Racism

Indicator Profile

Race is a social construct that divides people into categories based on nationality, ethnicity, phenotype, or other markers of social differences.¹ Racism, defined as an organized social system that devalues and disempowers racial groups regarded as inferior; reduces access to resources and opportunities such as employment, housing, education, and health care; and increases exposure to risk factors.^2,3 Research has consistently shown that racism drives racial/ethnic inequities in cardiovascular disease (CVD).^4,5According to the American Heart Association (AHA), people of color—including people who are Black/African American, Hispanic/Latino, American Indian/Alaska Native, and Asian—experience varying degrees of social disadvantage that puts these groups at increased risk of CVD and contributes to inequities in CVD outcomes.⁶

Table of Contents

Indicators

This document provides guidance for measuring six indicators related to racism that influence inequities across various social and environmental factors, leading to differential risks for developing CVD or differential access to and receipt of health care. The six racism indicators are measured at different levels of analysis, including individual, census tract, city, county, metropolitan area, and state.

Immigration Status

Why is this indicator relevant?

Immigration status, which refers to the way in which a person resides in the United States or has the authority to reside in the United States, has been linked to health inequities.⁷ Although immigration status does not equate to race, non-White immigrants are susceptible to racial profiling, discrimination, and anti-immigrant sentiment, which in turn contributes to psychological stress and increases the risk of negative health outcomes.⁸ Immigrant families, especially those who lack documentation, often lack health care resources such as insurance or a primary care physician; this may be related to attempts to avoid negative interactions with federal agencies.⁹ Immigrants often struggle with discrimination, language barriers, low income, and other socioeconomic challenges.

Studies suggest that foreign-born adults who reside in the United States have lower prevalence of CVD risk factors, lower incidence of stroke and coronary heart disease, and lower CVD mortality rates than those born in the United States.1^0,11 However, evidence also suggests that the protective effect of foreign birthplace on cardiovascular health decreases with increasing length of residency in the United States.^12,13,14

Acculturation, or the adoption of behavioral and social norms, is associated with poorer health behaviors and social isolation due to erosion of cultural and familial ties. Undocumented individuals face challenges in accessing care due to exclusion from public insurance programs and employer-based insurance, challenges that may affect CVD prevention, treatment, and management.¹⁵ Immigrants are also prone to higher levels of acculturative stress and chronic stress, which are risk factors for CVD.^16,17

South Asians (people from Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka) comprise one-quarter of the world’s population and are one of the fastest-growing ethnic groups in the United States. Compared with other racial/ethnic groups in the United States, South Asians have a higher atherosclerotic cardiovascular disease (ASCVD) risk and higher proportional mortality rate from ischemic heart disease.¹⁸ The risk, treatment, and outcomes of ASCVD among South Asians also vary by country of residence. Despite sharing the same genetic risk factors as South Asians living in their native countries, South Asians in the United States have different CVD outcomes, likely due to acculturation’s impact on health behaviors and to variations in socioeconomic status, education, health attitudes, and health insurance.¹⁹

Although studies have shown that recent Hispanic/Latino immigrants have better CVD outcomes than U.S.-born Hispanic/Latino adults, increasing duration of residency is associated with worsening CVD risk factors among Hispanic/Latino immigrants due to increased exposure to poorer diets, sedentary lifestyle, and increased stress and substance use.^20,21 Hispanic/Latino noncitizens face systemic barriers to accessing care, including difficulty in obtaining essential medications such as statins (lipid-lowering agents) that are critical for CVD prevention. A study examining the relationship between noncitizen concentration at the neighborhood level and statin nonadherence found that individuals living in neighborhoods with high noncitizen concentrations were more nonadherent to statins than those in Hispanic/Latino neighborhoods with fewer noncitizens.²²

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 state of a person’s citizenship in a country.
Data Availability	Census tract, ZIP code, city, county, congressional district, metropolitan division, metropolitan area, core-based statistical area (CBSA), state Subgroups: Age, sex, marital status, occupation, country of origin
Data Source(s)	Office of Homeland Security Yearbook of Immigration Statistics: The Office of Immigration Statistics at the U.S. Department of Homeland Security publishes the annual Yearbook of Immigration Statistics, a collection of tables that provide data on foreign nationals who, during a fiscal year, were granted lawful permanent residence (i.e., were admitted as immigrants or became legal permanent residents), were admitted into the United States on a temporary basis (e.g., tourists, students, workers), applied for asylum or refugee status, or were naturalized. Immigration information is available at the CBSA and state levels and can be stratified by age, sex, marital status, and occupation. Download available data for 2000–2020. Migration Policy Institute: The Migration Policy Institute uses data from the U.S. Census Bureau’s pooled 2015–2019 American Community Surveys (ACSs) to create a map of the geographical distribution of immigrants in the United States at the county, metropolitan areas, and state levels by country of origin. Data can also be downloaded in a Tableau, PowerPoint, or PDF file. Additionally, the Migration Policy Institute creates State Immigration Data Profiles, which use data from the U.S. Census Bureau. The State Immigration Data Profiles compile the number and percent of foreign residents and U.S.-born residents for each state for 1990, 2000, and 2019. These data can also be stratified by demographics (race/ethnicity, age, marital status), country of origin, and naturalization status. 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 provides estimates on the percentage of all people who were foreign born using data from the U.S. Census Bureau. Users can view the data by census tracts, ZIP codes, cities, counties, congressional districts, metropolitan divisions, and metropolitan areas using single-layer maps. Users can access this measure under Demographics > Foreign Born Population, Immigration > Foreign Born All. Data are available for 2000, 2006–2010, 2011–2015, and 2016–2020. Example Survey Instrument The following survey questions are available for assessing immigration status: American Community Survey (ACS) The U.S. Census Bureau’s ACS asks questions about a person’s place of birth, citizenship, and year of entry into the United States to create data about citizens, noncitizens, and the foreign-born population. To view ACS questions on place of birth, citizenship, and year of entry, please visit the U.S. Census Bureau’s website below. Link: https://www.census.gov/acs/www/about/why-we-ask-each-question/citizenship/

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Race-Consciousness

Why is this indicator relevant?

Race-consciousness reflects awareness and consciousness of stereotypes associated with one’s race/ethnicity, which may also result in heightened stress associated with the anticipation of experiencing racial bias, prejudice, or discrimination. Studies suggest that experiencing discrimination may be associated with greater race consciousness and that the anticipatory vigilance of race-consciousness has been linked to negative health outcomes such as lower self-reported overall health, poorer self-reported medication adherence, less sleep, and hypertension.^23,24

Several studies document that Black/African American patients report more race-consciousness than White patients. Among Black/African American persons, race-consciousness was associated with higher diastolic blood pressure and hypertension.^25,26

Race-consciousness reflects awareness and consciousness of stereotypes associated with one’s own race/ethnicity, which may also result in heightened stress associated with the anticipation of experiencing racial bias, prejudice, or discrimination. This indicator can be assessed by the following measures. Click on each measure to learn more:

Definition

The extent to which an individual anticipates or prepares for experiences of unfair treatment and/or discrimination.

