetes per million population.
    • New HIV cases per 100,000 population age 13 and over.
  • Disparities were not changing for 95% of measures, and disparities were widening for one measure: Emergency department visits for asthma per 10,000 population, ages 2–19.

End Stage Renal Disease Due to Diabetes

According to the Office of Minority Health, African American adults are 60 percent more likely than non-Hispanic White adults to have been diagnosed with diabetes by a physician and 3.5 times more likely to be diagnosed with end stage renal disease (ESRD) compared with non-Hispanic White people. During 2018, there were 131,636 newly reported cases of ESRD and diabetes was listed as the primary cause for nearly half (62,012).38

Resource

The U.S. Renal Data System of the National Institute of Diabetes and Digestive and Kidney Diseases tracks cases of ESRD in the ESRD Incident Count.

Line graph showing rate per million population. 
Key findings are in the text below the chart.

Figure 27

Adjusted incident rates of end stage renal disease due to diabetes per million population, 2001–2018 (lower rates are better). Key: AI/AN = American Indian or Alaska Native.

  • Data from 2001 to 2018 show that the disparity between Black people and White people is narrowing, but Black people are still showing a higher rate of ESRD due to diabetes (Figure 27).
  • Disparities have been persistent, with Black people having a higher incident rate of ESRD due to diabetes than White people in all years.

New HIV Cases

Line graph showing rate per 100,000 population. 
Key findings are in the text below the chart.

Figure 28

New HIV cases per 100,000 population age 13 and over, 2008–2019 (lower rates are better). Key: AI/AN = American Indian or Alaska Native, NHPI = Native Hawaiian/Pacific Islander. Note: All racial groups are non-Hispanic.

  • Data from 2008 to 2019 show that the disparity between Black people and White people was narrowing, but Black people are still showing a much higher rate of new HIV cases (45.3 per 100,000 population in 2019) compared with White people (5.3 per 100,000 population in 2019) (Figure 28).
  • The 2015 achievable benchmark was 4.2 per 100,000 population.
  • The top 10% of states that contributed to the achievable benchmark were Idaho, Iowa, Maine, West Virginia, and Wisconsin.

Disparities for Hispanic Populations

Hispanic groups experienced worse quality care than non-Hispanic White groups for about 40% of Healthy Living measures.

This section presents disparities in quality of care and, new in 2021, access to care for Hispanic populations. To provide context, findings for other ethnic and racial populations may be included. Additional details on disparities of care for other priority populations are presented in population-specific sections of this report.

Snapshot of Disparities in Access to Care for Hispanic Populations

Stacked bar chart showing number of access measures:
Hispanic vs. non-Hispanic White (n=14), Better, 0, Same, 3, Worse, 11. 
AI/AN vs. White (n=8), Better, 0, Same, 4, Worse, 4.
Asian vs. White (n=14), Better, 2, Same, 8, Worse, 4.
Black vs. White (n=15), Better, 0, Same, 7, Worse, 8.
NHPI vs. White (n=4), Better, 0, Same, 4, Worse, 0.
>1 Race vs. White (n=14), Better, 0, Same, 11, Worse, 3.

Figure 29

Number and percentage of access measures for which members of selected racial and ethnic groups experienced better, same, or worse access to care compared with White people, 2017, 2018, or 2019. Key: n = number of measures.

  • For the most recent year, Hispanic groups had worse access to care than non-Hispanic White groups for 79% of access measures (Figure 29).

Health Insurance

Hispanic populations have the highest uninsured rates of any racial or ethnic group in the United States. Variation occurs among subgroups, with Cubans having the highest percentage and Central Americans the lowest percentage.39 Disparities in insurance rates by income are also seen in the Hispanic population.

Columns showing percentage by income and race/ethnicity.
Key findings are in the text below the chart.

Figure 30

People under age 65 who were uninsured all year, 2018.

  • In 2018, poor (20.8%), low-income (21.9%), and middle-income Hispanic people (15.6%) were more likely to be uninsured compared with high-income Hispanic people (7.3%) (Figure 30).
Stacked bar chart showing number of quality measures:
Total (n=172), Better, 34, Same, 76, Worse, 62.
Person-Centered Care (n=17), Better, 2, Same, 10, Worse, 5.
Patient Safety (n=23), Better, 5, Same, 13, Worse, 5.
Care Coordination (n=18), Better, 5, Same, 5, Worse, 8.
Affordable Care (n=2), Better, 1, Same, 0, Worse, 1.
Effective Treatment (n=42), Better, 8, Same, 21, Worse, 13.
Healthy Living (n=70), Better, 13, Same, 27, Worse, 30.

Figure 31

Number and percentage of quality measures for which Hispanic groups experienced better, same, or worse quality of care compared with non-Hispanic White groups for the most recent data year, 2015, 2017, 2018, or 2019. Key: n = number of measures. Note: (more...)

  • Data for the most recent year show that quality care was worse for Hispanic groups compared with non-Hispanic White groups for 36% of all quality measures. Quality was better for Hispanic groups than for non-Hispanic White groups on 20% of all quality measures (Figure 31).

Largest Disparities

The measures with some of the largest disparities for Hispanic groups include:

  • New HIV cases per 100,000 population age 13 and over.
  • Home health care patients who had influenza vaccination during flu season.
  • People without a usual source of care who indicated a financial or insurance reason for not having a source of care.

New HIV Cases

Approximately 1.2 million people in the United States have HIV and about 13% of them do not know it and need testing. HIV continues to have a disproportionate impact on certain populations, particularly racial and ethnic minorities and gay, bisexual, and other men who have sex with men. New HIV infections declined from 2015 to 2019, after a period of general stability.40

Columns showing rate per 100,000 population:
2015 achievable benchmark: 4.2 per 100,000 population.
Total, 12.9
AI/AN, 10.5
Asian, 4.5
Black, 45.3
Hispanic, 20
NHPI, 13.9
White 5.3

Figure 32

New HIV cases per 100,000 population age 13 and over, 2019 (lower rates are better). Key: AI/AN = American Indian or Alaska Native; NHPI = Native Hawaiian/Pacific Islander. Note: Racial groups are non-Hispanic. Hispanic includes all races. The benchmark (more...)

  • In 2019, the rate of new HIV cases per 100,000 population age 13 and over was higher for Hispanic people (20.0 per 100,000 population) compared with non-Hispanic White people (5.3 per 100,000 population) (Figure 32).
  • The 2015 achievable benchmark was 4.2 per 100,000 population.
  • The top 10% of states that contributed to the benchmark were Idaho, Iowa, Maine, West Virginia, and Wisconsin.
Resources
  • Federal resources include the Let’s Stop HIV Together campaign (formerly known as Act Against AIDS), which has resources and partnerships aimed at stopping HIV stigma and promoting HIV testing, prevention, and treatment. This campaign provides Hispanic and Latino people with culturally and linguistically appropriate messages about HIV testing, prevention, and treatment.
  • Federal resources also include Ending the HIV Epidemic: A Plan for America, which aims to end the HIV epidemic in the United States by 2030. The plan leverages critical scientific advances in HIV prevention, diagnosis, treatment, and outbreak response by coordinating the highly successful programs, resources, and infrastructure of many HHS agencies and offices.

Influenza Vaccination Among Home Health Care Patients

Medicare defines home health care as a wide range of healthcare services that can be given in the home for an illness or injury. Patients can qualify for this service if they are under the care of a doctor who certifies that they need at least one service such as intermittent skilled nursing care, physical therapy, speech-language pathology, or continued occupational therapy services, and the patient must be home bound.

Home health care is usually less expensive, more convenient, and as effective as care in a hospital or skilled nursing facility. Home health care services include wound care for pressure sores or a surgical wound, patient and caregiver education, intravenous or nutrition therapy, and monitoring of serious illness and unstable health status.

Influenza vaccination is the primary method for preventing the illness and its severe complications, and annual vaccination is recommended for everyone age 6 months and over.41 All healthcare contacts, including hospitalizations, provide excellent opportunities for vaccination, particularly for people at the highest risk for complications and death from influenza.

Columns showing percentage:
2015 achievable benchmark: 94.1%
Total, 95.2
Hispanic, 90.4
Non-Hispanic Black, 93.3
Non-Hispanic White, 96

Figure 33

Home health care patients who had influenza vaccination during flu season, 2018. Notes: The benchmark calculation takes the average of the top 10% of states with statistically reliable data. U.S. territories are not included in the calculations. Some (more...)

  • In 2018, Hispanic home health care patients (90.4%) were less likely than non-Hispanic White home health care patients (96.0%) to receive an influenza vaccine (Figure 33).
  • The 2015 achievable benchmark was 94.1%.
  • The top 10% of states that contributed to the benchmark were Montana, Nebraska, North Dakota, South Dakota, Vermont, and Wisconsin.

Difficulty Accessing a Usual Source of Care

The AHRQ Medical Expenditure Panel Survey (MEPS) describes usual source of care as the particular medical professional, doctor’s office, clinic, health center, or other place where a person would usually go if sick or in need of advice about his or her health.

According to Healthy People 2020, patients with a usual source of care are more likely to receive recommended preventive services such as flu shots, blood pressure screenings, and cancer screenings.42

Columns showing percentage:
Total, 15.8
Hispanic, 26.3
Non-Hispanic Black, 15,2
Non-Hispanic White, 11.5

Figure 34

People without a usual source of care who indicate a financial or insurance reason for not having a source of care, 2018 (lower rates are better).

  • In 2018, the percentage of people without a usual source of care who indicate a financial or insurance reason for not having a source of care was higher for Hispanic people (26.3%) than for non-Hispanic White people (11.5%) (Figure 34).

Changes in Quality of Care for Hispanic Populations

Stacked bar chart showing number of quality measures:
Total (n=120), Improving, 79, Not Changing, 36, Worsening, 5.
Person-Centered Care (n=16), Improving, 9 Not Changing, 7, Worsening, 0.
Patient Safety (n=9), Improving, 7, Not Changing, 2, Worsening, 0.
Care Coordination (n=5), Improving, 1, Not Changing, 3, Worsening, 1.
Affordable Care (n=2), Improving, 0, Not Changing, 2, Worsening, 0.
Effective Treatment (n=29), Improving, 16, Not Changing, 10, Worsening, 3.
Healthy Living (n=59), Improving, 46, Not Changing, 12, Worsening, 1.

Figure 35

Number and percentage of all quality measures that were improving, not changing, or worsening over time, total for Hispanic groups and by priority area, from 2000 through 2015, 2017, 2018, or 2019. Key: n = number of measures. Note: For each measure with (more...)

  • Of the 120 quality measures with data for Hispanic groups, 66% were improving, 30% were not changing, and 4% were getting worse from 2000 through 2019 (Figure 35).xxii
  • Quality was improving for Hispanic groups for about three-fourths of Healthy Living and Patient Safety measures.
  • More than half of Effective Treatment measures improved and 10% of measures showed a worsening trend.

Over time, disparities have narrowed in end stage renal disease due to diabetes between Hispanic and non-Hispanic White people but Hispanic people still have a rate more than twice that of non-Hispanic White people.

