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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2024 Jan 2;79(4):gbad201. doi: 10.1093/geronb/gbad201

Discrimination and Cognition in Midlife Black Women: The Roles of Social Support and Spirituality

Jasmine S Dixon 1,✉, Dongwei Wang 2, Rebecca E Ready 3
Editor: Alyssa Gamaldo4
PMCID: PMC10939168  PMID: 38165061

Abstract

Objectives

Black women are at high risk for discrimination and cognitive impairment in late life. It is not known if discrimination is a risk factor for cognitive decline in Black women and if so, what factors are protective against the adverse cognitive effects of discrimination. Using the biopsychosocial model of gendered racism, we determined if discrimination is associated with poorer cognition in midlife Black women and if social support and/or spirituality would protect against the deleterious effects of discrimination on cognition.

Methods

Participants were midlife Black women (N = 669) from the Study of Women’s Health Across the Nation. Discrimination was measured by the Everyday Discrimination scale. Cognitive outcomes included episodic memory, processing speed, and working memory. Total social support, emotional support, instrumental support, and spirituality were assessed as protective factors.

Results

Contrary to expectations, structural equation modeling indicated that discrimination was associated with better immediate recall. For women with more emotional support, greater discrimination was associated with better immediate recall than for women with lower emotional support. Spirituality was not a significant moderator in the association between discrimination and cognition.

Discussion

Discrimination had unexpected positive associations with learning and attention-based cognitive skills for midlife Black women. Discrimination might enhance vigilance, which could be facilitated by higher levels of emotional support. There is an opportunity for clinical and public health interventions for cognitive health and discrimination focused on Black women to better incorporate emotional support as a coping resource.

Keywords: African American, Attention, Learning, Psychosocial factors, Stress exposure

Discrimination is associated with adverse physical and mental health outcomes in Black Americans (Williams et al., 2019). However, little is known about the association between discrimination and cognitive function in Black Americans (e.g., Barnes et al., 2012) and especially in Black women (Coogan et al., 2020). Black women are at the intersection of racial and gender health disparities and are more vulnerable to cognitive impairment compared to White women and Black men (Avila et al., 2019). Chronic exposure to discrimination might be one factor that accounts for disproportionate cognitive decline in Black women as they age. The current study will address associations between discrimination and cognition in Black midlife women, as well as determine Afrocentric values that might be protective of cognition outcomes.

Chronic stress is associated with cellular changes in the hippocampus, amygdala, and prefrontal cortex, which are brain regions that subserve many cognitive functions (McEwen, 1998). Discrimination is a chronic stressor for Black Americans (Sullivan et al., 2021) and thus can adversely affect cognitive function in midlife and older Black adults. Indeed, discrimination is associated with poorer cognition in Black older adult (Barnes et al., 2012) and racially diverse (Zahodne et al., 2020) samples. Although, there are a couple of exceptions (Meza et al., 2022; Pugh et al., 2021), discrimination is often associated with poorer proximal and longitudinal cognitive functioning.

Studies that focus on a single racial group are important to understand the heterogeneity within groups because individuals within racial groups are not a monolith. Differences within racial groups may be obscured when focusing on between-racial group differences. Within-group studies also help us to understand how culturally relevant constructs impact psychological processes for specific populations (Whitfield et al., 2008).

To date, there are a few studies about the association between discrimination and cognitive outcomes exclusively in Black Americans. One of the first studies on this topic found that in older Black American adults, discrimination in everyday social situations was associated with poorer episodic memory and perceptual speed but not working memory (Barnes et al., 2012). Everyday discrimination was associated with poorer episodic memory when Black older adults live in urban regional areas (Johnson et al., 2020). Black older adults with poorer memory at baseline, reported fewer resilience resources (including social support and religiosity) and more discrimination—measured as everyday and lifetime occurrences—than Black older adults with better memory (McDonough et al., 2023). Contrary to expectations, in Black older adults, lifetime discrimination was associated with better episodic memory, semantic memory, executive functioning, and global cognition (Meza et al., 2022). Further, everyday discrimination was associated with better semantic memory over time and with working memory at baseline in Black American older adults (Pugh et al., 2021). To make sense of these counterintuitive findings, Pugh et al. (2021) hypothesized that perceived discrimination may lead to greater vigilance (i.e., sustained attention) and thus better overall cognitive performance. Overall, the type of discrimination measured in these studies does not appear to account for the mixed results in the association between discrimination and cognition.

