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Race and other sociodemographic categories are differentially linked to multiple dimensions of interpersonal-level discrimination: Implications for intersectional, health research

  • Danielle L. Beatty Moody ,

    Contributed equally to this work with: Danielle L. Beatty Moody, Shari R. Waldstein, Daniel K. Leibel

    Roles Writing – original draft, Writing – review & editing

    dlbeatty@umbc.edu

    Affiliation Department of Human Services Psychology, University of Maryland, Baltimore, Maryland, United States of America

  • Shari R. Waldstein ,

    Contributed equally to this work with: Danielle L. Beatty Moody, Shari R. Waldstein, Daniel K. Leibel

    Roles Writing – original draft

    Affiliations Department of Human Services Psychology, University of Maryland, Baltimore, Maryland, United States of America, Division of Gerontology and Geriatric Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America, Geriatric Research Education and Clinical Center, Baltimore VA Medical Center, Baltimore, Maryland, United States of America

  • Daniel K. Leibel ,

    Contributed equally to this work with: Danielle L. Beatty Moody, Shari R. Waldstein, Daniel K. Leibel

    Roles Writing – original draft

    Affiliation Department of Human Services Psychology, University of Maryland, Baltimore, Maryland, United States of America

  • Lori S. Hoggard ,

    Roles Writing – original draft

    ‡ These authors also contributed equally to this work.

    Affiliation Department of Psychology, Rutgers University, New Brunswick, New Jersey, United States of America

  • Gilbert C. Gee ,

    Roles Writing – original draft

    ‡ These authors also contributed equally to this work.

    Affiliation Department of Community Health Sciences, University of California, Los Angeles, Los Angeles, California, United States of America

  • Jason J. Ashe ,

    Roles Writing – original draft

    ‡ These authors also contributed equally to this work.

    Affiliation Department of Human Services Psychology, University of Maryland, Baltimore, Maryland, United States of America

  • Elizabeth Brondolo ,

    Roles Writing – original draft

    ‡ These authors also contributed equally to this work.

    Affiliation Department of Psychology, St. John’s University, Queens, New York, New York, United States of America

  • Elias Al-Najjar ,

    Roles Writing – original draft

    ‡ These authors also contributed equally to this work.

    Affiliation Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, Maryland, United States of America

  • Michele K. Evans ,

    Roles Writing – original draft

    ‡ These authors also contributed equally to this work.

    Affiliation Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America

  • Alan B. Zonderman

    Roles Writing – original draft

    ‡ These authors also contributed equally to this work.

    Affiliation Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America

Abstract

Objectives

To examine whether intersections of race with other key sociodemographic categories contribute to variations in multiple dimensions of race- and non-race-related, interpersonal-level discrimination and burden in urban-dwelling African Americans and Whites.

Methods

Data from 2,958 participants aged 30–64 in the population-based Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study were used to estimate up to four-way interactions of race, age, gender, and poverty status with reports of racial and everyday discrimination, discrimination across multiple social statuses, and related lifetime discrimination burden in multiple regression models.

Results

We observed that: 1) African Americans experienced all forms of discrimination more frequently than Whites, but this finding was qualified by interactions of race with age, gender, and/or poverty status; 2) older African Americans, particularly African American men, and African American men living in poverty reported the greatest lifetime discrimination burden; 3) older African Americans reported greater racial discrimination and greater frequency of multiple social status-based discrimination than younger African Americans; 4) African American men reported greater racial and everyday discrimination and a greater frequency of social status discrimination than African American women; and, 5) White women reported greater frequency of discrimination than White men. All p’s < .05.

Conclusions

Within African Americans, older, male individuals with lower SES experienced greater racial, lifetime, and multiple social status-based discrimination, but this pattern was not observed in Whites. Among Whites, women reported greater frequency of discrimination across multiple social statuses and other factors (i.e., gender, income, appearance, and health status) than men. Efforts to reduce discrimination-related health disparities should concurrently assess dimensions of interpersonal-level discrimination across multiple sociodemographic categories, while simultaneously considering the broader socioecological context shaping these factors.

Introduction

In the United States (U.S.), discrimination continues to function chiefly as a consequence of dominant versus nondominant group membership, with minority categorizations delineated in accordance with sociohistorically established classifications [1, 2]. Arguably, race is the most impactful minority sociodemographic category in the U.S., driving striking and protracted economic, social, and health disparities among most racial minorities compared with Whites [35]. Most research on discrimination has examined the correlates of these experiences in one-on-one or interpersonal interactions and has focused on African Americans [e.g., 610]. The emphasis on African Americans is likely because of their distinct experiences—their unique passage to the U.S. and subsequent institutionalized enslavement and oppression; de jure and modern de facto discrimination; and their disproportionate burden of poor health [1, 3, 4, 10, 11]. However, Whites—the dominant racial group in the U.S.—are increasingly reporting interpersonal-level discrimination, which has been linked to poor health (e.g., [12]). While it is unsurprising that discrimination has adverse health consequences [13], we lack a rudimentary understanding of how the interactive association between race and other key sociodemographic categories is related to reports of interpersonal-level discrimination for African Americans and Whites.

Conceptualizing racism and racial discrimination

Racism is defined as “a system of oppression based on racial/ethnic group designations in which a pervasive ideology of racial superiority and inferiority provides the foundation for structural inequalities, intergroup conflict, discrimination, and prejudice” [4 p396]. Accordingly, racial discrimination—defined as actions by members of dominant racial groups that have negative or differential effects on members of nondominant racial groups [14]—is the behavioral manifestation of racism. Racial discrimination emanates from a system of oppression, based on power asymmetries that allow the dominant group to maintain access to unearned privileges, opportunities, and resources [1]. As we discuss the racism and discrimination—we use the sets of terms “African American” and “Black” and “White American” and “White,” respectively, to reflect these racialized groupings as used in the related literature.

Intersecting sociodemographic categories and dimensions of discrimination

When considering race, other sociodemographic categories for which marginalized identities are established may also affect experiences with discrimination. For many, race is experienced in tandem with other personal characteristics, including age, gender, and socioeconomic status (SES). For instance, the experiences of an older African American male with a lower SES may markedly differ from those of a younger White male with a higher SES. Thus, it may be insufficient to examine the “raced-experience” of African Americans without also considering age, gender, and SES—as each of these characteristics encompasses socially constructed meanings that may influence an individual’s perceived social standing and interpersonal-level interactions. As such, simultaneous consideration of an individual’s different minority or non-minority statuses may offer greater insight than simply studying these statuses in isolation. Indeed, it is plausible that each respective sociodemographic membership uniquely contributes to their experiences of discrimination, as well as towards the overall cumulative experience and impact of these events.

A current gap regarding interpersonal-level discrimination research is that this multidimensional construct is traditionally assessed through emphasis on one aspect or domain of discriminatory experiences [15]. Specifically, while some research does assess several dimensions concurrently [e.g., 1621], most research characterizing discrimination has not considered multiple facets of interpersonal discriminatory acts in a comprehensive manner, including their types (e.g., generic versus status specific [race- or gender-related]), context (e.g., workplace, court setting), frequency (e.g., daily, lifetime), form of threat (e.g., socio-emotional, physical), explicitness (e.g., covert versus overt), and consequences (e.g., posed obstacles, blocked opportunities). This is a particular limitation in descriptive discrimination research.

Understanding the differential patterning of these dimensions of discrimination as a function of an individual’s concurrent sociodemographic categories may enable greater precision in elucidating linkages to mental and physical health endpoints. This approach may also allow health disparities researchers to uncover previously obscured subgroups with a high risk of experiencing discrimination and who, in turn, face greater health vulnerabilities.

Sociodemographic variations in exposure to dimensions of discrimination

…Racism will always be a part of the world, a part of America…Hate in America, especially for African-Americans, is living every day…No matter how much money you have, no matter how famous you are…being black in America is tough…And we got a long way to go, for us as a society and for us as African-Americans, until we feel equal in America. [22]

–LeBron James

LeBron James made this statement in 2017, hours after his home was vandalized with racist graffiti. His response echoes the experience of many African Americans in the U.S.: racism is unyielding and pervasive, regardless of how much they accomplish. Indeed, over the last several years, a constant and growing barrage of national news reports have illuminated the myriad occurrences of racial discrimination experienced by African American women, men, and children across the country. White bystanders have requested unnecessary police intervention in situations where African Americans are engaging in routine, everyday activities such as bird watching, jogging, swimming in community pools, operating lemonade stands, entering their apartments, moving into new single-family homes, hosting neighborhood barbeques, or transacting business with their bank teller at a local financial institution [12, 2336]. Paralleling these news reports is an uptick in anti-Black hate crimes documented by the Federal Bureau of Investigation across 2017 and 2018 [24, 25]. Several empirical studies support these reports of racial discrimination against African Americans and also explore how other sociodemographic categories (e.g., age, gender, and SES) intersect with race and contribute to discriminatory experiences [3739].

Characterizing discrimination in blacks

Across decades of social science research, African Americans consistently self-report the highest levels of race-related and general interpersonal discrimination across major life domains. Indeed, 80 to 100% of African Americans report lifetime exposure to racial discrimination in daily life [2632]. In one longitudinal study, 90% of African Americans reported persistent racial discrimination at work; in the marketplace; when seeking employment, housing, or medical care; and when engaging with law enforcement and the judicial system across a seven-year span [40]. African Americans also report the highest levels of everyday discrimination [12], which includes day-to-day minor insults without attribution of cause (e.g., race, age, gender, SES). While racial differences in the overall lifetime burden of discrimination remain unclear, reports overwhelmingly demonstrate that African Americans experience discrimination more consistently throughout their lives as compared to other racial/ethnic groups [e.g., 3335].

Reports of interpersonal-level discrimination vary by age, gender, and SES, and these variations may also be modified by race. Regarding age, Gee et al., [36] posit that experiences with discrimination may be shaped by historical events across one’s life. For example, racial minorities living during particularly polarizing racialized periods, such as the Civil Rights era (i.e., 1954 to 1968), might be expected to report more accumulative discriminatory experiences than their younger peers or Whites [41]. However, findings are mixed with some studies showing that older African Americans report more overall and racial discrimination [4245] and some reporting no differences in reported discrimination according to age (e.g., [27, 4648]). Yet, there are also studies which demonstrate that younger African Americans report more overall and racial discrimination [12, 49, 50]. Notably, Gee and colleagues [36] emphasize the role of historical, racialized periods in shaping exposure to discrimination; a perspective which may shed light on why younger generations of African American adults would also report more of these experiences similar to older African Americans. Indeed, the accumulation of contemporary sociopolitical and structural discrimination, growing awareness of police-involved killings of unarmed African Americans, and the related wave of protests against social injustice over the last decade, likely serve as an unwelcome backdrop for the interpersonally discriminatory experiences that the younger generation faces on a daily basis. With regard to differences in exposure by age between African Americans and Whites, there is some evidence that older African Americans may report greater everyday discrimination than older Whites (e.g., [51]). Overall, the interplay between age and various types of discrimination experienced by African Americans remains poorly understood and has not been explored in comparison to White experiences.

