Socioeconomic Factors

Indicator Profile

Socioeconomic status refers to the absolute or relative levels of economic resources, power, and prestige closely associated with wealth of an individual, community, or country.¹ Socioeconomic status is a multidimensional construct comprising multiple factors, such as income, education, employment status, and other factors.²

Low socioeconomic status is associated with higher risk of developing and dying from cardiovascular disease (CVD).^3,4,5,6 Specifically, the American Heart Association notes that income level, educational attainment, and employment status at the individual and neighborhood level are consistently associated with CVD in high-income countries.⁷ Socioeconomic factors can affect health status directly at the individual level and can also influence broader household, neighborhood, or community-level characteristics, which can then affect health.

Socioeconomic factors affect one’s ability to engage in health activities, afford medical care and housing, and manage stress. For example, employment provides income, which enables access to housing, education, childcare, food, medical care, and other needs. At the community level, lower-income neighborhoods are less likely to have access to high quality health care.

Socioeconomic factors can also interact with or confound relationships between other variables and health. For example, the combined effects of socioeconomic status and race/ethnicity or sex can influence health differently across different groups.

Table of Contents - Socioeconomic Factors

Indicators

This document provides guidance for measuring five indicators related to socioeconomic factors that are associated with differential risks of developing CVD. The five socioeconomic indicators are measured at different levels of analysis, including city, county, and state.

Education

Why is this indicator relevant?

Education is strongly associated with life expectancy and morbidity.⁸ Research has shown that by the age of 25, a college graduate is expected to live a decade longer than a high school dropout.⁹Many studies, including a meta-analysis of hypertension research, have observed that lower levels of education are associated with a greater risk of CVD than higher levels of education are.^10,11,12For instance, one study showed that for men who completed graduate school, the likelihood of developing heart disease was 42%, compared with 59% for men who completed grade school only (the difference was statistically significant).¹³ Among women, the likelihood of developing heart disease was 28% for those who completed graduate studies, compared with 50% for women completing grade school only.¹⁴ Similarly, low educational attainment is an independent predictor of adverse outcomes for patients with coronary artery disease.¹⁵ Moreover, persistent racial disparities in educational attainment contribute to racial differences in heart disease mortality.¹⁶

Education plays an important role in health through its influence on multiple socioeconomic factors, such as employment, income, and other economic opportunities. Individuals with lower levels of educational attainment are more likely to lack sociopolitical power and economic resources, leading to adverse occupational, residential, and recreational conditions associated with negative health consequences. These adverse conditions lead to differential exposures to stressors (e.g., unemployment, crime, violence) and fewer resources (e.g., recreation, physical activities) to cope with the accumulation of stressors that contribute to a greater risk of hypertension.^17,18

School policies and exclusionary discipline practices, such as suspensions and expulsions, are applied unfairly by educators and have been shown to have a disproportionately negative impact on Black/African American children’s academic achievement.¹⁹ Exclusionary school discipline practices hinder educational attainment and exacerbate socioeconomic and health inequities. In addition to hindering academic achievement, expulsions and suspensions are correlated with substance use and worse mental health and social connectivity, which are risk factors for adverse health behaviors among adolescents, such as early sexual initiation; alcohol, tobacco, and drug use; violent behaviors; and gang involvement.^20,21 These adverse health behaviors in turn increase the risk of adverse health outcomes, including CVD.

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

Measure 1: City and County Minimum Wage
Definition	Proportion of the population age 25 and older who have less than a high school diploma, equivalent (i.e., GED), or some college.
Data Availability	Block group, census tract, ZIP code, city, county subdivision, county, congressional district, metro division, metro area, state Subgroups: Race/ethnicity, household income, gender, age
Data Source(s)	American Community Survey (ACS) 5-Year Files: The 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 and download the ACS 5-year files from the U.S. Census Bureau’s main data platform at the link below. Users can search “Educational Attainment” at the link below to access data on this measure. Users can create educational attainment estimates by various characteristics (e.g., income, gender, race/ethnicity, age) by downloading the ACS 5-Year Files. Data are available from 2009 to 2020 in various formats, including Excel, CSV, and ZIP. 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 U.S. Census Bureau ACS data on the percentage of population age 25 and older without a high school diploma. Users can access this measure under Social and Economic Factors > Economic Resources – Annual > Education – Less Than High School. 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. 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 empowerment to improve health equity. CHR&R uses data from the ACS to provide county-level estimates of the percentage of adults age 25 and over with a high school diploma or equivalent. Data can be stratified by age and race/ethnicity and can be accessed for many communities at the census tract or census block level. Users can access this measure under Ranked Measures > Health Factors > Social & Economic Factors > High School Completion. Data can be downloaded as an Excel workbook; years of data availability vary by state. Health Opportunity and Equity (HOPE) Initiative: The HOPE Initiative website provides state-level data on the proportion of adults age 25 and older who have attained at least some college education after graduating from high school. Data on the Post-Secondary Education indicator are available for 50 states via the web interface. Data are available from 2018 to 2020 and can be analyzed by race/ethnicity. Data files can be downloaded to Excel from the Resources section of the website. This measure can be found under the “Social & Economic Factors” domain and “Post-Secondary Education” indicator. 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 educational attainment data from the U.S. Census. Education data can be stratified by race/ethnicity. However, much of the data are unavailable at the county level and below. Users can access data on this measure under Education > Educational Attainment. Data years are dependent on the measures under Education Attainment (e.g., less than ninth grade, some high school, high school diploma). Example Survey Instrument The following survey questions are available for assessing educational attainment: American Community Survey (ACS) The U.S. Census Bureau’s ACS asks questions about the highest level of education a person has completed to create statistics about education. To view ACS survey questions on educational attainment, visit the U.S. Census Bureau’s website below. Link: https://www.census.gov/acs/www/about/why-we-ask-each-question/education/

Measure 1: City and County Minimum Wage
Definition	Percentage of public-school students with or without disabilities receiving one or more out-of-school suspensions during the school year.
Data Availability	School, school district, state Subgroups: Race/ethnicity, gender, disability status, English language learners
Data Source(s)	Civil Rights Data Collection (CRDC): The Civil Rights Data Collection (CRDC) is a biennial (i.e., every other school year) survey of public schools that has been required by Office of Civil Rights (OCR) since 1968. The CRDC collects data on leading civil rights indicators related to access and barriers to educational opportunity from preschool through 12th grade. Users can access this measure by selecting measures related to “out-of-school suspensions,” which are disaggregated by race, sex, disability status, and English language learner status via the data analysis tools provided on the site. “Comparison Graphs and Data” allow users to select up to 12 schools, districts, or states; “Detailed Graphs and Data” allow users to select up to two schools, districts, or states; and “Outcome Rate Calculator” allows users manually select up to 250 schools or districts to view suspension data. CSV files containing the entire CRDC data set for the user’s year of choice can be downloaded in a ZIP file for years between 2009 and 2018.

