Bookshelf

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

2022 National Healthcare Quality and Disparities Report [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2022 Oct.

National Healthcare Quality and Disparities Report" width="80px" height="100px" />

2022 National Healthcare Quality and Disparities Report [Internet].

PORTRAIT OF AMERICAN HEALTHCARE

Healthcare quality assessments evaluate health systems’ effectiveness to provide patient care that is timely, affordable, and based on reliable evidence. This section of the report provides a broad portrait of U.S. healthcare in consideration of those fundamental measures as well as additional factors that influence disparities in care. This information is provided to support contextual understanding of the structural factors, including societal inequities, that affect how today’s healthcare is organized, financed, and delivered.

Social Determinants of Health: prevalence of social, economic, environmental, and community conditions affecting health outcomes.

Demographics

Healthcare systems and providers in the United States serve a large and growing population. Over the 10 years between the 2010 Census and the 2020 Census, i the U.S. population increased 7.4% to 331,449,281 people, split nearly evenly between females (50.5%) and males (49.5%). 1

The following demographic data describe emerging trends related to the aging population, increased racial and ethnic diversity, and more Americans living in metropolitan areas.

The U.S. population is aging. Five-year estimates from the American Community Survey (ACS) show the median age increased from 36.9 years to 38.2 years between 2010 and 2020. Fewer babies being born and the oldest adults living longer account for much of this increase.

Figure 1

Distribution of people in the United States by 10-year age groups in 2010 and 2020.

In 2020, 6.0% of the population was under 5 years old, 12.6% was 5-14 years old, 13.2% was 15-24 years old, 13.9% was 25-34 years old, 12.7% was 35-44 years old, 12.7% was 45-54 years old, 12.9% was 55-64 years old, 9.4% was 65-74 years old, 4.7% was 75-84 years old, and 2.0% was 85 years and over in 2020 (Figure 1).

By comparison, in 2010, 6.6% was under 5 years old, 13.4% was 5-14 years old, 14.3% was 15-24 years old, 13.2% was 25-34 years old, 13.9% was 35-44 years old, 14.6% was 45-54 years old, 11.3% was 55-64 years old, 6.7% was 65-74 years old, 4.3% was 75-84 years old, and 1.7% was 85 years and over.

American Indian or Alaska Native (AI/AN). A person who has origins in any of the original peoples of North and South America (including Central America) and maintains tribal affiliation or community attachment.

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.

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.”

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.”

Native Hawaiian/Pacific Islander (NHPI). A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.

White. A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.

Much of the recent growth in racial and ethnic diversity can be attributed to a rise in the number of people who self-identify as two or more races, which increased 7.3 percentage points between 2010 and 2020. The percentage of people who identify as Asian alone also increased by 1.2 percentage points over the past decade, while the percentage of people who identify as Black, AI/AN, or NHPI remained at similar levels.

Figure 2

Distribution of people in the United States, by race, 2010 to 2020.

In the 2020 Census, 1.1% of people identified as AI/AN, 6.0% as Asian alone, 12.4% as Black or African American alone, 0.2% as NHPI alone, 61.6% as White alone, 10.2% as Two or More Races, and 8.4% as Some Other Race (Figure 2).

The 2010 Census reported percentages of racial and ethnic groups as 0.9% AI/AN alone, 4.8% Asian alone, 12.6% Black or African American alone, 0.2% NHPI alone, 72.4% White alone, 2.9% Two or More Races, and 6.2% Some Other Race.

Figure 3

Distribution of people in the United States, by ethnicity, 2010 to 2020.

The 2020 Census estimates that 18.7% of the population identify as Hispanic, while 81.3% identify as non-Hispanic (Figure 3).

A growing percentage of people resides in metropolitan communities. The Census Bureau reports that between 2010 and 2020, the percentage of people who live in metropolitan areas increased by 1.1 percentage points, while the percentage of people in nonmetropolitan counties decreased by the same amount.

Figure 4

Distribution of people in the United States, by location of residence, 2010 to 2020.

In the 2020 Census, 13.9% of people lived in nonmetropolitan counties and 86.1% lived in metropolitan counties (Figure 4).

In the 2010 Census, 15.0% of people lived in nonmetropolitan counties and 85.0% lived in metropolitan counties.

The NHQDR examines differences in health outcomes by rural-urban location of residence using the 2013 National Center for Health Statistics (NCHS) classification. Data on state-based rural-urban metrics are also available through the QDR State Snapshots.

Large Fringe Metropolitan: Counties in MSAs of 1 million or more population that do not qualify as large central areas. iii 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.

Micropolitan: Nonmetropolitan counties in a “micropolitan statistical area,” which OMB defines 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.

Figure 5 shows a map of U.S. county classifications according to the 2013 NCHS Urban-Rural Classification Scheme. iv

Figure 5

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

Health Measures

Measures of life expectancy and premature death have worsened in recent decades, suggesting that the United States has fallen farther away from its potential to promote and protect health. The following data quantify those trends.

Life expectancy has not kept pace with other nations. Life expectancy for the overall U.S. population decreased by 1.5 years, from 78.8 years in 2019 to 77.3 years in 2020. Much of this decline was attributable to the global COVID-19 public health emergency. The decline in life expectancy was greater in Hispanic (decrease by 3.0 years) and non-Hispanic Black (decrease by 2.9 years) groups than in non-Hispanic White groups (decrease by 1.2 years), widening an existing health disparity. v

U.S. life expectancy at birth lags behind the average life expectancy of 11 comparable Organisation for Economic Co-operation and Development (OECD) countries. vi The gap had grown steadily since 1980 and widened markedly in 2020, when life expectancy decreased more steeply in the U.S. than it had in comparable industrialized countries. The United States similarly lags behind peer OECD countries in mortality rates and premature death rates. 3

Figure 6

Life expectancy in United States vs. comparable OECD countries, 1980-2020. Note: Data are from the Centers for Disease Control and Prevention (CDC), Australian Bureau of Statistics, and Organization of Economic Co-operation and Development data. The 2019 (more. )

In 2019, before the COVID-19 global pandemic, average life expectancy in the United States was 78.8 years vs. 82.6 years in comparable OECD countries (Figure 6).

In 2020, average life expectancy in the United States was 77.0 years vs. 82.1 years in comparable OECD countries.

Exploring the reasons for this trend can provide insights for where the United States can improve healthcare delivery and associated outcomes, particularly trends in premature death and disease-related death, which are explored below.