Data regarding gender differences in overall and racial discrimination are also mixed [58]. Most studies have focused on African Americans and have examined gender differences within, rather than between, racial groups. Most, but not all, of these studies show that African American men report higher levels of overall and racial discrimination compared with African American women [12, 5254]. However, existing discrimination measures may not fully capture gender differences [40, 55] or encompass the intersectional nature of race and gender reflecting the experiences of African Americans [39, 56, 57]. African American men face pervasive, widely publicized, discriminatory events and are subject to hostile encounters with law enforcement and the judicial system [5860]. However, African American women also face chronic, unfair, hostile, and even deadly encounters across their lifetimes, which are lesser known, but are beginning to draw attention across national media platforms [e.g., 61, 62] and in empirical studies [e.g., 55]. Because their doubly marginalized status is often unaccounted for, empirical studies may not fully capture the discrimination that African American women face; therefore, examining how race interacts with gender and other key sociodemographic categories (e.g., SES) in relation to discrimination may be a step toward providing greater clarity on their experiences. African American men and women occupy marginalized statuses—both are from a minority race (African American) and African American women are also from a devalued gender grouping [6367]. These sociodemographic categories largely shape their ascribed level of threat and access to opportunities and benefits, and consequently, the types of discriminatory treatment they receive.

Many studies report that for African Americans, higher SES—whether measured by poverty level, income, or education—is associated with increased reports of various forms of self-reported discrimination [5, 33, 55, 68], especially for men [69, 70]. While some studies have reported no variation by SES in discriminatory experiences (e.g., [27]), others have shown that lower SES is associated with more frequent experiences of everyday or racial discrimination (e.g., [26, 41]) by African Americans and Whites. Few studies have explored whether African Americans face different types of discrimination as a function of their SES [47, 71, 72]. According to one study, African Americans with lower incomes and less education may face race-related stigma and physical threat or aggression, whereas those with higher incomes may face more racial discrimination in the workplace, indicating that SES variations may confer unique discriminatory experiences for African Americans [73]. Although studies of the interplay of SES and discrimination for Whites are limited, evidence suggests that those with greater poverty exposure and less education are more likely to report racial discrimination than their peers with a higher SES based on these indicators [33]. Therefore, SES differences in discrimination may vary by the metric used to measure SES and may also depend on the dimension of discrimination considered [47]. Notably, scholars have demonstrated that indicators of SES, including education and poverty, are not proxies for each another, nor are they interchangeable, but capture distinct information on SES [74]. Accordingly, we analyze the relationship of both poverty and education independently in relation to our outcomes of interest in this study.

Characterizing discrimination in whites

Embedded within the conceptualization of racism in the U.S. is the fundamental understanding that members of the dominant racial group, White Americans, are not, nor can they be, subject to systematic racism [1, 75]. Still, consideration must be given to the complex, intersecting histories of race, ethnicity, and Whiteness as related to discrimination in this country. For instance, prior to their acceptance into the broader racial grouping of White, some Euro-ethnic groups, such as Jewish and Greek people, experienced pervasive structural-level discrimination, from the “establishment” group, White Anglo-Saxon Protestants [76]. Against this backdrop, in contemporary reports, Whites are increasingly identifying their group as targets of racial discrimination. For instance, a 2017 nationally representative survey of Whites indicated that 55% believe anti-White discrimination exists, and 43% of non-Hispanic Whites from another survey believed racism against their group is widespread [26, 77]. However, in contrast to perceiving discrimination against their group [78], few Whites report personally experiencing racial discrimination. In a 2016 Pew poll, 30% of White respondents reported encountering racial discrimination, with only 2% identifying these as regularly occurring experiences [79]. These patterns of self-reported discrimination demonstrate that all groups might experience out-group threat, yet only some encounter discrimination on a daily basis. Notably, ethnic membership among Whites may contribute to variations in their experiences with discrimination. At the same time, these experiences in Whites are not fundamentally tied to an underlying system of structural racism. This foundational aspect of how race functions in the U.S. for Whites as compared to Blacks, has wholly shaped their differing experiences of racism and discrimination, including interpersonal-level racial/ethnic events.

While interpersonal discrimination related to race or ethnicity can happen to anyone, some researchers suggest that these reports in Whites may reflect other underlying factors central to the American cultural milieu. In this regard, several reports demonstrate growing concern and discomfort among Whites related to the rising themes of inclusivity and equity [8085] and shifting demographics in the U.S., as the U.S. Census Bureau predicts racial/ethnic minorities will comprise the majority of the nation’s population by 2044 [86]. These concerns may emanate from a zero-sum perspective—where members of a dominant group feel they “lose” if a nondominant group “wins” (e.g., by achieving equity and/or comprising the majority of the nation’s population), thereby producing negative racial sentiment in the dominant group [78, 80]. Indeed, per traditional social psychological theory, perceived alterations to the traditional social hierarchy and related privileges would be deemed threatening for any majority group [87]. Taken together, these factors may inspire negative sentiment and contribute to increased perceptions of race-based discrimination among Whites.

These factors (concerning the possible underlying reasons for these reports) notwithstanding, clearly, perception matters. Many studies show that the interpersonal experience of discrimination is real and valid for the target, regardless of the underlying cause, and that these experiences can impact biological processes that contribute to poor health outcomes [5, 8, 9]. Indeed, preliminary but growing evidence shows that discrimination is associated with negative health effects among Whites who affirm having these experiences [17, 20, 21, 50, 88].

Within Whites, intersecting gender and SES social identities may uniquely shape their reports of discrimination. For example, White women, given their juxtaposition of White racial privilege and devalued gender status, may experience particular forms of discrimination [e.g., see 89]. For instance, in a past national survey, White respondents (39.2%) were most likely to identify gender as the primary cause of discrimination they experienced [12]. Whites with a lower SES may also have unique discriminatory experiences or perceptions about discrimination [e.g., 90, 91]. Whether and how age, gender, and SES may shape Whites’ experiences with discrimination remains unknown. Characterizing reports of discrimination by Whites, particularly in the context of other key sociodemographic factors, can help clarify how these experiences negatively impact their health. In sum, comprehensive examination of these linkages in Whites—where they have been understudied—and African Americans, may shed much needed light on how age, gender, and SES uniquely or similarly shape their experiences of discrimination.

Guiding integrative theoretical framework

Our study draws on racial stratification theory [92, 93] and the racism-related stress [1], intersectionality [94, 95], and health disparities frameworks through considering four key tenets. First, macro-level U.S. sociohistorical dynamics have created a bifurcated grouping that deems Blacks as lowly valued and less desirable than Whites and other racial/ethnic groups [3, 96]. This culturally held understanding of the racial hierarchy consciously and unconsciously influences interpersonal interactions for African Americans and Whites [97, 98]. Although every discriminatory event that African Americans experience is not explicitly attributed to their race, their highly marginalized minority group membership still produces greater discriminatory exposure which may be characterized as race- or non-race-related [1]. Second, an individual likely experiences their social statuses as intersecting identities [95]. In this regard, intersectionality is a theoretical framework forwarded by Kimberly Crenshaw [95], a Black feminist scholar that posits that multiple social categories—such as race, age, gender, and SES—intersect at the individual-level to reflect established structural-level systems of privilege and oppression. Thus, considering race alongside other sociodemographic categories may allow greater understanding of their contributions to the experience of discrimination [7, 56, 94, 95, 99101]. Notably, this approach may shed light on the intersectional paradox [56], wherein an individual simultaneously occupies low and high social statuses (e.g., African American men or White women). Third, occupying a lower social status or multiple marginalized statuses may uniquely shape the types, contexts, frequency, and severity of discriminatory experiences. Finally, applying an integrative framework allows us to parse out the complex linkages between social statuses and interpersonal-level discrimination. This may be an important but overlooked step that may aid the resolution of equivocal findings in health disparities research related to racially disproportionate health outcomes and the contribution of social and psychological determinants of health. Indeed, disproportionate, chronic experiences with discrimination based on multiple aspects of one’s social identity may contribute to negative biopsychosocial sequelae via sustained psychological and physiological wear and tear (e.g., allostatic load).

Goals of current study

This study examines the complex linkages among key intersecting sociodemographic categories, specifically, race, age, gender, and SES, in relation to various dimensions of interpersonal-level discrimination in a cohort of socioeconomically diverse, urban-dwelling, middle-aged to older African American and White adults. Specifically, we propose that while both racial groups may experience discrimination, African Americans will report experiencing more interpersonal-level discrimination than Whites. Further, we expect different dimensions of discrimination, particularly, social-status-specific discrimination (e.g., racial) and non-social status specific discrimination (e.g., everyday, chronic experiences), as well as the overall burden of discrimination to vary across distinct, intersecting sociodemographic categories. While complex variations in the frequency, severity, and sources of discrimination likely occur along the lines of race, age, gender, and SES, most prior studies that examine discrimination as an endpoint adjust for one or more of these sociodemographic factors (e.g., [47, 49, 102]) without examining their potential interactions (for exceptions, see [53, 55]). Therefore, our exploratory investigation seeks to uncover how different combinations of these key sociodemographic categories may contribute to an individual’s experience of multidimensional discrimination. In turn, as discriminatory experiences are linked to a range of negative mental and physical health outcomes [9], our study will explore how exposure to discrimination is characterized by sociodemographic characteristics, which may also inform our understanding of associations between multidimensional discrimination and health endpoints. Thusly, the primary aim of the present study is to explicitly test the hypothesis that African American race—a key sociodemographic characteristic—will be associated with greater self-reported discrimination. Further, to address inconsistencies in the literature, we test up to 3-way interactions between race and age, gender, and SES in relation to the four concurrently-assessed measures of interpersonal-level discrimination (racial discrimination, discrimination across multiple social statuses, everyday discrimination, and lifetime discrimination burden).

Methods

Participants and procedure

Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) is a planned 20-year, prospective, population-based, longitudinal study, designed to investigate the associations among race, age, gender, and SES, and risk factors for health disparities [103]. An area probability sampling strategy stratified by 5-year age bands (30–64), sex, self-identified race (White or African American), and household income below or above the Federal household-size poverty level was utilized to determine the sample. HANDLS participants are a fixed cohort of 3,720 urban-dwelling, African American and White participants who were between 30 and 64 years old at baseline (2004–2009). Participants were drawn from an area probability sample of thirteen neighborhoods (contiguous census tracts) in the city of Baltimore, Maryland. In-home visits and mobile research vehicles were utilized to collect data. All participants provided written informed consent. The study protocol was approved by the Institutional Review Board at the National Institute of Environmental Health Sciences.

At initial selection for participation in HANDLS, participants were excluded if pregnant, within 6 months of cancer treatment, diagnosed with AIDS, limited in ability to provide written informed consent, unable to provide valid government-issued identification, or currently without a verifiable address. The current sample included 2,958 adults, of whom 1,924 were African American and 1,034 were White. However, the number of participants included in each analysis varied slightly because of missing data points on different discrimination measures.