The effects of discrimination on cognition deserve study in Black women—distinct from samples that combine Black men and women—because they are simultaneously exposed to racism and sexism. Intersectionality helps us understand unique experiences and the relatively low status of Black women in U.S. society. Intersectionality, derived from Black feminist theory (Collins, 2022), refers to the cumulative effect of gender and racial discrimination as well as discrimination against the identity of a “Black woman” (Crenshaw, 1989). Gendered racism (Essed, 1991) and gendered racial microaggressions (Lewis et al., 2017) are associated with poorer physical and mental health outcomes in Black women (e.g., Lewis et al., 2017). Given the negative impact of gender and racial discrimination on physical and mental health, it is reasonable to suspect that discrimination is a risk for poorer cognition in Black women as well.

The only three studies of the associations between discrimination and cognition in Black women used subjective measures of cognition (Coogan et al., 2020; Hill-Jarrett & Jones, 2022; Simons et al., 2023). Midlife and older Black women who reported higher institutional and daily racism were more likely to rate their subjective cognitive function as poor (Coogan et al., 2020). Older Black women who reported subjective cognitive complaints also reported experiencing more gendered racism (Hill-Jarrett & Jones, 2022). Discrimination was associated with subjective cognitive decline 19 years later in Black women (Simons et al., 2023). It is a limitation that the only evidence linking discrimination to cognitive outcomes in Black women used subjective measures of cognition. Whereas subjective memory complaints can be associated with greater likelihood of cognitive impairment progression (John et al., 2020), evidence is mixed (Ferraro et al., 2023). Thus, studies that utilize objective cognitive outcomes are needed in studies of discrimination in Black women.

There are two theoretical frameworks that are helpful in understanding how and why discrimination as a stressor impacts the cognitive health of Black women. The biopsychosocial model of perceived racism (Clark et al., 1999) states that when an event is appraised as racist, physiological stress and coping responses are activated, which, when chronically activated, ultimately result in poor health outcomes. This theory posits that there are biopsychosocial moderating variables (e.g., family health history, gender, self-esteem, etc.) that affect the strength of the association between racism and health.

The “biopsychosocial model of gendered racism” integrates the biopsychosocial model of perceived racism with an intersectional framework (Lewis, 2023). In this model, gendered racism leads to psychological and physiological stress responses that affect mental and physical health outcomes. This theory posits that coping responses mediate the associations between gendered racism and health, as well as sociodemographic factors that moderate associations between gendered racism and health. The current study will test aspects of the biopsychosocial model of gendered racism to better understand the association between discrimination and health in midlife Black women, using cognition as our health outcome measure.

The present study also will determine if resilience practices of Black women—namely, spirituality and social support—moderate associations between discrimination and cognition. For Black women, social support and spirituality are forms of resilience because of the cultural values of Black Americans. The cultural values of Black Americans are a combination of their historical experiences in the U.S. and traditional West African philosophies (Utsey et al., 2000). When faced with stress, Black Americans commonly rely on two coping resources grounded in Afrocentric cultural values and worldview: collective support from family, friends, and community, as well as engaging in spiritual practices (Utsey et al., 2000). Black women commonly use social support and spirituality when faced with discrimination due to their race and gender (Sullivan et al., 2021). Social support includes both instrumental as well as emotional support from family, friends, and wider community. Spirituality can be used to describe practices of a specific faith and formal organized religion, as well as practices that reflect beliefs in a higher spiritual being that are not bound to formalized religion (Mattis & Jagers, 2001). Preliminary evidence suggests positive associations between social support and cognitive outcomes (Ayotte et al., 2013; Sims et al., 2011), as well as between spirituality and cognitive outcomes (e.g., Henderson et al., 2022; Kraal et al., 2019) in Black adults and older adults. For example, providing social support was positively associated with fluid (e.g., verbal memory, processing speed, working memory) and crystallized (e.g., vocabulary) abilities in Black older adults (Ayotte et al., 2013). Further, religious service attendance and private prayer were associated with better episodic memory in an ethnically diverse sample of older adults that included Black older adults (Kraal et al., 2019).

