Health Care Access

Indicator Profile

Access to health care—both preventive care and treatment—is crucial for cardiovascular health. Research shows that by improving health care access, population-level cardiovascular disease (CVD) risk may be reduced.

For example, having health insurance is associated with earlier CVD detection and reduced risk of major cardiac events.¹ However, access to and use of health care services varies across population subgroups. Black/African American persons, Hispanic/Latino persons, American Indian/Alaska Native persons, people with lower incomes, and people who live in under-resourced neighborhoods are less likely to have access to quality health care.

Several factors influence health care access. In some communities, there is a shortage of primary care physicians, nurses, community health workers (CHWs), pharmacists, paramedics, and/or physical/occupational therapists; in others, health care clinics, pharmacies, and hospitals are inaccessible due to their location. Health care affordability also affects one’s ability to access health care.

Although the Affordable Care Act expanded insurance coverage to millions of Americans who have heart disease or risk factors for heart disease, nearly one-quarter of low-income Americans with CVD or cardiovascular risk factors remain uninsured. Similarly, approximately 13% of Black/African American adults, and 29% of Hispanic/Latino adults with CVD or CVD risk factors are uninsured.²

Even where health care is accessible, widespread differences in the quality of care provided can lead to differential health outcomes. Moreover, factors such as health literacy—which is notably lower within non-White communities, older adults, and individuals with less education—affects patients’ ability to make recommended healthy lifestyle changes and adhere to prescribed medication.³

Table of Contents

Indicators

Case Example

References

Indicators

This document provides guidance for measuring five indicators related to health care access that influence inequities in access to and use of health care services, leading to differential risks for developing CVD or complications from CVD. The five health care access indicators are measured at different levels of analysis, including block group, census tract, ZIP code, county, congressional district, metro division, metro area, and state.

Health Care Affordability

Why is this indicator relevant?

Health care affordability refers to the cost of health care services, health insurance premiums, deductibles, co-pays or co-insurance, and patients’ ability to pay for these.⁴ According to the 2018 National Center for Health Statistics National Health Interview Study, 14.2% of individuals in the U.S. lived in families that experienced problems paying medical bills in the past 12 months⁵ and more than 45% of adults between the ages of 18 to 64 with CVD reported financial hardship due to medical bills.⁶

Health insurance coverage (public or private) may increase patients’ ability to afford health care costs; however, even among those with health insurance, many people with CVD experience financial hardship due to the high costs of insurance deductible, copay, and coinsurance.⁷

The American Heart Association (AHA) reports that an estimated 7.3 million Americans with CVD are uninsured.⁸ In 2018, among people younger than 65, those who were uninsured were more likely than those who had Medicaid or private coverage to be in families experiencing problems paying medical bills.⁹ People who are uninsured also face challenges accessing preventive care, which is critical for early identification of cardiovascular risk factors.^10,11

Similarly, lack of insurance is associated with inadequate and untimely medical treatment access, resulting in greater risk of poor cardiovascular health outcomes.^12,13 Concerns with health care affordability result in patients avoiding or delaying seeking care. In a study of adults ages 50–64 years, 13.2% of respondents reported they did not get medical care in the past year; 11.9% avoided filling a prescription due to cost.¹⁴

This indicator can be assessed by the following measures. Click on each measure to learn more:

Definition

Portion of adults who report needing to see a doctor but could not because of cost.

Note: The literature uses “delayed care” and “avoided care” interchangeably.

Data Availability

State

Subgroups: Age, gender, race/ethnicity, education level, income, marital status, renter or owner status, urban/nonurban

Data Source(s)

Existing Data Source(s)

America’s Health Rankings (AHR)

The United Health Foundation’s AHR evaluates a comprehensive set of health, environmental, and socioeconomic data. The AHR website provides state-level analyses of CDC Behavioral Risk Factor Surveillance System (BRFSS) data on the percentage of adults who reported a time in the past 12 months when they needed to see a doctor but could not because of cost. Users can access this measure under Clinical Care > Access to Care – Annual > Avoided Care due to Cost. National and state-level estimates are provided by age, educational attainment, gender, income, and race/ethnicity for the most recent data. Current editions (2015–2021) can be explored online or downloaded in various formats including Excel, CSV, and ZIP. Past editions (1990–2014) are also available for download.

Link: https://www.americashealthrankings.org/explore/annual/measure/costburden/state/ALL

Kaiser Family Foundation

The Kaiser Family Foundation website provides state-level data on the proportion of adults who report not seeing a doctor in the past 12 months due to cost of care. Data for this measure are available annually for 2013–2020 and are sourced from the BRFSS. Data are available by race/ethnicity; however, some states do not have sufficient data for certain racial/ethnic groups. Data can be downloaded as a CSV file.

Link:

https://www.kff.org/other/state-indicator/percent-of-adults-reporting-not-seeing-a-doctor-in-the-past-12-months-because-of-cost-by-raceethnicity/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%22:%22asc%22%7D

Example Survey Instrument

The following survey is available for assessing avoided care due to cost:

Healthcare Access & Utilization Survey

The Healthcare Access & Utilization Survey was developed for the National Institutes of Health’s All of Us Research Program, which is a national effort to build one of the most diverse health databases. This survey asks questions about a participant’s access to and use of health care and includes several questions related to health care costs. Questions on avoidance of care due to cost are provided below. The entire instrument is available from NIH’s All of Us Research Program.

Link: https://www.researchallofus.org/wp-content/themes/research-hub-wordpress-theme/media/2019/02/Health_Care_Access.pdf

There are many reasons people delay getting medical care. Have you delayed getting care for any of the following reasons in the PAST 12 MONTHS?

Survey questions
	Yes	No	Don’t Know
Couldn’t afford the copay.
Your deductible was too high/or could not afford the deductible.
You had to pay out of pocket for some or all of the procedure.

DURING THE PAST 12 MONTHS, was there any time when you needed any of the following, but didn’t get it because you couldn’t afford it?

Survey questions
	Yes	No	Don’t Know
Prescription medicines
Mental health care or counseling
Emergency care
Dental care (including checkups)
Eyeglasses
To see a regular doctor or general health provider (in primary care, general practice, internal medicine, family medicine)
To see a specialist
Follow-up care

DURING THE PAST 12 MONTHS, were any of the following true for you?