Changes in Disparities for Hispanic Populations

Stacked bar chart showing number of quality measures:
Total (n=49), Improving, 2, Not Changing, 47, Worsening, 0.
Person-Centered Care (n=9), Improving, 0, Not Changing, 9, Worsening, 0.
Patient Safety (n=3), Improving, 0, Not Changing, 3, Worsening, 0.
Care Coordination (n=0).
Affordable Care (n=1), Improving, 0, Not Changing, 1, Worsening, 0.
Effective Treatment (n=12), Improving, 1, Not Changing, 11, Worsening, 0.
Healthy Living (n=24), Improving, 1, Not Changing, 23, Worsening, 0.

Figure 36

Number and percentage of all quality measures with disparity at baseline for which disparities related to race and ethnicity were improving, not changing, or worsening over time, total and by priority area, from 2000 through 2015, 2017, 2018, or 2019. (more...)

  • Of the 49 quality measures with a disparity at baseline, disparities between Hispanic and non-Hispanic White people did not change for 47 (96%) from 2000 through 2019 (Figure 36).
  • Two measures showed narrowing disparities—one Effective Treatment measure and one Healthy Living measure.
  • The two measures that showed improving disparities are:
    • Adjusted incident rates of end stage renal disease (ESRD) due to diabetes per million population.
    • Home health care patients whose shortness of breath decreased.
  • No measure showed widening disparities between Hispanic and non-Hispanic White people.
  • No Care Coordination measures with data for Hispanic groups were available.

End Stage Renal Disease

Diabetes is the leading cause of kidney disease in the United States. According to the National Institute of Diabetes and Digestive and Kidney Diseases, non-Hispanic White people experience diabetes and kidney disease at a lower rate than other racial and ethnic groups.43

Line graph showing rate per million population.
Key findings are in the text below the chart.

Figure 37

Adjusted incident rates of end stage renal disease due to diabetes per million population, 2001–2018 (lower rates are better).

  • Data from 2001 to 2018 show that the disparity between Hispanic and non-Hispanic White people was narrowing (Figure 37).
  • Rates of ESRD due to diabetes decreased for Hispanic people, from 410.0 per million population to 292.7 per million population.
  • Disparities have been persistent, with Hispanic populations having higher incident rates of ESRD due to diabetes than White people in all years.

Improved Breathing Among Home Health Care Patients

To assess the quality of care received by home health care patients, measures of wait time to see provider, timely initiation of care, ambulation, ability to get in and out of bed, bathing, toileting, dressing, pain, confusion, management of oral medications, influenza and pneumococcal vaccination, and shortness of breath are tracked.

Shortness of breath is uncomfortable. Many patients with heart or lung problems experience difficulty breathing and may tire easily or be unable to perform daily activities. Doctors and home health staff should monitor shortness of breath and may give advice, therapy, medication, or oxygen to help lessen this symptom.

Line graph showing percentage.
Key findings are in the text below the chart.

Figure 38

Home health care patients whose shortness of breath decreased, 2013–2018.

  • From 2013 to 2018, the disparity between Hispanic and non-Hispanic White people was narrowing for home health care patients whose shortness of breath decreased (Figure 38).
  • Both Hispanic people (53.7% to 74.5%) and non-Hispanic White people (66.7% to 80.9%) showed improvement over time.

Disparities for Native Hawaiian/Pacific Islander Populations

Native Hawaiian/Pacific Islander populations experienced worse quality care compared with White populations for about 40%of Person-Centered Care measures.

New in 2021, this section presents disparities in access to care for Native Hawaiian and Pacific Islander (NHPI) populations. To provide context, findings for other ethnic and racial populations may be included. Additional details on disparities of care for other priority populations are presented in population-specific sections of this report.

Snapshot of Disparities in Access to Care

Stacked bar chart showing number of access measures:
NHPI vs. White (n=4), Better, 0, Same, 4, Worse, 0. 
AI/AN vs. White (n=8), Better, 0, Same, 4, Worse, 4.
Asian vs. White (n=14), Better, 2, Same, 8, Worse, 4.
Black vs. White (n=15), Better, 0, Same, 7, Worse, 8.
>1 Race vs. White (n=14), Better, 0, Same, 11, Worse, 3.

Figure 39

Number and percentage of access measures for which NHPI groups experienced better, same, or worse access to care compared with White groups, 2017–2019. Key: AI/AN = American Indian or Alaska Native, NHPI = Native Hawaiian/Pacific Islander, n = (more...)

  • NHPI data were only available for four measures and all four measures showed that NHPI groups had the same access to care as White groups (Figure 39).
Stacked bar chart showing number of quality measures:
Total (n=81), Better, 15, Same, 43, Worse, 23.
Person-Centered Care (n=18), Better, 2, Same, 9, Worse, 7.
Patient Safety (n=12), Better, 1, Same, 9, Worse, 2.
Care Coordination (n=7), Better, 1, Same, 3, Worse, 3.
Affordable Care (n=0).
Effective Treatment (n=14), Better, 7, Same, 5, Worse, 2.
Healthy Living (n=30), Better, 4, Same, 17, Worse, 9.

Figure 40

Number and percentage of quality measures for which Native Hawaiian/Pacific Islander groups experienced better, same, or worse quality of care compared with White groups for the most recent data year, 2017, 2018, or 2019. Key: n = number of measures. (more...)

  • Data for the most recent year show that NHPI groups experienced worse quality care compared with White groups on 28% of all quality measures. Quality was better for NHPI groups than for White groups on 19% of all quality measures (Figure 40).
  • No Affordable Care measures with data for NHPI groups were available.

Largest Disparities

The measures with the largest disparities for NHPI populations include:

  • New HIV cases per 100,000 population age 13 and over.
  • Adults who reported that home health care providers always treated them with courtesy and respect in the last 2 months of care.
  • Home health care patients who had timely initiation of care.

New HIV Cases

HIV can affect anyone regardless of sexual orientation, race, ethnicity, gender, age, or geographic location. However, in the United States, some racial/ethnic groups are more affected than others, given their percentage of the population. This disparity occurs because some population groups have higher rates of HIV in their communities, thus raising the risk of new infections with each sexual or injection drug use encounter.

In addition, a range of social, economic, and demographic factors such as stigma, discrimination, income, education, and geographic region can affect people’s risk for HIV. In 2018, 42% of new HIV diagnoses were among Black people and 29% were among Hispanic people.44

While NHPI individuals represent 0.4% of the total population in the United States, their HIV case rate was more than twice that of the White population in 2019.

Columns showing rate per 100,000 population:
2015 achievable benchmark: 4.2.
Total, 12.9
AI/AN, 10.5
Asian, 4.5
Black, 45.3
NHPI, 13.9
White, 5.3
Hispanic, 20

Figure 41

New HIV cases per 100,000 population age 13 and over, 2019. Key: NHPI = Native Hawaiian or Pacific Islander Note: All racial groups are non-Hispanic. Hispanic includes all races. The benchmark calculation takes the average of the top 10% of states with (more...)

  • In 2019, the percentage of new HIV cases per 100,000 population age 13 and over was more than twice as high for NHPI groups (13.9 per 100,000 population) as for White groups (5.3 per 100,000 population) (Figure 41).
  • The 2015 achievable benchmark was 4.2 per 100,000 population.
  • The top 10% of states that contributed to the benchmark were Idaho, Iowa, Maine, West Virginia, and Wisconsin.

Treatment by Home Health Providers

Medicare defines home health care as a wide range of healthcare services that can be given in the home for an illness or injury. Patients can qualify for this service if they are under the care of a doctor who certifies that they need at least one service such as intermittent skilled nursing care, physical therapy, speech-language pathology, or continued occupational therapy services, and the patient must be home bound.

Home health care is usually less expensive, more convenient, and as effective as care in a hospital or skilled nursing facility. Home health care services include wound care for pressure sores or a surgical wound, patient and caregiver education, intravenous or nutrition therapy, and monitoring of serious illness and unstable health status.

The goal of home health care is to treat an illness or injury; help patients recover, regain independence, become as self-sufficient as possible, and maintain current condition or level of function; and slow decline.45

Column chart showing percentage.
2015 achievable benchmark: 95%
Total, 93.7
AI/AN, 91.3
Asian, 85.5
Black, 93
NHPI, 90.7
White, 94.4
>1 Race, 89.3

Figure 42

Adults who reported that home health care providers always treated them with courtesy and respect in the last 2 months of care, 2019. Note: The benchmark calculation takes the average of the top 10% of states with statistically reliable data. U.S. territories (more...)

  • In 2019, the percentage of adults who reported that home health care providers always treated them with courtesy and respect in the last 2 months was lower for NHPI people (90.7%) compared with White people (94.4%) (Figure 42).
  • The 2015 achievable benchmark was 95%.
  • The top 10% of states that contributed to the benchmark were Alabama, Louisiana, Mississippi, Rhode Island, South Carolina, and West Virginia. Guam was not included in the benchmark but its percentage was in the benchmark range.

Initiation of Home Health Care

Timely initiation of home health care is associated with lower risks of 30-day rehospitalization. Therefore, CMS requires that home health care services be initiated within 2 days of hospital discharge when ordered, except when the physician/provider authorizes a delay in the initiation of services due to an outpatient visit or the patient’s or family’s request.46

Columns showing percentage.
2015 achievable benchmark: 95%
Total, 94
AI/AN, 92.8
Asian, 92.3
Black, 92.5
NHPI, 91
White, 94.4
>1 Race, 93.3

Figure 43

Home health care patients who had timely initiation of care, 2018. Note: Initiation of care is defined by CMS as home health quality episodes in which the start or resumption of care date was on the physician-specified start or resumption of care date (more...)

  • In 2018, NHPI home health patients were less likely than White patients to receive timely initiation of care (91% vs. 94.4%) (Figure 43).
  • The 2015 achievable benchmark was 95%. At the current rate of progress, NHPI people should reach the benchmark in 5 years (trend data not shown).
  • The top 10% of states that contributed to the benchmark were Louisiana, Nebraska, North Dakota, South Dakota, and West Virginia.

Trends in Quality of Care for Native Hawaiian and Pacific Islander Populations

Nearly 45% of quality measures for NHPI groups showed improvement.

Stacked bar chart showing number of quality measures:
Total (n=68), Improving, 30, Not Changing, 34, Worsening, 4.
Person-Centered Care (n=12), Improving, 2, Not Changing, 9, Worsening, 1.
Patient Safety (n=12), Improving, 4, Not Changing, 7, Worsening, 1.
Care Coordination (n=8), Improving, 2, Not Changing, 5, Worsening, 1.
Affordable Care (n=0).
Effective Treatment (n=9), Improving, 6, Not Changing, 3, Worsening, 0.
Healthy Living (n=27), Improving, 16, Not Changing, 10, Worsening, 1.

Figure 44

Number and percentage of all quality measures that were improving, not changing, or worsening over time, total and by priority area, from 2001 through 2017, 2018, or 2019. Key: n = number of measures. Note: For each measure with at least four data points (more...)

  • Among the 68 quality measures with data for NHPI populations, 30 (44%) were improving, 34 (50%) were not changing, and 4 (6%) were getting worse from 2001 through 2019 (Figure 44).
  • No Affordable Care measures with data for NHPI populations were available.