In summary, this study aimed to investigate cross-sectional associations between discrimination and cognition in midlife Black women, as well as the potential protective roles of social support and spirituality for cognitive outcomes. It is hypothesized that (1) discrimination will be associated with poor cognition, (2a) Black women with greater social support will have a weaker association between discrimination and cognition than those with lower social support and, (2b) Black women with greater spirituality, will have a weaker association between discrimination and cognition than those with less spirituality.

Method

Participants

Participant data came from the Study of Women’s Health Across the Nation (SWAN; Sowers et al., 2000). SWAN is a multisite longitudinal epidemiological study that examines physical, biological, social, and psychological factors that affect health in midlife American women. SWAN study eligibility criteria included age 42–52 years old, not taking hormone medications, having a uterus and at least one ovary, and having a menstrual period within the past 3 months. Some participants (N = 158) were excluded if they had ≥10 c-reactive protein at initial visit, which is an indication of an acute inflammatory illness (O’Connor et al., 2009). Final sample of participants (N = 669) were women who identified as Black and completed at least one cognitive assessment. In SWAN, Black women were enrolled from four cities/areas (Boston, MA, Chicago, IL, Pittsburg, PA, and southeast Michigan; Harlow et al., 2022).

Procedures

Beginning at the initial visit in 1996–1997, participants completed follow-up visits approximately once a year. At each annual follow-up visit, questionnaires were administered to assess mental health, social factors, and physical health. Discrimination was collected at the initial visit and at the 1st, 2nd, and 3rd follow-up visits, whereas spirituality and social support were collected at the initial visit (see Supplementary Material). Cognition was measured at 4th, 6th, and 7th follow-up visits. Cognitive data at the 4th and 6th follow-up visits were used to control for practice effects from retesting; this strategy is consistent with prior research with the SWAN data set (Karlamangla et al., 2017). Thus, the cognitive outcome used in primary analyses was collected at the 7th follow-up visit. The use of these data was approved by the University of Massachusetts Institutional Review Board. This study was preregistered on Open Science Framework (OSF; https://osf.io/9ky7x/).

Measures

Discrimination

At the initial, 1st, 2nd, and 3rd follow-up visits, participants completed a 10-item modified version of the Everyday Discrimination scale (Williams et al., 1997). Item examples include “people act as if they think you are not smart” and “you or your family members are called names or insulted.” Item responses were rated from 1 = never to 4 = often. Scores were averaged across the 10 items so that the final score ranged between 1 and 4. Higher scores indicated more perceived everyday discrimination. The calculation of the discrimination variable is aligned with previous work completed in the SWAN data set (Lewis et al., 2012).

Confirmatory factor analysis (CFA) is a statistical approach to creating a latent variable from several items and it is known to better account for measurement error in estimating variables compared to other methods (Kline et al., 2016). The repeated measures of discrimination collected at initial, 1st, 2nd, and 3rd follow-up visits, function as distinct indicators of discrimination and when used to create a latent variable are a more reliable person-specific measure of discrimination than the baseline measurement alone. Three indices of model fit were used: root mean square error of approximation (RMSEA), comparative fit index (CFI), and standardized root mean square residuals (SRMR). A criterion of CFI > 0.90, RMSEA < 0.06, and SRMR < 0.08 was used to determine model fit (Hu & Bentler, 1999). We used CFA to create a discrimination latent variable using data from the initial, 1st, 2nd, and 3rd follow-up visits within Mplus software (Version 8, Muthén & Muthén, 2017). The four-indicator, one-factor model fit the data best [RMSEA = 0.058 (90% confidence interval [CI]: 0.031, 0.088); CFI = 0.988; and SRMR = 0.019]. Standardized factor loadings ranged from 0.75 to 0.83. Internal consistency was good (α = 0.88).

Social support

At initial visit, social support was measured by the four-item Medical Outcomes Study Social Support Survey (Sherbourne & Stewart, 1991). Example items include “someone you can count on to listen to you when you need to talk” (emotional support) and “someone to help with daily chores if you were sick” (instrumental support). Participants were asked how frequently they received each type of support as follows: 0 = none of the time, 1 = a little of the time, 2 = some of the time, 3 = most of the time, and 4 = all of the time. Higher mean scores indicated more social support received; scores ranged from 0 to 16. Internal consistency reliability was good for the SWAN sample (α = 0.84). Separate emotional support and instrumental support variables (two items each) were created from the total score and ranged from 0 to 8.

