
==== Front
PEC Innov
PEC Innov
PEC Innovation
2772-6282
Elsevier

S2772-6282(24)00082-7
10.1016/j.pecinn.2024.100334
100334
Full length article
Associations between perceived discrimination over the life course, subjective social status, and health literacy: A racial/ethnic stratification analysis
Bather Jemar R. jemar.bather@nyu.edu
ab⁎
Cuevas Adolfo G. ac
Harris Adrian a
Kaphingst Kimberly A. de
Goodman Melody S. ab
a Center for Anti-racism, Social Justice & Public Health, New York University School of Global Public Health, New York, NY 10003, USA
b Department of Biostatistics, New York University School of Global Public Health, New York, NY 10003, USA
c Department of Social and Behavioral Sciences, New York University School of Global Public Health, New York, NY 10003, USA
d Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
e Department of Communication, University of Utah, Salt Lake City, UT 84112, USA
⁎ Corresponding author at: Center for Anti-Racism, Social Justice & Public Health, New York University School of Global Public Health, 708 Broadway, 9th Floor, New York, NY 10003, USA. jemar.bather@nyu.edu
20 8 2024
15 12 2024
20 8 2024
5 1003345 2 2024
3 8 2024
18 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Objective

To analyze the relationship between perceived discrimination over the life course, social status, and limited health literacy (HL).

Methods

5040 adults who participated in the 2023 Survey of Racism and Public Health. We applied stratified multilevel models adjusted for sociodemographic characteristics.

Results

The average age was 47 years, 48% identified as White, 20% as Latinx, and 17% as Black. In the overall sample, we observed associations of perceived discrimination (b = 0.05, 95% CI: 0.01, 0.09), subjective social status (b = −0.16, 95% CI: −0.23, −0.10), and their interaction (b = 0.02, 95% CI: 0.01, 0.03). More perceived discrimination was associated with lower HL in the White and Multiracial participants. Higher subjective social status was associated with higher HL in the White and Latinx participants. There was a statistically significant interaction between perceived discrimination and subjective social status on HL among the White, Latinx, and Multiracial participants.

Conclusion

This analysis has implications for public health practice, indicating that multi-level interventions are needed to address limited HL.

Innovation

Our findings provide novel insights for identifying key SDOH indicators to assess in clinical settings to provide health literate care.

Highlights

• There is limited data on how social determinants of health are related to HL.

• Perceived discrimination and subjective social status (SSS) are associated with HL.

• The two social determinants of health interacted in their impact on HL.

• Understanding the predictors of HL is key to developing clinical screening tools.

Keywords

Racism
Social epidemiology
Social status
Low health literacy
Marginalized populations
Race
Abbreviations

SDOH Social Determinants of Health

BHLS Brief Health Literacy Screen

MacArthur SSS Scale MacArthur Scale of Subjective Social Status – Adult Version

AA/PI Asian American or Pacific Islander

AI/AN/A/ME/NA American Indian, Native American, Arab, Middle Eastern, or North African
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pmc1 Introduction

Improving health literacy is essential across all medical specialties [1]. Patients with limited health literacy often struggle with effective health self-management, such as medication non-adherence and drug label misinterpretation [[2], [3], [4]]. Limited health literacy is associated with worse glycemic control, cardiovascular and chronic diseases, and higher all-cause mortality rates [[5], [6], [7]]. These challenges have significant implications for healthcare costs, as patients with low health literacy are more likely to visit the emergency department and require hospitalization [2,4,8]. As a result, there has been growing interest in understanding the root causes of health literacy, such as social determinants of health, which may inform health literacy skills [[9], [10], [11]].

Perceived racial discrimination and social status have long been implicated as SDOH [[12], [13], [14], [15], [16], [17]]. Perceived discrimination is a subjective measure of how often an individual experiences unfair treatment due to race, ethnicity, or color [18]. Studies have linked perceived discrimination to elevated blood pressure, low infant birthweight, and autoimmune disease severity [[12], [13], [14]]. Social status, another subjective measure, assesses an individual's perceived social standing relative to others [19]. Higher perceived social status is associated with better mental and physical health and predicts health outcomes more accurately than traditional socioeconomic indicators such as income and educational attainment [[15], [16], [17]]. Despite their potential significance, perceived discrimination and social status remain relatively unexplored in the health literacy literature.