Changes in Disparities for Native Hawaiian and Pacific Islander Populations

Stacked bar chart showing number of quality measures:
Total (n=20), Improving, 2, Not Changing, 18, Worsening, 0.
Person-Centered Care (n=5), Improving, 0, Not Changing, 5, Worsening, 0.
Patient Safety (n=1), Improving, 0, Not Changing, 1, Worsening, 0.
Care Coordination (n=4), Improving, 0, Not Changing, 4, Worsening, 0.
Affordable Care (n=0).
Effective Treatment (n=4), Improving, 2, Not Changing, 2, Worsening, 0.
Healthy Living (n=6), Improving, 0, Not Changing, 6, Worsening, 0.

Figure 45

Number and percentage of all quality measures with disparity at baseline for which disparities related to race and ethnicity were improving, not changing, or worsening over time, total and by priority area, from 2008 through 2018 or 2019. Key: n = number (more...)

  • Disparities between NHPI and White populations did not change for most of the quality measures from 2008 through 2019. Of the 20 quality measures with a disparity at baseline, disparities were not changing for 18 measures (90%) (Figure 45).
  • No measure showed widening disparities, and only two measures showed narrowing disparities: People age 13 and over living with HIV who know their HIV status and People age 13 and over living with diagnosed HIV who had at least two CD4 or viral load tests performed at least 3 months apart during the last year.
  • No Affordable Care measures with data for NHPI people were available.

Knowledge of HIV Status

It is important for everyone to know his or her HIV status. Getting an HIV test is the first step for people living with HIV to get care and treatment and control the infection. Taking HIV medicine as prescribed helps people living with HIV to live a long, healthy life and protect their sex partners from HIV. About 85% of people with HIV in the United States know they have the virus. However, 15% (162,500) of people with HIV do not know they have the virus, and about 40% of new HIV infections come from them.

Half of people with HIV had the virus 3 years or more before diagnosis. Most people at high risk who did not get tested last year saw a healthcare provider during the year. Everyone should get tested at least once, and people at high risk should be tested at least once a year. Healthcare providers can diagnose HIV sooner if they test more people and test people at high risk more often.47

Line showing percentage.
2015 achievable benchmark: 90.2%.
Key findings are in the text below the chart.

Figure 46

People age 13 and over living with HIV who had knowledge of their HIV status, 2010–2019. Note: Data are statistically unreliable for NHPI groups in 2015. The benchmark calculation takes the average of the top 10% of states with statistically reliable (more...)

  • Data from 2010 to 2019 show that the disparity between NHPI people and White people was narrowing due to a larger increase in the percentage of NHPI people (69.8% to 83.6%) than White people (85.8% to 89.2%) who are living with HIV and had knowledge of their HIV status (Figure 46).
  • The 2015 achievable benchmark was 90.2%.
  • The top 10% of states that contributed to the benchmark were Connecticut, District of Columbia, Massachusetts, New Hampshire, and New York.

Viral Load Monitoring

Viral load is the amount of HIV in the blood of a person who has HIV. Viral load is highest during the acute phase of HIV and when HIV is untreated. People with HIV who keep an undetectable viral load (or stay virally suppressed) can live long, healthy lives. Having an undetectable viral load also helps prevent transmitting the virus to others through sex or sharing needles, syringes, or other injection equipment, and from mother to child during pregnancy, birth, and breastfeeding. Higher viral load increases the risk of transmitting HIV.48

Line graph showing percentage.
2015 achievable benchmark: 66.2%.
Key findings are in the text below the chart.

Figure 47

People age 13 and over living with diagnosed HIV who had at least two CD4 or viral load tests performed at least 3 months apart during the last year, 2014–2018. Note: The benchmark calculation takes the average of the top 10% of states with statistically (more...)

  • Data from 2014 to 2018 show that the disparity between NHPI and White populations was narrowing due to a larger increase in the percentage of NHPI people (50.2% to 55.7%) than White people (58.5% to 58.9%) living with diagnosed HIV who had at least two CD4 or viral load tests performed at least 3 months apart during the last year (Figure 47).
  • The 2015 achievable benchmark was 66.2%.
  • The top 10% of states that contributed to the benchmark were Connecticut, Iowa, Montana, and Oregon.
Resources

An example of a Department of Health and Human Services initiative to end the HIV epidemic is the Minority HIV/AIDS Fund (MHAF). This initiative has the goal of transforming HIV prevention, care, and treatment for communities of color by bringing federal, state, and community organizations together to design and test innovative solutions that address critical emerging needs; and by working to improve the efficiency, effectiveness, and impact of federal investments in HIV programs and services for racial and ethnic minorities.

MHAF supports Ending the HIV Epidemic: A Plan for America, a federal initiative designed to reduce the number of new HIV infections in the United States by 75% over 5 years and 90% by 2030.

MHAF also improves prevention, care, and treatment for racial and ethnic minorities through:

  • Innovation: The Fund designs and tests innovative programs and strategies to improve the efficiency, effectiveness, and impact of HIV programs in racial and ethnic minority communities.
  • Systems Change: Successes generated by the Fund are integrated into existing efforts, creating lasting changes across the federal HIV prevention, care, and treatment portfolio.
  • Strategic Partnerships and Collaboration: The Fund breaks down program silos and develops new ways for federal, state, and local agencies to work together in the community to improve outcomes for racial and ethnic minorities.

More information can be found at: https://www.hiv.gov/federal-response/smaif/smaif-in-action.

Disparities by Income

New in 2021, this section presents disparities in access to care by income groups. Additional details on disparities of care for other priority populations are presented in population-specific sections of this report.

Snapshot of Disparities in Access to Care

Stacked bar chart showing number of measures in each category:
Poor vs. High Income (n=14), better, 0, same, 3, worse, 11.
Low vs. High Income (n=14), better, 0, same, 4, worse, 10.
Middle vs. High Income (n=14), better, 0, same, 7, worse, 7.

Figure 48

Number and percentage of access measures for which members of selected income groups experienced better, same, or worse access to care compared with the high-income group, 2017, 2018, or 2019. Key: n = number of measures.

  • People in poor households had worse access to care than people in high-income households for 79% of access measures (Figure 48).
  • People in low-income households had worse access to care than people in high-income households for 71% of access measures.
  • People in middle-income households had worse access to care than people in high-income households for 50% of access measures.
Stacked columns showing number of measures in each category:
No measures were better.
Access, Total (n=14), same, 3, worse, 11.
Health Insurance (n=5), same, 0, worse, 5.
Source of Ongoing Care (n=2), same, 0, worse 2.
Timely Access to Care (n=3), same, 0, worse, 3.
Patient Perception of Need (n=4), same, 3, worse, 1.

Figure 49

Number and percentage of access measures for which people in poor households experienced better, same, or worse access to care compared with people in high-income households, by sub-area, 2017, 2018, or 2019. Key: n = number of measures.

  • For the most recent year, people in poor households had worse access to care than people in high-income households for 79% of access measures (Figure 49).
  • People in poor households had worse access to care than people in high-income households for 100% of health insurance, source of ongoing care, and timely access to care measures.
  • People in poor households had worse access to care than people in high-income households for a quarter of patient perception of need measures.

The measure with the largest disparities across all access to care subsections for people in poor households was people under age 65 with any private health insurance.

Columns showing percentage.
Key findings are in the text below the chart.

Figure 50

People under age 65 with any private health insurance, by income and ethnicity, 2019.

  • In 2019, among people under age 65, people in poor, low-income, and middle-income families were less likely than people in high-income families to have private health insurance (Figure 50).
  • In 2019, among people under age 65, Hispanic people of all income groups were less likely than non-Hispanic White people to have private health insurance.

The relationship between income and healthcare outcomes has been studied for many years, and researchers have shown the positive relationship between more income and better health outcomes.49, 50, 51, 52 Income is not the same as wealth, which can include assets other than income. Wealth is disproportionately dispersed among higher income categories, and research also shows a positive association between greater wealth and better health outcomes.

The NHQDR tracks disparities data for income and insurance categories. Income groups are based on the federal poverty level (FPL) for a family of four:

  • Poor: Less than 100% of FPL.
  • Low income: 100% to less than 200% of FPL.
  • Middle income: 200% to less than 400% of FPL.
  • High income: 400% or more of FPL

The poverty guidelines are issued annually in the Federal Register by the Department of Health and Human Services, Assistant Secretary for Planning and Evaluation. The guidelines vary by family size and there are different family income criteria for the contiguous 48 states, Alaska, and Hawaii. Criteria for U.S. territories are unavailable.53 For HCUP measures, income is based on median income of the patient’s ZIP Code and is divided into quartiles.

This section shows quality measures with the largest income disparities and trends in disparities.

Quality of care for high-income groups was better than for poor and low-income groups for more than half of all measures.

Stacked bar chart showing number of quality measures:
Total (n=350), Better, 9, Same, 157, Worse, 184.
Poor (n=117), Better, 6, Same, 44, Worse, 67.
Low Income (n=116), Better, 3, Same, 48, Worse, 65.
Middle Income (n=117), Better, 0, Same, 65, Worse, 52.

Figure 51

Number and percentage of quality measures for which income groups experienced better, same, or worse quality of care compared with the high-income group for the most recent data year, 2017, 2018, or 2019. Key: n = number of measures. Note: The most recent (more...)

  • Data for the most recent year show that high-income groups experienced better quality care than other income groups on 53% of all measures (Figure 51).
  • Poor and low-income groups experienced worse quality care compared with high-income groups on about 57% of the measures. Compared with high-income groups, middle-income groups experienced worse quality care on 44% of the measures.

Largest Disparities

The measure with the largest income disparities is “children ages 5–17 with untreated dental caries.”

Measures with the largest disparities for each income group include:

  • Children ages 5–17 with untreated dental caries (all income groups).
  • People without a usual source of care who indicated a financial or insurance reason for not having a source of care (all income groups).
  • People under age 65 whose family’s health insurance premium and out-of-pocket medical expenditures were more than 10% of total family income (middle income)
  • Children ages 19–35 months who received 1 or more doses of measles-mumps- rubella vaccine (low income)
  • Hospital admissions for short-term complications of diabetes per 100,000 population, adults (first quartile: lowest income).

Pediatric Dental Caries

Dental caries is one of the most common chronic diseases of childhood in the United States. Untreated caries can cause pain and infections that may lead to problems with eating, speaking, playing, and learning. Children who have poor oral health often miss more school and receive lower grades than children who do not.54

Columns showing percentage:
Total, 13
Poor, 19.4
Low Income, 16.9
Middle Income, 12.1
High Income 4.5

Figure 52

Children ages 5–17 with untreated dental caries, 2015–2018 (lower rates are better). Note: Poor refers to household incomes below the federal poverty level (FPL); low, the FPL to just below 200% of the FPL; middle, 200% to just below 400% (more...)

  • In 2015–2018, the measure with the largest income disparities among all income groups was children ages 5–17 with untreated dental caries (Figure 52).
  • In 2015–2018, the percentage of children ages 5–17 with untreated dental caries was higher for poor, low-income, and middle-income children compared with high-income children (19.4%, 16.9%, and 12.1%, respectively, vs. 4.5%).