Survey questions
	Yes	No	Don’t Know
You skipped medication doses to save money
You took less medicine to save money
You delayed filling a prescription to save money
You asked your doctor for a lower cost medication to save money
You bought prescription drugs from another country to save money
You used alternative therapies to save money

survey questions
	Very worried	Somewhat worried	Not at all worried	Don’t know
If you get sick or have an accident, how worried are you that you will be able to pay your medical bills?

Measure 1: City and County Minimum Wage
Definition	Individuals in families where out-of-pocket spending on health care, including premiums, accounted for more than 10% of annual income.
Data Availability	City, county, core-based statistical area (CBSA), state, census division, region Subgroups: Age, gender, income, race/ethnicity, education, health insurance coverage type
Data Source(s)	Existing Data Source(s) U.S. Census Bureau Current Population Survey (CPS) Annual Social and Economic Supplement (ASES): The CPS-ASEC files provide household-level data on family medical out-of-pocket expenditures and total family income. Annual data for 1998–2021 are available by age, gender, race/ethnicity, education attainment, insurance coverage type, and income level. The household survey data contain geographic identifiers and can be analyzed by city, county, CBSA, state, census division, and region. Because of the small sample size for each year, users typically combine 3 years of data (e.g., 2019–2021) to produce reliable estimates for population subgroups (e.g., by race/ethnicity). Using the CPS-ASES individual-level files requires some expertise in working with survey data and statistical analysis. State Health Compare: A State Health Access Data Assistance Center (SHADAC) project at the University of Minnesota, the State Health Compare website provides state-level estimates of the percentage of people with high medical cost burden, defined as the proportion of individuals in families where out-of-pocket spending on health care accounts for more than 10 percent of annual income. Annual data are available for 2017–2020 and can be analyzed by race/ethnicity, income, and employer coverage. Users can view data in map and tabular form and can download results, including margins of error, as a CSV file.

Measure 1: City and County Minimum Wage
Definition	Portion of relevant population who are uninsured or underinsured.
Data Availability	Block group, census tract, ZIP code, county subdivision, county, congressional district, metro division, metro area, state Subgroups: Age, race/ethnicity, income, employment, education, federal poverty level
Data Source(s)	County Health Rankings & Roadmaps (CHR&R): CHR&R is a program of the University of Wisconsin Population Health Institute. The CHR&R program provides data, evidence, guidance, and examples to build awareness of the multiple factors that influence health and support leaders in growing community power to improve health equity. CHR&R uses Census Bureau’s Small Area Health Insurance Estimates (SAHIE) program data to provide county-level estimates of the percentage of the population younger than 65 without health insurance. Demographic data on race/ethnicity, age, gender, and rural/urban are collected and categorized by using the U.S. Census Bureau definitions. Users can access this measure under Ranked Measures > Clinical Care > Access to Care > Uninsured. Data are downloadable as an Excel workbook; depending on the state, years of data availability will vary. Health Opportunity and Equity (HOPE) Initiative: The HOPE Initiative website provides state-level data on the proportion of the population younger than 65 who have health insurance. Health Insurance Coverage indicator data are available for all 50 states and Washington, D.C., via the web interface. In addition, data for 2018–2020 are available by race/ethnicity for all states and Washington, D.C. Data files are downloadable as Excel workbooks from the Resources section of the website. National Healthcare Quality and Disparities Report (NHQDR): The NHQDR presents trends for measures related to access to care, affordable care, care coordination, effective treatment, healthy living, patient safety, and person-centered care. The report presents, in chart form, the latest available findings on quality of and access to health care, as well as disparities related to race/ethnicity, income, and other social determinants of health. The report is produced annually, since 2003, with reports available for download for 2010–2021. The NHQDR’s Data Query is an interactive tool for accessing national and state benchmarks. Users can view individual state-specific benchmark results by selecting the state, subject area (e.g., health insurance), and topic (e.g., uninsured). In some cases, data are limited based on availability of specific measures by state. Other measures related to insurance status and coverage include, but are not limited to, proportion of individuals under age 65 with or without health insurance and reported coverage by any type of public or private health insurance. PolicyMap: PolicyMap is a data warehouse of more than 50,000 indicators on demographics, income and spending, housing, lending, quality of life, economy, education, health, and federal regulations. A free (basic) subscription provides access to indicators developed using publicly available data sources via a single-layer mapping tool. A paid (standard) subscription provides access to multilayer mapping, analysis tools, and data downloads (CSV format). PolicyMap’s data on health insurance coverage is based on the U.S. Census Bureau’s Decennial Census and American Community Survey (ACS). County-level data are available on the proportion of people who are uninsured (or insured) by race/ethnicity, age group, income level, and employment status using the single-layer maps. However, suppression of results due to insufficient data are an issue for some counties and smaller geographic levels for numerically smaller racial/ethnic groups. Users can access this measure under Health > Costs and Insurance > Health Insurance Coverage. Data are available for the years 2011–2015 and 2016–2020 and are available at the census tract, ZIP code, county subdivision, county, congressional district, metro division, metro area, and state levels. Users wanting to download the data for further analyses need to pay for a standard subscription. Example Survey Instrument The following survey questions are available for assessing insurance status: American Community Survey (ACS) The U.S. Census Bureau’s ACS asks questions about health insurance coverage to create statistics about the percentage of people covered by health insurance and the sources of health insurance. To view the ACS survey questions on health insurance coverage, visit the U.S. Census Bureau’s website below. Users can use the question on current health insurance or health coverage plans. Link: https://www.census.gov/acs/www/about/why-we-ask-each-question/health/

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Health Care Availability

Why is this indicator relevant?