Measures

Demographic characteristics.

Participants’ age, race (0 = White, 1 = African American), sex (0 = woman, 1 = man), gender (0 = female, 1 = male), annual household income, and educational attainment were assessed. Nine participants self-identified their ethnicity as Hispanic, all of whom also self-identified their race as White. Self-identified gender was determined based upon participant reports that their gender differed from their biological sex. Three participants identified as women whose biological sex assigned at birth was male. Poverty status was defined as annual household income (adjusted for household size) above (0) or below (1) 125% of the 2004 Health and Human Services poverty guidelines. Educational attainment was categorized into three levels (0 = less than high school, 1 = high school diploma or GED, or 2 = more than high school).

Discrimination measures.

Racial discrimination was assessed with a summed, six-item measure initially used in a large prospective, epidemiological cohort study [104] and subsequently included in the Experiences of Discrimination scale, which has adequate internal consistency (α = 0.74) and test-retest reliability (0.70; [105]). The measure included questions about whether individuals experienced racial discrimination at school, when seeking employment, at work, while getting housing, when getting medical care, or from police or in courts [106]. Respondents replied Yes (1) or No (0) to each item. Possible scores ranged from 0–6, with higher values indicating more racial discrimination. In our study population, this scale had strong internal consistency among African Americans (α = 0.82) and Whites (α = 0.77).

Discrimination across multiple social statuses was assessed using a summed, ten-item measure adapted from a previous measure of discrimination in healthcare settings [104, 107]. The measure included, “Overall, how much have you experienced prejudice or discrimination because of…” gender, race, ethnicity, income, age, religion, physical appearance, sexual orientation, health status, and disability. Participants responded on a 4-point scale ranging from 1 (not all all) to 4 (a lot). Possible scores can range from 10 to 40, with higher scores indicating more forms of discrimination were experienced more frequently. In our study population, this scale had strong internal consistency among African Americans (α = 0.84) and Whites (α = 0.74).

The Everyday Discrimination scale [50, 105] is a summed, nine-item measure assessing the frequency of day-to-day experiences of bias without attribution to race or ethnicity (e.g., “being treated with less courtesy”). Participants responded on a 6-point scale ranging from 1 (almost every day) to 6 (never). Responses were reverse scored and summed. Possible scores can range from 9–54, with higher scores indicating greater everyday discrimination. This scale has been shown to have strong internal consistency among African Americans and Whites (α = 0.88; [50]) and was similarly strong in our sample (α = 0.83 for African Americans and Whites when examined separately).

Lifetime discrimination burden was assessed with a summed, two-item measure. These items have been used in large-scale epidemiologic studies [104, 108, 109]: (1) “Overall, how much has discrimination interfered with you having a full and productive life?” and “Overall, how much harder has your life been because of discrimination?” Participants responded on a 4-point scale ranging from 1 (not at all) to 4 (a lot) for each item. Possible scores can range from 2–8, with higher scores indicating greater lifetime discrimination burden. In the present sample, these two items were strongly correlated among African Americans, r = 0.74, p < .001, and Whites, r = 0.79, p < .001.

Statistical plan

R version 1.2.1335 [110] was used for statistical analyses. Preliminary data analysis revealed positively skewed distributions and residual plots for all discrimination measures except everyday discrimination. Logarithmic- and square-root-transformations failed to correct the skewness of the distributions or residuals. Therefore, generalized linear models using an inverse Gaussian link function were run to examine moderating effects of age, gender, and poverty status on associations between race and the four discrimination outcomes. Models were run separately for each of the four discrimination measures. Analyses began with fully adjusted models, which contained all two-, three-, and four-way interactions and main effects of race, age, gender, and poverty status. If the four-way interaction effect was statistically significant (p-value < 0.05), the full model was retained. Conversely, if the four-way interaction effect was not significant, that interaction was removed, and the analysis proceeded through a backward elimination procedure [111]. This was repeated with higher order interactions that did not show statistically significant relationships in a stepwise fashion, beginning with the four-way interaction and followed by all three-way and two-way interactions in subsequent steps. This procedure continued until the highest-order interaction effect(s) that included race was identified, at which point the model was retained. All significant interactions were probed for simple effects and plotted to assist with interpretation. Models were also tested using the three-level education measure in lieu of poverty status to determine whether and how this SES indicator uniquely moderated the association of race with age and gender in relation to the discrimination outcomes. Finally, to determine whether Hispanic ethnicity influenced the results, analyses were rerun after excluding the nine participants who identified as Hispanic (see S2 File for detailed descriptions of these analyses and findings).

Adjustment for multiple comparisons were not made for several reasons, as outlined by other epidemiological researchers [112, 113]. Some of the primary reasons were because while these adjustments may reduce Type I errors, the likelihood of Type I errors cannot decrease without increasing the likelihood of Type II errors [114, 115], and also because adjusting for multiple comparisons may deem truly important group differences as nonsignificant. In addition, multiple comparison adjustments may also be problematic because their use implies that interpretation of a given effect is conditional on the number of tests performed, rather than on what the data demonstrate [112, 113]. Thusly, 95% confidence intervals were included for all effects in our regression model tables (see Tables 2 and 3) to provide information about the practical significance of our results.

Results

African Americans were significantly more likely to have household incomes below 125% of the poverty line and had higher scores on all discrimination measures than Whites (p’s < .001; Table 1). In addition, a significantly greater proportion of Whites identified as women (56.6%) compared to the proportion of African Americans who identified as women (52.6%; χ2(1) = 4.28, p = .038). All discrimination measures were positively correlated, demonstrating moderate (r = 0.35) to high (r = 0.62) correlations with one another (S1 Table).

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Table 1. Participant characteristics in the overall sample and stratified by race.

https://doi.org/10.1371/journal.pone.0251174.t001

We conducted race-stratified principal component analyses to assess the relationship among the four interpersonal discrimination indices used in this study (see S1 File). The results showed that a one-component (i.e., eigenvalue > 1) structure was the best fit for the data, explaining 60% and 57% of the shared variance for the four discrimination measures within African Americans and Whites, respectively. Further, Tucker’s coefficient of congruence revealed a high degree of congruence among the factor coefficients between African Americans and Whites (r = .89).

Analyses with poverty status

Analyses with poverty status as the indicator of SES revealed no significant four-way interactions among race, age, gender, and poverty status. After backward elimination, models revealed two significant three-way interaction effects of (a) Race × Age × Gender with lifetime discrimination burden, b = 0.03, p = .019, and (b) Race × Gender × Poverty Status with lifetime discrimination burden, b = 0.57, p = 0.040 (Table 2). As shown in Fig 1, simple effects analyses revealed that older age was associated with significantly greater lifetime discrimination burden among African American men, b = 0.03, p < .001, and African American women, b = 0.02, p = .006, with a more pronounced effect in African American men than African American women.

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Fig 1. Significant three-way interaction of race × age × gender with lifetime discrimination burden.

https://doi.org/10.1371/journal.pone.0251174.g001

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Table 2. Inverse Gaussian regression model estimating three-way interaction effects among race and age, gender, or poverty status with lifetime discrimination burden.

https://doi.org/10.1371/journal.pone.0251174.t002

Next, as shown in Fig 2, simple effects analyses also revealed that living in poverty (versus living above poverty) was associated with significantly greater lifetime discrimination burden among African American men, b = 1.03, p = .014, and African American women, b = 0.90, p = .033.

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Fig 2. Significant three-way interaction of race × gender × poverty status with lifetime discrimination burden.

https://doi.org/10.1371/journal.pone.0251174.g002

Further, the association between living in poverty and greater lifetime discrimination burden was stronger among African American men than African American women. In contrast, associations between age and poverty status with lifetime discrimination burden were nonsignificant among White men and women (p’s >.05).

There were no further significant three-way interactions for the other discrimination indices, thus we backward eliminated. Subsequent analyses revealed four significant two-way interaction effects (Table 3). Specifically, there were two significant two-way interactions of Race × Age with (a) racial discrimination, b = 0.02, p = .014, and (b) frequency of discrimination across sources, b = 0.05, p = .036. As demonstrated in Fig 3, among African Americans, older age was associated with greater (a) racial discrimination, b = 0.01, p = .002, and (b) frequency of discrimination across sources, b = 0.07, p < .001.

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Fig 3. Significant two-way interactions of race × age with (a) racial discrimination and (b) frequency of discrimination across sources.

https://doi.org/10.1371/journal.pone.0251174.g003

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Table 3. Inverse Gaussian regression model estimating two-way interaction effects among race and age, gender, or poverty status with multiple indices of discrimination.

https://doi.org/10.1371/journal.pone.0251174.t003

In contrast, age was not associated with these discrimination indices among Whites (p’s >.05). Next, there were three significant two-way interactions of Race × Gender with (a) racial discrimination, b = 0.59, p < .001, (b) frequency of discrimination across sources, b = 1.79, p < .001, and (c) everyday discrimination, b = 1.33, p = .038. As demonstrated in Fig 4, African American men (versus African American women) reported significantly greater (a) racial discrimination, b = 0.62, p < .001, (b) frequency of discrimination across sources, b = 0.72, p = .003, and (c) everyday discrimination, b = 1.67, p < .001.

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Fig 4. Significant two-way interactions of race × gender with (a) racial discrimination, (b) frequency of discrimination across sources, and (c) everyday discrimination.

https://doi.org/10.1371/journal.pone.0251174.g004

In contrast, White women reported significantly greater frequency of discrimination across sources than White men, b = -1.06, p = .002; no other significant differences were found between White men and women (p’s >.05).

Analyses with education

Secondary analyses that included education as the indicator of SES (in lieu of poverty status) also revealed no significant four-way interaction effects among race, age, gender, and education with any of the discrimination measures. After backward elimination, analyses revealed one significant three-way interaction effect (S2 Table). Specifically, the previously observed three-way interaction of Race × Age × Gender with lifetime discrimination burden remained significant with education was substituted for poverty status in the model, b = 0.04, p = .012. As was observed previously, greater age was associated with greater lifetime discrimination burden among African American men, b = 0.03, p < .001 (S1 Fig).

However, the association between age and lifetime discrimination burden among African American women was nonsignificant when education was substituted for poverty status in the model, b = 0.01, p = .055. Associations between age and lifetime discrimination burden among White men and women remained nonsignificant when education was substituted for poverty status (p’s >.05).

There were no further significant three-way interactions for the other discrimination indices, therefore we backward eliminated. As in the models with poverty status, analyses adjusting for education revealed significant two-way interactions of Race × Age with racial discrimination and frequency of discrimination across sources (p’s < .05), such that older age was associated with greater discrimination on these measures among African Americans but not Whites (see S3 Table and S2 Fig). Further, as was observed in the primary models with poverty status, analyses adjusting for education revealed significant two-way interactions of Race × Gender with racial discrimination, frequency of discrimination across sources, and everyday discrimination (p’s < .05; see S3 Table and S2 Fig).

