Skip to main content
Download PDF
  • Study Protocol
  • Open access
  • Published:

The heart of Detroit study: a window into urban middle-aged and older African Americans’ daily lives to understand psychosocial determinants of cardiovascular disease risk

BMC Psychiatry volume 23, Article number: 766 (2023) Cite this article

A Correction to this article was published on 16 November 2023

This article has been updated

Abstract

Background

Cardiovascular disease disproportionately affects African Americans. Psychosocial factors, including the experience of and emotional reactivity to racism and interpersonal stressors, contribute to the etiology and progression of cardiovascular disease through effects on health behaviors, stress-responsive neuroendocrine axes, and immune processes. The full pathway and complexities of these associations remain underexamined in African Americans. The Heart of Detroit Study aims to identify and model the biopsychosocial pathways that influence cardiovascular disease risk in a sample of urban middle-aged and older African American adults.

Methods

The proposed sample will be composed of 500 African American adults between the ages of 55 and 75 from the Detroit urban area. This longitudinal study will consist of two waves of data collection, two years apart. Biomarkers of stress, inflammation, and cardiovascular surrogate endpoints (i.e., heart rate variability and blood pressure) will be collected at each wave. Ecological momentary assessments will characterize momentary and daily experiences of stress, affect, and health behaviors during the first wave. A proposed subsample of 60 individuals will also complete an in-depth qualitative interview to contextualize quantitative results. The central hypothesis of this project is that interpersonal stressors predict poor cardiovascular outcomes, cumulative physiological stress, poor sleep, and inflammation by altering daily affect, daily health behaviors, and daily physiological stress.

Discussion

This study will provide insight into the biopsychosocial pathways through which experiences of stress and discrimination increase cardiovascular disease risk over micro and macro time scales among urban African American adults. Its discoveries will guide the design of future contextualized, time-sensitive, and culturally tailored behavioral interventions to reduce racial disparities in cardiovascular disease risk.

Background

Cardiovascular disease (CVD) has been the leading cause of death in the United States (US) for over 100 years [1, 2]. Intensive scientific efforts resulted in decades of steady decline in CVD morbidity and mortality rates; however, this improvement has stalled, and CVD rates have been stagnant in the US since 2010 [3, 4]. Racial disparities in CVD-related outcomes have remained a consistent problem [5,6,7,8,9]. In a study of the earliest nationally representative data on CVD mortality, from 1920 to 1955, rates in non-white Americans were “consistently higher” than in white Americans [8]. Such racial disparities have generally either remained unchanged or grown worse over time [10,11,12]. Current mortality rates for heart disease and stroke are 30% and 45% higher, respectively, among African Americans (AAs) compared to white Americans [13]. The scientific community has repeatedly called for action to examine and reduce racial CVD disparities [6, 13], yet these inequities are projected to deepen in coming years [14]. Overall CVD morbidity and mortality rates in the US are projected to rise through 2060, despite concurrent declines in CVD among white Americans; without meaningful intervention, racially minoritized groups will grow more disproportionately affected [14]. To reduce these disparities and reverse the recent stagnation in improvement of CVD rates, research must go beyond traditional CVD risk factors to explore the fundamental psychological, social, and environmental factors at play.

While many factors contribute to racial CVD disparities, racism may be particularly influential. Multiple theories and conceptual models converge to suggest that racism directly contributes to health disparities. Racism is a complex concept [15] and can be conceptualized as a set of beliefs, attitudes, structures, and actions that maintain systems of power and oppression along socially-defined racial lines [16, 17]. Three interrelated sources of racism have been delineated: structural, cultural, and individual [16, 18]. The Multidimensional Model of Racism-Related Stress [16] posits that these sources of racism are enacted across various contexts, including the interpersonal (e.g., interactions between individuals) and the sociopolitical (e.g., policy choices). Racism enacted across these sources and contexts results in the racism-related stress that is uniquely experienced by racially minoritized groups [16] and contributes to racial health disparities [17].

The interpersonal context is particularly important in the association between racism-related stress and racial health disparities. Interpersonal racism manifests as acts of prejudice and discrimination toward individuals. According to the Biopsychosocial Model of Racism [19], experiences of prejudice or discrimination are powerful stressors with psychological, behavioral, and biological sequelae. Repeated stressful experiences lead to chronic activation of the stress response system, resulting in the ‘wear and tear’ on the body (i.e., allostatic load [20] that has been found to partially explain black-white disparities in premature mortality [21]. Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis (e.g., flattened diurnal slopes, glucocorticoid resistance) and elevated systemic inflammation are critical components of the allostatic load model, and both contribute to CVD risk [22,23,24,25,26,27]. Past research has found that experiences of discrimination are associated with flatter diurnal cortisol slopes [28] and increased systemic inflammation [29, 30]. Furthermore, discrimination has been directly linked to increases in hypertension, carotid intima-media thickness, and incident of CVD [31,32,33]. Additionally, behavioral reactions to chronic stress (e.g., poor sleep quality) can also link experiences of stress and discrimination to poorer cardiovascular outcomes, possibly via pro-inflammatory mechanisms [34,35,36]. Thus, experiences of discrimination have psychological, biological, and behavioral consequences that negatively affect long-term health and contribute to racial health disparities.

The Biopsychosocial Model of Racism also recognizes that experiences of discrimination need to be considered in the context of other social determinants of health, such as socioeconomic status (SES) and interpersonal relationships. The association between low SES and increased CVD risk can be attributed to many factors [37], including increased inflammation and abdominal adiposity [38], elevated blood pressure [39, 40], increased tobacco use [41], and reduced psychosocial resources [42, 43]. Financial strain, a stressor associated with low SES, has been linked to experiencing more negative social interactions [44]. Patterns of negative social interactions have been found to be relatively stable over time, thus serving as a source of chronic stress [45, 49]. Among older adults, social and family networks can offer extensive support but can also be sources of interpersonal conflict [46, 47]. In turn, frequent negative interaction can negatively influence both physical and mental health [44, 48, 49]. For example, negative social interactions have been associated with increased carotid artery intima-medial thickness [50] and incident hypertension [48]. Loneliness and social isolation are similarly associated with increased CVD risk [51], possibly via pro-inflammatory mechanisms [52, 53]. Lastly, while higher SES is generally associated with lower CVD risk [37, 54], studies have found that racial disparities in CVD risk factors, such as hypertension, high cholesterol, obesity, diabetes, and metabolic syndrome remain constant or increase with increasing SES [55,56,57]. Higher SES may therefore not confer the same health benefits to AAs, likely due to the added burden of structural and interpersonal racism [55, 58].

The Intersectionality Theory [59] and more specifically the concepts of gendered racism [60] highlight the importance of considering gender-specific forms of discrimination. Intersectionality Theory recognizes that all individuals are members of multiple social groups simultaneously (e.g., racial groups, gender groups), and examining the effect of membership in any one social group in isolation is thus inherently limited [59]. Membership in multiple subordinated groups can produce additive or multiplicative effects on health. The intersection of race and gender has been a focal point of intersectionality theory since its inception [59, 61]. For example, Black women may experience “double discrimination”, that is the combined effects of discrimination based on both race and gender [61], and previous studies have shown that experiences of interpersonal gendered racism negatively affect self-reported mental and physical health in Black women [62]. Structural gendered racism, that is gendered racism that operates at the institutional and cultural level, also significantly contributes to racial disparities in health, including maternal mortality [63] and increased risk for COVID-19 [64]. Overall, the intersecting effects of gendered racism on health remain understudied.

These three theoretical models – the Multidimensional Model of Racism-Related Stress, the Biopsychosocial Model of Racism, and Intersectionality Theory – provide a framework for much-needed research examining the pathways through which racism and other psychosocial determinants of health impact CVD risk among AAs. Based on this theoretical foundation, The Heart of Detroit Study was designed to identify and conceptualize, through a mixed-method approach, the main psychosocial stressors most salient for urban middle-aged and older AAs and model the daily psychological, behavioral, and biological pathways through which these factors may exacerbate CVD risk in this population.

