Genderism, Sexism, and Heterosexism

Indicator Profile

Genderism, or bias resulting from a gender binary view, is a system of beliefs that perpetuates negative evaluations of gender nonconformity.¹

Sexism, defined as prejudice or discrimination based on one’s sex, stems from an ideology that one sex is superior to the other.²

Heterosexism is an ideological system that denies, denigrates, and stigmatizes any non-heterosexual form of behavior, identity, relationship, or community.³

Within health care systems, conscious or unconscious biases based on gender or sexual orientation, gender stereotypes, and sexism affect patient care. For example, various studies show that despite education efforts, health providers are more likely to screen, diagnose, and treat White men for cardiovascular disease (CVD) compared with women of all races and ethnicities. This gender bias in the prevention and management of CVD results in underdiagnosis, delayed care, ineffective care, and poorer outcomes among women.^4,5,6,7

Health care systems can achieve equitable outcomes by incorporating a more diverse and inclusive understanding of health in patient care and interventions.

Table of Contents

Indicators

Case Example

References

Indicators

This document provides guidance for measuring three indicators related to genderism, sexism, and heterosexism that influence social-environmental factors shown to increase the risk for developing CVD or result in differential access to and receipt of health care. The three indicators are measured at different levels of analysis, including individual, census tract, city, county, metropolitan area, and state levels.

Gender Discrimination

Why is this indicator relevant?

Gender is the economic, social, political, and cultural attributes and opportunities associated with being a woman, a man, or a person of another gender identity.⁷ Gender-based discrimination (i.e., conscious or unconscious biases and actions based on gender stereotypes) may be a cause of gender differences in health outcomes.⁸ Many women experience gender discrimination in the workplace, health care, higher education, housing, and the legal system, which has negative effects on economic opportunities, social well-being, and physical and mental health.^9,10 Emerging research suggests that the experience of discrimination, both institutional (e.g., health care) or interpersonal (e.g., microaggressions), may increase the body’s stress response over time, and that discrimination is linked to a range of poor health‐related behaviors, mental health outcomes, and physical health problems, including high blood pressure, heart disease, and self-reported health status.^11,12,13,14 The role of gender as a social determinant of health has been increasingly recognized within CVD research.¹⁵ As a result of more sex- and gender-specific CVD research, there has been a nearly 30% decline in the number of women dying from CVD in the United States.¹⁶ Despite this progress, continued research is needed to address persisting sex/gender inequities in CVD, particularly gender discrimination. Unconscious bias or lack of knowledge of gender differences in symptom presentation may affect the diagnosis, referral, and care of women for CVD. Gender discrimination and sexual harassment are considered chronic and persistent stressors that worsen cardiovascular health.¹⁷ Pathophysiologic mechanisms linking gender discrimination and CVD include changes in systolic blood pressure, cortisol secretions, pulse rate, and heart rate variability.¹⁸

Definition

Receipt of unfair treatment based on the social interpretation of one’s gender.

Data Availability

Individual

Data Source(s)

Example Survey Instrument

The following survey measures are available to measure gender 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, among other reasons. The full and short versions of the original scale can be found in the Williams et al. paper¹⁹ or at the link provided below. The short version of the EDS was developed for the Chicago Community Health Study.²⁰The modified short version below includes these response options and specifically asks about unfair treatment due to gender.

Link: https://scholar.harvard.edu/davidrwilliams/node/32397

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

Survey questions
	7 Almost every day	6 At least once a week	5 A few times a month	4 About once a month	3 A few times a year	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. This measure was originally developed to assess the frequency of how often a person has experienced discrimination because of their race/ethnicity.²¹ 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 (response choices of once, two or three times, and four or more times). The modified version below combines these response options and adapts the measure for gender discrimination.

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 gender?

Survey 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?

Schedule of Sexist Events (SSE)

SSE is a measure of lifetime and recent (past-year) sexist discrimination. The measure assesses the frequency with which a woman has experienced sexist events of various styles in a diversity of settings. SSE is a 20-item self-report measure that has been validated for measuring sexist degradation, sexism in distant relationships, sexism in close relationships, and sexism in the workplace.²²

Please think carefully about your life as you answer the questions below. For each question, read the question and answer it twice: answer once for what your ENTIRE LIFE (from when you were a child to now), and then once for what the PAST YEAR has been like. Choose the number that best describes events in YOUR ENTIRE LIFE and in the PAST YEAR, using these rules:

Questions 1-19:

1 = If the event NEVER happened to you

2 = If the event happened ONCE IN A WHILE (less than 10% of the time)

3 = If the event happened SOMETIMES (10-25% of the time)