There are several mechanisms by which perceived discrimination and subjective social status may impact health literacy. Individuals who perceive discrimination or occupy a lower subjective social status may face barriers to accessing healthcare services, obtaining accurate health information, and making informed decisions about their health. People who experience discrimination or perceive themselves to have a lower social status may be more likely to engage in unhealthy behaviors, such as smoking, excessive alcohol consumption, and lack of physical activity [20,21]. Additionally, they may have limited access to health information and resources, leading to lower health literacy levels. Experiences of discrimination and lower social status can impair cognitive processes, attention, and memory, and in turn, negatively affect comprehension of health information. Lastly, perceived discrimination and lower subjective social status can create power imbalances and communication barriers in healthcare settings. Patients who feel discriminated against or perceive a lower social status may be less likely to seek care, and, in turn, less likely to obtain health-promoting information.

Therefore, we analyzed the relationship between perceived discrimination over the life course, subjective social status, and limited health literacy among adults in the 2023 Survey of Racism and Public Health. Specifically, we used survey data from over 5000 study participants to investigate the following research questions:(1) Is perceived discrimination associated with health literacy?

(2) Is subjective social status associated with health literacy?

(3) Does the perceived discrimination association with health literacy vary across levels of subjective social status?

(4) Do these relationships vary across racial/ethnic groups?

We hypothesize that more experiences of discrimination across the life course is associated with limited health literacy. We hypothesize that this association will be more pronounced among those who perceive themselves as having low social status and those belonging to racial/ethnic minoritized groups.

2 Methods

2.1 Participant recruitment and data collection

We used data from the Survey of Racism and Public Health. This web-based cross-sectional survey included sociodemographic questions and asked participants about their experiences with discrimination, social status, financial and food insecurity, voting, policing, and health. Study participants were sourced through Qualtrics Research Services, which collected survey data based on inclusion/exclusion criteria, sample size, and target demographics. To be eligible, participants had to be at least 18 years old, speak and read English, and reside in the following states/territories: Connecticut, Delaware, District of Columbia, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Puerto Rico, Rhode Island, Vermont, and Virginia. The target age groups were 30% aged 18–34, 32% aged 35–54, and 38% aged 55 or older. The target racial/ethnic categories included an oversampling of minorities relative to their percentage in the US population: 50% White, 20% Black, 20% Hispanic, and 10% Other. Participants received compensation (e.g., gift cards) through Qualtrics's third-party vendors. Additional details about the study protocol can be found elsewhere [22,23]. Informed consent was obtained prior to survey participation, and the study protocol was approved by the New York University Institutional Review Board (IRB-FY2023-7408).

2.2 Analytic sample

Participants were recruited from March 10 to April 12, 2023 (Fig. 1). Out of 9096 potential participants, 44.4% (n = 4037) did not consent (n = 1106), were removed through Qualtrics data cleaning services (n = 2389) or did not complete the survey (n = 542). Qualtrics removed responses for the following reasons: non-sensical answers, duplicates, responses to questions not making sense (e.g., height being too short or too tall), bots, contradictory responses, suspicious weight, suspicious race and origin, invalid IP, and gibberish. The remaining participants totaled over 5000 (n = 5059) who were eligible, agreed to participate, and completed the survey. Of these, 12 (0.2%) participants did not provide information on their race/ethnicity, and 7 (0.1%) participants did not complete the BHLS, resulting in a final analytic sample of 5040 study participants.Fig. 1 Recruitment of Survey of Racism and Public Health study participants, March 10 to April 12, 2023.

Fig. 1

2.3 Dependent variable

2.3.1 Health literacy

We evaluated health literacy using the Brief Health Literacy Screen (BHLS), a validated subjective measure comprised of three self-reported Single Item Literacy Screeners [[24], [25], [26]]. These items gauge an individual's difficulty reading hospital materials, difficulty learning about their medical condition, and confidence in filling out medical forms. Each item was rated on a 5-point Likert scale and then summed to create a BHLS index (Cronbach's α = 0.69). Lower scores indicate higher health literacy (range = 3–15).

2.4 Independent variables

2.4.1 Discrimination across the life course

We assessed perceived discrimination with an adapted version of the Major Experiences of Discrimination Scale [18]. Four items measured participants' experiences with discrimination throughout their lives. Participants indicated how often they were mistreated because of their race, color, or ethnicity during different life stages: childhood, teenage years, adulthood, and the past year. Response options ranged from never (1) to always (5) on a 5-point scale and were summed to create a composite perceived life course discrimination index (Cronbach's α = 0.92). Higher values reflected more frequent experiences with discrimination across the life course (range = 4–20).