Data Availability

Individual

Data Source(s)

Example Survey Instrument

The following survey is available to measure racism-related vigilance:

Heightened Vigilance Scale

The Heightened Vigilance Scale measures efforts to protect oneself from general discrimination and minimize exposure.2⁷ This scale was originally developed as a six-item self-report measure for the 1995 Detroit Area Study and was shortened as a four-item scale for the Chicago Community Adult Health Study. Both versions are available for download at the link and require slight modification to the opening question (“In your day-to-day life, how often do you…”) in order to measure racism-related vigilance. The abbreviated scale is provided below and has been modified to measure efforts to protect oneself from racism-related discrimination (added, “…because of your race/ethnicity.”).

Link: https://scholar.harvard.edu/files/davidrwilliams/files/discrimination_resource_july_2020.pdf

In your day-to-day life, how often do you do the following things because of your race/ethnicity:

Race/Ethnicity table
Event	Frequency
1. You try to prepare for possible insults from other people before leaving home	Almost every day At least once a week A few times a month A few times a year Less than once a year Never
2. Feel that you always have to be very careful about your appearance (to get good service or avoid being harassed)	Almost every day At least once a week A few times a month A few times a year Less than once a year Never
3. Carefully watch what you say and how you say it	Almost every day At least once a week A few times a month A few times a year Less than once a year Never
4. Try to avoid certain social situations and places	Almost every day At least once a week A few times a month A few times a year Less than once a year Never

Measure 1: City and County Minimum Wage
Definition	An individual’s experience and response to differential treatment based on race/ethnicity.
Data Availability	Individual, state
Data Source(s)	Behavioral Risk Factor Surveillance System (BRFSS): CDC’s BRFSS is a state-level, multistage-cluster sampling telephone survey of U.S. residents that collects data on self-reported demographics, health behaviors, and preventive health practices. The Reactions to Race module is an optional module (states elect to add optional modules to the state questionnaire) that assesses race consciousness, perceptions of differential treatment by race/ethnicity in health care and work settings, and experiences of emotional and physical symptoms resulting from race-based treatment. The Reactions to Race module was added in 2002, and years of data availability vary by state. Questionnaires and data are downloadable. Example Survey Instrument The following survey is available to measure reactions to race: Reactions to Race on CDC’s BRFSS The Reactions to Race module is a six-item measure from the CDC BRFSS used to assess an individual’s experience and response to differential treatment based on race. The questions involve asking how often the individual thinks about their race. To view survey items from the Reactions to Race module, visit the BRFFSS link below and search for “Reactions to Race.” Link: https://www.cdc.gov/brfss/questionnaires/pdf-ques/2004brfss.pdf [PDF – 380 KB]

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Racial Income Gap

Why is this indicator relevant?

When the Civil Rights Act of 1964 was passed, it became illegal for employers to discriminate based on race.²⁸ Although the Civil Rights Act eliminated many legally sanctioned (de jure) forms of discrimination, many persist, including in hiring and promotion practices. Such experiences contribute to racial/ethnic inequities in income, particularly the racial income gap (RIGap), which is the difference in median income between racial and ethnic groups.²⁹

The United States has seen a rise in racial income inequality in recent decades, with wealth inequality following a similar trend.³⁰ For instance, in 2019, the median income for Black/African American households was roughly 60% of the median income for White households. Furthermore, the median White family had eight times the wealth of the median Black/African American family and five times the wealth of the median Hispanic/Latino family.³¹ Another study found that Black/African American persons earned about 38% less than White persons did.³² Research suggests that factors such as educational inequality, unemployment differences, and government policies contribute to the RIGap.³³ Furthermore, racial income inequality has had adverse societal and health consequences, including racial disparities in health care and homeownership, as well as increases in violent crime and suicide.³⁴ One study observed that higher levels of RIGap at the ZIP-code level were associated with high levels of perceived discrimination, behavioral avoidance, and anxiety.³⁵ Other studies reported that aggregated measures of income equality demonstrated a relationship with health outcomes such as mortality, self-rated health, and risk of coronary heart disease, with the strongest effects observed between county and state levels of income inequality and individual health.^36,37

Several studies suggest that substantial disparities in CVD prevalence exist between the highest-resourced groups and the remainder of the population.^38,39,40 County-level measures of median income and income inequality are also associated with county- and individual-level CVD mortality rates.^41,42 Racial income inequality may affect CVD risk through several pathways, including environmental, occupational, and neighborhood exposures affecting psychosocial, metabolic, and behavioral risk factors for CVD.^43,44,45,46 The chronic stress due to social dysfunction in unequal communities may result in heightened blood pressure (BP), leading to the adoption of unhealthy coping behaviors (e.g., smoking, unhealthy eating, alcohol consumption), which can affect cardiovascular and other chronic diseases.^47,48,49,50 Additionally, income inequality is associated with increased crime.^51,52 Perceived lack of safety from increased crime may lead to reduced outdoor physical activity, leading to increased body mass index, increased BP, and other risk factors for CVD.⁵³ Moreover, income disparities are linked to poor access to care, and people in lower-income groups are less likely to utilize preventive services for CVD.5⁴

The racial income gap refers to the differences in median income between racial and ethnic groups. This indicator can be assessed by the following measures. Click on each measure to learn more:

Definition

A measure of economic and/or social (race/ethnic) spatial polarization that can be used from the very local level (e.g., census tract) up to the neighborhood or city/town level.⁵⁵ The ICE quantifies the extent to which people in a specified area are concentrated in the top versus bottom extremes of a specified social distribution using the following formula: ICEi = (Ai – Pi)/Ti

Where Ai is the number of people in the most privileged extreme, Pi is the number in the most deprived extreme, and Ti is the total population in geographic area i. ICEi ranges from −1 to 1; a value of −1 means that 100% of the population is concentrated in the most deprived group, and a value of 1 means that 100% of the population is concentrated in the most privileged group.⁵⁶

ICE allows for the selection of two groups for comparison and can be used as three separate measures of economic and racial privilege⁵⁶:

ICE for income (economic segregation or income gap)
ICE for race/ethnicity (racial segregation)
ICE for race/ethnicity + income (racialized economic segregation or racial income gap)

Data Availability

Census tract, county, metropolitan area, state

Subgroups: Race/ethnicity

Data Source(s)

American Community Survey (ACS) 5-Year Summary Files: A dataset for the ICE has not yet been developed and made publicly available. Interested parties would need to download the underlying data and develop the index themselves, using the methods described in Additional Measurement Considerations. Full methodology on the ICE is available from Krieger et al.^57,58,59 Users can download income statistics from the U.S. Census Bureau’s ACS 5-year files and create a ratio between racial/ethnic groups. Income data are available annually for 2010–2020. ACS 5-year files can be downloaded from the U.S. Census Bureau’s main data platform at the link below.