Methods and study design

Project overview

The Heart of Detroit Study is an intensive longitudinal mixed-method study that combines Ecological Momentary Assessment (EMA), biological, and qualitative approaches. The sample consists of self-identified AA participants between the ages of 55 and 75 living in the Tri-County area of Metro Detroit. The Heart of Detroit Study is a prospective longitudinal study in which participants are assessed extensively using a multimodal design at baseline (Wave 1) then followed up 2 years later (Wave 2). Wave 1 consists of two home visits, separated by a seven-day period of EMA data collection. A subset of participants will be asked to participate in an additional semi-structured qualitative interview. Wave 2 of quantitative data collection will take place two years after the Wave 1 baseline and consists of one home visit.

Participants

Inclusion and exclusion criteria

Individuals who self-identify as AA, are between the ages of 55 and 75 years, and are able to read and write in English are potentially eligible to participate in Wave 1. The main exclusion criteria include a history of CVD at baseline assessment, HIV/AIDS infection, neurodegenerative disease, psychiatric disorders, or cognitive impairment, and having plans to move out of the study area within the next two years. The proposed sample size is 500 individuals.

Recruitment and enrollment

Participants are being recruited through the Wayne State Institute of Gerontology Participant Research Pool, a pool of AAs living in Metro Detroit who are willing to participate in research [65]. Other recruitment methods include online advertisements, advertisements placed in the community, and snowball sampling. All potential participants complete an initial phone screening after which eligible individuals are scheduled for their first home visit.

Procedures

Study procedures were approved by the Wayne State University Institutional Review Board. During the first home visit, participants provide written informed consent, and eligibility is confirmed. Following the consent and eligibility procedures, participants complete questionnaires about demographic information, physical and psychological health, health behaviors, cognitive abilities, and personal life experiences. For the next seven consecutive days, participants self-assess blood pressure each morning and evening using a provided home blood pressure monitor, provide four saliva samples per day at specified times, and record information about these measures in a daily log. Additionally, participants are asked to wear an actigraphy watch and carry a cell phone to complete EMA measures from awakening until bedtime. Both the actigraphy watch and the cell phone are provided by the study and are considered minimally invasive. Following the daily monitoring period, a second home visit is conducted. At the second home visit, height, weight, waist and hip circumference, peak expiratory flow, heart rate variability, and blood pressure are measured by study staff; hair and venous blood samples are also collected. Participants also complete questionnaires about health behaviors at this visit. A proposed subsample of 100 participants will provide capillary blood microsamples from a finger-prick. Blood microsamples are collected by research staff; however, these devices can also be used for personal, home-based collection. Blood volumes collected through blood microsamples are adequate for most clinically-relevant biomarkers including those related to the environment, inflammation, and nutrition even in times of decreased access to healthcare [66]. Two years after Wave 1, Wave 2 will be conducted. Wave 2 consists of a single home visit, in which participants will be asked to complete demographic, health, behavioral, cognitive, psychological, and experiential questionnaires. During this visit, study staff will collect many of the measures and questionnaires collected at Wave 1, including hair and blood samples. A subset of participants will be asked to complete a semi-structured qualitative interview intended to contextualize and help explain the quantitative findings. Participants will be compensated up to $460 for their time and participation in the study.

Measures

Background and anthropometric variables

Demographic information (e.g., age, sex, and SES) are assessed once per wave during home visits. Measures of neighborhood quality are obtained via questionnaire; census block group data will be used to generate a Neighborhood Adversity Index and Residential Segregation measure that have been linked to stress and health outcomes [67, 68]. Other background information is also collected, including measures of personality traits, cognitive function, and mental health. Anthropometric variables, including height, weight, and waist circumference, are measured by study staff.

Stress and discrimination

Stress is a multifaceted process comprised of several domains that occur across various timescales [69,70,71]. To leverage the multimodal study design and obtain a more nuanced understanding of the sources, mediators, and consequences of stress in urban AAs, several measures of stress are being used.

Global measures of stress are assessed once per wave; these measures capture chronic stressors and major life event stressors. Chronic stressors assessed include neighborhood adversity [72], everyday experiences of discrimination [73], loneliness [74], and caregiving [75]. To enable a closer examination of questions related to intersectionality, gender-specific questionnaires about experiences of discrimination and exposure to stereotypes are also used, such as the Gendered Racial Microaggressions scale [76] and the African American Men’s Gendered Racism Stress Inventory [77]. Major life events are infrequent but dramatic stressors, including major experiences of discrimination such as being unfairly fired or denied a promotion [78].

Examination of momentary (within-day) and daily (within-person) stressors and their impact on long-term health is a central goal of The Heart of Detroit Study. To assess within-person variability in these stressors, EMA questions are asked four times per day (9 a.m., 1 p.m., 5 p.m., and 9 p.m.) across seven consecutive days. These questions focus on current mood, recent (i.e., since the last EMA prompt) stressful experiences such as negative social interactions or experiences of discrimination, social context (e.g., engagement in social activities or interactions), and stress-related health behaviors such as smoking.

Lastly, cortisol is being used to assess the physiological impact of stress. Dysregulation of the HPA axis, which can be reflected in certain patterns of diurnal cortisol secretion, is an important mechanism through which stress may negatively affect health [22, 43, 79, 80]. While salivary cortisol enables analysis of individual differences in daily cortisol levels and day-to-day variability in HPA axis activation, hair cortisol levels are a complementary measure of systemic cortisol exposure across longer time scales [81]. Examination of both salivary and hair cortisol in The Heart of Detroit Study will allow for the examination of physiological stress across multiple time scales. During the daily diary period of Wave 1, participants collect four saliva samples per day (immediately at waking, 30 min after waking, prior to dinnertime, and at bedtime), enabling assessment of diurnal cortisol patterns. Compliance with sampling times is assessed by a microchip that records openings of each sampling vial (MEMS trackcap; Aardex, Denver, CO). Study staff collect one hair sample per wave for cortisol analysis during home visits. Cortisol concentrations will be quantified with a commercially available luminescence immunoassay (IBL International, Hamburg, Germany) at the Dresden LabService (Dresden, Germany).

Emotional and behavioral mediators

Coping mechanisms are important mediators in the stress-health association [82]; consequently, several measures of coping styles and strategies are assessed, including measures of religious coping. Emotional reactivity to stress, such as increased negative affect, may also mediate the association between stressful experiences and health outcomes [83, 84] and is assessed during both waves. Access to and use of quality social support also appears to be important in the stress-health relationship [85], while loneliness and social isolation have been linked to increased CVD risk [51, 86]. Consequently, several measures of social support [87], social isolation [88], and loneliness [74] are included. Changes in health behaviors, such as increased alcohol use or reduced sleep quality, are considered key mechanisms linking stress to poor health [89]; health behaviors, such as alcohol consumption [90], tobacco use [90], and sleep habits [91] are therefore assessed. Given the overarching goal of examining mediators between stress and health across varying time scales, emotional and behavioral mediators of stress are measured using both global questionnaires assessing habitual health behaviors and EMA questions. Sleep is assessed both subjectively via questionnaire and daily diaries, and objectively using actigraphy data.

Markers of CVD risk

Heart rate variability (HRV) is a noninvasive marker of autonomic nervous system activation [92] that is associated with both CVD risk and risk factors [93, 94] and may be a mediator between stress and CVD risk [95]. HRV is measured once per wave by study staff using a Polar v800 Heart Rate Monitor (or Polar Watch) and respiration band. After being fitted with the monitor and respiratory band, participants are asked to rest in a seated position while heart rate variability is measured for 5 consecutive minutes.

