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American Journal of Epidemiology logoLink to American Journal of Epidemiology
. 2024 Jul 3;194(1):142–151. doi: 10.1093/aje/kwae176

Exposure to structural racism–related state laws is associated with worse cardiovascular health among US adults, BRFSS 2011 and 2013

Belinda L Needham 1,✉, Darya Dokshina 2, Talha Ali 3, Kristi L Allgood 4, Megan Douglas 5, Akilah Dulin 6, Nancy L Fleischer 7, Amber E Johnson 8, Annie Ro 9, Madina Agénor 10
PMCID: PMC13368606  PMID: 38960630

Abstract

The objective of this study was to determine whether exposure to structural racism–related state laws is associated with cardiovascular health among a racially and ethnically diverse sample of US adults. Data were from the Database of Structural Racism–Related State Laws and the Behavioral Risk Factor Surveillance System (BRFSS). The sample included 958 019 BRFSS 2011 and 2013 respondents aged 18 years or older from all 50 US states. The exposure was a summary index of 22 state laws related to the criminal legal system, economics and labor, education, health care, housing, immigration, and political participation. The outcome was the American Heart Association’s Life’s Simple 7 (LS7), a summary index of 7 cardiovascular health indicators. Linear regression models included fixed effects for year and state to control for time trends and unmeasured, time-invariant, state-level contextual factors. In the full sample, a 1 SD increase in the structural racism state legal index was associated with a 0.06-unit decrease in the LS7 (b = −0.06; 95% CI, −0.09 to 0.02; P = .001), controlling for individual- and state-level covariates. Contrary to expectations, stratified models revealed no statistically significant differences by race and ethnicity in the association between the structural racism state legal index and the LS7.

Keywords: structural racism, legal epidemiology, cardiovascular health

Introduction

Racial and ethnic inequities in cardiovascular outcomes are well established,1-3 but the underlying mechanisms are not fully understood. Building on research examining interpersonal discrimination as a risk factor for adverse health outcomes,4-7 scholars have begun to explore the ways in which structural racism—the system of interconnected laws, policies, practices, and norms that foster racial inequity by creating differential access to resources and opportunities—8-12 contributes to inequitable health outcomes among racially and ethnically minoritized populations.13-15 However, few studies have examined the impact of structural racism on cardiovascular-related outcomes, and existing research is limited by the use of self-report16-19 and proxy measures of structural racism.16,20-28 In this study, we combined data from the Database of Structural Racism–Related State Laws29 and the Behavioral Risk Factor Surveillance System (BRFSS) to examine the association between exposure to structural racism–related state laws and the American Heart Association’s Life’s Simple 7 (LS7), a summary index of 7 cardiovascular health indicators.30

Structural racism is a relatively new area of scientific inquiry, and researchers have yet to reach a consensus about the best way to measure exposure to it. Among studies that have examined cardiovascular outcomes, exposure to structural racism has been measured by self-reports of discrimination within 1 or more institutional settings, such as school or work,17-19 and by area-level indicators of racial inequity that serve as proxies for exposure to racially discriminatory laws and policies in various institutional domains, such as education and housing.16,20-26 A limitation of self-reported measures of discrimination within institutional settings is that it is not possible to know whether unfair treatment was the result of structural racism, interpersonal racism, or both.31 Moreover, because structural racism determines access to resources and opportunities,8 it need not be perceived by an individual to have consequences for health and well-being. A limitation of area-level measures of racial inequity, such as the White student to Black student high school graduation ratio or the index of dissimilarity (a measure of racial residential segregation), is that these measures capture the downstream consequences of structural racism rather than the upstream causes of inequities, such as laws and policies.31

To address the limitations of measurement approaches used in previous research, this study directly measured exposure to structural racism–related state laws across multiple interrelated domains. Although the 1964 Civil Rights Act barred overt racial discrimination in the United States, there are many contemporary state laws that have differential impacts on racially and ethnically minoritized groups. Notable examples include mandatory minimum sentencing laws,32,33 voter identification laws,34,35 and minimum wage laws.36,37 Laws like these vary across states and, depending on the way they are written, may either be harmful or protective for racially and ethnically minoritized groups. Laws are important to study because they have the potential to affect population health and health equity by determining access to flexible resources, such as freedom, political power, and income, that help people avoid and/or mitigate risks for disease.38 Access to these socially valued resources additionally may influence health through psychosocial mechanisms, such as perceptions of fairness.39

Building on recent work by a team of public health researchers and legal scholars who used systematic policy surveillance methods to develop a comprehensive database of structural racism–related US state laws,29 we created a summary index of 22 state laws related to the criminal legal system, economics and labor, education, health care, housing, immigration, and political participation. We then linked this new structural racism state legal index to BRFSS survey data to examine the following hypotheses:

Our first hypothesis is that an increase in the structural racism state legal index will be associated with poorer cardiovascular health, as indicated by a lower total score on the LS7. Our second hypothesis is that the association between the structural racism state legal index and the LS7 score will be more pronounced among non-Hispanic Black, Hispanic, non-Hispanic Asian, and another race/multiracial adults compared with non-Hispanic White adults.

