Educational Attainment and Cardiovascular Disease Symptoms among Americans Entering Mid-Adulthood

Abstract

Although the education-health gradient is well documented, relatively little is known about its application to cardiovascular disease (CVD), the leading cause of mortality in the United States. Furthermore, critical nuances across educational levels and demographic subgroups remain underexplored. The authors examine the association between education and CVD among U.S. adults entering mid-adulthood, leveraging a novel symptom-based CVD index capturing clinical indicators such as angina or dyspnea and a detailed 13-category measure of educational attainment. Waves I and V of the nationally representative National Longitudinal Study of Adolescent to Adult Health sample (n = 11,806) are analyzed for the full cohort and by sex/gender and race/ethnicity; models also account for adolescent confounders and adulthood social and economic mediators. The findings reveal profound disparities: adults with professional or doctoral degrees report 0.6 CVD symptoms on average, versus 1.8 among those without high school credentials, a threefold difference. However, there are also important deviations from the expected gradient. Adults with sub-baccalaureate education report no fewer symptoms than high school graduates, whereas General Educational Development diploma recipients resemble dropouts more than diploma holders. Racial/ethnic patterns differ markedly: most strikingly, Black adults derive no significant CVD returns from college completion compared with high school graduation. These patterns challenge assumptions about education’s universal benefits and underscore the importance of structural and intersectional approaches to addressing health disparities and designing equitable health policies.

Keywords

cardiovascular disease education health gradient sex/gender race/ethnicity

The well-established education-health gradient posits that higher educational attainment is consistently associated with better health outcomes. More than two decades ago, two prominent scholars declared that “health, by any definition and by any measures, increases with the level of education” (Mirowsky and Ross 2003:7). More recently, the gradient has been referred to as a “social fact” (Montez et al. 2009:625). However, despite the extensive literature documenting and explaining the gradient, fundamental questions remain open. These are not just academic questions, but critical unknowns about the health of the U.S. adult population.

Unknown #1: The Educational Gradient for Cardiovascular Disease

Cardiovascular disease (CVD) has long been, and remains, the leading cause of mortality among U.S. adults (Ahmad and Anderson 2021; Ahmad, Cisewski, and Anderson 2024) and a key reason for the high mortality rates in the United States compared with other countries (Bor et al. 2024). The poor cardiovascular health among younger cohorts is a particular concern for the future. In the nationally representative National Longitudinal Study of Adolescent to Adult Health (Add Health) cohort, fewer than one in four adults had “good” cardiovascular health at 24 to 34 years of age (Lawrence et al. 2018).

Despite the clear importance of CVD for population health, relatively few social science studies on the education gradient have considered this outcome. Most focused on self-rated health (Bauldry 2014; Ericsson et al. 2024; Frase and Bauldry 2022; Lynch and von Hippel 2016; Zajacova, Hummer, and Rogers 2012; Zhang, Solazzo, and Gorman 2020), mortality (Halpern-Manners et al. 2020; Leive and Ruhm 2022; Masters, Link, and Phelan 2015; Montez et al. 2019; Warren et al. 2020), or mental health (Bauldry 2015; McFarland and Wagner 2015; Muñoz and Santos-Lozada 2021). Even studies of cardiovascular health have typically operationalized CVD in terms of risk factors, such as body mass index, blood pressure, C-reactive protein and other biomarkers, self-reported health behaviors, and sometimes medication use and doctor-diagnosed self-reports of diabetes or heart disease (Johnson et al. 2022; Lawrence et al. 2018; Noppert et al. 2021; Zajacova and Johnson-Lawrence 2016).

The problem of inferring the education gradient in CVD from studies of other health domains is that disparities “may vary depending on the domain of interest” (Hayward and Sheehan 2016:356). Numerous scholars have noted that educational effects differ across health domains, such as physical health, mental health, chronic conditions, and general self-rated health (Andersson, Wilkinson, and Maralani 2024; Cutler and Lleras-Muney 2008; Vable et al. 2018; Zajacova and Johnson-Lawrence 2016). Specifically for CVD, the education gradient varied across biomarkers or biomarker indices not only in size, but also in shape and significance for specific education levels (Bell et al. 2018; Johnson et al. 2022; Noppert et al. 2021; Zajacova and Johnson-Lawrence 2016). Educational patterns also differ for CVD conditions versus CVD mortality (Magnani et al. 2024).

Thus, a major contribution of our study is our use of a CVD symptom index as the outcome. This index, added to Add Health Wave V by clinicians specifically to capture disease burden in mid-adulthood, aggregates clinical indicators similar to those used in medical assessments of CVD (E. Whitsel, personal communication, September 19, 2023). Prior analyses of CVD with the Add Health data calculated the Framingham risk score or used other composite measures combining biomarkers with self-reported behaviors, doctor-diagnosed conditions, and medication use (e.g., Allgood et al. 2024; Bravo et al. 2023; Gooding et al. 2016; Lippert, Houle, and Walsemann 2022; Noppert et al. 2021; Park, Kim, and Kim 2023). In contrast, our index reflects symptoms and functional limitations, offering a more accurate picture of individuals’ present CVD symptoms rather than risk for future cardiovascular problems. Our approach thus reveals real-time cardiovascular strain, particularly among groups underrepresented in clinical diagnoses, such as women and racial/ethnic minority adults.

Unknown #2: The Functional Form of the Education Gradient for CVD

The education-health gradient is among the most documented relationships in social science (e.g., Conti, Heckman, and Urzua 2010; Cutler and Lleras-Muney 2008; Frase and Bauldry 2022; Groot and Maassen van den Brink 2007; Ross, Masters, and Hummer 2012; Sasson 2016; Schellekens and Ziv 2020). According to fundamental cause theory (Link and Phelan 1995), social factors such as educational attainment shape health via numerous mechanisms that facilitate access to health-relevant resources such as sufficient income, safe housing and neighborhoods, or access to quality health care.

A growing number of studies, however, have revealed anomalies that challenged the universality of the gradient. One important anomaly pertains to the sub-baccalaureate (subBA) group, which is large and heterogeneous, comprising adults who completed only some college all the way to those with associate degrees. Adults with subBA attainment were sometimes found to report health and health behaviors no better than their peers with only a high school diploma despite the additional schooling (Muñoz and Santos-Lozada 2021; Rosenbaum 2012; Schoenborn, Adams, and Peregoy 2013; Zajacova and Johnson-Lawrence 2016; Zajacova, Rogers, and Johnson-Lawrence 2012; Zajacova et al. 2020). In some studies, this pattern was evident in the data although the authors did not engage with the findings (Johannes et al. 2010; Weinberger et al. 2018). Nonetheless, the lack of health returns for adults with subBA schooling can be considered an anomaly insofar as their additional education might not be associated with correspondingly better health.

Another anomaly has been observed among adults whose highest degree is the General Educational Development (GED) diploma, sometimes referred to as high school equivalency diploma. Despite the assumption of equivalency built into the very purpose of the degree, numerous studies found that GED diploma recipients had significantly worse health than their supposed peers with a high school diploma and instead were more similar to high school dropouts (Fuller-Thomson, Grossman, and MacNeil 2024; Kenkel, Lillard, and Mathios 2006; Liu, Chavan, and Glymour 2013; Zajacova 2012; Zajacova and Everett 2014; Zajacova and Montez 2017). The GED disadvantage has been described for other important life domains such as work, economic well-being, or family formation (Heckman, Humphries, and Kautz 2014; Jepsen 2017; Ou 2008).

Patterns that contradict the expected education-health gradient may indicate instances where additional schooling or credentials do not equate to greater access to such resources, or where educational gains are counterbalanced by adverse factors. For example, subBA attainment may improve economic prospects (Kim and Tamborini 2019; Rhodes 2024), but college noncompleters carry student debt (Fishman and Gutin 2021) and may experience wide-ranging negative consequences of the attrition itself (Dennison 2022; Reynolds and Baird 2010), ultimately negating potential health benefits.

CVD outcomes, as expected, exhibit educational disparities, although there has been limited research examining these disparities in detail. Education is associated with CVD mortality (Khan et al. 2023) and risk factors (Hamad et al. 2019; Hu, Yang, and Yang 2024; Jiang, Boylan, and Zilioli 2022; Johnson et al. 2022), including in Add Health (Lawrence et al. 2018; Noppert et al. 2021). Only one analysis to date explored educational attainment in sufficient granularity: Lawrence et al. (2018), examining CVD risk factors, found no educational disparities among individuals with lower levels of attainment, but observed a pronounced gradient beginning at the associate degree level and especially at the bachelor’s level. As noted earlier, however, findings for risk factors cannot be assumed to generalize to CVD symptoms. These are conceptually and empirically distinct constructs. Education gradients vary substantially from relatively flat associations for biomarkers to steeper ones for mortality (Hoffmann and Kröger 2021). Relatively few studies have examined CVD prevalence (Magnani et al. 2024), and none, to our knowledge, has examined CVD symptoms. Our study thus contributes in two important ways: by investigating the functional form of the educational gradient in detail and by focusing on CVD symptoms, an underexamined dimension in the literature.

Unknown #3: The Role of Sex/Gender and Race/Ethnicity in the Education-CVD Gradient

Education, as a key dimension determining social conditions for individuals across the life course, has long been understood as a fundamental cause of health (Link and Phelan 1995). However, racism (Phelan and Link 2015; Williams, Lawrence, and Davis 2019) and sexism (Homan 2019, 2024) have independent fundamental causal roles in shaping population health. Moreover, race/ethnicity and sex/gender intersect with socioeconomic conditions in complex ways to affect health. Thus, the education-health gradient may differ by sex/gender and race/ethnicity. We use the term sex/gender because both concepts are important to CVD (Clayton and Gaugh 2022) and because this study and cited research do not distinguish between biological and social pathways and use measures that conflate the two concepts.