Spirituality

A latent variable of spirituality was created from three items collected at the initial visit. The three items were chosen for analysis from five items after a series of CFA were conducted to determine the best model based on fit indices, factor loadings, and reliability statistics. Further rationale for how the three items were chosen from the SWAN data set is explained in Supplementary Material. Importance of religious faith or spirituality ranged from “not at all important” to “very important.” Spirituality being a source of strength and comfort ranged from “none” to “a great deal.” Frequency of prayer or meditation ranged from “none” to “nearly every day to 4 or more times a week.” The three items were negatively skewed; thus, a cube transformation was performed; the “spirituality as a source of strength” indicator remained above −2. A three-indicator, one-factor model fit the data best and met the criteria on all fit indices [RMSEA = 0.000 (90% CI: 0.000, 0.000); CFI = 1.00; and SRMR = 0.001]. Standardized factor loadings ranged from 0.65 to 0.83. Reliability analysis conducted on the spirituality latent variable was α = 0.71, indicating an acceptable reliability for our sample.

East Boston Memory Test

The East Boston Memory Test (EBMT) measured verbal episodic memory (Albert et al., 1991). Participants were read a short story and then completed an immediate and 10-min delay recall; both immediate and delayed recall scores ranged between 0 and 12 points, with higher scores indicating better cognitive performance. The EBMT demonstrates convergent validity with the Wechsler Memory Scale-Revised Logical Memory scores (WMS-R; Wechsler, 1987).

Symbol Digit Modalities Test

The Symbol Digit Modalities Test (SDMT) measured processing speed (Smith, 1982). Participants were timed on how quickly they could match numbers with symbols; scores ranged from 0 to 110 points, with higher scores indicating better cognitive performance. The SDMT demonstrates convergent validity with Wechsler Adult Intelligence Scale-Fourth Edition Coding (Sheridan et al., 2006; Wechsler, 2008).

Digit span backwards

Digit span backwards from the Wechsler Adult Intelligence Scale-Third Edition measured working memory (Tulsky et al., 1997). Participants were asked to repeat a series of numbers backwards; scores ranged from 0 to 12 points, with higher scores indicating better cognitive performance.

Covariates

For this study, covariates included age, income, education, depressive symptoms, allostatic load, and practice effects. Age was measured in years. Income and education were measured as categorical variables. Income ranged from less than $19,999 to greater than $100,000. Education ranged from less than high school degree to postgraduate education. Depressive symptoms were measured by the 20-item Center for Epidemiological Studies (CES-D; Radloff, 1977), with a total score created by averaging scores from the initial, 1st, 2nd, and 3rd follow-up visits and ranged from 0 to 60. The total score was positively skewed; thus, a log-transformed variable was used in primary analysis. Practice effects were measured from the first to second cognitive measurement (4th to 6th follow-up) by calculating reliable change scores for the four cognitive outcomes using a standardized regression-based change formula (Duff, 2012). Further details on practice effect calculations are in Supplementary Material.

Allostatic load variables from the initial, 1st, and 3rd follow-up visits were created by summing 10 biomarkers considered to be high risk, which cover the cardiovascular, metabolic, inflammatory, and neuroendocrine systems (systolic and diastolic blood pressure, total cholesterol, high-density lipoprotein [HDL], triglycerides, glucose, body mass index, waist-hip ratio, c-reactive protein, and dehydroepiandrosterone-sulfate [DHEA-S]; Seeman et al., 2001). For each biomarker, the high-risk cutoff values were determined based on sample distribution at each visit, which was the 75th quartile for all biomarkers except HDL and DHEA-S that used the 25th quartile as high risk. Several biomarkers were not collected at the 2nd follow-up; thus, those visit data were not included in analyses. A CFA was conducted to create a latent variable for allostatic load from data collected from the initial, 1st, and 3rd follow-up visits, thus there are repeated measures of allostatic load. Missingness for allostatic load indicators was less than 50% and detailed in Supplementary Table 5. A three-indicator, one-factor model fit the data best [RMSEA = 0.000 (90% CI: 0.000, 0.000); CFI = 1.00; and SRMR = 0.003]. Factor loadings ranged from 0.84 to 0.86. Internal consistency of the latent variable was good, α = 0.89.

