Social Stratification and Risk for Cardiovascular Disease: Examination of Emotional Suppression as a Pathway to Risk

Abstract

This article examines whether emotional suppression is associated with socioeconomic position (SEP) in a community sample of Black and White men, and whether emotional suppression may help explain the aggregation of multiple biopsychosocial risk factors for cardiovascular disease at lower SEP (social support, depression, cardiovascular stress reactivity). Aim 1 tests whether multiple indicators of SEP show a consistent graded association with self-reported trait suppression, and whether suppression mediates associations between SEP and perceived social support and depressive affect. Aim 2 tests whether suppression during a laboratory anger recall task mediates associations between SEP and cardiovascular reactivity to the task. All measures of higher SEP were associated with lower suppression. Findings in this racially diverse sample of adult men suggest that socioeconomic disparities in emotional suppression may be more likely to confer cardiovascular risk through disruption of affect and social relationships, than through direct and immediate physiological pathways.

Keywords

socioeconomic position emotion regulation depression social support cardiovascular physiology

Lower socioeconomic position (SEP) is associated with worse mental and physical health, including cardiovascular disease (CVD). This relationship does not simply reflect the effects of poverty. Rather, there is considerable evidence that the SEP-health association is monotonic, extending beyond poverty to explain relative differences in health across levels of SEP (e.g., Adler, 2009; Matthews, Gallo, & Taylor, 2010). This graded relationship between SEP and health has been interpreted as evidence that social rank may influence physical health in addition to objective resources (e.g., income; Cundiff & Matthews, 2017). Furthermore, because low SEP confers risk for poor health independent of traditional biologic and behavioral risk factors and health care access (e.g., Pampel, Krueger, & Denney, 2010), researchers hypothesize that social stratification of psychosocial risk factors partially explain the SEP-health gradient (Adler, 2009; Cundiff & Smith, 2017; Fuller-Rowell, Curtis, Chae, & Ryff, 2018; Gallo & Matthews, 2003; Matthews & Gallo, 2011; J. E. Phillips & Klein, 2010). Presumably, such gradients in psychosocial processes link low SEP with CVD through more frequent, severe, and long-lasting psychophysiological stress responses (for a review, see Boylan, Cundiff, & Matthews, 2018). Over long periods of time, these responses promote the quicker development, progression, and adverse course of CVD (e.g., Taylor, Repetti, & Seeman, 1997).

There are a number of psychosocial factors that appear to be socially stratified and may link SEP to cardiovascular risk (Matthews & Gallo, 2011). One strategy for identifying such factors is to consider psychosocial factors shown to be related to social hierarchy in experimental research and also related to cardiovascular risk. Such an approach may also contribute to our understanding of the clustering of psychosocial risk at lower levels of SEP by identifying common pathways to multiple psychosocial risk factors for CVD.

Suppression as a Potential Common Pathway From SEP to Multiple Risk Factors for CVD

The social display of emotions, or its absence (Ekman, Sorenson, & Friesen, 1969), specifically emotional suppression, is strongly linked to relative status within dyads and local social hierarchies. During interactions with higher ranking others, lower ranking individuals more often suppress emotions (e.g., Matsumoto, 1990) and are generally more constrained in their affect and behavior (e.g., for a meta-analysis see Hall, Coats, & LeBeau, 2005; Lammers, Stoker, & Stapel, 2010). Most studies in humans are laboratory manipulations in primarily White, college samples, and typically examine power or status differentials during a social interaction. However, such findings raise the possibility that emotional suppression may also be socially stratified at the level of broad indicators of social rank (Cundiff & Smith, 2017). Such findings would be consistent with evolutionary explanations for rank-based differences in the social display of emotions, which suggest that suppression of emotions by lower ranking individuals serves the adaptive function of avoiding attack and ingratiating the self to higher ranking individuals (Diefendorff, Morehart, & Gabriel, 2010; Gruenfeld, Inesi, Magee, & Galinsky, 2008; Guinote, 2007; Lammers et al., 2010). Strong evidence from nonhuman primate models also support this hypothesis, showing that social rank causally influences behavioral constraint in female rhesus macaques (Kohn et al., 2016). No study to our knowledge has examined whether there is a gradient in suppression associated with multiple broad, health-relevant indicators of SEP in humans (cf. Langner, Epel, Matthews, Moskowitz, & Adler, 2012).

If emotional suppression reliably varies by broad indicators of SEP, as it does by relative status in social interactions, then it may be a viable mechanism linking lower SEP to risk for CVD. Below we outline how emotional suppression may confer increased risk for CVD both directly (via physiology) and indirectly (via psychosocial risk).

SEP, Social Support, and Suppression

Social relationships and support are vital to health, showing effects on mortality comparable with factors such as smoking, physical activity, and body mass index (Holt-Lunstad, Smith, & Layton, 2010). This important social resource is not evenly distributed across socioeconomic strata in either humans or nonhuman primates (e.g., Silk, Cheney, & Seyfarth, 2013; Turner & Marino, 1994). For example, lower socioeconomic status (SES; e.g., income, education, occupation) is associated with lower marital quality and lower levels of perceived and received social support in humans (Karney & Bradbury, 2005; Schafer & Vargas, 2016; Turner & Marino, 1994). Non-human primates of higher social rank also evidence more and stronger social bonds (Silk et al., 2013; Snyder-Mackler et al., 2016).

A separate line of evidence suggests that emotional suppression can influence the quality of social relationships (Gross & John, 2003). Although the theorized goal of emotion regulation is to influence affect (intrapersonal consequence), the majority of emotion regulation efforts occur in social contexts (Gross, Richards, & John, 2006). Experimental data show that momentary suppression (though not reappraisal) can disrupt the formation of social bonds and the quality of interpersonal interactions (Butler et al., 2003). Furthermore, higher trait-level suppression is associated with worse social functioning as reported by both self- and peer-reports, ruling out the possibility that this cross-sectional association is simply due to common method variance or response bias (Gross & John, 2003). Perhaps most convincingly, one prospective study showed that both stable trait differences and increases in the use of suppression in a new environment (college) predicted lower social support, less closeness to others, and lower social satisfaction (Srivastava, Tamir, McGonigal, John, & Gross, 2009) as measured by multiple raters (self and peer) and over multiple weeks. Hence, there is strong evidence that suppression can disrupt the quality of social relationships and the receipt of social support.

SEP, Depression, and Suppression

Depressive symptoms and disorders predict the initial development, severity, and adverse course (e.g., reduced survival) of CVD (Gan et al., 2014; Kabir et al., 2009; Leung et al., 2012; Meijer et al., 2013). Furthermore, these symptoms and disorders are more common at lower levels of SEP, showing a dose-response relationship with income and education for example (for a meta-analysis, see Lorant et al., 2003).

Suppression may link SEP and depression. Social rank theory (Gilbert, 2000, 2006) argues that perceptions of lower social rank and inferiority lead to submissive behavioral strategies such as suppression, which can increase negative affect and depression. Consistent with animal models mentioned above, this theory argues that submissive strategies such as emotional and behavioral suppression are enacted instinctually as a form of self-defense to avoid negative attention and aggression from higher ranking others. Unfortunately this protective behavior appears to come with a cost. For example, experimental manipulations of suppression of negative affect have been shown to exacerbate (rather than dampen) the experience of negative emotions (Wenzlaff & Wegner, 2000), and emotional reactivity and regulation are key components of evidence-based theories of depressive disorders (e.g., Barlow, 2004; Gross & Muñoz, 1995). Only one study to our knowledge has tested whether higher levels of SEP (as indicated by education) are associated with lower levels of depressive symptoms via emotional suppression (Langner et al., 2012). Results support the idea that suppression may mediate the association between SEP and depressive symptoms.