Health outcomes, including CVD, differ across sex/gender because of many social and biological factors. We might expect that the relationship between education and health differs for men and women because of structural sexism, variation in economic returns, differences in selection into educational levels, or biological pathways. When comparing education’s effects on health for men and women, findings have been mixed. Sometimes the education gradient is comparable for men and women (Cutler and Lleras-Muney 2008; Vable et al. 2018; Zajacova 2006; Zhang et al. 2020). For mortality, there seems to be a tendency toward a steeper gradient for men (Ross et al. 2012), although the differences are often only marginal (Hummer and Hernandez 2013; Montez et al. 2009; Zajacova and Hummer 2009). For health, in contrast, studies tend to find a steeper gradient for women, for instance for self-rated health, depression, or physical impairment (Cutler and Lleras-Muney 2008; Ross and Mirowsky 2010; Ross et al. 2012).

The sex/gender patterns for education’s effects on CVD specifically remain unresolved. Men have higher CVD risk factors (Clark et al. 2014), incidence (Domanski et al. 2025), and mortality (Centers for Disease Control and Prevention 2023). Although no studies to our knowledge have formally tested differences in the education-CVD association, some reported similar educational patterns across sex/gender for CVD risk factors (Lawrence et al. 2018) and CVD mortality (Khan et al. 2023). Others found a steeper gradient for women for incident coronary heart disease (Thurston et al. 2005) and cardiometabolic biomarkers (Zajacova and Johnson-Lawrence 2016). These mixed findings may stem from the fact that different CVD measures can yield distinct gradient patterns (Noppert et al. 2021), underscoring the need to investigate sex/gender moderation in the context of CVD symptoms.

Investigating how race and ethnicity may moderate the educational gradient in CVD symptoms is also needed, as historical and contemporary racism may lead to differences in the education-health relationship. One important potential pathway is captured in the Stress Process model (Brown, Mitchell, and Ailshire 2020; Pearlin 1989; Richardson, Goodwin, and Hummer 2021), whereby discrimination heightens exposure to chronic and acute stressors, leading to sustained physiological stress and ultimately chronic inflammation that harms health (DeAngelis, Hargrove, and Hummer 2022).

Prior research demonstrates mixed findings for educational differences in health for racial/ethnic groups, depending on the outcome and subgroups examined. Overall, Black Americans derive fewer health benefits from higher education, a phenomenon referred to as diminished returns (Assari 2020; Assari and Bazargan 2019; Assari et al. 2020). These reduced benefits are also evident in CVD risk factors and morbidity (Bell et al. 2018; Holmes and Zajacova 2014; Magnani et al. 2024). Hispanic and Asian Americans’ health displays important heterogeneity by outcome, and by immigrant status and ethnic group (Alam et al. 2021; Guadamuz et al. 2021; Lopez-Neyman et al. 2022). For CVD, studies of risk factors indicate that Hispanic and Asian Americans may have a flatter education gradient (Ciciurkaite 2021; Goldman et al. 2025; Kimbro et al. 2008). Together, the literature shows that there are differences in the education-health gradient by race/ethnicity, but the findings are inconclusive and it is difficult to hypothesize what these patterns may look like for CVD symptoms.

Unknown #4: How Much of the Education-CVD Symptom Association Is Accounted for by Early-Life Social Context or Adult Socioeconomic and Social Circumstances?

Educational disparities in cardiovascular health reflect the accumulation of social and health-related factors across the life course. To account for early-life conditions that may shape both educational attainment and adult health, we adjust for adolescent background characteristics, including family socioeconomic status, academic performance, and health (Conti and Heckman 2010; Hayward and Gorman 2004; Jackson 2009). These models offer a view of the education-CVD gradient after accounting for early-life social context.

We also examine the education-CVD association after accounting for adulthood socioeconomic and social conditions, domains often viewed as key resources through which education shapes health (Cutler and Lleras-Muney 2008; Mirowsky and Ross 2003; Phelan, Link, and Tehranifar 2010). We focus on these material and social resources in line with a structural fundamental cause approach that emphasizes upstream influences (Link and Phelan 1995). Although not intended as a test of mediation, changes in the gradient after adjustment can illuminate the contribution of these factors. For example, if the disparity between high school and BA decreases, it would suggest that the health advantages of higher education operate in part through these factors. In contrast, if the disadvantage at the subBA level remains unchanged, other unmeasured factors may be involved.

We examine these factors not only for the full sample but also in the sex/gender- and race/ethnicity-stratified analyses, as the underlying confounders and mechanisms linking education to health may operate differently across population groups. Theories on gender differences in health including structural sexism which highlights the robust differential in power and resources for men and women (Homan 2019, 2024), and resource substitution/multiplication which identifies the utility of education as a resource for women compared with men (Ross and Mirowsky 2006; Ross et al. 2012), propose that the pathways into education and from education to health likely differ by sex/gender. Research using frameworks on diminished returns and structural racism suggest multiple reasons for race/ethnic differences in the education-health relationship, including fewer financial and occupational opportunities and psychosocial consequences for upward mobility and racism-related stressors (Assari 2020; Assari and Bazargan 2019; DeAngelis et al. 2022). Although empirical work on this question in the CVD realm remains limited, Shah, Huang, et al. (2024) found that key economic and social factors played markedly different explanatory roles in racial/ethnic disparities in CVD risk factors. Although not intended as causal or formal mediation analyses, accounting for confounders and mechanisms can shed light on how the education gradient in CVD for the population and its subgroups may be structured by both earlier and contemporaneous social contexts.

The Present Study: Aims and Contributions

We examine the Add Health cohort as participants approach midlife, focusing on a unique index of CVD symptoms, and drawing on a detailed educational attainment measure, for the full cohort as well as by sex/gender and race/ethnicity. Our study is the first to use the set of measures that have been specifically designed for Add Health Wave V with the aim or capturing a clinical assessment of CVD symptoms. Additionally, the longitudinal structure of Add Health allows examination of the role of confounders, with background factors measured prior to adulthood, and mechanisms, with a breadth of information on social factors at early midlife.

Methods

Data

We use data from Add Health (Harris 2009). This large and nationally representative ongoing panel survey has followed a cohort of respondents since 1994–1995, when they were 12 to 19 years old; the last wave available at the time of this writing was Wave V, conducted in 2016–2018, when the cohort was 38 years old on average (Harris et al. 2019).

Add Health is ideal for our study because the dataset includes (1) a unique set of measures explicitly developed to capture symptoms of CVD at the early mid-adulthood stage, (2) a detailed measure of educational attainment, (3) a large sample size that allows separate examination of major population subgroups, (4) prospectively collected rich covariates across the adolescent-to-adult life course critical to capturing confounders and mechanisms, and (5) recent information on a U.S. cohort entering midlife.

Analytic Sample

Of the 20,745 original Add Health respondents, 12,300 participated in Wave V interviews. Our analysis is based on 11,806 of these respondents (96 percent) who provided valid education and CVD symptom answers. Missing information on covariates was imputed using a chained equations approach with 10 rounds of multiple imputation. We applied the Wave V weights and accounted for the study’s complex survey design to ensure representativeness.

Variables

The outcome is an index of CVD symptoms. Add Health includes a unique set of six indicators that describe a range of symptoms and conditions closely linked to key CVDs (E. Whitsel, personal communication, September 19, 2023):

doctor-diagnosed high blood pressure (hypertension, a major risk factor for CVD),

chest pain experienced during physical exertion (angina, a symptom of coronary artery disease, a type of CVD),

calf pain experienced during physical exertion (claudication, a symptom of peripheral artery disease),

shortness of breath during physical exertion (dyspnea, a significant manifestation of several underlying CVD conditions),

swelling in the feet and ankles, except during pregnancy (edema, a manifestation of several CVD conditions, including heart failure and poor venous return), and

limitations in climbing several flights of stairs (a functional measure often used in clinical assessments to gauge the status of cardiovascular health in terms of its impact on everyday functioning).

We created a summation index ranging from 0 to 6, following precedent on summing up elevated biomarkers of cardiovascular risk (Hoffmann and Kröger 2021). We analyze the index as a count variable, as this specification accurately reflected the intended operationalization (E. Whitsel, personal communication, September 19, 2023). We examined multiple alternative specifications, including a confirmatory factor analysis–based latent variable, as well as a binary indicator based off the summation index, and found the results similar to those shown below.

The key independent variable is educational attainment. We use education as reported in the latest wave (Wave V) to capture current completed levels of schooling. Educational attainment is measured in 13 mutually exclusive categories defined as the highest completed level (see Table 1). High school graduate is the omitted level in most regression analyses, as in prior studies (e.g., Andersson et al. 2024; Zajacova et al. 2012); more importantly, it shows the “returns” to postsecondary education relative to only secondary school completion and the “penalty” of incomplete secondary schooling level.

Table 1.

Distribution of Key Variables and CVD Symptom Counts in Each Category.

Note: CC = community college; CI = confidence interval; CVD = cardiovascular disease; GED = General Educational Development; HS = high school; NH = non-Hispanic; VTE = vocational or technical postsecondary education. The column for mean CVD symptom count is shaded from green = lowest count to red=highest count.

Covariates

All models control for sex/gender and race/ethnicity. Our measure of sex/gender reflects whether respondents reported they were male or female in Wave I. We do not include age in regression models, because this covariate varies little across the cohort; its inclusion does not yield any analytic benefits. The second set of covariates represents potential confounders. Measured in Wave I, this set includes individual and family-of-origin variables measured in adolescence: parental education and (logged) household income, educational expectations, Peabody Picture Vocabulary Test (a standardized test capturing verbal cognitive skills), self-reported grades earned in recent English and mathematics classes, self-rated health, smoking, and body mass index. The third set of covariates captures potential mediators measured in Wave V. These include measures of economic well-being (income, assets, employment status) and social support and integration (marital or cohabiting status, number of close friends, and regular religious service attendance). Table 1 shows information on sex/gender and race/ethnicity; Supplemental Table S2 presents details on all other covariates.