Analytic Strategy

Descriptive statistics were run in SPSS version 23.0 to characterize the sample and to evaluate the data for normal distribution and outliers (IBM Corp., 2015). For the primary analyses, regression models predicting each outcome separately at the 7th follow-up were completed in sequence as follows: (1) predictors only (discrimination, social support, emotional support, instrumental support, and spirituality), (2) inclusion of demographic covariates (age, income, education, and practice effect) and (3) inclusion of depressive symptoms and allostatic load (Figure 1).

Figure 1.

Figure 1.

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Model diagrams of path analyses. (A) Main effects model regressing Immediate Recall on Discrimination. The same models were run separately for the other cognitive outcomes (Delayed Recall, Processing Speed, and Working Memory) collected at the 7th follow-up visit. (B) Two-Way interaction model with Discrimination × Spirituality. (C) Two-Way interaction model with Discrimination × Total Social Support. The same models were run separately for Discrimination × Emotional Support and Discrimination × Instrumental Support interactions. Discrimination indicators were collected at the initial, 1st, 2nd, and 3rd follow-up visits. Spirituality indicators were collected at the initial visit. Social support was collected at the initial visit. Cognitive Outcomes were collected at the 7th follow-up visit.

Preliminary analyses

Random-coefficient models indicated that there was significant between-participant variability to be explained in immediate recall, delayed recall, processing speed, and working memory scores at the 7th follow-up visit, but not in their respective linear slopes (Supplementary Table 11). Because there was no longitudinal growth found in the four outcome variables, primary analyses focused on the cross-sectional/proximal associations between discrimination and cognition. Structural regression models via structural equation modeling (SEM) were conducted with Mplus for all primary analyses. Path modeling that utilizes SEM allows variables in the model to be latent or observed and another benefit is that missing data are accounted for using full information maximum likelihood estimation. For their respective moderation models, interaction terms were created between discrimination and the following variables: total social support (observed), emotional support (observed), instrumental support (observed), and spirituality (latent). All two-way interaction models were run separately (Figure 1).

Unstandardized coefficients were used to illustrate effect sizes. All covariates except practice effects were grand mean centered. A Bonferroni correction was used to determine the statistical significance of findings for the four model comparisons. Thus, to account for the four outcome variables, the p value was adjusted to .013 (α = 0.05/4) for all primary analyses.

Results

Participants

Most of the women had a family income ≥$20,000, had at least a high school degree, and were currently married or living as married (Table 1). Most of the women endorsed high spirituality (e.g., 82% for religious faith is “very important” to them; Table 1). Most women endorsed low instances of discrimination but when they did experience discrimination it was largely because of race, ethnicity, and/or gender (Table 1; Supplementary Table 4). Correlations between predictors, moderators, and outcome variables are reported in Supplementary Tables 5 and 6.

Table 1.

Sample Sociodemographic Characteristics at the Initial Visit

Variables M (SD) or %
Age 45.72 (2.65)
Family income
 Less than $19,999 20.4
 $20,000–$49,999 43.5
 $50,000–$99,999 30.4
 $100,000 or more 5.8
Education attainment
 Less than high school 5.3
 High school graduate 21.2
 Some college/technical school 40.2
 College graduate 16.5
 Post graduate education 16.7
Marital status
 Single, never married 21.6
 Currently married/living as married 47.0
 Separated or divorced 27.5
 Widowed 3.9
Cognitive outcome
 Immediate recall 9.93 (1.81)
 Delayed recall 9.68 (1.96)
 Processing speed 51.09 (11.60)
 Working memory 5.93 (2.24)
Practice effecta
 Immediate recall −0.019 (1.00)
 Delayed recall −0.02 (1.00)
 Processing speed 0.002 (1.00)
 Working memory 0.001 (1.00)
Discriminationb
 Initial visit 1.95 (0.52)
 1st follow- up 1.94 (0.50)
 2nd follow-up 1.88 (0.51)
 3rd follow-up 1.87 (0.54)
How important is your religious faithc
 Not at all important 0.5
 Not very important 2.0
 Somewhat important 15.5
 Very important 82.1
How often do you pray/meditatec
 Never 0.5
 Less than once a year 1.7
 Yearly/few times a year 3.3
 Monthly/few times a month 6.0
 At least once a week/1–3 times a week 18.8
 Nearly every day/4 or more times a week 69.7
How much is religion a source of strength/comfortc
 None 0.8
 A little 12.2
 A great deal 87.1
Total social supportc 12.04 (3.45)
 Emotional support 6.32 (1.70)
 instrumental support 5.72 (2.04)
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Notes: SD = standard deviation. N = 669.