Health care availability is typically defined as the geographic proximity of providers and facilities in relation to an individual and reflects the capacity of medical service markets to adequately meet the needs of the local population.^15,16 Limited availability of health care resources, including the number of primary care physicians, nurse practitioners, and pharmacists per capita, presents a barrier that may reduce access to health services and increase the risk of poor health outcomes.¹⁷

In the United States, nearly 84,000,000 people live in Primary Care Health Professional Shortage Areas.¹⁸ Primary care serves as the usual and ongoing source of care that is associated with enhanced access to other health care services, including preventive services such as blood pressure screenings; better health outcomes; and a decrease in hospitalization and emergency department visits. Primary care can also help counteract the negative effect of poor economic conditions on health.¹⁹

Safety net providers focus on providing care to uninsured, poor, Medicaid, or other vulnerable patients. Safety net providers typically rely on Medicaid, Medicare, or charitable funding and typically offer essential health services and enabling or “wraparound” services (e.g., language interpretation, transportation, childcare, nutrition and social support services) specifically targeted to the needs of the vulnerable populations.^20,21 The availability of safety net providers is linked to improved access of care among uninsured persons.²² One critical component of the health care safety net are Federally Qualified Health Centers (FQHCs). FQHC service availability is positively associated with access to care for the uninsured and having a usual source of care for those with Medicaid.²³ Having access to care and a usual source of care may facilitate CVD screening and increase opportunities for patients to receive preventive care and information about CVD risk behaviors from a health care provider.²⁴

Health care availability is typically defined as the geographic proximity of providers and facilities in relation to an individual and reflects the capacity of medical service markets to adequately meet the needs of the local population. This indicator can be assessed by the following measures. Click on each measure to learn more:

Measure 1: City and County Minimum Wage
Definition	The nurse practitioner ratio can be defined as the number of advanced practice registered nurses (APRNs) per 1,000 population or the number of APRNs per 100,000 people. An APRN is a registered nurse with education beyond the basic nursing education and certified by a nationally recognized professional organization in a nursing specialty or meeting other criteria established by a board of nursing. APRN includes advanced practice midwives, certified registered nurse anesthetists, clinical nurse specialists, and nurse practitioners.
Data Availability	City, county, state Subgroups: Income, race/ethnicity
Data Source(s)	Area Health Resources Files (AHRF): The AHRF contains county- and state-level data on demographics, rural/urban categorization, health professional and facility supply, utilization, expenditures, and the local environment. State and county FIPS (Federal Information Processing Standard) codes are provided, allowing the data to be merged with external datasets. Data for the “Nurse Workforce Survey Data” are available for download for 1977-2018 through various formats (e.g., ASCII, SAS, SPSS, STATA). Users can use an interactive dashboard and filter “Health Profession” by “Nurse,” as well as filter by “Health Profession Subgroup” for the years 2018–2019, 2019–2020, and 2020–2021. Data on the dashboard can be analyzed by poverty status and race/ethnicity. The estimates by subgroup represents the number of APRNs per 100,000 subgroup population. For example, the APRN availability by poverty is defined as the number of APRN per 100,000 persons living in poverty. National Provider Identifier (NPI): The NPI is a Health Insurance Portability and Accountability Act (HIPAA) Administrative Simplification Standard. The NPI is a unique identification number for covered health care providers. Covered health care providers and all health plans and health care clearinghouses must use the NPI in the administrative and financial transactions adopted under HIPAA. The NPI is a 10-position, intelligence-free numeric identifier (i.e., a 10-digit number). This means the numbers do not carry other information about health care providers, such as the state in which they live or their medical specialty. The NPI must be used in lieu of legacy provider identifiers in the HIPAA standards transactions. Users can search the registry by city, county, or state level to view the number and type of health care providers in the given area, including FQHCs. The type of health care provider is provided in the Primary Taxonomy column of the search results. Data are available for download via CSV format. PolicyMap: PolicyMap is a data warehouse of more than 50,000 indicators on demographics, income and spending, housing, lending, quality of life, economy, education, health, and federal regulations. A free (basic) subscription provides access to indicators developed by using publicly available data sources via a single-layer mapping tool. A paid (standard) subscription provides access to multilayer mapping, analysis tools, and data downloads (CSV format). PolicyMap uses Health Resources and Services Administration (HRSA) data to provide the rate of advanced practice nurse practitioners per 1,000 people. Users can access this measure under Health > Access to Medical Care > Health Professionals > Advanced Practice Nurses > Nurse Practitioners. Data is available for the years 2010–2016.
Additional Measurement Considerations	This indicator may require skills in pulling secondary data, setting up a database, and conducting descriptive statistical analysis and reporting. To measure the magnitude of disparities in health care availability, users should calculate metrics for specific subgroups. This facilitates setting tailored targets, measuring baseline disparities, and tracking trends by population groups that matter for advancing health equity. Demographic categories to consider for data disaggregation are race/ethnicity as defined by the Office of Management and Budget (OMB), gender, socioeconomic status, sexual orientation, immigration status, ability status, and geography. One method to consider for setting equity targets is the HOPE Initiative’s approach. This method consists of averaging the proportion of the top five geographic units within a jurisdiction for the highest-performing socioeconomic groups. This method helps set targets based on actual population performance using socioeconomic status, which is strongly associated with health outcomes. One limitation to this approach is the assumption that the highest-performing groups are in favorable health. More details on this methodological approach are provided in the Benchmark Development section. Link: https://hopeinitiative.s3.amazonaws.com/uploads/5f74bfbfdbdb27001e41986b-HOPE%20Methods_Technical%20Documentation_FINAL.pdf As a piloted indicator in DHDSP’s HEI Pilot Study, sites found that county-level data were most useful to provide multiple data points for analysis. A large sample size and a broader set of counties are needed to enhance the utility of this indicator. Health care availability metrics should be interpreted alongside health care needs. It is important to consider the level of need in a community, because some areas with high rates of disease may have high health care availability due to high need for health services.