Approach

Univariate and Bivariate Estimation

We summarize educational attainment, gender, and race/ethnicity and also estimate the mean CVD symptom total at each level of these variables (Table 1). We then display the estimated prevalence of the six CVD components and the distribution of their count (Table 2). Supplemental descriptive statistics include the sample sizes at each level of education by sex/gender and race (Supplemental Table S1), the distribution of all covariates used in analyses (Supplemental Table S2), and a cross-tabulation of background characteristics and respondent’s own educational attainment (Supplemental Table S3), which highlights how the respondents’ attainment is linked to their circumstances while growing up.

Table 2.

Distribution of CVD Symptoms.

	Proportion, Weighted	95% CI
CVD symptoms
Dyspnea	.36	(.35–.38)
Physical limitations	.24	(.23–.26)
Hypertension	.19	(.18–.21)
Claudication	.18	(.17–.19)
Edema	.17	(.15–.18)
Angina	.08	(.07–.09)
CVD symptom count
0	.43	(.42–.45)
1	.24	(.23–.25)
2	.14	(.13–.15)
3	.09	(.09–.10)
4	.05	(.05–.06)
5	.03	(.02–.03)
6	.01	(.01–.01)

Note: N = 11,806. Components are ordered from most to least prevalent. CI = confidence interval; CVD = cardiovascular disease.

Multivariable Regression Analyses

We estimate negative binomial models of CVD symptom index in the full sample as a function of educational attainment plus covariates (Table 3). We build a series of three nested models: model 1 controls only for sex/gender and race/ethnicity, model 2 adds confounders (background characteristics from Wave I), and model 3 adds potential mechanisms (adulthood economic and social factors from Wave V). The negative binomial link is appropriate for the distribution of the dependent variable. Finally, we estimate similar models but stratified by sex/gender and by race/ethnicity. To obtain reliable estimates in the smaller subpopulations, we aggregate education categories into four levels: less than high school or GED, high school completion, some postsecondary education, and college degree or more (Table 4).

Table 3.

Count Ratios for Cardiovascular Disease Symptom Index by Detailed Education, Full Sample.

	Model 1	Model 2	Model 3
Education (reference: HS)
Less than HS	1.40***	1.38***	1.27***
GED diploma	1.32**	1.24*	1.21*
Some VTE	1.26**	1.26**	1.23**
Some community college	1.11	1.15*	1.17*
Some college	1.10	1.15*	1.19**
VTE degree	.96	.96	.99
AA degree	.94	.98	1.04
BA degree	.71***	.82**	.91
Some beyond BA	.84	.90	.98
MA	.60***	.69***	.78**
Some beyond MA	.54***	.64***	.72**
Doctorate	.45***	.57***	.63***
Demographics
Female (reference: male)	1.26***	1.27***	1.24***
Race/ethnicity (reference: NH White)
NH Black	1.11*	1.08	.97
Hispanic	.89*	.87*	.88*
Asian	.87	.94	.96
Other	1.25**	1.18*	1.09
Additional covariates
Wave I confounder variables		✓	✓
Wave V mechanism variables			✓

Note: Results from multiply imputed negative binomial regression models adjusted for National Longitudinal Study of Adolescent to Adult Health complex sampling design. N = 11,806. Complete results showing estimates for all covariates are provided in Supplemental Table S4. Supplemental Table S2 shows the distribution of the covariates added in Models 2 and 3. Model 1 controls for gender and race/ethnicity. Model 2 controls for gender and race/ethnicity and hypothesized confounders. Model 3 controls for gender and race/ethnicity, hypothesized confounders, and mechanisms. GED = General Educational Development; HS = high school; NH = non-Hispanic; VTE = vocational or technical postsecondary education.

p < .05. **p < .01. ***p < .001.

Table 4.

Count Ratios for Cardiovascular Disease Symptom Index by Aggregated Education: Full Sample and Stratified by Gender and Race/Ethnicity.

		By Gender		By Race/Ethnicity
	All	Men	Women	NH White	NH Black	Hispanic	Asian	Other
Model 1
Less than HS or GED diploma	1.37***	1.43***	1.30***	1.40***	1.53**	1.20	1.04	1.11
SubBA	1.06	1.09	1.02	1.02	1.29	1.02	.62**	1.42
College graduate or more	.66***	.69***	.63***	.59***	.90	.74*	.58**	1.10
Model 2
Less than HS or GED	1.32***	1.38***	1.24**	1.30***	1.56**	1.07	.89	1.43
SubBA	1.10	1.14	1.06	1.09	1.31*	.93	.52**	1.55*
College graduate or more	.78***	.81*	.75***	.75***	.98	.75*	.47***	1.16
Model 3
Less than HS or GED	1.24***	1.30**	1.17	1.23**	1.44**	1.03	.98	1.42
SubBA	1.13*	1.17*	1.10	1.13	1.34*	.94	.55**	1.52
College graduate or more	.87*	.89	.85*	.84*	1.09	.81	.49**	1.16

Note: Results from multiply imputed negative binomial regression models adjusted for National Longitudinal Study of Adolescent to Adult Health complex sampling design. N = 11,806. The reference category is HS. Values in boldface type indicate which level of education differs significantly for a given racial/ethnic group compared with the same education effect for NH White adults. None of the nine gender-by-education interaction terms was statistically significant. Model 1 controls for gender and race/ethnicity or stratifies by these characteristics. Model 2 also controls for the set of hypothesized confounders. Model 3 also controls for the set of hypothesized mechanisms. GED = General Educational Development; HS = high school; NH = non-Hispanic; subBA = sub-baccalaureate.

p < .05. **p < .01. ***p < .001.

Visualizations

We present findings graphically as average predicted counts of CVD symptoms. We show the detailed education gradient for the full sample (Figure 1) and also the aggregated education categories from sex/gender- and race/ethnicity-stratified models (Figure 2). The figures recast the findings in absolute terms rather than, as tables above, in relative terms vis-à-vis the omitted high school level.

Figure 1.

Predicted count of CVD symptoms by education. (Plot A) Predicted CVD symptom count net of gender and race (model 1). (Plot B) Predicted CVD symptom count net of gender and race and confounders (model 2). (Plot C) Predicted CVD symptom count net of gender and race, confounders, and mediators (model 3).

Figure 2.

Predicted count of CVD symptoms by education, by gender and race/ethnicity.

Supplemental Analyses and Sensitivity Checks

We conducted extensive auxiliary analyses to assess the robustness of our conclusions to different model specifications. For interested readers, Supplemental Table S4 shows the estimates for all covariates from models in Table 3 (the article includes only estimates for education, sex/gender, and race/ethnicity for parsimony). Supplemental Table S5 also shows significance tests for all pairwise comparisons from model 1 in Table 3. This shows whether any two pairs of education levels differ significantly from one another. Supplemental Table S6 shows a comparable set of pairwise comparisons from all sex/gender- and race/ethnicity-stratified models in Table 4. Supplemental Table S7 reestimates models from Table 3 using the Poisson link, and Supplemental Table S8 does the same for models from Table 4. For readers interested in examining the associations for men and women in more detail, the sample sizes allowed an estimation of sex/gender-stratified models using the original 13-category educational attainment specification (Supplemental Table S9).

We conclude with two important sensitivity checks. First, we check how sensitive our findings are to reclassifying the GED category in the stratified analyses. In the results shown in Table 4, we included the GED recipients with less than high school. In Supplemental Table S10, we reestimate the stratified models with GED recipients categorized with high school graduates as the equivalency assumption for this degree. Second, we check whether the CVD symptom index findings may be influenced by hypertension. Self-reported hypertension is different from the other five measures in that it is not just perceived difficulty but requires that a respondent reports a doctor’s prior diagnosis. Because of the widespread underreporting of hypertension (Everett and Zajacova 2015), including in the Add Health cohort (Nguyen et al. 2011), we reestimate models of CVD symptoms that omit hypertension (Supplemental Table S11). The findings from these models, as with other sensitivity checks, yield substantively identical results and only modestly differing point estimates.

Results

Table 1 shows the distribution of education, sex/gender, and race/ethnicity in the target population and the mean number of CVD symptoms at each level of these covariates. The largest educational category is BA degree with 20 percent of the cohort. High school diploma is the second largest, with 13 percent. The five subBA levels combined constitute 41 percent of the cohort; of those, 25 percent earned no postsecondary degree while 6 percent earned vocational or technical degrees and 10 percent earned associate’s degrees. To complete the description, 14 percent have at least a master’s degree, and at the other end of the spectrum, 5 percent did not complete secondary schooling and 4 percent did so via the GED program.

The mean CVD symptom count in the cohort is 1.22 (95 percent confidence interval [CI] = 1.16, 1.28); that is, the average person in the cohort has more than one of the six CVD symptoms. The table highlights the large disparities in CVD symptoms by attainment: adults with the highest attainment (professional or doctoral) have 0.60 (95 percent confidence interval [CI] = 0.49, 0.71) symptoms, while adults without a high school diploma have three times as many, or 1.82 (95 percent confidence interval [CI] = 1.63, 2.00). Women report more symptoms than men (1.32 vs. 1.12, with nonoverlapping confidence intervals). Finally, there are large racial/ethnic differences: non-Hispanic (NH) White and Hispanic adults have a similar number of symptoms (1.20 and 1.14, respectively). Compared with these two groups, NH Black adults have more (1.40) and NH Asian adults fewer (0.94) symptoms, with largely nonoverlapping confidence intervals.

Table 2 displays the key descriptive statistics of the CVD symptom index. Dyspnea and physical limitations are most common (36 percent and 24 percent prevalence, respectively), while angina is least prevalent (8 percent); hypertension, claudication, and edema are experienced by 19 percent, 18 percent, and 17 percent of the cohort, respectively. More than half of the cohort (57 percent) has at least one CVD risk factor or symptom.

Three supplemental tables provide additional information for the sample and variables. Supplemental Table S1 confirms the data include sufficient sample sizes for subgroup analyses, especially as we collapse attainment into four levels for race/ethnicity-stratified models. Supplemental Table S2 provides univariate descriptive statistics for all covariates, while Supplemental Table S3 shows the bivariate relationships between early circumstances and respondents final educational attainment level, highlighting the importance of considering these potential confounders in our analyses.