aPractice effects are calculated from visit 4 and visit 6 outcome scores.

bDiscrimination scores ranged from 1 to 4. Higher scores indicate greater discrimination.

cSpirituality and social support were collected at the initial visit. Total social support ranged from 0 to 16; emotional and instrumental support ranged from 0 to 8.

Primary Analyses

Contrary to predictions, greater discrimination was associated with better immediate recall, after accounting for all covariates (b = 0.72, SE = 0.22, p = .001; Table 2). Discrimination was not a significant predictor for any of the other cognitive outcomes (Supplementary Tables 12–14). Emotional support was a significant moderator of the association between discrimination and immediate recall scores, after accounting for all covariates (b = 0.36, SE = 0.11, p = .002; Table 3). For simple slope comparisons, defined as emotional support standard deviations: 1 standard deviation below, mean, and 1 standard deviation above, indicated that for women who experience average (b = 0.80, SE = 0.22, p < .001) and high (b = 1.40, SE = 0.30, p < .001) levels of emotional support, greater discrimination was associated with better immediate recall compared to women with low emotional support (b = 0.19, SE = 0.29, p = .505; Table 3, Figure 2). Total social support and instrumental support were not significant moderators between discrimination and any cognitive outcomes (Supplementary Tables 15–18 and 22–25, respectively). Spirituality was not a significant moderator of the association between discrimination and any of the four cognitive outcomes (Supplementary Tables 26–29).

Table 2.

Main Effects Model Regressing Immediate Recall on Discrimination

Variables Model 1 Model 2 Model 3
Coefficient (SE) Coefficient (SE) Coefficient (SE)
Discrimination 0.55 (0.22)* 0.54 (0.21)* 0.72 (0.22)**
Age — 0.03 (0.03) 0.02 (0.03)
Income — 0.24 (0.10) 0.20 (0.10)
Education — 0.20 (0.08)* 0.17 (0.08)
Practice effect — 0.42 (0.08)** 0.40 (0.08)**
Depressive symptoms — — −0.50 (0.19)*
Allostatic load — — −0.002 (0.06)
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Notes: SE = standard error; Model 1 = predictors only; Model 2 = predictors and demographic covariates; Model 3 = predictors, demographic, and biopsychological covariates.

* p < .013.

** p < .001.

Table 3.

Two-Way Interaction Model Regressing Immediate Recall on Discrimination × Emotional Support Interaction

Variables Model 1 Model 2 Model 3
Coefficient (SE) Coefficient (SE) Coefficient (SE)
Discrimination 0.71 (0.22)** 0.66 (0.21)* 0.80 (0.22)**
Emotional support 0.10 (0.05) 0.07 (0.05) 0.04 (0.05)
Discrimination × emotional support 0.40 (0.12)** 0.36 (0.11)* 0.36 (0.11)*
Age — 0.02 (0.03) 0.02 (0.03)
Income — 0.20 (0.10) 0.17 (0.10)
Education — 0.21 (0.07)* 0.18 (0.08)
Practice effect — 0.40 (0.08)** 0.38 (0.08)**
Depressive symptoms — — −0.42 (0.20)
Allostatic load — — −0.02 (0.06)
Simple slope comparisona
 Low emotional support 0.19 (0.29)
 Average emotional support 0.80 (0.22)**
 High emotional support 1.40 (0.30)**
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Notes: SE = standard error; Model 1 = predictors only; Model 2 = predictors and demographic covariates; Model 3 = predictors, demographic, and biopsychological covariates.

aStandard deviation (SD) of emotional support was 1.696. Low emotional support = 1 SD below mean; average emotional support = mean; high emotional support = 1 SD above.