Measure 1: City and County Minimum Wage
Definition	Safety net providers are “those providers that organize and deliver a significant level of health care and other related services to uninsured, Medicaid, and other vulnerable patients.”²⁵ Safety net providers include some hospitals (e.g., public, children’s, teaching, and community hospitals serving low-income individuals), community health centers, Federally Qualified Health Centers (FQHCs),* migrant health centers, health services programs for the homeless or public housing residents, school-based clinics, and some home health agencies.²⁶ ^*An FQHC is a community health center that qualifies for enhanced reimbursement, beyond standard Medicare and Medicaid, from the HRSA Health Center Program, due to its focus on health disparities and work to empower people who live in areas that are medically underserved with high-quality patient care.
Data Availability	County, state
Data Source(s)	Area Health Resources Files (AHRF): The AHRF contains county- and state-level data on demographics, rural/urban categorization, health professional and facility supply, utilization, expenditures, and the local environment. State and county FIPS (Federal Information Processing Standard) codes are provided, allowing the data to be merged with external datasets. The AHRF contains information on the number of FQHCs within a county linked to states. Users can consolidate these data with county-level population data, also provided in the AHRF, to calculate the number of FQHCs by population. Users can search reports on health care facilities and providers for the following: the Centers for Medicare & Medicaid Services Health Center Facilities Report, Federally Qualified Health Centers and Look-Alikes, Hill-Burton Facilities Obligated to Provide Free or Reduced-Cost Health Care, and the Ryan White HIV/AIDS Recipients and Sub-Recipients Report. Data have been maintained annually starting with the 1970s; however, data availability varies by measure. Data are available for download as CSV or other formats. Health Center Program Uniform Data System (UDS) Data Overview: HRSA Health Center Program awardees and look-alikes are required by the HRSA Health Center program to report on patient characteristics, services provided, clinical processes and health outcomes, patients’ use of services, staffing, costs, and revenues through the UDS. The UDS Data Overview tool allows users to access patient characteristic and performance data for FQHCs and look-alikes by state and by program type (i.e., program awardee or look-alike). Users can find data tables by selecting the program type (“Select Health Center Program Type”) and state (“Select State/Territory”). Data are available for the most recent 5 years and can be downloaded in Excel format via a ZIP file. PolicyMap: PolicyMap is a data warehouse of more than 50,000 indicators on demographics, income and spending, housing, lending, quality of life, economy, education, health, and federal regulations. A free (basic) subscription provides access to indicators developed using publicly available data sources via a single-layer mapping tool. A paid (standard) subscription provides access to multilayer mapping, analysis tools, and data downloads (CSV format). PolicyMap uses HRSA data to provide the number of Federally Qualified Health Centers (FQHCs). Users can access this data under Health > Access to Medical Care > Facilities > Federally Qualified Health Centers. Users can access the number of FQHCs for 2005–2017 at the county and state levels. Users can also view a map layer that indicates the location of every FQHC and look-alike in the United States under Health > Facility Locations > Community Health Centers and Look-Alikes. Each FQHC location includes data on patient demographics, patient insurance and income, and patient health conditions. Flex Monitoring Team (FMT) Critical Access Hospital Locations List: The FMT is a consortium of researchers from the Universities of Minnesota, North Carolina at Chapel Hill, and Southern Maine that examines data and conducts research on Critical Access Hospitals (CAHs)* to assess quality, financial, and community measures at the national, state, and hospital level. FMT maintains a national listing of CAHs by name, city, ZIP code, state using data from the Centers for Medicare & Medicaid Services (CMS). The Critical Access Hospital Locations List is updated regularly and includes historical data from 2004. American Hospital Association Annual Survey Database: The American Hospital Association conducts an annual survey of hospitals which collects data on facility location, facility characteristics, services, utilization, staffing, finances, insurance, and alternative payment models from more than 6,200 hospitals and 400 health care systems. The American Hospital Association survey is often used to identify safety net hospitals in a geographic area of interest. Users can define geographic area using the facility location data (e.g., city, state) and hospital safety net status can be determined by facility characteristics (e.g., teaching status, nonprofit status, public ownership), Medicaid caseload (e.g., percentage of inpatient discharges that are Medicaid), revenue by payer (e.g., receipt of Medicaid disproportionate share hospital payments), and uncompensated care (e.g., bad debt, charity care). The survey instrument and survey data are available annually from the American Hospital Association’s website; however, users must request access and pay for access. Hospital Provider Cost Report: Medicare-certified hospitals are required to submit an annual cost report to the Medicare program. CMS maintains cost report data through the Healthcare Cost Report Information System (HCRIS). The Hospital Provider Cost Report includes hospital-level data on facility location, facility characteristics, utilization data, cost and charges, and financial statement data. Cost report data are often used to identify safety net hospitals in a geographic area of interest. Users can define geographic area using the facility location data (e.g., street address, ZIP code, city, state, core-based statistical area [CBSA]) and hospital safety net status can be determined based on facility characteristics (e.g., teaching status, nonprofit status, public ownership), Medicaid caseload (e.g., percentage of inpatient discharges that are Medicaid), Medicaid disproportionate share hospital payment (DSH) status (i.e., DSH index*), and uncompensated care (e.g., proportions of uninsured patients, self-pay patients, or charity care). HCRIS is updated annually. User guidance and datasets are available for years 2011–2019 and can be downloaded as a CSV file. CAHs are located in rural areas more than 35 miles from another hospital (or more than 15 miles in areas with mountainous terrain or that have only secondary roads available, or they have been certified as a “necessary provider” by their state prior to January 1, 2006), provide 24-hour emergency services, have a maximum of 25 inpatient beds, and maintain an annual average length of stay of 96 hours or less for their acute care patients. **The DSH index is a function of a hospital’s total inpatient days from patients on Supplemental Security Income (SSI) with Medicare and the total inpatient days from non-Medicare patients on Medicaid.
Additional Measurement Considerations	This indicator may require skills in pulling secondary data, setting up a database, and conducting descriptive statistical analysis and reporting. To measure the magnitude of disparities in health care availability, users should calculate metrics for specific subgroups. This facilitates setting tailored targets, measuring baseline disparities, and tracking trends by population groups that matter for advancing health equity. Demographic categories to consider for data disaggregation are race/ethnicity as defined by the Office of Management and Budget (OMB), gender, socioeconomic status, sexual orientation, immigration status, ability status, and geography. One method to consider for setting equity targets is the HOPE Initiative’s approach. This method consists of averaging the proportion of the top five geographic units within a jurisdiction for the highest-performing socioeconomic groups. This method helps set targets based on actual population performance using socioeconomic status, which is strongly associated with health outcomes. A limitation to this approach is the assumption that the highest-performing groups are in favorable health. More details on this methodological approach are provided in the Benchmark Development section. Link: https://hopeinitiative.s3.amazonaws.com/uploads/5f74bfbfdbdb27001e41986b-HOPE%20Methods_Technical%20Documentation_FINAL.pdf As a piloted indicator in DHDSP’s HEI Pilot Study, sites found that county-level data were most useful to provide multiple data points for analysis. A large sample size and a broader set of counties are needed to enhance the utility of this indicator. Identifying the number of safety net hospitals can be difficult due to the absence of a standard definition.^27,28,29,30 Safety net hospitals are generally recognized as hospitals that provide essential care to patients regardless of ability to pay, insurance status, or immigration status. These hospitals usually serve a substantial share of uninsured, Medicaid, and other vulnerable patients.^31,32 Common ways to identify safety net providers include payer mix (e.g., Medicaid, uninsured, private insurance), hospital characteristics (e.g., teaching status, public ownership, nonprofit status), patient case mix (e.g., socioeconomic status, health status), Medicaid disproportionate share hospital payment (DSH) status, Medicaid caseload (e.g., percentage of inpatient discharges that are Medicaid), and/or the level of uncompensated care.^33,34 Health care availability metrics should be interpreted alongside health care needs. It is important to consider the level of need in a community, because some areas with high rates of disease may have high health care availability due to high need for health services.