Table 3 presents results of regression models for the full sample. For parsimony, the table shows only coefficients associated with education (see Supplemental Table S4 for full results and Supplemental Table S5, which summarizes all within-model pairwise significance tests by omitting each education level in turn). Model 1 controls for sex/gender and race/ethnicity. Compared with high school graduates, adults who did not complete high school have 40 percent greater expected count of CVD symptoms. A college degree, in contrast, is associated with 29 percent lower, an MA with 40 percent lower, and a doctoral or professional degree with 55 percent lower symptom count.

Some results do not fit the expected gradient. The subBA levels, whether completed or not, are all associated with symptom count comparable with that of high school graduates with no postsecondary training (or even a greater number of symptoms, in the case of some vocational or technical postsecondary education schooling). GED graduates have 32 percent higher count of CVD symptoms compared with those with a high school diploma.

Model 2 shows the gradient net of the cohort’s adolescent characteristics. Two divergent tendencies are evident. For most schooling levels, the differences (vis-à-vis high school graduates) attenuate to varying degrees, in line with prior research. On the other hand, for two of the subBA levels, the negative “returns” became statistically significant, exposing a suppression effect. That is, net of background characteristics, attending a postsecondary institution but leaving without a degree is associated with a significantly worse CVD than just completing high school.

Model 3 includes potential mechanisms through which educational attainment might influence health. Here, again, there is generally a further attenuation of the education gradient as expected; in fact, there is no significant difference between high school and a BA degree in these models. On the other hand, these early midlife factors do not attenuate the subBA penalty for those without a degree: they have roughly 20 percent higher CVD symptom count than their peers who only completed high school and never attended postsecondary institutions.

Figure 1 visualizes these patterns in absolute levels instead of relative risk, using average predicted count of CVD symptoms. Plot A recasts results from model 1, plot B from model 2, and plot C from model 3. Plot A highlights the strong overall gradient and the monotonic behavior of the most common certifications: less than high school, high school, BA, and advanced degrees. However, the plot also makes evident the higher than expected CVD symptom counts for all subBA levels, as well as the nonequivalent cardiovascular health of the GED recipients who resemble high school dropouts more than high school graduates. Alternatively, it is the high school graduates who have lower CVD symptom counts than would be expected, relative to their peers with lower and higher schooling levels.

In plots B and C, net of potential confounders and mechanisms, the gradient overall becomes less steep. The largest change in the gradient derives from the addition to confounders in model 2. Yet the subBA paradox, especially for the noncompleters, as well as the nonequivalence of the GED, remain evident.

Table 4 summarizes the education gradients by sex/gender and by race/ethnicity for aggregated education levels, and also indicates where observed differences are statistically significant across demographic subgroups. For sex/gender, there are no statistically significant differences between men and women in the patterns across education levels and covariate sets. Supplemental Table S5 adds sex/gender-stratified models with the original attainment detail and corroborates the similarity between men and women regardless of the granularity of the attainment specification.

In contrast, there are large racial/ethnic differences. NH Black adults have no significant “returns” to higher education: their CVD symptoms are statistically equivalent for high school graduates, those with subBA, and even college graduates, while high school noncompleters have a much higher CVD symptom count. In contrast, for Hispanic adults, there is no significant “penalty” for not completing high school while the college completion ‘premium’ is significant and statistically indistinguishable from the returns to NH White adults. Asian Americans are the only group where subBA education is associated with positive, and substantively large, “returns” to CVD symptoms; in fact, the point estimate is nearly as large as it is for college completion.

Figure 2 recasts these results in absolute terms as average predicted CVD symptom counts. We note two patterns. First, although there are differences in the CVD overall across groups (i.e., they are higher for women than men, and for NH Black than for Hispanic and Asian adults). Second, the shape of the gradient differs across racial/ethnic groups as described in Table 4: the BA premium vis-à-vis high school completion is pronounced among NH White adults, more muted among Hispanic and NH Asian adults, and not evident among NH Black adults.

Discussion

This study offers a new perspective on the relationship between education and cardiovascular health by leveraging a unique, clinically grounded symptom-based index of CVD in a diverse cohort of U.S. adults entering mid-adulthood. We documented disparities across a detailed range of educational levels and assessed variation by sex/gender and race/ethnicity. The findings reveal both striking overall inequalities and important deviations from the expected gradient. The deviations emerge both at specific attainment levels (among GED holders and adults with subBA education) and across population groups, with notable differences in the education-CVD association by race/ethnicity. The results challenge our assumption of a universal education-health gradient.

CVD Symptoms among Adults Entering Midlife

Our findings document concerning trends in elevated risk for key CVD clinical symptoms, extending evidence on troubling levels of CVD risk factors in U.S. adults born 1977 and 1984 (Lawrence et al. 2018). At an average age of just 38 years, a majority (57 percent) of the cohort reports at least one CVD symptom. The most common are dyspnea (shortness of breath during physical activity), reported by 36 percent of the cohort, and limitations when climbing stairs (24 percent). Nearly one in five experience calf pain during physical exertion and swelling in the feet or ankles, pointing to early signs of peripheral vascular problems or venous insufficiency, conditions that signal an increased risk for future cardiovascular events in this cohort.

The Education-CVD Gradient

Our results highlight stark disparities by education in CVD symptom burden, adding to prior evidence linking education and CVD risk factors, events, and mortality (Johnson et al. 2022; Kubota et al. 2017; Magnani et al. 2024; Noppert et al. 2021; Zacher 2023). Adults with professional or doctoral degrees report only 0.6 CVD symptoms on average, compared with 1.8 symptoms among those without a secondary credential, a more than threefold difference that reflects deep social stratification in cardiovascular health, even in early midlife.

The detailed educational categories revealed patterns that deviate from the expected monotonic gradient. Adults with subBA attainment—some postsecondary vocational or college education or a vocational/technical or associate’s degree—have more schooling than high school graduates but do not have fewer CVD symptoms; many, in fact, have significantly more symptoms. Even the completion of a vocational/technical or associate degree does not confer an advantage, compared with high school graduates. Additionally, adults who completed their secondary credential via the GED report significantly more CVD symptoms than high school graduates. This is inconsistent with the understanding of the GED as an equivalent to a high school diploma (GED Testing Service 2014; Heckman, Humphries, and Mader 2010; Heckman et al. 2014).

The prior paragraph can be reinterpreted by focusing the advantage of high school graduates rather than the underperformance of the subBA and GED groups. That is, perhaps it is the high school diploma group that’s anomalous in having better cardiovascular health than expected from the overall gradient. This is a useful reinterpretation of the literature on anomalies, which takes the “deficit perspective” by focusing on the groups with worse-than-expected health. Instead, perhaps the literature should examine what characteristics explain the better than expected cardiovascular health of high school graduates. For example, it could be that these individuals have particularly stable life course and occupational trajectories that also benefit their health.

Confounders and Mechanisms for the Education-CVD Symptom Relationship

The differences in CVD symptoms across educational levels are only partly reduced when considering early-life circumstances that may shape both educational attainment and health in adulthood, and midlife social and economic conditions that may link education to improved cardiovascular health. These findings support causal studies concluding that the CVD gradient is not merely an artifact of confounding (Hamad et al. 2019; Hu et al. 2024; Liu et al. 2024), including studies analyzing the same Add Health cohort (Lawrence et al. 2018; Noppert et al. 2021). Economic and social factors in early midlife also contribute meaningfully to the elevated CVD symptom burden among adults with lower education. That is, if these factors were more favorable among the less educated, cardiovascular symptoms would likely be lower than observed, highlighting the potential of midlife environments to mitigate educational disparities in health.

These factors also offer insight into the documented anomalies. Our models show that some of the high school–GED difference is due to the more disadvantaged background of GED recipients and their lower level of economic and social resources in adulthood. Taking those differences into account attenuates the GED penalty, but it remains significant and substantively large (about 21 percent higher count of CVD symptoms relative to high school graduates). The GED has been associated with “high-risk” behaviors (Kurti et al. 2016) and lower noncognitive skills (Heckman 2008; Heckman and Rubinstein 2001), characteristics that might be associated with risky health behaviors that may harm the cardiovascular system.

In contrast, adjusting for the subBA group’s relatively favorable early-life circumstances and midlife social and economic conditions amplifies the observed anomalies. That is, if all groups had similar background and adult circumstances, the symptom gap between subBA and high school graduates would be even greater. This paradox aligns with research showing that subBA credentials yield labor market returns (Kim and Tamborini 2019; Schudde and Bernell 2019; Schudde and Shea 2022), and suggests that other, unmeasured, factors may explain the subBA anomaly. Most people who went to college expect to earn a bachelor’s degree (Rosenbaum, Ahearn, and Rosenbaum 2017; Santos Laanan 2003), and falling short of that aspiration may undermine self-esteem (Hoeschler and Backes-Gellner 2017), worsen mental health (Faas et al. 2018), and impose financial burdens such as student debt (Houle 2013), which may adversely affect cardiovascular health.

Sex/Gender and the Education-CVD Gradient

Analyses stratified by sex/gender reveal that the education-CVD symptom gradients for women and men are virtually identical, regardless of whether education is measured using four broad levels or the full 13-category detail. This aligns with prior research showing no sex/gender differences in education gradients for CVD risk factors (Lawrence et al. 2018) and CVD mortality (Khan et al. 2023). From a theoretical perspective, resource-substitution reasoning suggests education should yield larger health dividends for women by compensating for gender-linked resource deficits, whereas resource-multiplication and structural sexism frameworks predict smaller returns for women because institutional sexism limits the translation of schooling into health-promoting resources (Homan 2019, 2024; Ross and Mirowsky 2006, 2010). Our results indicate that, at least for early- to midlife cardiovascular symptomatology, these countervailing forces cancel out, producing net parity in the education-CVD gradient.