Methods

Data and sample

The primary data sources for this study were the BRFSS40 and the Database of Structural Racism–Related State Laws.29 The US Centers for Disease Control and Prevention launched the BRFSS in 1984 to collect state-level data on health-related risk behaviors, chronic health conditions, and the use of preventive services. Data are collected annually via telephone survey, using random-digit dialing techniques for landlines and cell phones in all 50 states, the District of Columbia, and 3 US territories. Noninstitutionalized civilians aged 18 years or older are eligible to participate, and the sample is representative of the population of each state. The Database of Structural-Racism Related State Laws is a comprehensive database of state laws related to structural racism.29 The database was developed by a team of public health researchers and legal scholars using systematic policy surveillance methods (see Agénor et al.29 for additional details). Data are available from 2010 through 2013 for all 50 states and the District of Columbia. Additional data sources for state-level covariates included the US Census Bureau population estimates,41 the American Community Survey (ACS),42 and the Correlates of State Policy Project,43 a public database containing more than 3000 policy-related variables. We used state identifiers to link data from the BRFSS surveys to data from the other sources. Although we had exposure data from 2010 through 2013, outcome data were only available in odd years. Thus, we limited our analysis to 2011 and 2013. The analytic sample included 958 019 BRFSS 2011 and 2013 respondents from all 50 US states with nonmissing data on race and ethnicity.

Outcome: LS7

The LS7 includes 7 health-related indicators: smoking status, body mass index (BMI; an indirect estimate of excess adiposity that does not account for differences by race and ethnicity, sex, gender, or age44), physical activity, diet, total cholesterol level, blood pressure, and blood glucose level.30 Traditionally, each indicator is classified as “ideal” (score = 2), “intermediate” (score = 1), or “poor” (score = 0), according to criteria established by the American Heart Association and summed, with total scores ranging from 0 to 14. For this study, we used a version of the LS7 that has been adapted for use in BRFSS.45,46 Each indicator was classified as ideal (1) or not ideal (0) and summed, with total scores ranging from 0 to 7. Higher scores indicate better cardiovascular health. Indicators include current smoking status (1 = smoked < 100 cigarettes in lifetime, or smoked ≥100 cigarettes in lifetime but not currently smoking), BMI (1 = 18.5 to 24.9 kg/m2), physical activity (1 = performed 150 minutes of moderate intensity activity or 75 minutes of vigorous intensity activity or an equivalent combination in the past week), fruit and vegetable consumption (1 = met sex- and age-specific dietary guidelines for fruit and vegetable intake in the past month3), diagnosis of high cholesterol (1 = not diagnosed with high cholesterol), diagnosis of hypertension (1 = not diagnosed with high blood pressure), and diagnosis of diabetes (1 = not diagnosed with diabetes). All LS7 indicators were self-reported.

Exposure: structural racism state legal index

The structural racism state legal index was created using information from the Database of Structural Racism–Related State Laws.29 The index includes 22 laws related to the criminal legal system, economics and labor, education, health care, housing, immigration, and political participation, as shown in Table 1. Drawing on previous research examining structural xenophobia,47 a group of public health researchers and legal scholars categorized each law as potentially protective/inclusionary or discriminatory/exclusionary based on their legal and public health expertise. Laws were coded as potentially protective/inclusionary and assigned a score of -1 if they disproportionately extended benefits or resources or prevented penalty or harm among racially or ethnically minoritized people. Laws were coded as potentially discriminatory/exclusionary and assigned a score of 1 if they disproportionately undermined access to benefits or resources or penalized or harmed racially or ethnically minoritized people. Given that structural racism often manifests as “inaction in the face of need,”48 laws that maintain the status quo were coded as potentially discriminatory. For example, in the case of minimum-wage adjustments, states that require minimum-wage adjustments according to the cost of living or inflation were assigned a score of −1, whereas states that do not require such adjustments and those that follow federal minimum wage were assigned a score of 1. Annual index scores are continuous, with total scores potentially ranging from −22 to 22. A negative score on the structural racism state legal index indicates a state that has a greater number of protective laws than discriminatory laws, whereas a positive score indicates a state that has a greater number of discriminatory laws than protective laws. A score of 0 indicates a state that has an equal number of protective and discriminatory laws. Thus, a higher score on the structural racism state legal index indicates exposure to a greater number of potentially discriminatory state laws.

Table 1.

Structural racism state legal index coding, 2011 and 2013.