Measure 1: City and County Minimum Wage
Definition	Percentage of public school students with or without disabilities receiving expulsions with or without educational services.
Data Availability	School, school district, state Subgroups: Race/ethnicity, gender, disability status, English language learners
Data Source(s)	Civil Rights Data Collection (CRDC): The Civil Rights Data Collection (CRDC) is a biennial (i.e., every other school year) survey of public schools required by the Office of Civil Rights (OCR) since 1968. Users can access this measure by selecting “Expulsions (with and without educational services combined),” with data disaggregated by race, sex, disability status, and English language learner status, via the data analysis tools provided on the site. “Comparison Graphs and Data” allow users to select up to 12 schools, districts, or states; “Detailed Graphs and Data” users to select up to two schools, districts, or states; and “Outcome Rate Calculator” allows users to manually select up to 250 schools or districts to view school expulsion data. CSV files containing the entire CRDC data set for the user’s year of choice can be downloaded in a ZIP file for years between 2009 and 2018.

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Employment Status

Why is this indicator relevant?

Employment status (whether an individual is working to earn wages) is consistently identified as an indicator of socioeconomic status strongly associated with health outcomes.²² Employment status affects health through both physical and psychosocial pathways. Employees may be exposed to hazardous physical, chemical, or biological agents from the occupational setting. Unstable employment can lead to loss of compensation and employee benefits (e.g., health insurance), creating psychosocial stress. In the United States, health care is accessed through predominantly employer-sponsored health insurance plans.²³ Loss of employment results in loss of health care insurance coverage. The short-term unemployed tend to experience the greatest barriers to health care access, as they may not be able to take advantage of public benefits.²⁴ Even in cases where individuals may qualify for public insurance assistance through the Consolidated Omnibus Budget Reconciliation Act (COBRA) or other public programs, the copayments and deductibles are often too costly for those with reduced or no steady income. The long-term unemployed and those not able to work may be eligible for Medicaid. Those who are self-employed also experience barriers to health care, as individual insurance plans are often not as comprehensive as employer-sponsored plans.²⁵ Barriers to health care access due to loss of employer-sponsored health coverage are associated with reduced health care utilization and unfavorable health outcomes, including CVD.^26,27

Beyond the impact of employment status on access to health insurance, employment type may directly affect the risk for heart disease. In a recent meta-analysis, shift work, including rotational and night shift work, was associated with a 26% increased risk of coronary heart disease (CHD) morbidity and an approximately 20% increased risk of CHD and CVD mortality.²⁸Increased risk develops after 5 years of shift work and increases 7.1% for every 5 additional years of shift work. The association seems to be strongest for rotating shift schedules (i.e., people work a mix of irregular day and night hours) rather than fixed day- or night-only shifts. Causal factors include disruptions to circadian rhythms and poor health behaviors associated with shift work.²⁹

Measure 1: City and County Minimum Wage
Definition	Proportion of the population age 16 and older in the United States labor force that is employed or unemployed and seeking work.
Data Availability	Block group, census tract, ZIP code, city, county subdivision, county, congressional district, metro division, metro area, state Subgroups: Race/ethnicity, household income, gender, age, educational attainment
Data Source(s)	American Community Survey (ACS) 5-Year 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 and download the ACS 5-year files from the U.S. Census Bureau’s main data platform at the link below. Users who want to create unemployment estimates by other characteristics (e.g., income, gender, age) or other geographic levels can download the ACS 5-year files. Data on the employment status of people age 16 and over are derived from multiple questions in the ACS. Users can search “Employment Status” at the link below to access this measure. Data are available from 2009 to 2020 in various formats, including Excel, CSV, and ZIP and can be viewed online for 2010 to 2020. America’s Health Rankings (AHR): Produced by the United Health Foundation, AHR evaluates a comprehensive set of health, environmental, and socioeconomic data. The AHR website provides state-level data from the U.S. Census Bureau’s ACS on the percentage of civilian population ages 16–64 who are unemployed. Estimates are provided by age, gender, and race/ethnicity for the most recent data. Users can access this measure under the Social and Economic Factors > Economic Resources – Annual > Economic Hardship Index > Unemployment. 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. 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 empowerment to improve health equity. CHR&R uses the Local Area Unemployment Statistics (LAUS) program of the U.S. Bureau of Labor Statistics to provide county-level estimates of the percentage of population age 16 and older unemployed but seeking work. Users can access this measure under Ranked Measures > Health Factors > Social & Economic Factors > Unemployment. Data are downloadable as an Excel workbook; years of data availability vary by state. Health Equity and Opportunity (HOPE) Initiative: The HOPE Initiative website provides state-level data on employment. Data are available by race/ethnicity for individual states via the web interface. In addition, data on all states are available by race/ethnicity and educational attainment via a downloadable Excel workbook from the Resources section of the website. Data are available for 50 states and Washington, D.C., via the web interface. In addition, data on all states are available for the years 2018–2020 via a downloadable Excel workbook from the Resources section of the website. This measure can be accessed under the “Social & Economic Factors” domain and the “Employment” indicator. National Equity Atlas: The National Equity Atlas provides the unemployment data for the working-age population (2–-64). The National Equity Atlas calculates the unemployment rate by race/ethnicity, education, gender, nativity, and ancestry for each year and geography. The unemployment rate is the number of people who are out of work divided by the number who are in the labor force, defined as working or actively seeking employment (over the previous 4 weeks). Data are available for 1990–2019, and data for 2010-2019 represent 5-year averages (e.g., 2015–2019). Users can access this measure from the “Economic Vitality” indicator group and the “Unemployment” indicator and can download data as an Excel workbook. 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 unemployment data from the U.S. Bureau of Labor Statistics (BLS). Users can access this measure under Economy > Employment and Unemployment and can drill down further within this measure. Data for this measure are available from 2000 to 2022. Example Survey Instrument The following survey questions are available for assessing employment status: American Community Survey (ACS) The U.S. Census Bureau’s ACS asks about whether a person worked last week and, if the answer is no, why they were not working, whether they plan to return to work, and when they last worked. To view ACS survey questions on employment status, visit the U.S. Census Bureau’s website. Link: https://www.census.gov/acs/www/about/why-we-ask-each-question/employment/