Suppression and Autonomic Physiology

Suppression may also directly influence psychophysiology during stress, which is implicated in the development and progression of CVD. Cardiovascular reactivity, and to a lesser extent, high-frequency heart rate variability (HF-HRV; an index of parasympathetic influence on the heart) have both been linked to risk for CVD (Chida & Steptoe, 2010; Steptoe & Kivimaki, 2013). In addition, cardiovascular reactivity and HF-HRV have also both been examined as potential biological correlates of emotion regulation. Although suppression is typically conceptualized as a risk factor for poor physical health, a recent meta-analysis examining experimentally induced suppression across a number of physiological parameters (e.g., electroencephalogram [EEG], heart rate [HR]) showed that greater suppression was associated with concurrently less cardiovascular reactivity compared with a control group (Webb, Miles, & Sheeran, 2012).

With regard to HF-HRV, two major psychological theories (Porges, 1995, 2007; Thayer & Lane, 2007, 2009) and supporting empirical research suggest that the regulation of emotions and social behavior may be correlated with HF-HRV, and both tonic levels and phasic changes of HF-HRV are linked not only to regulation of emotion and social behavior but also to the establishment and maintenance of social bonds (Porges, 1995; Porges & Furman, 2011). However, a recent meta-analysis of the association between tonic or resting HF-HRV and experimentally assessed self-control (broadly defined) found only a small positive association that became statistically insignificant after correcting for publication bias (Zahn et al., 2016). Furthermore, theory and some studies suggest that efforts to regulate emotions are associated with a concurrent rise in HF-HRV (Butler, Wilhelm, & Gross, 2006; Thayer & Lane, 2009, p. 85), which works to slow HR and so should be health-protective from a cardiovascular stress reactivity perspective.

Although self- and emotion-regulation have been assessed in many different ways in past studies examining HF-HRV, a few studies have specifically assessed emotional suppression (Zahn et al., 2016). Although some of these studies have found that experimental manipulations of suppression are associated with phasic increases in HF-HRV (Butler et al., 2006), others have found no association (e.g., Campbell-Sills, Barlow, Brown, & Hofmann, 2006; Roberts, Levenson, & Gross, 2008). Importantly, although examining associations between changes in HF-HRV and self- or emotion-regulation is consistent with current trends in the literature and influential theories, changes in other autonomic parameters such as blood pressure (BP), whose phasic changes have better established links to cardiovascular risk than do HR or HF-HRV (Chida & Steptoe, 2010), are also linked to suppression (Webb et al., 2012) and perhaps better distinguish it from other emotional responses (e.g., Dan-Glauser & Gross, 2015). Finally, much of the literature examining physiological correlates of suppression examines primarily White college students. Race may be associated with differential use of emotional suppression and the psychophysiological correlates of suppression may differ by race (Dorr, Brosschot, Sollers, & Thayer, 2007; Roberts et al., 2008).

Study Aims

In the analyses reported here, we integrate and extend prior research by examining the viability of suppression as a common behavioral pathway linking SEP to psychosocial and biological risk factors for poor cardiovascular health in a diverse community sample of Black and White men (see Figure 1 for an overview). In Aim 1, we examine whether there is a socioeconomic gradient in general trait patterns of suppression, and whether higher rates of suppression among lower SEP persons may help explain socioeconomic gradients in social support and depressive affect. This would suggest a viable indirect pathway from SEP to CVD through the general tendency to suppress emotions and subsequent disruption of social relationships and depressive affect. In Aim 2, in a subsample from the same community sample, we move beyond general trait-level suppression and reliance on self-reports by examining whether there is a socioeconomic gradient in spontaneous state suppression during anger recall as rated by self-report and objective observer ratings. We also examine whether greater emotional suppression during this laboratory task is associated with concurrent changes in measures of cardiovascular stress reactivity that have been previously theoretically and empirically linked to both cardiovascular risk and emotional suppression (see section “Suppression and Autonomic Physiology” above), including HF-HRV thought to index regulatory effort. We did not examine other impedance cardiography outcomes that were collected but not believed to be related to the pathways examined here. If state suppression is associated with lower SEP and more damaging physiological stress reactivity, this would suggest a viable direct pathway from SEP to CVD through suppression and its momentary physiological correlates.

Figure 1.

Conceptual model and breakdown of pathways examined by each study.

Method

Participants

Participants were drawn from the Pittsburgh Youth Study, a longitudinal school-based study of boys who were initially recruited from the Pittsburgh Public Schools in 1987-1988 (see Loeber, Farrington, Stouthamer-Loeber, & White, 2008). The goal of the original study was to examine risk and protective factors for antisocial behavior and juvenile delinquency, and boys identified at the Top 30% on the screening risk measure, and a roughly equal number of boys randomly selected from the remainder of the distribution were selected for longitudinal follow-up. A subset of men in the Pittsburgh Youth Study were re-contacted in young adulthood (mean age 32 years) to participate in the current study, and were eligible if they were alive, had not refused further participation at an earlier visit, were not severely mentally disabled, and were not currently incarcerated. In total, 358 men participated in all or some portion of the protocol (72% of those eligible). More specifically, letters were sent inviting the men to participate in the follow-up project and nonresponses were followed up by phone. If interested, men came to the lab and signed consent forms after verbal explanation and time to read the form. If participants were unable to come to the lab but were willing to complete interviews and questionnaires, then consent was attained by reading and explaining the consent form and procedures by phone followed by sending a copy of the form through the mail. In total, 307 men participated in the laboratory protocol involving the anger recall task. As previously reported (Boylan, Jennings, & Matthews, 2016), the sample completing the laboratory protocol did not differ from the original study sample on race, initial SES, or number of health problems in childhood (ps > .05). Both protocols for obtaining consent (in person and by phone) were approved by the Institutional Review Board at the University of Pittsburgh.

Trait Measures (Aim 1)

SEP

Objective (e.g., income, education, occupation) and subjective (e.g., subjective social status) measures of SEP are overlapping but distinct constructs with apparently independent contributions to physical health (e.g., Adler, 2009; Cundiff & Matthews, 2017). Hence, SEP was measured in multiple ways: (a) family size adjusted annual income, (b) Hollingshead (1975) scores which combine occupational prestige and educational attainment, and (c) the subjective social status ladders (Cundiff, Smith, Uchino, & Berg, 2013; Operario, Adler, & Williams, 2004). Household income was adjusted for family size and then log transformed due to skew (Schwartz, 1985). Hollingshead scores were calculated using updated occupational titles and rankings (updates and justification can be found at http://socialclass-bpd.wikispaces.com/file/view/Barratt_Simplifed_Measure_of_Social_Status.pdf). Subjective social status was measured in relation to both others in the United States and others in the “community,” where community is defined by the rater.