Additional
Measurement Considerations

Calculating the ICE can be a complex task for nonacademic practitioners. Pre-calculated ICE data are not publicly available.

In addition, although this index may consider both racial and economic inequality jointly whereas other measures do not, it is still an aggregate measure, and interpreting the results is not straightforward. For example, results close to 0 may indicate either that almost no one in the geography has high or low income or that a relatively equal number of residents belong to the two extremes.

Finally, like most existing measures, the ICE allows for the incorporation of only two groups and does not provide insights about other potentially disadvantaged groups.

The following steps provide guidance on ICE calculations. This sample calculation compares wealthy White people with impoverished Black/African American persons.

Download income data by race/ethnicity from the U.S. Census Bureau’s ACS 5-year files

Household Income In Dollars
- Table Name: P052
- Variables:
- P052001: Total

Household Income In Dollars (Black Alone Householder)*
- Table Name: P151B
- Variables:
- P151B001: Total
- P151B002: Less than $10,000
- P151B003: $10,000 to $14,999
- P151B004: $15,000 to $19,999
- P151B005: $20,000 to $24,999

Household Income In Dollars (White Alone, Not Hispanic Or Latino Householder)*
- Table Name: P151I
- Variables:
- P151I001: Total
- P151I013: $75,000 to $99,999
- P151I014: $100,000 to $124,999
- P151I015: $125,000 to $149,999
- P151I016: $150,000 to $199,999
- P151I017: $200,000 or more

Outline the definition of “privileged” and “deprived” group

Income: $100K vs. $25K, or $75K vs. $25K
Race: non-Hispanic White vs. non-Hispanic Black
Income and race: non-Hispanic White household making $100K vs. non-Hispanic Black household making $25K

Calculate ICE measure using the following formula

Formulas
Conceptual formula	Data formula
[(White non-Hispanic over US$75 k) – (Black alone under US$25 k)] / Total Population Household Income	[(SF3_P151I013+SF3_P151I014+SF3_P151I015+SF3_P151I016+SF3_P151I017)-(SF3_P151B002+SF3_P151B003+SF3_P151B004+SF3_P151B005)] / SF3_P052001

*Note: Variable names are based on the 2000 Census. Variable names are different by year.

Measure 1: City and County Minimum Wage
Definition	The ratio of median household income by race.
Data Availability	Census tract, county, metropolitan area, state Subgroups: Race/ethnicity
Data Source(s)	American Community Survey (ACS) 5-Year Summary Files: The American Community Survey (ACS) from the U.S. Census Bureau is an ongoing survey that provides data annually. The 5-year estimates from the ACS represent data collected over a period of time. Users can access tools such as Detail Tables, Subject Tables, Data Profiles, and Comparison Profiles. Users can search “Median Household Income” at the link below to access data on this measure. Data on median income in the past 12 months are available annually for 2010–2020. Users can access median income estimates by various characteristics (e.g., gender, race/ethnicity, age) by downloading the U.S. Census Bureau’s ACS 5-year files from the U.S. Census Bureau’s main data platform at the link below. In order to understand the racial income gap, users can create a ratio of median income by race by dividing the median income of one race by the median income of another race (e.g., the median income of White households divided by the median income of Black/African American households). 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 provide county-level estimates on median household income and includes estimates by race/ethnicity. CHR&R uses data from the U.S. Census Bureau’s Small Area Income and Poverty Estimates (SAIPE). Users can access this measure under Additional Measures > Social & Economic Factors > Median Household Income. Data for this measure are available for download as an Excel workbook, and years of data availability vary by state. In order to understand the racial income gap, users can create a ratio of median income by race by dividing the median income of one race by the median income of another race (e.g., the median income of White households divided by the median income of Black/African American households). PolicyMap: PolicyMap is a data warehouse of more than 50 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). Users can view five categories/ranges of median household income by race/ethnicity. Users can access this measure under Incomes & Spending > Income > Household > Median Household Income > By Race or Ethnicity. PolicyMap uses U.S. Census Bureau data for this measure. Data are available for 2000, 2006–2010, 2011–2015, and 2016–2020. In order to understand the racial income gap, users can create a ratio of median income by race by dividing the median income of one race by the median income of another race (e.g., the median income of White households divided by the median income of Black/African American households). Example Survey Instrument The following survey questions are available for assessing racial income gap: American Community Survey (ACS) The U.S. Census Bureau’s ACS asks questions about the funds a person receives from various sources to create statistics about income, assistance, earnings, and poverty status. In order to understand the racial income gap, users have to calculate total income in past 12 months for respondents by race and calculate the ratio of income by race by dividing the income of one race by another race (e.g., the total income of White households divided by the total income of American Indian/Alaska Native households). To view ACS questions on income and race, please visit the U.S. Census Bureau’s website below. Links: https://www.census.gov/acs/www/about/why-we-ask-each-question/income/ https://www.census.gov/acs/www/about/why-we-ask-each-question/race/ https://www.census.gov/acs/www/about/why-we-ask-each-question/ethnicity/

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Racial Residential Segregation

Why is this indicator relevant?

Racial residential segregation refers to the physical separation of races in residential settings and serves as a proxy for structural racism due to the systematic disinvestment of neighborhoods among historically marginalized groups that occurs along with segregation.^60,61 Racial residential segregation and its systematic disinvestments have negative economic, educational, employment, and environmental consequences that lead to systematic discrimination in housing and lending and ultimately affect downstream health outcomes. On average, individuals from historically marginalized groups—particularly Black/African American persons, Hispanic/Latino persons, and American Indian/Alaska Native persons, are more likely to have lower high school graduation rates, to have individual and household incomes below the federal poverty level (annual income thresholds set by the federal government to determine financial eligibility criteria⁶²), and to lack insurance and regular access to quality primary care due to structural racism resulting from residential segregation.⁶³

Research suggests that concentrated poverty, poor housing environments, and inequitable access to health care and education are key pathways through which racial residential segregation affects cardiovascular outcomes and that the socioeconomic factors associated with segregation can explain more of the disparities in CVD mortality than traditional cardiovascular risk factors can.⁶⁴ Using data from the Multi-Ethnic Study of Atherosclerosis, Kershaw et al. found that Black/African American people living in neighborhoods (defined by census tracts) segregated from other racial/ethnic groups are at greater risk for CVD than Black/African American persons living in more racially/ethnically integrated neighborhoods. Kershaw et al. found the reverse relationship for White persons—that is, White persons living in less integrated neighborhoods had better cardiovascular outcomes.⁶⁵ However, this effect disappeared after adjusting for neighborhood poverty.⁶⁶ No effects were observed for Hispanic/Latino Americans. In a later review of the literature, Kershaw and Albrecht reported that this pattern held among most studies examining the effects of neighborhood (census-tract) and metropolitan-level segregation on CVD risk, especially among Black/African American individuals.⁶⁷