Difficulty Accessing a Usual Source of Care

People with lower incomes may experience difficulty accessing affordable care and are less likely to have a usual source of care that is readily accessible.51 People who are unwell and have low incomes are also more likely to experience poverty.51

In 2018, the measure with the second largest income disparities among all income groups was people without a usual source of care who indicated a financial or insurance reason for not having a source of care.

Columns showing percentage:
Total 15.8
Poor 23.6
Low Income 25.9
Middle Income 16.1
High Income 7.2

Figure 53

People without a usual source of care who indicated a financial or insurance reason for not having a source of care, 2018 (lower rates are better).

  • In 2018, the percentage of people without a usual source of care who indicated a financial or insurance reason for not having a source of care was higher for poor, low-income, and middle-income people compared with high-income people (23.6%, 25.9%, and 16.1%, respectively, vs. 7.2%) (Figure 53).

High Family Medical Expenditures

The most prominent barriers to healthcare coverage include affordability, eligibility for public coverage in a person’s state, immigration status, and lack of familiarity with signup procedures.55 Poor health may require a family to spend more on healthcare, resulting in less income. Costs will vary based on each person or family’s needs and may inhibit a family’s ability to reach other goals.51

In 2018, the measure with the third largest income disparities among middle-income people was people under age 65 whose family’s health insurance premium and out-of-pocket medical expenditures were more than 10% of total family income

Columns showing percentage:
Total 17.5
Poor 24.2
Low Income 21.9
Middle Income 21.9
High Income 10.7

Figure 54

People under age 65 whose family’s health insurance premium and out-of-pocket medical expenditures were more than 10% of total family income, 2018 (lower rates are better).

  • In 2018, the percentage of people under age 65 whose family’s health insurance premium and out-of-pocket medical expenditures were more than 10% of total family income was higher for middle-income people compared with high-income people (21.9% vs. 10.7%) (Figure 54).

Childhood Vaccinations

Childhood vaccinations are an important part of preventing disease. Consistently high childhood immunization rates have greatly reduced the rates of death, disability, and illness from communicable diseases such as chicken pox, diphtheria, measles, meningococcal meningitis, mumps, polio, rubella, tetanus, and whooping cough.

In the decade before the measles vaccine became available, an average of 549,000 measles cases and 495 measles deaths were reported annually in the United States. Of the reported cases, approximately 48,000 people were hospitalized from measles and each year, 1,000 people developed chronic disability from acute encephalitis caused by measles.56

Mumps complications include orchitis, oophoritis, mastitis, meningitis, encephalitis, pancreatitis, and hearing loss.57

Before the rubella vaccine became available, one noted outbreak infected 12.5 million people, 11,000 pregnant women lost their babies, 2,100 newborns died, and 20,000 babies were born with congenital rubella syndrome.58

Columns showing percentage:
Total 92.1
Poor 90.3
Low Income 90.3
Middle Income 91.8
High Income 95.8

Figure 55

Children ages 19–35 months who received 1 or more doses of measles-mumps-rubella vaccine, 2018. Note: The benchmark calculation takes the average of the top 10% of states with statistically reliable data. U.S. territories are not included in the (more...)

  • In 2018, the percentage of children ages 19–35 months who received 1 or more doses of measles-mumps-rubella vaccine was lower for children from poor (90.3%) and low-income (90.3%) families compared with children from high-income families (95.8%) (Figure 55).
  • The 2015 achievable benchmark was 96.4%.
  • The top 5 states that contributed to the achievable benchmark were Connecticut, Delaware, Iowa, Maine, Nebraska, and Vermont.

Hospital Admissions for Diabetes Complications

More than 100 million people living in the United States have diabetes or are at risk for diabetes.59 Compared with some other countries, the rate of hospital admissions for short-term complications of diabetes, which include ketoacidosis, hyperosmolarity, and coma, is higher in the United States.60 Such complications may be related to kidney disease, hypertension, vision problems, pain, or other issues.

Columns showing rate per 100,000 population:
Total, 90
First Quartile (Lowest Income), 145.3
Second Quartile, 99.3
Third Quartile, 73.6
Fourth Quartile (Highest Income), 45

Figure 56

Hospital admissions for short-term complications of diabetes per 100,000 population, adults, 2018 (lower rates are better).

  • In 2018, the rate of hospital admissions for short-term complications of diabetes was three times as high for adults in the lowest income group (145.3 per 100,000 population) compared with adults in the highest income group (45.0 per 100,000 population) (Figure 56).

Trends in Quality of Care for Income Groups

Poor, low-income, and middle-income people had a higher percentage of improving measures compared with high-income people.

Stacked bar chart showing number of quality measures:
Total (n=260), Improving, 143, Not Changing, 103, Worsening, 14.
Poor (n=65), Improving, 37, Not Changing, 24, Worsening, 4.
Low Income (n=65), Improving, 37, Not Changing, 25, Worsening, 3.
Middle Income (n=65), Improving, 37, Not Changing, 24, Worsening, 4.
High Income (n=65), Improving, 32, Not Changing, 30, Worsening, 3.

Figure 57

Number and percentage of all quality measures that were improving, not changing, or worsening over time, total and by income group, from 2000 through 2016, 2017, 2018, or 2019. Key: n = number of measures. Note: For each measure with at least four data (more...)

  • The percentage of measures that showed improvement was 57% for poor people, low-income people, and middle-income people, and 49% for high-income people (Figure 57).

Changes in Income Disparities

Most disparities by income showed no statistically significant changes over time.

Stacked bar chart showing number of quality measures:
Total (n=117), Improving, 1, Not Changing, 111, Worsening, 5.
Poor (n=41), Improving, 0, Not Changing, 39, Worsening, 2.
Low Income (n=41), Improving, 1, Not Changing, 38, Worsening, 2.
Middle Income (n=35), Improving, 0, Not Changing, 34, Worsening, 1.

Figure 58

Number and percentage of quality measures with disparity at baseline for which disparities related to income were improving, not changing, or worsening over time, 2000 through 2016, 2017, 2018, or 2019. Key: n = number of measures. Note: Different data (more...)

  • Disparities by income were unchanged for about 95% of quality measures (Figure 58).
  • Only one measure showed narrowing disparities and five measures showed widening disparities.

The measure that showed improvement in disparities was:

  • Adolescents ages 16–17 who received 1 or more doses of meningococcal conjugate vaccine (low income).

Measures that showed worsening disparities were:

  • Emergency department visits involving opioid-related diagnoses per 100,000 population (first and second quartiles: lowest and second lowest income).
  • Hospital inpatient stays involving opioid-related diagnoses per 100,000 population (first, second, and third quartiles: lowest, second lowest, and second highest income).

Adolescent Vaccination

Meningococcal disease refers to any illness caused by bacteria called Neisseria meningitidis, also known as meningococcus. These illnesses are often severe and can be deadly. They include infections of the lining of the brain and spinal cord (meningitis) and bloodstream infections (bacteremia or septicemia).61

Vaccines can help prevent meningococcal disease. Two types of meningococcal vaccines are available in the United States:

  • Meningococcal conjugate or MenACWY vaccines, which help protect against four types of the bacteria that cause meningococcal disease (serogroups A, C, W, and Y).
  • Serogroup B meningococcal or MenB vaccines, which help protect against serogroup B meningococcal disease.

According to CDC, all children ages 11 to 12 years old should get a meningococcal conjugate vaccine, with a booster dose at 16 years old.62

Line graph showing percentage.
2015 achievable benchmark: 96.2%.
Key findings are in the text below the chart.

Figure 59

Adolescents ages 16–17 who received 1 or more doses of meningococcal conjugate vaccine, 2008–2018. Note: The benchmark calculation takes the average of the top 10% of states with statistically reliable data. U.S. territories are not included (more...)

  • In 2008, 31.9% of low-income adolescents ages 16–17 received 1 or more doses of meningococcal conjugate vaccine, and by 2018, the percentage had increased to 86.4% (Figure 59).
  • From 2008 to 2018, the percentage of high-income adolescents ages 16–17 who received 1 or more doses of meningococcal conjugate vaccine increased from 46.8% to 89.8%.
  • Data from 2008 to 2018 show that disparities between adolescents in high-income households and in poor households were narrowing over time and both populations were improving.
  • The 2015 achievable benchmark was 96.2%. At the current rate of increase, the benchmark could be achieved in 2 years for all income groups.
  • The top 5 states that contributed to the achievable benchmark were Indiana, Michigan, New Jersey, Pennsylvania, and Rhode Island.

Emergency Department Visits Involving Opioids

The U.S. opioid overdose epidemic continues to evolve. In 2016, 66.4% of the 63,632 drug overdose deaths involved an opioid. In 2017, among 70,237 drug overdose deaths, 47,600 (67.8%) involved opioids, with increases across age groups, racial and ethnic groups, county urbanization levels, and multiple states. From 2013 to 2017, synthetic opioids contributed to increases in drug overdose death rates in several states. From 2016 to 2017, synthetic opioid-involved overdose death rates increased 45.2%.63

Line graph showing rate per 100,000 population:
2015 achievable benchmark: 65.2 per 100,000 population.
Key findings are in the text below the chart.

Figure 60

Emergency department visits related to opioid use per 100,000 population, 2005–2018. Key: 1st Quartile = <$48000, 2nd Quartile = $48,000–$60,999, 3rd Quartile = $61,000–$81,999, and 4th Quartile = >$82,000. Note: (more...)

  • In 2005, the rate of emergency department visits involving opioid-related diagnoses among people in the lowest income group was 104.9 per 100,000 population, and by 2018, the rate had increased to 348.1 per 100,000 population (Figure 60).
  • In 2005, the rate of emergency department visits involving opioid-related diagnoses among people in the second lowest income group was 90.2 per 100,000 population, and by 2018, the rate had increased to 231 per 100,000 population.
  • In 2005, the rate of emergency department visits involving opioid-related diagnoses among people in the third income group was 83.2 per 100,000 population, and by 2018, the rate had increased to 195.7 per 100,000 population.
  • In 2005, the rate of emergency department visits involving opioid-related diagnoses among people in the highest income group was 65.5 per 100,000 population, and by 2018, the rate had increased to 146.8 per 100,000 population.
  • Data from 2005 to 2018 show that disparities between high-income and poor and low-income people were widening over time and both populations were worsening.
  • The 2015 achievable benchmark was 65.2 per 100,000. No income group showed progress toward the benchmark.
  • The top 10% of states contributing to the achievable benchmark were Iowa, Kansas, Nebraska, and South Dakota.

Hospital Stays Involving Opioids

Increased availability and overuse of opioid medications have contributed to adverse outcomes for patients, including increased risk of opioid use disorder, misuse of medication, and overdoses.64 The National Survey on Drug Use and Health shows that in 2020, nearly 9.5 million people age 12 and over misused opioids in the past year.65 This treatment measure examines inpatient stays associated with opioid-related diagnoses.

Line graph showing rate per 100,000 population.
2015 achievable benchmark: 102.9 per 100,000 population.
Key findings are in the text below the chart.