Suppression

Suppression is an emotion regulation strategy wherein individuals do not outwardly express internally felt emotions (Gross & Levenson, 1997). The Emotion Regulation Questionnaire (ERQ; Gross & John, 2003) was used to assess individual differences in general emotional suppression. Subjects were asked to use a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree) to answer items such as “I control my emotions by not expressing them.” This scale is not specific to the suppression of any one emotion. It has been widely used and shown to have acceptable reliability and construct validity (Gross & John, 2003). This scale demonstrated adequate internal consistency in this sample (Cronbach’s α = .71). Higher scores indicate greater emotional suppression.

Perceived social support

The Social Provisions scale is a 20-item scale designed to assess perceived social support. Using a 4-point Likert-type scale, participants were asked to indicate the extent of their agreement with statements such as “There are people I can depend on to help if I really need it” and “There is no one I can turn to for guidance in times of stress.” This measure has demonstrated excellent internal consistency (Cronbach’s α = .92; Cutrona, Russell, & Rose, 1986), as well as discriminant, convergent, and predictive validity with diverse samples (e.g., Aquino, Russell, Cutrona, & Altmaier, 1996; Baron, Cutrona, Hicklin, Russell, & Lubaroff, 1990; Cutrona & Russell, 1987). Internal consistency was also very high for the current sample (Cronbach’s α = .93). The scale is scored by summing all items after reverse coding negatively worded items. Higher scores indicate increased levels of social support.

Depression

The Recent Mood and Feelings questionnaire is a 13-item scale, which assesses depressive symptomology and is commonly used in epidemiological studies of children and adolescents (Angold, Costello, Messer, & Pickles, 1995; Costello & Angold, 1988). This scale was used because it had been used repeatedly in the longitudinal study from which participants were recruited. Participants are asked to rate their agreement with statements such as “You didn’t enjoy anything at all” and “You hated yourself” regarding their feelings over the previous 2 weeks using a 3-point scale (0 = not true, 1 = sometimes true, 2 = true). This scale demonstrated good internal consistency in the current sample (Cronbach’s α = .88).

Lab Task and State Measures (Aim 2)

Anger recall task

The full laboratory procedure has been described in detail elsewhere (Boylan et al., 2016). In summary, participants engaged in a 10-min minimally engaging baseline period (Jennings, Kamarck, Stewart, Eddy, & Johnson, 1992) followed by counterbalanced mental arithmetic (3 min) and mirror tracing (3 min) tasks, and then the anger recall speech task (3-min preparation, 4-min speech), which involved recounting a recent angry event in the presence of a research assistant. Participants were given the following instructions for the anger recall task:

Everyone gets angry from time to time, so I would like for you to think of a time in the last 6 months when you became extremely angry. Something that you can still get upset about, that may keep you awake or keeps coming back to make you angry again. Think about what made you angry and how upset you got. I want you to really try and get back into the situation in your mind and re-experience it.

Self-reports of suppression during anger recall

After the task, participants were asked to rate their affective responses to the task using the following questions: (a) Felt almost as strongly during my speech as during the situation, (b) Started feeling bodily reactions during my speech, and (c) Felt irritated and angry during my speech. Response options were on a 5-point Likert-type scale ranging from “not at all” to “extremely.” The items were reverse scored so that higher ratings reflect less emotion or bodily reactions, suggesting greater state suppression given the context of an anger recall task. Results of a principal components analysis revealed that 63% of the variance in these three items could be explained by a common underlying factor and that each individual item was highly correlated with this underlying factor (r = .70, .84, and .82, respectively). Hence, we aggregated the three items measuring state suppression by standardizing each item score and then aggregating across the three items.

Observer-reports of suppression during anger recall

Speeches were video-recorded for coding by independent observers. After watching the videos, coders responded to the question, “How emotional was the participant during his speech” using a 5-point Likert-type scale ranging from “no emotions” to “very emotional.” These ratings served as objective observer assessments of emotional suppression during the task. The item was reverse scored so that higher ratings reflect less emotional expression during the anger recall task.

Measures of Cardiovascular Physiology

HR and HF-HRV

HR and HF-HRV (.12-.4 Hz) were collected from electrocardiogram (ECG) signals using a modified lead II configuration. Other details of collection and derivation of HR and HF-HRV have been described elsewhere (Boylan et al., 2016). Measures were calculated over each entire experimental epoch (e.g., speech task). Trained staff examined each record and edited as necessary, deleting artifactual data points, routinely less than 5% of any data file. We took the natural log of HF-HRV (sometimes labeled “RSA” for respiratory sinus arrhythmia) to correct for positive skew.

BP

Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were monitored during the laboratory protocol using a CARESCAPE Dinamap V100 Vital Signs Monitor (GE Medical Systems Information Technologies, Inc.) with a standard occluding cuff placed on the participant’s nondominant arm. BP collection occurred every 2 min during the 10-min baseline and twice during the anger recall task.

Covariates

Race (coded as White = 1, Black = 0), body mass index, and respiration rate were included as covariates. The risk stratification index used in the original study to guide recruitment was also covaried. Body mass index was calculated based on measurements taken by staff at the laboratory visit (weight in kg/height in m²). Respiration rate was measured using a respiratory belt placed over the diaphragm that changed electrical resistance with chest volume changes.

Overview of Analyses

We examined bivariate correlations of our primary study variables in SPSS and used an SPSS macro (Indirect; Preacher & Hayes, 2008) to test mediational paths and their significance. This macro uses bootstrapping to provide estimates of the indirect (mediated) effect of the independent variable on the dependent variable. Bootstrapping provides high statistical power and also reduces the risk of Type I error compared with other common techniques for testing mediation such as the Sobel test.

Results

Aim 1: Does General Trait Suppression Help Explain SEP Disparities in Trait Social Support and Depressive Affect?

Correlations among primary study variables are presented in Table 1. As expected, all measures of SEP were significantly positively correlated with one another. White (compared with Black) race was also significantly correlated with higher SEP (with the exception of community subjective social status [SSS]) as well as greater social support and lower trait levels of suppression. Individuals who report lower SEP in any form also report greater use of suppression, and this association was graded in nature (Figure 2, Panel A). Similarly, lower SEP in all forms was also associated with less perceived social support (Figure 2, Panel B) in a graded manner. As expected, higher trait levels of suppression were also associated with lower social support and greater depressed affect (Table 1).

Table 1.

Bivariate Correlations and Descriptive Statistics of Primary Study Variables in Aim 1.

	Hollingshead	Income	U.S. rank	Community rank	Trait suppression	Social support	Depressive affect
Race	.27*	.44*	.17*	.02	−.13*	.15*	−.01
Hollingshead		.58*	.31*	.27*	−.18*	.31*	−.17*
Income			.34*	.23*	−.23*	.33*	−.15*
U.S. rank				.46*	−.19*	.25*	−.18*
Community rank					−.20*	.27*	−.29*
Trait suppression						−.44*	.32*
Social support							−.42*
M (SD)	32.4 (15.3)	US$43,640 (US$40,050)	4.0 (1.9)	5.3 (1.7)	9.6 (2.0)	64.9 (9.4)	4.7 (4.6)

Note. Race is coded 1 = Black, 2 = White.

p < .05.

Figure 2.

Multi-method assessment of the social stratification of emotional suppression and social support: Panel A—There is a graded inverse relationship between socioeconomic position and emotional suppression; Panel B—There is a graded relationship between socioeconomic position and social support.