Measure 1: City and County Minimum Wage
Definition	A demographic measure of evenness that examines how two population groups are distributed across component geographic areas (e.g., census tracts) that make up a larger area (e.g., a county). The index of dissimilarity with higher values indicates greater segregation between two groups, ranging from 0 (complete integration) to 100 (complete segregation). If an area’s dissimilarity index of Black/African American to White persons is 65, then 65% of Black/African American persons would need to move to another area to make White and Black/African American persons evenly distributed across all areas.
Data Availability	City, county, metropolitan area, state
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 provides county-level indices of dissimilarity where higher values indicate greater residential segregation between Black/African American and majority-White county residents. CHR&R use data from the U.S. Census Bureau’s ACS 5-year files. Demographic data on race/ethnicity, age, gender, and rural/urban are collected and categorized using the U.S. Census Bureau definitions. Users can access this measure under Additional Measures > Social & Economic Factors > Residential Segregation – Black/White American or Residential Segregation – non-White/White. Data are available for download as an Excel workbook, and years of data availability vary by state. Russell Sage Foundation American Communities Project: The Russell Sage Foundation American Communities Project’s Diversity and Disparities website calculates dissimilarity indices for a variety of racial/ethnic groupings (i.e., non-Hispanic White persons, non-Hispanic Black/African American persons, Hispanic/Latino persons, Asian persons, and American Indian/Alaska Native persons) at the city, metropolitan area, and metropolitan division levels. The Diversity and Disparities website calculates dissimilarity indices using data from U.S. Census Bureau’s Decennial Census. Data are available for 1980, 1990, 2000, and 2010; users can download them from the link below.
Additional Measurement Considerations	The Racial Residential Segregation indicator addresses only a few dimensions of segregation. The measures provided for this indicator assess only racial evenness and racial concentration in communities. However, studying other dimensions of residential segregation, including isolation, exposure, clustering, centralization, and hyper-segregation, is crucial.

Measure 1: City and County Minimum Wage
Definition	A measure of economic and/or social (race/ethnic) spatial polarization that can be used at the very local level (e.g., census tract) up to the neighborhood or city/town level.⁶ It quantifies the extent to which people in a specified area are concentrated in the top vs. bottom extremes of a specified social distribution using the following formula: ICEi = (Ai – Pi)/Ti Where Ai is the number of people in the most privileged extreme, Pi is the number in the most deprived extreme, and Ti is the total population in geographic area i. ICEi ranges from −1 to 1; a value of −1 means that 100% of the population is concentrated in the most deprived group, and a value of 1 means that 100% of the population is concentrated in the most privileged group.⁶⁸ ICE allows for the selection of two groups for comparison and can be used as three separate measures of economic and racial privilege⁶⁹: ICE for income (economic segregation or income gap) ICE for race/ethnicity (racial segregation) ICE for race/ethnicity + income (racialized economic segregation or racial income gap)
Data Availability	Census tract, county, metropolitan area, state Subgroups: Race/ethnicity
Data Source(s)	American Community Survey (ACS) 5-Year Summary Files: A dataset for the ICE has not yet been developed and made publicly available. Interested parties would need to download the underlying data and develop the index themselves using the methods described in Additional Measurement Considerations. Full methodology on the ICE is available from Krieger et al.^70,71,72 Users can download population count data from the U.S. Census Bureau’s ACS 5-year files and create a ratio between racial/ethnic groups. To access data on this measure, users can search for “Hispanic or Latino origin by race” or by the variable name “B03002” in the link below. Data on Hispanic or Latino origin by race are available annually from 2010 to 2020. ACS 5-year files can be downloaded from the U.S. Census Bureau’s main data platform at the link below.
Additional Measurement Considerations	The Racial Residential Segregation indicator in the Racism Indicator Profile addresses only a few dimensions of segregation. The measures provided in this document assess only racial evenness and racial concentration in communities. However, studying other dimensions of residential segregation, including isolation, exposure, clustering, centralization, and hyper-segregation, is crucial. Calculating the ICE can be a complex task for nonacademic practitioners. Pre-calculated ICE data are not publicly available. In addition, although this index may consider both racial and economic inequality jointly whereas other measures do not, it is still an aggregate measure, and interpreting the results is not straightforward. For example, results close to 0 may indicate either that almost no one in the geography has high or low income or that a relatively equal number of residents belong to the two extremes. Finally, like most existing measures, the ICE allows for the incorporation of only two groups and does not provide insights about other potentially disadvantaged groups. The following steps provide guidance on ICE calculations. This calculation example compares White persons with Black/African American persons. Download race/ethnicity data from the U.S. Census Bureau’s ACS 5-year files Hispanic or Latino Origin by Race; Table Name: B03002 Outline the definitions of “privileged” and “deprived” groups High racial privilege: non-Hispanic White Low racial privilege: non-Hispanic Black Calculate the ICE measure using the following formula The ICE for race/ethnicity is calculated as the number of people with high racial privilege subtracted by the number of people with low racial privilege in a given area, divided by the total population in the given area.⁷³ ((No. of people self-identified as “White non-Hispanic”) – (No. of people self-identified as “Black non-Hispanic”)) / No. of total population with race/ethnicity data

Measure 1: City and County Minimum Wage
Definition	An individual’s appraisal of racial diversity in their neighborhood.
Data Availability	Individual
Data Source(s)	Example Survey Instrument The following survey question is available for assessing perceived racial diversity: National Survey of American Life (NSAL) Self-Administered Questionnaire (SAQ) The NSAL was a comprehensive study of mental health among African Americans and was designed to investigate intra- and inter-group racial and ethnic differences in mental disorders, psychological distress, informal and formal service use, stressors, risk and resilient factors, and coping resources.⁷⁴ The study was conducted by the Program for Research on Black Americans (PRBA) within the Institute for Social Research at the University of Michigan. The NSAL self-administered questionnaire asks several questions about group and personal identity (racial awareness and identity), racial relations (e.g., interracial contact), political attitudes, and job and financial stressors, including one question about racial composition of neighborhoods. The full questionnaire is available at the University of Michigan Institute for Social Research’s website. To view the question on racial composition of neighborhoods, users can select the “Variables” tab and search for variable “G31” or download the entire survey from the “Data and Documentation” tab and search for question “G31.” Link: https://www.icpsr.umich.edu/web/ICPSR/studies/27121/summary
Additional Measurement Considerations	The Racial Residential Segregation indicator addresses only a few dimensions of segregation. The measures provided for this indicator assess only racial evenness and racial concentration in communities. However, studying other dimensions of residential segregation, including isolation, exposure, clustering, centralization, and hyper-segregation, is crucial.