Figure 7

Ten leading causes of death, based on age-adjusted mortality, United States, 2016-2020. Note: Suicide was one of the 10 leading causes of death from 2016 to 2019 but was replaced by COVID-19 in 2020.

Heart disease and cancer remained the leading causes of death in 2020, accounting for 168.2 deaths and 144.1 deaths per 100,000 population, respectively) (Figure 7).

In 2020, COVID-19 became the third leading cause of death, accounting for 85.0 deaths per 100,000 population.

Drug overdose and other unintentional poisonings accounted for 41.0% of the 57.6 deaths per 100,000 population that were due to unintentional injury. Other prevalent conditions contributing to unintentional injury deaths include accidental falls followed by motor vehicle accidents, unintentional suffocation, and drowning. vii

Death rates from heart disease, which had been in decline, rose between 2019 and 2020. Death rates from unintentional injury, Alzheimer’s disease, and diabetes also increased.

Opioid use and violence have been powerful contributors to premature deaths. Years of potential life lost (YPLL) is a measure of premature death. It adjusts mortality statistics for age at death, estimating the average time that a person would have lived had she or he not died prematurely. Thus, YPLL highlights conditions that affect younger populations and accounts for the social and economic costs of premature death.

Figure 8

Ten leading causes of years of potential life lost, 2016-2020.

Unintentional injury (a plurality is due to opioid overdose) is by far the leading contributor to YPLL, due to its prevalence and effects on people across the age spectrum (Figure 8).

After reaching the United States in 2020, COVID-19 became the seventh leading contributor to YPLL (248.0 years per 100,000 population in 2020).

The effects of increased violence and lethality of violent means on younger populations are reflected in rising rates of YPLL due to unintentional injury (1,021.8 to 1,271.2 years per 100,000 population between 2016 and 2020) and homicide (225.3 to 286.0 years per 100,000 population between 2016 and 2020).

Liver disease (often resulting from alcohol use or hepatitis infections caused by injection drug use).

Chronic diseases contribute to many of the leading causes of death and years of potential life lost. Chronic diseases are conditions that last 1 year or more and require ongoing medical attention or limit activities of daily living or both. viii Six in 10 adults in the United States have a chronic disease, and 4 in 10 have two or more chronic conditions. 4 Chronic conditions contribute to 7 of the 10 leading causes of death and 6 of the 10 leading causes of premature death. 5

Communities, healthcare delivery organizations, and providers can also build capacity to serve the specific needs of people with chronic diseases. People with a chronic disease typically require ongoing support to monitor and, if needed, adjust treatment during their lifetime. When people have multiple chronic diseases, each may interact with others in complex ways.

For example, hypertension and chronic kidney disease both increase risk for developing heart disease. However, hypertension also increases the risk for developing chronic kidney disease and complicates its management if it develops. Likewise, chronic kidney disease exacerbates high blood pressure and complicates the management of hypertension. Thus, people with multiple chronic diseases often benefit from interdisciplinary, coordinated healthcare services that can address their clinical needs as well as their health priorities, social needs, and health-related behaviors. 6

Experts have noted that acute, episodic healthcare services, such as those typically delivered in hospitals, are often inadequate to prevent and mitigate the impact of chronic disease on the nation’s health. 7 , 8 They instead point to primary care and community-based strategies as having the greater potential to meet the challenges posed by these conditions. 9 , 10

Social Determinants of Health

Considerable evidence indicates that social determinants of health (SDOH)—social, economic, environmental, and community conditions—may have a stronger influence on people’s health outcomes than clinical services provided by healthcare delivery systems. 11 Healthcare delivery systems and healthcare workers must account for SDOH when addressing patients’ health concerns.

This section describes the extent to which SDOH factors are present among people in the United States, thus providing estimates for issues healthcare delivery systems must address to produce optimal health outcomes.

The importance of understanding SDOH is underscored in Healthy People 2030, which sets national objectives for improving health and well-being. Healthy People 2030 describes five SDOH domains that can influence health outcomes (Figure 9). One of the domains—Health Care Access and Quality—accounts for a population’s ability to receive healthcare services when needed. That includes having healthcare services nearby and having insurance to cover the cost of receiving services. The NHQDR summarizes multiple measures related to the Healthcare Access and Quality domain later in this report.

Figure 9

Social Determinants of Health.

Screening for treatable health conditions (such as diabetes, cholesterol, HIV, and breast, prostate, and colon cancer),

The NHQDR mostly reports on disparities related to 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. 14 Thus, almost no older adults lack insurance coverage since almost all are covered, at minimum, by public insurance (Medicare).

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.

Figure 10

People under 65 years of age with public, private, or no health insurance, 2020.

Of those who had health insurance, approximately 27% had public insurance (Medicaid or a combination of Medicare and Medicaid), while just under three-fourths had private insurance, often from an employer.

The distribution of people who have health insurance varies by demographic factors, including race, location, and other characteristics.

Figure 11

People with any health insurance, by race, ethnicity, and location of residence, 2020.

Among racial and ethnic groups, non-Hispanic Asian groups (92.4%) were the most likely to have any health insurance, followed by non-Hispanic White (92.2%), non-Hispanic Multiracial (89.7%), non-Hispanic Black (88.1%), non-Hispanic NHPI (85.6%), Hispanic (77.6%), and non-Hispanic AI/AN (72.9%) groups (Figure 11).

Among location of residence, people in large fringe metro counties (i.e., “suburbs,” 90.1%) were most likely to have any health insurance, followed by people in medium metro areas (89.3%), small metro areas (88.5%), large central metro areas (i.e., “cities,” 88.0%), noncore counties (i.e., “rural,” 85.9%) and micropolitan areas (i.e., “small towns,” 84.5%).

Economic stability is associated with better health. The Economic Stability domain accounts for a population’s ability to maintain steady employment and afford items needed to remain healthy, such as housing, utilities, food, and medications. It also considers how health issues, such as arthritis or health-related disabilities, can limit a person’s ability to work, earn income, and accumulate wealth. Employment, income (the amount a person earns each year), and wealth (their net worth and assets) all enhance health.

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. 15 , 16 , 17 , 18 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.