Domain Law Coding a
Criminal legal Stand your ground −1: Duty to retreat
1: Castle doctrine or “stand your ground”; or self-defense laws do not address “stand your ground”
Criminal legal Mandatory minimum sentencing −1: State does not impose mandatory minimum sentencing
1: State imposes mandatory minimum sentencing
Criminal legal Racial profiling data collection −1: State mandates racial profiling data collection from traffic stops
1: State does not mandate racial profiling data collection from traffic stops
Criminal legal Laws prohibiting racial profiling −1: State explicitly prohibits racial profiling or has explicit punishment for racial profiling
1: State does not prohibit racial profiling
Criminal legal Racial profiling training −1: State requires racial profiling training for law enforcement
1: State does not require racial profiling training for law enforcement
Criminal legal Stop and identification: failure to respond penalty −1: State does not penalize people for failing to provide identification upon request by a law enforcement officer
1: State penalizes people in some form for failing to provide identification upon request by a law enforcement officer
Economics and labor Federal minimum-wage comparison −1: State’s minimum wage is >$0.50 more than the federal minimum wage
1: State’s minimum wage is ≤$0.50 more than the federal minimum wage
Economics and labor Limits on minimum-wage −1: State does not limit minimum wage
1: State prohibits any city/town from setting minimum wage above the federal minimum wage or limits the amount; or state follows federal minimum wage
Economics and labor Minimum-wage adjustments −1: State requires minimum-wage adjustments according to the cost of living or inflation
1: State does not require minimum-wage adjustments according to the cost of living or inflation; or state follows federal minimum wage
Economics and labor Minimum-wage exemptions −1: State does not include an exemption leading to a salary below the federal minimum wage
1: State permits employers to pay some employees below the federal minimum wage based on ≥1 exemption; or state follows federal minimum wage
Economics and labor Predatory lending −1: State provides comprehensive consumer protection (including credit and/or real estate)
1: State does not provide consumer protection for real estate or credit; or state has no consumer protection law
Immigration Undocumented immigrant identification −1: State allows undocumented immigrants to obtain a driver’s license or state identification
1: State does not allow undocumented immigrants to obtain a driver’s license or state identification
Immigration and healthcare Undocumented immigrant health insurance coverage −1: State provides health insurance coverage to all immigrant children
1: State provides health insurance coverage to documented immigrant children only or state does not provide coverage for immigrant children
Immigration and education Undocumented immigrant in-state tuition −1: Undocumented immigrants are eligible for in-state tuition benefits
1: Undocumented immigrants are not eligible for in-state tuition benefits
Political participation Voter identification requirement −1: No identification required
1: Identification required
Political participation and criminal legal Voter disenfranchisement −1: No disenfranchisement for people with criminal convictions
1: Voting rights restored automatically after release from prison, voting rights restored after certain conditions are met, or felons are permanently disenfranchised
Education Corporal punishment in schools −1: State has outlawed corporal punishment in public schools
1: State has no law on corporal punishment in public schools; or corporal punishment is permitted
Housing Fair housing: domestic violence −1: State allows victims of domestic violence to break a lease without penalty or victims are protected against discrimination in housing
1: State imposes penalties on victims of domestic violence when breaking a lease and requires proof; or state has no law
Housing Fair housing: source of income −1: State prohibits landlords from discriminating against tenants based on source of income
1: State has no law on or does not prohibit landlords from discriminating based on source of income
Housing Eviction notice −1: 3+ days notice
1: No notice
Housing and criminal legal Fair housing: drug felony convictions −1: State does not specifically exempt individuals from fair housing protections who have been convicted of a drug felony
1: State exempts persons from fair housing protections who have been convicted of a drug felony or state has no law on discrimination in housing based on drug felony conviction
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aCoding is as follows: −1 = protective/inclusionary; 1 = discriminatory/exclusionary.

Effect modifier: race and ethnicity

Race and ethnicity were self-reported and categorized as non-Hispanic Black, Hispanic, non-Hispanic Asian, non-Hispanic White, and another race/multiracial, which includes Native Hawaiian or other Pacific Islander, American Indian or Alaska Native, more than 1 race, and another race not listed. To ensure adequate power for the examination of effect modification, groups with fewer than 10 000 respondents summed across survey years were combined into a single category, as described in the previous sentence.

Covariates

Individual-level covariates measured in BRFSS included the following: race and ethnicity categories (non-Hispanic Black [reference], White, Hispanic, non-Hispanic Asian, and another race/multiracial), age (18-24 years [reference], 25-34, 35-44, 45-54, 55-64, ≥65 years), sex (female, male [reference]), and marital status (married or partnered; widowed, divorced, separated or never married [reference]). All individual-level covariates were self-reported, except sex, which was determined by the interviewer. Time-varying state-level covariates for 2011 and 2013 included the following: percentage of non-Hispanic Black people and percentage of Hispanic people, from the US Census Bureau population estimates41; percentage of people living in poverty, from the ACS42; percentage of adults with a bachelor’s degree or higher, from the ACS42; and citizen ideology, from the Correlates of State Policy Project.43 Citizen ideology is a continuous measure; higher values indicate that residents are more politically liberal.49 Race and ethnicity, sex, and marital status were conceptualized as potential confounders because they are associated with the exposure and are risk factors for the outcome but are not on the causal pathway from exposure to outcome. State-level covariates were conceptualized as potential confounders or potential mediators, given uncertainty about the temporal relationship with the exposure. For example, states with more Black residents may be more likely to pass racially discriminatory laws, making the percentage of non-Hispanic Black people a potential confounder. However, it is also possible that Black people may choose to move out of a state that has recently passed new racially discriminatory laws, making the percentage of non-Hispanic Black people a potential mediator.