Measure 1: City and County Minimum Wage
Definition	Full-time wage and salary workers working a non-regular daytime schedule (including evening, night, rotating, irregular, and split shifts).
Data Availability	National Subgroups: Gender, race/ethnicity, age, educational attainment, earnings
Data Source(s)	U.S. Bureau of Labor Statistics: The U.S. Bureau of Labor Statistics provides national estimates of the total number and percentage of individuals working a non-daytime schedule by shift. Data are available by age, gender, race/ethnicity, educational attainment, occupation type, and earnings. The latest data release is for 2017–2018; however, data for 1997, 2001, and 2004 can be accessed in the Archives under “Workers on Flexible and Shift Schedules.” Example Survey Instrument The following survey measure is available to assess shift work: American Time Use Survey (ATUS) Leave and Job Flexibilities Module The U.S. Bureau of Labor Statistics (BLS) ATUS measures the amount of time people spend doing various activities, such as paid work, childcare, volunteering, and socializing. The 2017–2018 ATUS Leave and Job Flexibilities Module questionnaire asks respondents about access to paid and unpaid leave from their jobs, job flexibility, and work schedules. The module covers five areas: access to paid leave, access to unpaid leave, leave taken in last week, job flexibility and work schedules, and non-use of leave. The entire questionnaire is available on the BLS website. To access questions for this measure, search for “job flexibility and work schedules.” Link: https://www.bls.gov/tus/questionnaires/lvmquestionnaire1718.pdf
Additional Measurement Considerations	Data for the percent working alternative shifts are available only at the national level. National estimates can be used as a starting point to understand shift work trends but may not provide meaningful insights for health care organizations that want to learn about their specific patient population.

Measure 1: City and County Minimum Wage
Definition	Any person working an 8-hour or more shift past midnight.
Data Availability	Public Use Microdata Areas (PUMAs), state, region, division Subgroups: Gender, race/ethnicity, age, educational attainment, income Public Use Microdata Areas (PUMAs) are geographies defined by the U.S. Census for providing statistical and demographic information. PUMAs contain at least 100,000 people, do not overlap, and are contained within a single state.
Data Source(s)	American Community Survey (ACS) Public Use Microdata Sample (PUMS): The ACS PUMS files are a set of untabulated records about individual people or housing units. The U.S. Census Bureau’s ACS PUMS files enable users to create custom estimates and tables that are not available through ACS pre-tabulated data products. The ACS asks a question about time of arrival to work. The question is usually analyzed with another survey item asking about the time the respondent departs for work, to calculate commute time; however, it can also be used alone to identify the proportion of the population working regular evening or nighttime shifts. Unfortunately, there are no questions about schedule regularity in the ACS. ACS PUMS provides 1-year and 5-year estimates on time of arrival at work by race/ethnicity, gender, household income, age, and educational attainment. Users can navigate to “Time of arrival at work – hour and minute” for this measure. Data users can access the ACS PUMS files through the U.S. Census Bureau’s “Accessing PUMS Data” website. Data are available for PUMA, state, region, and division levels for 2005–2020.
Additional Measurement Considerations	The percent working regular evening or nighttime shifts measure uses ACS PUMS data. ACS PUMS data provide information for a Public Use Microdata Area (PUMA), which is a community-level area with no less than 100,000 people. This geographical level has statistical utility but may not have practical application for health care organizations. Users can use a PUMA-to-County crosswalk from the Missouri Census Data Center to generate pseudo county-based statistics from PUMA estimates. PUMS also provides data for state, region, and division levels, but these geographical levels are very large and do not provide granular information. Link: https://mcdc.missouri.edu/geography/PUMAs.html

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Income

Why is this indicator relevant?

The relationship between income and health is well established. Households with incomes below the federal poverty level (annual income thresholds set by the federal government to determine financial eligibility criteria³⁰) have high levels of illness and premature mortality.^31,32,33Individuals with lower incomes lack economic resources, resulting in social disadvantage, poor education, poor working conditions, housing insecurity, and residence in unsafe neighborhoods. These negative environmental and psychosocial factors affect behavioral and physiological pathways that have proximal effects on health, including increased morbidity and mortality.³⁴The United States has experienced a rise in income inequality, with widening racial gaps in wealth.^35,36 For every dollar of wealth that White households have, Asian, Hispanic/Latino, and Black/African American households have 83 cents, 7 cents, and 6 cents, respectively.³⁷ It is estimated that in the United States, the gap in life expectancy between the top 1% of wage earners and the bottom 1% is 14.6 years for men and 10.1 years for women.³⁸Moreover, over the past few decades life expectancy has increased among the wealthiest 20%, while the remaining 80% have not experienced any gains in life expectancy.³⁹

A growing body of evidence points to income-based disparities in CVD.^40,41,42,43 One study found that the richest 20% of study participants had healthier levels of biomarkers for cardiovascular disease, including body mass index, systolic blood pressure, and high-density lipoproteins relative to the poorest 80% of participants.⁴⁴Individuals with lower incomes are more likely to experience adverse psychosocial factors that can induce a physiological stress response, resulting in higher circulating levels of catecholamines, higher cortisol levels, and increased blood pressure, which are all risk factors for CVD.^45,46,47

Individuals with lower incomes lack economic resources, resulting in social disadvantage, poor education, poor working conditions, housing insecurity, and residence in unsafe neighborhoods. This indicator can be assessed by the following measures. Click on each measure to learn more:

Measure 1: City and County Minimum Wage
Definition	An individual’s appraisal of ability to meet current financial needs.
Data Availability	Individual
Data Source(s)	Example Survey Instrument The following survey is recommended to measure perceived financial strain: Financial Strain Index The Financial Strain Index was developed for Welfare, Children, and Families: A Three-City Study, which assessed the well-being of low-income children and families in the post–welfare reform era and explored how families responded to welfare reform in terms of employment, schooling, and income.⁴⁸The Financial Strain Index is a 15-item self-reported measure that assesses the level of difficulty in meeting financial obligations and the availability of financial resources. The full survey from Welfare, Children, and Families: A Three-City Study is available at the University of Michigan Institute for Social Research’s website. To view the questionnaire and coding instructions, users can navigate to the “Data and Documentation” tab, download the “DS15 Main Interview Data, Wave 1” file, then search for “FINANCIAL STRAIN INDEX.” Link: https://icpsr.umich.edu/index.html/web/DSDR/studies/4701