Additionally, these analyses revealed three new significant two-way interactions of Race × Education for (a) racial discrimination, b = 0.27, p = .001, (b) frequency of discrimination across sources, b = 0.64, p = .011, and (c) everyday discrimination, b = 1.16, p = .003 (see S3 Table). Among African Americans, greater educational attainment was associated with greater racial discrimination, b = 0.20, p < .001, and frequency of discrimination across sources, b = 0.41, p = .001 (S4 Fig); these effects were not observed among Whites (p’s >.05). Finally, among Whites, lower educational attainment was associated with greater everyday discrimination, b = -0.82, p = .007, whereas no association was found among African Americans, b = 0.34, p = .153 (see S4 Fig).

Discussion

We examined patterns of multiple dimensions of interpersonal-level discrimination across key social statuses—race, age, gender, and SES—in an urban population of middle-aged to older African American and White adults. Our conceptual framework integrated elements from several relevant approaches including the racism-related stress framework [1], social stress theory [116, 117], and the intersectionality framework [93, 94]. Broadly, we observed that African Americans experienced all forms of discrimination more frequently than Whites, but this relationship was qualified by interactions of race with age, gender, and/or poverty status. In our study population, older African Americans, particularly older African American men, and African American men living in poverty, reported the greatest lifetime discrimination burden. Older African Americans also reported greater racial discrimination and greater frequency of social status-based discrimination than younger African Americans. African American men reported greater racial and everyday discrimination and a higher frequency of social status-based discrimination than African American women. We also observed that White women reported a greater frequency of discrimination than White men. Our findings show how race alongside age, gender, and SES shape experiences of interpersonal-level discrimination across two racial groups with divergent histories and contemporary lived experiences in the U.S.

Patterning of discrimination

Race and age.

A predominant theme in the current study demonstrated interactive patterning of race with age, such that older African Americans reported a greater frequency of discrimination across multiple social statuses, racial discrimination, and everyday discrimination. These findings are consistent with some recent studies (e.g., [36, 118]) that suggest age may partially reflect the cumulative burden of discrimination among African Americans and serve as a valuable proxy for exposure to social adversity across the lifespan [37]. While previous results from studies regarding age and interpersonal-level discrimination in African Americans are equivocal [58], these inconsistencies might result from methodological differences, such as not using the most relevant timeframe to measure exposure (e.g., past 12 months versus past 3 years) or using unidimensional measures that limit a fuller examination of the range of discriminatory experiences that African Americans face. Our findings support the life course perspective of discrimination, which suggests that while age and time function as biological processes, they can also function as markers of social significance [36]. African Americans coming of age closer in time to the Civil Rights Movement would have experienced a social and cultural milieu greatly influenced by race and discriminatory practices. Almost a third (30%) of the current participants lived through the Civil Rights Movement era. Our results support that older African Americans have faced more discrimination coinciding with historical periods of entrenched de jure and ensuing de facto structural racism through which resources, opportunities for advancement, and equity in protection are obtained. Our findings suggest that a finer grained delineation of interpersonal-level discrimination for African Americans may require gauging the contribution of how variations in sociohistorical and contemporary racism may contribute to their lived experiences.

Race, age, and gender.

We also observed an interactive relationship between race, age, and gender—chiefly that older African American men reported the greatest lifetime burden of discrimination. This pattern is consistent with a preponderance of prior research suggesting that African American men report more self-reported discrimination (overall and race-based discrimination; although a few exceptions exist, see [5] for a review) than African American women. However, our results indicate that the overall burden of all discriminatory experiences, including variations in experiences by race and gender, may be predicated in part upon age [37]. The intersectional paradox may be appropriate for contextualizing this pattern. Specifically, in alignment with the hierarchy of sociodemographic categories in the U.S., African American men represent a social threat [6466], because they occupy a minority sociodemographic (race), as well as a majority sociodemographic position of advantage (gender). As a result, their ideal superordinate category (gender) status may confer certain benefits, while their subgroup status as African Americans confers risks. Specifically, the primacy of their maleness in U.S. society, which can (or should) provide assurances of privilege and freedom in a patriarchal culture, is juxtaposed with their (highly stigmatized) race, which may invoke stereotypical depictions of them as violent, threatening, and listless [67, 119]. Indeed, African American men have been and continue to be key targets of race-based mistreatment, degradation, and violence in the U.S. (e.g., [58]). Further, older age may allow for greater cumulative exposure to discrimination as well as the effects of ageism per se [120].

Of course, it is difficult to tease apart the influence of age from other relevant factors, including historical period. Older African Americans in our sample would have come of age during the mid-1950s to 1970s when the Civil Rights Movement and de facto discrimination and racism would have been major aspects of their everyday lives. Our assessment of lifetime discrimination captured how much discrimination interfered with their ability to have a full and productive life, and how much harder it made their lives. It is plausible that the combination of the race-related transitions impacting all aspects of society and the related challenges they faced during this particular period, alongside their race and gender, left an indelible impression on their life course, shaping their opportunities and, consequently, their achievements, in ways that had cascading effects across their lives, creating a marked burden of discrimination. Indeed, our results are further supported by scores of narratives documenting the tumultuous and trying nature of this historical period for African American men (e.g., [121123]). In sum, our findings may shed a critical light on the intersecting nature of race, age, and gender for older African American men in the context of historical discrimination. Future work exploring their life course narratives would help to further elucidate these linkages and their lived experiences of their sociodemographic memberships.

Race and gender.

African American men reported greater frequency of discrimination across multiple social statuses, racial discrimination, and everyday discrimination than African American women. As discussed above, these results are largely consistent with the predominant finding across the literature that African American men report greater discrimination than African American women [5]. For instance, in an epidemiological study of 4,452 African Americans, men were more likely to report everyday and racial discrimination than women [124]. However, researchers have suggested that the greater discrimination burden reported by African American men is driven by a higher prevalence of racist experiences among men, a greater willingness of men to affirm these experiences, or from measurement challenges wherein discriminatory experiences gendered as female are not as adequately captured [55]. Indeed, for African Americans, the complex patterning of discrimination when race and gender are simultaneously considered raises questions about the nature of discriminatory experiences of African American women ([20, 21]). The current findings largely demonstrate that when the experience of discrimination is qualified by gender, African American men report more of these experiences. Qualitative evidence suggests that the lower prevalence of self-reported discrimination in African American women may not reflect an actual lower frequency of discriminatory experiences, but rather, differences in the types of discrimination they encounter across specific domains [i.e., 55; for further discussion see, 20, 21] and limited assessments of discrimination occurring at the intersection of their race and gender. For example, while African American men may be more likely to experience “criminal profiling” characterized by unwarranted police encounters based on unfounded judgments that they are physical threats, African American women may face “interpersonal incivilities” characterized by subtle expressions of disrespect, inappropriate breaches of social boundaries or attention, or being treated as invisible [55]. Thus, the discrimination burden of African American women may simply be comprised of different discriminatory experiences that may be as common or as great in magnitude as those experienced by African American men. In addition, African American women are doubly marginalized, as explained by the concept of intersectional invisibility [125]: African American women are completely marginalized in relation to the combination of their African American and gender [63, 125]. Therefore, African American women face both interracial and cross-racial sexism, as demonstrated in emerging empirical research (e.g., [20, 21]) and anecdotal and cultural accounts (e.g., [126]) that may not be well assessed in traditional racial or gender discrimination measures. Future research should examine gendered racism specific to the sociohistorical context that disparately shapes the experiences of African American men and women.

We found only one significant relationship for Whites in relation to discrimination—gender. White women reported a significantly greater frequency of discrimination across social statuses than White men; approximately 60% of White women identified their gender as a source of discrimination, followed by income and appearance. Thus, while White women still experience sexism, neither race, age, nor SES predicted their experiences with discrimination. These findings further bolster the intersectionality paradox, as some of White women’s statuses may serve as protective (e.g., race), while others may allow for discriminatory interactions (e.g., gender). Furthermore, these findings may partially reflect the longstanding, delicate connection between White women and African Americans in their struggles for equality. The Abolitionist Movement and the subsequent Women’s Suffrage Movement fought for equality with respective emphases on race and gender as single minority social statuses. A prevailing tension-invoking sentiment was that if African American men were to have freedom, surely White women should at the least possess liberties and civic participatory rights [absent consideration of African American women; 64, 127]. Consequently, White women’s experiences with gender discrimination, but not with racial discrimination, align with the sociohistorical lineage of these statuses in the U.S.

Race, gender, and SES.

Our results illustrated how poverty and education are differentially associated with discrimination for African Americans as compared with Whites. When the role of poverty was considered, we observed that African Americans living in poverty (versus above poverty) reported greater lifetime discrimination burden, an effect that was more pronounced among African American men than African American women. The pronounced effect observed in African American men may reflect their distinct, intersecting statuses in American society. African American men continue to face a unique set of obstacles as related to SES and their lived experience of race-related adversity in the U.S. Compared with African American women and White men or women, African American men have much lower employment and high school completion rates and higher incarceration rates [128] and relatedly, have worse trajectories for income, wealth, and education—core conduits for disrupting poverty. These gender-by-race disparities may be underscored by perceptions of African American males as threatening, criminal, and violent. Indeed, these perceptions emerge in childhood, contributing to harsher and more punitive reactions from their teachers [129]. These perceptions also are linked to more adverse consequences during interactions with law enforcement and the criminal justice system—shown by their increased risk of being killed by law enforcement and their receiving unfair trials and harsher sentences [130, 131]. The collective impact of these types of race-related adversities on African American males may stymie their ability to rise from or avoid descending into poverty in adulthood and certainly impedes their ability to lead more fruitful lives overall. In sum, these factors may shed light on why the lifetime burden of discrimination is particularly meaningful for the lived experience of African American men living in poverty.

When we examined the role of education as the SES indicator, we observed some distinct patterns. For instance, greater educational attainment was related to greater discrimination exposure across social statuses for African Americans but not Whites, and lower educational attainment was related to greater everyday discrimination among Whites but not for African Americans. Our findings for African Americans are underscored by several theories including John Henryism [132], skin-deep resilience [133], and diminishing returns [134], which altogether assert that while African Americans may strive to attain higher levels of achievement, they may derive less benefit from attaining these outcomes. Higher educational attainment may not be commensurate with greater equity in interpersonal treatment for African Americans, but instead may exacerbate exposure to discrimination based on race and other social statuses they hold, which does not occur for Whites with similar achievements. African Americans may expect that they will have greater access to services and opportunities commensurate with their striving for upward mobility, in alignment with the American ideology that education is the “great equalizer” of differences and inequity [135]. Yet, our results echo those of other recent studies that suggest that achieving more appears to confer less benefit for African Americans and may be a source of risk: upwardly mobile African Americans have been found to experience more interpersonal discrimination than their SES-stable African American and White counterparts [136] or their African American peers with lower SES [17]. Our observation of greater discrimination across social status and race for African Americans suggests that there is something distinct about these social statuses as related to their educational attainment. For instance, it could suggest that African Americans with greater education are more likely to navigate predominately White settings or have interactions that yield more social status-based discrimination, especially racial discrimination [e.g., 137]. In this regard, our assessment of racial discrimination does capture these events across major life domains including, school, when seeking employment, housing, or medical care and at work, or in interactions with the police or legal system. Thusly, it is plausible that they are more likely to have access to more diverse settings as a result of their educational attainment, but yet face pushback based on their minority social memberships in those settings.