Statistical analysis

First, we performed multiple imputation using chained equations to account for missing data.50 All study variables were included in the imputation model, and 10 data sets were imputed under the missing at random assumption. The amount of missing data ranged from 0% to 13.9% (Table S1). Next, we calculated weighted and unweighted descriptive statistics for all study variables. We then ran a series of linear regression models to test study hypotheses. All regression models included fixed effects for year and state to control for time trends and unmeasured, time-invariant, state-level contextual factors (eg, history of Jim Crow laws). In model 1, we regressed the LS7 score on the structural racism state legal index (measured in the same year as the outcome and scaled by the SD for ease of interpretation), controlling for potential confounders, including individual-level race and ethnicity, age, sex, and marital status. We did not adjust for education, income, or health insurance status, because those factors may be on the causal pathway from state-level structural racism to cardiovascular health, particularly if a respondent’s level of exposure to structural racism–related state laws at the time of the BRFSS survey was highly correlated with their level of exposure prior to adulthood. In model 2, we added the following time-varying, state-level covariates to model 1: percentage of non-Hispanic Black people, percentage of Hispanic people, percentage of people living in poverty, percentage of adults with a bachelor’s degree or higher education, and citizen ideology. These covariates were added in a second step, because it is unclear whether they are confounders or mediators, as previously discussed.

Next, we examined race-stratified models to determine whether the association between the structural racism state legal index and the LS7 score was more pronounced among non-Hispanic Black, Hispanic, non-Hispanic Asian, and another race/multiracial respondents compared with non-Hispanic White respondents. We examined race-stratified models rather than models with interaction terms to allow for differences by race and ethnicity in associations between the covariates and the outcome. We tested the equality of coefficients for the structural racism state legal index across the race-stratified models via seemingly unrelated estimation.51

Finally, we performed 2 secondary analyses. First, we examined income-stratified models in the full sample and by race and ethnicity to determine whether the association between the structural racism state legal index and the LS7 score was more pronounced among respondents with family income below the federal poverty threshold (poverty income ratio [PIR] < 1) compared with respondents with family income 1 to 3 times (PIR = 1-3) or more than 3 times (PIR > 3) the federal poverty threshold. Next, we used logistic regression models to examine individual components of the LS7 score as outcomes in the full sample and by race and ethnicity. All analyses were conducted in STATA 17.0 using procedures that account for the complex sampling design of BRFSS by incorporating strata and primary sampling unit indicators, as well as sample weights.

Results

Weighted descriptive statistics for all study variables are shown for the full sample in Table 2 (unweighted descriptive statistics are shown for the full sample in Table S2, and weighted and unweighted descriptive statistics for the individual LS7 components are shown by race and ethnicity in Tables S3 and S4). In the full sample, the mean LS7 score was 3.7 (SD = 1.2). Scores on the structural racism state legal index ranged from −11 to 17, with a mean of 4.5 (SD = 8.0). Each state’s score on the index is shown by year in Table S5. States with the lowest scores on the index in 2011 were Washington and California; states with the highest scores were Georgia, Indiana, and Tennessee. Just under 12% of the sample was non-Hispanic Black, 15.0% was Hispanic, 4.3% was non-Hispanic Asian, 66.1% was non-Hispanic White, and 3.1% of the sample were people of another race or multiracial.

Table 2.

Weighted descriptive statistics for all study variables, Behavioral Risk Factor Surveillance System 2011 and 2013 (n = 958 019).

Characteristic % or Mean (SD) a
Age group (years), %
 18-24 13.0
 25-34 17.4
 35-44 17.1
 45-54 18.5
 55-64 16.0
 ≥65 18.1
Sex, %
 Female 51.4
 Male 48.6
Race/ethnicity, %
 Non-Hispanic Black 11.5
 Non-Hispanic White 66.1
 Hispanic 15.0
 Non-Hispanic Asian 4.3
 Another race/multiracial 3.1
Marital status, %
 Married or partnered 55.9
 Not married, not partnered 44.1
State percentage of Black people, mean (SD), range 11.8 (7.5), 0.4-35.3
State percentage of Hispanic people, mean (SD), range 14.7 (11.7), 1.1-43.7
State percentage of people with a college degree or higher education, mean (SD), range 29.6 (4.5), 18.9-40.5
State percentage of people in poverty, mean (SD), range 14.7 (2.7), 7.6-22.5
State citizen ideology, mean (SD), range 49.5 (11.4), 21.0-91.9
Structural Racism State Legal Index, mean (SD), range 4.5 (8.0), −11 to 17
LS7 score, mean (SD), range 3.7 (1.2), 0-7
Individual LS7 components
 Smoking status, % ideal 80.8
 Body mass index, % ideal 34.7
 Physical activity, % ideal 50.4
 Diet, % ideal 14.3
 Cholesterol, % ideal 37.1
 Hypertension, % ideal 68.0
 Diabetes, % ideal 87.3
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aUnless otherwise indicated.

bLS7, Life’s Simple 7.

Results of the linear regression models examining the association between the structural racism state legal index and the LS7 score in the full sample are shown in models 1 and 2 in Table 3. All models included fixed effects for year and state. As shown in model 1, a 1 SD increase in the structural racism state legal index was associated with a 0.03-unit decrease in the LS7 score (b = −0.03; 95% CI, −0.07 to 0.00; P = .049), controlling for individual-level confounders (race and ethnicity, age, sex, marital status). As shown in model 2, adding controls for time-varying state-level covariates (percentage of non-Hispanic Black people, percentage of Hispanic people, percentage of people with a bachelor’s degree or higher education, percentage of people living in poverty, citizen ideology) that may be on the causal pathway from the exposure to the outcome did not attenuate the association between the structural racism state legal index and the LS7 score (b = −0.06; 95% CI, −0.09 to 0.02; P = .001).

Table 3.