2.4.2 Social status

We used the MacArthur Scale of Subjective Social Status – Adult Version (MacArthur SSS Scale) to measure subjective social status [19]. Other researchers have employed this scale across diverse racial and ethnic groups [[27], [28], [29], [30]]. The MacArthur SSS Scale asks participants to rank themselves on a ladder compared to others in their community. Specifically, participants in the survey were asked: “Think of this ladder as representing where people stand in our society. At the top of the ladder are the people who are the best off, those who have the most money, most education, and best jobs. At the bottom are the people who are the worst off, those who have the least money, least education, and the worst jobs or no job. Where would you place yourself on this ladder? Please select the number that best represents where you would be on this ladder.” Response options range from 1 (lowest) to 10 (highest). We treated this variable as continuous in the analysis.

2.4.3 Sociodemographic factors

The survey also collected information on the participants' demographics. We recoded the categorical variables for this analysis. Demographic covariates include age (measured continuously in years), race/ethnicity, gender identity (woman, man), educational attainment (≤high school, some college, ≥college degree), marital status (married/living with partner vs. other), employment status (full/part-time vs. other), number of dependents (0, 1, 2+), and zip code. Race/ethnicity was recategorized as White, Hispanic/Latinx (Latinx), Black/African American (Black), Asian American/Pacific Islander (AA/PI), Multiracial, and American Indian/Native American/Arab/Middle Eastern/North African (AI/AN/A/ME/NA). Latinx was inclusive of race as all other groups are non-Hispanic (e.g., non-Hispanic White, non-Hispanic Black). We classified “other” gender identity (n = 29) as missing due to the small sample size. The “other” marital status category includes those who were divorced, separated, widowed, or never married. The “other” employment status category consists of those on temporary leave, unemployed, not working by choice (e.g., disability, student), independent contractors, or business owners. We used rural-urban commuting area codes from the US Department of Agriculture to classify zip codes as rural or urban [31].

2.5 Analytic strategy

We calculated frequencies or means to describe the characteristics of the study sample. Next, we performed bivariate analyses using the Kruskal-Wallis rank sum test and Pearson's Chi-squared test to examine descriptive factors by race/ethnicity. We applied multilevel linear regression models to explore the relationship between perceived discrimination across the life course, social status, and limited health literacy. We nested participants by zip code to account for the correlation of health literacy outcomes among respondents residing in the same area. Covariates were determined a priori based on previous health literacy studies [[32], [33], [34], [35]]. We fit adjusted models for the overall sample and each racial/ethnic group and tabulate the estimates (β) with their corresponding 95% confidence intervals. In the supplementary materials, we provided additional bivariate analyses using a dichotomous health literacy measure. Consistent with other studies [3,26], we classified individuals with total BHLS scores of 6 or greater as having limited health literacy and those with scores <6 as having adequate health literacy. All statistical analyses were conducted using R [36].

3 Results

3.1 Study population characteristics

Table 1 presents the characteristics of the overall study population and stratified by racial/ethnic group. The average age was 47.0 years (SD: 17.5), with an equal gender identity distribution (man: 50.3%; woman: 49.7%). Most of the respondents identified as White (48.0%) or Latinx (20.4%), had obtained at least some college education (74.5%), were married or living with a partner (53.0%), were engaged in full- or part-time employment (57.0%), had no dependents (55.8%), and resided in an urban area (91.8%). The average health literacy score was 5.1 (SD = 2.4), and 35.4% were classified as having limited health literacy according to the BHLS (Supplemental Table 1). The mean perceived discrimination score was 8.0 (SD = 3.8), implying that, on average, individuals in this sample reported low levels of discrimination over the life course. The average subjective social status score was 5.9 (SD = 2.0), demonstrating that, on average, study participants perceived themselves a little above average within society.Table 1 Study participant characteristics, Survey of Racism and Public Health, 2023.