High blood pressure has the strongest evidence for CVD causation of any traditional risk factor [96], and there are marked racial disparities in the prevalence and treatment of high blood pressure [97]. While single measures of blood pressure strongly predict CVD risk, within-person variability in blood pressure predicts CVD risk above and beyond mean blood pressure [98]. Thus, two approaches to blood pressure measurement are used in The Heart of Detroit Study. First, participants self-assess blood pressure each day of the seven-day daily monitoring period using an automated device (Omron BP710N) twice per day, after awakening in the morning and before bed at night. At each time point, blood pressure is measured in triplicate, with a two-minute rest between measures. The average of these three readings is provided by the device. Participants are asked to record the time and results of each assessment. Additionally, study staff assess participants’ blood pressure once per wave using an automated device (Omron 907XL), which is considered the gold standard for office-based measurement [99]. Following the HRV measurement at each assessment, blood pressure is measured three times using an automated protocol which includes a two-minute rest between readings; the average of these three readings is taken.

Immune dysregulation and chronic inflammation are key mediators linking stress to CVD [89, 100]. Evidence links experiences of discrimination to increased inflammation; however, additional research using a greater variety of immune markers is needed to better characterize this association [29]. Consequently, The Heart of Detroit Study assesses a range of immune-related biomarkers derived from blood samples, including C-Reactive Protein (CRP), circulating inflammatory cytokines, and ex vivo lipopolysaccharide-stimulated inflammatory cytokine production. Both pro-inflammatory (e.g., IL-6) and anti-inflammatory cytokines (e.g., IL-10) are measured under basal and stimulated conditions. For basal cytokines, a portion of each blood sample is centrifuged (15 min x 1500 g), and 400 µl aliquots of the resulting supernatant are stored in a − 80oC freezer. For ex vivo stimulated cytokine production, 1 ml of whole blood from each sample is combined with a 3 ml solution of lipopolysaccharide (LPS; E. coli, Sigma) to create a 1 µg/ml LPS solution, and then incubated at 37 °C in 5% CO2 for 4 h on a rotational shaker. The samples are then centrifuged (15 min x 1500 g), aliquoted into 500 µl samples, and stored at -80oC. Plasma samples are then analyzed in Dr. Christopher Engeland’s Stress and Immunity laboratory at the Pennsylvania State University using V-Plex multiplex assays (Meso Scale Diagnostics, Rockville MD) following the manufacturer protocol. Other markers of immune function, such as white blood cell counts, will also be measured at the Detroit Medical Center. In addition to immune-related factors, blood samples will be also used to assess lipids, including total cholesterol and low-density lipoprotein [LDL] cholesterol, using Beckman Coulter Clinical Chemistry analyzers at the Detroit Medical Center. Additional blood is also being collected for future genetic, epigenetic, and gene expression analyses.

Qualitative interview

Following partial completion of Wave 1, a subsample of participants (proposed N = 60) will be recruited to take part in semi-structured qualitative interviews. The subsample will be selected based on stress levels and CVD risk levels during Wave 1 (high stress/high CVD risk, high stress/low CVD risk, low stress/high CVD risk, low stress/low CVD risk). Semi-structured interviews are conducted at participants’ homes using the Lifeline Interview Method (LIM) [101] (Assink & Schroots, 2010). The LIM adds value to a traditional semi-structured interview because charting the life-line (a) primes individuals to think about the key events in their life course, (b) provides an organizing structure to ensure a standardized, yet comprehensive approach to discussing the overall life course, and (c) provides anchoring points to discuss the prevailing conditions, perceptions, and responses tied to each event. The interview consists of the participant tracing major ups and downs in their life on a graph and marking major life events on that line. The interview questions are focused on each event asking for detail about: what happened, their appraisal (effects, feelings) of it, how they responded, outcomes of the event, conditions at the time (work, family, finances, housing/neighborhood, social relationships, roles, health), and that event relationships with other events on the line. A final question asks the participant to reflect on their overall lifeline. Efforts are made by the team and collaborators to ensure that the interview questions are culturally informed.

Analytic plan

The Heart of Detroit Study includes collection and analysis of both quantitative and qualitative data. EMA data collected for seven consecutive days will evaluate the momentary and daily dynamics through which negative affect, health behaviors, and interpersonal stressors, namely discrimination [102, 103], social isolation [104], and negative interactions with close others [105], impact cortisol secretion, blood pressure, and sleep. Quantitative data will also be used to investigate concurrent and prospective associations of psychosocial stressors and health behaviors with biomarkers of cumulative stress, inflammation, and CVD surrogate endpoints. Qualitative data will allow us to probe more deeply into life conditions, processes, and behaviors beyond the quantitative measures to understand and begin to explain how various social and individual determinants of health affect each subgroup.

Quantitative data analyses

Multilevel structural equation models (MSEM) will evaluate contributions of interpersonal stress experiences (e.g., discrimination, negative social interactions) to altered cortisol secretion, elevated blood pressure, and impaired sleep at least partially through elevated negative affect and adverse health behaviors (e.g., smoking). The MSEM framework can parse within-person and between-person covariation to test whether stressful experiences within daily life contribute to increased momentary negative affect, engagement in negative health behaviors, and, subsequently, to alterations in cortisol secretion (i.e., flattened daily cortisol slopes), elevated evening blood pressure, and worsened sleep. The capacity of the MSEM framework to statistically parse within-person and between-person covariation also facilitates adjustment for any potential between-person covariation (i.e., at level 3) to simultaneously account for theoretically proposed and empirically demonstrated between-person associations among stress, negative affect, negative health behaviors, cortisol secretion, blood pressure, and sleep [82, 106,107,108,109,110]. Three-level models will be constructed to examine within-person and within-day associations (Level 1), within-person and between-day associations (Level 2), and between-persons associations (Level 3) among the variables of interest.

Structural equation model (SEM) and MSEM frameworks will also examine associations of baseline interpersonal stress exposure, averaged over the 7-day recording period, with adverse health outcomes, hair cortisol, inflammation, resting blood pressure, and HRV both concurrently and prospectively over the 2-year follow-up period. For example, health outcomes, hair cortisol, inflammation, resting blood pressure, and HRV at baseline and latent change from baseline to 2-year follow-up will be regressed onto latent interpersonal stress exposure at baseline. Similarly, the MSEM approach will be extended to evaluate concurrent and prospective associations of reactivity to interpersonal stress (i.e., the within-person covariation of interpersonal stress with negative affect [emotional reactivity] and negative health behaviors [behavioral reactivity]) with adverse health outcomes, hair cortisol, inflammation, resting blood pressure, and HRV.

Qualitative data analysis

To examine the data from the semi-structured qualitative interviews, Grounded Theory Analysis (GTA) will be applied to the interview transcripts and field notes. In the GTA method, key conceptual categories are constructed from a dataset to theoretically explain the most important processes that emerge from the data [111]. Following established GTA procedures, coders will be sensitized to examine the conditions, contexts, actions, and consequences present in the text. Codes will be progressively honed and aggregated via a constant comparative analysis to develop core conceptual categories. Conceptual categories will then be connected in a way that explains how conditions, dynamics, and behaviors (for instance) are related to stress and CVD risk.

Sample size calculation

To calculate the sample size required for the EMA component, power analyses based on Monte Carlo simulation were conducted with 2,000 independent trials. While uncommon in multilevel models, pseudo-standardized regression paths at levels 1 and 2 (i.e., b) were estimated by simulating associated variances at 1 given little prior research to guide the a priori determination of variance estimates. A sample of 500 people, with 35 assessments over 7 days, provides > 80% statistical power to detect (1) small associations of momentary interpersonal stress with current negative affect or negative health behaviors (b = 0.03), and (2) small associations of daily interpersonal stress with negative affect, negative health behaviors, elevated blood pressure, or altered cortisol secretion (b = 0.08).

To calculate the sample size required for CVD-related outcomes, power analyses were based on two-tailed significance tests using an α of 0.05 and are reported in an effect size r metric (r = sqrt(t2/t2 + df); [112]), which is computed from t values associated with betas obtained from MSEM. A sample of 500 individuals provide > 80% statistical power to evaluate small and medium effects (|r|= 0.14).