Figure 61

Hospital inpatient stays involving opioid-related diagnoses per 100,000 population, 2005–2018. Key: 1st Quartile = <$48000, 2nd Quartile = $48,000–$60,999, 3rd Quartile = $61,000–$81,999, and 4th Quartile = >$82,000. (more...)

  • In 2005, the rate of hospital inpatient stays involving opioid-related diagnoses among people in the lowest income group was 179.6 per 100,000 population, and by 2018, the rate had increased to 382.1 per 100,000 population (Figure 61).
  • In 2005, the rate of hospital inpatient stays involving opioid-related diagnoses among people in the second lowest income group was 125.5 per 100,000 population, and by 2018, the rate had increased to 288.7 per 100,000 population.
  • In 2005, the rate of hospital inpatient stays involving opioid-related diagnoses among people in the second highest income group was 117.2 per 100,000 population, and by 2018, the rate had increased to 252.1 per 100,000 population.
  • In 2005, the rate of hospital inpatient stays involving opioid-related diagnoses among people in the highest income group was 98.1 per 100,000 population, and by 2018, the rate had increased to 191.6 per 100,000 population.
  • Data from 2005 to 2018 show that disparities between people in the highest quartile and people in the other three quartiles were widening over time and all populations were worsening.
  • The 2015 achievable benchmark was 102.9 per 100,000. There is no evidence of progress toward the benchmark.
  • The top 10% of states that contributed to the achievable benchmark were Georgia, Iowa, Nebraska, Texas, and Wyoming.

Disparities by Insurance Status

Health insurance increases access to healthcare, including preventive care and services for chronic disease and major health conditions. Evidence from observational studies and randomized controlled trials such as the Oregon Health Insurance Experiment links having health insurance coverage with positive outcomes. These outcomes include:

  • Increased financial security,
  • Access to primary care,
  • Adherence to prescription medications,
  • Screening for treatable health conditions (such as diabetes, cholesterol, HIV, and breast, prostate, and colon cancer),
  • Improved perceptions of health,
  • Reduced depression symptoms, and
  • Earlier detection of cancer.66, 67

This section examines disparities and trends by insurance status among people ages 0–64 years. It focuses on people less than age 65 years because more than 98% of Americans 65 years and over have Medicare.68 Thus, almost no older adults lack insurance coverage since almost all are covered, at minimum, by public insurance (Medicare).

Insurance status for people ages 0–64 years consists of three categories:

  • Private Insurance: Person has access to insurance from a private insurer.
  • Public Insurance: Person receives insurance from one or more government-sponsored sources, including Medicaid, State Children’s Health Insurance Program (S-CHIP), state-sponsored or other government-sponsored health plans, Medicare, and military and veteran health plans.
  • Uninsured: Person does not have any health insurance.

It should be noted that the Indian Health Service (IHS) is not considered a health plan for this report. IHS is a healthcare system, which offers comprehensive healthcare services to AI/AN individuals. Currently, IHS serves 2.7 million AI/AN people who belong to 574 federally recognized tribes in 37 states. Non-IHS data sources, including CDC’s National Center for Health Statistics, also track disparities for AI/AN populations and are the source of data for health disparities for this population.

The bar chart (Figure 62) summarizes comparisons between people with private health insurance (the reference group) and people with public health insurance or no insurance for 69 quality of care measures for which data by insurance status are available.

Quality of care for uninsured people was better than quality for those with private insurance on only 7% of measures.

Stacked bar chart showing number of quality measures:
Total (n=130), better, 11, same, 55, worse, 64.
Public (n=69), better, 7, same, 35, worse, 27.
Uninsured (n=61), better, 4, same, 20, worse, 37.

Figure 62

Number and percentage of quality measures for which insurance groups experienced better, same, or worse quality of care compared with reference group (privately insured), 2016, 2017, or 2018. Key: n = number of measures. Note: The difference between two (more...)

  • Compared with those with private insurance, people with public insurance experienced better quality care for 10% of measures. Uninsured people experienced better quality care for 7% of measures (Figure 62).
  • For 3 of the 69 measures with data by insurance status, people with public insurance and uninsured people both had better quality care than people with private insurance:
    • People under age 65 whose family’s health insurance premium and out-of-pocket medical expenditures were more than 10% of total family income.
    • Deaths per 1,000 adult hospital admissions with heart failure.
    • Deaths per 1,000 adult hospital admissions with pneumonia.
  • Compared with people with private insurance, people with public insurance had worse quality care for 39% of measures, and uninsured people had worse quality care for 61% of measures.

The measures with the largest disparities between people with public health insurance and people with private insurance reflect differences in access to care and in quality of care experienced by patients. The measures with the largest disparities between people with no insurance and those with private insurance reflect differences in access to primary care providers and the routine healthcare services they deliver.

Largest Disparities for People With Public Insurance

Among different public insurance programs, Medicaid and S-CHIP alone cover approximately one-fourth of Americans,69 of whom nearly two-thirds are seniors, children, or disabled people.70 While outcomes are often worse for people with public insurance, some of the differences in health outcomes may be explained by factors other than public insurance. For example, injuries, disabilities, and preexisting illnesses that can contribute to negative health outcomes are also reasons many people qualify for public health insurance.71 Thus, on average, people with public insurance begin with worse baseline health than people with no insurance or those with private insurance. Public insurance serves as a safety net for people with limited options after experiencing disabling injury or illness.

The three quality measures with the largest disparities between people with public insurance and people with private insurance are:

  • People without a usual source of care who indicated a financial or insurance reason for not having a source of care.
  • Women under age 70 treated for breast cancer with breast-conserving surgery who received radiation therapy to the breast within 1 year of diagnosis.
  • Adults who had a doctor’s office or clinic visit in the last 12 months whose health providers sometimes or never showed respect for what they had to say.

Difficulty Accessing a Usual Source of Care

Having a usual primary care provider is associated with higher likelihood of receiving appropriate care, including preventive care services. Patients with a usual source of care also report better provider-patient communication and increased trust in the provider, both of which are linked to treatment adherence and better health.72

Column chart showing percentage:
Total, 15.8
Private Insurance, 8.7
Public Insurance, 17.9
Uninsured, 43.5

Figure 63

People without a usual source of care who indicated a financial or insurance reason for not having a source of care, 2018 (lower rates are better).

  • In 2018, the percentage of people without a usual source of care who indicated a financial or insurance reason for not having a source of care was more than twice as high for adults with public insurance (17.9%) compared with adults with private insurance (8.7%) (Figure 63).
  • In 2018, the percentage of people without a usual source of care who indicated a financial or insurance reason for not having a source of care was more than 5 times as high for uninsured adults (43.8%) compared with adults with private insurance (8.7%).

Receiving Appropriate Treatment After Lumpectomy for Breast Cancer

When women with early stage breast cancer undergo breast-conserving surgery (also called lumpectomy), combining surgical treatment with radiation therapy improves outcomes.73 Observational studies have reported that adding radiation therapy reduces the risk of recurrence by half and reduces the risk of death from breast cancer by a sixth.74

Column chart showing percentage:
Total, 89
Private Insurance, 91.3
Public Insurance Only, 83.2
Uninsured, 88.7

Figure 64

Women under age 70 treated for breast cancer with breast-conserving surgery who received radiation therapy to the breast within 1 year of diagnosis, 2017.

  • In 2017, the percentage of women who underwent breast-conserving surgery for breast cancer and received radiation therapy within 1 year of surgery was significantly lower for women with public insurance (83.2%) than for women with private insurance (91.3%) (Figure 64).
  • The percentage of women who underwent breast-conserving surgery for breast cancer and received radiation therapy within 1 year of surgery was also lower for women with no insurance (88.7%) than for women with private insurance (91.3%).

Providers Who Showed Respect for What Patients Had to Say

Patient-centered care encompasses qualities of compassion, empathy, and responsiveness to the needs, values, and preferences of individuals. It is linked to greater patient participation in their care, lower risk of misdiagnosis due to poor communication, and better patient outcomes.75, 76

Column chart showing percentage:
Total, 6.9
Private Insurance, 6.4
Public Insurance Only, 12.2
Uninsured, 13.3

Figure 65

Adults who had a doctor’s office or clinic visit in the last 12 months whose health providers sometimes or never showed respect for what they had to say, 2017 (lower rates are better).

  • In 2017, the percentage of adults who had a doctor’s office or clinic visit in the last 12 months who reported their health providers sometimes or never showed respect for what they had to say was nearly twice as high for people with public insurance (12.2%) compared with people with private insurance (6.4%) (Figure 65).
  • The percentage of adults who had a doctor’s office or clinic visit in the last 12 months who reported their health providers sometimes or never showed respect for what they had to say was also higher for people without health insurance (13.3%) than for people with private insurance (6.4%).

Largest Disparities for Uninsured People

Approximately 12% of Americans under age 65, or 32.5 million people, lack health insurance.77 The three quality measures with the largest disparities between uninsured people and people with private insurance are:

  • People without a usual source of care who indicated a financial or insurance reason for not having a source of care (Figure 63).
  • Children ages 0–17 with a wellness checkup in the past 12 months.
  • Adults who received a blood pressure measurement in the last 2 years.

Wellness Visits for Children

Wellness visits are important opportunities to assess the physical, emotional, and social development of children and adolescents, screen for health risks, and influence health behaviors, such as eating habits and physical activity, which often extend into adulthood.78 Having health insurance facilitates access to providers for recommended well-child visits.

Column chart showing percentage:
Total, 93.8
Private Insurance, 94.6
Public Insurance Only, 95.2
Uninsured, 74.1

Figure 66

Children ages 0–17 with wellness checkup in the past 12 months, by insurance status, 2019.

  • In 2019, children with no insurance (74.1%) were less likely to receive a wellness visit in the preceding 12 months than children with either public (95.2%) or private (94.6%) insurance (Figure 66).

Blood Pressure Screening

Hypertension, also called high blood pressure, affects about one-third of U.S. adults. It can damage the heart, blood vessels, kidneys, and other parts of the body over time, but it is often asymptomatic until complications, such as stroke, heart attack, heart failure, and chronic kidney disease, develop. If hypertension is identified early, providers can offer patients a range of treatment that lowers the risk for complications.79

Column chart showing percentage:
Total, 94.2
Private Insurance, 94.1
Public Insurance Only, 94.4
Uninsured, 75.6

Figure 67

Adults without hypertension who had their blood pressure measured in the last 2 years, 2019.

  • In 2019, uninsured adults (75.6%) were less likely to receive screening for high blood pressure in the last 2 years than adults covered by public (94.4%) or private (94.1%) insurance (Figure 67).

Changes in Quality of Care by Insurance Status

More than half of quality measures for those with private and public insurance were improving but only one-third of quality measures for uninsured people showed improvement.

Stacked bar chart showing number of quality measures:
Total (n=104), Improving, 50, Not Changing, 49, Worsening, 5.
Private (n=35), Improving, 19, Not Changing, 15, Worsening, 1.
Public (n=35), Improving, 20, Not Changing, 13, Worsening, 2.
Uninsured (n=34), Improving, 11, Not Changing, 21, Worsening, 2.

Figure 68

Number and percentage of all quality measures that were improving, not changing, or worsening, total and by insurance status, from 2000 through 2015, 2017, 2018, or 2019. Key: n = number of measures. Note: For each measure with at least four data points (more...)