We observed that Whites with less education reported greater everyday discrimination than Whites with more education. Our findings suggest that attaining higher education serves a protective function for Whites, similar to Colen et al. [136] who found that as Whites realize greater upward SES mobility over time, they also experience less exposure to acute and chronic discrimination. In contrast to African Americans, Whites with more education in our study were more likely to affirm being treated with greater respect and courtesy than Whites with less education. This may demonstrate that experiences of feeling unfairly treated could—to some extent—hinge upon SES for both African Americans and Whites. These findings in Whites may suggest that class influences their perception of respect and fairness in interpersonal interactions, but do not suggest that their race bears upon these experiences.

In sum, these findings on the interactive role of race and SES, demonstrate at least three critical considerations. First, they provide strong support for the importance of investigating the intersectional linkages of race with other key sociodemographic factors, particularly SES, to further elucidate patterning of self-reported discrimination. Second, these findings demonstrate that experiences of and burdens associated with discrimination may be differentially linked to specific SES indicators and these linkages are not the same for Whites and African Americans. Different dimensions of SES may operate in unique ways [74] revealing variations in discriminatory experiences across key dimensions. Third, whereas Whites’ ascent up the SES ladder accrues better treatment, for African Americans lower and higher SES are both associated with greater discrimination risk [138]. This may suggest that there is never any relief for African Americans from discrimination whether or not they achieve greater SES.

Further elucidation of multidimensional interpersonal discrimination

Although there is an emerging focus on discrimination as a multidimensional phenomenon [1618], most prior research on interpersonal-level discrimination has either focused on a singular facet of discrimination or has only assessed discrimination among African Americans (e.g., Jackson Heart Study; [109]). However, when considered in aggregate, the above findings suggest that it may be important to study both race- and non-race-related experiences of discrimination in African Americans and Whites for several reasons.

One reason is that some discriminatory acts may not be explicitly race-related while other acts are. Individuals occupying multiple disadvantaged memberships may face discrimination associated not only with the respective minority memberships that they hold, but also across a combination of their disadvantaged and advantaged statuses [139]. In addition, simultaneously capturing multiple dimensions of discrimination may provide greater insight into the patterning of these experiences across various sociodemographic groupings, which may subsequently inform a host of other research areas [140].

Another factor to consider is that studies focusing on Whites’ self-reported experiences and their roles in conveying discrimination may be complementary to existing discrimination research. Specifically, examining the underlying conscious and unconscious psychological processes and the continuum on which discriminatory behavior is expressed (i.e., from subtle, non-race-related to blatantly race-related) may allow for a greater understanding of the rationale for White’s self-reports and their involvement in espousing discrimination. Also, in some discriminatory acts, the perpetrator or the target may be unaware that their experience is race-related, as factors such as personality, situational contexts, or unconscious Anti-Black bias can shape expressed behaviors [80, 141, 142].

Relatedly, it may be important to consider that differences across African Americans and Whites in their experiences of discrimination, particularly in the context of higher versus lower SES, may also bear upon cross-race empathy. That is, because Whites experience less discrimination and less burden overall, and particularly so as they gain higher education, they may not understand why discrimination (and perhaps other race-related adversity) are such a hindrance for African Americans, especially those who have also attained higher SES [143]. While Whites are also adversely affected by poverty, they are less so than African Americans. Research addressing these factors holds great promise for elucidating the cycle of discrimination that is commonplace in U.S. society.

Strengths and limitations

A primary strength of this study is its inclusion of African Americans and Whites, as most studies of interpersonal-level discrimination have focused on racial or ethnic minorities, without surveying Whites on their perceptions of discrimination. We also examined whether multiple sociodemographic memberships might shape an individual’s experiences with discrimination, a little-explored avenue of research. In addition, we also included the interaction between race and SES in our analysis, which researchers have argued is valuable to examine, despite the inherent difficulty in disentangling the overlapping influences of each sociodemographic category [144]. Finally, building on a nascent area of discrimination research [17, 18], we considered multiple forms of interpersonal-level discrimination, while most past research has focused on a single form. Our approach allowed for a comprehensive investigation of the central question: how do experiences of interpersonal-level discrimination vary in accordance with intersections among race, age, gender, and SES?

As with all studies, our research had limitations. First, we did not evaluate SES indicators other than poverty and education; including other SES indicators (e.g., wealth and assets [47]) may further advance our understanding of the relationship of SES and interpersonal-level discrimination, especially in the context of race. Second, we surveyed only two racial groups; including other racial groups in this type of analysis would shed greater light on variations in the relationships between sociodemographic characteristics and interpersonal-level discrimination. In this regard, future studies should also explore the contribution of ethnicity within and between racial groups to shed further light on within racial group diversity and related differences in marginalization that may be specific to ethnicity, but not fully reflected by race. Additionally, given the continuous diversification of the American landscape, greater attention must be given biracial identities as well. Third, the present study examined self-identified gender (versus biological sex). The HANDLS study did not explicitly ask participants to self-identify their gender, thusly, except for three respondents who indicated to research staff that their self-identified gender differed from their biological sex, we were unable to determine whether all participants’ gender identities differed from their biological sex. Relatedly, we were also unable to examine non-binary gender identities or gender fluidity. A more inclusive characterization of gender in the HANDLS sample might have added other dimensions to our analysis. Fourth, although the present findings are not generalizable to the national population, independent demographic empirical analyses have demonstrated that this cohort is representative of populations from 14 U.S. cities with similar densities and racial distributions [145]. Fifth, the present analyses do not formally test for measurement invariance (e.g., through multigroup confirmatory factor analysis) to determine whether Whites and African Americans can be compared across dimensions of interpersonal discrimination. However, results from the race-stratified principal component analyses of the four interpersonal discrimination indices used in this study (see S1 File) and the calculation of Tucker’s coefficient of congruence determined that these indices are likely measuring similar underlying constructs in both racial groups. Finally, our results should be interpreted cautiously and viewed as preliminary given their overall exploratory nature and because we did not apply p-value adjustments for multiple comparisons.

Future directions: Looking to health

Inherent in the statement by LeBron James was an awareness that discrimination is embedded within the structure of U.S. culture—a view widely held by theorists, historians, and researchers attentive to the implications of race as a social construct. To this end, the consequences of longstanding racial discrimination in the U.S. are apparent in well-documented racial inequities across core life domains, including those related to health, social relationships, education, civic and legal processes and institutions, and the economy [146]. In each of these domains, striking and protracted racial disparities in access, opportunities, resources, and welfare exist, in which African Americans fare more poorly than most other minority racial and ethnic groups, especially Whites. Understanding that these population-level, race-based patterns are not coincidental or the result of individual-level choices or behaviors [96] warrants that greater attention be given to the multilevel influences that support and reinforce individual-level discrimination processes.

Applying a socioecological perspective to elucidate the linkages among race, other sociodemographic categories, and the disproportionate burden of poor health on racial and ethnic minorities will help increase understanding of the full impact of discrimination on health. At least three considerations should be considered in future research. First, comprehensive investigation of the concurrent structural, cultural, individual, and intraindividual-level aspects of discrimination and racism is needed for a more inclusive picture of how these processes inform opportunities and well-being. Further, such work must engage the sociohistorical landscape underpinning these dynamics and examine the macro-level vestiges of de jure racism emerging as de facto racism [4]. For instance, recent studies drawing on interdisciplinary resources and methodologies have demonstrated that contemporary (1968–2014) heart disease mortality rates decline more slowly in U.S. counties with stronger legacies of African American slavery [147]; nationwide police killings of unarmed African Americans significantly impact mental health for African Americans but not Whites [148]; and neighborhood desirability ratings based on race and other minority sociodemographic categories from almost a century ago predict present day greater neighborhood violence risk in urban U.S. settings [149]. Altogether, these studies illustrate the relevance of historical and structural racism in shaping experiences at the interpersonal level [150] and highlight the need for more descriptive research to explicate linkages among intersecting minority sociodemographic categories and multilevel- and multidimensional-discrimination to better understand the intergenerational transmission of health inequity across a myriad of endpoints.

Second, emphasis on understanding more nuanced and ambiguous interpersonal-level discriminatory experiences, as well as vicarious, cultural-, and media-related experiences, should be considered as they may reflect the modern zeitgeist and current events [1] shaping day-to-day life. Indeed, the current findings provide strong support for such future work as we demonstrate that middle-aged to older African Americans—in a single cohort—are having diverse experiences with interpersonal-level discrimination across key forms—including racial and everyday discrimination, discrimination across multiple social statuses, and lifetime burden of discrimination. Also, the current report highlights the variability of these discriminatory events, showing their interactive linkages with other relevant sociodemographic categories, in addition to race. These findings help to draw needed attention to the complexity of the lived experience of discrimination. Therefore, this work points to the need for future empirical investigations which not only assess better-established forms of discrimination (e.g., racial and everyday), concurrently, but also more fully engages a multidimensional framework, wherein a broader array of discriminatory experiences is examined (e.g., vicarious race-related and vicarious-everyday). Altogether, this would add greatly to our understanding of cumulative exposure to discrimination as well.

Finally, other sociodemographic memberships are also shaped by the broader socioecological context and the related social hierarchy, and a more complete engagement of them (e.g., sexual- and gender-related minority statuses, ableness) and how they comprise an individual’s identity is necessary, if not essential, in health disparities research. Future research should give greater attention to nuanced intersectional paradoxes for particular groups (e.g., higher SES African Americans; [151]) and intersectional invisibilities for others (e.g., higher SES, minority race, and physical ableness), alongside more commonly assessed sociodemographic categories (e.g., gender). In sum, a comprehensive research approach which considers the sociohistorical lineage of sociodemographic categories, discrimination, and racism, alongside their present-day enactments at each level of society and their collective production of inequity across the key domains of American life, is needed for all, as LeBron James stated, to “feel equal in America” [12].