Measure 1: City and County Minimum Wage
Definition	The primary care physician ratio can be defined as the number of primary care physicians per 1,000 population, the number of primary care physicians per 100,000 population, or the number of residents per primary care physician.
Data Availability	County, state Subgroups: Income, race/ethnicity
Data Source(s)	Area Health Resources Files (AHRF): The AHRF contains county- and state-level data on demographics, rural/urban categorization, health professional and facility supply, utilization, expenditures, and the local environment. State and county FIPS (Federal Information Processing Standard) codes are provided, allowing the data to be merged with external datasets. Users wanting to analyze primary care physician availability can download AHRF directly from the HRSA website. Data have been maintained annually since the 1970s; however, data availability varies by measure. Data are available for download via CSV file or other formats. Users can use an interactive dashboard and filter the health profession by “M.D.” and the health profession subgroup by “Primary Care” for 2018–2019, 2019–2020, and 2020–2021. Dashboard data can be analyzed by poverty status and race/ethnicity. The estimates by subgroup represents the number of primary care physicians per 100,000 subgroup population. For example, the availability of primary care physicians by the number of Black/African American persons is defined as the number of primary care physicians per 100,000 Black/African American residents. County Health Rankings & Roadmaps (CHR&R): CHR&R is a program of the University of Wisconsin Population Health Institute. The CHR&R program provides data, evidence, guidance, and examples to build awareness of the multiple factors that influence health and support leaders in growing community power to improve health equity. CHR&R uses Area Health Resource File data to provide county-level estimates of the ratio of population to primary care physicians. Users can access this measure under Ranked Measures > Health Factors > Clinical Care > Access to Care > Primary Care Physicians. Data are downloadable as an Excel workbook; depending on the state, years of data availability will vary. Health Opportunity and Equity (HOPE) Initiative: The HOPE Initiative website provides state-level data on the proportion of people living in counties with a population-to-primary care physician ratio of less than 2,000:1 through its Access to Primary Care indicator. Data are available by race/ethnicity for all 50 states and Washington, D.C., via the web interface. In addition, data on all states are available by race/ethnicity, and socioeconomic status for 2018–2020 via a downloadable Excel workbook from the Resources section of the website.PolicyMap is a data warehouse of more than 50,000 indicators on demographics, income and spending, housing, lending, quality of life, economy, education, health, and federal regulations. A free (basic) subscription provides access to indicators developed using publicly available data sources via a single-layer mapping tool. A paid (standard) subscription provides access to multilayer mapping, analysis tools, and data downloads (CSV format). PolicyMap uses HRSA data to provide the number of primary care physicians per 1,000 people at the state and county levels for all states. Users can access this measure under Health > Access to Medical Care > Health Professionals > Doctors. Data for this measure is available for 2010–2016. PolicyMap: PolicyMap is a data warehouse of more than 50,000 indicators on demographics, income and spending, housing, lending, quality of life, economy, education, health, and federal regulations. A free (basic) subscription provides access to indicators developed using publicly available data sources via a single-layer mapping tool. A paid (standard) subscription provides access to multilayer mapping, analysis tools, and data downloads (CSV format). PolicyMap uses HRSA data to provide the number of primary care physicians per 1,000 people at the state and county levels for all states. Users can access this measure under Health > Access to Medical Care > Health Professionals > Doctors. Data for this measure is available for 2010–2016.
Additional Measurement Considerations	This indicator may require skills in pulling secondary data, setting up a database, and conducting descriptive statistical analysis and reporting. To measure the magnitude of disparities in health care availability, users should calculate metrics for specific subgroups. This facilitates setting tailored targets, measuring baseline disparities, and tracking trends by population groups that matter for advancing health equity. Demographic categories to consider for data disaggregation are race/ethnicity as defined by the Office of Management and Budget (OMB), gender, socioeconomic status, sexual orientation, immigration status, ability status, and geography. One method to consider for setting equity targets is the HOPE Initiative’s approach. This method consists of averaging the proportion of the top five geographic units within a jurisdiction for the highest-performing socioeconomic groups. This method helps set targets based on actual population performance using socioeconomic status, which is strongly associated with health outcomes. A limitation to this approach is the assumption that the highest-performing groups are in favorable health. More details on this methodological approach are provided in the Benchmark Development section. Link: https://hopeinitiative.s3.amazonaws.com/uploads/5f74bfbfdbdb27001e41986b-HOPE%20Methods_Technical%20Documentation_FINAL.pdf As a piloted indicator in DHDSP’s HEI Pilot Study, sites found that county-level data were most useful to provide multiple data points for analysis. A large sample size and a broader set of counties are needed to enhance the utility of this indicator. Health care availability metrics should be interpreted alongside health care needs. It is important to consider the level of need in a community, because some areas with high rates of disease may have high health care availability due to high need for health services.

Measure 1: City and County Minimum Wage
Definition	The pharmacy ratio is the number of pharmacies per 100,000 population.
Data Availability	County, metro area
Data Source(s)	PolicyMap: PolicyMap is a data warehouse of more than 50,000 indicators on demographics, income and spending, housing, lending, quality of life, economy, education, health, and federal regulations. A free (basic) subscription provides access to indicators developed using publicly available data sources via a single-layer mapping tool. A paid (standard) subscription provides access to multilayer mapping, analysis tools, and data downloads (CSV format). PolicyMap uses County Business Patterns data to provide the number of pharmacies per 100,000 people at the county and metro levels. Users can access this measure under Health > Access to Medical Care > Pharmacies. Data are available for the years 2003–2019.
Additional Measurement Considerations	This indicator may require skills in pulling secondary data, setting up a database, and conducting descriptive statistical analysis and reporting. To measure the magnitude of disparities in health care availability, users should calculate metrics for specific subgroups. This facilitates setting tailored targets, measuring baseline disparities, and tracking trends by population groups that matter for advancing health equity. Demographic categories to consider for data disaggregation are race/ethnicity as defined by the Office of Management and Budget (OMB), gender, socioeconomic status, sexual orientation, immigration status, ability status, and geography. One method to consider for setting equity targets is the HOPE Initiative’s approach. This method consists of averaging the proportion of the top five geographic units within a jurisdiction for the highest-performing socioeconomic groups. This method helps set targets based on actual population performance using socioeconomic status, which is strongly associated with health outcomes. A limitation to this approach is the assumption that the highest-performing groups are in favorable health. More details on this methodological approach are provided in the Benchmark Development section. Link: https://hopeinitiative.s3.amazonaws.com/uploads/5f74bfbfdbdb27001e41986b-HOPE%20Methods_Technical%20Documentation_FINAL.pdf As a piloted indicator in DHDSP’s HEI Pilot Study, sites found that county-level data were most useful to provide multiple data points for analysis. A large sample size and a broader set of counties are needed to enhance the utility of this indicator. Health care availability metrics should be interpreted alongside health care needs. It is important to consider the level of need in a community, because some areas with high rates of disease may have high health care availability due to high need for health services.