Linear regression models examining the association between the structural racism state legal index and the Life’s Simple 7 score, Behavioral Risk Factor Surveillance System 2011 and 2013 (n = 958 019).

  Model 1 a Model 2 b
  b (95% CI) P value b (95% CI) P value
Structural racism state legal indexc −0.03 (−0.07 to 0.00) .049 −0.06 (−0.09 to 0.02) .001
Race/ethnicity
 Non-Hispanic Black Refd Ref
 Non-Hispanic White 0.35 (0.33-0.37) <.001 0.35 (0.33-0.37) <.001
 Hispanic 0.14 (0.11-0.17) <.001 0.14 (0.11-0.17) <.001
 Non-Hispanic Asian 0.50 (0.46-0.54) <.001 0.50 (0.46-0.54) <.001
 Another race/multiracial 0.14 (0.11-0.17) <.001 0.14 (0.12-0.17) <.001
Age group, years
 18-24 Ref Ref
 25-34 −0.38 (−0.40 to −0.36) <.001 −0.38 (−0.40 to −0.36) <.001
 35-44 −0.48 (−0.50 to −0.46) <.001 −0.38 (−0.50 to −0.46) <.001
 45-54 −0.58 (−0.60 to −0.56) <.001 −0.58 (−0.60 to −0.56) <.001
 55-65 −0.63 (−0.65 to −0.61) <.001 −0.63 (−0.65 to −0.61) <.001
 ≥65 −0.62 (−0.64 to −0.60) <.001 −0.62 (−0.64 to −0.60) <.001
Sex
 Male Ref Ref
 Female 0.26 (0.25-0.27) <.001 0.26 (0.25-0.27) <.001
Marital status
 Not married or partnered Ref Ref
 Married or partnered 0.19 (0.18-0.20) <.001 0.19 (0.18-0.20) <.001
State percentage of Black people 0.02 (−0.08 to 0.11) .710
State percentage of Hispanic people −0.05 (−0.09 to 0.00) .044
State percentage of people with a college   degree or higher education 0.90 (−0.53 to 2.33) .215
State percentage of people in poverty 0.00 (0.00-0.01) .375
Citizen ideology 0.00 (0.00, 0.00) .101
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aAll models include fixed effects for year and state.

bThe structural racism state legal index is scaled by the SD.

cRef, reference.

Linear regression models stratified by race and ethnicity are shown in models 1 and 2 in Table 4. All models included fixed effects for year and state. As shown in model 1, which controlled for individual-level confounders, a 1 SD increase in the structural racism state legal index was associated with a 0.12-unit decrease in the LS7 score among Black respondents (b = −0.12; 95% CI, −0.25 to 0.01; P = .067), a 0.04-unit decrease among non-Hispanic White respondents (b = −0.04; 95% CI, −0.07 to 0.00; P = .048), a 0.05-unit decrease among Hispanic respondents (b = −0.05; 95% CI, −0.18 to 0.08; P = .440), a 0.07-unit decrease among non-Hispanic Asian respondents (b = −0.07; 95% CI, −0.34 to 0.19; P = .584), and a 0.03-unit increase among another race/multiracial respondents (b = 0.03; 95% CI, −0.17 to 0.22; P = .800). Results were similar in model 2, which controlled for both individual- and state-level covariates. Comparisons of regression coefficients across the race-stratified models revealed no statistically significant differences in the association between the structural racism state legal index and the LS7 score (results not shown).

Table 4.

Race-stratified linear regression models examining the association between the structural racism state legal index and the Life’s Simple 7 score, Behavioral Risk Factor Surveillance System 2011 and 2013 (n = 958 019).