Table 1Characteristic	N	Overall
N = 5040	White
N = 2420	Latinx
N = 1027	Black
N = 879	AA/PI
N = 419	Multiracial
N = 158	AI/AN/A/ME/NA
N = 137	p-value1	
Age	5038								<0.001	
Mean (SD)		47.0 (17.5)	56.2 (15.9)	34.8 (12.7)	40.8 (15.3)	41.9 (14.3)	38.2 (14.6)	41.9 (14.6)		
Range		18.0, 90.0	18.0, 90.0	18.0, 88.0	18.0, 83.0	18.0, 86.0	18.0, 79.0	18.0, 75.0		
Gender identity, No. (%)	5010								<0.001	
Man		2519 (50.3%)	1533 (63.5%)	358 (35.3%)	346 (39.5%)	165 (39.7%)	56 (35.9%)	61 (44.9%)		
Woman		2491 (49.7%)	881 (36.5%)	655 (64.7%)	529 (60.5%)	251 (60.3%)	100 (64.1%)	75 (55.1%)		
Educational attainment, No. (%)	5016								<0.001	
≤High School		1280 (25.5%)	497 (20.6%)	329 (32.2%)	320 (36.5%)	52 (12.4%)	39 (24.7%)	43 (31.9%)		
Some College		1639 (32.7%)	750 (31.1%)	353 (34.6%)	329 (37.6%)	81 (19.4%)	69 (43.7%)	57 (42.2%)		
≥College Degree		2097 (41.8%)	1161 (48.2%)	339 (33.2%)	227 (25.9%)	285 (68.2%)	50 (31.6%)	35 (25.9%)		
Marital status, No. (%)	5038								<0.001	
Other		2368 (47.0%)	909 (37.6%)	534 (52.0%)	589 (67.1%)	186 (44.4%)	80 (50.6%)	70 (51.1%)		
Married/Living with partner		2670 (53.0%)	1510 (62.4%)	493 (48.0%)	289 (32.9%)	233 (55.6%)	78 (49.4%)	67 (48.9%)		
Employment status, No. (%)	5005								<0.001	
Other		2154 (43.0%)	1179 (49.3%)	357 (34.8%)	350 (39.9%)	137 (32.8%)	75 (47.5%)	56 (41.2%)		
Full/part-time		2851 (57.0%)	1211 (50.7%)	669 (65.2%)	527 (60.1%)	281 (67.2%)	83 (52.5%)	80 (58.8%)		
# Children, No. (%)	5039								<0.001	
0		2812 (55.8%)	1410 (58.3%)	515 (50.1%)	491 (55.9%)	240 (57.3%)	91 (57.6%)	65 (47.4%)		
1		998 (19.8%)	473 (19.5%)	190 (18.5%)	198 (22.6%)	81 (19.3%)	28 (17.7%)	28 (20.4%)		
2+		1229 (24.4%)	537 (22.2%)	322 (31.4%)	189 (21.5%)	98 (23.4%)	39 (24.7%)	44 (32.1%)		
Residence, No. (%)	5040								<0.001	
Rural		414 (8.2%)	315 (13.0%)	33 (3.2%)	26 (3.0%)	7 (1.7%)	17 (10.8%)	16 (11.7%)		
Urban		4626 (91.8%)	2105 (87.0%)	994 (96.8%)	853 (97.0%)	412 (98.3%)	141 (89.2%)	121 (88.3%)		
Health literacy	5040								<0.001	
Mean (SD)		5.1 (2.4)	4.8 (2.3)	5.7 (2.6)	5.3 (2.6)	5.2 (2.3)	5.1 (2.3)	5.9 (2.5)		
Range		3.0, 15.0	3.0, 15.0	3.0, 14.0	3.0, 14.0	3.0, 13.0	3.0, 11.0	3.0, 15.0		
Perceived discrimination	5038								<0.001	
Mean (SD)		8.0 (3.8)	6.3 (3.2)	9.0 (3.8)	9.8 (3.8)	9.4 (3.3)	10.4 (3.8)	9.5 (3.9)		
Range		4.0, 20.0	4.0, 20.0	4.0, 20.0	4.0, 20.0	4.0, 20.0	4.0, 20.0	4.0, 20.0		
Subjective social status	5040								<0.001	
Mean (SD)		5.9 (2.0)	6.1 (1.9)	5.7 (2.1)	5.4 (2.1)	6.2 (1.8)	5.4 (2.0)	5.5 (2.2)		
Range		1.0, 10.0	1.0, 10.0	1.0, 10.0	1.0, 10.0	1.0, 10.0	1.0, 10.0	1.0, 10.0		
Latinx = Hispanic/Latinx, Black = Black/African American, AA/PI = Asian American/Pacific Islander, AI/AN/A/ME/NA = American Indian/Native American/Arab/Middle Eastern/North African, SD = Standard Deviation.