* p < .013.

** p < .001.

Figure 2.

Figure 2.

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Emotional Support Moderating Discrimination and Immediate Recall. Discrimination scores were averaged across the 10 items so that the final score ranged between 1 (low discrimination) and 4 (high discrimination). Higher scores indicate more perceived everyday discrimination. SD = standard deviation. a Low emotional support = 1 SD below; average emotional support = mean; high emotional support = 1 SD above.

Discussion

Discrimination was associated with better immediate recall in a sample of midlife Black women, which was unexpected. Emotional support yielded surprising results as well. For women with average and high emotional support, greater discrimination was associated with better immediate recall. Unexpectedly, spirituality was not a significant moderator between discrimination and cognition.

The majority of research finds that discrimination is associated with poor cognitive outcomes in Black older adults (e.g., Johnson et al., 2020). Our data are contradictory to these findings. There are a few possible reasons for why we did not find that discrimination was associated with adverse cognitive outcomes. First, measures of cognitive constructs in previous studies used multiple measures of a cognitive domain, which might have been more reliable than the single measures used in this study (Barnes et al., 2012).

Second, most women in the current study reported low levels of discrimination exposure. The low discrimination is perplexing because the midlife sample was enrolled in SWAN in 1996–1997, was born during the Jim Crow era and Civil Rights Movement, and was likely exposed to explicitly sanctioned discrimination during early life (Harlow et al., 2022). However, it is possible that our sample experienced less lifetime discrimination than older Black adult samples, who are the population most often the focus of studies on the association between discrimination and cognition. Mean level differences and/or variability of discrimination between midlife and older adults might have contributed to our anomalous findings that discrimination was associated with better cognition.

That said, our surprising results—that discrimination was associated with better immediate recall—are consistent with two studies in Black older adults that found discrimination was associated with better cognition (Meza et al., 2022; Pugh et al., 2021). For example, everyday discrimination was associated with better working memory at baseline and better semantic memory over time in Black older adults (Pugh et al., 2021). In another study, lifetime discrimination was associated with better executive functioning, episodic memory, semantic memory, and global cognition in Black older adults (Meza et al., 2022).

The hormesis model of psychosocial stress might help understand unexpected positive associations between discrimination and cognition. The hormesis model posits that exposure to low-moderate stress levels can yield better neuropsychological performance; however, once a higher stress level is reached, a toxic threshold is achieved, thus resulting in poor neuropsychological performance (Oshri et al., 2022). This theory suggests that stress exposure leads to neural activation and affects neuropsychological functioning. It is possible that discrimination exposure at low levels activates vigilance, which would allow women to better attend to incoming information, thus resulting in a better immediate recall performance. Vigilance or vigilant attention refers to an individual’s ability to sustain attention on a stimuli over a period of time (Warm et al., 2008). Indeed, in Black adults, racial discrimination was associated with altered functional connectivity of the amygdala and insula, which are neural correlates of vigilance (Webb et al., 2022). In cognitively healthy Black older adults, everyday discrimination was associated with variations in insula functional connectivity to other brain regions including the dorsolateral prefrontal cortex and the insula plays a key role in assessing trustworthiness (Han et al., 2021). Likewise, in Black women, greater racial discrimination was associated with activation of emotion regulation and fear inhibition (ventromedial prefrontal cortex) and visual attention (middle occipital cortex) networks (Fani et al., 2021).

Total social support was not significantly associated with cognitive outcomes after accounting for other psychosocial factors, such as education and depressive symptoms. Our findings were inconsistent with previous studies that found social support to be positively associated with cognition in midlife Black adults (e.g., Sims et al., 2011). However, in the current study, the association between discrimination and immediate recall was stronger for Black women who have greater emotional support compared to those with less emotional support. In other words, the effect of discrimination on cognition—although positive—is larger for women with greater psychosocial resources (i.e., emotional support) rather than those with low resources. The hormesis model of psychosocial stress can again be useful in understanding these findings because it incorporates moderation effects. Results of a study testing this theory found that the association between low-moderate stress levels and better neuropsychological performance is stronger for individuals with higher levels of psychosocial resources (Oshri et al., 2022).