Race/Ethnicity and the Education-CVD Gradient

We find striking heterogeneity in the education-CVD symptom gradient across racial/ethnic groups, challenging assumptions about universal education benefits. Notably, Black adults experience no significant returns to college completion compared with high school graduation, while simultaneously experiencing the steepest penalty for incomplete secondary education. This pattern echoes a similar absence of returns to higher schooling levels in the Add Health cohort for allostatic load (Richardson et al. 2021), suggesting that the finding may apply to multiple dimensions of health. Conceptually, it aligns with the diminished returns hypothesis (Assari 2020; Assari et al. 2020; Barsha et al. 2023; Curry 2020; Johnson et al. 2022), which posits that Black adults derive smaller health benefits from advanced education compared with White adults. Structural racism may lead to a lower quality of educational experiences, as well as barriers preventing the translation of educational achievements into health-promoting resources for Black Americans that encompass limited access to economic opportunities, quality health care, and neighborhood resources (Rochmes 2024; Williams et al. 2019).

In line with prior studies, Hispanic adults show a flatter gradient for CVD symptoms compared with NH Whites, with no significant penalty for incomplete high school nor return to subBA education, but significant returns to college completion. The latter suggests that structural barriers operate differently than for other racial/ethnic groups, while the patterns at lower levels of schooling might reflect protective cultural factors, perhaps partly linked to the higher proportion of recent immigrants in the community or family social support systems at lower education levels (Boen and Hummer 2019; Goldman et al. 2006; Kimbro et al. 2008). It is important to note that the relatively flat gradient for Hispanic adults may mask important differences across ethnic subgroups (Elias et al. 2023).

Finally, Asian Americans also present a unique pattern: no CVD symptom differences between high school education or less, but the largest postsecondary returns of any group. Asian Americans, in fact, are the only groups where subBA education confers significant CVD benefits. This exceptional pattern possibly reflects the substantial heterogeneity within this population and varying social determinants across Asian ethnic subgroups, including immigration status, English proficiency, and health care access (Shah, Kandula, et al. 2024).

Taken together, the racial/ethnic heterogeneity in education-CVD associations suggests that demographic groups vary in the selection and operant mechanisms through which education influences their health (Assari, Cobb, and Bazargan 2019). Explanations for variations in educational pattern for Black, Hispanic, and Asian Americans compared with NH Whites differ, with each group demonstrating distinct sociohistorical determinants of health (Goldman et al. 2025). These results further challenge assumptions about uniform education-health relationships and highlight the critical role of structural factors in shaping how educational resources translate into cardiovascular health benefits.

Limitations

Several caveats limit the generalizability and interpretation of these findings. First, although the narrow age range in the Add Health study is not a limitation in and of itself, the findings are based on the experiences of the 1977–1984 cohorts and their health as they enter mid-adulthood and thus may not necessarily apply to other birth cohorts or other ages. Second, our outcome is self-reported and includes a doctor-diagnosed condition (e.g., hypertension), which may be disproportionately underreported (Everett and Zajacova 2015). However, we reanalyzed the data without hypertension and found all conclusions unchanged, so this element of the CVD index does not bias our findings. Third, the analysis is observational, descriptive and correlational; a causal analysis is beyond the scope of our aims. We encourage a causal analysis of the Add Health CVD symptom data, which would add an important dimension—a focus on CVD—to the sizable and growing literature exploring the causality of the education-health gradient (Avendano, de Coulon, and Nafilyan 2020; Ericsson et al. 2024; Fletcher 2015; Lawrence 2017). Fourth, we examine select upstream mechanisms of socioeconomic and social conditions, but future work may consider more proximal mechanisms such as psychosocial resources and health behaviors. Last, our study focused on vertical attainment patterns. We were not able to incorporate major horizontal aspects of education, such as achievement, quality of institution, college majors, and other distinguishing characteristics that may have independent effects on health outcomes (Montez et al. 2018; Reimer and Pollak 2009).

Conclusions

The study highlights the nuanced and complex relationship between educational attainment and cardiovascular health among U.S. adults entering mid-adulthood. By leveraging a unique, clinically grounded symptom-based index that captures actual cardiovascular strain rather than risk factors or biomarkers, our findings provide critical insights into the cardiovascular health status of this cohort. The symptoms, including dyspnea, angina, and functional limitations, represent important harbingers of future cardiovascular events as this population ages, making our findings particularly consequential for understanding long-term population health trajectories.

Our findings highlight large overall education disparities, such that the lowest educated have three times the number of CVD symptoms compared with the highest educated adults. The findings also reveal important anomalies. Adults with subBA attainment have no better cardiovascular health than high school graduates, and GED recipients have significantly more symptoms than high school graduates. Given the large sizes of the affected groups—57 million U.S. adults with subBA attainment levels (U.S. Census Bureau 2021) and 10 percent of all secondary credentials awarded as GEDs (Heckman et al. 2014)—these patterns matter substantially for population-level health disparities. Our findings also reveal striking racial/ethnic heterogeneity that challenges fundamental assumptions about education’s universal benefits. Black adults derive no significant cardiovascular health returns from college completion compared with high school graduation, a stark contrast to substantial benefits observed among White and Asian adults. Given that CVD remains the leading cause of mortality, these symptom-based disparities represent early warning signs of serious future cardiovascular events that demand urgent policy attention.

Supplemental Material

sj-docx-1-srd-10.1177_23780231251371345 – Supplemental material for Educational Attainment and Cardiovascular Disease Symptoms among Americans Entering Mid-Adulthood

Supplemental material, sj-docx-1-srd-10.1177_23780231251371345 for Educational Attainment and Cardiovascular Disease Symptoms among Americans Entering Mid-Adulthood by Anna Zajacova, Samuel Fishman and Elizabeth M. Lawrence in Socius

Footnotes

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Add Health is directed by Robert A. Hummer and funded by National Institute on Aging cooperative agreements U01 AG071448 (Hummer) and U01AG071450 (Aiello and Hummer) at the University of North Carolina at Chapel Hill. Waves I to V data are from the Add Health Program Project, grant P01 HD31921 (Harris) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Add Health was designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill.

ORCID iDs

Anna Zajacova

Samuel Fishman

Elizabeth M. Lawrence

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Anna Zajacova is a professor of sociology at the University of Western Ontario, where she studies population health with a focus on health determinants, trends, and disparities, particularly among midlife and older U.S. adults. Her research has been supported by the National Institutes of Health and the Social Sciences and Humanities Research Council of Canada and published in Demography, Pain, the Journal of Health and Social Behavior, and other leading outlets.

Samuel Fishman’s research examines economic stratification and population health and intersection between these two topics. His work has been published in outlets such as the American Journal of Sociology, Social Science Research, SSM—Population Health, and the Journal of Marriage and Family. He completed his PhD at the University of North Carolina at Chapel Hill in 2019 and completed a postdoctoral position at Duke University in 2021. He is currently an assistant professor at Baylor University. His research agenda focuses on intergenerational and early life determinants of education outcomes and the relationship between educational and financial inequality with population health.

Elizabeth M. Lawrence is an associate professor of sociology at the University of Nevada, Las Vegas. Her research examines how social structures and contexts differentially shape health behaviors, health outcomes, and mortality. She uses a life course perspective to understand how educational and health disparities develop over time and across developmental stages. Her work has been published in journals such as the Journal of Health and Social Behavior and Social Science & Medicine.

References

Ahmad

Farida B.

Anderson

Robert N.

2021. “The Leading Causes of Death in the US for 2020.” JAMA 325(18):1829–30.

Ahmad

Farida B.

Cisewski

Jodi A.

Anderson

Robert N.

2024. “Leading Causes of Death in the US, 2019-2023.” JAMA 332(12):957–58.

Alam

Towfiqul

Echeverria

Sandra E.

DuPont-Reyes

Melissa J.

Vasquez

Elizabeth

Murillo

Rosenda

Gonzalez

Tailisha

Rodriguez

Fatima

. 2021. “Educational Attainment and Prevalence of Cardiovascular Health (Life’s Simple 7) in Asian Americans.” International Journal of Environmental Research and Public Health 18(4):1–12.

Allgood

Kristi L.

Fleischer

Nancy L.

Morenoff

Jeffrey

Assari

Shervin

Needham

Belinda L.

2024. “Do Police Encounters Increase the Risk for Cardiovascular Disease? Police Encounters and Framingham 30-Year Cardiovascular Risk Score.” Journal of Racial and Ethnic Health Disparities 11(1):348–63.

Andersson

Matthew A.

Wilkinson

Renae

Maralani

Vida

. 2024. “Revisiting Health Disparities Linked to ‘Some College’: Incorporating Gender and High School Experiences.” Social Currents 11(2):159–80.

Assari

Shervin

. 2020. “Blacks’ Diminished Health Returns of Educational Attainment: Health and Retirement Study.” Journal of Medical Research and Innovation 4(2):e000212.

Assari

Shervin

Bazargan

Mohsen

. 2019. “Minorities’ Diminished Returns of Educational Attainment on Hospitalization Risk: National Health Interview Survey (NHIS).” Hospital Practices and Research 4(3):86–91.

Assari

Shervin

Cobb

Sharon

Bazargan

Mohsen

. 2019. “Race by Gender Differences in the Protective Effects of Education and Income against Subsequent Changes in Self-Rated Health, Physical Activity, and Body Mass Index among Older Americans.” Journal of Health Economics and Development 1(2):9–21.

Assari

Shervin

Cobb

Sharon

Saqib

Mohammed

Bazargan

Mohsen

. 2020. “Diminished Returns of Educational Attainment on Heart Disease among Black Americans.” Open Cardiovascular Medicine Journal 14:5–12.

10.

Avendano

Mauricio

de Coulon

Augustin

Nafilyan

Vahe

. 2020. “Does Longer Compulsory Schooling Affect Mental Health? Evidence from a British Reform.” Journal of Public Economics 183:1–34.

11.

Barsha

Rifath Ara Alam

Assari

Shervin

Hossain

Mian B.