Measure 1: City and County Minimum Wage
Definition	The ICE is 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.⁴⁹ It 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: ICE_i = (Ai – P_i)/T_i 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.⁵⁰ ICE allows for the selection of two groups for comparison and can be used as three separate measures of economic and racial privilege5¹: 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
Data Source(s)	American Community Survey (ACS) 5-Year 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.^52,53,54 Users can download income data from the U.S. Census Bureau’s ACS 5-year files and create a ratio between the 80th and 20th percentiles. To access data on this measure, users can search for “Household Income” or by the variable name “B19001” in the link below. Data on household income 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	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. Download income data from the U.S. Census Bureau’s ACS Five-Year files Household Income In Dollars; Table Name: B19001 Outline the definition of “privileged” and “deprived” group High income: ≥$100K Low income: ≤$25K Calculate the ICE measure using the following formula The ICE for income is calculated as the number of people in high-income households minus the number of people in low-income households in a given area, divided by the total population in the given area.⁵⁵Cut-points for high and low income are 80th vs. 20th percentile. (No. of people in households with income over $100K) – (No. of people in households with income under $25K) / No. of total population with household income data Example Survey Instrument The following survey questions are available for assessing median household income: American Community Survey (ACS) The U.S. Census Bureau’s ACS asks about the funds a person receives from various sources to create statistics about income, assistance, earnings, and poverty status. ACS questions on income are provided at the link below. To calculate household income, the incomes of all related family members who live together are added up, meaning the questionnaire must be completed for all household members. For more information on income survey questions, please visit the U.S. Census Bureau’s website. Link: https://www.census.gov/acs/www/about/why-we-ask-each-question/income/

Measure 1: City and County Minimum Wage
Definition	The median income of households (combined gross income of all household members, defined as a group of people who are 15 years and older living together) in a specified geographic area (household members do not need to be related).
Data Availability	Block group, census tract, ZIP code, city, county subdivision, county, congressional district, metro division, metro area, state Subgroups: Race/ethnicity, gender, age, educational attainment
Data Source(s)	American Community Survey (ACS) 5-Year 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 and download the ACS 5-year files from the U.S. Census Bureau’s main data platform at the link below. Users can search “Median Household Income” at the link below to access data on this measure. Users can create household income estimates by various characteristics (e.g., gender, race/ethnicity, age) by downloading the ACS 5-year files. Data are available from 2005 to 2020 in various formats, including Excel, CSV, and ZIP. 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 empowerment to improve health equity. CHR&R uses data from the U.S. Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) program to provide county-level estimates of median household income. Users can access this measure under Additional Measures > Social & Economic Factors > Median Household Income. Data for this measure is available for download as an Excel workbook; depending on the state, years of data availability vary. 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. In addition, there is an option to view the median family income for single female-headed families with children. Users can access this measure under Incomes & Spending > Income > Household > Median Household Income and drill down further within this measure to look at “All Household,” “By Race or Ethnicity,” or “By Housing Tenure.” PolicyMap uses U.S. Census Bureau data for this measure. Data are available for 2000, 2006–2010, 2011–2015, and 2016–2020. Small Area Income and Poverty Estimates (SAIPE) Program: The U.S. Census Bureau’s SAIPE program provides annual estimates of income and poverty statistics for all school districts, counties, and states. The main objective of this program is to provide estimates of income and poverty for the administration of federal programs and the allocation of federal funds to local jurisdictions. State and local programs also use the income and poverty estimates for distributing funds and managing programs. Data are available for 1989, 1993, and 1995–2020. Beginning with the SAIPE program’s estimates for 2005, ACS data are used in the estimation procedure; prior years used data from the Annual Social and Economic Supplements of the Current Population Survey.

Measure 1: City and County Minimum Wage
Definition	The median income of families (defined as the combined gross income of people in a household who are related by birth, marriage, or adoption) in a specified geographic area.
Data Availability	Block group, census tract, ZIP code, city, county subdivision, county, congressional district, metro division, metro area, state Subgroups: Race/ethnicity, household income, gender, age, educational attainment
Data Source(s)	Decennial Survey of Population and Housing: The U.S. Census Bureau’s Decennial Survey of Population and Housing is an ongoing survey that is collected every 10 years. Users can search “Median Family Income” to access data on this measure and can create household income estimates by various characteristics (e.g., gender, race/ethnicity, age) by downloading the files. Data from 2000, 2010, and 2020 are available in various formats, including Excel, CSV, and ZIP. American Community Survey (ACS) 5-Year Files: The 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 and download the ACS 5-year files from the U.S. Census Bureau’s main data platform at the link below. Users can search “Median Family Income” at the link below to access data on this measure and can create family income estimates by various characteristics (e.g., gender, race/ethnicity, age) by downloading the ACS 5-year files. Data from 2005–2020 are available in various formats, including Excel, CSV, and ZIP. 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). Users can view five categories/ranges of median family income. In addition, there is an option to view the median family income for single female-headed families with children. Users can access this measure under Incomes & Spending > Income > Family > Median Family Income and drill down further within this measure. Measures using Census data are available for 2000, 2006–2010, 2011–2015, and 2016–2020.

Measure 1: City and County Minimum Wage
Definition	Percentage of adults whose household income is higher than 250% of the federal poverty level.
Data Availability	Block group, census tract, ZIP code, city, county subdivision, county, congressional district, metro division, metro area, state Subgroups: Race/ethnicity, gender, age, educational attainment
Data Source(s)	American Community Survey (ACS) 5-Year 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 and download the ACS 5-year files from the U.S. Census Bureau’s main data platform at the link below. Users can search “Poverty” at the link below to access data on this measure and can create poverty estimates by various characteristics (e.g., gender, race/ethnicity, age) by downloading the ACS 5-year files. Data from 2009–2020 are available in various formats, including Excel, CSV, and ZIP. Health Opportunity and Equity (HOPE) Initiative: The HOPE Initiative website provides state-level data on the proportion of adults living in households with income greater than 250% federal poverty level. Data are available by race/ethnicity for 50 states via the web interface for the years 2018–2020. In addition, data on all states are available by race/ethnicity and educational attainment via a downloadable Excel workbook from the Resources section of the website. Users can access this data by navigating to the “Social and Economic Factors” domain and “Livable Income” indicator.