Measure 1: City and County Minimum Wage
Definition	The pharmacist ratio is the number of pharmacists per 100,000 population.
Data Availability	State
Data Source(s)	Area Health Resources Files (AHRF): The AHRF contains county- and state-level data on demographics, rural/urban categorization, health professional and facility supply, utilization, expenditures, and the local environment. State and county FIPS (Federal Information Processing Standard) codes are provided, allowing the data to be merged with external datasets. Users wanting to analyze pharmacist availability at the state-level can download AHRF directly from the HRSA website. Data are available for download via CSV file or other formats. Users can use an interactive dashboard and filter the “Health Profession” by “Pharmacist” for 2018-2019, 2019-2020, and 2020-2021.
Additional Measurement Considerations	This indicator may require skills in pulling secondary data, setting up a database, and conducting descriptive statistical analysis and reporting. To measure the magnitude of disparities in health care availability, users should calculate metrics for specific subgroups. This facilitates setting tailored targets, measuring baseline disparities, and tracking trends by population groups that matter for advancing health equity. Demographic categories to consider for data disaggregation are race/ethnicity as defined by the Office of Management and Budget (OMB), gender, socioeconomic status, sexual orientation, immigration status, ability status, and geography. One method to consider for setting equity targets is the HOPE Initiative’s approach. This method consists of averaging the proportion of the top five geographic units within a jurisdiction for the highest-performing socioeconomic groups. This method helps set targets based on actual population performance using socioeconomic status, which is strongly associated with health outcomes. A limitation to this approach is the assumption that the highest-performing groups are in favorable health. More details on this methodological approach are provided in the Benchmark Development section. Link: https://hopeinitiative.s3.amazonaws.com/uploads/5f74bfbfdbdb27001e41986b-HOPE%20Methods_Technical%20Documentation_FINAL.pdf As a piloted indicator in DHDSP’s HEI Pilot Study, sites found that county-level data were most useful to provide multiple data points for analysis. A large sample size and a broader set of counties are needed to enhance the utility of this indicator. Health care availability metrics should be interpreted alongside health care needs. It is important to consider the level of need in a community, because some areas with high rates of disease may have high health care availability due to high need for health services.

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Medically Underserved Areas

Why is this indicator relevant?

Medically Underserved Areas/Populations (MUA/Ps) are physician shortage designations that are sister programs to the Health Professional Shortage Area (HPSA), which provide similar benefits to communities in need.³⁵ MUA/Ps are designated by HRSA as having too few primary care providers, high infant mortality, high poverty, or a high older adult population.³⁶ Individuals living in medically underserved areas often face economic, cultural, or linguistic barriers to health services and preventive care,³⁷ which is associated with earlier identification of cardiovascular risk factors,^38,39 and inadequate and untimely access to medical treatment, resulting in greater risk of poor cardiovascular health outcomes.^40,41

If a population group does not meet the criteria for an MUA/P, but exceptional conditions exist as barriers to health services, they can be designated with a recommendation from the state’s governor. A list of Governor-Designated Secretary-Certified Shortage Areas for MUA/Ps for each state is available on the HRSA site.⁴²

Medically Underserved Areas/Populations (MUA/Ps) are physician shortage designations. MUA/Ps are designated by the Health Resources and Services Administration as having too few primary care providers, high infant mortality, high poverty, or a high older adult population. This indicator can be assessed by the following measure. Click on the measure to learn more:

Measure 1: City and County Minimum Wage
Definition	MUAs are areas where a specific population group is underserved, including groups with economic, cultural, or linguistic barriers to primary medical care.
Data Availability	Census tract
Data Source(s)	PolicyMap: PolicyMap is a data warehouse of more than 50,000 indicators on demographics, income and spending, housing, lending, quality of life, economy, education, health, and federal regulations. A free (basic) subscription provides access to indicators developed using publicly available data sources via single-layer mapping tool. A paid (standard) subscription provides access to multilayer mapping, analysis tools, and data downloads (CSV format). PolicyMap uses HRSA data to provide a census tract-level map of Medically Underserved Areas. The current census tract-level map reflects 2022 MUAs. Users can access this measure under Health > Access to Medical Care > Medically Underserved Areas.

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Health Care Effectiveness and Quality

Why is this indicator relevant?

Whether an individual has a primary care physician influences key aspects of the quality of care that individual receives (care coordination, person-centered care). According to an article in the Annals of Internal Medicine, data obtained from patients over the past 15 years show that most Americans have a primary care physician. Although having a primary care provider does not guarantee quality of care, it does support achieving improved health outcomes.^43,44

The 2021 National Healthcare Quality and Disparities Report found that Black/African American, Hispanic/Latino, and American Indian/Alaska Native communities experience significant disparities in all domains of health care quality compared with White persons.⁴⁵ People of color tend to receive lower-quality health care than White persons, even when insurance status, income, age, and severity of conditions are comparable. For example, Black/African American and Hispanic/Latino patients are less likely to be given appropriate cardiac medications, diagnostic tests, and treatments.⁴⁶ Lack of health insurance, poor routine health care access, low socioeconomic status, and language barriers contribute to racial/ethnic disparities in screening and treatment.^47,48 Statin prescribing and statin use for atherosclerotic cardiovascular disease (ASCVD) prevention varies by race. A study that analyzed data from 2013-2020 National Health and Nutrition Examination Survey found that and was much lower in Black/African American (20%) and Hispanic/Latino participants (15.4%) than White participants (27.9%).⁴⁹