Missing data and model assumptions

All SEM and MSEM analyses will use full information maximum likelihood (FIML) estimation with a sandwich estimator robust to deviations from multivariate normality [113,114,115] and data missing at random conditional on modeled covariates [116, 117]. Additional techniques to account for missing data will be considered, including multiple imputations using chained equations (MICE) [118] and inverse probability weighting to calibrate the data [119, 120].

Discussion

The Heart of Detroit Study is a multi-faceted project that seeks to attain a more comprehensive understanding of the relationship between psychosocial stress and CVD risk in urban AA middle-aged and older adults. The study design allows for examination of links between short-term processes (i.e., momentary and daily stress) and cardiovascular health trajectories across multiple timespans (moments, days, years), methodological approaches (quantitative, qualitative), and biobehavioral systems (e.g., subjective and objective health behaviors; HPA axis, immune system, cardiovascular system functioning). The inclusion of EMA and daily diary measures enables a fine-grained and ecologically valid examination of interpersonal stressors, such as experiences of discrimination, and associated emotional and behavioral responses in this population. Including both EMA and daily diary approaches allows for better characterization of the contextualized, event-dependent links between psychosocial stressors and CVD risk on a within-person level, facilitating the development of more targeted and effective interventions. By including multiple temporal scales (momentary assessment, daily assessment, and two-year follow-up), this study is well-suited to examine the pathways between both moment-to-moment and day-to-day variability in individuals’ experiences and long-term change in their cardiovascular health. Finally, the qualitative portion of our mixed-methods approach will supplement and contextualize quantitative findings, providing insight into participants’ experiences of and responses to the stressors in their daily lives.

The Heart of Detroit Study will provide valuable insights into the direct and indirect associations between stress and CVD risk in AAs through the incorporation of both traditional (e.g., blood lipids) and novel (e.g., stimulated pro-inflammatory cytokine production) biomarkers of CVD risk. Examination of these biomarkers will allow for examination of the role of multiple biological pathways through which interpersonal stressors may influence health across time. The use of EMA, daily diaries, and in-depth qualitative interviews combined with a detailed biobehavioral characterization of participants’ CVD risk will help contextualize previously identified psychosocial stressors and behaviors among AAs and identify the optimal timing for interventions to mitigate subclinical CVD progression. These results will help inform contextualized, culturally tailored, and adaptive behavioral interventions aimed at reducing racial disparities in CVD, an area of research that remains underdeveloped.

While the primary focus of The Heart of Detroit Study is on the health effects of experiences of interpersonal stressors, including interpersonal racism, structural racism remains the critical determinant of racial disparities in CVD outcomes [13, 121]. For example, historical discriminatory lending and insurance policies, such as redlining, created a structural barrier that prevented racially minoritized groups from moving and resulted in racially segregated neighborhoods [122]. In addition, these policies have led to environmental injustices, manifested, for example, with racially minoritized groups being most impacted by pollution and pollutant-induced chronic diseases [123]. This race-based residential segregation remains a fundamental driver of racial health disparities: by fueling the concentration of poverty and producing inequitable access to resources at both the individual and community level, racial segregation drives racial disparities in CVD-related outcomes [13, 58, 124,125,126]. Detroit has consistently been one of the most segregated metropolitan areas in the US [127, 128]. Past research conducted in Detroit has found that redlining and racial segregation are important predictors of health outcomes, including poor access to health care [129], low birth weight [130], high childhood blood lead levels [131], and poor self-rated health [132], disproportionately affecting AA residents [125]. The Heart of Detroit Study includes measures of both subjective and objective neighborhood quality, with the intention of examining the impacts of these aspects of structural racism on health.

The Heart of Detroit Study focuses exclusively on AA participants. Much existing research on racial health disparities has used a between-group approach, and this research has made important contributions to our understanding of the existence and extent of such disparities. However, between-group comparisons may be insufficient to fully understand racial health disparities [133]; there are theoretical and empirical reasons for why assessments conducted strictly within the AA population will deepen scientific understanding of the health of racially minoritized groups. These reasons include the untested and unjust assumption that Whites are a standard from which AAs deviate (Cultural Deviance Model), the falsified assumptions that group differences exist only for outcomes of interest and not for underlying processes (Cultural Equivalence Model), and the importance of investigating within-group heterogeneity with appropriate statistical power [133, 134]. The exclusive focus on AAs is a strength of our study and a fruitful departure from the Cultural Deviance and Cultural Equivalence models. The Heart of Detroit Study will enable us to both address the long-standing underrepresentation of AAs in studies on daily psychosocial determinants of CVD, and appropriately capture culturally relevant dynamics and within-group heterogeneity, which would go under-examined in a multiracial version of this study. Moreover, because certain social stressors are not comparable across races, a single race study will help avoid problems related to residual confounding [135]. For example, use of a within-group design enables a more nuanced understanding of the effect of SES on the health of AAs. Importantly, socioeconomic distinctions between racial groups are multifaceted and cannot easily be summarized into individual metrics like income [135]; some of the observed racial and ethnic health disparities are therefore likely the result of unmeasured socioeconomic differences [136]. To assess SES more accurately within our sample, The Heart of Detroit Study includes a range of subjective and objective measures of SES across individual, family, and neighborhood levels. Results from The Heart of Detroit Study could therefore help disentangle the effects of race and SES on CVD risk.

Conclusion

Racial disparities in CVD morbidity and mortality remain a pressing public health problem, with AAs at substantially higher risk. While scientific efforts have led to improvements in CVD treatment, progress in reducing racial disparities has been slow, at least in part due to insufficient research investigating the underlying social and psychological determinants of CVD risk in minoritized groups, such as AAs. Past research has been hampered by limited inclusion of AA participants, confounding by SES, and by a lack of understanding of daily stressors and subsequent behavioral and emotional responses in this population. Our results are expected to contribute to the establishment of a comprehensive model of the psychosocial determinants of CVD risk in urban AA middle-aged and older adults, which can help the development of more culturally and individually tailored treatments and interventions to reduce CVD in this population.

Data Availability

Not applicable.

Change history

Abbreviations

AAs:

African Americans

CVD:

cardiovascular disease

EMA:

Ecological Momentary Assessment

FIML:

full information maximum likelihood

GTA:

Grounded Theory Analysis

HPA:

hypothalamic-pituitary-adrenal

HRV:

Heart rate variability

LDL:

low-density lipoprotein

MICE:

multiple imputations using chained equations

MSEM:

Multilevel structural equation modeling

SEM:

Structural equation modeling

SES:

socioeconomic status

References

  1. Kris-Etherton PM, Sapp PA, Riley TM, Davis KM, Hart T, Lawler O. The dynamic interplay of healthy lifestyle behaviors for Cardiovascular Health. Curr Atheroscler Rep. 2022;24(12):969–80.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Oblast T. Decline in deaths from Heart Disease and Stroke—United States, 1900–1999. JAMA. 1999;282(8):724.

    Article  Google Scholar 

  3. Mehta NK, Abrams LR, Myrskylä M. US life expectancy stalls due to cardiovascular disease, not drug deaths. Proc Natl Acad Sci USA. 2020;117(13):6998–7000.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Sidney S, Quesenberry CP, Jaffe MG, Sorel M, Nguyen-Huynh MN, Kushi LH, et al. Recent Trends in Cardiovascular Mortality in the United States and Public Health Goals. JAMA Cardiol. 2016;1(5):594.

    Article  PubMed  Google Scholar 

  5. Boyle E. Biological patterns in hypertension by race, sex, Body Weight, and skin color. JAMA. 1970;213(10):1637.

    Article  PubMed  Google Scholar 

  6. Cooper ES. Cardiovascular diseases and stroke in African Americans: a call for action. J Natl Med Assoc. 1993;85(2).

  7. Javed Z, Haisum Maqsood M, Yahya T, Amin Z, Acquah I, Valero-Elizondo J et al. Race, Racism, and Cardiovascular Health: Applying a Social Determinants of Health Framework to Racial/Ethnic Disparities in Cardiovascular Disease. Circ: Cardiovascular Quality and Outcomes [Internet]. 2022 Jan [cited 2023 Feb 2];15(1). Available from: https://www.ahajournals.org/doi/https://0-doi-org.brum.beds.ac.uk/10.1161/CIRCOUTCOMES.121.007917.