1 Kruskal-Wallis rank sum test; Pearson's Chi-squared test.

We found statistically significant differences in sociodemographic characteristics across racial/ethnic groups (all p < 0.001). White respondents were the oldest (mean age = 56.2, SD: 15.9) and had the lowest proportion of woman participants (36.5%) and participants who worked full- or part-time (50.7%). Additionally, White respondents had the highest proportion of participants who were married or living with a partner (62.4%) and had no dependents (58.3%). In contrast, Black participants had the lowest proportion of participants who were married or living with a partner (32.9%). Latinx participants were the youngest (mean age = 34.8, SD: 12.7) and had the highest proportions of woman participants (64.7%). AA/PI had the highest proportions of those living in an urban area (98.3%), those with at least some college education (87.6%), and those who worked full- or part-time (67.2%).

We observed statistically significant differences in measures of health literacy, perceived discrimination, and social status across racial/ethnic groups (all p < 0.001, Table 1). When comparing racial/ethnic groups, the mean health literacy scores ranged from 4.8 (White, SD: 2.3) to 5.9 (AI/AN/A/ME/NA, SD: 2.5). White participants had the lowest mean discrimination score (mean = 6.3, SD: 3.2), while Multiracial participants had the highest (mean = 10.4, SD: 3.8). Black (mean = 5.4, SD: 2.1) and Multiracial (mean = 5.4, SD: 2.0) participants had the lowest subjective social status score, whereas AA/PI participants had the highest (mean = 6.2, SD: 1.8).

3.2 Associations with health literacy among the study sample

Table 2 displays the adjusted associations between health literacy, perceived discrimination, and social status in the overall sample. The statistically significant association of perceived discrimination (b = 0.049, 95% CI: 0.005, 0.094) suggests that more frequent perceived discrimination experiences over the life course are associated with lower health literacy. However, this association depends on the level of subjective social status. The interaction term between perceived discrimination and subjective social status (b = 0.021, 95% CI: 0.014, 0.089) was found to be statistically significant, in addition to the independent association of subjective social status (b = −0.163, 95% CI: −0.230, −0.096).Table 2 Adjusted associations between health literacy, perceived discrimination, and social status among the overall sample, Survey of Racism and Public Health, 2023.

Table 2	Overall (n = 4944)	
	b	95% CI	
Perceived discrimination	0.049	0.005, 0.094	
Subjective social status	−0.163	−0.230, −0.096	
Perceived discrimination*Subjective social status	0.021	0.014, 0.028	
CI = Confidence interval. Model adjusted for age, race/ethnicity, gender identity, educational attainment, marital status, employment status, number of children, and residence.

To illustrate this combined association, we plotted the predicted marginal means for subjective social status ratings of low (1), middle (5), and high (10), controlling for race/ethnicity, gender identity, education, marital and employment status, dependents, and location (Fig. 2). Among individuals who reported never experiencing discrimination throughout their lives (perceived discrimination score = 4), those who perceived themselves as high (social status = 10; predicted mean health literacy score = 4.09) on the social status ladder had better health literacy than those who perceived themselves as low (social status = 1; predicted mean health literacy score = 4.78) or middle (social status = 5; predicted mean health literacy score = 4.47) on the ladder. However, this relationship reversed among individuals who reported frequent discrimination (perceived discrimination score = 20). For instance, those who perceived themselves as low (social status = 1; predicted mean health literacy score = 5.92) on the social status ladder had better health literacy than those who perceived themselves as high (social status = 10; predicted mean health literacy score = 8.30) or middle (social status = 5; predicted mean health literacy score = 6.97) on the ladder.Fig. 2 Predicted marginal means from the multilevel model of the overall sample, adjusted for race/ethnicity, gender identity, education, marital and employment status, dependents, and location, Survey of Racism and Public Health, 2023.

Fig. 2

3.3 Associations with health literacy by race/ethnicity

Table 3 compares the associations between health literacy, perceived discrimination, and social status stratified by racial/ethnic groups. The table reveals that many statistically significant associations observed in the overall sample persisted among White participants, with some strengthened associations. These associations include perceived discrimination (bWhite= 0.082, 95% CI: 0.010, 0.155; bOverall = 0.049, 95% CI: 0.005, 0.094), social status (bWhite= −0.188, 95% CI: −0.275, −0.102; bOverall = −0.163, 95% CI: −0.230, −0.096), and the interaction between perceived discrimination and subjective social status (bWhite= 0.026, 95% CI: 0.016, 0.037; bOverall = 0.021, 95% CI: 0.014, 0.028).Table 3 Adjusted associations between health literacy, perceived discrimination, and social status across racial/ethnic groups, Survey of Racism and Public Health, 2023.