Measure 1: City and County Minimum Wage
Definition	An index ranging from 0 to 1 that displays information about racial segregation. Index values below .20 suggest less segregation, and index values above .40 suggest more segregation. The Theil index is a measure of how evenly members of racial/ethnic groups (i.e., White, Black/African American, American Indian/Alaska Native, Asian, Native Hawaiian, Pacific Islander, and other people) are distributed within a region, calculated by comparing the diversity of all subregions (census blocks) to the region as a whole. By design, the Theil index weights subregions used in the calculations according to their population size.
Data Availability	Block group, census tract, county, metropolitan 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 calculates the Theil index using data from U.S. Census Bureau’s Decennial Census. Users can view the Theil Index for census block groups, census tracts, counties, and metropolitan areas using single-layer maps. Users can access this measure under Demographics > Race, Ethnicity, Diversity > Segregation > Theil Index. Theil index data are only available for 2010.
Additional Measurement Considerations	The Racial Residential Segregation indicator addresses only a few dimensions of segregation. The measures provided for this indicator assess only racial evenness and racial concentration in communities. However, studying other dimensions of residential segregation, including isolation, exposure, clustering, centralization, and hyper-segregation, is crucial.

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Racial/Ethnic Discrimination and Trauma

Why is this indicator relevant?

Racial/ethnic discrimination is defined as any distinction, exclusion, restriction, or preference based on race, descent, or national or ethnic origin that has the purpose or effect of nullifying or impairing the recognition, enjoyment, or exercise, on an equal footing, of human rights and fundamental freedoms in the political, economic, social, cultural, or any other field of public life.⁷⁵ The anticipation and experience of racial/ethnic discrimination have been linked to negative health behaviors and outcomes such as increased substance use and abuse, elevated stress, less sleep, and increased depressive symptoms.^76,77,78

Relating to cardiovascular risk factors and outcomes, past research has found robust and consistent associations between reports of discrimination and ambulatory blood pressure (BP).⁷⁹ For example, exposure to racial/ethnic discrimination across the life course is associated with elevated BP in Black/African American persons and Hispanic/Latino adults.⁸⁰ One probable pathway is that racial discrimination can lead to both acute and chronic stress responses within multiple physiological systems. Specifically, exposure to discrimination results in increases in heart rate and BP that, with repeated exposure over time, can result in increased risk for hypertension and coronary artery calcification.⁸¹ However, findings in this area of research have been inconsistent; other studies find no association between racial/ethnic discrimination and CVD outcomes.

Racial trauma, or race-based traumatic stress, refers to the mental and emotional injury caused by encounters with racial bias and ethnic discrimination, racism, and hate crimes.⁸² Trauma is distinct from experiences of discrimination in that it captures events that are extreme, overwhelming, and horrific in impact.⁸³ Victims of trauma can experience both short- and long-term adverse physiological effects, because the brain has been shown to react to and sometimes retain the resulting emotions and trauma.⁸⁴ For instance, racial trauma has been shown to affect individuals’ health behaviors and psychosocial factors, such as the amount of stress and sleep health, which directly affect neurobiological mediators, such as the cardiovascular system, hypothalamic–pituitary–adrenocortical axis, brain systems, and the immune system. These neurobiological mediators ultimately lead to an increased risk for CVD.⁸⁵

Racial/ethnic discrimination is defined as any distinction, exclusion, restriction, or preference based on race, descent, or national or ethnic origin with the purpose or effect of nullifying or impairing the recognition, enjoyment, or exercise, on an equal footing, of human rights and fundamental freedoms in the political, economic, social, cultural, or any other field of public life. This indicator can be assessed by the following measures. Click on each measure to learn more:

Definition

Unfair, negative, and/or differential treatment on the basis of race/ethnicity.

Data Availability

Individual

Data Source(s)

Example Survey Instrument

The following surveys are available to measure racial/ethnic discrimination:

The Everyday Discrimination Scale (EDS)

The EDS is a general measure of unfair treatment. Possible attributions for unfair treatment include race, gender, and sexual orientation. The full and short versions of the original scale were published by Williams et al.⁸⁶ The short version of the EDS was developed for the Chicago Community Health Study.⁸⁷ We recommend adding a response option of “About once a month” to capture experiences ranging between “A few times a month” and “A few times a year.” The modified short version below includes these response options and specifically asks about unfair treatment due to race/ethnicity.

In your day-to-day life, how often have any of the following things happened to you because of your race/ethnicity?

Race/Ethnicity questions
	7 Almost every day	6 At least once a week	5 A few times a month	4 About once a month	2 Less than once a year	1 Never
You are treated with less courtesy or respect than other people
You receive poorer service than other people at restaurants or stores
People act as if they think you are not smart
People act as if they are afraid of you
You are threatened or harassed

Experiences of Discrimination Measure (EOD)

The EOD measure has been used to study the stress of experiencing discrimination and how the experience of discrimination may contribute to trauma. The race version of the EOD Scale assesses how often a person has experienced discrimination because of their race/ethnicity.⁸⁸ The original measure uses a two-step approach: Participants reporting having experienced discrimination on a particular item (with responses choices of no or yes) were then asked how often this occurred (once, two or three times, and four or more times). The modified version below combines these response options.

Have you ever experienced discrimination, been prevented from doing something, or been hassled or made to feel inferior in any of the following situations because of your race/ethnicity, or color?

Race/Ethnicity questions
	1 No	2 Yes, once	3 Yes, two or three times	4 Yes, four or more times
At school?
Getting hired or getting a job?
At work?
Getting housing?
Getting medical care?
Getting service at a store or restaurant?
Getting credit, bank loans, or a mortgage?
On the street or in a public setting?
From the police or in the courts?

Additional Measurement Considerations

The EDS has become the subject of further study, because some researchers claim the scale should be coded, or weighted, to account for various groups having different cultural conceptions and reactions to discrimination or to more accurately reflect the impact of chronic discrimination.⁸⁶ Michaels et al. investigate a novel, chronicity-based coding and a more conventional, frequency-based coding and describe how different scoring methods affect exposure classification.⁸⁹

Definition

Experiences of direct racism (e.g., racial micro-stressors, harassment, discrimination) or vicarious racism (e.g., witnessing or hearing about the experiences of racism of others in one’s racial/ethnic group).

Data Availability

Individual

Data Source(s)

Example Survey Instrument

The following survey is available to measure aspects of racial trauma:

Race-Related Events Scale (RES)

This scale captures exposure to stressful and potentially traumatizing experiences of racism. The full scale, which can be found in a Waelde et al. paper, is a 22-item checklist that has been validated for measuring direct and vicarious experience of trauma. The total RES score is the number of race-related events that were endorsed.⁹⁰

Please circle “YES” if this has ever happened to you because of your race or ethnicity or “NO” if it has not.