Results of mediation analyses are presented in Tables 2 and 3 (last column tests the indirect/mediated effect) for social support and depression, respectively. For all measures of SEP, tested independently, lower suppression partially accounted for the association between higher SEP and greater social support. Likewise, lower suppression partially accounted for the association between higher SEP and lower depression for all measures of SEP.

Table 2.

Aim 1 Results of Mediation Analyses Linking Lower Socioeconomic Position to Lower Perceived Social Support Through Higher Trait Emotional Suppression.

Independent variable(s)	IV to suppression (a path)	Suppression to social support (b path)	Total effect on social support (c path)	Direct effect of IV on social support (c′ path)	Indirect effect of IV on social support (95% CI)
Race	−0.59*	−2.1*	2.8*	1.59, ns	1.2 [0.33, 2.25]
Adjusted income	−0.48*	−1.8*	3.1*	2.2*	0.86 [0.41, 1.44]
Hollingshead	−0.02*	−1.9*	0.19*	0.15*	0.04 [0.02, 0.07]
U.S. SSS	−0.22*	−2.0*	1.3*	0.82*	0.44 [0.20, 0.73]
Community SSS	−0.23*	−2.0*	1.5*	1.1*	0.44 [0.20, 0.74]
Race controlling OSS	−0.16, ns	−1.7*	−0.12, ns	−0.39, ns	0.27 [–0.52, 1.1]

Note. R² for all models was between .21 and .28. CIs are estimated from 5,000 bootstrap resamples. Adjusted income is log-transformed family size adjusted income. Race is coded 1 = Black, 2 = White. CI = confidence interval; SSS = aggregate of U.S. SSS and community SSS; OSS = objective social status, which is an aggregate of adjusted income and Hollingshead score.

p < .05.

Table 3.

Aim 1 Results of Mediation Analyses Linking Lower Socioeconomic Position to Higher Depressive Affect Through Higher Trait Emotional Suppression.

Independent variable(s)	IV to suppression (a path)	Suppression to depression (b path)	Total effect on depression (c path)	Direct effect of IV on depression (c′ path)	Indirect effect of IV on depression (95% CI)
Adjusted income	−0.48*	0.75*	−0.60*	−0.24, ns	−0.36 [−0.67, −0.15]
Hollingshead	−0.02*	0.70*	−0.05*	−0.03*	−0.02 [−0.03, −0.01]
U.S. SSS	−0.20*	0.68*	−0.50*	−0.36*	−0.13 [−0.25, −0.06]
Community SSS	−0.23*	0.63*	−0.78*	−0.64*	−0.14 [−0.28, −0.07]

Note. R² for all models was between .12 and .16. CIs are estimated from 5,000 bootstrap resamples. Adjusted income is log-transformed family size adjusted income. CI = confidence interval.

p < .05.

The first row of Table 2 also shows that suppression mediates the association between race and social support; however, this pathway becomes nonsignificant after controlling for objective SEP (income and Hollingshead score; last row of Table 2). We did not test this pathway with depression (race → suppression → depression) as there was no association between race and depression, consistent with epidemiological data showing that Blacks tend to report equal or lesser depression compared with Whites (Williams et al., 2007; but see also Brown, Bromberger, Schott, Crawford, & Matthews, 2014).

Aim 2: Does State Anger Suppression Help Explain SEP Disparities in Cardiovascular Stress Reactivity to a Lab Task?

Bivariate correlations of self- and observer-reports of suppression with measures of SEP and stress reactivity are reported in Table 4. Observer ratings of suppression during the anger recall task were significantly associated with self-reports of suppression during the same task (r = .28, p < .001; Table 4). Neither self- nor observer-reports of suppression during the anger recall task were associated with any indicator of SEP (Table 4), and these associations remained nonsignificant when race and SEP were mutually adjusted (results not shown).

Table 4.

Bivariate Correlations Among Self-Reports and Observer Ratings of Suppression During an Anger Recall Task With Socioeconomic Position and Concurrent Changes in Physiology From Aim 2.

	Self-reported suppression during task (r)	Observer-rated suppression during task (r)
Race	.03	−.08
Hollingshead	−.09	.07
Income	.03	.01
U.S. rank	.00	.03
Community rank	.04	.09
∆HF-HRV	.19*	.21*
∆HR	−.18*	−.14*
∆SBP	−.21*	−.22*
∆DBP	−.16*	−.15*

Note. HF-HRV = high-frequency heart rate variability; HR = heart rate; SBP = systolic blood pressure; DBP = diastolic blood pressure.

Both observer ratings and self-reports of suppression during the anger recall task were correlated with increased HF-HRV during the task and smaller increases in HR (Table 4 and Figure 3), indicating parasympathetic activation concurrent with greater emotion regulation. Observer ratings and self-reports of suppression were also associated with less change in SBP and DBP during anger recall (Table 4 and Figure 4). Hence, individuals who suppress emotions more during an anger recall task show less change in HR and BP and greater parasympathetic engagement during anger recall.

Figure 3.

Associations between observer ratings and self-reports of suppression and changes in HR and HF-HRV during an anger recall task.

Figure 4.

Associations between observer ratings and self-reports of suppression and changes in SBP and DBP during an anger recall task.

Subjective social status and family size adjusted income were not significantly correlated with cardiovascular reactivity during the anger recall task. However, the Hollingshead Index was significantly associated with greater increases in HR (r = .13, p = .03), SBP (r = .15, p = .01), and DBP (r = .17, p < .01), but not HF-HRV (r = .07, p = .22). We did not test mediational pathways (SEP → state suppression → cardiovascular reactivity) as no indicator of SEP was associated with suppression during the task (Table 4).

In regression analyses controlling for race, SES, body mass index, risk group, and respiration rate, observer-reports and self-reports of suppression during the anger recall task remained significantly associated with more parasympathetic activation and less change in HR (see Table 5; Figure 3). Self- and observer-reports of suppression during the task were all associated with increases in HF-HRV during the task (greater suppression, greater increase in HF-HRV). Greater suppression was also consistently associated with smaller increases in HR during the task across both measures of suppression (Figure 3). Table 5 also shows that both ratings of suppression during the task were associated with smaller increases in both SBP and DBP during the task (see Figure 4). Hence, emotional suppression, at least in the context of suppressing anger in the presence of a stranger, appears to be related to a more salubrious momentary autonomic profile.

Table 5.

Results of Regression Analyses Examining Self-Reports and Observer Ratings of Suppression With Heart Rate, Heart Rate Variability, and Blood Pressure During an Anger Recall Task.

Indicator of emotional suppression	∆HF-HRV				∆HR				∆SBP				∆DBP
Indicator of emotional suppression	b	β	SE	R ²	b	β	SE	R ²	b	β	SE	R ²	b	β	SE	R ²
Self-reported suppression	0.08*	.20	.02	.05	−0.08*	−.19	.02	.07	−1.1*	−.19	.33	.08	−0.52*	−.13	.23	.07
Observer-rated suppression	0.21*	.18	.07	.05	−0.18*	−.15	.07	.06	−3.72*	−.22	.97	.08	−1.61*	−.14	.67	.06

Note. Regression weights (betas) for HF-HRV are estimated from models controlling for race, risk group, socioeconomic status, respiration rate, and body mass index. Regression weights for HR, SBP, and DBP are estimated from models controlling for race, risk group, socioeconomic status, and body mass index. HF-HRV = high-frequency heart rate variability; HR = heart rate; SBP = systolic blood pressure; DBP = diastolic blood pressure; b = unstandardized beta; β = standardized beta.

p < .05.