Federal guidelines defining the poverty level are issued annually in the Federal Register by the Department of Health and Human Services, Assistant Secretary for Planning and Evaluation. ix The poverty guideline (PG) (or poverty threshold) varies by family size and there are different family income criteria for the contiguous 48 states, Alaska, and Hawaii. The poverty guidelines are not defined for Puerto Rico, the U.S. Virgin Islands, American Samoa, Guam, the Republic of the Marshall Islands, the Federated States of Micronesia, the Commonwealth of the Northern Mariana Islands, or Palau. 19

For most measures, the NHQDR tracks disparities data based on the ratio of household income to the PG for the household’s size. For measures drawn from AHRQ’s Healthcare Cost and Utilization Project (HCUP), income is defined using the median income of the patient’s residential ZIP Code. x

Figure 12

U.S. household Income distribution by percent population, 2020. Note: Percentiles add to 100. Ranges represent quartiles but each quartile may represent less than or more than 25% of the population. The last quartile is divided into two groups, showing (more. )

In 2020, the median household income was $64,994 (data not shown). The lowest quartile of individual households earned less than $35,000 annually, while the highest quartile of households earned $120,000 or more each year (Figure 12).

More than one-quarter of households (26.2%) earned less than $35,000 per year; the top 10% of households earned $200,000 or more per year.

The Census estimates that 12.8% of the population lives in poverty. Poverty is a state in which a person or household lacks sufficient financial resources to afford basic needs, such as food, shelter, or clothing. Poverty also hinders people from participating in community life, engaging in healthy activities, or accessing healthcare services when needed. Thus, people who live in poverty are particularly at risk for poor quality of care and undesirable health outcomes.

In 2020, the PG, which is used to determine income-to-PG ratios, was $12,760 for a one-person household, $17,240 for a two-person household, $21,720 for a three-person household, and $26,200 for a four-person household.

Figure 13

Cumulative percentage of U.S. households with different ratios of income to poverty, 2020.

In 2020, 12.8% of the population had annual household incomes equal to or lower than the poverty threshold (Figure 13).

Almost 60% (59.5%) of the population had household incomes at or lower than 400% of the poverty threshold (meaning that more than 40% percent had household incomes at or higher than 400% of the poverty threshold).

Social connectedness is associated with better health. The Social and Community Context domain accounts for the influence that positive and negative relationships with family, friends, coworkers, and the broader community can have on health. This domain includes the ability to communicate with healthcare providers and navigate social norms in healthcare delivery processes. Interpersonal relationships and rapport with clinicians are difficult to measure in a population. However, a few statistics provide a window on this domain.

Five-year estimates from the American Community Survey report that 86.5% of the population were born in the United States, and 93.0% of the population are U.S. citizens. Of the 13.5% who were born outside the United States, 6.9% are naturalized U.S. citizens and 6.6% are not naturalized U.S. citizens. 20

Nearly four-fifths (78.5%) of the population 5 years and over speak English as their primary language at home. Spanish is the second most spoken language in the United States (13.2%). Asian and Pacific Island languages account for 3.5%, other Indo-European languages for 3.7%, and other languages for 1.1%. 21

Access to high quality education is associated with better health. The Education Access and Quality domain accounts for the association between having a higher level of education and living a longer, healthier life. Access to high-quality formal education can improve economic stability, enhance the likelihood of engaging in healthy behaviors, and improve a person’s ability to understand and adhere to medical treatment.

Figure 14

Percentage of people in the United States enrolled in school, by age, 2020.

Just under half (47.3%) of children between 3 and 4 years old are enrolled in school. Nearly all children (94.6%) between 5 and 17 years are enrolled in school. School enrollment declines steadily after age 18 years (Figure 14).

Most adults age 25 years and over in the United States (88.5%) have a high school diploma. Approximately one-third have a bachelor’s degree, and about 13% have a graduate or professional degree. About 20% of adults attended college but did not get a degree, and about 9% have an associate’s degree. About 5% of the adult population did not attend school beyond eighth grade (data not shown). 22

Health quality is influenced by community characteristics. The Neighborhood and Built Environment domain accounts for the influence that physical infrastructure (e.g., access to transportation, access to healthy food options, spaces for engaging in physical activity, and access to high-speed internet) and the environment (e.g., air quality, water quality) have on a population’s health.

Broadband internet access is an example of the built environment as a social determinant of health. With healthcare delivery organizations expanding telehealth-based services, patients’ access to healthcare services may come to depend on access to high-speed internet. Currently, about 85.0% of people in the United States have a broadband internet subscription. However, access varies by a person’s household income. Nearly 15% have no internet, and less than 1% have dial-up only. 23 AHRQ has a data visualization on poverty and broadband access.

Healthcare Delivery Systems

The United States must have an adequate healthcare delivery infrastructure to meet population needs. Americans receive healthcare from a complex ecosystem of people, institutions, organizations, and resources. The healthcare workforce includes more than 60 occupations that provide direct care to patients, as well as many other administrative, technological, and support occupations.

Healthcare infrastructure includes diverse organizations, such as hospitals; long-term care facilities; home care services; ambulatory surgery centers; clinics; public health departments; health insurance plans; and various industries that produce medications, medical devices, and healthcare technological applications.

Staffing shortages may compromise the capacity to care for patients. Delivering high-quality care often requires that the right number and combination of healthcare workers are available and can work together effectively. For example, routine surgical procedures can be delayed if only a surgeon is present. Safe, high-quality procedures may require anesthesiologists, nurses, pharmacy staff, laboratory technicians, staff who clean operating rooms, staff to sterilize and safely store instruments, and other professions.

Reports of hospital and nursing home staff shortages due to increased healthcare worker turnover, burnout, prioritization of family obligations, illness, and death during the COVID-19 public health emergency have raised concerns about whether the United States has the capacity to deliver safe, high-quality care. Data from the Bureau of Labor Statistics (BLS) offers support for these concerns but also highlight important nuances.

The BLS classifies healthcare delivery “establishments” into major types of settings: ambulatory healthcare, hospitals, and nursing and residential care facilities. Its Standard Occupational Classifications lists more than 60 different healthcare occupations that provide direct care services in those settings. Other, non-direct-care occupations, such as office managers, security personnel, and catering, also work in healthcare settings.

This section uses data from the BLS Current Employment Survey and BLS Current Population Survey to describe overall workforce trends and trends for several types of healthcare occupations: physicians, registered nurses, advanced practice registered nurses (APRNs), and three sets of other groups of healthcare occupations, classified by level of education needed to enter their profession. xi , 24

Healthcare workers are diverse in terms of race and ethnicity. However, diversity varies among different types of occupations (Figure 15).