  8. Moriyama IM, Woolsey TD, Stamler J. Observations on possible factors responsible for the sex and race trends in cardiovascular-renal mortality in the United States. J Chronic Dis. 1958;7(5):401–12.

    Article  CAS  PubMed  Google Scholar 

  9. Murray CJL, Kulkarni SC, Michaud C, Tomijima N, Bulzacchelli MT, Iandiorio TJ et al. Eight Americas: Investigating mortality disparities across races, counties, and race-counties in the United States. Novotny T, editor. PLoS Med. 2006;3(9):e260.

  10. Carnethon MR, Pu J, Howard G, Albert MA, Anderson CAM, Bertoni AG et al. Cardiovascular Health in African Americans: A Scientific Statement From the American Heart Association. Circulation [Internet]. 2017 Nov 21 [cited 2023 Apr 13];136(21). Available from: https://www.ahajournals.org/doi/https://0-doi-org.brum.beds.ac.uk/10.1161/CIR.0000000000000534.

  11. Singh G. Widening Socioeconomic and Racial Disparities in Cardiovascular Disease Mortality in the United States, 1969–2013. Int J MCH AIDS [Internet]. 2014 [cited 2023 Feb 3];3(2). Available from: http://mchandaids.org/index.php/IJMA/article/view/44.

  12. Williams DR, Race. Socioeconomic status, and Health the added Effects of racism and discrimination. Ann N Y Acad Sci. 1999;896(1):173–88.

    Article  CAS  PubMed  Google Scholar 

  13. Churchwell K, Elkind MSV, Benjamin RM, Carson AP, Chang EK, Lawrence W et al. Call to action: Structural racism as a fundamental driver of health disparities: A presidential advisory from the American Heart Association. Circulation [Internet]. 2020 Dec 15 [cited 2023 Feb 1];142(24). Available from: https://www.ahajournals.org/doi/https://0-doi-org.brum.beds.ac.uk/10.1161/CIR.0000000000000936.

  14. Mohebi R, Chen C, Ibrahim NE, McCarthy CP, Gaggin HK, Singer DE, et al. Cardiovascular Disease Projections in the United States based on the 2020 Census estimates. J Am Coll Cardiol. 2022;80(6):565–78.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Headley C. Philosophical analysis and the Problem of defining racism: Kalumba KM. Editor Philosophia Africana. 2006;9(1):1–16.

    Article  Google Scholar 

  16. Harrell SP. A multidimensional conceptualization of racism-related stress: implications for the well-being of people of color. Am J Orthopsychiatry. 2000;70(1):42–57.

    Article  CAS  PubMed  Google Scholar 

  17. Paradies YC. Defining, conceptualizing and characterizing racism in health research. Crit Public Health. 2006;16(2):143–57.

    Article  Google Scholar 

  18. Jones JM. Prejudice and racism. Reading, MA: Addison-Wesley; 1972.

    Google Scholar 

  19. Clark R, Anderson NB, Clark VR, Williams DR. Racism as a Stressor for African Americans. American Psychologist. 1999.

  20. McEwen BS, Stellar E. Stress and the Individual: mechanisms leading to Disease. ARCH INTERN MED. 1993;153:2093–101.

    Article  CAS  PubMed  Google Scholar 

  21. Duru OK, Harawa NT, Kermah D, Norris KC. Allostatic load burden and racial disparities in Mortality. J Natl Med Assoc. 2012;104(1–2):89–95.

    PubMed  Google Scholar 

  22. Cohen S, Janicki-Deverts D, Doyle WJ, Miller GE, Frank E, Rabin BS, et al. Chronic stress, glucocorticoid receptor resistance, inflammation, and disease risk. Proc Natl Acad Sci USA. 2012;109(16):5995–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Knight EL, Jiang Y, Rodriguez-Stanley J, Almeida DM, Engeland CG, Zilioli S. Perceived stress is linked to heightened biomarkers of inflammation via diurnal cortisol in a national sample of adults. Brain Behav Immun. 2021;93:206–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Miller GE, Blackwell E. Turning up the heat: inflammation as a mechanism linking chronic stress, Depression, and Heart Disease. Curr Dir Psychol Sci. 2006;15(6):269–72.

    Article  Google Scholar 

  25. Rodriguez JM, Monsalves-Alvarez M, Henriquez S, Llanos MN, Troncoso R. Glucocorticoid resistance in chronic diseases. Steroids. 2016;115:182–92.

    Article  CAS  PubMed  Google Scholar 

  26. Wirtz PH, von Känel R. Psychological stress, inflammation, and Coronary Heart Disease. Curr Cardiol Rep. 2017;19(11):111.

    Article  PubMed  Google Scholar 

  27. Wyatt SB, Williams DR, Calvin R, Henderson FC, Walker ER, Winters K. Racism and Cardiovascular Disease in African Americans. Am J Med Sci. 2003;325(6):315–31.

    Article  PubMed  Google Scholar 

  28. Zilioli S, Jiang Y, Byrd D, Joseph N. Lifetime discrimination, habitual and daily everyday discrimination, and diurnal cortisol among older African Americans adults. Psychoneuroendocrinology. 2023;152:106089.

    Article  PubMed  Google Scholar 

  29. Cuevas AG, Ong AD, Carvalho K, Ho T, Chan SW (Celine), Allen JD Discrimination and systemic inflammation, et al. editors. A critical review and synthesis. Brain, Behavior, and Immunity. 2020;89:465–79.

  30. Lewis TT, Aiello AE, Leurgans S, Kelly J, Barnes LL, Brain. Behav Immun. 2010;24(3):438–43.

    Article  CAS  Google Scholar 

  31. Camelo LV, Machado AV, Chor D, Griep RH, Mill JG, Brant LCC, et al. Racial discrimination is associated with greater arterial stiffness and carotid intima-media thickness: the ELSA-Brasil study. Ann Epidemiol. 2022;72:40–7.

    Article  PubMed  Google Scholar 

  32. Everson-Rose SA, Lutsey PL, Roetker NS, Lewis TT, Kershaw KN, Alonso A, et al. Perceived discrimination and Incident Cardiovascular events: the multi-ethnic study of atherosclerosis. Am J Epidemiol. 2015;182(3):225–34.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Forde AT, Sims M, Muntner P, Lewis T, Onwuka A, Moore K, et al. Discrimination and hypertension risk among African Americans in the Jackson Heart Study. Hypertension. 2020;76(3):715–23.

    Article  CAS  PubMed  Google Scholar 

  34. McEwen BS. Protective and damaging effects of stress mediators: central role of the brain. Dialog Clin Neurosci. 2006;8(4):367–81.

    Article  Google Scholar 

  35. McEwen BS. Central effects of stress hormones in health and disease: understanding the protective and damaging effects of stress and stress mediators. Eur J Pharmacol. 2008;583(2–3):174–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ong AD, Williams DR. Lifetime discrimination, global sleep quality, and inflammation burden in a multiethnic sample of middle-aged adults. Cult Divers Ethnic Minor Psychol. 2019;25(1):82–90.

    Article  Google Scholar 

  37. de Mestral C, Stringhini S. Socioeconomic Status and Cardiovascular Disease: an update. Curr Cardiol Rep. 2017;19(11):115.

    Article  PubMed  Google Scholar 

  38. Steptoe A, Hiltl TJ, Dowd JB, Hamer M. Socioeconomic status and central adiposity as determinants of stress-related biological responses relevant to cardiovascular disease risk. Brain Behav Immun. 2019;77:16–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Leng B, Jin Y, Li G, Chen L, Jin N. Socioeconomic status and hypertension: a meta-analysis. J Hypertens. 2015;33(2):221–9.

    Article  CAS  PubMed  Google Scholar 

  40. Vathesatogkit P, Woodward M, Tanomsup S, Hengprasith B, Aekplakorn W, Yamwong S, et al. Long-term effects of socioeconomic status on incident hypertension and progression of blood pressure. J Hypertens. 2012;30(7):1347–53.