  Non-Hispanic Black Non-Hispanic White Hispanic Non-Hispanic Asian Another race/multiracial
  b (95% CI) P value b (95% CI) P value b (95% CI) p-value b (95% CI) P value b (95% CI) P value
Model 1 a
Structural racism state legal indexb −0.12 (−0.25 to 0.01) .067 −0.04 (−0.07 to 0.00) .048 −0.05 (−0.18 to 0.08) .440 −0.07 (−0.34 to 0.19) .584 0.03 (−0.17 to 0.22) .800
Age, years (refc: 18-24)
 25-34 −0.41 (−0.47 to −0.35) <.001 −0.38 (−0.41 to −0.36) <.001 −0.34 (−0.40 to −0.29) <.001 −0.34 (−0.46 to −0.22) <.001 −0.34 (−0.44 to −0.25) <.001
 35-44 −0.60 (−0.66 to −0.54) <.001 −0.47 (−0.49 to −0.44) <.001 −0.41 (−0.47 to −0.36) <.001 −0.34 (−0.47 to −0.21) <.001 −0.50 (−0.61 to −0.40) <.001
 45-54 −0.88 (−0.94 to −0.83) <.001 −0.52 (−0.54 to −0.49) <.001 −0.57 (−0.63 to −0.51) <.001 −0.41 (−0.56 to −0.25) <.001 −0.65 (−0.75 to −0.55) <.001
 55-65 −0.97 (−1.03 to −0.91) <.001 −0.54 (−0.57 to −0.52) <.001 −0.74 (−0.81 to −0.67) <.001 −0.50 (−0.65 to −0.35) <.001 −0.63 (−0.74 to −0.53) <.001
 ≥65 −0.95 (−1.01 to −0.89) <.001 −0.54 (−0.56 to −0.51) <.001 −0.80 (−0.87 to −0.73) <.001 −0.55 (−0.72 to −0.38) <.001 −0.65 (−0.75 to −0.55) <.001
Sex (ref: male)
 Female 0.04 (0.00-0.07) .040 0.31 (0.29-0.32) <.001 0.25 (0.22-0.29) <.001 0.29 (0.23-0.36) <.001 0.18 (0.12-0.24) <.001
Marital status (ref: not married or partnered)
Married/partnered 0.11 (0.08-0.15) <.001 0.22 (0.21-0.23) <.001 0.10 (0.06013) <.001 0.09 (0.00-0.17) .052 0.15 (0.09-0.21) <.001
Model 2 a
Structural racism state legal index −0.11 (−0.27 to 0.04) .146 −0.04 (−0.08 to −0.01) .026 −0.09 (−0.22 to 0.05) .220 −0.23 (−0.50 to 0.04) .098 −0.06 (−0.25 to 0.13) .548
Age, years (ref: 18-24)
 25-34 −0.41 (−0.47 to −0.35) <.001 −0.38 (−0.41 to −0.36) <.001 −0.34 (−0.40 to −0.29) <.001 −0.34 (−0.46 to −0.22) <.001 −0.35 (−0.45 to −0.25) <.001
 35-44 −0.60 (−0.66 to −0.54) <.001 −0.47 (−0.49 to −0.44) <.001 −0.42 (−0.47 to −0.36) <.001 −0.34 (−0.47 to −0.21) <.001 −0.51 (−0.61 to −0.40) <.001
 45-54 −0.88 (−0.94 to −0.83) <.001 −0.52 (−0.54 to −0.49) <.001 −0.57 (−0.63 to −0.51) <.001 −0.41 (−0.57 to −0.25) <.001 −0.65 (−0.75 to −0.55) <.001
 55-65 −0.97 (−1.03 to −0.91) <.001 −0.54 (−0.57 to −0.52) <.001 −0.74 (−0.81 to −0.67) <.001 −0.50 (−0.65 to −0.35) <.001 −0.63 (−0.74 to −0.53) <.001
 ≥65 −0.95 (−1.01 to −0.89) <.001 −0.54 (−0.56 to −0.51) <.001 −0.80 (−0.87 to −0.73) <.001 −0.55 (−0.72 to −0.38) <.001 −0.65 (−0.75 to −0.55) <.001
Sex (ref: male)
Female 0.04 (0.00-0.07) .039 0.31 (0.29-0.32) <.001 0.25 (0.22-0.29) <.001 0.29 (0.23-0.36) <.001 0.18 (0.13-0.24) <.001
Marital status (ref: not married or partnered)
 Married or partnered 0.11 (0.08-0.15) <.001 0.22 (0.21-0.23) <.001 0.10 (0.06013) <.001 0.09 (0.00-0.17) .054 0.15 (0.09-0.21) <.001
State percentage of Black people 0.02 (−0.27 to 0.32) .884 0.00 (−0.09 to 0.10) .940 −0.14 (−0.48 to 0.20) .425 −0.01 (−0.72 to 0.69) .975 0.14 (−0.34 to 0.61) .572
State percentage of Hispanic people 0.09 (−0.06 to 0.25) .238 −0.04 (−0.09 to 0.02) .168 −0.12 (−0.28 to 0.04) .134 −0.27 (−0.65 to 0.11) .162 −0.20 (−0.45 to 0.05) .122
State percentage of people with a college degree or higher education 4.89 (−1.80 to 11.58) .152 −0.93 (−2.31 to 0.44) .184 6.97 (−0.53 to 14.47) .068 11.06 (0.59 to 21.52) .038 3.48 (−2.30 to 9.25) .237
State percentage of people living in poverty 0.00 (−0.01 to 0.02) .844 0.00 (0.00 to 0.01) .610 0.00 (−0.03 to 0.03) .835 0.01 (−0.04 to 0.06) .639 0.01 (−0.02 to 0.04) .650
Citizen ideology 0.00 (−0.01 to 0.01) .487 0.00 (0.00 to 0.00) .468 −0.01 (−0.01 to 0.00) .116 0.00 (−0.01 to 0.02) .527 0.00 (−0.01 to 0.01) .778
Open in a new tab

aAll models include fixed effects for year and state.

bThe structural racism state legal index is scaled by the SD. Coefficients for the structural racism state legal index were not significantly different across racial and ethnic groups.

cRef, reference.

Results of secondary analyses examining models stratified by income are shown in Table S6. In the full sample, there was no evidence of effect modification by income in model 1. In model 2, which controlled for both individual and state-level covariates, the association between the structural racism state legal index and the LS7 score was statistically significant among respondents in the middle (PIR = 1-3: b = −0.06; 95% CI, −0.12 to 0.00; P = .036) and highest (PIR > 3: b = −0.05; 95% CI, −0.10 to 0.00; P = .042) PIR categories but not the lowest category (PIR < 1: b = −0.08; 95% CI, −0.21 to 0.06; P = .258). Comparisons of coefficients across the income-stratified models revealed that these coefficients were not statistically different from one another (results not shown). In race-stratified models, there was no evidence of effect modification by income in either model 1 or model 2 for non-Hispanic White, Hispanic, non-Hispanic Asian, and another race/multiracial respondents. Among non-Hispanic Black respondents, in model 1 only, the association between the structural racism state legal index and the LS7 score was statistically significant among those in the lowest PIR category (b = −0.38; 95% CI, −0.66 to −0.09; P = .011) but not the middle (b = −0.16; 95% CI, −0.37 to 0.04; P = .111) or highest (b = 0.06; 95% CI, −0.16 to 0.27; P = .589) categories. Comparisons of coefficients across the income-stratified models for Black respondents revealed that the coefficients for the lowest and highest PIR categories were statistically different from one another (P = .02; results not shown).