Figure 15

Percent distribution of race and ethnicity in different healthcare occupations.

The healthcare workforce included approximately 16.1 million workers as of January 2022, which was approximately 2% lower than it had been in January 2020 (immediately before the COVID-19 public health emergency). BLS data indicate worker experiences varied widely by which sector of healthcare delivery they worked in.

Figure 16

Number of workers employed and at work in ambulatory healthcare, hospitals, and nursing and residential care facilities, January 2020-January 2022.

The number of employed and at work ambulatory healthcare workers decreased by 16.2% in April 2020, near the beginning of the COVID-19 public health emergency (Figure 16). However, employment in this setting quickly recovered and returned to higher levels than reported in January 2020. In January 2022, there were 8,023,100 workers in this setting.

The number of workers employed and at work in hospitals decreased by 3.2% between March 2020 and May 2020. Employment in this setting has since increased but remained 2% below levels reported in January 2020, a statistically significant difference. In January 2022, there were 5,126,100 workers in this setting.

The number of workers employed in the nursing and residential care facilities setting decreased steadily from 2020 to 2022. In January 2022, it was 12.1% lower than it had been in January 2020, a statistically significant decrease. In January 2022, there were 2,968,500 workers in this setting.

Data from the BLS Current Population Survey provide less statistically stable estimates due to smaller sample sizes. However, they allow examination of employment trends by occupation and worker demographic characteristics. The findings suggest that loss of workers in less highly educated professions explains most of the decrease in healthcare workforce size.

Figure 17

Number of nurses, advanced practice registered nurses, and physicians employed and at work in any healthcare setting, January 2020-January 2022.

The number of nurses employed and at work exhibited time-limited swings of up to 15.3% between September 2020 and May 2021, but the overall workforce size has not changed significantly since January 2020 (Figure 17).

There were no statistically significant changes in the overall number of APRNs and physicians employed and at work from January 2020 through January 2022.

Figure 18

Number of workers in other healthcare occupations employed and at work in any healthcare setting, by education needed to enter the occupation, January 2020-January 2022.

Since then, employment for workers in this group only partially recovered. At the end of January 2022, the number remained 10.2% lower than it had been in January 2020, a statistically significant decrease.

At the end of January 2022, there were 2,029,000 healthcare workers in this category employed and at work.

By comparison, between January 2020 and the end of January 2022, there were no statistically significant changes in employment levels for healthcare workers in professions requiring a bachelor’s degree or higher level of education. At the end of January 2022, there were 1,399,000 healthcare workers in occupations requiring bachelor’s or master’s degrees and 765,000 workers in occupations requiring doctorate degrees.

In a complex U.S. healthcare system, medical offices remain by far the setting most commonly visited for care. In any given year, most people in the United States interact with healthcare delivery systems through routine office-based physician visits. A smaller percentage of people seek emergency care services, and even fewer require hospitalization. In 2020, 83.4% of adults and 94.0% of children had an office visit with a doctor or other healthcare professional in the past year. 25 For comparison, 19.0% of adults had an emergency department visit that year. 25 In 2018, only 7.4% of people in the United States required an overnight hospital stay. 26

Ambulatory Medical and Surgical Offices

In 2018, there were 860.4 million medical physician office visits, or 267.1 visits per 100 people. Just over half (136.6 visits per 100 person) were with a primary care provider. Approximately one-quarter of encounters (67.1 visits per 100 people) were with medical specialists, and just under one-quarter (63.3 visits per 100 people) were with surgical specialists. 27

Figure 19

Major reasons for office-based physician visits, by patient age, 2018.

Overall, most office visits (39.0%) were for managing one or more chronic conditions, followed by evaluating a new problem (24.0%), providing preventive care services (23.0%), and performing pre- or postoperative evaluation (8%) (Figure 19).

Among children less than 18 years old, this overall pattern of visits differs, giving greater emphasis to visits for new problems and preventive services.

The 10 leading principal reasons for visits account for less than half (41.6%) of all reasons for all office visits. The list illustrates the wide scope of healthcare services delivered in ambulatory settings. It also highlights primary care offices’ counseling, medication maintenance, and followup activities, which are central to successfully managing chronic diseases. Error! Bookmark not defined .

Living in proximity to primary care services could improve a person’s likelihood of receiving high-quality care for chronic disease. However, many communities in the United States report limited or no access to primary care, especially nonmetropolitan communities. The Health Resources and Services Administration (HRSA) has designated 7,955 locations, population groups, and healthcare facilities as Primary Care Health Professional Shortage Areas (HPSAs). More equitable distribution of primary care providers may reduce the number of primary care HPSAs. HRSA reports that there were 256,220 full-time-equivalent primary care providers in 2018 28 and estimates that 16,461 additional practitioners would fulfill the needs of existing HPSAs. 29

Figure 20

Counties where all, part, or none of the county is a Primary Care HPSA.

Overall, 1,963 (62.5%) of 3,141 counties and county equivalents are classified as “whole county shortage areas.” Of these, 562 (28.6%) are metropolitan counties and 1,401 (71.4%) are nonmetropolitan counties (Figure 20).

In contrast, only 169 (5.4%) counties and county equivalents are classified as having “no primary care shortage area.” Approximately two-thirds (112 or 66.3%) are metropolitan counties while only 57 (33.7%) are nonmetropolitan counties.

Of the approximately 79 million people who live in counties where the entire county has been designated a primary care HPSA, 51 million (64.6%) are in metropolitan counties and 28 million (35.4%) are in nonmetropolitan counties. 30

Of the approximately 226 million people who live in counties where part of the county has been designed a primary care HPSA, 210 million (92.9%) are in metropolitan counties, and 16 million (7.0%) live in nonmetropolitan counties. 29

Approximately 22.6 million people live in counties where none of the county has been designated a primary care HPSA. Nearly all (21 million or 92.9%) live in metropolitan counties. 29

Emergency Departments

Emergency departments (EDs) play a critical role in healthcare delivery systems as a provider of acute care and an important gateway for hospitalization. 31 Their central role in healthcare delivery is supported in part by the Emergency Medical Treatment and Labor Act, which requires hospitals to provide acute medical care to all patients, regardless of their demographic characteristics or ability to pay. 32 In 2019, there were approximately 151 million ED visits, or 46.6 visits per 100 people. 33

Figure 21

Triage status of emergency department visits, 2019.