    Article  CAS  PubMed  Google Scholar 

  41. Redondo-Bravo L, Fernández-Alvira JM, Górriz J, Mendiguren JM, Sanz J, Fernández-Friera L, et al. Does Socioeconomic Status Influence the risk of subclinical atherosclerosis? J Am Coll Cardiol. 2019;74(4):526–35.

    Article  PubMed  Google Scholar 

  42. Matthews KA, Gallo LC, Taylor SE. Are psychosocial factors mediators of socioeconomic status and health connections?: a progress report and blueprint for the future. Ann N Y Acad Sci. 2010;1186(1):146–73.

    Article  PubMed  Google Scholar 

  43. Zilioli S, Imami L, Slatcher RB. Socioeconomic Status and Health-Related Biology: Links Between Socioeconomic Disadvantage, Psychological Factors, and HPA Activity in MIDUS. In: Ryff CD, Krueger RF, editors. The Oxford Handbook of Integrative Health Science [Internet]. Oxford University Press; 2018 [cited 2023 Jan 19]. p. 430–42. Available from: https://0-academic-oup-com.brum.beds.ac.uk/edited-volume/34662/chapter/295347655.

  44. Lincoln KD, Financial, Strain. Negative interactions, and mastery: pathways to Mental Health among older African Americans. J Black Psychol. 2007;33(4):439–62.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Krause N, Rook KS. Negative Interaction in Late Life: issues in the Stability and Generalizability of Conflict Across Relationships. The Journals of Gerontology: Series B. 2003;58(2):P88–99.

    Article  Google Scholar 

  46. Cichy KE, Lee JE. Family as a naturally occurring stressor: race, psychosocial factors, and daily health. In: Ryff CD, Krueger RF, editors. The Oxford handbook of integrative health science. Oxford University Press; 2018. pp. 173–90.

  47. Rook KS. Social networks in later life: weighing positive and negative Effects on Health and Well-Being. Curr Dir Psychol Sci. 2015;24(1):45–51.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Sneed RS, Cohen S. Negative social interactions and incident hypertension among older adults. Health Psychol. 2014;33(6):554–65.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Newson JT, Mahan TL, Rook KS, Krause N. Stable negative social exchanges and health. Health Psychol. 2008;27(1):78–86. https://0-doi-org.brum.beds.ac.uk/10.1037/0278-6133.27.1.78

    Article  Google Scholar 

  50. Joseph NT, Kamarck TW, Muldoon MF, Manuck SB. Daily Marital Interaction Quality and Carotid Artery Intima-Medial thickness in healthy middle-aged adults. Psychosom Med. 2014;76(5):347–54.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Xia N, Li H, Loneliness. Social isolation, and Cardiovascular Health. Antioxid Redox Signal. 2018;28(9):837–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Smith KJ, Gavey S, RIddell NE, Kontari P, Victor C. The association between loneliness, social isolation and inflammation: a systematic review and meta-analysis. Neurosci Biobehavioral Reviews. 2020;112:519–41.

    Article  CAS  Google Scholar 

  53. Zilioli S, Jiang Y. Endocrine and immunomodulatory effects of social isolation and loneliness across adulthood. Psychoneuroendocrinology. 2021;128:105194.

    Article  CAS  PubMed  Google Scholar 

  54. Wang T, Li Y, Zheng X. Association of socioeconomic status with cardiovascular disease and cardiovascular risk factors: a systematic review and meta-analysis. J Public Health (Berl) [Internet]. 2023 Jan 21 [cited 2023 Mar 6]; Available from: https://0-link-springer-com.brum.beds.ac.uk/https://0-doi-org.brum.beds.ac.uk/10.1007/s10389-023-01825-4.

  55. Bell C, Thorpe R, Bowie J, LaVeist T. Race disparities in cardiovascular disease risk factors within socioeconomic status strata. Ann Epidemiol. 2018;28.

  56. Ogden CL, Yanovski SZ, Carroll MD, Flegal KM. The epidemiology of obesity. Gastroenterology. 2007;132(6):2087e102.

    Article  Google Scholar 

  57. Karlamangla AS, Merkin SS, Crimmins EM, Seeman TE. Socioeconomic and ethnic disparities in Cardiovascular Risk in the United States, 2001–2006. Ann Epidemiol. 2010;20(8):617–28.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Mujahid MS, Gao X, Tabb LP, Morris C, Lewis TT. Historical redlining and cardiovascular health: the multi-ethnic study of atherosclerosis. Proc Natl Acad Sci USA. 2021;118(51):e2110986118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Cole ER. Intersectionality and research in psychology. Am Psychol. 2009;64(3):170–80.

    Article  PubMed  Google Scholar 

  60. Essed P. Understanding Everyday Racism: an interdisciplinary theory. Volume 2. Sage; 1991.

  61. Crenshaw K. Demarginalizing the intersection of race and sex: a black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. Univ Chic Legal Forum. 1989;139–67.

  62. Lewis JA, Williams MG, Peppers EJ, Gadson CA. Applying intersectionality to explore the relations between gendered racism and health among black women. J Couns Psychol. 2017;64(5):475–86.

    Article  PubMed  Google Scholar 

  63. Patterson EJ, Becker A, Baluran DA. Gendered racism on the body: an Intersectional Approach to maternal mortality in the United States. Popul Res Policy Rev. 2022;41(3):1261–94.

    Article  Google Scholar 

  64. Laster Pirtle WN, Wright T. Structural gendered racism revealed in pandemic Times: intersectional approaches to understanding race and gender Health Inequities in COVID-19. Gend Soc. 2021;35(2):168–79.

    Article  Google Scholar 

  65. Chadiha LA, Washington OGM, Lichtenberg PA, Green CR, Daniels KL, Jackson JS. Building a Registry of research volunteers among older urban African Americans: recruitment processes and outcomes from a community-based Partnership. Gerontologist. 2011;51(Supplement 1):106–15.

    Article  Google Scholar 

  66. Matthews MM, Kim TG, Shibata S, Shibata N, Butcher C, Hyun J, et al. COVID-19 serological survey using micro blood sampling. Sci Rep. 2021;11(1):9475.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Cutchin MP, Eschbach K, Mair CA, Ju H, Goodwin JS. The socio-spatial neighborhood estimation method: an approach to operationalizing the neighborhood concept. Health Place. 2011;17(5):1113–21.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Ellis DA, Cutchin MP, Templin T, Carcone AI, Evans M, Weissberg-Benchell J, et al. Effects of family and neighborhood risks on glycemic control among young black adolescents with type 1 diabetes: findings from a multi‐center study. Pediatr Diabetes. 2021;22(3):511–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Epel ES, Crosswell AD, Mayer SE, Prather AA, Slavich GM, Puterman E, et al. More than a feeling: a unified view of stress measurement for population science. Front Neuroendocr. 2018;49:146–69.

    Article  Google Scholar 

  70. Ong AD, Fuller-Rowell T, Burrow AL. Racial discrimination and the stress process. J Personal Soc Psychol. 2009;96(6):1259–71.

    Article  Google Scholar 

  71. Pearlin L, Menaghan EG, Lieberman MA, Mullan JT. The stress process. J Health Soc Behav. 1989;22(4):337–56.

    Article  Google Scholar 

  72. Zilioli S, Slatcher RB, Fritz H, Booza JC, Cutchin MP. Brief report: Neighborhood disadvantage and hair cortisol among older urban African Americans. Psychoneuroendocrinology. 2017;80:36–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Sternthal MJ, Slopen N, Williams DR. Racial diparities in health: how much does stress really matter? Du Bois Rev. 2011;8(1):95–113.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Hughes ME, Waite LJ, Hawkley LC, Cacioppo JT. A short scale for measuring loneliness in large surveys: results from two Population-Based Studies. Res Aging. 2004;26(6):655–72.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Thornton M, Travis SS. Analysis of the reliability of the modified caregiver strain index. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2003;58(2):127–32.