Measure 1: City and County Minimum Wage
Definition	Proportion of workers earning at least $15 per hour.
Data Availability	City, region, state Subgroups: Race/ethnicity, gender, nativity
Data Source(s)	National Equity Atlas: The National Equity Atlas provides data on the percentage of full-time wage and salaried workers, ages 25–64, earning at least $15 per hour (in 2019 dollars) at the state, regional, and city levels and by race/ethnicity, gender, and nativity. Data for 1980–2000 are based on surveys conducted in those years but reflect income from the preceding year, while data for 2010 and 2019 represent 5-year averages (e.g., 2015–2019). Users can access “Wages: $15/hr.” under the “Economic Vitality” indicator. Data are available for download via Excel workbook.

Measure 1: City and County Minimum Wage
Definition	Estimated per capita income (individual level).
Data Availability	Block group, census tract, ZIP code, city, county subdivision, county, congressional district, metro division, metro area, state Subgroups: Race/ethnicity, household income, gender, age, educational attainment
Data Source(s)	America’s Health Rankings (AHR): AHR provides state-level analyses of U.S. Census Bureau ACS data on per capita income in the past 12 months, in inflation-adjusted dollars to data year. Users can create estimates by various characteristics (e.g., household income, gender, education attainment, race/ethnicity, age) by downloading the ACS 5-year files and completing the analyses directly. Users can access this measure under Social and Economic Factors > Economic Resources – Annual > Economic Hardship Index > Per Capita Income. Data for 1990–2021 are available in various formats, including Excel, CSV, and ZIP. 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 and data downloads (CSV format). PolicyMap uses U.S. Census data to provide estimates on per capita income. Users can access this measure under Incomes & Spending > Income > Per Capita and can drill down further within this measure. Measures using Census data are available for 2000, 2006–2010, 2011–2015, and 2016–2020.

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Food Insecurity

Why is this indicator relevant?

Food insecurity, defined as the disruption of food intake or eating patterns due to insufficient financial resources and other resources,⁵⁶ is closely related to income and unemployment and is widely recognized as a risk factor for chronic diseases, such as hypertension, coronary heart disease (CHD), hepatitis, stroke, cancer, asthma, diabetes, arthritis, chronic obstructive pulmonary disease (COPD), and kidney disease.^57,58In 2020, it was estimated that 10.5% of U.S. households experienced food insecurity, and the prevalence of food insecurity was notably higher for single-parent households and Black/African American and Hispanic/Latino households.⁵⁹

Food insecurity negatively affects health and increases the risk for CVD through three pathways: unhealthy nutrition; monetary trade-offs; and psychological distress.⁶⁰First, food insecurity is associated with poorer diet quality, which may lead to metabolic dysregulation, fat accumulation, or insulin resistance.^61,62,63Second, relieving and mitigating food insecurity often involves monetary trade-offs between purchasing food or medication that may severely limit people’s ability to manage chronic conditions properly.^64,65Third, food insecurity is strongly associated with psychological distress, lower self-efficacy, and depressive symptoms, triggering physiological stress responses (e.g., elevated cortisol levels) and unhealthy coping behaviors (e.g., excessive drinking, smoking, drug use).^66,67,68

Food insecurity is defined as the disruption of food intake or eating patterns due to insufficient financial resources and other resources. This indicator can be assessed by the following measure. Click on the measure to learn more:

Measure 1: City and County Minimum Wage
Definition	Household-level lack of consistent access to food.
Data Availability	Individual, city, county, core-based statistical area (CBSA), consolidated statistical area (CSA), state
Data Source(s)	Current Population Survey, Food Security Supplement (CPS-FSS): The CPS-FSS is conducted annually in December and provides data on food access and adequacy, food spending, and sources of food assistance. Data are available from 1995 to 2022. Household-level data from 2010 onward are available for download from the Census.gov FTP site as an Excel file; .dat files are available on compact disc for years before 2010. The National Bureau of Economic Research provides files for reading these .dat files into SAS and Stata (https://data.nber.org/data/cps_progs.html). Results can be disaggregated by householder race/ethnicity, age, income, educational attainment and by geographic location (CBSA, CSA, city, and county). Using this data requires expertise in statistical analysis and survey weighting. U.S. Department of Agriculture (USDA) Reports on Household Food Security in the United States: The USDA publishes annual downloadable reports that provide data on the proportion of households that are food-insecure by state (3-year averages), as well as changes in the proportion of households that were food insecure over time. Reports are available annually starting in 1999. To download reports, navigate to the provided link, select “Reports” on the right-hand side, click “See all,” and then search for “Household Food Security in the United States.” Example Survey Instrument The following surveys are available to measure food insecurity: U.S. Household Food Security Survey Module and Adult Food Insecurity Survey Module Both the Household Food Security Survey Module and the Adult Food Insecurity Survey Module provide primary data collection on food insecurity. The Household Food Security Survey Module is an 18-item survey, and the Adult Food Insecurity Survey Module is a 10-item survey. Both surveys use a three-stage survey design with screener; survey respondents are screened at three stages to determine whether they should proceed with the survey. Screening keeps respondent burden to the minimum needed to obtain reliable data. Most households in a general population survey are asked only three questions (five if there are children in the household). The survey and detailed scoring instructions are downloadable in PDF or Word format. Although the Adult Food Insecurity Survey is “less redundant, improves comparability of food security statistics between households with and without children and among households with children in different age ranges, and avoids asking questions about children’s food security, which can be sensitive in some survey contexts,” it does not provide specific information on food security of children.⁶⁹ Link: https://www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-u-s/survey-tools/#household Six-Item Short Form of the Food Security Survey Module For users who cannot implement the 18-item or 10-item surveys, the short-form 6-item scale provides a reasonably reliable substitute. It uses a subset of the standard 18-item survey. The survey and detailed scoring instructions are downloadable in PDF or Word format. Link: https://www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-u-s/survey-tools/#household Note: USDA also offers surveys in Spanish and Chinese at the link above. Household Food Insecurity Access Scale (HFIAS) HFIAS was developed by USAID’s Food and Nutrition Technical Assistance Project to assess household food insecurity. HFIAS takes approximately 15 minutes to administer and asks respondents several questions about food intake, anxieties or concerns about household food supply, and food quality. There are nine occurrence questions that ask whether a specific condition or experience of food insecurity ever occurred in the past 30 days. Each occurrence question is followed by a frequency-of-occurrence question asking how often the reported condition or experience occurred. If the condition or experience of food insecurity did not occur in the past 30 days, the frequency-of-occurrence question is skipped. The survey and detailed guidance are provided in the link below. Link: https://www.fantaproject.org/sites/default/files/resources/HFIAS_ENG_v3_Aug07.pdf

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Housing Insecurity

Why is this indicator relevant?