Supporting information

S1 Fig. Significant three-way interaction of race × age × gender with lifetime discrimination in analyses with education.

https://doi.org/10.1371/journal.pone.0251174.s001

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S2 Fig. Significant two-way interactions of race × age with (a) racial discrimination and (b) frequency of discrimination across sources in analyses with education.

https://doi.org/10.1371/journal.pone.0251174.s002

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S3 Fig. Significant two-way interactions of race × gender with (a) racial discrimination, (b) frequency of discrimination across sources, and (c) everyday discrimination in analyses with education.

https://doi.org/10.1371/journal.pone.0251174.s003

(PDF)

S4 Fig. Significant two-way interactions of race × education with (a) racial discrimination, (b) frequency of discrimination across sources, and (c) everyday discrimination in analyses with education.

https://doi.org/10.1371/journal.pone.0251174.s004

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S5 Fig. Significant two-way interaction of gender × education with lifetime discrimination burden among non-Hispanic participants.

https://doi.org/10.1371/journal.pone.0251174.s005

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S1 File. Supplementary principal component analysis.

https://doi.org/10.1371/journal.pone.0251174.s006

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S2 File. Supplementary methods and results.

https://doi.org/10.1371/journal.pone.0251174.s007

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S1 Table. Bivariate correlations among discrimination measures.

https://doi.org/10.1371/journal.pone.0251174.s008

(DOCX)

S2 Table. Inverse Gaussian regression model estimating three-way interactions among race and age, gender, or education with lifetime discrimination burden.

https://doi.org/10.1371/journal.pone.0251174.s009

(DOCX)

S3 Table. Inverse Gaussian regression models estimating two-way interactions among race and age, gender, or education with racial discrimination, frequency of discrimination across sources, and everyday discrimination.

https://doi.org/10.1371/journal.pone.0251174.s010

(DOCX)

S4 Table. Inverse Gaussian regression model estimating three-way interaction effects among race and age, gender, or poverty status with lifetime discrimination burden after excluding Hispanic Whites.

https://doi.org/10.1371/journal.pone.0251174.s011

(DOCX)

S5 Table. Inverse Gaussian regression model estimating two-way interaction effects among race and age, gender, or poverty status with multiple indices of discrimination after excluding Hispanic Whites.

https://doi.org/10.1371/journal.pone.0251174.s012

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S6 Table. Inverse Gaussian regression model estimating three-way interactions among race and age, gender, or education with lifetime discrimination burden after excluding Hispanic Whites.

https://doi.org/10.1371/journal.pone.0251174.s013

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S7 Table. Inverse Gaussian regression models estimating two-way interactions among race and age, gender, or education with racial discrimination, frequency of discrimination across sources, and everyday discrimination after excluding Hispanic Whites.

https://doi.org/10.1371/journal.pone.0251174.s014

(DOCX)

Acknowledgments

We would like to thank K. Bagamian (Bagamian Scientific Consulting), for her editorial and writing suggestions.