Race/Ethnicity table
Have any of these events happened to you?
1. Treated rudely or coldly because of my race or ethnicity	Yes	No
2. Ignored because of my race or ethnicity	Yes	No
3. Treated unfairly by teacher or boss because of my race or ethnicity	Yes	No
4. Insulted or called an insulting name because of my race or ethnicity	Yes	No
5. Told to leave a place and not come back because of my race or ethnicity	Yes	No
6. Followed by someone because of my race or ethnicity	Yes	No
7. Harassed by police or security guards because of my race or ethnicity	Yes	No
8. Verbal conflict with someone because of my race or ethnicity	Yes	No
9. Physical fight with someone because of my race or ethnicity	Yes	No
10. Someone hurt my family member because of his/her race or ethnicity	Yes	No
11. Someone threw something at me because of my race or ethnicity	Yes	No
12. Someone pushed or shoved me because of my race or ethnicity	Yes	No
13. Someone stole something from me because of my race or ethnicity	Yes	No
14. Someone chased me because of my race or ethnicity	Yes	No
15. Someone beat me or hurt me because of my race or ethnicity	Yes	No
16. Threatened with a knife, gun or other weapon because of my race or ethnicity	Yes	No
17. Someone threatened to kill me because of my race or ethnicity	Yes	No
18. Heard about someone (who is the same race or ethnicity as me) getting injured or killed because of their race or ethnicity	Yes	No
19. Saw someone (who is the same race or ethnicity as me) get treated in a racist or prejudiced way	Yes	No
20. Saw someone (who is the same race or ethnicity as me) almost get seriously injured or killed because of their race or ethnicity	Yes	No
21. Saw someone (who is the same race or ethnicity as me) seriously injured because of their race or ethnicity	Yes	No
22. Saw someone (who is the same race or ethnicity as me) get killed because of their race or ethnicity	Yes	No

Additional Measurement Considerations

These questionnaires can be adapted to measure specific experiences during specific developmental periods during the life course, such as childhood, adolescence, and adulthood.⁹¹

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Redlining

Why is this indicator relevant?

Historical redlining was the practice of systematically designating areas with higher numbers of Black/African American persons, immigrants, and working-class residents as “hazardous.”⁹² This practice was enacted by the Federal Housing Administration in the 1930s and led to institutionalized discriminatory lending practices that denied mortgages in diverse and working-class neighborhoods.⁹² Ultimately, historical redlining resulted in disinvestment, poverty concentration, White flight, and further racial residential segregation. Since its inception, the definition of redlining has evolved to encapsulate discriminatory housing practices more broadly. The contemporary definition of redlining refers to the systematic denial of services to residents of certain neighborhoods or communities associated with a certain racial/ethnic group.⁹³

Although the historical practice of redlining was abolished in 1968, communities in historically redlined areas are still socioeconomically disadvantaged and more likely to have a higher concentration of Blacks/African American residents. Historical redlining has also had a measurable impact on health outcomes. Residence in historically redlined areas is associated with worse physical and mental health, as well as higher prevalence of adverse outcomes after inpatient hospitalization, post-operative mortality, pre-term births, gunshot-related injuries, asthma, heat-related illness (i.e., urban heat island effect), and chronic conditions.⁹⁴

According to ecosocial theory, which describes how multiple levels of influence impact the distribution of disease in populations, redlining may drive disparities in CVD risk and outcomes.^95,96,97 Social, environmental, economic, and biologic factors interact to affect physiological, metabolic, and cardiovascular systems.⁹⁸ Indeed, a recent study finds that Black/African American adults residing in historically redlined areas are more likely to have lower cardiovascular health scores across seven CVD risk factors.⁹⁹

The contemporary definition of redlining refers to the systematic denial of services to residents of certain neighborhoods or communities associated with a certain racial/ethnic group. This indicator can be assessed by the following measures. Click on each measure to learn more:

Measure 1: City and County Minimum Wage
Definition	A collection of digital maps showing areas in urban centers that were redlined, developed by the HOLC from 1935 to 1940, covering approximately 250 cities. The maps include color-coded areas based on grades assigned to them by HOLC officers. Grades were assigned based on input from mortgage lenders, developers, and real estate appraisers and were used as a measure of creditworthiness and risk on neighborhood and metropolitan levels. Area grades range from A to D, with A denoting “Excellent,” B denoting “Still Desirable,” C denoting “Definitely Declining,” and D denoting “Hazardous.” The digital maps are publicly available to help the public understand the effects of federal housing policy and local implementation in their own communities.
Data Availability	Neighborhood Subgroups: Race/ethnicity, household type, age, educational attainment
Data Source(s)	Mapping Inequality Website: The Mapping Inequality: Redlining in New Deal America project is a collaboration among the University of Richmond, the University of Maryland, Virginia Tech, and Johns Hopkins University. The Mapping Inequality website organizes HOLC files by state and city. Maps can be downloaded as scanned PDFs, georectified images, and shapefiles. Users can also download scanned images of the original written descriptions of risk areas within a city. 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). The PolicyMap dataset contains HOLC data (1935–1940) for 148 urban regions in 28 states. Users can access this measure under Lending > Historic Lending Boundaries > Home Owner’s Loan Corporation Risk Maps (1935–1940).
Additional Measurement Considerations	The following steps provide guidance for calculating redlining using HOLC maps. For additional details, please review Mujahid et al.’s article.⁹⁸ Upload data into ArcGIS Download redlining map shapefiles for cities of interest here: https://dsl.richmond.edu/panorama/redlining/ Merge if downloading multiple cities Download the census-tract boundary shapefile Load all HOLC shapefiles and census-tract boundaries into ArcGIS and ensure that the coordinate systems of both types of shapefiles match Note: The remaining steps require the purchase of an ArcGIS Pro license. Calculate the overlap between census tract and HOLC areas Launch the Intersect tool from Toolboxes > System Toolboxes > Analysis Tools > Statistics toolset > Tabulate Intersection (Analysis) Follow this guide to calculate the overlapping land area: https://pro.arcgis.com/en/pro-app/latest/tool-reference/analysis/tabulate-intersection.htm Input: Zone features: HOLC maps Class features: Census tracts Calculate the HOLC scores of census tracts Export the table from ArcGIS to do calculations in R (can also be done in ArcGIS if you code in Python) Group by census tract IDs, then calculate a weighted HOLC score based on the percentage of the census tract that is in a specific HOLC score (e.g., A = 1, B = 2, C = 3, D = 4) Recommended to also keep a variable to keep track of how many distinct HOLC areas are covering each census tract if needed for sensitivity analysis (i.e., how many tracts were covered by more than one HOLC area) Results The locator will output a table with unique census-tract identifiers and a weighted HOLC score for each tract.

Measure 1: City and County Minimum Wage
Definition	The percentage of owner-occupied one- to four-unit home mortgages with a reported rate spread (i.e., where the annual percentage rate [APR] is more than 1.5 percentage points higher than the average prime offer rate). This category was previously referred to as “subprime” loans.
Data Availability	Unknown (access require subscription) Subgroups: Race/ethnicity
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). The PolicyMap dataset contains high-cost mortgage loan data from the Federal Financial Institutions Examination Council (FFIEC) for 2002–2009 Q3 and 2010 or later. However, these data are available only to paid subscribers.