Housing insecurity is commonly defined as high housing cost relative to income, but it also has been used as an umbrella term to describe multiple housing issues, such as poor housing quality, unstable occupancy, overcrowding, and unsafe neighborhoods.^70,71,72,73Housing-insecure adults are more likely to delay medical care and utilize emergency care, have poorer health care access, experience adverse mental health outcomes, and have higher prevalence of substance use than individuals with stable housing do.^74,75,76

Eviction and foreclosure are associated with exposure to violence, depression, anxiety, increased alcohol use, psychological distress, and suicide.^77,78,79Housing insecurity can be linked to CVD risk and related mortality due to downstream consequences of psychological distress and competing stressors (i.e., spending on housing rather than medical care). Increased exposure to secondhand smoke is common in low-income and public housing. Secondhand smoke interferes with the normal functioning of the heart, blood, and vascular systems; damages the lining of blood vessels; and causes blood platelets to become stickier, which increases the risk of heart attacks, strokes, and development of coronary heart disease.8⁰ Cardiotoxic air pollutants from poor-quality homes are also associated with increased risk for CVD.⁸¹

Homeownership offers stable housing and is a protective factor for mental health. Homeowners report higher self-esteem and happiness than renters and people experiencing housing insecurity, results that could reduce stress, a common risk factor for cardiovascular health.⁸²Homeownership is also associated with better psychosocial health, such as reduced burden of depression.⁸³

Housing insecurity is commonly defined as high housing cost relative to income but also has been used as an umbrella term to describe multiple housing issues, such as poor housing quality, unstable occupancy, overcrowding, and unsafe neighborhoods. This indicator can be assessed by the following measures. Click on each measure to learn more:

Measure 1: City and County Minimum Wage
Definition	Proportion of households living in a home that they own.
Data Availability	Block group, census tract, ZIP code, county subdivision, county, congressional district, metro division, metro area, state
Data Source(s)	American Housing Survey (AHS): The AHS is sponsored by the U.S. Department of Housing and Urban Development (HUD) and conducted by the U.S. Census Bureau. It is the most comprehensive national housing survey in the United States. Users can access the AHS Table Creator to view data and download data as a CSV or Excel file. Data are available by metro area, state, and nationally. The years that data are available depend on state and measure. Health Opportunity and Equity (HOPE) Initiative: The HOPE Initiative website provides state-level data from 2018 on the proportion of households living in homes that they own, broken out by race/ethnicity. In addition, data on all states by householder race/ethnicity and educational attainment are available as a downloadable Excel workbook from the Resources section of the website. This measure can be found under the “Physical Environment” domain and “Home Ownership” indicator. 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 U.S. Census data to provide estimates on the percent of all households that own a home. Users can view the proportion of households that own their homes at all geographic levels noted above. Race/ethnicity and age subgroupings can be viewed using the single-layer maps available to basic subscribers. However, insufficient data becomes an issue at the county and smaller geographic levels. Users can access data on this measure under Housing > Homeowners and Renters and can drill down further within this measure by race, age, mortgage status, and crowdedness. Data are from the U.S. Census, and the years of data availability vary by measure and state. Example Survey Instrument The following survey questions are available for assessing home ownership: American Community Survey (ACS) The U.S. Census Bureau’s ACS asks questions about whether a home is owned or rented to create statistics about home ownership and renters. To view the ACS survey questions on home ownership, visit the U.S. Census Bureau’s website below. Link: https://www.census.gov/acs/www/about/why-we-ask-each-question/ownership/

Definition

Housing issues such as periods of homelessness; paying more than 50% of income on housing costs; difficulty paying rent, mortgage, or utility bills; and difficulty finding safe, adequate, and affordable housing.

Data Availability

City, town, minor civil division, county, state

Subgroups: Educational attainment, race/ethnicity, income level, age, disability status

Data Source(s)

U.S. Department of Housing and Urban Development, Comprehensive Housing Affordability Strategy (HUD-CHAS): The HUD-CHAS website provides query and data download tools that allow users to view data on a variety of housing-related indicators. These indicators focus on low-income households and are commonly used by HUD to demonstrate the extent of housing problems and needs experienced by populations across the United States. The query tool displays data for various housing-related indicators at a place (city/town), minor civil division, county, or state level and is useful for users interested in creating estimates disaggregated by household income for a particular locale. The data tool allows users to download complete data files (CSV format) for a specified 5-year period and geographic level. Downloaded files include data at HUD-specified geographic areas for the entire United States. Downloaded files include tables by race/ethnicity, income level, household size, presence of a household member with a disability, and various other subgroup options; they also provide margins of error for all estimates. For the query tool, users can choose data years going back to 2000; however, due to coding errors in the older data, the 2000 and 2005–2007 estimates should not be compared to estimates using 2006–2017 data. For this reason, the data download tool includes only 2006–2017 data.