4 = If the event happened ALOT (26-49% of the time)

5 = If the event happened MOST OF THE TIME (50-70% of the time)

6 = If the event happened ALMOST ALL OF THE TIME (more than 70% of the time)

Question 20:

1= Same as now

2= Little different

3= Different in many ways

4= Different in a lot of ways

5= Different in most ways

6= Totally different

Measure 1: City and County Minimum Wage
	How many TIMES IN YOUR ENTIRE LIFE?	How many times IN THE PAST YEAR?
1. How many times have you been treated unfairly by teachers or professors because you are a woman?
2. How many times have you been treated unfairly by your employer‚ boss‚ or supervisors because you are a woman?
3. How many times have you been treated unfairly by your co-workers‚ fellow students or colleagues because you are a woman?
4. How many times have you been treated unfairly by people in service jobs (by store clerks‚ waiters‚ bartenders‚ waitresses‚ bank tellers‚ mechanics‚ and others) because you are a woman?
5. How many times have you been treated unfairly by strangers because you are a woman?
6. How many times have you been treated unfairly by people in helping jobs (by doctors‚ nurses‚ psychiatrists‚ case workers‚ dentists‚ school counselors‚ therapists‚ pediatricians‚ school principals‚ gynecologists‚ and others) because you are a woman?
7. How many times have you been treated unfairly by neighbors because you are a woman?
8. How many times have you been treated unfairly by your boyfriend‚ husband‚ or other important man in your life because you are a woman?
9. How many times were you denied a raise‚ a promotion‚ tenure‚ a good assignment‚ a job‚ or other such thing at work that you deserved because you are a woman?
10. How many times have you been treated unfairly by your family because you are a woman?
11. How many times have people made inappropriate or unwanted sexual advances to you because you are a woman?
12. How many times have people failed to show you the respect you deserve because you are a woman?
13. How many times have you wanted to tell someone off for being sexist?
14. How many times have you been really angry about something sexist that was done to you?
15. How many times were you forced to take drastic steps (such as filing a grievance‚ filing a lawsuit‚ quitting your job‚ moving away‚ and other actions) to deal with some sexist thing that was done to you?
16. How many times have you been called a sexist name like bitch‚ cunt‚ chick‚ or other names?
17. How many times have you gotten into an argument or a fight about something sexist that was said or done to you or done to somebody else?
18. How many times have you been made fun of‚ picked on‚ pushed‚ shoved‚ hit‚ or threatened with harm because you are a woman?
19. How many times have you heard people making sexist jokes or degrading sexual jokes?
20. How different would your life have been now if you HAD NOT BEEN treated in a sexist and unfair way?

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.²³Michaels et al. investigate a novel, chronicity-based coding and a more conventional, frequency-based coding and describe how different scoring methods affect exposure classification and assessment of the relationship between EDS and hypertension among African American women.²⁴

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Gender Income Gap

Why is this indicator relevant?

The movement for equal pay dates back to the 1860s. Although the Equal Pay Act was signed into law in 1963, the gender income gap, or the difference between the median wages or salaries of men and women, still persists.²⁵ In 2020, among full-time and part-time workers in the United States, women earned 84% of what men earned.²⁶ Factors driving the gap include human capital or productivity factors such as education, skills, and workforce experience (e.g., unequal access to education); occupational segregation (e.g., overrepresentation of men in the finance sector); gender-specific temporal flexibility constraints, which can affect promotions and remuneration (e.g., caregiving responsibilities among women may limit work); gender discrimination in hiring, promotion, task assignment, and/or compensation (e.g., exclusion from hiring due to gender); and undervaluing the work of women due to conscious or unconscious gender biases (e.g., women receiving less compensation than men despite the same level of productivity and the same quality of results).^27,28

Compared with men, women are more likely to be in low-paid, nonunionized sectors of the economy, to have interrupted careers, or to work part-time, which contributes to the gender income gap and may affect access to health care.^29,30 In the United States, health care is predominantly accessed through employer-sponsored health insurance plans, which are typically limited to full-time and high-wage workers. The gender income gap has been linked to gender-based disparities in depression and anxiety disorders.³¹ Moreover, aggregated measures of income equality demonstrate a relationship with health outcomes such as mortality, self-rated health, and risk of coronary heart diseases and with the strongest effects observed between county or state levels of income inequality and individual health.^32,33