Measure 1: City and County Minimum Wage
Definition	The percentage of owner-occupied one- to four-unit home mortgage loans that were denied.
Data Availability	Unknown (access require subscription) Subgroups: Race/ethnicity
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). The PolicyMap dataset contains mortgage denial data from the Federal Financial Institutions Examination Council (FFIEC); however, these data are available only to paid subscribers.

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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: Los Angeles County Department of Public Health
Focus Area: Racism
Measure: HOLC Risk Maps

This content presents an example of how one health department, the Los Angeles County Department of Public Health (LACDPH), used data on the health equity indicator redlining. Redlining is the systematic denial of services to residents of certain neighborhoods or communities based on race or ethnicity.¹⁰⁰Residence in historically redlined areas is associated with worse physical and mental health as well as higher adverse outcomes after inpatient hospitalization, postoperative mortality, and prevalence of preterm births, gunshot-related injuries, asthma, heat-related illness (i.e., urban heat island effect), and other chronic conditions.¹⁰¹ LACDPH used Home Owners’ Loan Corporation (HOLC) risk maps to measure redlining. The HOLC risk maps are a collection of digital maps showing areas in urban centers that were redlined and can be used to understand the effects of historical discriminatory federal housing policy on local communities. For more information on the HOLC risk maps measure, please see the redlining indicator in the Racism Indicator Profile.

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

Health Equity in Action: Los Angeles County Department of Public Health

LACDPH serves more than 10 million residents, of which 48.4% are Hispanic/Latino, 28.3% are White, 14.4% are Asian American, and 8.5% are Black/African American. LACDPH is committed to developing and implementing policies and programs that support the department’s and the county government’s priority to advance health equity. For example, LACDPH’s Center for Health Equity assists other programs within the department to address many of these disparities. As part of the effort to advance this goal, LACDPH agreed to work with CDC to pilot selected health equity indicators it was not previously collecting to determine applicability to the department’s overall work in this area of public health practice.

Overview of LACDPH's data collection methods

Indicator: Redlining

Measure: HOLC Risk Maps

Data Collection Method: Secondary data analysis

Data Source: Home Owners’ Loan Corporation (HOLC) maps

Data Availability: Census tract

Application/Use: Spatial analysis of redlining data overlaid with chronic disease prevalence data at the census tract level.

Data Collection and Analysis Methods. As part of the pilot, LACDPH tested the collection and use of redlining data using HOLC maps. This data source is publicly available through the University of Richmond’s Digital Scholarship Lab and provides geocoded redlining data from the 1930s through the 1940s. Following the recommended steps provided in the Racism Health Equity Indicator Profile, the LACDPH team uploaded HOLC shapefiles into ArcGIS and conducted a spatial analysis to visualize patterns of redlining across Los Angeles County. The team created two color coded maps (Figure 1 and Figure 2) displaying the 2014 chronic disease prevalence and the 2014 HOLC risk scores for each census tract. The 2014 chronic disease prevalence data was derived from the CDC 500 Cities Project, which uses small area estimation methods to obtain chronic disease measures at the city-/census-tract level for the 500 largest cities in the United States.

Findings. Findings from the analysis suggest a lasting effect of redlining on chronic disease prevalence in Los Angeles County. The LACDPH team, however, noted that the present analysis does not adequately account for the various confounding factors contributing to health inequities. Additional analyses are needed to better understand the confounding factors and to draw inferences.

Facilitators. Given LACDPH’s ongoing focus on health equity work, there was strong senior leadership support to explore new indicators of health disparities and social determinants of health. This support, combined with the availability of CDC support, allowed staff to dedicate time to this analysis. Staff members’ expertise in ArcGIS enabled the team to efficiently use the shapefiles to identify redlined areas and develop visual depictions of redlining patterns.

Challenges and Limitations. While the HOLC map shapefiles are accessible and free to download using ArcGIS, access and use of additional data elements and mapping features (e.g., HOLC score, import of census tract boundaries) requires an ArcGIS Pro license. LACDPH was able to create choropleth maps using ArcGIS despite this barrier. Another limitation of the HOLC maps is that they are available only for a limited number of cities and therefore cannot be used to map patterns across all geographies, especially rural areas.

Future Plans. LACDPH noted that redlining as an indicator is helpful for understanding the historical context that shapes current patterns of differential access to resources across neighborhoods. The team plans to share findings from the analysis of redlining and chronic disease rates with key partners, including local health agencies, to support the geographic prioritization of health and social services interventions as part of their ongoing efforts to reduce health inequities.

Figure 1. Map of HOLC Risk Scores by Hypertension Prevalence

A colored map of the prevalence of hypertension and Home Owner’s Loan Corporation grades by census tracts in Los Angeles County. The boundaries represent census tracts and the color coded shading represents the relationship between 1939 Home Owner’s Loan Corporation grades and 2014

Image description: A colored map of the prevalence of hypertension and Home Owner’s Loan Corporation grades by census tracts in Los Angeles County. The boundaries represent census tracts and the color coded shading represents the relationship between 1939 Home Owner’s Loan Corporation grades and 2014 hypertension prevalence, represented in a two-way contingency table at the bottom of the map. Home Owner’s Loan Corporation grades are based on neighborhoods’ quality of housing, history of sale and rent values, and racial and ethnic identity and class of residents. The color green represents the highest grade of A or “Best”, representing low risk for banks and lenders; blue represents “Still Desirable” or B grade; orange represents “Definitely Hazardous” or C grade; and purple represents “Hazardous” or D grade. Yellow indicates missing Home Owner’s Loan Corporation data. Hypertension prevalence was broken into terciles of low, medium, and high. Darker shades of green, blue, orange, and purple indicate a higher percentage of adults ages 18 years or older who have hypertension. Grey indicates missing hypertension and Home Owner’s Loan Corporation data.

Figure 2. Map of HOLC Risk scores by Diabetes Prevalence

Image description: A colored map of the type 2 diabetes prevalence and Home Owner’s Loan Corporation grades by census tracts in Los Angeles County. The boundaries represent census tracts and the color-coded shading represents the relationship between 1939 Home Owner’s Loan Corporation grades and 2014 type 2 diabetes prevalence, represented in a two-way contingency table at the bottom of the map. Home Owner’s Loan Corporation grades are based on neighborhoods’ quality of housing, history of sale and rent values, and racial and ethnic identity and class of residents The color green represents the highest grade of A or “Best”, representing low risk for banks and lenders; blue represents “Still Desirable” or B grade; orange represents “Definitely Hazardous” or C grade; and purple represents “Hazardous” or D grade. Yellow indicates missing Home Owner’s Loan Corporation data. Diabetes prevalence was broken into terciles of low, medium, and high. Darker shades of green, blue, orange, and purple indicate a higher percentage of adults ages 18 years or older who have type 2 diabetes. Grey indicates missing type 2 diabetes and Home Owner’s Loan Corporation data.