Among visits with triage data available, nearly two-thirds (65.7%) of visits were classified as “urgent” or higher acuity, 30.3% were classified as “semiurgent,” and only a few (3.9%) were deemed “nonurgent” (Figure 21).

Although the number of freestanding EDs (defined as EDs that are not physically attached to a hospital) has increased in recent years, most EDs are located within hospitals. 34

Hospitals

Hospitals are organizations that bring together different types of healthcare professionals, diagnostic and therapeutic equipment, and services, typically to provide medical and surgical care for short-term (acute) illnesses. 35 In 2022, the American Hospital Association (AHA) counted 6,093 hospitals with a total of 920,531 staffed beds in the United States. 36 Most are community hospitals.

Most hospitals (3,483 or 57.2%) are affiliated with a health system, which the AHA defines as “a central organization linking either two or more hospitals, or a hospital and three or more non-acute care entities, such as a multispecialty outpatient office or a skilled nursing facility.” xiii Health systems have the potential to extend the efficiencies hospitals offer by linking them to a broader network of resources and services than any individual hospital can provide onsite.

Hospitals Serving Communities That Experience Higher Risk for Poor Health Outcomes

The NHQDR focuses additional attention to care delivered by three types of hospitals that play an important role in rural areas and other at-risk communities. The Healthcare Cost and Utilization Project (HCUP), which supplies data for many NHQDR measures, defines minority-serving hospitals (MSHs) as hospitals with the 25% highest number of discharges for people who are not identified as non-Hispanic White race/ethnicity. HCUP similarly defines safety net hospitals (SNHs) as hospitals that have the highest 25% of hospital discharges paid for by Medicaid or uninsured. (It should be noted, however, that academic literature offers varying definitions for SNHs.)

MSHs and SNHs are often large, located in metropolitan centers, and classified as teaching hospitals. Although the MSH and SNH designations do not confer additional resources on hospitals, they provide a useful window for understanding differences in hospital performance.

Critical Access Hospitals (CAHs) are facilities that meet certain statutory and regulatory criteria. Such criteria include having fewer than 25 acute care inpatient beds, providing 24/7 emergency care services, being located more than 35 miles from another hospital or CAH (with exceptions), and maintaining an annual average length of stay of 96 hours or less. 37 CAHs are thus smaller than most hospitals, and most are located in rural communities (Figure 22). The Centers for Medicare & Medicaid Services (CMS) certifies a facility as a CAH if it (1) is located in a state that has established a Medicare rural hospital flexibility program; (2) is designated as a CAH by the State in which it is located; and (3) meets other criteria CMS may require.

Figure 22

Distribution of critical access hospitals in the United States, 2022.

Hospital availability in nonmetropolitan (rural) communities is of particular interest to the nation’s health. In sparsely populated communities, hospitals may be the only source for routine and specialized services that would otherwise be unavailable. They also are often the only source of emergency and after-hours care. Thus, when rural hospitals are unavailable or stop providing services, access to healthcare services may be hindered.

For example, 135 rural hospitals closed between 2010 and 2020. The Government Accountability Office (GAO) recently examined the effects of rural hospital closures on healthcare services and found people who lived in a closed hospital’s service area had to travel considerably farther to access dental, mental health, substance use, and obstetric services, as well as services typically associated with hospital care (Figure 23). 38

Figure 23

Median distance people in the service area of a rural hospital that offered a selected healthcare service in 2012 traveled to receive the service after the hospital closed, 2018.

Data from the North Carolina Rural Health Research Program suggests that rural hospitals have closed at an accelerating pace. Although concern was heightened during the early phases of the COVID-19 public health emergency, data point to a trend that preceded the emergency (Figure 24). The NHQDR team continues to monitor this trend.

Figure 24

Number of rural hospital closures by year, 2005-2020.

Hospital Bed Capacity

Although hospitals provide a wide range of services, not every hospital provides every service, and not every staffed bed may be appropriate for every need. The AHA notes that 789,354 (85.7%) staffed beds are in community hospitals, 36 and 696,233 (76%) beds are in hospitals affiliated with health systems. 39 However, only some support general healthcare activities, while many staffed beds are intended for specialized purposes, such as intensive care, care for children, or labor and delivery.

For example, the AHA estimates that 112,359 (12.2%) staffed hospital beds are designated for providing intensive care services. 36 However, the specific types of critical care services they provide vary (Figure 25). While a hospital where the need for medical-surgical intensive care beds has exceeded capacity may realistically reallocate a cardiac intensive care bed to treat an adult with pneumonia-induced respiratory failure, it would be much more challenging to reallocate neonatal intensive care beds for the same purpose.

Figure 25

Types of staffed intensive care beds in community hospitals, 2019.

The number and type of hospital beds available in a community, especially in relation to specific needs, may provide a more meaningful way to assess the United States’ capacity to anticipate and meet demand for hospital services. During the COVID-19 public health emergency, the Centers for Disease Control and Prevention’s (CDC) National Healthcare Safety Network established a system for estimating general medical and intensive care bed capacity at national and state levels. The system allowed estimates to be updated biweekly to provide federal decision makers with timely guidance. Although no longer updated after July 2020, the hospital capacity dashboard (https://www.cdc.gov/nhsn/covid19/report-overview.html) still provides valuable information about the distribution of acute care services in the United States.

Personal Healthcare Expenditures

“Personal healthcare expenditures” measures the total amount spent to treat individuals with specific medical conditions. It includes all the medical goods and services used to treat or prevent a specific disease or condition in a specific person. These include hospital care; professional services; other health, residential, and personal care; home healthcare; nursing care facilities and continuing care retirement communities; and retail outlet sales of medical products. 40

Hospital care accounted for nearly 40 percent of healthcare spending (Figure 26). Although relatively few people in the United States require hospitalization, the people who do often need care that is complex, labor intensive, and expensive. Thus, acute and post-acute care services account for almost half of the nation’s personal healthcare expenditures.

Figure 26

Distribution of personal healthcare expenditures by type of expenditure, 2020. Key: CCRCs = continuing care retirement communities. Note: Personal healthcare expenditures are outlays for goods and services related directly to patient care. These expenditures (more. )

Private insurance paid for more healthcare than any other source ( Figure 27 ).

Figure 27

Personal healthcare expenditures, by source of funds, 2020. Note: Data are available at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsHistorical.html. Personal healthcare (more. )

Private insurance accounted for 32% of hospital, 37% of physician, 13% of home health, 9% of nursing home, 42% of dental, and 40% of prescription drug expenditures (data not shown).