    Article  Google Scholar 

  76. Lewis JA, Neville HA. Construction and initial validation of the gendered racial Microaggressions Scale for Black women. J Couns Psychol. 2015;62(2):289–302.

    Article  PubMed  Google Scholar 

  77. Schwing AE, Wong YJ, Fann MD. Development and validation of the african american men’s gendered racism stress inventory. Psychol Men Masculinity. 2013;14(1):16–24.

    Article  Google Scholar 

  78. Williams DR, Gonzalez HM, Williams S, Mohammed SA, Moomal H, Stein DJ. Perceived discrimination, race and health in South Africa. Soc Sci Med. 2008;67(3):441–52.

    Article  PubMed  PubMed Central  Google Scholar 

  79. DeMorrow S. Role of the hypothalamic–pituitary–adrenal Axis in Health and Disease. IJMS. 2018;19(4):986.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Quax RA, Manenschijn L, Koper JW, Hazes JM, Lamberts SWJ, van Rossum EFC, et al. Glucocorticoid sensitivity in health and disease. Nat Rev Endocrinol. 2013;9(11):670–86.

    Article  CAS  PubMed  Google Scholar 

  81. Russell E, Koren G, Rieder M, Van Uum S. Hair cortisol as a biological marker of chronic stress: current status, future directions and unanswered questions. Psychoneuroendocrinology. 2012;37(5):589–601.

    Article  CAS  PubMed  Google Scholar 

  82. Schneiderman N, Ironson G, Siegel SD. Stress and health: psychological, behavioral, and Biological Determinants. Annu Rev Clin Psychol. 2005;1(1):607–28.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Katsarou AL, Triposkiadis F, Panagiotakos D. Perceived stress and vascular disease. Where Are We Now? Angiology. 2013;64(7):529–34.

    PubMed  Google Scholar 

  84. Lovallo WR, Gerin W. Psychophysiological reactivity: mechanisms and pathways to Cardiovascular Disease. Psychosom Med. 2003;65(1):36–45.

    Article  PubMed  Google Scholar 

  85. Cohen S, McKay G. Social support, stress and the buffering hypothesis: a theoretical analysis. Handbook of psychology and health. Routledge; 2022. 253–67.

  86. Paul E, Bu F, Fancourt D. Loneliness and risk for Cardiovascular Disease: mechanisms and future directions. Curr Cardiol Rep. 2021;23(6):68.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Krause N. Longitudinal study of social support and meaning in life. Psychol Aging. 2007;22(3):456–69.

    Article  PubMed  Google Scholar 

  88. Cohen S, Skoner DP, Rabin BS. Social Ties and susceptibility to the Common Cold. JAMA. 1997;277(24):1940–4.

    Article  CAS  PubMed  Google Scholar 

  89. Osborne MT, Shin LM, Mehta NN, Pitman RK, Fayad ZA, Tawakol A. Disentangling the links between psychosocial stress and Cardiovascular Disease. Circ: Cardiovasc Imaging. 2020;13(8):e010931.

    PubMed  Google Scholar 

  90. Cohen S, Tyrrell DA, Russell MA, Jarvis MJ, Smith AP. Smoking, alcohol consumption, and susceptibility to the common cold. Am J Public Health. 1993;83(9):1277–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193–213.

    Article  CAS  PubMed  Google Scholar 

  92. Sztajzel J. Heart rate variability: a noninvasive electrocardiographic method to measure the autonomic nervous system. Swiss Med Wkly [Internet]. 2004 Sep 4 [cited 2023 Mar 2]; Available from: https://smw.ch/index.php/smw/article/view/411.

  93. Schuster AK, Fischer JE, Thayer JF, Mauss D, Jarczok MN. Decreased heart rate variability correlates to increased cardiovascular risk. Int J Cardiol. 2016;203:728–30.

    Article  PubMed  Google Scholar 

  94. Xhyheri B, Manfrini O, Mazzolini M, Pizzi C, Bugiardini R. Heart Rate Variability today. Prog Cardiovasc Dis. 2012;55(3):321–31.

    Article  PubMed  Google Scholar 

  95. Kemp AH, Quintana DS. The relationship between mental and physical health: insights from the study of heart rate variability. Int J Psychophysiol. 2013;89(3):288–96.

    Article  PubMed  Google Scholar 

  96. Fuchs FD, Whelton PK. High blood pressure and Cardiovascular Disease. Hypertension. 2020;75(2):285–92.

    Article  CAS  PubMed  Google Scholar 

  97. Balfour PC, Rodriguez CJ, Ferdinand KC. The role of hypertension in race-ethnic disparities in Cardiovascular Disease. Curr Cardiovasc Risk Rep. 2015;9(4):18.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Stevens SL, Wood S, Koshiaris C, Law K, Glasziou P, Stevens RJ et al. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. BMJ. 2016;i4098.

  99. The SPRINT Research Group. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med. 2015;373(22):2103–16.

    Article  PubMed Central  Google Scholar 

  100. Black PH, Garbutt LD. Stress, inflammation and cardiovascular disease. J Psychosom Res. 2002;52(1):1–23.

    Article  PubMed  Google Scholar 

  101. Assink MH, Schroots JJ. The dynamics of autobiographical memory: using the LIM, lifeline interview method. Göttingen, Germany: Hogrefe Publishing; 2010.

    Google Scholar 

  102. Dolezsar CM, McGrath JJ, Herzig AJM, Miller SB. Perceived racial discrimination and hypertension: a comprehensive systematic review. Health Psychol. 2014;33(1):20–34.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Lewis TT, Williams DR, Tamene M, Clark CR. Self-reported experiences of discrimination and Cardiovascular Disease. Curr Cardiovasc Risk Rep. 2014;8(1):365.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Steptoe A, Shankar A, Demakakos P, Wardle J. Social isolation, loneliness, and all-cause mortality in older men and women. Proc Natl Acad Sci USA. 2013;110(15):5797–801.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. De Vogli R, Chandola T, Marmot MG. Negative aspects of Close Relationships and Heart Disease. Arch Intern Med. 2007;167(18):1951.

    Article  PubMed  Google Scholar 

  106. Brondolo E, Brady N, Thompson S, Tobin JN, Cassells A, Sweeney M, et al. Perceived racism and negative affect: analyses of trait and state measures of Affect in a community sample. J Soc Clin Psychol. 2008;27(2):150–73.

    Article  PubMed  PubMed Central  Google Scholar 

  107. Lo Martire V, Caruso D, Palagini L, Zoccoli G, Bastianini S. Stress & sleep: a relationship lasting a lifetime. Neurosci Biobehavioral Reviews. 2020;117:65–77.

    Article  Google Scholar 

  108. Mroczek DK, Almeida DM. The effect of daily stress, personality, and Age on Daily negative affect. J Pers. 2008;72(2):355–78.

    Article  Google Scholar 

  109. O’Connor DB, Thayer JF, Vedhara K. Stress and health: a review of psychobiological processes. Annu Rev Psychol. 2021;72(1):663–88.

    Article  PubMed  Google Scholar 

  110. Pepin JL, Borel AL, Tamisier R, Baguet JP, Levy P, Dauvilliers Y. Hypertension and sleep: overview of a tight relationship. Sleep Med Rev. 2014;18(6):509–19.

    Article  PubMed  Google Scholar 

  111. Charmaz K. Constructing grounded theory: a practical guide through qualitative analysis. United Kingdom: SAGE Publications; 2006. p. 224.

    Google Scholar 

  112. Rosenthal R, Rosnow RL, Rubin DB. Contrasts and effect sizes in behavioral research: A correlationalapproach. Cambridge University Press; 2000.

  113. Huber PJ. The behavior of maximum likelihood estimates under nonstandard conditions. Proc Fifth Berkeley Symp Math Stat Probab. 1967;1:221–33.

    Google Scholar 

  114. Muthén LK, Muthén BO, MPlus. The comprehensive modeling program for applied researchers user guide. Los Angeles, CA: Muthén & Muthén; 2012.