References

  1. 1. 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. pmid:10702849
  2. 2. Kaiser CR, Major B. A social psychological perspective on perceiving and reporting discrimination. Law Soc Inq. 2006;31(4): 801–830.
  3. 3. Bonilla-Silva E. Rethinking racism: Toward a structural interpretation. Am Sociol Rev. 1997;62: 465–480.
  4. 4. Gee GC, Ford CL. Structural racism and health inequities: Old issues, new directions. Du Bois Rev. 2011;8(1): 115–132. pmid:25632292
  5. 5. Paradies Y. A systematic review of empirical research on self-reported racism and health. Int J Epidemiol. 2006;35(4): 888–901. pmid:16585055
  6. 6. Ben J, Cormack D, Harris R, Paradies Y. Racism and health service utilisation: A systematic review and meta-analysis. PLoS One. 2017 Dec 18. pmid:29253855
  7. 7. Lewis TT, Cogburn CD, Williams DR. Self-reported experiences of discrimination and health: Scientific advances, ongoing controversies, and emerging issues. Annu Rev Clin Psychol. 2015;1: 407–440.
  8. 8. Paradies Y, Ben J, Denson N, Elias A, Priest N, Pieterse A, et al. Racism as a determinant of health: A systematic review and meta-analysis. PLoS One. 2015;10(9): e0138511. pmid:26398658
  9. 9. Pascoe EA, Richman LS. Perceived discrimination and health: A meta-analytic review. Psychol Bull. 2009;135(4): 531–554. pmid:19586161
  10. 10. Quillian L, Pager D, Hexel O, Midtbøen AH. Meta-analysis of field experiments shows no change in racial discrimination in hiring over time. Proc Natl Acad Sci. 2017;114(41): 10870–10875. pmid:28900012
  11. 11. Lemelle AJ. Conceptual operational, and theoretical overview of African American health related disparities for social and behavioral interventions. In: Reed W, Taylor SE, editors. Handbook of African American Health. New York: Springer; 2011. pp. 3–33.
  12. 12. Kessler RC, Mickelson KD, Williams DR. The prevalence, distribution, and mental health correlates of perceived discrimination in the United States. J Health Soc Behav. 1999;40(3): 208–230. pmid:10513145
  13. 13. Clark R, Anderson NB, Clark VR, Williams DR. Racism as a stressor for African Americans: A biopsychosocial model. Am Psychol. 1999;54(10): 805–816. pmid:10540593
  14. 14. Williams DR, Neighbors HW, Jackson JS. Racial/ethnic discrimination and health: Findings from community studies. Am J Public Health. 2003;93(2): 200–208. pmid:12554570
  15. 15. Krieger N. Embodying inequality: A review of concepts, measures, and methods for studying health consequences of discrimination. Int J Health Serv. 1999;29(2): 295–352. pmid:10379455
  16. 16. Okhomina VI, Glover L, Taylor H, Sims M. Dimensions of and responses to perceived discrimination and subclinical disease among African-Americans in the Jackson heart study. J Racial Ethn Health Disparities. 2018;5(5): 1084–1092. pmid:29313298
  17. 17. Pantesco EJ, Leibel DK, Ashe JJ, Waldstein SR, Katzel LI, Liu HB, et al. Multiple forms of discrimination, social status, and telomere length: Interactions within race. Psychoneuroendocrinology. 2018;98: 119–126. pmid:30138832
  18. 18. Beatty Moody DL, Taylor AD, Leibel DK, Al-Najjar E, Katzel LI, Davatzikos C, et al. Lifetime discrimination burden, racial discrimination, and subclinical cerebrovascular disease among African Americans. Health Psychol. 2019;38(1): 63. pmid:30474995
  19. 19. Moody DLB, Leibel DK, Darden TM, Ashe JJ, Waldstein SR, Katzel LI, et al. Interpersonal-level discrimination indices, sociodemographic factors, and telomere length in African-Americans and Whites. Biol Psychol. 2019;141: 1–9. pmid:30553820
  20. 20. Bey GS, Jesdale BM, Forrester S, Person SD, and Kiefe C. Intersectional effects of racial and gender discrimination on cardiovascular health vary among black and white women and men in the CARDIA Study. SSM Popul Health. 2019. pmid:31334327
  21. 21. Bey GS, Person SD, and Kiefe C. Gendered race and setting matter: Sources of complexity in the relationships between reported interpersonal discrimination and cardiovascular health in the CARDIA study. J Racial Ethn Health Disparities. 2020;7(4):687–697. pmid:31939080.
  22. 22. Kilgore A. ‘Being Black in America is tough’: LeBron James responds to racist vandalism incident. The Washington Post. 2017 May 31 [Cited 2017 June 1]. https://www.washingtonpost.com/news/sports/wp/2017/ 05/31/ being- black-in-america-is-tough-lebron-james-responds-to-racist-vandalism-incident/?utm_term=.705212306496
  23. 23. Shah K. Ahmaud Arbery: Former police officer and son charged in shooting of Black jogger. The Guardian. 2020 May 7 [cited 2020 Aug 25]. http://www.theguardian.com/us-news/2020/may/07/ahmaud-arbery-former-police-officer-and-son-charged-in-shooting-of-black-man
  24. 24. Department of Justice Federal Bureau of Investigation. About hate crime statistics [Internet]. Criminal Justice information services Division. Clarksburg, West Virginia;2017. [cited 20 August 2020]. https://ucr.fbi.gov/hate-crime/2017
  25. 25. Department of Justice Federal Bureau of Investigation. About hate crime statistics [Internet]. Criminal Justice Information Services Division. Clarksburg, West Virginia;2018. [cited 20 August 2020]. https://ucr.fbi.gov/hate-crime/2018
  26. 26. National Public Radio, Robert Wood Johnson Foundation, & Harvard T. H. Chan School of Public Health. Discrimination in America: Experience and views of African Americans. Final summary. 2018—[cited 2018 Dec 17]. https://www.rwjf.org/content/dam/farm/reports/surveys_and_polls/2018/rwjf443620
  27. 27. Forman TA, Williams DR, Jackson JS, Gardner C. Race, place, and discrimination. JAI Press. 1997;9: 231–261.
  28. 28. Klonoff EA, Landrine H. Cross-validation of the schedule of racist events. J Black Psychol. 1999;25(2): 231–254.
  29. 29. Krieger N, Sidney S. Racial discrimination and blood pressure: The CARDIA Study of young Black and White adults. Am J Public Health. 1996;86(10): 1370–1378. pmid:8876504
  30. 30. Landrine H, Klonoff EA. The schedule of racist events: A measure of racial discrimination and a study of its negative physical and mental health consequences. J Black Psychol. 1996;22(2): 144–168.
  31. 31. Peters RM. Racism and hypertension among African Americans. West J Nurs Res. 2004;26(6): 612–631. pmid:15359074
  32. 32. Bleich SN, Findling MG, Casey LS, Blendon RJ, Benson JM, SteelFisher GK, et al. Discrimination in the United States: Experiences of Black Americans. Health Serv Res. 2019;54: 1399–1408. pmid:31663124
  33. 33. Borrell LN, Kiefe CI, Diez-Roux AV, Williams DR, Gordon-Larsen P. Racial discrimination, racial/ethnic segregation, and health behaviors in the CARDIA study. Ethn Health. 2013;18(3): 227–243. pmid:22913715
  34. 34. Ayalon L, Gum AM. The relationships between major lifetime discrimination, everyday discrimination, and mental health in three racial and ethnic groups of older adults. Aging Ment Health. 2011;15(5): 587–594. pmid:21815851
  35. 35. Lee RT, Perez AD, Boykin CM, Mendoza-Denton R. On the prevalence of racial discrimination in the United States. PLoS One. 2019 Jan 10. pmid:30629706
  36. 36. Gee GC, Walsemann KM, Brondolo E. A life course perspective on how racism may be related to health inequities. Am J Public Health. 2012;102(5): 967–974. pmid:22420802
  37. 37. Gee CG, Hing A, Mohammed S, Tabor DC, Williams DR. Racism and the life course: Taking time seriously. Am J Public Health. 2019;109: S43–S47. pmid:30699016
  38. 38. Ro AE, Choi KH. Social status correlates of reporting gender discrimination and racial discrimination among racially diverse women. Women & Health. 2009;49(1): 1–15. pmid:19485231
  39. 39. Lewis TT, Van Dyke ME. Discrimination and the health of African Americans: The potential importance of intersectionalities. Curr Dir Psychol Sci. 2018;27(3): 176–182. pmid:30655654
  40. 40. Seaton EK, Tyson K. The intersection of race and gender among Black American adolescents. Child Dev. 2018;90(1): 62–70. pmid:29777539
  41. 41. Klarman MJ. From Jim Crow to civil rights: The supreme court and the struggle for racial equality. 1st ed. New York: Oxford University Press; 2004.
  42. 42. English D, Lambert SF, Evans MK, Zonderman AB. Neighborhood racial composition, racial discrimination, and depressive symptoms in African Americans. Am J Community Psychol. 2014;54(3): 219–228.
  43. 43. Sigelman L, Welch S. A dream deferred: Black attitudes toward race and inequality. 1st ed. Cambridge: Cambridge University Press; 1991.
  44. 44. Stancil TR, Hertz-Picciotto I, Schramm M, Watt-Morse M. Stress and pregnancy among African-American women. Pediatr Perinat Epidemiol. 2000;14: 127–135. pmid:10791655
  45. 45. Steffen PR, McNeilly M, Anderson N, Sherwood A. Effects of perceived racism and anger inhibition on ambulatory blood pressure in African Americans. Psychosom Med. 2003;65(5): 746–750. pmid:14508015
  46. 46. Brondolo E, Kelly KP, Coakley V, Gordon T, Thompson S, Levy E, et al. The Perceived ethnic discrimination questionnaire: Development and preliminary validation of a community version. J Appl Soc Psychol. 2005;35(2): 335–365.
  47. 47. Brondolo E, Beatty DL, Cubbin C, Pencille M, Saegert S, Wellington R, et al. Sociodemographic variations in self-reported racism in a community sample of Blacks and Latino. J Appl Soc Psychol. 2009;39(2): 407–429.
  48. 48. Clark R. Interethnic group and intraethnic group racism: Perceptions and coping in Black university students. J Black Psychol. 2004;30: 506–526.
  49. 49. Broman CL, Mavaddat R, Hsu SY. The experience and consequences of perceived racial discrimination: A study of African Americans. J Black Psychol. 2000;26(2): 165–180.
  50. 50. Williams DR, Yu Y, Jackson JS, Anderson NB. Racial differences in physical and mental health: Socio-economic status, stress and discrimination. J Health Psychol. 1997;2(3): 335–351. pmid:22013026
  51. 51. Lewis TT, Barnes LL, Bienias JL, Lackland DT, Evans DA, Mendes de Leon CF. Perceived discrimination and blood pressure in older African American and White adults. J Gerontol. 2009;64: 1002–1008. pmid:19429703
  52. 52. Banks KH, Kohn-Wood LP, Spencer M. An examination of the African American experience of everyday discrimination and symptoms of psychological distress. Community Ment Health J. 2006;42(6): 555–570. pmid:16897412
  53. 53. Seaton EK, Caldwell CH, Sellers RM, Jackson JS. An intersectional approach for understanding perceived discrimination and psychological well-being among African American and Caribbean Black youth. Dev Psychol. 2010;46(5): 1372–1379. pmid:20822246
  54. 54. Sellers RM, Shelton JN. The role of racial identity in perceived racial discrimination. J Pers Soc Psychol. 2003;84(5): 1079–1092. pmid:12757150
  55. 55. Kwate NOA, Goodman MS. Racism at the intersections: Gender and socioeconomic differences in the experience of racism among African Americans. Am J Orthopsychiatry. 2015;85: 397–408. pmid:26460700
  56. 56. Bowleg L. The problem with the phrase women and minorities: Intersectionality-an important theoretical framework for public health. Am J Public Health. 2012;102(7): 1267–1273. pmid:22594719
  57. 57. Brownlow BN, Sosoo EE, Long RN, Hoggard LS, Burford TI, Hill LK. Sex differences in the impact of racial discrimination on mental health among Black Americans. Curr Psychiatry Rep. 2019;21: 112. pmid:31686220
  58. 58. Edwards F, Lee H, Esposito M. Risk of being killed by police use of force in the United States by age, race–ethnicity, and sex. Proc Natl Acad Sci. 2019;116(34): 16793–16798. pmid:31383756
  59. 59. Stolzenberg L, D’Alessio SJ, Eitle D. Race and cumulative discrimination in the prosecution of criminal defendants. Race Justice. 2013;3(4): 275–299.
  60. 60. Gelman A, Fagan J, Kiss A. An analysis of the New York City police department’s “stop-and-frisk” policy in the context of claims of racial bias. J Am Stat Assoc. 2007;102(479): 813–823.
  61. 61. #Sayhername campaign [Internet]. The African American Policy Forum. [cited 2020 Aug 20]. https://aapf.org/sayhername
  62. 62. Brown M, Ray R, Summers E, Fraistat N. # SayHerName: A case study of intersectional social media activism. Ethn Racial Stud. 2017;40(11): 1831–1846.
  63. 63. Sesko AK, Biernat M. Prototypes of race and gender: The invisibility of Black women. J Exp Soc Psychol. 2010;46(2): 356–360.
  64. 64. Collins PH. Black feminist thought: Knowledge, consciousness, and the politics of empowerment. 1st ed. New York: Routledge; 1991.
  65. 65. Cooper FR. Against bipolar Black masculinity: Intersectionality, assimilation, identity performance, and hierarchy. UC Davis Law Rev. 2005;39: 853–906. Available from: http://docplayer.net/39068098-Against-bipolar-black-masculinity-intersectionality-assimilation-identity-performance-and-hierarchy.html
  66. 66. Wingfield AH. The modern mammy and the angry Black man: African American professionals’ experiences with gendered racism in the workplace. Race Gend Cl. 2007;14(1/2): 196–212.
  67. 67. Smiley C, Fakunle D. From “brute” to “thug:” The demonization and criminalization of unarmed Black male victims in America. J Hum Behav Soc Environ. 2016;26(3–4): 350–366. pmid:27594778
  68. 68. Brown TN. Measuring self-perceived racial and ethnic discrimination in social surveys. Sociol Spectr. 2001;21(3): 377–392.
  69. 69. Gong F, Xu J, Takeuchi DT. Racial and ethnic differences in perceptions of everyday discrimination. Sociol Race Ethn. 2017;3(4): 506–521.
  70. 70. Hudson DL, Neighbors HW, Geronimus AT, Jackson JS. Racial discrimination, John Henryism, and depression among African Americans. J Black Psychol. 2016;42(3): 221–243. pmid:27529626
  71. 71. Bird ST, Bogart LM. Perceived race-based and socioeconomic status (SES)-based discrimination in interactions with health care providers. Ethn Dis. 2001;11(3): 554–563. pmid:11572421
  72. 72. Van Dyke ME, Vaccarino V, Quyyumi AA, Lewis TT. Socioeconomic status discrimination is associated with poor sleep in African-Americans, but not Whites. Soc Sci Med. 2016;153: 141–147. pmid:26896878
  73. 73. Carliner H, Delker E, Fink DS, Keyes KM, Hasin DS. Racial discrimination, socioeconomic position, and illicit drug use among U.S. Blacks. Soc Psychiatry Psychiatr Epidemiol. 2016;51(4): 551–560. pmid:26810670