Supplementary analyses controlling for felt anger

Recalled events may not have been equally anger-provoking across participants, leading to less emotionality not attributable to suppression. To account for this potential limitation of our operationalization of suppression, we statistically controlled for felt anger (e.g., participants’ response to the question “felt irritated and angry during my speech”) in regression analyses examining observer ratings of suppression with outcomes. In fully adjusted regression analyses (comparable with those in Table 5), observer ratings of suppression remained similarly and significantly associated with HF-HRV (b = .20, SE = .08, p = .010, R² = .08), SBP (b = −3.74, SE = 1.09, p = .001, R² = .10), and DBP (b = −1.72, SE = .77, p = .027, R² = .07), and associations with HR became marginally significant though the magnitude of the effect remained similar (b = −.14, SE = .08, p = .075, R² = .05).

Discussion

Findings from this community sample of Black and White men contribute to our understanding of whether and how suppression may play a role in the link between SEP and health. Our findings are consistent with evolutionary explanations for rank-based differences in the social display of emotions, which suggest that suppression of emotions is enacted more by lower ranking individuals because it serves the adaptive function of avoiding attack and ingratiating the self to higher ranking individuals (Diefendorff et al., 2010; Gruenfeld et al., 2008; Guinote, 2007; Lammers et al., 2010). A primary strength of the current study is the multi-method testing of multiple theoretically plausible pathways in a racially diverse community sample. Testing full mediation pathways and including significant numbers of non-White participants are weaknesses in the current body of literature examining SEP-health disparities and psychosocial factors (for a review, see Matthews, Gallo, & Taylor, 2010) as well as physiological factors (e.g., laboratory stress reactivity; Boylan et al., 2018).

Findings from Aim 1 provide multi-method evidence for a graded relationship between SEP and suppression (Figure 2, Panel A) as well as SEP and social support (Figure 2, Panel B), and confirm previous findings for a graded relationship between SEP and depressed affect. Notably, there were no race differences in trait suppression in this sample of Black and White men after accounting for the fact that Black men, on average, also reported lower SEP (Table 2; cf. Gross & John, 2003 who sampled college students). Furthermore, cross-sectional mediation analyses provide initial evidence that greater trait suppression may partially explain socioeconomic disparities in social support and depressed affect (Tables 2 and 3), both well-established psychosocial risk factors for CVD (and other physical health problems). Suppression accounted for a larger percentage of the variance between SEP and social support than for SEP and depression (see Tables 2 and 3 notes). Significant mediation was found independent of which measure of social rank was used as the predictor. Results for both social support and depressive affect may show this highly consistent pattern due to the fact that social display rules most strongly dictate the suppression of negative emotions at lower status. Suppression of negative emotions increases negative affect and likely interferes with stress buffering social support (e.g., if you don’t display distress, others don’t know you’re in need of support and so are less likely to provide support). Consistent with this explanation, recent work suggests that suppression results in reduced opportunities for receiving social support because it masks one’s true/authentic emotional experience from others (English & John, 2013; Landau et al., 2011). It is difficult to engender social support if others are not aware of the person’s need, and difficult to feel close to and supported by others (and vice-versa) while guarding or hiding one’s own experience.

In Aim 2, no indicator of SEP was reliably associated with self- or observer-reports of suppression during the task. Further only one indicator of SEP, the Hollingshead Index, was reliably associated with increases in HR and BP during the task. These results are directly counter to the idea that emotional suppression may be a pathway linking SEP to poor cardiovascular health through concurrent changes in autonomic physiology (Figure 1, Aim 2).

Although not reliably associated with SEP, self- and observer-ratings of suppression during the anger recall task were consistently associated with greater parasympathetic activation (positive change in HF-HRV) and less cardiovascular reactivity (HR, SBP, DBP), even after controlling for participants’ felt anger (Table 5; Figures 3 and 4). These findings are consistent with meta-analytic findings (Webb et al., 2012) and views such as the neurovisceral integration model (Thayer & Lane, 2009), which proposes a positive relationship between HF-HRV and emotion regulation. However, findings are inconsistent with the view of suppression as a risk factor for poor physical health due to associated momentary changes in cardiovascular physiology. Lower BP reactivity presages better—not worse—cardiovascular outcomes (Chida & Steptoe, 2010) and phasic decreases in HF-HRV during laboratory stress tasks are associated with poorer physical health, such as greater coronary artery calcification (Gianaros et al., 2005). Hence, findings here suggest that suppression is associated with a healthier autonomic profile as more suppression is associated with a phasic increase in HF-HRV and greater downregulation of autonomic physiology.

Notably, it has been argued that self-regulation is a limited resource that can be depleted (Baumeister, Bratslavsky, Muraven, & Tice, 1998; Hagger, Wood, Stiff, & Chatzisarantis, 2010; Muraven, Tice, & Baumeister, 1998; Thayer & Lane, 2009). Thus, it is possible that frequent momentary increases in HF-HRV associated with emotion regulation result in failures of self-regulation and decreases in HF-HRV during subsequent stressors, consistent with some evidence from married couples (e.g., Smith et al., 2011). In addition, some data appear to suggest that greater physiological reactivity to stress is associated with better mental health, and these findings have led some researchers to suggest that moderate stress reactivity may be optimal for overall mental and physical health as high reactivity presages poor physical health but low reactivity has been associated with poor cognitive and behavioral health (e.g., A. C. Phillips, Ginty, & Hughes, 2013).

Findings here could also be interpreted as additional evidence of physiological and psychosocial decoupling; the costs of suppression may have immediate physiological benefits (adaptive) but psychosocial costs (maladaptive). For example, there is some recent evidence to suggest that greater parasympathetic reactivity in daily life (which is theoretically health-protective) is associated with poorer social and affective functioning (Hamilton & Alloy, 2017). Notably, reports of incongruent findings for psychological and biological outcomes have been found primarily in high-risk samples or subgroups (e.g., Brody et al., 2013; Dich, Doan, & Evans, 2017; Miller, Cohen, Janicki-Deverts, Brody, & Chen, 2016), and no such subgroup analyses or interactions were tested here.

Limitations

Results and conclusions of these data should be interpreted in light of study limitations. Although our sample was diverse in terms of race and SES, we studied only men, and these results may not generalize to women. The sample was also limited geographically in that all participants grew up in and around the city of Pittsburgh. In Aim 1, data are cross-sectional, and this is an important limitation for their interpretation; mediation analyses cannot establish that SEP leads to individual differences in suppression, which in turn lead to poorer social support or greater depressed affect (e.g., Maxwell, Cole, & Mitchell, 2011). Although the sample stems from a longitudinal sample, suppression was only measured at the last wave and so mediation analyses cannot be performed longitudinally. It is possible that the association between emotional suppression and social support is bidirectional, reflecting a transactional cycle between individual behavior and interpersonal experience (e.g., Kiesler, 1996). However, there are experimental and prospective studies and strong theory (see Introduction above) suggesting that the order of effects presented here (SEP → suppression → social support/depression) is plausibly causal, and we interpret these data in light of those previous findings.