Table 3	White	Latinx	Black	AA/PI	Multiracial	AI/AN/A/ME/NA	
	n = 2371	n = 1006	n = 867	n = 413	n = 155	n = 132	
	b	95% CI	b	95% CI	b	95% CI	b	95% CI	b	95% CI	b	95% CI	
Perceived discrimination	0.082	0.010, 0.155	0.008	−0.096, 0.111	0.083	−0.020, 0.186	0.081	−0.133, 0.295	0.249	0.026, 0.471	0.030	−0.248, 0.308	
Subjective social status	−0.188	−0.275, −0.102	−0.181	−0.348, −0.014	−0.024	−0.214, 0.165	−0.107	−0.434, 0.220	0.409	−0.067, 0.885	−0.305	−0.804, 0.194	
Perceived discrimination*Subjective social status	0.026	0.016, 0.037	0.026	0.010, 0.042	0.008	−0.010, 0.025	0.003	−0.029, 0.035	−0.042	−0.082, −0.002	0.021	−0.023, 0.065	
Latinx = Hispanic/Latinx, Black = Black/African American, AA/PI = Asian American/Pacific Islander, AI/AN/A/ME/NA = American Indian/Native American/Arab/Middle Eastern/North African, CI = Confidence Interval.

Model adjusted for age, gender identity, educational attainment, marital status, employment status, number of children, and residence.

Bold font indicates 95% confidence interval does not include 0.

Similar to the overall sample results, the combined association of perceived discrimination and subjective social status on health literacy among the White sample population can be interpreted by examining the predicted marginal means (not shown). Among White individuals who reported never experiencing discrimination across their life course (perceived discrimination score = 4), those who perceived themselves as high (social status score = 10; predicted mean health literacy score = 3.92) on the social status ladder had better health literacy compared to those who perceived themselves as low (social status score = 1; predicted mean health literacy score = 4.66) or middle (social status score = 5; predicted mean health literacy score = 4.33) on the ladder. However, this relationship reversed among individuals who reported frequent discrimination (perceived discrimination score = 20). Particularly, those who perceived themselves as low (social status score = 1; predicted mean health literacy score = 6.41) on the social status ladder had better health literacy than those who perceived themselves as high (social status score = 10; predicted mean health literacy score = 9.48) or middle (social status score = 5; predicted mean health literacy score = 7.77).

Among Latinx participants, we observed statistically significant associations of subjective social status (b = −0.181, 95% CI: −0.348, −0.014) and the interaction between perceived discrimination and subjective social status (b = 0.026, 95% CI: 0.010, 0.042). Among Multiracial participants, we found statistically significant associations of perceived discrimination (b = 0.249, 95% CI: 0.026, 0.471) and the interaction between perceived discrimination and subjective social status (b = −0.042, 95% CI: −0.082, −0.002). We did not find statistically significant associations of perceived discrimination, subjective social status, and their interaction among participants identified as Black, Asian American/Pacific Islander, or American Indian/Native American/Arab/Middle Eastern/North African.

4 Discussion and conclusion

4.1 Discussion

We investigated the relationship between perceived discrimination across the life course, subjective social status, and health literacy. We observed that among the overall, White, and Multiracial participants, more perceived discrimination was associated with lower health literacy. Among the overall, White, and Latinx participants, we found that higher subjective social status was associated with higher health literacy. There was a statistically significant interaction between perceived discrimination and subjective social status on health literacy among the overall, White, Latinx, and Multiracial participants. These findings underpin the importance of examining social determinants of health literacy.

The negative relationship between perceived discrimination over the life course in the overall sample may be due to the weathering hypothesis [37,38]. This theory suggests that cumulative exposure to socioeconomic disadvantage adversely affects health outcomes [37,38]. Given the link between health literacy and health [[39], [40], [41], [42], [43], [44], [45], [46]], it is plausible that cumulative exposure to discrimination also negatively impacts health literacy. Life course discrimination may be an additional barrier to obtaining higher health literacy among populations with low income and education levels [[47], [48], [49], [50], [51]]. Our findings further suggest that subjective social status may modify the relationship between perceived discrimination and health literacy. Study participants with the most experiences of perceived discrimination and the highest self-ranking of social status tended to have limited health literacy. This may indicate that even when individuals believe they have reached the highest social level in society, their health literacy may still be plagued by internalized racism.