Results of secondary analyses examining individual components of the LS7 score are shown in Table S7. For the full sample, in both models 1 and 2, a 1 SD increase in the structural racism state legal index was associated with lower odds of ideal BMI (model 1: OR = 0.94 [95% CI, 0.88-1.00], P = .051; model 2: OR = 0.93 [95% CI, 0.87-1.00], P = .038), ideal diet (model 1: OR = 0.89 [95% CI, 0.83-0.97], P = .007; model 2: OR = 0.87, [95% CI, 0.80-0.95], P = .001), and ideal hypertension (model 1: OR = 0.92 [95% CI, 0.86-0.98], P = .012; model 2: OR = 0.92 [95% CI, 0.86-0.98]; P = .016). The structural racism state legal index was not associated with ideal smoking status, ideal physical activity, ideal cholesterol level, or ideal diabetes. Results were similar in race-stratified models for the individual LS7 indicators.

Discussion

In December 2020, the American Heart Association issued a call to action to address structural racism as a fundamental cause of disparities in cardiovascular morbidity and mortality.52 To date, however, few empirical studies have examined the impact of structural racism on cardiovascular outcomes, and existing research has been limited by the use of self-report16-19 and proxy measures of structural racism.16,20-28 To address this limitation, we directly measured exposure to 22 structural racism–related state laws in 7 domains relevant to population health. We found that an increase in the summary index of structural racism–related US state laws was associated with poorer cardiovascular health, as indicated by a lower total score on the AHA’s LS7 index. Contrary to expectations, we found that this association was similar across racial and ethnic groups.

Although the association between the structural racism state legal index and the LS7 score was statistically significant, it was small in magnitude. In the model adjusted for individual-level confounders, a 1 SD increase in the exposure was associated with a 0.03-unit decrease in the outcome, which ranged from 0 to 7 and had a SD of 1.2. Previous research using the traditional LS7 measure, which ranges from 0 to 14, found that a 1-point higher score on the LS7 was associated with a 27% lower hazard of incident cardiovascular disease events and a 31% lower hazard of cardiovascular mortality.53 Thus, the magnitude of clinical significance is unclear.

In general, previous studies have found that exposure to structural racism is associated with poorer cardiovascular outcomes, particularly among Black Americans. For example, Lukachko et al.21 found that living in a state with greater Black-White disparities in political participation and representation, employment, education, and criminal legal involvement was associated with higher odds of past-year myocardial infarction among Black people but either null or lower odds of myocardial infarction among White people, and Dougherty et al.20 found that greater county-level Black-White disparities in segregation, education, incarceration, poverty, and access to health care were associated with higher BMI among Black people but lower BMI among White people. In contrast to these results, we found that exposure to structural racism–related state laws was negatively associated with cardiovascular health for all racial and ethnic groups, including White respondents, who should, in theory, derive social and economic benefits from these laws. Additional research is needed to understand this unexpected finding, which is consistent with some recent work suggesting that policies designed to maintain White supremacy (eg, failure to adopt Medicaid expansion, repeal of gun control laws) may also harm the health of White Americans, particularly those with low socioeconomic status.54,55 We explored this possibility in secondary analyses stratified by income and found that the association between the structural racism state legal index and the LS7 score did not differ by income among White respondents. However, a higher score on the structural racism state legal index was associated with worse cardiovascular health among Black respondents who lived below the poverty threshold compared with those whose total family income was more than 3 times the poverty threshold, suggesting that exposure to structural racism–related state laws is more harmful for Black people with low vs high income.

Strengths, limitations, and directions for future research

Key strengths of this study include direct measurement of exposure to structural racism across multiple domains; examination of multiple racial and ethnic groups in a large, representative sample of US adults in all 50 states; and use of fixed effects models to control for unmeasured, time-invariant, state-level confounders. First, we used the Database of Structural Racism–Related State Laws,29 which was developed by a team of public health researchers and legal scholars using systematic policy surveillance methods, to directly measure exposure to state laws related to structural racism in 7 legal domains. This approach is consistent with the conceptualization of structural racism as a “race discrimination system” that operates across many interrelated domains10 and represents an important step forward in the measurement of structural racism in cardiovascular health research. Next, although most research on structural racism and health has focused on Black-White disparities, BRFSS provided adequate power to determine whether exposure to structural racism–related state laws was more detrimental to the health of non-Hispanic Black, Hispanic, and non-Hispanic Asian respondents compared with White respondents. Findings revealed small differences across racial and ethnic groups that were not statistically significant. Finally, we used fixed effects models to control for all time-invariant, state-level confounders, which strengthens causal inference by reducing the threat of omitted variable bias.