    Google Scholar 

  115. White HA, Heteroskedasticity-Consistent. Covariance Matrix Estimator and a direct test for heteroskedasticity. Econometrica. 1980;48(4):817.

    Article  Google Scholar 

  116. Enders CK. Applied missing data analysis. New York, NY: Guilford; 2010.

    Google Scholar 

  117. Little TD, Jorgensen TD, Lang KM, Moore EWG. On the Joys of Missing Data. J Pediatr Psychol. 2014;39(2):151–62.

    Article  PubMed  Google Scholar 

  118. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377–99.

    Article  PubMed  Google Scholar 

  119. Li L, Shen C, Li X, Robins JM. On weighting approaches for missing data. Stat Methods Med Res. 2013;22(1):14–30.

    Article  PubMed  Google Scholar 

  120. Seaman SR, White IR. Review of inverse probability weighting for dealing with missing data. Stat Methods Med Res. 2013;22(3):278–95.

    Article  PubMed  Google Scholar 

  121. Bailey ZD, Krieger N, Agénor M, Graves J, Linos N, Bassett MT. Structural racism and health inequities in the USA: evidence and interventions. The Lancet. 2017;389(10077):1453–63.

    Article  Google Scholar 

  122. Mitchell B, Franco J. Home Owners’ Loan Corporation redlining maps: the persistent structure of segregation and economic inequality. Washington, D.C.: National Community Reinvestment Coalition; 2018.

    Google Scholar 

  123. Deivanayagam TA, English S, Hickel J, Bonifacio J, Guinto RR, Hill KX et al. Envisioning environmental equity: climate change, health, and racial justice. The Lancet. 2023;S0140673623009194.

  124. Collins C, Williams DR. Segregation and mortality: the Deadly Effects of Racism? Sociol Forum. 1999;14(3):495–523.

    Article  Google Scholar 

  125. Schulz AJ, Williams DR, Israel BA, Lempert LB. Racial and spatial relations as fundamental determinants of Health in Detroit. Milbank Q. 2002;80(4):677–707.

    Article  PubMed  PubMed Central  Google Scholar 

  126. Williams DR, Collins C. Racial residential segregation: a Fundamental cause of racial disparities in Health. Public Health Rep. 2001;116.

  127. Farley R, Frey WH. Changes in the segregation of Whites from Blacks during the 1980s: small steps toward a more Integrated Society. Am Sociol Rev. 1994;59(1):23.

    Article  Google Scholar 

  128. Florida R, Mellander C. Segregated city: the geography of economic segregation in America’s metros. Martin Prosperity Institute; 2015.

  129. Dai D. Black residential segregation, disparities in spatial access to health care facilities, and late-stage breast cancer diagnosis in metropolitan Detroit. Health Place. 2010;16(5):1038–52.

    Article  PubMed  Google Scholar 

  130. Grady S, Darden J. Spatial methods to study local racial residential segregation and Infant Health in Detroit, Michigan. Ann Assoc Am Geogr. 2012;102(5):922–31.

    Article  Google Scholar 

  131. Moody HA, Darden JT, Pigozzi BWm. The relationship of Neighborhood Socioeconomic differences and racial residential segregation to Childhood blood lead levels in Metropolitan Detroit. J Urban Health. 2016;93(5):820–39.

    Article  PubMed  PubMed Central  Google Scholar 

  132. McClure E, Feinstein L, Cordoba E, Douglas C, Emch M, Robinson W, et al. The legacy of redlining in the effect of foreclosures on Detroit residents’ self-rated health. Health Place. 2019;55:9–19.

    Article  PubMed  Google Scholar 

  133. Whitfield KE, Allaire JC, Belue R, Edwards CL. Are comparisons the answer to understanding behavioral aspects of aging in racial and ethnic groups? The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2008;63(5):P301–8.

    Article  PubMed  PubMed Central  Google Scholar 

  134. Cauce AM, Coronado N, Watson J. Conceptual, methodological, and statistical issues in culturally competent research. In: Hernandez M, Isaacs MR, editors. Promoting cultural competence in children’s mental health services. Baltimore, MD: Paul H. Brookes Publishing Co.; 1998. pp. 305–29.

    Google Scholar 

  135. Kaufman JS, Cooper RS, McGee DL. Socioeconomic status and health in Blacks and Whites: the problem of residual confounding and the resiliency of race. Epidemiology. 1997;8(6):621–8.

    CAS  PubMed  Google Scholar 

  136. Braveman PA, Cubbin C, Egerter S, Chideya S, Marchi KS, Metzler M, et al. Socioeconomic status in Health Research: one size does not fit all. JAMA. 2005;294(22):2879.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the research participants for the time and effort they are contributing to this research. The authors would also like to thank the Institute of Gerontology (IoG) at Wayne State University, all the research assistants in the Biopsychosocial Health Laboratory, Daniel Saleh, James Mallare, and Julian Bruinsma for their help in data collection, management, and analysis.

Authors’ information (optional): Not applicable.

Funding

This study was funded by the National Heart, Lung, and Blood Institute.

Author information

Author notes

  1. Kristin M. Davis, Katherine Knauft contributed equally to this work.

Authors and Affiliations

  1. Department of Psychology, Wayne State University, 5057 Woodward Avenue, Detroit, MI, 48202, USA

    Kristin M. Davis, Katherine Knauft & Samuele Zilioli

  2. College of Human Medicine, Michigan State University, East Lansing, MI, 48824, USA

    Lena Lewis

  3. Institute of Environmental Health Sciences, Department of Pharmacology, Wayne State University, Detroit, MI, 48201, USA

    Michael Petriello

  4. Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA

    Lauren Petrick

  5. Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, 48201, USA

    Francesca Luca

  6. Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, 48201, USA

    Francesca Luca

  7. Department of Psychology, Pepperdine University, Malibu, CA, 90265, USA

    Nataria T. Joseph

  8. School of Occupational Therapy, Pacific Northwest University of Health Sciences, Yakima, WA, 98901, USA

    Heather Fritz & Malcolm Cutchin

  9. Department of Psychology, University of Windsor, Windsor, ON, N9B 1B4, Canada

    Lance Rappaport

  10. Departments of Emergency Medicine and Physiology, Wayne State University, Detroit, MI, 48201, USA

    Phillip Levy

  11. Department of Biobehavioral Health, Pennsylvania State University, University Park, PA, 16802, USA

    Christopher G. Engeland

  12. Ross and Carol Nese College of Nursing, Pennsylvania State University, University Park, PA, 16802, USA

    Christopher G. Engeland

  13. Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, MI, 48201, USA

    Samuele Zilioli

Authors
  1. Kristin M. Davis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katherine Knauft
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lena Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael Petriello
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lauren Petrick
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Francesca Luca
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Nataria T. Joseph
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Heather Fritz
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Malcolm Cutchin
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lance Rappaport
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Phillip Levy
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Christopher G. Engeland
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Samuele Zilioli
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.M.D. and K.K. drafted and provided substantial revisions to the manuscriptL.L. provided substantial revisions to the manuscriptM.P., L.P., F.L., N.T.J., H.F., M.C., L.R., P.L., and C.G.E., conceived of and designed the Heart of Detroit StudyS.Z. conceived of and designed the Heart of Detroit Study, and provided substantial revisions to the manuscriptAll authors reviewed the manuscript.

Corresponding author

Correspondence to Samuele Zilioli.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Study procedures were approved by the Wayne State University Institutional Review Board (approval number IRB-21-01-3152). All participants will provide written informed consent, and all methods will be conducted in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original version of this article was revised: the author name has been updated.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Davis, K.M., Knauft, K., Lewis, L. et al. The heart of Detroit study: a window into urban middle-aged and older African Americans’ daily lives to understand psychosocial determinants of cardiovascular disease risk. BMC Psychiatry 23, 766 (2023). https://0-doi-org.brum.beds.ac.uk/10.1186/s12888-023-05148-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s12888-023-05148-2

Keywords

BMC Psychiatry

ISSN: 1471-244X

Contact us