Apata

Jummai

Sheikhattari

Payam

. 2023. “Black Americans’ Diminished Return of Educational Attainment on Tobacco Use in Baltimore City.” Journal of Racial and Ethnic Health Disparities 10(6):3178–87.

12.

Bauldry

Shawn

. 2014. “Conditional Health-Related Benefits of Higher Education: An Assessment of Compensatory versus Accumulative Mechanisms.” Social Science & Medicine 111:94–100.

13.

Bauldry

Shawn

. 2015. “Variation in the Protective Effect of Higher Education against Depression.” Society and Mental Health 5(2):145–61.

14.

Bell

Caryn N.

Thorpe

Roland J.

Bowie

Janice V.

LaVeist

Thomas A.

2018. “Race Disparities in Cardiovascular Disease Risk Factors within Socioeconomic Status Strata.” Annals of Epidemiology 28(3):147–52.

15.

Boen

Courtney E.

Hummer

Robert A.

2019. “Longer—but Harder—Lives? The Hispanic Health Paradox and the Social Determinants of Racial, Ethnic, and Immigrant–Native Health Disparities from Midlife through Late Life.” Journal of Health and Social Behavior 60(4):434–52.

16.

Bor

Jacob

Raquib

Rafeya

Himmelstein

David

Woolhandler

Steffie

Stokes

Andrew C.

2024. “Causes of Excess Deaths in the US Relative to Other Wealthy Nations, 1999–2020: A Population Autopsy.” medRxiv. Retrieved September 6, 2025. https://www.medrxiv.org/content/10.1101/2024.07.26.24311071v1.

17.

Bravo

Mercedes A.

Fang

Hancock

Dana B.

Johnson

Eric O.

Mullan Harris

Kathleen

. 2023. “Long-Term Air Pollution Exposure and Markers of Cardiometabolic Health in the National Longitudinal Study of Adolescent to Adult Health (Add Health).” Environment International 177:107987.

18.

Brown

Lauren L.

Mitchell

Uchechi A.

Ailshire

Jennifer A.

2020. “Disentangling the Stress Process: Race/Ethnic Differences in the Exposure and Appraisal of Chronic Stressors among Older Adults.” Journals of Gerontology: Series B, Psychological Sciences and Social Sciences 75(3):650–60.

19.

Centers for Disease Control and Prevention. 2023. “Heart Disease Deaths.” Retrieved June 22, 2025. https://www.cdc.gov/nchs/hus/topics/heart-disease-deaths.htm.

20.

Ciciurkaite

Gabriele

. 2021. “Race/Ethnicity, Gender and the Ses Gradient in Bmi: The Diminishing Returns of Ses for Racial/Ethnic Minorities.” Sociology of Health & Illness 43(8):1754–73.

21.

Clark

Cari Jo

Alonso

Alvaro

Spencer

Rachael A.

Pencina

Michael

Williams

Ken

Everson-Rose

Susan A.

2014. “Predicted Long-Term Cardiovascular Risk among Young Adults in the National Longitudinal Study of Adolescent Health.” American Journal of Public Health 104(12):e108–15.

22.

Clayton

Janine Austin

Gaugh

Michelle D.

2022. “Sex as a Biological Variable in Cardiovascular Diseases: JACC Focus Seminar 1/7.” Journal of the American College of Cardiology 79(14):1388–97.

23.

Conti

Gabriella

Heckman

James J.

2010. “Understanding the Early Origins of the Education–Health Gradient.” Perspectives on Psychological Science 5(5):585–605.

24.

Conti

Gabriella

Heckman

James J.

Urzua

2010. “The Education-Health Gradient.” American Economic Review 100(2):234–38.

25.

Curry

Gwenetta Denise

. 2020. “The Impact of Educational Attainment on Black Women’s Obesity Rate in the United States.” Journal of Racial and Ethnic Health Disparities 7(2):345–54.

26.

Cutler

David M.

Lleras-Muney

Adriana

. 2008. “Education and Health: Evaluating Theories and Evidence.” Pp. 29–60 in Making Americans Healthier: Social and Economic Policy as Health Policy, edited by Schoeni

R. F.

House

J. S.

Kaplan

G. A.

Pollack

New York: Russell Sage.

27.

DeAngelis

Reed T.

Hargrove

Taylor W.

Hummer

Robert A.

2022. “Skin Tone and the Health Returns to Higher Status.” Demography 59(5):1791–1819.

28.

Dennison

Christopher R.

2022. “Dropping out of College and Dropping into Crime.” Justice Quarterly 39(3):585–611.

29.

Domanski

Michael J.

Wu Colin

Tian

Xin

Haiou

Shalhoub

Ruba

Miao

Rui

Ahmed

A. Hasan

, et al. 2025. “Association of Race with Risk of Incident Cardiovascular Disease, Coronary Heart Disease, Heart Failure, and Stroke.” Journal of the American College of Cardiology: Advances 4(6):101811.

30.

Elias

Sabrina

Turkson-Ocran

Ruth-Alma

Koirala

Binu

Byiringiro

Samuel

Baptiste

Diana

Himmelfarb

Cheryl R.

Commodore-Mensah

Yvonne

. 2023. “Heterogeneity in Cardiovascular Disease Risk Factors among Latino Immigrant Subgroups: Evidence from the 2010 to 2018 National Health Interview Survey.” Journal of the American Heart Association 12(10):e027433.

31.

Ericsson

Malin

Finch

Brian

Karlsson

Ida K.

Gatz

Margaret

Reynolds

Chandra A.

Pedersen

Nancy L.

Mosing

Miriam A.

2024. “Educational Influences on Late-Life Health: Genetic Propensity and Attained Education.” Journals of Gerontology: Series B, Psychological Sciences and Social Sciences 79(1):gbad153.

32.

Everett

Bethany

Zajacova

Anna

. 2015. “Gender Differences in Hypertension and Hypertension Awareness among Young Adults.” Biodemography and Social Biology 61(1):1–17.

33.

Faas

Caitlin

Benson

Mark J.

Kaestle

Christine E.

Savla

Jyoti

. 2018. “Socioeconomic Success and Mental Health Profiles of Young Adults Who Drop out of College.” Journal of Youth Studies 21(5):669–86.

34.

Fishman

Samuel H.

Gutin

Iliya

. 2021. “Debts of Despair: Education, Financial Losses, and Precursors of Deaths of Despair.” SSM—Population Health 14:100759.

35.

Fletcher

Jason M.

2015. “New Evidence of the Effects of Education on Health in the US: Compulsory Schooling Laws Revisited.” Social Science & Medicine 127:101–107.

36.

Frase

Robert T.

Bauldry

Shawn

. 2022. “The Expansion of Higher Education and the Education-Health Gradient in the United States.” Social Currents 9(1):70–86.

37.

Fuller-Thomson

Esme

Grossman

Robin

MacNeil

Andie

. 2024. “Is the Health of Older Americans with a GED Equivalent to Their Peers with a High School Diploma?” International Journal of Aging and Human Development 99(1):47–65.

38.

GED Testing Service. 2014. “Annual Statistical Report on the GED^® Test: The Close of the 2002 Series GED^® Test.” Washington, DC: GED Testing Service.

39.

Goldman

Noreen

Bai Allen

Norrina

Pratt

Boriana

Ning

Hongyan

Schneper

Lisa

Lloyd-Jones

Donald

Notterman

Daniel

. 2025. “Racial and Ethnic Variation in Socioeconomic Differentials in Young Adult Cardiovascular Health.” American Journal of Epidemiology. doi:10.1093/aje/kwaf100.

40.

Goldman

Noreen

Kimbro

Rachel T.

Turra

Cassio M.

Pebley

Anne R.

2006. “Socioeconomic Gradients in Health for White and Mexican-Origin Populations.” American Journal of Public Health 96(12):2186–93.

41.

Gooding

Holly C.

Milliren

Carly

Shay

Christina M.

Richmond

Tracy K.

Field

Alison E.

Gillman

Matthew W.

2016. “Achieving Cardiovascular Health in Young Adulthood—Which Adolescent Factors Matter?” Journal of Adolescent Health 58(1):119–21.

42.

Groot

Wim

Maassen van den Brink

Henriette

. 2007. “The Health Effects of Education.” Economics of Education Review 26:186–200.

43.

Guadamuz

Jenny S.

Kapoor

Karan

Lazo

Mariana

Eleazar

Andrea

Yahya

Tamer

Kanaya

Alka M.

Cainzos-Achirica

Miguel

, et al. 2021. “Understanding Immigration as a Social Determinant of Health: Cardiovascular Disease in Hispanics/Latinos and South Asians in the United States.” Current Atherosclerosis Reports 23(6):25.

44.

Halpern-Manners

Andrew

Helgertz

Jonas

Warren

John Robert

Roberts

Evan

. 2020. “The Effects of Education on Mortality: Evidence from Linked U.S. Census and Administrative Mortality Data.” Demography 57(4):1513–41.

45.

Hamad

Rita

Nguyen

Thu T.

Bhattacharya

Jay

Glymour

M. Maria

Rehkopf

David H.

2019. “Educational Attainment and Cardiovascular Disease in the United States: A Quasi-experimental Instrumental Variables Analysis.” PLoS Medicine 16(6):e1002834.

46.

Harris

Kathleen M.

2009. “The National Longitudinal Study of Adolescent to Adult Health (Add Health), Waves I & II, 1994–1996; Wave III, 2001–2002; Wave IV, 2007-2009” [Machine-Readable Data File and Documentation]. Vol. 2021. Chapel Hill: Carolina Population Center, University of North Carolina at Chapel Hill.

47.

Harris

Kathleen M.

Halpern

Carolyn T.

Whitsel

Eric A.

Hussey

Jon M.

Killeya-Jones

Ley A.

Tabor

Joyce

Dean

Sarah C.

2019. “Cohort Profile: The National Longitudinal Study of Adolescent to Adult Health (Add Health).” International Journal of Epidemiology 48(5):1415–15.

48.

Hayward

Mark D.

Gorman

Bridget K.

2004. “The Long Arm of Childhood: The Influence of Early-Life Social Conditions on Men’s Mortality.” Demography 41(1):87–107.