  • From 2000 through 2019, for people with private insurance, 54% of measures were improving, 43% of measures were not changing, and 3% of measures were worsening (Figure 68).
  • For people with public insurance, 57% of measures were improving, 37% of measures were not changing, and 6% of measures were worsening.
  • For people with no insurance, 32% of measures were improving, 62% of measures were not changing, and 6% of measures were worsening.

The measures that improved for people covered by public or private insurance, but not for those who lacked insurance, reflects the role health insurance plays in accessing preventive healthcare services for children:

  • Adults who had a doctor’s office or clinic visit in the last 12 months whose health providers always asked them to describe how they will follow the instructions.
  • Infants born in the calendar year who received breastfeeding exclusively through 3 months.
  • Children ages 2–17 for whom a health provider gave advice within the past 2 years about the amount and kind of exercise, sports, or physically active hobbies they should have.
  • Children ages 2–17 for whom a health provider gave advice within the past 2 years about healthy eating.
  • Children who had their height and weight measured by a health provider within the past 2 years.
  • Children 41–80 lb for whom a health provider gave advice within the past 2 years about using a booster seat when riding in the car.

Only one measure showed improvement for people with public insurance or no insurance, but not for people with private insurance:

  • People under age 65 whose family’s health insurance premium and out-of-pocket medical expenditures were more than 10% of total family income.

The measures that improved only for people with private health insurance suggest improved provider-patient interactions and increased rates of influenza vaccination:

  • People with a usual source of care who usually asks about prescription medications and treatments from other doctors.
  • Adults ages 18 and over who received influenza vaccination in the last flu season.
  • Children ages 6 months to 17 years who received influenza vaccination in the last flu season.

The measures that improved only for people with public health insurance may reflect improving trends in access to primary care and dental care:

  • Children ages 2–17 who had a dental visit in the calendar year.
  • Children ages 2–17 who received a preventive dental service in the calendar year.
  • People without a usual source of care who indicated a financial or insurance reason for not having a source of care.

Only one measure showed improvement for uninsured people but not people covered by public or private health insurance. This measure is examined in more detail below:

  • Adults age 40 and over with diagnosed diabetes who received a flu vaccination in the calendar year.

Changes in Disparities by Insurance

Although many measures of healthcare quality improved over time, disparities between groups by health insurance status changed for only one measure (Figure 69).

Stacked bar chart showing number of quality measures:
Total (n=38), Improving, 1, Not Changing, 37, Worsening, 0.
Public (n=16), Improving, 0, Not Changing, 16, Worsening, 0.
Uninsured (n=21), Improving, 1, Not Changing, 21, Worsening, 0.

Figure 69

Number and percentage of quality measures with disparity at baseline for which disparities related to insurance were improving, not changing, or worsening, 2000 through 2017, 2018, or 2019. Key: n = number of measures.

  • Disparities by insurance status for most quality measures did not change (Figure 69).
  • Only one measure showed improvement over time in disparities between uninsured people and people with private insurance: Adults age 40 and over with diagnosed diabetes who received a flu vaccination in the calendar year.

Receipt of Flu Vaccine by Patients With Diabetes

Some patients are at higher risk of contracting the flu. These include children, older adults, and people with diabetes. The flu also has a greater likelihood of exacerbating diabetes in affected patients.80 The only measure showing decreased disparities by insurance status is:

  • Adults age 40 and over with diagnosed diabetes who received a flu vaccination in the calendar year.

The disparity reduction for this measure reflects stagnant outcomes for patients with private insurance while outcomes for uninsured patients showed improvement.

Line graph showing percentage.
Key findings are in the text below the chart.

Figure 70

Adults age 40 and over with diagnosed diabetes who received a flu vaccination in the calendar year, 2008–2018. Note: Data for uninsured people did not meet criteria for statistical reliability in 2017 and 2018.

  • The percentage of uninsured adults age 40 and over with diabetes who received a flu vaccine increased from 36.7% in 2008 to 49.7% in 2016.
  • The percentage of adults with diabetes and public or private insurance who received a flu vaccine showed no statistically significant changes (Figure 70).
Resources

CDC has prepared several patient and provider resources, including a web page on flu and diabetes.

Disparities by Residence Location

Where people live affects their access to healthcare and the quality of services they receive. Research shows that healthcare disparities by residence location exist for both adults and children.81, 82, 83, 84, 85, 86 Socioeconomic differences may contribute to the disparities: residents of inner-city and rural communities are more likely to live in poverty, more likely to engage in unhealthy behaviors (e.g., smoking), and less likely to have health insurance than people who live in suburbs.87

Differences in population density may also contribute to disparities that are specific to each location. Inner-city residents may live in crowded or inadequate housing that exposes them to higher levels of environmental pollutants, contagious vectors, mental distress, and violence compared with people who live in suburban and rural communities.88 By contrast, reduced economies of scale, longer travel times to access goods and services, and decreased opportunities for social contact in rural communities may limit the availability of healthcare services and increase risk for diseases related to social isolation.89, 90

This section examines disparities in quality of care by residence location.

Residence Location Groups

The analyses in this section use the 2013 National Center for Health Statistics (NCHS) classification,91 which are the most recent categories used by NCHS.

The 2013 scheme includes six urbanization categories:

  • Four are metropolitan county designations derived from census-defined metropolitan statistical areas (MSAs). MSAs are areas containing a large population center and adjacent communities that have a high degree of economic and social integration with that core. MSAs have at least 50,000 residents and include an urban core with population density of at least 1,000 people per square mile and adjacent areas with at least 500 people per square mile:
    • Large Central Metropolitan: Counties in an MSA of 1 million or more residents:
      1. That contain the entire population of the largest principal city of the MSA, or
      2. Whose entire population is contained within the largest principal city of the MSA, or
      3. That contain at least 250,000 residents of any principal city in the MSA.
      Examples of Large Central Metro areas are Denver County, Colorado; Washington, DC; and Cook County, Illinois.
    • Large Fringe Metropolitan: Counties in MSAs of 1 million or more population that do not qualify as large central areas.xxiii Large Fringe Metropolitan areas are also described as suburban areas. Examples of Large Fringe Metro areas are San Bernardino County, California; Broward County, Florida; and Bergen County, New Jersey.
    • Medium Metropolitan: Counties in MSAs of 250,000 to 999,999 population. Examples of Medium Metro areas are Scott County, Kentucky; York County, Maine; and Douglas County, Nebraska.
    • Small Metropolitan: Counties in MSAs of less than 250,000 population. Examples of Small Metro areas are Baldwin County, Alabama; Wayne County, North Carolina; and Allen County, Ohio.
  • The remaining two categories are nonmetropolitan county designations, which are defined as not meeting the criteria for being an MSA (i.e., population less than 50,000 inhabitants or population density less than 500 people per square mile):
    • Micropolitan: Nonmetropolitan counties in a “micropolitan statistical area,” which are defined as counties that are less densely populated than MSAs and centered around smaller urban clusters with 2,500–49,999 inhabitants. Examples of Micropolitan areas are Woodward County, Oklahoma; Cherokee County, South Carolina; and Harrison County, West Virginia.
    • Noncore: Nonmetropolitan counties that are outside of a micropolitan statistical area. Noncore counties are also described as rural. Examples of Noncore areas are Wallowa County, Oregon; Bedford County, Pennsylvania; and Crane County, Texas.

When examining trends, it is important to recognize that the key differences between the 2013 NCHS Urban-Rural Classification scheme and the earlier 2006 version are in how it describes small metropolitan, micropolitan, and noncore areas. The 2013 classification broadens the inclusion criteria for each of these residence locations. All other definitions are unchanged (Table 1).92

Table 1. NCHS Urban-Rural Classification Scheme, 2006 vs. 2013.

Table 1

NCHS Urban-Rural Classification Scheme, 2006 vs. 2013.

Figure 71 shows a map of U.S. county classifications according to the 2013 NCHS Urban-Rural Classification system.

Map of United States showing county groupings, which are described in the text after the map.

Figure 71

Map showing 2013 NCHS Urban-Rural County Classifications in the United States.

The NHQDR uses the NCHS classification to analyze performance of quality measures that have data available by residence location. Data on state-based performance metrics are also available through the NHQDR State View.93

With the State View tool, users can explore the quality of their state’s healthcare and compare their state’s data with national data or data from the best performing states. Users can access a state dashboard showing performance compared with benchmarks for more than 80 measures. Some of these measures are also stratified by subpopulations to show disparities.

Overview of Disparities by Residence Location

In the most recent data year, 34% of measures had better performance in large fringe metro areas than in other locations while only 4% of measures showed worse performance (Figure 72). Relative to large fringe metro counties, nonmetropolitan (i.e., micropolitan and noncore) areas had the largest number of measures that showed worse quality care, followed by small metro and large central metro areas. Large central metro and noncore areas had the largest number of measures that showed better quality care.

Nonmetropolitan areas had the largest number of measures showing worse quality care compared with large fringe metropolitan areas, followed by small metropolitan and large central metropolitan areas.

Stacked bar chart showing percentage of measures that were better, same, or worse:
Total, better, 17, same, 319, worse, 129.
Large Central Metro, better, 5, same, 70, worse, 21.
Medium Metro, better, 0, same, 78, worse, 17.
Small Metro, better, 3, same, 67, worse, 23.
Micropolitan, better, 3, same, 54, worse, 35.
Noncore, better, 6, same, 50, worse, 33.

Figure 72

Number and percentage of quality measures for which residents of selected locations experienced better, same, or worse quality of care compared with large fringe metropolitan areas, 2017, 2018, or 2019. Key: n = number of measures. Note: Definitions of (more...)

  • Nonmetropolitan (micropolitan and noncore) areas showed worse quality care than large fringe metro areas on 45% and 37% of measures, respectively, and better quality care on 3% and 7% of measures for which data are available by location of residence (Figure 72).
  • Large central metro areas showed worse quality care than large fringe metro areas on 22% of measures and better quality care for 5% of measures.

Examining the specific measures where nonmetropolitan areas and large central metro areas experienced better or worse care relative to large fringe metro areas highlights issues where these locations share similar concerns and where they differ. Large central metro, micropolitan, and noncore areas overlapped on six quality of care measures, where all three experienced worse quality than large fringe metro areas. However, they did not overlap in any of the measures for which they experienced better quality of care. Instead, measures where a residence location at one end of the urban-rural spectrum experienced better quality care were frequently the same measure where the residence location at the other end of the spectrum experienced worse quality care.

The six quality of care measures for which large central metropolitan, micropolitan, and noncore areas all experienced worse quality care than large fringe metros are:

  • Adults who had a doctor’s office or clinic visit in the last 12 months whose health providers sometimes or never listened carefully to them.
  • Children ages 2–17 who had a dental visit in the calendar year.
  • Children over 80 lb for whom a health provider gave advice within the past 2 years about using lap or shoulder belts when riding in a car.
  • Hospital admissions for short-term complications of diabetes per 100,000 population, adults.
  • Hospital admissions for lower extremity amputations per 1,000 population, adults age 18 and over with diabetes.
  • Reclosure of postoperative abdominal wound dehiscence per 1,000 abdominopelvic-surgery admissions of length 2 or more days, adults.