  74. 74. 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): 2879–2888. pmid:16352796
  75. 75. Jones CP. Levels of racism: A theoretic framework and a gardener’s tale. Am J Public Health. 2000;90(8): 1212. pmid:10936998
  76. 76. Yetman NR. Majority and minority: The dynamics of race and ethnicity in American life. 6th ed. Pearson; 1999. pp. 224.
  77. 77. Jones JM. Six in 10 Americans say racism against Blacks is widespread [Internet]. Gallup. 2016 Aug 17. [cited 20 August 2020] Available from: https://news.gallup.com/poll/194657/six-americans-say-racism-against-blacks-widespread.aspx
  78. 78. Norton MI, Sommers SR. Whites see racism as a zero-sum game that they are now losing. Perspect Psychol Sci. 2011;6(3): 215–218. pmid:26168512
  79. 79. Pew Research Center. On views of race and inequality, blacks and whites are worlds apart. 2016 June 27.
  80. 80. Craig MA, Richeson JA. On the precipice of a “majority-minority” America: Perceived status threat from the racial demographic shift affects White Americans’ political ideology. Psychol Sci. 2014;25(6): 1189–1197. pmid:24699846
  81. 81. Craig MA, Richeson JA. Information about the US racial demographic shift triggers concerns about anti-White discrimination among the prospective White “minority”. PLoS One. 2017;12(9). pmid:28953971
  82. 82. Craig MA, Richeson JA. “More diverse yet less tolerant? How the increasingly-diverse racial landscape affects White Americans’ racial attitudes.” Pers Soc Psychol Bull. 2014;40(6): 750–61. pmid:24625658
  83. 83. Outten RH, Schmitt MT, Miller DA, Garcia AL. “Feeling threatened about the future: Whites’ emotional reactions to anticipated ethnic demographic changes.” Pers Soc Psychol Bull. 2012;38(1): 14–25. pmid:21844094
  84. 84. Skinner AL, Cheadle JE. “The ‘Obama effect’? Priming contemporary racial milestones increases implicit racial bias among Whites.” Soc Cogn. 2016;34(6): 544–58.
  85. 85. Danbold F, Huo YJ. “No longer ‘all-American’?: Whites’ defensive reactions to their numerical decline.” Soc Psychol Personal Sci. 2015;6(2): 210–18.
  86. 86. US Census Bureau [Internet]. New census bureau report analyzes US population projections. 2015 Mar 3. [cited 20 August 2020]. https://www.census.gov/newsroom/press-releases/2015/cb15-tps16.html
  87. 87. Wilkins CL, Hirsh AA, Kaiser CR, Inkles MP. The threat of racial progress and the self-protective nature of perceiving anti-White bias. Group Process Intergroup Relat. 2017;20(6): 801–812.
  88. 88. Peterson LM, Matthews KA, Derby CA, Bromberger JT, Thurston RC. The relationship between cumulative unfair treatment and intima media thickness and adventitial diameter: The moderating role of race in the study of women’s health across the nation. Health Psychol. 2016;35(4): 313. pmid:27018722
  89. 89. Levin S, Sinclair S, Veniegas RC, Taylor PL. Perceived discrimination in the context of multiple group memberships. Psychol Sci. 2002;13(6): 557–560. pmid:12430842
  90. 90. Discrimination in America. Experiences and views of White Americans [Internet]. 2017 Nov. [cited 20 August 2020]. https://cdn1.sph.harvard.edu/wp-content/uploads/sites/94/2017/11/NPR-RWJF-HSPH-Discrimination-Whites-Final-Report.pdf
  91. 91. Bower KM, Thorpe RJ, LaVeist TA. Perceived racial discrimination and mental health in low-oncome, urban-dwelling Whites. Int J Health Serv. 2013;43(2): 267–280. pmid:23821905
  92. 92. Bonilla-Silva E. Racism without racists: Color-blind racism and the persistence of racial inequality in America. 3rd ed. Lanham, MD: Rowman & Littlefield; 2009.
  93. 93. Feagin J. Systemic racism: A theory of oppression. 1st ed. London, England: Routledge; 2006.
  94. 94. Cole ER. Intersectionality and research in psychology. Am Psychol. 2009;64(3): 170–180. pmid:19348518
  95. 95. Crenshaw K. Demarginalizing the intersection of race and sex: A Black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. Univ Chic Leg Forum. 1989;140: 139–167. Available from https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1052&context=uclf
  96. 96. Jones JM. Prejudice and racism. 2nd ed. New York: McGraw-Hill Publishers; 1996
  97. 97. Wilkins CL, Kaiser CR. Racial progress as threat to the status hierarchy: Implications for perceptions of anti-White bias. Psychol Sci. 2014;25(2): 439–446. pmid:24343099
  98. 98. Richeson JA, Shelton JN. When prejudice does not pay: Effects of interracial contact on executive function. Psychol Sci. 2003;14(3): 287–290. pmid:12741756
  99. 99. Rosenthal L. Incorporating intersectionality into psychology: An opportunity to promote social justice and equity. Am Psychol. 2016;71(6): 474. pmid:27571527
  100. 100. Brondolo E. Racial and ethnic disparities in health: Examining the contexts that shape resilience and risk. Psychosom Med. 2015;77(1): 2–5. pmid:25551202
  101. 101. Else-Quest NM, Hyde JS. Intersectionality in quantitative psychological research: I. theoretical and epistemological issues. Psychol Women Q. 2016;40(2): 155–170.
  102. 102. Barnes LL, de Leon CF, Lewis TT, Bienias JL, Wilson RS, Evans DA. Perceived discrimination and mortality in a population-based study of older adults. Am J Public Health. 2008;98(7): 1241–1247. pmid:18511732
  103. 103. Evans MK, Lepkowski JM, Powe NR, LaVeist T, Kuczmarski MF, Zonderman AB. Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS): Overcoming barriers to implementing a longitudinal, epidemiologic, urban study of health, race, and socioeconomic status. Ethn Dis. 2010;20: 267–275. pmid:20828101
  104. 104. MacArthur Research Network on SES & Health. Psychosocial Notebook: Discrimination [Internet]. UCSF. 2008 [cited 2020 Aug 25]. https://macses.ucsf.edu/research/psychosocial/discrimination.php
  105. 105. MacArthur Research Network on SES & Health. Psychosocial Notebook: MacArthur midlife survey [Internet]. UCSF. 2008 [cited 2020 Aug 25]. https://www.macses.ucsf.edu/research/psychosocial/midmac.php
  106. 106. Krieger N. Racial and gender discrimination: Risk factors for high blood pressure?. Soc Sci Med. 1990;30(12): 1273–1281. pmid:2367873
  107. 107. LaVeist TA, Rolley NC, Diala C. Prevalence and patterns of discrimination among US health care consumers. Int J Health Serv. 2003;33: 331–344. pmid:12800890
  108. 108. Friedman EM, Williams DR, Singer BH, Ryff CD. Chronic discrimination predicts higher circulating levels of E-selectin in a national sample: The MIDUS study. Brain Behav Immun. 2009;23(5): 684–692. pmid:19171188
  109. 109. Sims M, Wyatt SB Gutierrez ML, Taylor HA, Williams DR. Development and psychometric testing of a multidimensional instrument of perceived discrimination among African Americans in the Jackson Heart Study. Ethn Dis. 2009 Aug 11;19(1): 56–64. pmid:19341164
  110. 110. RStudio | Open source & professional software for data science teams [Internet]. Rstudio.com. 2020. [cited 20 August 2020]. http://www.rstudio.com/
  111. 111. Morrell CH, Pearson JD, Brant LJ. Linear transformations of linear mixed-effects models. Am Stat. 1997;51(4): 338–343.
  112. 112. Perneger TV. What’s wrong with Bonferroni adjustments. BMJ. 1998;316(7139): 1236–1238. pmid:9553006
  113. 113. Savitz DA, Olshan AF. Multiple comparisons and related issues in the interpretation of epidemiologic data. Am J Epidemiol. 1995;142(9): 904–908. pmid:7572970
  114. 114. Feise RJ. Do multiple outcome measures require p-value adjustment?. BMC Med Res Methodol. 2002;2: 8. pmid:12069695
  115. 115. Rothman KJ. No adjustments are needed for multiple comparisons. Epidemiol. 1990;1(1): 43–46. pmid:2081237
  116. 116. Pearlin LI. The sociological study of stress. J Health Soc Behav. 1989;30(3): 241–256. pmid:2674272
  117. 117. Thoits PA. Stress and health: Major findings and policy implications. J Health Soc Behav. 2010;51: S41–S53. pmid:20943582
  118. 118. Beatty Moody DL, Waldstein SR, Tobin JN, Cassells A, Schwartz JC, Brondolo E. Lifetime racial/ethnic discrimination and ambulatory blood pressure: The moderating effect of age. Health Psychol. 2016;35(4): 333–342. pmid:27018724
  119. 119. Ferber AL. The construction of Black masculinity: White supremacy now and then. J Sport Soc Issues. 2007;31(1): 11–24.
  120. 120. Gee GC, Pavalko EK, Long JS. Age, cohort and perceived age discrimination: Using the life course to assess self-reported age discrimination. Soc Forces. 2007;86(1): 265–290.
  121. 121. Buckner TR, Caster P. Fathers, preachers, rebels, men: Black masculinity in US history and literature, 1820–1945. The Ohio State University Press; 2011.
  122. 122. Bibb H. The life and adventures of Henry Bibb: An American slave. Madison, WI: University of Wisconsin Press; 2001.
  123. 123. Gates HL. The classic slave narratives. Harlow, England: Penguin Books; 1987.
  124. 124. Sims M, Diez-Roux AV, Gebreab SY, Brenner A, Dubbert P, Wyatt S, et al. Perceived discrimination is associated with health behaviours among African-Americans in the Jackson Heart Study. J Epidemiol Community Health. 2016;70(2): 187–194. pmid:26417003
  125. 125. Purdie-Vaughns V, Eibach RP. Intersectional invisibility: The distinctive advantages and disadvantages of multiple subordinate-group identities. Sex Roles. 2008;59(5–6): 377–391.
  126. 126. Essed P. Understanding everyday racism: An interdisciplinary theory. 2nd ed. District of Columbia: Sage Publications; 1991.
  127. 127. Beale F. Double jeopardy: To be Black and female. words of fire: An anthology of African-American feminist thought. 1st ed. New York: The New Press; 1995.
  128. 128. Chetty R, Hendren N, Jones MR, Porter SR. Race and economic opportunity in the United States: An intergenerational perspective. Q J Econ. 2020;135(2): 711–783.
  129. 129. Gilliam WS, Maupin AN, Reyes CR, Accavitti M, Shic F. Do early educators’ implicit biases regarding sex and race relate to behavior expectations and recommendations of preschool expulsions and suspensions. Yale University Child Study Center. 2016;9(28): 1–16.
  130. 130. Hetey RC, Eberhardt JL. The numbers don’t speak for themselves: Racial disparities and the persistence of inequality in the criminal justice system. Curr Dir Psychol Sci. 2018;27(3): 183–187.
  131. 131. United States Sentencing Commission. Demographic differences in sentencing: An update to the 2012 Booker report. Fed Sentencing Rep. 2018;30(3): 212–29.
  132. 132. James SA. John Henryism and the health of African-Americans. Cult Med Psychiatry. 1994;18(2): 163–82. pmid:7924399
  133. 133. Brody GH, Yu T, Chen E, Miller GE, Kogan SM, Beach SR. 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. Psychol Sci. 2013;24(7): 1285–1293. pmid:23722980
  134. 134. Farmer MM, Ferraro KF. Are racial disparities in health conditional on socioeconomic status?. Soc Sci Med. 2005;60(1): 191–204. pmid:15482878
  135. 135. Mann H. (1848). Twelfth annual report to the Massachusetts Board of Education. The republic and the school: Horace Mann and the education of free men. New York, NY: Teachers College Press.
  136. 136. Colen CG, Krueger PM, Boettner BL. Do rising tides lift all boats? Racial disparities in health across the lifecourse among middle-class African-Americans and Whites. SSM Popul Health. 2018;6: 125–135. pmid:30258971
  137. 137. Fuller-Rowell TE, Cogburn CD, Brodish AB, Peck SC, Malanchuk O, Eccles JS. Racial discrimination and substance use: Longitudinal associations and identity moderators. J Behav Med. 2012;35(6): 581–590. pmid:22113318
  138. 138. Brondolo E, Blair IV, Kaur A. Biopsychosocial mechanisms linking discrimination to health: A focus on social cognition. In: Major B, Dovidio JF, Link BG, editors. The Oxford handbook of stigma, discrimination, and health. Oxford University Press; 2018. pp. 219–240.
  139. 139. Harnois CE, Bastos JL, Shariff-Marco S. Intersectionality, contextual specificity, and everyday discrimination: Assessing the difficulty associated with identifying a main reason for discrimination among racial/ethnic minority respondents. Sociol Methods Res. 2020 Mar 30.
  140. 140. Krieger N. Discrimination and health inequities. Int J Health Serv. 2014;44(4): 643–710. pmid:25626224
  141. 141. Glaser J, Knowles ED. Implicit motivation to control prejudice. J Exp Soc Psychol. 2008;44(1): 164–172.
  142. 142. Quillian L. Does unconscious racism exist?. Soc Psychol Q. 2008;71(1): 6–11.
  143. 143. Brondolo E, Byer K, Gianaros PJ, Liu C, Prather AA, Thomas K, et al. Stress and health disparities: Contexts, mechanisms, and interventions among racial/ethnic minority and low-socioeconomic status populations [Internet]. American Psychological Association (APA). 2017 Dec. [cited 20 August 2020]. http://www.apa.org/pi/health-disparities/resources/stress-report.aspx
  144. 144. Williams DR, Mohammed SA, Leavell J, Collins C. Race, socioeconomic status, and health: Complexities, ongoing challenges, and research opportunities. Ann N Y Acad Sci. 2010;1186(1): 69–101.
  145. 145. Lepkowski J. HANDLS Generalizability, 2010 and HANDLS principle cities clusters analysis. Baltimore, MD: National Institutes on Aging. 2011.
  146. 146. National Urban League. State of Black America report [Internet]. New York: NY; 2018 [cited 2017 Dec 17]. 12 p. http://soba.iamempowered.com/system/files/NUL-SOBA2018Executive%20Summary.pdf
  147. 147. Kramer MR, Black NC, Matthews SA, James SA. The legacy of slavery and contemporary declines in heart disease mortality in the U.S. South. SSM Popul Health. 2017;3: 609–617. pmid:29226214
  148. 148. Bor J, Venkataramani AS, Williams DR, Tsai AC. Police killings and their spillover effects on the mental health of Black Americans: A population-based, quasi-experimental study. Lancet. 2018;392(10144): P302–310. pmid:29937193
  149. 149. Jacoby SF, Dong B, Beard JH, Wiebe DJ, Morrison CN. The enduring impact of historical and structural racism on urban violence in Philadelphia. Soc Sci Med. 2018;199: 87–95. pmid:28579093
  150. 150. Jones C. P. Confronting institutionalized racism. Phylon. 2002;50(1/2): 7–22.
  151. 151. Assari S. Unequal gain of equal resources across racial groups. Int J Health Policy Manag. 2018;7(1): 1–9. pmid:29325397