Several studies suggest that substantial disparities in CVD prevalence exist between the highest-income group and the remainder of the population.^34,35 County-level measures of median income and income inequality are also associated with county-level CVD mortality rates.^36,37 Income may affect CVD risk through several pathways, including via its impact on environmental, occupational, and neighborhood exposures affecting psychosocial, metabolic, and behavioral risk factors for CVD.^38,39,40,41 The stress or anxiety related to income inequality may result in heightened blood pressure or could contribute to the adoption of unhealthy coping behaviors (e.g., smoking, unhealthy eating, alcohol consumption), which can affect cardiovascular and other chronic diseases.^42,43,44,45 Additionally, income inequality is linked to poor access to care, and the working poor are less likely to utilize preventive services for CVD.⁴⁶

The gender income gap is the difference between the median wages or salaries of men and women. This indicator can be assessed by the following measure. Click on the measure to learn more:

Measure 1: Median Household Income by Gender
Definition	Ratio of median household income by gender.
Data Availability	Individual, census tract, county, metropolitan area, national Subgroups: Gender
Data Source(s)	Existing Data Source(s) American Community Survey (ACS) 5-Year Summary Files The ACS from the U.S. Census Bureau is an ongoing survey that provides data annually. The 5-year estimates from the ACS are “period” estimates that 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 create median income estimates by various characteristics (e.g., gender, race/ethnicity, age) by downloading the ACS 5-year files from the U.S. Census Bureau’s main data platform at the link below. In order to understand the gender income gap, users can search “Income” at the link below and create a ratio of median income by gender by dividing the median income of one gender by another gender (i.e., the median income of women/median income of men). Data on the median income in the past 12 months are available for 2010–2020, in various formats, including Excel, CSV, and ZIP. Link: https://data.census.gov/cedsci/ Current Population Survey (CPS) The CPS, sponsored jointly by the U.S. Census Bureau and the U.S. Bureau of Labor Statistics (BLS), is the primary source of labor force statistics for the population of the United States. The CPS measures hourly and weekly earnings of wage and salary workers. The data provided are before taxes and other deductions and include overtime pay, commissions, or tips received. Earnings data are available by demographic characteristics such as age, sex, and race/ethnicity. Users can access this measure under “Earning” and download data in multiple formats, including PDF and Excel. Data are available for 1979–2022. Link: https://www.bls.gov/cps/earnings.htm Example Survey Instrument The following survey questions are available for assessing gender 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. To view the ACS survey questions on income and sex, visit the U.S. Census Bureau’s websites below. In order to understand the gender income gap, users have to calculate total income in past 12 months for respondents by gender and calculate the ratio of income by gender by dividing the income of one gender by another gender (e.g., the total income of women/total income of men). 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/sex/

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LGBTQIA+ Discrimination

Why is this indicator relevant?

Lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other sexual orientation and gender identity populations (LGBTQIA+) are more likely to experience discrimination.⁴⁷ LGBTQIA+ people have experienced a long history of discrimination across multiple domains, including in health care, employment, housing, policing, the judicial system, and other social institutions. LGBTQIA+ discrimination also includes criminalization of sexual orientations, designation as mental illness, hate crimes and harassment, as well as exclusion from workplaces, schools, services, and public spaces.

An extensive body of research shows that exposure to LGBTQIA+ discrimination harms mental and physical health and may result in depression, anxiety, suicidality, post-traumatic stress disorder (PTSD), substance use, psychological distress, elevated stress hormone levels, CVD, and poor self-reported health.^48,49 The Institute of Medicine (IOM) issued a landmark report in 2011 that identified several risk factors for poor health outcomes among LGBTQIA+, including inadequate training of health care providers, discrimination, harassment, poverty, and less or lack of health insurance coverage.⁵⁰ Recognizing the health inequities among this population, the National Institutes of Health (NIH) established the Sexual & Gender Minority Research Office in 2015 and designated LGBTQIA+ people as a health disparity population in 2016.⁵¹ Additionally, the U.S. Department of Health and Human Services (HHS) Healthy People initiative added improving the safety, health, and well-being of lesbian, gay, bisexual, and transgender (LGBT) individuals as a new objective for Healthy People 2020.⁵²

Evidence suggests that LGBTQIA+ adults experience inequities across several cardiovascular risk indicators compared with their cisgender and heterosexual counterparts. These inequities are driven by disproportionately greater exposure to psychosocial stressors across the life span.⁵³ A systematic review of CVD in sexual minorities found that sexual minorities are at elevated risk for CVD due to increased tobacco use, alcohol consumption, illicit drug use, poor mental health, and elevated body mass index.⁵⁴

Lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other sexual orientation and gender identity populations (LGBTQIA+) are more likely to experience discrimination, or unfair treatment, due to their sexual orientation. This indicator can be assessed by the following measure. Click on the measure to learn more:

Definition

Unfair treatment due to sexual orientation and/or gender identity.