Example Survey Instrument

The following survey is available to measure housing insecurity:

Housing Stability Index

This 10-item survey was developed for the Safe Housing and Rent Assistance Evaluation (SHARE) study, which analyzed housing instability as a predictor of poor health. The Housing Instability Index is a count of 10 possible risk factors for housing instability in the past 6 months.⁸⁴ Eight items are dichotomous (yes/no responses). Question 10 should be recoded so that 0 represents a response of “likely” and 1 represents a response of “unlikely,” and Question 4 should be reverse-coded so that a response of “no” is counted as a risk factor. Scoring guidance is available from Rollins et al.⁸⁵

Survey questions
Have any of these events happened to you in the past six months?
1. In the past 6 months, have you had to live somewhere that you did not want to live?	Yes	No
2. In the past 6 months, have you had difficulty (or were unable to) paying for your housing?	Yes	No
3. Have you had trouble getting housing in the past 6 months?	Yes	No
4. Do you expect that you will be able to stay in your current housing for the next 6 months?	Yes	No
5. In the past 6 months, have you had to borrow money or ask friends/ family or others for money to pay your rent/mortgage payment?	Yes	No
6. In the past 6 months, have you moved more than twice?	Yes	No
7. Have you had trouble with a landlord in the past 6 months?	Yes	No
8. In the past 6 months, has your landlord threatened to evict you?	Yes	No
9. In the past 6 months, have you been served an eviction notice?	Yes	No
10. How likely is it that you will be able to pay for your housing (e.g., rent/mortgage) this month?	Yes	No

Additional Measurement Considerations

As a piloted indicator in the Health Equity Indicator Pilot, sites found that adding housing insecurity questions into their social determinant of health (SDOH) screening tools and electronic health record (EHR) workflows created actionable opportunities to facilitate referrals to community resources.

For more measures related to housing, please see the Housing indicator in the Neighborhood Characteristics Indicator Profile.

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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: Northwell Health
Focus Area: Socioeconomic Factors
Measure: Housing Instability

This content presents an example of how one healthcare site, Northwell Health, collected data on the health equity indicator, housing insecurity. Housing insecurity encompasses several dimensions of housing problems people may experience, including affordability, safety, quality, instability, and loss of housing.⁸⁶ Housing-insecure adults are more likely to delay medical care and utilize emergency care, have poorer healthcare access, experience adverse mental health outcomes, and have higher prevalence of substance use, compared to individuals with stable housing.^87,88,89Housing insecurity can be linked to cardiovascular risk and mortality due to downstream consequences of psychological distress and competing stressors (i.e., spending on housing rather than medical care).⁹⁰Northwell Health used the housing instability measure to assess housing insecurity. Housing stability refers to housing issues such as periods of homelessness; paying more than 50% of income on housing costs; difficulty paying rent, mortgage, or utility bills; and difficulty finding safe, adequate, and affordable housing. For more information on the Housing Instability measure, please see the Housing Insecurity indicator in the Socioeconomic Factors Indicator Profile.

The description of Northwell Health’s data analysis methods, data sources, challenges, and facilitators 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: Northwell Health

Northwell Health is New York state’s largest health care provider, with an annual patient population of more than 2 million across 22 hospitals and 830 outpatient facilities. Northwell employs more than 16,600 affiliated physicians, of which 4,200 are members of Northwell’s multi-specialty physicians’ group. Northwell’s service area extends across the downstate New York region, composed of more than six counties, including Queens County, which has a population of more than 2.2 million and is the most linguistically diverse county in the country. In 2019, the racial/ethnic composition of patients in Northwell’s acute care settings was 55.3%White and 44.6% non-White (15.3% Black/African American, 12.7% Hispanic/Latino, 7.3% Asian, and 9.2% other race/ethnicities). Northwell engages with multiple councils and work groups, including community-based and faith-based organizations, academic institutions, schools, and state and local health departments, to achieve its mission of “improving health and quality of life” and advancing health equity.

Overview of Northwell Health’s data collection methods

Indicator: Housing Insecurity

Measure: Housing Instability

Data Collection Method: Primary data collection

Data Source: Patient intake forms

Data Availability: Patient-level

Application/Use: Plan to analyze the relationship between housing insecurity and access to cardiovascular care in the foreseeable future.

Data Collection Method. Given Northwell’s existing social determinants of health (SDOH) data collection infrastructure pertaining to housing insecurity, the health system opted out of this particular aspect of the pilot to test housing insecurity data collection as specified in the Socioeconomic Factors Health Equity Indicator Profile. However, they shared information about their data collection and analysis processes for this indicator as part of the pilot of select health equity measures.

Although responses are voluntary, Northwell collects survey data on SDOH from patients using a screening tool administered to patients that varies depending on their care setting within the health system. Please see below for housing-related questions from the SDOH screening tool. Completed data from the screening process are then entered into the relevant electronic medical records of the respective sites within the health system. Northwell has reported that out of all screenings in the inpatient setting that were identified as having social needs, 15.3% indicated housing insecurity concerns.

Northwell has a robust data set of health equity metrics, including SDOH data, within its data systems, reflecting data from inpatients, emergency department patients, and ambulatory patients spanning the last 15 years. The SDOH data from its screening tool are collected from three ambulatory patient sites, 13 hospitals, and one emergency department, within its health home, and among its clinical initiatives that are part of its value-based contracting. Northwell reported that at this time, the SDOH screening process is unique to the setting in which the screener is implemented. For example, in inpatient settings, nurses complete the SDOH screener with patients as part of their initial nursing assessment. Patients identified as having social needs during the initial nursing assessment are directed to an on-site social worker for further review and are connected to the necessary social and community services to address their health-related social needs. In Northwell’s outpatient settings, paper screeners are completed during patients’ annual visits. Data are then entered into the REDCap database and reviewed to inform the ongoing improvement of its services provided in outpatient settings. Northwell’s emergency departments implement a more targeted approach of screening patients at higher risk of medical complications, including uninsured patients, underinsured patients, and patients with a select set of chronic diseases and comorbidities. These patients are assigned a care coordinator who conducts the SDOH screening, which helps Northwell better adapt social and community resources to meet patients’ needs. Northwell Health is currently in the process of revising and standardizing SDOH screening protocols and plans to analyze data across all care settings to better understand the relationship between housing insecurity and access to cardiovascular care. With specificity to health disparities in CVD, Northwell’s Department of Cardiology is currently in the process of formalizing their governance and road map on the best ways to address cardiovascular health disparities associated with adverse health-related social needs.

Facilitators. Northwell’s organizational focus and prioritization of health equity provide the foundation to support the systematic collection and analysis of SDOH data, including data on housing insecurity, and integration of this effort into the clinical workflow. Leadership buy-in and support from clinicians and subject matter experts have further facilitated this process.