49.

Hayward

Mark D.

Sheehan

Connor M.

2016. “Does the Body Forget? Adult Health, Life Course Dynamics, and Social Change.” Pp. 355–68 in Handbook of the Life Course, edited by Shanahan

Mortimer

Kirkpatrick Johnson

Cham, Switzerland: Springer.

50.

Heckman

James J.

2008. “Schools, Skills, and Synapses.” Economic Inquiry 46(3):289–324.

51.

Heckman

James J.

Humphries

John Eric

Kautz

Tim

. 2014. The Myth of Achievement Tests: The GED and the Role of Character in American Life. Chicago: University of Chicago Press.

52.

Heckman

James J.

Humphries

John E.

Mader

Nicholas S.

2010. “The GED.” NBER Working Paper No. 16064. Cambridge, MA: National Bureau of Economic Research.

53.

Heckman

James J.

Rubinstein

Yona

. 2001. “The Importance of Noncognitive Skills: Lessons from the GED Testing Program.” American Economic Review 91(2):145–49.

54.

Hoeschler

Peter

Backes-Gellner

Uschi

. 2017. “Shooting for the Stars and Failing: College Dropout and Self-Esteem.” Zurich, Switzerland: Department of Business Administration, University of Zurich.

55.

Hoffmann

Rasmus

Kröger

Hannes

. 2021. “Comparing the Educational Gradients in Three Cardiovascular Disease-Specific Health Measures.” Longitudinal and Life Course Studies 12(4):591–607.

56.

Holmes

Christopher J.

Zajacova

Anna

. 2014. “Education as ‘the Great Equalizer’: Health Benefits for Black and White Adults.” Social Science Quarterly 95(4):1064–85.

57.

Homan

Patricia

. 2019. “Structural Sexism and Health in the United States: A New Perspective on Health Inequality and the Gender System.” American Sociological Review 84(3):486–516.

58.

Homan

Patricia

. 2024. “Health Consequences of Structural Sexism: Conceptual Foundations, Empirical Evidence and Priorities for Future Research.” Social Science & Medicine 351:116379.

59.

Houle

Jason N.

2013. “Disparities in Debt: Parents’ Socioeconomic Resources and Young Adult Student Loan Debt.” Sociology of Education 87(1):53–69.

60.

Mengjin

Yang

Tao

Yang

Yuejin

. 2024. “Causal Associations of Education Level with Cardiovascular Diseases, Cardiovascular Biomarkers, and Socioeconomic Factors.” American Journal of Cardiology 213:76–85.

61.

Hummer

Robert A.

Hernandez

Elaine M.

2013. “The Effect of Educational Attainment on Adult Mortality in the United States.” Population Bulletin 68(1):2.

62.

Jackson

Margot I.

2009. “Understanding Links between Adolescent Health and Educational Attainment.” Demography 46(4):671–94.

63.

Jepsen

Christopher

. 2017. “Second Chance for High School Dropouts? A Regression Discontinuity Analysis of Postsecondary Educational Returns to the GED.” Journal of Labor Economics 35:S273–304.

64.

Jiang

Yanping

Boylan

Jennifer Morozink

Zilioli

Samuele

. 2022. “Effects of the Great Recession on Educational Disparities in Cardiometabolic Health.” Annals of Behavioral Medicine 56(5):428–41.

65.

Johannes

Catherine B.

Kim T.

Zhou

Xiaolei

Johnston

Joseph A.

Dworkin

Robert H.

2010. “The Prevalence of Chronic Pain in United States Adults: Results of an Internet-Based Survey.” Journal of Pain 11(11):1230–39.

66.

Johnson

Amber E.

Herbert

Brandon M.

Stokes

Natalie

Brooks

Maria M.

Needham

Belinda L.

Magnani

Jared W.

2022. “Educational Attainment, Race, and Ethnicity as Predictors for Ideal Cardiovascular Health: From the National Health and Nutrition Examination Survey.” Journal of the American Heart Association 11(2):e023438.

67.

Kenkel

Donald

Lillard

Dean

Mathios

Alan

. 2006. “The Roles of High School Completion and GED Receipt in Smoking and Obesity.” Journal of Labor Economics 24(3):635–60.

68.

Khan

Najah

Javed

Zulqarnain

Acquah

Isaac

Hagan

Kobina

Khan

Madiha

Valero-Elizondo

Javier

Chang

Ryan

, et al. 2023. “Low Educational Attainment Is Associated with Higher All-Cause and Cardiovascular Mortality in the United States Adult Population.” BMC Public Health 23(1):900.

69.

Kim

Changhwan

Tamborini

Christopher R.

2019. “Are They Still Worth It? The Long-Run Earnings Benefits of an Associate Degree, Vocational Diploma or Certificate, and Some College.” RSF: The Russell Sage Foundation Journal of the Social Sciences 5(3):64–85.

70.

Kimbro

Rachel Tolbert

Bzostek

Sharon

Goldman

Noreen

Rodríguez

Germán

. 2008. “Race, Ethnicity, and the Education Gradient in Health.” Health Affairs 27(2):361–72.

71.

Kubota

Yasuhiko

Heiss

Gerardo

MacLehose

Richard F.

Roetker

Nicholas S.

Folsom

Aaron R.

2017. “Association of Educational Attainment with Lifetime Risk of Cardiovascular Disease: The Atherosclerosis Risk in Communities Study.” JAMA Internal Medicine 177(8):1165–72.

72.

Kurti

Allison N.

Klemperer

Elias M.

Zvorsky

Ivori

Redner

Ryan

Priest

Jeff S.

Higgins

Stephen T.

2016. “Some Context for Understanding the Place of the General Educational Development Degree in the Relationship between Educational Attainment and Smoking Prevalence.” Preventive Medicine 92:141–47.

73.

Lawrence

Elizabeth M.

2017. “Why Do College Graduates Behave More Healthfully Than Those Who Are Less Educated?” Journal of Health and Social Behavior 58(3):291–306.

74.

Lawrence

Elizabeth M.

Hummer

Robert A.

Domingue

Benjamin W.

Mullan Harris

Kathleen

. 2018. “Wide Educational Disparities in Young Adult Cardiovascular Health.” SSM—Population Health 5:249–56.

75.

Leive

Adam A.

Ruhm

Christopher J.

2022. “Education Gradients in Mortality Trends by Gender and Race.” Journal of Human Capital 16(1):47–72.

76.

Link

Bruce G.

Phelan

. 1995. “Social Conditions as Fundamental Causes of Disease.” Journal of Health and Social Behavior 35(Extra Iss.):80–94.

77.

Lippert

Adam M.

Houle

Jason N.

Walsemann

Katrina M.

2022. “Student Debt and Cardiovascular Disease Risk among U.S. Adults in Early Mid-life.” American Journal of Preventive Medicine 63(2):151–59.

78.

Liu

Sze Yan

Chavan

Niraj R.

Glymour

M. Maria

. 2013. “Type of High-School Credentials and Older Age ADL and IADL Limitations: Is the GED Credential Equivalent to a Diploma?” Gerontologist 53(2):326–33.

79.

Liu

Wei

Lin

Quan

Fan

Zongjing

Cui

Jie

Yang

. 2024. “Education and Cardiovascular Diseases: A Mendelian Randomization Study.” Frontiers in Cardiovascular Medicine 11:1320205.

80.

Lopez-Neyman

Stephanie M.

Davis

Kathleen

Zohoori

Namvar

Broughton

K. Shane

Moore

Carolyn E.

Miketinas

Derek

. 2022. “Racial Disparities and Prevalence of Cardiovascular Disease Risk Factors, Cardiometabolic Risk Factors, and Cardiovascular Health Metrics among US Adults: NHANES 2011–2018.” Scientific Reports 12(1):19475.

81.

Lynch

Jamie L.

von Hippel

Paul T.

2016. “An Education Gradient in Health, a Health Gradient in Education, or a Confounded Gradient in Both?” Social Science & Medicine 154:18–27.

82.

Magnani

Jared W.

Ning

Hongyan

Wilkins

John T.

Lloyd-Jones

Donald M.

Allen

Norrina B.

2024. “Educational Attainment and Lifetime Risk of Cardiovascular Disease.” JAMA Cardiology 9(1):45–54.

83.

Masters

Ryan K.

Link

Bruce G.

Phelan

Jo C.

2015. “Trends in Education Gradients of ‘Preventable’ Mortality: A Test of Fundamental Cause Theory.” Social Science & Medicine 127(Spec. Iss.):19–28.

84.

McFarland

Michael J.

Wagner

Brandon G.

2015. “Does a College Education Reduce Depressive Symptoms in American Young Adults?” Social Science & Medicine 146:75–84.

85.

Mirowsky

John

Ross

Catherine E.

2003. Education, Social Status, and Health. New York: Aldine de Gruyter.

86.

Montez

Jennifer Karas

Hayward

Mark D.

Brown

Dustin C.

Hummer

Robert A.

2009. “Why Is the Educational Gradient of Mortality Steeper for Men?” Journal of Gerontology: Psychological and Social Sciences 64(5):625–34.

87.

Montez

Jennifer Karas

Zajacova

Anna

Hayward

Mark D.

Woolf

Steven H.

Chapman

Derek

Beckfield

Jason

. 2019. “Educational Disparities in Adult Mortality across US States: How Do They Differ, and Have They Changed since the Mid-1980s?” Demography 56(2):621–44.

88.

Montez

Jennifer Karas

Zhang

Wencheng

Zajacova

Anna

Hamilton

Tod G.

2018. “Does College Major Matter for Women’s and Men’s Health in Midlife? Examining the Horizontal Dimensions of Educational Attainment.” Social Science & Medicine 198:130–38.

89.

Muñoz

Ismael G.

Santos-Lozada

Alexis R.

2021. “Educational Attainment and Psychological Distress among Working-Age Adults in the United States.” SSM—Mental Health 1:100003.

90.

Nguyen

Quynh C.

Tabor

Joyce W.