Micropolitan and noncore areas collectively experienced better quality care than large fringe metro areas on seven unique measures (nine measures total). Of these, five were measures where large central metro areas experienced worse quality care than large fringe metro areas:

  • Emergency department visits with a principal diagnosis related to substance use disorder only, per 100,000 population.
  • Hospital admissions for asthma per 100,000 population, adults ages 18–39.
  • Hospital admissions for asthma per 100,000 population, children ages 2–17.
  • Hospital admissions for hypertension per 100,000 population, adults age 18 and over.
  • HIV infection deaths per 100,000 population.

Large central metro areas experienced better quality care than large fringe metro areas on five measures. Of these, three were measures where micropolitan or noncore areas experienced worse quality care than large fringe metro areas:

  • Hospital admissions for community-acquired pneumonia per 100,000 population, adults age 18 and over.
  • Lung cancer deaths per 100,000 population per year.
  • Suicide deaths among people age 12 and over per 100,000 population.

Largest Disparities

The three measures with the largest disparities between large fringe metro areas and other locations vary. The differences may reflect differing healthcare needs for each location. In the most recent available data years, the three measures with the largest disparities relative to large fringe metro areas follow for each location.

  • Large Central Metro:
    • HIV infection deaths per 100,000 population
    • Hospital admissions for asthma per 100,000 population, children ages 2–17
    • Emergency department visits with a principal diagnosis related to substance use disorder only, per 100,000 population
  • Medium Metro:
    • Hospital admissions for short-term complications of diabetes per 100,000 population, children ages 6–17
    • Adults who received a blood cholesterol measurement in the last 5 years
    • Infant mortality per 1,000 live births, birth weight 2,500 grams or more
  • Small Metro:
    • Children ages 3–5 who ever had their vision checked by a health provider
    • Hospitalizations and emergency department encounters for heart failure
    • Infant mortality per 1,000 live births, birth weight 2,500 grams or more
  • Micropolitan:
    • Emergency department visits with a principal diagnosis related to dental conditions
    • Adults who received a blood cholesterol measurement in the last 5 years
    • Children ages 3–5 who ever had their vision checked by a health provider
  • Noncore:
    • Hospital admissions for community-acquired pneumonia per 100,000 population, adults age 18 and over
    • Deaths per 1,000 hospital admissions with expected low mortality
    • Infant mortality per 1,000 live births, birth weight 2,500 grams or more

The following figures these measures in detail.

Deaths From HIV Infection

New HIV diagnoses and HIV prevalence are concentrated primarily in large U.S. metropolitan areas, with Atlanta, Baton Rouge, Miami, New Orleans, and Orlando leading the list of areas with the highest rate of new diagnoses. Atlanta, Baton Rouge, Miami, New Orleans, and New York lead the list of areas with the highest rates of people living with HIV.94

Columns showing deaths per 100,000 population:
2015 achievable benchmark: 0.75 per 100,000 population.
Total, 1.5
Large Central Metro, 2.3
Large Fringe Metro, 1.1
Medium Metro, 1.4
Small Metro, 1.3
Micropolitan, 1.1
Noncore, 0.9

Figure 73

HIV infection deaths per 100,000 population, 2018 (lower rates are better). Note: The benchmark calculation takes the average of the top 10% of states with statistically reliable data. U.S. territories are not included in the calculations. Some benchmarks (more...)

  • In 2018, the death rate from HIV infections was higher in large central metro areas (2.3 per 100,000 population) compared with the rate in large fringe metro areas (1.1 per 100,000 population) (Figure 73).
  • The 2015 achievable benchmark was 0.75 per 100,000 population. At the current rate of increase, overall, the benchmark could be achieved in 4 years for large central metro areas and in 2 years for large fringe metro areas (trend data not shown).
  • The top 10% of states that contributed to the achievable benchmark were Kansas, Kentucky, Minnesota, Missouri, Ohio, and Washington.

An HHS initiative to eliminate new HIV infections is underway. The goal is “to reduce new HIV infections in the United States by 75 percent in five years and by 90 percent by 2030.”95 Federal efforts to reduce HIV-related mortality include the promotion of treatment therapies such as antiretroviral therapy, as well as pre-exposure prophylaxis and postexposure prophylaxis.96

Several HHS agencies provide a federal response to the HIV epidemic, including HRSA’s HIV/AIDS Bureau, which administers the Ryan White HIV/AIDS Program (RWHAP). RWHAP is the largest federal program focused exclusively on providing HIV care and treatment to patients with inadequate or no insurance. Through RWHAP’s partnerships, more than 512,000 people receive care annually.

Hospital Admissions for Asthma

Asthma is the most common chronic lung condition among children under 17 years in the United States.97 Children with asthma may experience debilitating exacerbations triggered by environmental exposures, such as fumes, airborne viruses, and cold air, but appropriate treatment in ambulatory care settings can reduce patients’ risk for exacerbations.98, 99

Research has linked access to primary care, continuity of care by a provider, and adherence to preventive care plans to improved quality of care and fewer hospital admissions for chronic conditions such as asthma.100

Columns showing admissions per 100,000 population
Total, 81.5
Large Central Metro, 116.3
Large Fringe Metro, 71.3
Medium Metro, 74.1
Small Metro, 55.3
Micropolitan, 52.1
Noncore, 43.1

Figure 74

Hospital admissions for asthma per 100,000 population, children ages 2–17, 2018 (lower rates are better).

  • In 2018, the rate of hospital admissions for children ages 2–17 with asthma was more than 60% higher in large central metro areas (116.3 per 100,000 population) than in large fringe metro areas (71.3 per 100,000 population) (Figure 74).

Emergency Department Visits for Substance Use

Illicit drug use and subsequent overdose deaths have risen in both metropolitan and nonmetropolitan areas over the past two decades. Overdose death rates in rural areas exceeded rates in urban areas between 2007 and 2015,101 overlapping with the second wave of opioid overdose deaths.102 However, more recent data show that overdose death rates in the third wave of opioid overdose deaths are highest in urban communities.103

Columns showing visits per 100,000 population:
Total, 556.4
Large Central Metro, 642.8
Large Fringe Metro, 452.7
Medium Metro, 547.5
Small Metro, 537.3
Micropolitan, 487.3
Noncore, 364.3

Figure 75

Emergency department visits with a principal diagnosis related to substance use disorder only per 100,000 population, 2018 (lower rates are better).

  • In 2018, the rate of adult emergency department visits with a principal diagnosis related to substance use disorder was 42% higher in large central metro areas (642.8 per 100,000 population) than in large fringe metro areas (452.7 per 100,000 population) (Figure 75).

Hospital Admissions for Short-Term Complications of Diabetes

Type 1 diabetes is one of the most common chronic diseases in childhood. It is caused by insulin deficiency, resulting from an autoimmune reaction that destroys insulin-producing beta-cells in the pancreas. In children and adolescents, the most common complications of diabetes are short-term problems that result from blood sugars going too low or too high: hypoglycemia, ketoacidosis, and diabetic coma.104 Access to healthcare providers who can prescribe medications and teach patients how to self-manage their health can reduce risks for short-term complications and prevent emergency visits and hospitalizations.105

Columns showing admissions per 100,000 population:
Total, 28
Large Central Metro, 29.9
Large Fringe Metro, 23.6
Medium Metro, 32.1
Small Metro, 27.8
Micropolitan, 25
Noncore, 27.9

Figure 76

Hospital admissions for short-term complications of diabetes per 100,000 population, children 6–17, 2018 (lower rates are better).

  • In 2018, the rate of hospitalizations among children ages6–17 years due to short-term complications of diabetes mellitus was 36% higher in medium metro areas (32.1 per 100,000 population) than in large fringe metro areas (23.6 per 100,000 population) (Figure 76).

Cholesterol Check

Medications and lifestyle modifications that lower cholesterol reduce the risk of heart attacks and strokes in people who may have underlying atherosclerosis (i.e., cardiovascular disease).106 Intermittent laboratory testing for cholesterol by a healthcare provider can identify atherosclerosis in otherwise healthy people and help them make informed treatment decisions to lower their risk of heart attacks and strokes. Thus, access to screening for cholesterol is an important component of efforts to improve cardiovascular health.107

Columns showing percentage:
Total, 89.5
Large Central Metro, 92.1
Large Fringe Metro, 91
Medium Metro, 88.1
Small Metro, 87.4
Micropolitan, 83.5
Noncore, 85.9

Figure 77

Adults who received a blood cholesterol measurement in the last 5 years, 2019.

  • In 2019, the percentage of adults who received a blood cholesterol measurement in the last 5 years was lower in micropolitan (83.5%) and medium metropolitan areas (88.1%) than in large fringe metro areas (91.0%) (Figure 77).

Infant Mortality

Infant mortality is the death of infants before their first birthday. It is a key health indicator that reflects baseline maternal and infant health, as well as healthcare services delivered before, during, and immediately after an infant’s birth. In 2018, the five leading causes of infant death were birth defects, preterm birth and low birth weight, injuries (e.g., suffocation), sudden infant death syndrome, and maternal pregnancy complications.108

Columns showing deaths per 1,000 live births:
Total, 2.0
Large Central Metro, 1.8
Large Fringe Metro, 1.7
Medium Metro, 2.2
Small Metro, 2.4
Micropolitan, 2.6
Noncore, 2.9

Figure 78

Infant mortality per 1,000 live births, birth weight 2,500 grams or more, 2017 (lower rates are better).

  • In 2017, the percentage of infant deaths among live births weighing 2,500 grams or more was significantly higher in medium metro (2.2%), small metro (2.4%), micropolitan (2.6%), and noncore (2.9%) areas than in large fringe metro areas (1.7%) (Figure 78).

Pediatric Vision Exams

Pediatric vision screenings are efficient eye examinations that primary care providers, trained laypeople (e.g., in schools), and eye care specialists perform to detect issues that warrant a more comprehensive eye examination by a specialist. They are crucial for identifying conditions that could lead to blindness, life-threatening illness, and problems with school performance if left untreated.109

Research shows that periodic vision screening in early childhood reduces the risk of vision loss at age 7 years by more than 50%.110 Thus, access to vision screening throughout childhood is important to ensure children’s health.

Column chart showing percentage:
Total, 70.7
Large Central Metro, 73.5
Large Fringe Metro, 77.3
Medium Metro, 68.3
Small Metro, 62.0
Micropolitan, 58.9

Figure 79

Children ages 3–5 who ever had their vision checked by a health provider, 2018. Note: Data for noncore areas are not shown because the data were statistically unreliable.

  • In 2018, the percentage of children ages 3–5 years who had their vision checked by a health provider was lower in micropolitan (58.9%) and small metropolitan areas (62.0%) than in large fringe metro areas (77.3%) (Figure 79).