Medicare accounted for 25% of hospital, 24% of physician, 34% of home health, 20% of nursing home, 2.0% of dental, and 32% of prescription drug expenditures (data not shown).

Medicaid accounted for 17% of hospital, 11% of physician, 33% of home health, 27% of nursing home, 9% of dental, and 10% of prescription drug expenditures (data not shown).

Out-of-pocket payments accounted for 3% of hospital, 7% of physician, 10% of home health, 23% of nursing home, 37% of dental, and 13% of prescription drug expenditures (data not shown).

Private health insurers covered more prescription drug expenditures than other payers.

Figure 28

Prescription drug expenditures, by source of funds, 2020. Note: Data are available at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsHistorical.html. Personal healthcare (more. )

Private health insurance companies accounted for 40.4% of retail drug expenses ($140.9 billion in 2020).

Other third-party payers had the smallest percentage of costs (1.3%), which represented $4.4 billion in retail drug costs.

Geographic Variations in Care

States have been described as “laboratories of democracy” 41 ; variations in quality of care and health disparity measures provide indicators to guide efforts to improve state-specific healthcare delivery.

State-level data show that healthcare quality and disparities vary widely depending on state and region. Although a state may perform well in overall quality, the same state may face significant disparities in healthcare access or disparities within specific areas of quality.

State-level analysis included 179 measures for which state data were available. Of these measures, 137 are core measures and 42 are supplemental measures from the National CAHPS xiv Benchmarking Database, which provides state data for core measures with Medical Expenditure Panel Survey national data only. The state healthcare quality analysis included all 179 measures, and the state disparities analysis included 110 measures for which state-by-race or state-by-ethnicity data were available.

State-level data are also available for 110 supplemental measures. These data are available from the Data Query tool on the NHQDR website but are not included in data analysis.

Quality varied between States, but in some regions nearby States had similar quality scores.

Figure 29

Overall quality of care, by state, 2016-2021. Note: All state-level measures with data were used to compute an overall quality score for each state based on the number of quality measures above, at, or below the average across all states. States were (more. )

Five states in the Northeast region (Maine, Massachusetts, New Hampshire, Pennsylvania, and Rhode Island), four in the Midwest region (Iowa, Minnesota, North Dakota, and Wisconsin), and two states in the West region (Colorado and Utah) had the highest overall quality scores.

Seven states in the West region (Alaska, Arizona, California, Montana, Nevada, New Mexico, and Wyoming), five states in the South region (District of Columbia, xv Georgia, Mississippi, Oklahoma, and Texas), and New York had the lowest overall quality scores.

More information about healthcare quality in each state can be found on the NHQDR Data Tools website, https://datatools ​.ahrq.gov/nhqdr.

The disparities map (Figure 30) shows average differences in quality of care for AI/AN, Asian, Black, Hispanic, NHPI, and multiracial people compared with the reference group, non-Hispanic White or White people. States with fewer than 50 data points are excluded. Racial and ethnic disparities varied across the United States.

Many factors may account for the variation in disparities between states. Factors may include differences in prevalence of chronic conditions, policies that limit care for behavioral risk factors, and lack of availability of infrastructure that allows easy access to quality healthcare.

Figure 30

Average differences in quality of care for American Indian or Alaska Native, Asian, Black, Hispanic, Native Hawaiian/Pacific Islander, and multiracial people compared with non-Hispanic White or White people, by state, 2018-2021. Note: All measures in (more. )

Five states in the West region (Arizona, Hawaii, Idaho, Oregon, and Washington), four states in the South region (Arkansas, Kentucky, Virginia, and West Virginia), Kansas, and New Jersey had the fewest racial and ethnic disparities overall (Figure 30).

Four states in the North region (Connecticut, Massachusetts, New York, and Pennsylvania), three states in the Midwest region (Illinois, Minnesota, and Ohio), and three states in the South region (District of Columbia, North Carolina, and Texas) had the most racial and ethnic disparities overall.

References

U.S. Census Bureau. Quick Facts, United States, .v2021. July 1, 2021. https://www ​.census.gov ​/quickfacts/fact/table/US/PST045221. Accessed September 23, 2022.

Ingram DD, Franco SJ. 2013 NCHS Urban-Rural Classification Scheme for Counties. Vital Health Stat 2014 Apr;2(166). https://www ​.cdc.gov/nchs ​/data/series/sr_02/sr02_166.pdf. Accessed October 4, 2022. [PubMed : 24776070 ]

Kurani N, Wager E. Quality of care: How does the quality of the U.S. health system compare to other countries? Peterson-KFF Health System Tracker. September 30, 2021. https://www ​.healthsystemtracker ​.org/chart-collection ​/quality-u-s-healthcare-system-compare-countries/. Accessed September 23, 2022.

Centers for Disease Control and Prevention. Chronic Diseases in America. Last reviewed May 2022. https://www ​.cdc.gov/chronicdisease ​/resources ​/infographic/chronic-diseases.htm. Accessed October 4, 2022.

Centers for Disease Control and Prevention. How You Can Prevent Chronic Diseases. https://www ​.cdc.gov/chronicdisease ​/about/prevent/index.htm. Accessed October 4, 2022.

University of Wisconsin Population Health Institute. Chronic Disease Management Programs. Last updated July 2017. https://www ​.countyhealthrankings ​.org/take-action-to-improve-health ​/what-works-for-health ​/strategies/chronic-disease-management-programs. Accessed October 4, 2022.

Decker SL, Schappert SM, Sisk JE. Use of medical care for chronic conditions. Health Aff (Millwood). 2009 Jan–Feb;28(1):26–35. 10.1377/hlthaff.28.1.26. Accessed October 4, 2022. [PubMed : 19124849 ] [CrossRef]

Siu AL, Spragens LH, Inouye SK, Morrison RS, Leff B. The ironic business case for chronic care in the acute care setting. Health Aff (Millwood). 2009 Jan–Feb;28(1):113–25. 10.1377/hlthaff.28.1.113. Accessed October 4, 2022. [PubMed : 19124861 ] [CrossRef]

Reynolds R, Dennis S, Hasan I, Slewa J, Chen W, Tian D, Bobba S, Zwar N. A systematic review of chronic disease management interventions in primary care. BMC Fam Pract. 2018 Jan 9;19(1):11. 10.1186/s12875-017-0692-3. Accessed October 4, 2022. [PMC free article : PMC5759778 ] [PubMed : 29316889 ] [CrossRef]

Smith SM, Wallace E, O’Dowd T, Fortin M. Interventions for improving outcomes in patients with multimorbidity in primary care and community settings. Cochrane Database Syst Rev. 2021 Jan 15;1(1):CD006560. 10.1002/14651858.CD006560.pub4. Accessed October 4, 2022. [PMC free article : PMC8092473 ] [PubMed : 33448337 ] [CrossRef]

Magnan S. Social Determinants of Health 101 for Health Care: Five Plus Five. NAM Perspectives. Discussion Paper. Washington, DC: National Academy of Medicine; 2017. https://nam ​.edu/social-determinants-of-health-101-for-health-care-five-plus-five/. Accessed September 23, 2022.