Although efforts are underway to expand the Database of Structural Racism–Related State Laws, the current database only includes 4 years of data, and the LS7 indicators were only measured in BRFSS in 2 of these years. Thus, an important limitation of this study is the short amount of time over which to observe changes in the number of racially discriminatory laws in the states, which increases the risk of Type II error by reducing statistical power.56 Another limitation is the measurement of exposure and outcome in the same year. Although behaviors like smoking and physical activity can change quickly in response to an environmental stressor, health outcomes such as hypertension and diabetes develop more slowly. Thus, the measurement approach used in this study may be better suited to the subset of behavioral indicators included in the LS7, particularly among respondents whose early life exposure to racially discriminatory state laws differs significantly from their current exposure. Secondary analyses examining individual components of the LS7 score revealed that a higher score on the structural racism state legal index was associated with lower odds of 2 out of 4 behavioral indicators (ideal BMI and diet) and 1 out of 3 health outcomes (ideal hypertension). Ideally, future studies should measure current and lifetime exposure to structural racism. Other important directions for future research include examining how sex, gender identity, socioeconomic status, and sexual orientation identity interact with race and ethnicity to modify the association between exposure to structural racism–related state laws and cardiovascular health; examining individual laws as predictors of cardiovascular health to make results more directly actionable; and examining biometric health data, which is more accurate than self-reported data. Finally, laws are only 1 component of structural racism. Measures that capture multiple aspects of structural racism, including laws, policies, and institutional norms and practices, are needed to further our understanding of the health-related consequences of exposure to structural racism.

Conclusions

This study used an innovative approach to directly measure exposure to potentially discriminatory state laws, an important dimension of structural racism. We found that an increase in structural racism–related state laws was associated with worse cardiovascular health. Contrary to expectations, this association was similar across racial and ethnic groups. Studies examining exposure to structural racism over a longer period are needed to confirm or refute the results presented here. In addition, more work is needed to identify the mechanisms underlying the association between exposure to racially discriminatory state laws and cardiovascular health and to identify strategic points of intervention to improve cardiovascular health and reduce racial and ethnic health inequities.

Supplementary Material

Web_Material_kwae176
web_material_kwae176.zip (49.9KB, zip)

Acknowledgments

The Database of Structural Racism–Related State Laws, used in this study, was funded by a pilot grant from Harvard Catalyst awarded to S. Bryn Austin.

Contributor Information

Belinda L Needham, Department of Epidemiology, University of Michigan, Ann Arbor, MI, United States .

Darya Dokshina, Department of Epidemiology, University of Michigan, Ann Arbor, MI, United States .

Talha Ali, Department of Community Health, Tufts University, Medford, MA, United States.

Kristi L Allgood, Department of Epidemiology and Biostatistics, Texas A&M University, College Station, TX, United States.

Megan Douglas, Department of Community Health and Preventive Medicine, Morehouse School of Medicine, Atlanta, GA, United States.

Akilah Dulin, Department of Behavioral and Social Sciences, Brown University, Providence, RI, United States.

Nancy L Fleischer, Department of Epidemiology, University of Michigan, Ann Arbor, MI, United States .

Amber E Johnson, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.

Annie Ro, Department of Health, Society, and Behavior, University of California, Irvine, CA, United States.

Madina Agénor, Department of Behavioral and Social Sciences, Brown University, Providence, RI, United States.

Supplementary material

Supplementary material is available at the American Journal of Epidemiology online.

Funding

The authors declare no funding.

Conflict of interest

The authors declare no conflicts of interest.

Data availability

Data from the Database of Structural Racism–Related State Laws are available through the Brown Digital Repository (https://repository.library.brown.edu/studio/). A data use agreement is required to gain access to the data. Data from the Behavioral Risk Factor Surveillance System can be downloaded from the BRFSS website (https://www.cdc.gov/brfss/annual_data/annual_data.htm). US Census Bureau population estimates can be found on the US Census Bureau website (https://www.census.gov/programs-surveys/popest.html). Data from the American Community Survey can be downloaded from the US Census Bureau website (https://www.census.gov/programs-surveys/acs/data.html). Data from the Correlates of State Policy Project can be downloaded from the Michigan State University Institute for Public Policy and Social Research website (https://ippsr.msu.edu/public-policy/correlates-state-policy).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Web_Material_kwae176
web_material_kwae176.zip (49.9KB, zip)

Data Availability Statement

Data from the Database of Structural Racism–Related State Laws are available through the Brown Digital Repository (https://repository.library.brown.edu/studio/). A data use agreement is required to gain access to the data. Data from the Behavioral Risk Factor Surveillance System can be downloaded from the BRFSS website (https://www.cdc.gov/brfss/annual_data/annual_data.htm). US Census Bureau population estimates can be found on the US Census Bureau website (https://www.census.gov/programs-surveys/popest.html). Data from the American Community Survey can be downloaded from the US Census Bureau website (https://www.census.gov/programs-surveys/acs/data.html). Data from the Correlates of State Policy Project can be downloaded from the Michigan State University Institute for Public Policy and Social Research website (https://ippsr.msu.edu/public-policy/correlates-state-policy).

Articles from American Journal of Epidemiology are provided here courtesy of Oxford University Press

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