Patients with access to a regular primary care physician receive more effective and higher-quality health care. They also report lower overall health care costs, improved health outcomes, fewer hospitalizations, less duplication in treatment, and lower prevalence of health care disparities.⁵⁰ A study in a California hospital asked patients about their access to care, chronic medical conditions, and propensity to seek health care. The study found that communities with perceived poor access to medical care had higher prevalence of hospitalizations for chronic disease and noted that “improving access to care is more likely than patients’ propensity to seek health care or eliminating variation in physician practice style to reduce hospitalizations for chronic conditions.”⁵¹

Patients with access to a regular primary care physician receive more effective and higher quality health care. This indicator can be assessed by the following measures. Click on each measure to learn more:

Measure 1: City and County Minimum Wage
Definition	Percentage of adults who report having a personal doctor or health care provider.
Data Availability	State Subgroups: Age, educational attainment, gender, income, race/ethnicity, urban/nonurban
Data Source(s)	America’s Health Rankings (AHR): The United Health Foundation’s AHR evaluates a comprehensive set of health, environmental, and socioeconomic data. The AHR website provides state-level analyses of CDC Behavioral Risk Factor Surveillance System (BRFSS) data on the percentage of adults who reported having a personal doctor or health care provider. Users can access this measure under Dedicated Health Care Provider. National and state-level estimates are provided by age, educational attainment, gender, income, and race/ethnicity for the most recent data. Current editions (2015–2021) can be explored online or downloaded in various formats, including Excel, CSV, and ZIP. Past editions (1990–2014) are also available for download. Health Opportunity and Equity (HOPE) Initiative: The HOPE Initiative website provides state-level data on the proportion of adults age 25 years and older who report having someone they consider their personal health care provider, using 3-year merged BRFSS data. Three years of data were used for these analyses to ensure reliable estimates. Data are available by race/ethnicity for all 50 states and Washington, D.C., via the web interface. In addition, data on all states are available by race/ethnicity, educational attainment, and income relative to the federal poverty line for 2018–2020 via a downloadable Excel workbook from the Resources section of the website. Kaiser Family Foundation: The Kaiser Family Foundation website provides state-level data by race/ethnicity on the proportion of adults who report not having a personal doctor or health care provider. Data for this measure are available annually for 2013–2020. Data are downloadable as a CSV file. National Healthcare Quality and Disparities Report (NHQDR): The NHQDR presents trends for measures related to access to care, affordable care, care coordination, effective treatment, healthy living, patient safety, and person-centered care. The report presents, in chart form, the latest available findings on quality of and access to health care, as well as disparities related to race/ethnicity, income, and other social determinants of health. The report has been produced annually since 2003, with reports available for download for 2010–2021. Users can view individual state-specific benchmark achievement on the percentage of people with specific ongoing care sources by selecting the state, subject area (e.g., access to care), and topic (e.g., structural access). In some cases, data are limited based on availability of specific measures by state.

Measure 1: City and County Minimum Wage
Definition	Preventable hospitalizations are hospital admissions for conditions that might not have required hospitalization had they been managed successfully by primary care providers in outpatient settings. Because many cases of inpatient hospitalizations are more costly than outpatient or primary care and can be prevented by quality primary care, preventable hospitalizations are measures of health care quality and efficiency.
Data Availability	County, state Subgroups: Age, gender, race/ethnicity, Medicare eligibility type
Data Source(s)	County Health Rankings & Roadmap (CHR&R): CHR&R is a program of the University of Wisconsin Population Health Institute. The CHR&R program provides data, evidence, guidance, and examples to build awareness of the multiple factors that influence health and support leaders in growing community power to improve health equity. CHR&R uses the Centers for Medicare & Medicaid Services (CMS) Office of Minority Health (OMH) Mapping Medicare Disparities (MMD) Tool to provide county-level estimates of hospital stay rates for ambulatory-sensitive conditions per 1,000 Medicare enrollees. Data are available by age, gender, and race/ethnicity. Users can access this measure under Ranked Measures > Clinical Care > Quality of Care > Preventable Hospital Stays. Data are downloadable as an Excel workbook; depending on the state, years of data availability will vary. Mapping Medicare Disparities Tool: The CMS OMH developed the MMD Tool. The Agency for Healthcare Research and Quality developed the Prevention Quality Indicators (PQIs) measure within the MMD Tool. The PQIs estimate the rate of preventable hospitalizations. There are 14 PQIs, 11 of which are condition-specific (e.g., heart failure, hypertension, diabetes) and three of which are composite measures (i.e., overall, acute conditions, and chronic conditions). Case rates per 100,000 population for all 14 PQIs are available via the tool’s Population View; users can disaggregate by age, gender, race/ethnicity, and dual eligibility. Case rates are suppressed for small populations, which are most relevant at the county level. Data are downloadable in CSV format and are available for single-year (2012–2020) or multiple-year ranges (e.g., 2012–2018, 2012–2019, 2012–2020).

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Health Literacy

Why is this indicator relevant?

Personal health literacy is the degree to which individuals can find, understand, and use information and services to inform health-related decisions and actions for themselves and others.⁵²

The 2003 National Assessment of Adult Literacy survey determined that 36% of U.S. adults had basic or below-basic health literacy. Limited health literacy is more prevalent among non-White racial/ethnic groups, older adults, and individuals with less education.⁵³

Adults experiencing low health literacy have less knowledge about their medical conditions and are less likely to use preventive health services. People with low health literacy also experience greater difficulties in performing disease self-management, are more likely to seek care in emergency departments and be admitted to the hospital, and experience higher mortality.⁵⁴

Health literacy affects individuals’ ability to prevent and manage CVD, including understanding guideline-based blood pressure recommendations and recognizing the signs and symptoms of stroke.⁵⁵

Health literacy is the degree to which individuals can find, understand, and use information and services to inform health-related decisions and actions for themselves and others. This indicator can be assessed by the following measure. Click on the measure to learn more:

Measure 1: City and County Minimum Wage
Definition	The ability to use literacy skills to read and understand health-related information.
Data Availability	Individual
Data Source(s)	Example Survey Instruments The following surveys are available to measure adult health literacy: The Health Literacy Questionnaire (HLQ) The HLQ is a widely used scale for measuring health literacy; it has been tested and validated in more than 12 countries.^56,57 The HLQ covers nine conceptually distinct areas of health literacy to assess the needs and challenges of a wide range of people and organizations. Users must request access to the HLQ. Link: https://healthliteracy.bu.edu/hlq Test of Functional Health Literacy in Adults (TOFHLA) The TOFHLA measures both reading comprehension and numeracy. It is designed to assess adult literacy in the health care setting.⁵⁸ It was developed using actual hospital-related materials, such as prescription bottle labels and appointment slips. Both long and short versions are available. The short version of the TOFHLA (S-TOFHLA) reduced the long form from 17 numeracy items and three prose passages to four numeracy items and two prose passages and reduced the survey administration time from 22 minutes to 12.⁵⁹ Users can request access to the S-TOFHLA. Link: https://healthliteracy.bu.edu/s-tofhla Newest Vital Sign (NVS) The NVS is a valid and reliable screening tool designed to assess a patient’s health literacy skills quickly and simply within 3 minutes.⁶⁰ The survey instrument is an ice cream nutrition label that is accompanied by six questions. Users can access NVS survey in English or Spanish and scoring instructions from the Pfizer website. Link: https://www.pfizer.com/products/medicine-safety/health-literacy/nvs-toolkit
Additional Measurement Considerations	CDC provides additional resources for measuring health literacy at https://www.cdc.gov/healthliteracy/learn/UnderstandingLiteracy.html

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Case Example

This case example was developed from the Health Equity Indicators (HEI) Pilot Study. Seven health care organizations participated in the HEI Pilot Study from January 2022 to April 2022 to pilot-test a subset of HEIs in order to assess the feasibility of gathering and analyzing data on these indicators within health care settings. The pilot case examples document participating sites’ experiences with data collection and lessons learned from piloting the HEIs.

Site: Trinity Health
Focus Area: Health Care Access
Measure: Primary Care Physician Ratio

This content presents an example of how one health care site, Trinity Health used data on the health equity indicator health care availability. Health care availability is typically defined as the geographic proximity of providers and facilities in relation to an individual and reflects the capacity of medical service markets to adequately meet the needs of the local population.^61,62 Limited availability of health care resources, including the number of primary care physicians, nurse practitioners, and pharmacists per capita, presents a barrier that may reduce access to health services and increase the risk of poor health outcomes.⁶³ Having access to care and a usual source of care may facilitate CVD screenings and increase opportunities for patients to receive preventive care and information about CVD risk behaviors from a health care provider.⁶⁴Trinity Health used the primary care physician ratio to measure health care availability. The primary care physician ratio is defined as the ratio of the population to the number of primary care providers.⁶⁵ For more information on the primary care physician ratio measure, please see the Health Care Access indicator in the Health Care Availability Indicator Profile.

The description of Trinity Health’s data analysis methods, data sources, challenges, and facilitators summarized below reflects insights gathered during the Health Equity Indicators for CVD Pilot Study, conducted between January and April of 2022.

Health Equity in Action: Trinity Health

Trinity Health is a Catholic-based primary care health system with treatment centers (referred to as ministries) across the country. Trinity Health agreed to work with CDC to pilot this health equity indicator to determine applicability to its broader health equity efforts. To support this pilot, the health system collaborated with one of their ministries, Saint Joseph Mercy Ann Arbor (SJMAA), and the Michigan Data Analytics (MDA) team. The SJMAA serves the Ypsilanti and Ann Arbor areas. In 2021, the U.S. Census Bureau reports estimated the Ypsilanti population to be 20,113, with 26.8% identified as Black/African American. The city of Ann Arbor was reported to have a population estimate of 121,536, with 7% identified as Black/African American.

Overview of Trinity Health’s data collection methods

Indicator: Health Care Availability

Measure: Primary Care Physician Ratio

Data Collection Method: Secondary, Secondary, Primary

Data Source: AMA Physician Master Files, CMS National Provider Identifier file, Electronic medical records

Data Availability: County-level, County-level, Patient-level

Application/Use: Analysis of relationship between readmission levels for congestive heart failure and health care availability, by patient ZIP code

Data Collection and Analysis Methods. As part of the pilot of select health equity indicators, Trinity Health tested the collection of data on health care availability using two data sources: the American Medical Association (AMA) Physician Master Files via the Health Resources & Services Administration’s (HRSA) Area Health Resources Files and the Centers for Medicare and Medicaid Services (CMS) National Provider Identifier (NPI) file. The AMA Physician Master File is a data set of all physicians in the United States and contains county-level estimates of the number of physicians in the county and the number of individuals served per physician. The CMS NPI file contains unique identification numbers for covered health care providers. With the NPI file, users can search the registry by county, city, or state to view the number and type of health care providers in a chosen area.

In addition, Trinity Health recruited patients from their inpatient hospital and outpatient congestive heart failure (CHF) clinic for primary data collection and obtained patient ZIP codes from electronic medical records. Using secondary data files and primary data on patient ZIP codes, the MDA team generated primary care physician and nurse practitioner ratios by matching patients’ ZIP codes to the corresponding county. They then used the data as an explanatory covariate in SJMAA’S CHF readmission analysis to inform their strategy for readmitting patients with CHF, based on health care availability within the counties they serve.

Facilitators. Trinity Health’s leadership team supported this work. The MDA team partnership and availability of a biostatistician facilitated the synthesis and analysis of data from the different data sources. In addition, the team had institutional review board approvals to gather ZIP code data.

Challenges and Limitations. The team recruited only 40 patients for ZIP code data. Due to this small sample size of patients, there were not enough data points from different counties to allow county-level analysis of health care availability.

Future Plans. Although the data sources were accessible and data collection for this indicator was feasible, a large sample size and a broader set of counties is required for maximum utility, enabling meaningful connections to CVD outcomes. To mitigate the challenge of limited county representation when using a county-level measure, Trinity Health plans to broaden their sample size by expanding recruitment efforts for collecting ZIP code data to include patients who receive home care. This will allow continued exploration of how to use this indicator in ongoing CHF patient studies. Additionally, the chief quality officer and MDA team developed a dashboard that tracks various health outcomes by race. Trinity Health plans to explore the dashboard data in the next few months.

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References

Alcalá HE, Albert SL, Roby DH, Beckerman J, Champagne P, Brookmeyer R, et al. Access to care and cardiovascular disease prevention: A cross-sectional study in 2 Latino communities. Medicine (Baltimore). 2015;94(34):e1441. doi:10.1097/MD.0000000000001441
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Institute of Medicine Committee on the Consequences of Uninsurance. Care Without Coverage: Too Little, Too Late. National Academies Press; 2002. Accessed July 24, 2022. http://www.ncbi.nlm.nih.gov/books/NBK220639/
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