Challenges and Limitations. Northwell has implemented an SDOH screening tool adapted to its various care settings across the health system. However, Northwell has acknowledged that there are challenges to collecting housing insecurity and SDOH data, a situation also seen in national trends. One of the main challenges has been the ongoing constraints on available time and resources. Northwell also acknowledged the stigma of housing insecurity as a challenge to successful data collection. To mitigate this challenge, Northwell is focused on building trust with its patients to ensure these questions are voluntary and will in no way affect the delivery of care, by building partnerships within the community through ongoing efforts to educate and raise awareness of the links between health and housing and why such questions are being asked. Similarly, provider comfort with the topic was initially a known challenge, which is continually addressed through ongoing training.

Future Plans. As part of their ongoing efforts to address health inequity, Northwell plans to use the data from their SDOH program, including the housing insecurity indicator, to inform enhancements to the SDOH screening tool, staff training, patient resources, community health needs assessments, and community service plans. Northwell Health has the foundation of established processes and infrastructure at large to link SDOH factors to health outcomes with the future intention of analyzing individual SDOH factors such as housing insecurity and linking them to specific health outcomes such as cardiovascular health. Furthermore, Northwell is continuously developing targeted community outreach initiatives across its catchment area to better address these adverse social needs.

Housing-related Outpatient and Inpatient SDOH Screening Questions

For the outpatient setting:

1A. Do you worry that in the next 2 months, you/your family may not have a safe or stable place to live?

1B. Where are you living now?

Do you worry that the place you are living now is making you sick because of cigarette smoke exposure?
Do you worry that the place you are living now is making you sick because of mold or dampness or water leaks?
Do you worry that the place you are living now is making you sick because of rodents or bugs?
Do you worry that the place you are living now is making you sick because of peeling paint?
Do you worry that the place you are living now is making you sick because of broken appliances?
Do you worry that the place you are living now is making you sick because of the elevator not working?
Do you worry that the place you are living now is making you sick because of not having enough heat?

For the inpatient setting:

Do you have a safe place to live?
Do you worry the place you are living is making you sick?

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Field Notes

This field note showcases 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: Kansas City, Missouri Health Department (KCMO)
Focus Area: Socioeconomic Factors
Measure: Out-of-School Suspension

This content presents an example of how one health department, Kansas City, Missouri (KCMO) Health Department (KCMO), used data on the health equity indicator education. Education policies and exclusionary school discipline practices, such as suspension, hinder educational attainment and exacerbate socioeconomic and health inequities. In addition to hindering academic achievement, expulsions and suspensions are correlated with substance use and worse mental health and social connectivity, which are risk factors for adverse health behaviors, such as early sexual initiation; alcohol, tobacco, and drug use; violent behaviors; and gang involvement, among adolescents.^91,92 These adverse health behaviors in turn increase the risk of adverse health outcomes, including CVD. KCMO Health Department used the out-of-school suspension measure to assess education. Out-of-school suspension is defined as the percentage of public school students with or without disabilities receiving one or more out-of-school suspensions during the school year.⁹³ For more information on the out-of-school suspension measure, please see the education indicator in the Socioeconomic Factors Indicator Profile.

Representatives from the KCMO Health Department 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, KCMO shared the following example of how they accessed and analyzed data on education, including data sources, challenges, and facilitators. Their approach and lessons learned are summarized below.

The KCMO Health Department serves residents of Kansas City, Missouri, a large city spanning more than 300 square miles with a population of 508,090. The city has 16 school districts, including 10 public school districts. About 55% of the population is White, 35% is Black/African American, and 10% is Hispanic/Latino. In 2017, Kansas City was the fifth most economically and racially segregated city in the United States.⁹⁴ Historical disinvestment and structural racism are considered contributing factors to health disparities in Kansas City. In some neighborhoods, the gap in life expectancy between White and Black/African American residents is as large as 17 years.⁹⁵

Overview of KCMO Health Department’s data collection methods

Indicator: Education

Measure: Out-of-School Suspension

Data Collection Method: Secondary

Data Source: U.S. Department of Education’s Civil Rights Data Collection

Data Availability: School district

Application/Use: Comparison of school suspension rates by student race/ethnicity. Analysis findings informed reforms to school disciplinary policies.

Data Collection and Analysis Methods. KCMO Health Department accessed publicly available school suspension rate data from the U.S. Department of Education’s Civil Rights Data Collection. KCMO Health Department gathered school suspension rates for each Kansas City school district and analyzed the data by student race/ethnicity for the 2015-16 academic year.

Findings. The analysis found that young Black/African American students were five times more likely than their White peers to be removed from Kansas City classrooms for disciplinary infractions during the 2015-16 academic year. Furthermore, Black/African American students received longer suspensions and were more likely to be suspended for reasons other than illegal substances, physical violence, and weapons than their White and Hispanic/Latino classmates.

Given the link between school attendance and physical, mental, and social well-being, KCMO Health Department is working with Kansas City school districts to amend disciplinary policies to ensure that students are no longer being disciplined unjustly. KCMO presented their data findings to the school districts to promote disciplinary policy reform and to reduce the number of students being suspended, especially Black/African American students. Additionally, the school board champions and non-profits such as the Metro Organization for Racial and Economic Equity (More2) advocated for changes to the suspension policy. As a result, the Kansas City Public School board of education voted to revise the code of conduct to limit out-of-school suspensions for students in pre-kindergarten through fifth grade in 2021. Moreover, school discipline is included in KCMO’s 2022–2027 Community Health Improvement Plan (CHIP). One of the goals in the 2022–2027 CHIP is to increase trauma-informed and anti-racist education and practices, with the specific objective of reducing the number of suspensions by 10% each year.⁹⁶

Facilitators. The mayor was particularly invested in improving school outcomes at the time, and the support from the mayor and the KCMO Health Commission was critical to bringing school district leaders to the table. District leaders were convened at the invitation of the Office of the Mayor and the Health Commission, rather than the Health Department. Additionally, school district leadership became more invested in discussions and taking action when it was made clear that findings would be de-identified. School-specific suspension data were not made publicly available, and only aggregate data were published. Community support and advocacy from the school board champions and More2 was also instrumental in driving school policy change.

Challenges and Limitations. Primary data collection was not possible because KCMO Health Department had not built a sufficient level of trust with school districts prior to data collection. Although there were no challenges in accessing and downloading secondary data from the U.S. Department of Education, the analysis team at KCMO Health Department would have preferred to collect data directly from schools. In terms of barriers to policy implementation, teachers reported needing more classroom support and resources, such as behavioral interventions and trainings on de-escalation, cultural awareness, trauma-informed care, and restorative justice, in order to successfully limit suspensions.

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References

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