Sommers BD, Gawande AA, Baicker K. Health insurance coverage and health - what the recent evidence tells us. N Engl J Med. 2017 Aug 10;377(6):586–93. https://www ​.nejm.org/doi/10 ​.1056/NEJMsb1706645. Accessed September 23, 2022. [PubMed : 28636831 ]

Allen H, Baicker K, Finkelstein A, Taubman S, Wright BJ; Oregon Health Study Group. What the Oregon Health Study can tell us about expanding Medicaid. Health Aff (Millwood). 2010 Aug;29(8):1498–506. https://www ​.healthaffairs ​.org/doi/10.1377/hlthaff.2010.0191. Accessed September 23, 2022. [PMC free article : PMC3533495 ] [PubMed : 20679654 ]

Institute of Medicine Committee on the Consequences of Uninsurance. Care Without Coverage: Too Little, Too Late. 4. The Difference Coverage Could Make to the Health of Uninsured Adults. Washington, DC: National Academies Press; 2002. https://www ​.ncbi.nlm ​.nih.gov/books/NBK220633/. Accessed September 23, 2022. [PubMed : 25057604 ]

Khullar D, Chokshi DA. Health, Income, & Poverty: Where We Are & What Could Help. Health Affairs Health Policy Brief. 2018 Oct 4. https://www ​.healthaffairs ​.org/do/10.1377/hpb20180817 ​.901935/full/. Accessed September 23, 2022.

Larrimore J. Does a higher income have positive health effects? Using the earned income tax credit to explore the income-health gradient. Milbank Q 2011 Dec;89(4):694–727. https://www ​.ncbi.nlm ​.nih.gov/pmc/articles/PMC3250638/. Accessed September 23, 2022. [PMC free article : PMC3250638 ] [PubMed : 22188352 ]

Shaw KM, Theis KA, Self-Brown S, Roblin DW, Barker L. Chronic disease disparities by county economic status and metropolitan classification, Behavioral Risk Factor Surveillance System, 2013. Prev Chronic Dis. 2016 Sep 1;13:E119. https://www ​.ncbi.nlm ​.nih.gov/pmc/articles/PMC5008860/. Accessed September 23, 2022. [PMC free article : PMC5008860 ] [PubMed : 27584875 ]

Bor J, Cohen GH, Galea S. Population health in an era of rising income inequality: USA, 1980-2015. Lancet. 2017 Apr 8;389(10077):1475–90. 10.1016/S0140-6736(17)30571-8. Accessed September 23, 2022. [PubMed : 28402829 ] [CrossRef]

Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services. U.S. Federal Poverty Guidelines Used To Determine Financial Eligibility for Certain Federal Programs. In effect as of January 12, 2022. https://aspe ​.hhs.gov ​/topics/poverty-economic-mobility ​/poverty-guidelines ​/prior-hhs-poverty-guidelines-federal-register-references ​/2020-poverty-guidelines. Accessed October 4, 2022.

American Community Survey, 5-year estimates, 2020, Table S2801. https://data ​.census.gov ​/cedsci/table?q=internet&tid ​=ACSST5Y2020.S2801. Accessed September 23, 2022.

U.S. Bureau of Labor Statistics. Occupational Outlook Handbook. Last modified September 2022. https://www ​.bls.gov/ooh/. Accessed September 23, 2022.

Centers for Disease Control and Prevention, National Center for Health Statistics. National Health Interview Survey Interactive Data Query Systems. Last reviewed February 2022. https://www ​.cdc.gov/nchs/nhis/SHS.htm. Accessed September 23, 2022.

Lucas JW, Benson V. Summary Health Statistics: National Health Interview Survey, 2018. Table P-10a. National Center for Health Statistics. 2019. https://www ​.cdc.gov/nchs ​/nhis/SHS/tables.htm. Accessed October 4, 2022.

Centers for Disease Control and Prevention, National Center for Health Statistics. National Ambulatory Medical Care Survey: 2018 National Summary Tables. https://www ​.cdc.gov/nchs ​/data/ahcd/namcs_summary ​/2018-namcs-web-tables-508.pdf. Accessed September 23, 2022.

Health Resources and Services Administration. Primary Care Workforce Projections. https://bhw ​.hrsa.gov ​/data-research/projecting-health-workforce-supply-demand ​/primary-health. Accessed September 23, 2022.

Health Resources and Services Administration. Health Workforce Shortage Areas. https://data ​.hrsa.gov ​/topics/health-workforce/shortage-areas. Accessed September 23, 2022.

Rural Health Information Hub. Population in Primary Care HPSAs (Health Professional Shortage Area) for Metro and Nonmetro Counties, 2022. https://www ​.ruralhealthinfo.org/charts/6. Accessed September 23, 2022.

Schuur JD, Venkatesh AK. The growing role of emergency departments in hospital admissions. N Engl J Med. 2012;367 (5):391–3. https://www ​.nejm.org/doi/10 ​.1056/NEJMp1204431. Accessed September 23, 2022. [PubMed : 22784039 ]

Fields WW, Asplin BR, Larkin GL, Marco CA, Johnson LA, Yeh C, Ghezzi KT, Rapp M. The Emergency Medical Treatment and Labor Act as a federal health care safety net program. Acad Emerg Med. 2001;8(11):1064–9. 10.1111/j.1553-2712.2001.tb01116.x. Accessed September 23, 2022. [PubMed : 11691669 ] [CrossRef]

National Center for Health Statistics. National Hospital Ambulatory Medical Care Survey: 2019 Emergency Department Summary Tables. Table 7. Triage status of emergency department visits, by selected patient characteristics: United States, 2019. https://www ​.cdc.gov/nchs ​/data/nhamcs/web_tables ​/2019-nhamcs-ed-web-tables-508 ​.pdf. Accessed October 4, 2022.