Entzel

Pamela P.

Lau

Yan

Suchindran

Chirayath

Hussey

Jon M.

Halpern

Carolyn T.

, et al. 2011. “Discordance in National Estimates of Hypertension among Young Adults.” Epidemiology 22(4):532–41.

91.

Noppert

Grace A.

Gaydosh

Lauren

Harris

Kathleen Mullan

Goodwin

Andrea

Hummer

Robert A.

2021. “Is Educational Attainment Associated with Young Adult Cardiometabolic Health?” SSM—Population Health 13:1–12.

92.

Suh-Ruu

. 2008. “Do GED Recipients Differ from Graduates and School Dropouts? Findings from an Inner-City Cohort.” Urban Education 43(1):83–117.

93.

Park

Hansol

Kim

Taehoon

Kim

Jinho

. 2023. “Longitudinal Pathways from Adolescent Depressive Symptoms to Cardiovascular Disease Risk in Adulthood.” Social Science & Medicine 318:115657.

94.

Pearlin

Leonard I.

1989. “The Sociological Study of Stress.” Journal of Health and Social Behavior 30(3):241–56.

95.

Phelan

Jo C.

Link

Bruce G.

2015. “Is Racism a Fundamental Cause of Inequalities in Health?” Annual Review of Sociology 41:311–30.

96.

Phelan

Jo C.

Link

Bruce G.

Tehranifar

Parisa

. 2010. “Social Conditions as Fundamental Causes of Health Inequalities: Theory, Evidence, and Policy Implications.” Journal of Health and Social Behavior 51(1 Suppl.):S28–40.

97.

Reimer

David

Pollak

Reinhard

. 2009. “Educational Expansion and Its Consequences for Vertical and Horizontal Inequalities in Access to Higher Education in West Germany.” European Sociological Review 26(4):415–30.

98.

Reynolds

John R.

Baird

Chardie L.

2010. “Is There a Downside to Shooting for the Stars? Unrealized Educational Expectations and Symptoms of Depression.” American Sociological Review 75(1):151–72.

99.

Rhodes

Alec P.

2024. “Labor Market Inequality, Debt, and the Consequences of Sub-baccalaureate Higher Education.” Sociological Quarterly 65(1):11–37.

100.

Richardson

Liana J.

Goodwin

Andrea N.

Hummer

Robert A.

2021. “Social Status Differences in Allostatic Load among Young Adults in the United States.” SSM—Population Health 15:100771.

101.

Rochmes

Jane

. 2024. “Educational Inequality as a Consequence and Cause of Race.” Sociological Forum 39(3):231–49. doi:10.1111/socf.13016.

102.

Rosenbaum

Janet E.

2012. “Degrees of Health Disparities: Health Status Disparities between Young Adults with High School Diplomas, Sub-baccalaureate Degrees, and Baccalaureate Degrees.” Health Services and Outcomes Research Methodology 12(2–3):156–68.

103.

Rosenbaum

James E.

Ahearn

Caitlin E.

Rosenbaum

Janet E.

2017. “College for All: New Opportunities through Community Colleges.” Pp. 1–19 in Bridging the Gaps: College Pathways to Career Success, edited by Rosenbaum

J. E.

Ahearn

C. E.

Rosenbaum

J. E.

New York: Russell Sage.

104.

Ross

Catherine

Masters

Ryan

Hummer

Robert A.

2012. “Education and the Gender Gaps in Health and Mortality.” Demography 49(4):1157–83.

105.

Ross

Catherine E.

Mirowsky

John

. 2006. “Sex Differences in the Effect of Education on Depression: Resource Multiplication or Resource Substitution?” Social Science & Medicine 63(5):1400–13.

106.

Ross

Catherine E.

Mirowsky

John

. 2010. “Gender and the Health Benefits of Education.” Sociological Quarterly 51(1):1–19.

107.

Santos Laanan

Frankie

. 2003. “Degree Aspirations of Two-Year College Students.” Community College Journal of Research and Practice 27(6):495–518.

108.

Sasson

Isaac

. 2016. “Trends in Life Expectancy and Lifespan Variation by Educational Attainment: United States, 1990–2010.” Demography 53(2):269–93.

109.

Schellekens

Jona

Ziv

Anat

. 2020. “The Role of Education in Explaining Trends in Self-Rated Health in the United States, 1972–2018.” Demographic Research 42:383–98.

110.

Schoenborn

Charlotte A.

Adams

Patricia F.

Peregoy

Jennifer A.

2013. “Health Behaviors of Adults: United States, 2008–2010.” Vital and Health Statistics 10(257).

111.

Schudde

Lauren

Bernell

Kaitlin

. 2019. “Educational Attainment and Nonwage Labor Market Returns in the United States.” AERA Open 5(3):18.

112.

Schudde

Lauren

Shea

Meghan

. 2022. “Heterogeneity in the Returns to Credits for Public Two-Year College Entrants.” Research in Higher Education 63(2):337–67.

113.

Shah

Nilay S.

Huang

Xiaoning

Petito

Lucia C.

Bancks

Michael P.

Kanaya

Alka M.

Talegawkar

Sameera

Farhan

Saaniya

, et al. 2024. “Social and Psychosocial Determinants of Racial and Ethnic Differences in Cardiovascular Health: The Masala and Mesa Studies.” American Journal of Preventive Cardiology 17:100636.

114.

Shah

Nilay S.

Kandula

Namratha R.

Commodore-Mensah

Yvonne

Morey

Brittany N.

Patel

Shivani A.

Wong

Sally

Yang

Eugene

, et al. 2024. “Social Determinants of Cardiovascular Health in Asian Americans: A Scientific Statement from the American Heart Association.” Circulation 150(16):e296–315.

115.

Thurston

Rebecca C.

Kubzansky

Laura D.

Kawachi

Ichiro

Berkman

Lisa F.

2005. “Is the Association between Socioeconomic Position and Coronary Heart Disease Stronger in Women Than in Men?” American Journal of Epidemiology 162(1):57–65.

116.

U.S. Census Bureau. 2021. “Table 1. Educational Attainment of the Population 18 Years and over, by Age, Sex, Race, and Hispanic Origin: 2020.” Retrieved September 25, 2021. https://www.census.gov/data/tables/2020/demo/educational-attainment/cps-detailed-tables.html.

117.

Vable

Anusha M.

Cohen

Alison K.

Leonard

Stephanie A.

Glymour

Maria M.

Duarte

Catherine D. P.

Yen

Irene H.

2018. “Do the Health Benefits of Education Vary by Sociodemographic Subgroup? Differential Returns to Education and Implications for Health Inequities.” Annals of Epidemiology 28(11):759–66.

118.

Warren

John Robert

Muller

Chandra

Hummer

Robert A.

Grodsky

Eric

Humphries

Melissa

. 2020. “Which Aspects of Education Matter for Early Adult Mortality? Evidence from the High School and Beyond Cohort.” Socius. doi:10.1177/2378023120918082.

119.

Weinberger

Andrea H.

Gbedemah

Misato

Martinez

Adriana M

Nash

Denis

Galea

Sandro

Goodwin

Renee D.

2018. “Trends in Depression Prevalence in the USA from 2005 to 2015: Widening Disparities in Vulnerable Groups.” Psychological Medicine 48(8):1308–15.

120.

Williams

David R.

Lawrence

Jourdyn A.

Davis

Brigette A.

2019. “Racism and Health: Evidence and Needed Research.” Annual Review of Public Health 40:105–25.

121.

Zacher

Meghan

. 2023. “Educational Disparities in Hypertension Prevalence and Blood Pressure Percentiles in the Health and Retirement Study.” Journals of Gerontology: Series B, Psychological Sciences and Social Sciences 78(9):1535–44.

122.

Zajacova

Anna

. 2006. “Education, Gender, and Mortality: Does Schooling Have the Same Effect on Mortality for Men and Women in the US?” Social Science & Medicine 63(8):2176–90.

123.

Zajacova

Anna

. 2012. “Health in Working-Age Americans: Adults with a High School Equivalency (GED) Diploma Are Similar to Dropouts, Not High School Graduates.” American Journal of Public Health 102(S2):S284–90.

124.

Zajacova

Anna

Everett

Bethany G.

2014. “The Nonequivalent Health of High School Equivalents.” Social Science Quarterly 95(1):221–38.

125.

Zajacova

Anna

Hummer

Robert A.

2009. “Gender Differences in Education Gradients in All-Cause Mortality for White and Black Adults Born 1906–1965.” Social Science & Medicine 69(4):529–37.

126.

Zajacova

Anna

Hummer

Robert A.

Rogers

Richard G.

2012. “Education and Health among U.S. Working-Age Adults: A Detailed Portrait across the Full Educational-Attainment Spectrum.” Biodemography and Social Biology 58(1):40–61.

127.

Zajacova

Anna

Johnson-Lawrence

Vicki

. 2016. “Anomaly in the Education-Health Gradient: Biomarker Profiles among Adults with Subbaccalaureate Attainment Levels.” Social Science & Medicine—Population Health 2(12):360–64.

128.

Zajacova

Anna

Montez

Jennifer Karas

. 2017. “The Health Penalty of the GED: Testing the Role of Noncognitive Skills, Health Behaviors, and Economic Factors.” Social Science Quarterly 98(1):1–15.

129.

Zajacova

Anna

Rogers

Richard G.

Grodsky

Eric

Grol-Prokopczyk

Hanna

. 2020. “The Relationship between Education and Pain among Adults Aged 30–49 in the United States.” Journal of Pain 21(11–12):1270–80.

130.

Zajacova

Anna

Rogers

Richard G.

Johnson-Lawrence

Vicki

. 2012. “Glitch in the Gradient: Additional Education Does Not Uniformly Equal Better Health.” Social Science & Medicine 75(11):2007–12.

131.

Zhang

Zhe

Solazzo

Alexa

Gorman

Bridget K.

2020. “Education and Health: The Joint Role of Gender and Sexual Identity.” SSM—Population Health 12:100668.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.09 MB