Data Availability

Individual

Data Source(s)

Example Survey Instrument

The following survey measures are available to measure LGBTQIA+ 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, among other reasons. The full and short versions of the original scale can be found in the Williams et al. paper.⁵⁵ The short version of the EDS was developed for the Chicago Community Health Study.⁵⁶ The modified short version below includes these response options and specifically asks about unfair treatment due to sexual orientation and gender identity.

Link: https://scholar.harvard.edu/davidrwilliams/node/32397

In your day-to-day life, how often have any of the following things happened to you because of your sexual orientation and/or gender identity?

Survey questions
	7 Almost every day	6 At least once a week	5 A few times a month	4 About once a month	3 A few times a year	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. This measure was originally developed to assess the frequency of how often a person has experienced discrimination because of their race/ethnicity.⁵⁷ 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 (response choices of once, two or three times, and four or more times). The modified version below combines these response options and adapts the measure for LGBTQIA+ discrimination.

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 sexual orientation and/or gender identity?

Sexual orientation or gender questions
	1 No	2 Yes	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?

Daily Heterosexist Experiences Questionnaire (DHEQ)

DHEQ is a 50-item self-report measure designed to assess day-to-day minority stress experienced by diverse LGBTQIA+ populations.⁵⁸ DHEQ captures nine dimensions of the unique aspects of minority stress including harassment and discrimination, gender expression, parenting, victimization, family of origin, vicarious trauma, isolation, and HIV/AIDS. The instrument and scoring details are available from the Research on Intersectional Sexual and Gender Identity Experiences (RISE LAB) at Palo Alto University.

Link: https://www.paloaltou.edu/sites/default/files/The%20Daily%20Heterosexist%20Experiences%20Questionnaire%20%28DHEQ%29.pdf

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. 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 and assessment of the relationship between EDS and hypertension among African American women.²⁴

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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: Dignity Health St. Joseph’s Hospital and Medical Center
Focus Area: Genderism, Sexism, Heterosexism
Measure: Median Household Income by Gender

This content presents an example of how one health care site, Dignity Health St. Joseph’s Hospital and Medical Center in Phoenix, Arizona, used data on the health equity indicator gender income gap. The gender income gap is defined as the difference between the median earnings of men and women relative to the median earnings of men.⁵⁹ This serves as a proxy indicator for genderism/sexism.

Compared with men, women are more likely to be in low-paid, nonunionized sectors of the economy, have interrupted careers, or work part-time, all of which contribute to the gender income gap and can affect access to health care.^60,61 Health care is predominantly accessed through employer-sponsored health insurance plans, which are typically limited to full-time and high-wage workers.

County-level measures of median income and income inequality are also associated with county-level CVD mortality rates and individual-level risk.^62,63 The stress or anxiety related to income inequality may result in heightened blood pressures or could contribute to the adoption of unhealthy coping behaviors (e.g., smoking, unhealthy eating, alcohol consumption), which can affect CVD and other chronic diseases.^64,65,66 Dignity Health St. Joseph’s Hospital and Medical Center used the median household income by gender measure to access the gender income gap. The median household income by gender measure is defined as the ratio of median household income by gender.

For more information on the median household income by gender measure, please see the gender income gap indicator in the Genderism, Sexism, and Heterosexism Indicator Profile.

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

Health Equity in Action: Dignity Health St. Joseph’s Hospital and Medical Center

Serving the approximately 4.3 million residents of Maricopa County, Dignity Health St. Joseph’s Hospital and Medical Center is part of CommonSpirit Health^®, which is one of the nation’s largest nonprofit health care systems, with more than 1,000 care sites and 140 hospitals in 21 states. Dignity Health St. Joseph’s Hospital and Medical Center collaborated with the Office of Informatics & Epidemiology within the Maricopa County Department of Public Health (MCDPH) to develop a plan for addressing community health needs, which includes a focus on health equity. Dignity Health St. Joseph’s Hospital and Medical Center’s Heart and Vascular Institute and the Community Benefit and Health Equity Department participated in the CDC pilot reviewing select health equity indicators, including the gender income gap indicator. The pilot supported Dignity Health’s core mission of advancing social justice and improving health for all, especially for the most vulnerable. Some of the hospital’s current health equity efforts also include improving interoperability and analytic capabilities for data systems that contain health equity measures, standardizing health equity data collection processes, visualizing health equity data, and ultimately, using data to inform decisions to address health inequities.