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Field Note

These field notes showcase other examples of health equity measurement and evaluation at health care organizations, such as health departments. It is important to note that the examples in the field notes are not derived from the HEI Pilot Study and therefore may reflect slightly different uses or definitions of HEIs. In some cases, the HEIs presented in the field notes may not perfectly align with the measurement definition and guidance provided in the HEI Profiles.

Health Department: Massachusetts Department of Public Health (MDPH)
Focus Area: Racism
Measure: Index of Concentration at the Extremes

This content presents an example of how one health department, the Massachusetts Department of Public Health (MDPH), used data on the health equity indicator racial income gap. Racial income gap is the difference in median income between racial and ethnic groups.¹⁰² Racial income inequality may affect health such as cardiovascular disease (CVD) risk through several pathways, including structural and institutional drivers such as social policies, as well as through community and individual drivers, such as environmental, occupational, and neighborhood exposures affecting psychosocial, metabolic, and behavioral risk factors for CVD.^{103,104,105,106} MDPH used the Index of Concentration at the Extremes (ICE) to measure racial income gap. ICE is a measure of economic and/or social (racial/ethnic) spatial polarization that can be used at a highly granular level (i.e., census tract) up to the neighborhood or city/town level.^107,108,109 For more information on the ICE measure, please see the Racial Income Gap indicator in the Racism Indicator Profile.

Representatives from MDPH participated in a subject matter expert panel to provide input for the development of CDC’s Health Equity Indicators for CVD Toolkit. As part of this process, MDPH shared an example of how they accessed and analyzed data on racial income gap, including data sources, challenges, and facilitators. MDPH’s approach and lessons learned are summarized below.

Health Equity in Action: Massachusetts Department of Public Health (MDPH)

MDPH’s CVD work employs a broad health equity framework, addressing a range of health inequities and adverse social determinants of health (SDOH). This includes populations affected by institutional and structural factors, such as historic and present-day racism and poverty; populations with disabilities; and populations with sexual orientation and gender identity (SOGI)–based inequities. MDPH participates in multiple national CDC-supported CVD prevention and management programs, including the Paul Coverdell National Acute Stroke Program (Coverdell Program), Improving the Health of Americans through Prevention and Management of Diabetes and Heart Disease and Stroke cooperative agreement (1815 Cooperative Agreement), and Innovative State and Local Public Health Strategies to Prevent and Manage Diabetes, Heart Disease, and Stroke (1817 Cooperative Agreement). The Coverdell Program focuses on improving the quality of stroke care by collaborating with providers across the stroke care continuum. The DP18-1815 and DP18-1817 Cooperative Agreements are focused on supporting the use of clinical quality measures, team-based care, and clinical-community linkages to address CVD prevention and management. The DP18-1815 Cooperative Agreement applies evidence-based interventions, while the DP18-1817 Cooperative Agreement applies innovative approaches. Through these programs, MDPH works with health care organizations and community-based organizations to meet the clinical and social needs of communities that are medically underserved. MDPH is also engaged in statewide efforts to develop additional data infrastructure, disseminate materials, and conduct surveillance projects with an equity lens.

Overview of MDPH's data collection methods

Indicator: Racial Income Gap

Measure: Index of Concentration at the Extremes (ICE)

Data Collection Method: Secondary data analysis

Data Source: American Community Survey (ACS)

Data Availability: Zip code

Application/Use: Comparative analysis of median household income by race/ethnicity and by ZIP code.

Data Collection and Analysis Methods. MDPH calculated ICE from the American Community Survey (ACS) to elucidate racial income inequality at the ZIP code level throughout Massachusetts. The MDPH team pulled household income data from the ACS, using income thresholds of $25,000 and $120,000. The team then compared populations with the greatest deprivation (non-Hispanic Black, Hispanic, and American Indian/Alaska Native with household income below $25,000 per year) and the population with the highest privilege (non-Hispanic White with household income of at least $120,000 per year). The ICE algorithm that MDPH utilized was modeled after a previous publication assessing racial income inequality in Boston, Massachusetts.^110,111

ICE served as an important foundation, from which MDPH proceeded to develop new composite measures to assess broader SDOH and a more comprehensive assessment of the intersection of multiple forms of oppression. Using principal component analysis (PCA) of ACS data, MDPH created a measure that not only integrated inputs used in the calculation of ICE but also included additional race and ethnicity, poverty, public assistance, and health insurance data. MDPH derived from the PCA a composite measure, where a large score indicates a large percentage of Hispanic and non-Hispanic Black populations who predominantly work in the entertainment, recreation, accommodation, and food service industries and who experience disproportionate levels of unemployment and poverty. This measure represents the intersection of multiple forms of oppression and inequity and has subsequently been used to inform MDPH’s CVD efforts.

Findings. The identification of granular geographies in Boston served as a powerful example of how MDPH leveraged ICE and the PCA to identify communities in Massachusetts most affected by racism and other forms of oppression. A history of redlining and present-day underinvestment in communities of color in Boston shows striking contrasts between neighborhoods, even those directly adjacent to one another. MDPH’s former approaches used health outcome data aggregated across Boston to prioritize intervention areas. The results of such aggregated analyses suggested that Boston ranked in the middle percentiles on various metrics, rather than highlighting neighborhoods of greatest need. Using ICE and the PCA permitted MDPH to make more robust, data-driven decisions in the selection of priority communities. These measures have also informed the implementation of CVD cooperative agreements. MDPH leveraged ICE and the PCA to identify five communities in Massachusetts to implement the DP18-1817 Cooperative Agreement and the Coverdell Program. Although the outcomes of these cooperative agreements are downstream, ICE and the PCA permitted MDPH to integrate upstream root causes in selection of the communities and design of program interventions.

In addition to helping identify priority areas for program implementation, the ICE and PCA findings have facilitated conversations with MDPH leadership and key partners to promote awareness of health equity. For example, the outbreak of COVID-19 reduced access to ambulatory care and caused a large uptake of telehealth services. MDPH leveraged ICE and the PCA, along with other data sources, to identify communities most at risk of being unable to access telehealth services to communicate with MDPH leadership. These data help to reframe conversations around structural and institutional factors that must be addressed to supplement micro-level patient care efforts and inform broader public health policy.

Facilitators. The free and publicly available input data from the ACS allowed for this data analysis. The MDPH team also had strong support from department leadership and from epidemiologists with years of experience in biostatistics and machine learning, which facilitated this analysis and the team’s broader health equity work.

Challenges and Limitations. Even with leadership support, MDPH, as a state agency, faces organizational constraints that limit its ability to make policy or macro-level changes to address the identified health equity challenges. Funding limitations are also a significant barrier to their health equity work.

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