In Aim 2, we examined natural variations in emotional suppression rather than experimentally manipulating suppression. This may have provided a more accurate indication of individuals’ tendency to suppress and the associated physiology. However, we cannot infer causality between suppression and changes in physiology because our study design was not experimental with respect to suppression. Our ability to adequately control for respiration during speech is also imperfect because speech interferes with the otherwise cyclical nature of respiration and thus respiration is less accurately captured and quantified, making statistical control for respiration less precise during speech. Hence, the possibility exists that the positive association between HF-HRV and suppression reflects that more suppression is associated with a slower rate and less volume of speech. This limitation in the measurement of respiration is a limitation for HF-HRV to a greater extent than for findings related to HR or BP.

Both sets of analyses are lacking a more detailed assessment of which emotions are suppressed, with whom, and to what immediate functional end (e.g., was suppression an adaptive coping strategy in this situation?). Such details may change the nature of the associations studied here. For example, at higher levels of SEP, anger may be more socially acceptable and so result in less maladaptive alienation of social support (e.g., Tiedens, 2001). However, in the context of marriage, the expressive suppression of sadness and other “soft” emotions may be the most important factor, as suppression of these emotions in particular can seriously impede connection and social support in this context (e.g., Sanford & Rowatt, 2004). More detail concerning the social context and the emotions being suppressed may show that lower SEP is associated with greater suppression of certain emotions in certain contexts that are particularly detrimental to the receipt of social support and/or depressogenic. Similarly, we examined suppression during an anger recall task in the presence of a stranger, and suppression may differentially correlate with autonomic reactivity based on task parameters (e.g., recalling anger vs. recalling sadness or joy; expressing an emotion in the presence of a stranger vs. a close other).

Finally, exposure to situations more likely to evoke suppression may be an important moderating factor of any impact of suppression (and its physiological correlates) on health. Ecological momentary assessments, which allow researchers to capture data on the frequency of exposure to social processes such as suppression in the natural environment, as well as acute physiological effects (e.g., changes in ambulatory HR or BP) and psychosocial sequelae, appear particularly well-suited in efforts to examine whether social behaviors and experiences may contribute to SEP disparities in health.

Conclusion

Despite these limitations and considerations, the current work contributes to our understanding of SEP-health disparities. Examining the viability of modifiable biopsychosocial pathways is a necessary next step in efforts to ameliorate well-established socioeconomic health disparities. The studies presented here examine the viability of suppression as a common pathway linking SEP to psychosocial and biological risk factors for poor cardiovascular health in a community sample of Black and White men. Findings reveal a socioeconomic gradient in trait patterns of suppression and that higher rate of suppression among lower SEP persons partially explain socioeconomic gradients in social support and depressive affect, but not stress physiology. Hence, findings suggest that suppression may be more likely to confer cardiovascular risk through disruption of affect and social relationships, than through direct and immediate physiological pathways.

Supplemental Material

Cundiff_OnlineAppendix – Supplemental material for Social Stratification and Risk for Cardiovascular Disease: Examination of Emotional Suppression as a Pathway to Risk

Supplemental material, Cundiff_OnlineAppendix for Social Stratification and Risk for Cardiovascular Disease: Examination of Emotional Suppression as a Pathway to Risk by Jenny M. Cundiff, J. Richard Jennings and Karen A. Matthews in Personality and Social Psychology Bulletin

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Preparation of this manuscript was supported by Grants #HL07560 and R01 #HL111802 from the National Institutes of Health.

Supplemental Material

Supplemental material is available online with this article.

ORCID iD

Jenny M. Cundiff

References

Adler

N. E.

(2009). Health disparities through a psychological lens. American Psychologist, 64, 663-673.

Angold

Costello

E. J.

Messer

S. C.

Pickles

(1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5, 251-262.

Aquino

J. A.

Russell

D. W.

Cutrona

C. E.

Altmaier

E. M.

(1996). Employment status, social support, and life satisfaction among the elderly. Journal of Counseling Psychology, 43, 480-489.

Barlow

D. H.

(2004). Anxiety and its disorders: The nature and treatment of anxiety and panic. New York, NY: Guilford Press.

Baron

R. S.

Cutrona

C. E.

Hicklin

Russell

D. W.

Lubaroff

D. M.

(1990). Social support and immune function among spouses of cancer patients. Journal of Personality and Social Psychology, 59, 344-352.

Baumeister

R. F.

Bratslavsky

Muraven

Tice

D. M.

(1998). Ego depletion: Is the active self a limited resource? Journal of Personality and Social Psychology, 74, 1252-1265.

Boylan

J. M.

Cundiff

J. M.

Matthews

K. A.

(2018). Socioeconomic status and cardiovascular responses to standardized stressors: A systematic review and meta-analysis. Psychosomatic Medicine, 80(3), 278-293.

Boylan

J. M.

Jennings

J. R.

Matthews

K. A.

(2016). Childhood socioeconomic status and cardiovascular reactivity and recovery among Black and White men: Mitigating effects of psychological resources. Health Psychology, 35, 957-966.

Brody

G. H.

Chen

Miller

G. E.

Kogan

S. M.

Beach

S. R.

(2013). Is resilience only skin deep? Rural African Americans’ socioeconomic status-related risk and competence in preadolescence and psychological adjustment and allostatic load at age 19. Psychological Science, 24, 1285-1293.

10.

Brown

Bromberger

J. T.

Schott

L. L.

Crawford

Matthews

K. A.

(2014). Persistence of depression in African American and Caucasian women at midlife: Findings from the Study of Women Across the Nation (SWAN). Archives of Women’s Mental Health, 17, 549-557.

11.

Butler

E. A.

Egloff

Wilhelm

F. H.

Smith

N. C.

Erickson

E. A.

Gross

J. J.

(2003). The social consequences of expressive suppression. Emotion, 3, 48-67.

12.

Butler

E. A.

Wilhelm

F. H.

Gross

J. J.

(2006). Respiratory sinus arrhythmia, emotion, and emotion regulation during social interaction. Psychophysiology, 43, 612-622.

13.

Campbell-Sills

Barlow

D. H.

Brown

T. A.

Hofmann

S. G.

(2006). Effects of suppression and acceptance on emotional responses of individuals with anxiety and mood disorders. Behaviour Research and Therapy, 44, 1251-1263.

14.

Chida

Steptoe

(2010). Greater cardiovascular responses to laboratory mental stress are associated with poor subsequent cardiovascular risk status: A meta-analysis of prospective evidence. Hypertension, 55, 1026-1032.

15.

Costello

E. J.

Angold

(1988). Scales to assess child and adolescent depression: Checklists, screens, and nets. Journal of the American Academy of Child & Adolescent Psychiatry, 27, 726-737.

16.

Cundiff

J. M.

Matthews

K. A.

(2017). Is subjective social status a unique correlate of physical health? A meta-analysis. Health Psychology, 36, 1109-1125. doi:10.1037/hea0000534

17.

Cundiff

J. M.

Smith

T. W.

(2017). Social status, everyday interpersonal processes, and coronary heart disease: A social psychophysiological view. Social & Personality Psychology Compass, 11(4), e12310.

18.