Gutierrez C, Lindor RA, Baker O, Cutler D, Schuur JD. State regulation of freestanding emergency departments varies widely, affecting location, growth, and services provided. Health Aff (Millwood). 2016 Oct 1;35(10):1857–66. https://www ​.healthaffairs ​.org/doi/10.1377/hlthaff.2016.0412. Accessed September 23, 2022. [PubMed : 27702960 ]

Liu JB, Kelz RR. Types of hospitals in the United States. JAMA. 2018 Sep 11;320(10):1074. 10.1001/jama.2018.9471. Accessed September 23, 2022. [PubMed : 30208457 ] [CrossRef]

American Hospital Association. Fast Facts on U.S. Hospitals, 2022. Updated January 2022. https://www ​.aha.org/system ​/files/media/file ​/2022/01/fast-facts-on-US-hospitals-2022.pdf. Accessed October 17, 2022.

Rural Health Information Hub. Critical Access Hospitals (CAHs). Last reviewed September 2021. https://www ​.ruralhealthinfo ​.org/topics/critical-access-hospitals. Accessed September 23, 2022.

Report to the Ranking Member, Committee on Homeland Security and Governmental Affairs, United States Senate. Rural Hospital Closures: Affected Residents Had Reduced Access to Healthcare Services. GAO-21-93. Washington, DC: Government Accountability Office; December 2020. https://www ​.gao.gov/products/gao-21-93. Accessed September 23, 2022.

Centers for Medicare & Medicaid Services. National Health Expenditure Accounts: Methodology Paper, 2020: Definitions, Sources, and Methods. https://www ​.cms.gov/files ​/document/definitions-sources-and-methods.pdf. Accessed September 23, 2022.

Sutherland G, Supreme Court of the United States. U.S. Reports: New State Ice Co. v. Liebmann, 285 U.S. 262 (1932). https://www ​.loc.gov/item/usrep285262/. Accessed September 23, 2022.

Footnotes

In this report, “Census” refers to the decennial census.

For more information, refer to Dobis EA, Krumel Jr TP, Cromartie J, Conley KL, Sanders A, Ortiz R. Rural America at a Glance: 2021 Edition. Washington, DC: U.S. Department of Agriculture, Economic Research Service. EIB-230. https://www ​.ers.usda ​.gov/webdocs/publications ​/102576/eib-230.pdf?v=4409. Accessed October 13, 2022.

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 quality and access to healthcare.

Readers examining long-term trends should note that the 2013 NCHS Urban-Rural Classification scheme is similar to the 2006 version that preceded it. Although minor differences between the two classification schemes may result in counties being classified in different categories, a 2014 analysis comparing the two classification schemes found that only 286 of 3,143 counties (9.1%) had different category assignments. (See Ingram DD, Franco SJ. 2013 NCHS Urban-Rural Classification Scheme for Counties. Vital Health Stat 2. 2014 Apr;166:1–73. https://www ​.cdc.gov/nchs ​/data/series/sr_02/sr02_166.pdf [PubMed : 24776070 ].)

In August 2022, the National Center for Health Statistics released Provisional Life Expectancy Estimates for 2021. Although the final data were not released in time to include in the 2022 NHQDR, the provisional estimates indicate that overall life expectancy in the United States declined for a second consecutive year, from 77.0 years in 2020 to 76.1 years in 2021. Excess deaths due to COVID-19 accounted for most of the decrease in life expectancy, followed by excess deaths due to unintentional injuries, heart disease, liver disease, and suicide.

Compared with averaged data for Australia, Austria, Belgium, Canada, France, Germany, Japan, the Netherlands, Sweden, Switzerland, and the United Kingdom.

The Centers for Disease Control and Prevention National Center for Health Statistics distinguishes unintentional causes of death from deaths due to self-injury (Suicide) and deaths due to intentional violence (Homicide). In 2020, suicide was the 12 th leading cause of death and homicide was the 16 th leading cause of death.

Examples of chronic diseases include: diseases of the brain, such as stroke and traumatic brain injury; affective disorders, such as depression, anxiety, bipolar disorder, and schizophrenia; vascular diseases, such as high blood pressure, high cholesterol, heart disease, and stroke; metabolic disorders, such as diabetes and thyroid disease; digestive diseases, such as Crohn’s disease; liver diseases, such as cirrhosis; kidney diseases, such as chronic kidney disease; diseases of the joints, such as arthritis; and diseases of the blood, such as thalassemia and sickle cell disease.

Measures using data from the Healthcare Cost and Utilization Project analyze health outcomes by community-level household income. In 2020, the median households in the lowest earning quartile of ZIP Codes earned $49,999 or less each year, while the median household in the highest earning quartiles of ZIP Codes earned $86,000 or more. More detail can be found at https://www ​.hcup-us.ahrq ​.gov/db/vars/zipinc_qrtl/nisnote.jsp.

Our analysis grouped healthcare occupations into three categories according to the level of education typically required to enter a profession. Examples of occupations requiring an associate’s degree or less education are dental hygienist, medical assistant, phlebotomist, emergency medical technician (EMT), licensed practical nurse (LPN), and licensed vocational nurse (LVN). Examples of occupations requiring a bachelor’s or master’s degree are occupational therapist, dietitian, and laboratory technician. Examples of occupations requiring a doctorate or equivalent training are pharmacist and podiatrist.

Terms in this list are based on the National Ambulatory Medical Care Survey’s Reason for Visit Classification for Ambulatory Care, defined in the 2018 Public Use File Documentation. https://ftp ​.cdc.gov/pub ​/Health_Statistics ​/NCHS/Dataset_Documentation ​/NAMCS/doc2018-508.pdf.

The AHRQ Comparative Health System Performance Initiative similarly defines a health system as “an organization that includes at least one hospital and at least one group of physicians that provides comprehensive care (including primary and specialty care) who are connected with each other and with the hospital through common ownership or joint management.” More information and additional resources for examining health systems may be found in the AHRQ Compendium of Health Systems: https://www ​.ahrq.gov ​/chsp/data-resources/compendium.html.

CAHPS is the Consumer Assessment of Healthcare Providers and Systems.

For purposes of this report, the District of Columbia is treated as a state.

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