Overview of Dignity Health St. Joseph’s data collection methods

Indicator: Gender Income Gap

Measure: Median Household Income by Gender

Data Collection Method: Secondary data analysis

Data Source: American Community Survey (ACS)

Data Availability: County-level

Application/Use: Calculation of ratio of women’s earnings to men’s earnings.

Data Collection and Analysis Methods. As part of the pilot reviewing select health equity indicators, Dignity Health St. Joseph’s Hospital and Medical Center tested the gender income gap indicator using the median household income measure from the American Community Survey (ACS). In collaboration with MCDPH, the Dignity Health St. Joseph’s Hospital and Medical Center team extracted median household income data from the 2019 ACS for Maricopa County. Using income data for men and for women, the team calculated the ratio of women’s income to men’s income. One of the analysis findings indicates that women earn 77.6% of men’s earnings across all industries in Maricopa County. The Dignity Health St. Joseph’s Hospital and Medical Center team plans to collect and analyze data at additional geographic levels by leveraging the Dignity Health Community Needs Index (CNI) to identify high-need ZIP codes.

Facilitators. Strong support from Dignity Health St. Joseph’s Hospital and Medical Center patient care and executive leadership teams at the local, division, and national levels greatly facilitated data collection and analysis for the pilot. Overall, teams at Dignity Health St. Joseph’s Heart and Vascular Institute and the hospital’s Community Benefit and Health Equity Department found that the pilot accelerated efforts to refine the process in which health equity indicator data are captured and measured for broader health equity work. Given the availability of dedicated staff and community resources, Dignity Health St. Joseph’s Hospital and Medical Center reported that they have the necessary capabilities to continue working with MCDPH’s Office of Informatics & Epidemiology to address health equity efforts.

Challenges and Limitations. The team reported no barriers to data collection.

Future Plans. The ACS data set allows for additional analyses to provide further insights on gender income gap and health impact. Potential future analyses include comparing income ratios across the different counties Dignity Health serves in Arizona and comparing income ratios by rural/urban areas. Dignity Health St. Joseph’s Hospital and Medical Center can use the analysis insights in relation to health outcomes to guide future health equity interventions.

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This does not hold true when data are examined by race/ethnicity. Patterns in employment type, unemployment, and earnings vary by race/ethnicity. Compared with White women, a higher proportion of Black/African American men are considered working poor. Black/African American men have the highest unemployment rates and the lowest earnings out of all race/gender groups. For more information, refer to the Racial Income Gap indicator in the Racism Indicator Profile.
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Kim D, Kawachi I, Hoorn SV, Ezzati M. Is inequality at the heart of it? Cross-country associations of income inequality with cardiovascular diseases and risk factors. Soc Sci Med. 2008;66(8):1719–32. doi:10.1016/j.socscimed.2007.12.030
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Caceres BA, Brody A, Luscombe RE, Primiano JE, Marusca P, Sitts EM, Chyun D. A systematic review of cardiovascular disease in sexual minorities. Am J Public Health. 2017;107(4):e13–21. doi:10.2105/AJPH.2016.303630
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Spiggle T. The gender pay gap: Why it’s still here. Forbes. 2021. Accessed December 12, 2022. https://www.forbes.com/sites/quad/2022/12/05/why-marketers-must-streamline-their-mx-as-they-face-economic-headwinds/?sh=663e222f4a7c
Fee E. The gender gap in wages and health. Health Affairs. 1991;10(4), 302–5. doi:10.1377/hlthaff.10.4.302
Khatana SAM, Venkataramani AS, Nathan AS, Dayoub EJ, Eberly LA, Kazi DS, et al. Association between county-level change in economic prosperity and change in cardiovascular mortality among middle-aged US adults. JAMA. 2021;325(5):445–53. doi:10.1001/jama.2020.26141
Massing MW, Rosamond WD, Wing SB, Suchindran CM, Kaplan BH, Tyroler HA. Income, income inequality, and cardiovascular disease mortality: relations among county populations of the United States, 1985 to 1994. South Med J. 2004;97(5):475–84. doi:10.1097/00007611-200405000-00012
Pabayo R, Kawachi I, Gilman SE. US State-level income inequality and risks of heart attack and coronary risk behaviors: longitudinal findings. Int J Public Health. 2015;60(5):573–88. doi:10.1007/s00038-015-0678-7
Diez Roux AV. Residential environments and cardiovascular risk. J Urban Health. 2003;80(4):569–89. doi: 10.1093/jurban/jtg065
Kubzansky LD, Seeman TE, Glymour MM. Biological pathways linking social conditions and health. In: Berkman LF, Kawachi I, eds. Social Epidemiology. 2nd ed. Oxford University Press; 2014:512–61.

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