Cundiff

J. M.

Smith

T. W.

Uchino

B. N.

Berg

C. A.

(2013). Subjective social status: Construct validity and associations with psychosocial vulnerability and self-rated health. International Journal of Behavioral Medicine, 20, 148-158.

19.

Cutrona

C. E.

Russell

D. W.

(1987). The provisions of social relationships and adaptation to stress. Advances in Personal Relationships, 1(1), 37-67.

20.

Cutrona

C. E.

Russell

D. W.

Rose

(1986). Social support and adaptation to stress by the elderly. Psychology and Aging, 1, 47-54.

21.

Dan-Glauser

E. S.

Gross

J. J.

(2015). The temporal dynamics of emotional acceptance: Experience, expression, and physiology. Biological Psychology, 108, 1-12.

22.

Dich

Doan

S. N.

Evans

G. W.

(2017). In risky environments, emotional children have more behavioral problems but lower allostatic load. Health Psychology, 36, 468-476.

23.

Diefendorff

J. M.

Morehart

Gabriel

(2010). The influence of power and solidarity on emotional display rules at work. Motivation and Emotion, 34, 120-132.

24.

Dorr

Brosschot

J. F.

Sollers

J. J.

Thayer

J. F.

(2007). Damned if you do, damned if you don’t: The differential effect of expression and inhibition of anger on cardiovascular recovery in Black and White males. International Journal of Psychophysiology, 66, 125-134.

25.

Ekman

Sorenson

E. R.

Friesen

W. V.

(1969). Pan-cultural elements in facial displays of emotion. Science, 164(3875), 86-88.

26.

English

John

O. P.

(2013). Understanding the social effects of emotion regulation: The mediating role of authenticity for individual differences in suppression. Emotion, 13, 314-329.

27.

Fuller-Rowell

T. E.

Curtis

D. S.

Chae

D. H.

Ryff

C. D.

(2018). Longitudinal health consequences of socioeconomic disadvantage: Examining perceived discrimination as a mediator. Health Psychology, 37(5), 491-500.

28.

Gallo

L. C.

Matthews

K. A.

(2003). Understanding the association between socioeconomic status and physical health: Do negative emotions play a role? Psychological Bulletin, 129, 10-51.

29.

Gan

Gong

Tong

Sun

Cong

Dong

. . . Lu

(2014). Depression and the risk of coronary heart disease: A meta-analysis of prospective cohort studies. BMC Psychiatry, 14, Article 371.

30.

Gianaros

P. J.

Salomon

Zhou

Owens

J. F.

Edmundowicz

Kuller

L. H.

Matthews

K. A.

(2005). A greater reduction in high-frequency heart rate variability to a psychological stressor is associated with subclinical coronary and aortic calcification in postmenopausal women. Psychosomatic Medicine, 67, 553-560.

31.

Gilbert

(2000). The relationship of shame, social anxiety and depression: The role of the evaluation of social rank. Clinical Psychology & Psychotherapy, 7, 174-189.

32.

Gilbert

(2006). Evolution and depression: Issues and implications. Psychological Medicine: A Journal of Research in Psychiatry and the Allied Sciences, 36, 287-297.

33.

Gross

J. J.

John

O. P.

(2003). Individual differences in two emotion regulation processes: Implications for affect, relationships, and wellbeing. Journal of Personality and Social Psychology, 85, 348-362.

34.

Gross

J. J.

Levenson

R. W.

(1997). Hiding feelings: The acute effects of inhibiting negative and positive emotion. Journal of Abnormal Psychology, 106, 95-103.

35.

Gross

J. J.

Muñoz

R. F.

(1995). Emotion regulation and mental health. Clinical Psychology: Science and Practice, 2, 151-164.

36.

Gross

J. J.

Richards

J. M.

John

O. P.

(2006). Emotion regulation in everyday life. In Snyder

D. K.

Simpson

J. A.

Hughes

J. N.

(Eds.), Emotion regulation in families: Pathways to dysfunction and health (pp. 13-35). Washington, DC: American Psychological Association.

37.

Gruenfeld

D. H.

Inesi

M. E.

Magee

J. C.

Galinsky

A. D.

(2008). Power and the objectification of social targets. Journal of Personality and Social Psychology, 95(1), 111-127.

38.

Guinote

(2007). Power affects basic cognition: Increased attentional inhibition and flexibility. Journal of Experimental Social Psychology, 43, 685-697.

39.

Hagger

M. S.

Wood

Stiff

Chatzisarantis

N. L.

(2010). Ego depletion and the strength model of self-control: A meta-analysis. Psychological Bulletin, 136, 495-525.

40.

Hall

J. A.

Coats

E. J.

LeBeau

L. S.

(2005). Nonverbal behavior and the vertical dimension of social relations: A meta-analysis. Psychological Bulletin, 131, 898-924.

41.

Hamilton

J. L.

Alloy

L. B.

(2017). Physiological markers of interpersonal stress generation in depression. Clinical Psychological Science, 5, 911-929. doi:10.1177/2167702617720211

42.

Hollingshead

(1975). Four factor index of social status. Retrieved from https://artlesstanzim.files.wordpress.com/2014/05/hollinghead-four-factors-2.pdf

43.

Holt-Lunstad

Smith

T. B.

Layton

J. B.

(2010). Social relationships and mortality risk: A meta-analytic review. PLoS Medicine, 7(7), e1000316. doi:10.1371/journal.pmed.1000316

44.

Jennings

J. R.

Kamarck

Stewart

Eddy

Johnson

(1992). Alternate cardiovascular baseline assessment techniques: Vanilla or resting baseline. Psychophysiology, 29, 742-750.

45.

Kabir

A. A.

Srinivasan

S. R.

Sultana

Chen

Wei

C. Y.

Berenson

G. S.

(2009). Association between depression and intima-media thickness of carotid bulb in asymptomatic young adults. American Journal of Medicine, 122, 1151.e1-1151.e8.

46.

Karney

B. R.

Bradbury

T. N.

(2005). Contextual influences on marriage implications for policy and intervention. Current Directions in Psychological Science, 14, 171-174.

47.

Kiesler

D. J.

(1996). Contemporary interpersonal theory and research: Personality, psychopathology, and psychotherapy. New York, NY: John Wiley.

48.

Kohn

J. N.

Snyder-Mackler

Barreiro

L. B.

Johnson

Z. P.

Tung

Wilson

M. E.

(2016). Dominance rank causally affects personality and glucocorticoid regulation in female rhesus macaques. Psychoneuroendocrinology, 74, 179-188.

49.

Lammers

Stoker

J. I.

Stapel

D. A.

(2010). Power and behavioral approach orientation in existing power relations and the mediating effect of income. European Journal of Social Psychology, 40, 543-551.

50.

Landau

M. J.

Vess

Arndt

Rothschild

Z. K.

Sullivan

Atchley

R. A.

(2011). Embodied metaphor and the “true” self: Priming entity expansion and protection influences intrinsic self-expressions in self-perceptions and interpersonal behavior. Journal of Experimental Social Psychology, 47, 79-87.

51.

Langner

C. A.

Epel

E. S.

Matthews

K. A.

Moskowitz

J. T.

Adler

N. E.

(2012). Social hierarchy and depression: The role of emotion suppression. The Journal of Psychology, 146, 417-436.