Hospital and Emergency Visits for Heart Failure

Heart failure is an important cause of morbidity and mortality in the United States, accounting for 379,800 deaths in 2018.111, 112 It is also the most common and expensive reason for preventable hospitalizations, with more than 1 million admissions and $11.2 billion in total costs in 2017. Access to appropriate treatment in ambulatory care settings can help patients safely avoid emergency visits and hospital admissions for this condition.113

Columns showing rate per 100,000 population:
Total, 525.7
Large Central Metro, 502.2
Large Fringe Metro, 434.9
Medium Metro, 512.9
Small Metro, 634.0
Micropolitan, 663.6
Noncore, 713.3

Figure 80

Hospitalizations and emergency department encounters for heart failure per 100,000 population, 2018 (lower rates are better).

  • In 2018, the rate of emergency department visits and hospitalizations per 100,000 population for heart failure was significantly higher in micropolitan (663.6 visits) and noncore (713.3 visits) areas than in large fringe metro areas (434.9 visits) (Figure 80).

Emergency Department Visits for Dental Conditions

Oral health is a vital component of a person’s overall health and well-being. Untreated oral disease can affect appetite, leading to nutritional problems; cause chronic pain, interfering with sleep and work; and has been associated with diabetes, heart and lung disease, stroke, and poor birth outcomes.114

Preventive dental care, including early detection, treatment, and management of problems, promotes good oral health. When people lack access to a usual source of dental care, they often will seek relief in emergency departments, which are equipped to meet only emergency dental care needs.115

Column chart showing visits per 100,000 population:
Total, 290.2
Large Central Metro, 230.1
Large Fringe Metro, 210.3
Medium and Small Metro, 334.8
Micropolitan and Noncore, 459.7

Figure 81

Emergency department visits with a principal diagnosis related to dental conditions per 100,000 population, 2018 (lower rates are better).

  • In 2018, the rate of ED visits related to dental conditions in micropolitan and noncore areas combined (459.7 per 100,000 population) was more than twice the rate in large fringe metro areas (210.3 per 100,000 population) (Figure 81).

Hospital Admissions for Pneumonia

Community-acquired pneumonia (CAP) is an acute lung infection acquired outside of a hospital setting. A person with CAP may present with symptoms that range from mild fever and productive cough to severe infection and inability to breathe without mechanical ventilation.116

CAP results in substantial morbidity, mortality, and costs in the United States. As the fourth leading reason for hospitalizations in 2018, it accounted for 740,700 admissions and $7.7 billion in healthcare costs.117 In 2019, it was the underlying cause of death in 43,881 individuals (13.4 deaths per 100,000 population).118

CAP hospitalizations are often avoidable. Administering pneumococcal vaccines to high-risk groups can prevent infections, and early evaluation and treatment by a healthcare provider can prevent hospitalizations.

Column chart showing admissions per 100,000 population:
Total, 184.5
Large Central Metro, 146.6
Large Fringe Metro, 171.2
Medium Metro, 171.3
Small Metro, 199.7
Micropolitan, 244.5
Noncore, 330.2

Figure 82

Hospital admissions for community-acquired pneumonia per 100,000 population, adults age 18 and over, 2018 (lower rates are better).

  • In 2018, the rate of hospital admissions for CAP was nearly twice as high in noncore areas (330.2 per 100,000 population) as in large fringe metro areas (171.2 per 100,000 population) (Figure 82).

Unexpected Deaths After Hospital Admission

Death during a hospital admission may indicate that patients received unsafe or inappropriate care, particularly if a patient dies while being treated for problems with low mortality risk.

Column chart showing deaths per 1,000 hospital admissions:
Total, 0.52
Large Central Metro, 0.45
Large Fringe Metro, 0.45
Medium Metro, 0.57
Small Metro, 0.5
Micropolitan, 0.66
Noncore, 0.81

Figure 83

Deaths per 1,000 hospital admissions with expected low mortality, 2018 (lower rates are better).

  • In 2018, the death rate for conditions with expected low mortality was nearly twice as high in noncore areas (0.81 per 1,000 admission) as in large fringe metro areas (0.45 per 1,000 admission) (Figure 83).

Changes in Quality of Care by Residence Location

The bar chart in Figure 84 summarizes trends in 45 quality of care measures for which data are available by geographic location.

Among the six geographic locations, noncore areas had the fewest improving trends and the most worsening trends.

Stacked bar chart showing number of quality measures:
Large Central Metro (n=45), Improving, 23, Not Changing, 19, Worsening, 3.
Large Fringe Metro (n=42), Improving, 21, Not Changing, 17, Worsening, 4.
Medium Metro (n=42), Improving, 18, Not Changing, 19, Worsening, 5.
Small Metro (n=44), Improving, 18, Not Changing, 22, Worsening, 4.
Micropolitan (n=43), Improving, 21, Not Changing, 19, Worsening, 3.
Noncore (n=43), Improving, 14, Not Changing, 23, Worsening, 6.

Figure 84

Number and percentage of all quality measures that were improving, not changing, or worsening, total and by residence location, from 2002 through 2010, 2011, 2013, 2016, 2017, 2018, or 2019. Key: n = number of measures. Note: For each measure with at (more...)

  • Noncore areas had the fewest measures with improving trends (33%) and the most measures with worsening trends (14%) (Figure 84).
  • Among the remaining geographic locations, large central metro and large fringe metro areas had the most measures with improving trends (51% and 50%, respectively). Large central metro and micropolitan areas had the fewest measures with worsening trends (7%).

Changes in Disparities by Residence Location

The bar chart in Figure 85 summarizes trends in disparities between large fringe metro areas and other locations for 13 measures for which data are available by geographic location. Overall, disparities between large fringe metropolitan counties and other areas did not change during the most recent data year available. The only measure that showed narrowing disparities was “hospital inpatient stays involving opioid-related diagnoses,” which resulted from worsening opioid-related hospitalization rates in large fringe metro areas, instead of improving trends in other locations.

The only disparity that improved was due to a worsening trend for large fringe metropolitan counties instead of improvement in other locations.

In the 2019 NHQDR, two other measures had similarly shown narrowing disparities due to worsening trends in large fringe metro areas: “people unable to get or delayed in getting needed medical care due to financial or insurance reasons” and “people unable to get or delayed in getting needed prescription medicines due to financial or insurance reasons.” These measures are not reported this year due to lack of data availability.

Stacked bar chart showing number of quality measures:
Reference group is large fringe metro--no disparities were worsening (widening).
Total (n=13), Improving, 1, Not Changing, 12. 
Large Central Metro (n=13), Improving, 1, Not Changing, 12.
Medium Metro (n=4), Improving, 0, Not Changing, 4.
Small Metro (n=4), Improving, 0, Not Changing, 4.
Micropolitan (n=10), Improving, 0, Not Changing, 10.
Noncore (n=12), Improving, 0, Not Changing, 12.

Figure 85

Number and percentage of quality measures with disparity at baseline for which disparities related to residence location were improving or not changing, 2002 through 2015, 2016, 2017, or 2018. Key: n = number of measures. Note: A total of 13 measures (more...)

  • Disparities by residence location remained unchanged for most quality measures (Figure 85).

Inpatient Stays Due to Opioid Use

The opioid epidemic constitutes a continuing public health emergency119 that affects the entire United States. The 2020 National Survey on Drug Use and Health estimates that nearly 9.5 million people misused opioids in the past year,65 and data from the Centers for Disease Control and Prevention (CDC) indicate that rates of nonfatal and fatal overdose continue to rise in multiple states and territories.

CDC estimates that 49,860 of 70,630 drug overdose deaths (70.6%) involved opioids in 2019, affecting multiple age groups, racial and ethnic groups, and geographic regions.120 Rising rates of hospital admissions for opioid-related diagnoses echo this trend. They also indicate that narrowing disparities between geographic locations represent worsening trends in large fringe metro areas instead of improving trends in large central metro areas.

Line graph showing rate per 100,000 population.
2015 achievable benchmark: 102.9 per 100,000 population.
Key findings are in the text below the chart.

Figure 86

Hospital inpatient stays involving opioid-related diagnoses per 100,000 population, 2005–2018 (lower rates are better). Note: The benchmark calculation takes the average of the top 10% of states with statistically reliable data. U.S. territories (more...)

  • From 2005 to 2018, the gap in opioid-related hospitalization rates in large central metro areas and in large central fringe metro areas narrowed (Figure 86). However, the reduced disparity was due to rates of opioid-related hospitalizations rising faster in large fringe metro areas. This undesirable trend began to plateau in 2016 but remains well above the 2015 achievable benchmark of 102.9 hospitalizations per 100,000 population. (For this measure, a low value is more desirable, so rates above the achievable benchmark indicate suboptimal quality of care.)
  • In 2005, the rate was 111.5 per 100,000 population in large fringe metro areas vs. 195.8 per 100,000 population in large central metro areas. In 2017, rates in both geographic areas had risen to peak 288.4 admissions per 100,000 population in large fringe metro areas and 314.6 admissions per 100,000 population in large central metro areas.
  • In 2018, the most recent year for which data are available, hospitalization rates for opioid-related disorders had plateaued at 268.7 per 100,000 population in large fringe metro areas and 307.3 per 100,000 population in large central metro areas, approximately 3 times as high as the achievable benchmark of 102.9 per 100,000 population.
  • The top 10% of states that contributed to the achievable benchmark were Georgia, Iowa, Nebraska, Texas, and Wyoming. In 2016–2017, no state reached the benchmark.
Resources

In 2017, HHS launched a departmentwide initiative with a five-point strategy to combat the opioid epidemic. Many agencies supported this initiative by establishing specific research opportunities, resources, and data to support providers, patients, and researchers. More information is available at https://www.hhs.gov/opioids/. Other federal resources are discussed in detail in the Quality of Care – Trends in Effective Treatment section of this report.

Footnotes

xix

Due to a change in the Healthcare Cost and Utilization Project (HCUP) data, the same measures reported in past reports are not represented in this report. HCUP converted all measures from International Classification of Diseases, Ninth Revision (ICD-9) to Tenth Revision (ICD-10) codes in October 2015, thus changing the outcomes of these measures. Therefore, trend data are not directly comparable at this time.

xx

Due to a change in the Healthcare Cost and Utilization Project (HCUP) data, the same measures reported in past reports are not represented in this report. HCUP converted all measures from International Classification of Diseases, Ninth Revision (ICD-9) to Tenth Revision (ICD-10) codes in October 2015, thus changing the outcomes of these measures. Therefore, trend data are not available at this time.

xxi

Due to a change in the Healthcare Cost and Utilization Project (HCUP) data, the same measures reported in past reports are not represented in this report. HCUP converted all measures from International Classification of Diseases, Ninth Revision (ICD-9) to Tenth Revision (ICD-10) codes in October 2015, thus changing the outcomes of these measures. Therefore, trend data are not available at this time.

xxii

Due to a change in the Healthcare Cost and Utilization Project (HCUP) data, the same measures reported in past reports are not represented in this report. HCUP converted all measures from International Classification of Diseases, Ninth Revision (ICD-9) to Tenth Revision (ICD-10) codes in October 2015, thus changing the outcomes of these measures. Therefore, trend data are not available at this time.

xxiii

For comparisons across residence locations, large fringe MSAs (large city suburbs) are used as the reference group since these counties have the lowest levels of poverty and typically have the best healthcare quality and access to healthcare.

Copyright Notice

This document is in the public domain and may be used and reprinted without permission. Citation of the source is appreciated.

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