Spirituality did not have significant associations with cognition in Black midlife women nor did it moderate associations between discrimination and cognition. These results were contrary to study hypotheses and surprising because spirituality can be beneficial for Black women’s cognitive health later in life (e.g., Henderson et al., 2022). For example, greater religiosity, which involves religious values, beliefs, and coping was associated with better global cognitive functioning in older Black women (Henderson et al., 2022). It is unclear why spirituality did not play a significant role in the association between discrimination and cognition given that the sample endorsed overall high spirituality. Perhaps at midlife, when variability on cognitive outcomes is low, spirituality is less important for cognition than other important demographic factors, such as income, education, practice effects, and depressive symptoms.

This study highlights that emotional support is an important culturally relevant resource for Black women. Clinical and public health interventions for Black women should consider incorporating or accounting for the important role that emotional support has in their lives. For example, interventions for cognitive health and coping with discrimination could emphasize the use of support networks especially those composed of other Black women (Lewis et al., 2013). Black women often seek emotional support from other Black women when facing discrimination because these social encounters help to validate their unique experiences and build a sense of sisterhood and community (Davis et al., 2019).

Although study results indicated that discrimination was associated with better cognition in midlife women who endorsed greater emotional support, that does not mean discrimination is good for Black women’s cognitive health, especially if the links are due to enhanced vigilance associated with discrimination experiences. Study findings are an exception to the bulk of the existing literature indicating that discrimination is associated with poorer cognition. The present study highlights that there is still more to learn about the complex associations between discrimination and cognition within the context of emotional support for Black women during midlife.

Although this study was intended to be longitudinal, due to insufficient between-person variability in cognition across time, it was implemented as a cross-sectional design. The cross-sectional design prevents us from determining causality between discrimination and cognitive outcomes. Limited variability in discrimination, social support, and spirituality may have had inhibited our ability to fully test our hypotheses. Black women in the SWAN sample were recruited from four sites in Northeast and Midwestern urban settings, thus limiting the generalizability of the sample to those from rural areas and other parts of the United States (i.e., Western and Southern states; Harlow et al., 2022).

Future research could examine neural correlates of vigilance as a mediator in the association between discrimination and cognition in Black women. Activation and/or alteration of these neural networks may partially explain the association between discrimination and cognitive functioning. Future research should examine the longitudinal associations between discrimination and cognition in midlife Black women. A longitudinal study can help determine if the short-term positive effect of discrimination on cognitive functioning extends to long-term associations within this population and why there may be differences. Future research could examine midlife Black women’s appraisal of discrimination experiences and use of coping resources in qualitative research. Qualitative research could inform how discrimination exposure is experienced by Black women and how it relates to if and how they use culturally relevant resources such as emotional support to cope.

Supplementary Material

gbad201_suppl_Supplementary_Material
gbad201_suppl_supplementary_material.docx (66.2KB, docx)

Contributor Information

Jasmine S Dixon, Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, USA.

Dongwei Wang, Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, USA.

Rebecca E Ready, Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, USA.

Alyssa Gamaldo, (Psychological Sciences Section).

Funding

Study of Women’s Health Across the Nation has grant support from United States Department of Health and Human Services, National Institutes of Health (NR004061); United States Department of Health and Human Services, National Institutes of Health, National Institute on Aging (AG012495, AG012505, AG012539, AG012546, AG012553, AG012554); United States Department of Health and Human Services, National Institutes of Health, National Institute of Nursing Research (AG012535); and United States Department of Health and Human Services. National Institutes of Health. Office of Research on Women’s Health (AG012531).

Conflict of Interest

None.

Data Availability

The publicly available data sets from The Study of Women’s Health Across the Nation were distributed by the Inter-University Consortium for Political and Social Research (ICPSR). Researchers seeking data can make request/access through ICPSR who own the rights to the data. This study was preregistered on Open Science Framework.

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Associated Data

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

Supplementary Materials

gbad201_suppl_Supplementary_Material
gbad201_suppl_supplementary_material.docx (66.2KB, docx)

Data Availability Statement

The publicly available data sets from The Study of Women’s Health Across the Nation were distributed by the Inter-University Consortium for Political and Social Research (ICPSR). Researchers seeking data can make request/access through ICPSR who own the rights to the data. This study was preregistered on Open Science Framework.

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