52.

Leung

Flora

Gravely

Irvine

Carney

Grace

(2012). The impact of pre-morbid and post-morbid depression onset on mortality and cardiac morbidity among coronary heart disease patients: A meta-analysis. Psychosomatic Medicine, 74, 786-801.

53.

Loeber

Farrington

D. P.

Stouthamer-Loeber

White

H. R.

(2008). Violence and serious theft: Development and prediction from childhood to adulthood. New York, NY: Taylor & Francis.

54.

Lorant

Deliege

Eaton

Robert

Philippot

Ansseau

(2003). Socioeconomic inequalities in depression: A meta-analysis. American Journal of Epidemiology, 157, 98-112.

55.

Matsumoto

(1990). Cultural similarities and differences in display rules. Motivation and Emotion, 15, 195-214.

56.

Matthews

K. A.

Gallo

L. C.

(2011). Psychological perspectives on pathways linking socioeconomic status and physical health. Annual Review of Psychology, 62, 501-530.

57.

Matthews

K. A.

Gallo

L. C.

Taylor

S. E.

(2010). Are psychosocial factors mediators of socioeconomic status and health connections? Annals of the New York Academy of Sciences, 1186, 146-173.

58.

Maxwell

S. E.

Cole

D. A.

Mitchell

M. A.

(2011). Bias in cross-sectional analyses of longitudinal mediation: Partial and complete mediation under an autoregressive model. Multivariate Behavioral Research, 46, 816-841.

59.

Meijer

Conradi

Bos

Anselmino

Carney

Denollet

. . . De Jong

(2013). Adjusted prognostic association of depression following myocardial infarction with mortality and cardiovascular events: Individual patient data meta-analysis. British Journal of Psychiatry, 203, 90-102.

60.

Miller

G. E.

Cohen

Janicki-Deverts

Brody

G. H.

Chen

(2016). Viral challenge reveals further evidence of skin-deep resilience in African Americans from disadvantaged backgrounds. Health Psychology, 35, 1225-1234.

61.

Muraven

Tice

D. M.

Baumeister

R. F.

(1998). Self-control as a limited resource: Regulatory depletion patterns. Journal of Personality and Social Psychology, 74, 774-789.

62.

Operario

Adler

N. E.

Williams

D. R.

(2004). Subjective social status: Reliability and predictive utility for global health. Psychology & Health, 19, 237-246.

63.

Pampel

F. C.

Krueger

P. M.

Denney

J. T.

(2010). Socioeconomic disparities in health behaviors. Annual Review of Sociology, 36, 349-370.

64.

Phillips

A. C.

Ginty

A. T.

Hughes

B. M.

(2013). The other side of the coin: Blunted cardiovascular and cortisol reactivity are associated with negative health outcomes. International Journal of Psychophysiology, 90, 1-7.

65.

Phillips

J. E.

Klein

W. M.

(2010). Socioeconomic status and coronary heart disease risk: The role of social cognitive factors. Social & Personality Psychology Compass, 4, 704-727.

66.

Porges

S. W.

(1995). Orienting in a defensive world: Mammalian modifications of our evolutionary heritage. A polyvagal theory. Psychophysiology, 32, 301-318.

67.

Porges

S. W.

(2007). The polyvagal perspective. Biological Psychology, 74, 116-143.

68.

Porges

S. W.

Furman

S. A.

(2011). The early development of the autonomic nervous system provides a neural platform for social behaviour: A polyvagal perspective. Infant and Child Development, 20, 106-118.

69.

Preacher

K. J.

Hayes

A. F.

(2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879-891.

70.

Roberts

N. A.

Levenson

R. W.

Gross

J. J.

(2008). Cardiovascular costs of emotion suppression cross ethnic lines. International Journal of Psychophysiology, 70, 82-87.

71.

Sanford

Rowatt

W. C.

(2004). When is negative emotion positive for relationships? An investigation of married couples and roommates. Personal Relationships, 11, 329-354.

72.

Schafer

M. H.

Vargas

(2016). The dynamics of social support inequality: Maintenance gaps by socioeconomic status and race? Social Forces, 94, 1795-1822.

73.

Schwartz

J. E.

(1985). The utility of the cube root of income. Journal of Official Statistics, 1(1), 5-19.

74.

Silk

Cheney

Seyfarth

(2013). A practical guide to the study of social relationships. Evolutionary Anthropology: Issues, News, and Reviews, 22, 213-225.

75.

Smith

T. W.

Cribbet

M. R.

Nealey-Moore

J. B.

Uchino

B. N.

Williams

P. G.

MacKenzie

Thayer

J. F.

(2011). Matters of the variable heart: Respiratory sinus arrhythmia response to marital interaction and associations with marital quality. Journal of Personality and Social Psychology, 100, 103-119.

76.

Snyder-Mackler

Kohn

J. N.

Barreiro

L. B.

Johnson

Z. P.

Wilson

M. E.

Tung

(2016). Social status drives social relationships in groups of unrelated female rhesus macaques. Animal Behaviour, 111, 307-317.

77.

Srivastava

Tamir

McGonigal

K. M.

John

O. P.

Gross

J. J.

(2009). The social costs of emotional suppression: A prospective study of the transition to college. Journal of Personality and Social Psychology, 96, 883-897.

78.

Steptoe

Kivimaki

(2013). Stress and cardiovascular disease: An update on current knowledge. Annual Review of Public Health, 34, 337-354.

79.

Taylor

S. E.

Repetti

R. L.

Seeman

(1997). Health psychology: What is an unhealthy environment and how does it get under the skin? Annual Review of Psychology, 48, 411-447.

80.

Thayer

J. F.

Lane

R. D.

(2007). The role of vagal function in the risk for cardiovascular disease and mortality. Biological Psychology, 74, 224-242.

81.

Thayer

J. F.

Lane

R. D.

(2009). Claude Bernard and the heart-brain connection: Further elaboration of a model of neurovisceral integration. Neuroscience & Biobehavioral Reviews, 33, 81-88.

82.

Tiedens

L. Z.

(2001). Anger and advancement versus sadness and subjugation: The effect of negative emotion expressions on social status conferral. Journal of Personality and Social Psychology, 80, 86-94.

83.

Turner

R. J.

Marino

(1994). Social support and social structure: A descriptive epidemiology. Journal of Health and Social Behavior, 35, 193-212.

84.

Webb

T. L.

Miles

Sheeran

(2012). Dealing with feeling: A meta-analysis of the effectiveness of strategies derived from the process model of emotion regulation. Psychological Bulletin, 138, 775-808.

85.

Wenzlaff

R. M.

Wegner

D. M.

(2000). Thought suppression. Annual Review of Psychology, 51, 59-91.

86.

Williams

D. R.

Gonzalez

H. M.

Neighbors

Nesse

Abelson

J. M.

Sweetman

Jackson

J. S.

(2007). Prevalence and distribution of major depressive disorder in African Americans, Caribbean blacks, and non-Hispanic whites: Results from the National Survey of American Life. Archives of General Psychiatry, 64, 305-315.

87.

Zahn

Adams

Krohn

Wenzel

Mann

C. G.

Gomille

L. K.

. . . Kubiak

(2016). Heart rate variability and self-control—A meta-analysis. Biological Psychology, 115, 9-26.

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

0.30 MB