We observed statistically significant associations of perceived discrimination, subjective social status, and their interaction with health literacy among those racialized as White. We did not find statistically significant associations among populations racialized as Black, Asian American/Pacific Islander, and American Indian/Native American/Arab/Middle Eastern/North African. These null findings could be attributed to little variation in the frequency of interpersonal racism experienced among racially minoritized populations [52]. Despite differences in income and education levels, it is likely that no within-group variation exists because we assessed race/ethnicity using a social construct [[53], [54], [55], [56], [57], [58], [59], [60], [61]]. Future investigations should explore within-group differences based on an objective skin color measurement [[62], [63], [64], [65], [66], [67], [68], [69]].

Limited data exist on the intersection between perceived discrimination, social status, and health literacy. A study by Goodman et al. explored the association between self-reported segregation across the life course and health literacy among Suffolk County, NY health center patients [32]. The authors found that patients who reported attending predominantly White junior high schools were more likely to have adequate health literacy than those who reported attending non-predominantly White junior high schools. Additionally, this study revealed a similar association between self-reports of living in predominantly White neighborhoods and adequate health literacy. Zou et al. conducted a mediation analysis on health literacy, subjective social status, and depressive symptoms in heart failure patients [70]. In their subjective social status and health literacy model, Zou et al. found that higher perceived social status correlated with better health literacy.

Improving organizational health literacy is a viable solution for mitigating the impacts of cumulative discrimination exposure [[71], [72], [73]]. Organizational health literacy is “the degree to which organizations equitably enable individuals to find, understand, and use information and services to inform health-related decisions and actions for themselves and others [73].” Researchers have argued that physicians and trainees should be held more accountable for progress in increasing health literacy among minoritized patients [[71], [72], [73]]. Even though physicians and trainees take required patient communication courses, Coleman et al. contend that physicians and trainees continue to overcomplicate patient-provider communication [71]. The findings from the present study underscore the need for physicians and trainees to consider patient's past discrimination experiences. Understanding these experiences can potentially improve how providers interact with their patients, especially when socially disadvantaged patients interact with providers from historically advantaged racial backgrounds [[74], [75], [76], [77], [78]].

Several limitations should be considered when interpreting our study's findings. First, the sample was limited to online computer and mobile device users, excluding individuals without computer or mobile access. Social factors may disproportionately impact the health literacy of populations without computer or mobile access. Second, this study's findings may not be generalizable because the study recruitment strategy required participants to reside in a geographic region comprised mainly of Northeastern states/territories and be English-speaking. Participants who speak English as a second language may not fully understand English [79], which, in turn, may result in low health literacy scores. Our inferences may have been susceptible to selection bias because we oversampled Latinx participants and recruited participants from Puerto Rico but required the survey to be completed in English [80]. Future studies are needed to investigate the reliability of the BHLS in populations of varying English proficiency. Future iterations of the Survey of Racism and Public Health should be administered in multiple languages. Our results may also not be generalizable since 42% of the study sample were college graduates, a higher proportion than the US population (34%) [81]. Additionally, sample participants generally reported low levels of discrimination over the life course. As with all self-reported survey measures, there is the potential for recall and social desirability bias in participant responses [82]. We were able to discern a statistically significant association between life course discrimination and health literacy, but it will be important to examine this relationship further in a sample with greater variability in perceived discrimination.

Third, we stratified the analyses based on a social construct of race [[53], [54], [55], [56], [57], [58], [59], [60], [61]]. Fourth, we excluded participants who did not report demographic characteristics, such as race, that might marginalize them. We also classified 29 individuals in the other gender identity category as missing. Future generalizable analyses should explore associations of perceived discrimination, social status, and health literacy among gender-minoritized individuals. This would require the oversampling of gender-minoritized individuals. Fifth, assessing discrimination across the life course introduces potential recall bias and measurement error. Sixth, the BHLS does not assess other aspects of health literacy, such as visual literacy, numeracy, and oral communication [25].

Additionally, the alpha reliability coefficient for the BHLS was 0.69, slightly below the adequate level, possibly due to the difference between our sample and the BHLS validation sample [24]. Future research is needed to confirm whether our findings hold when using functional health literacy measures, such as the Test of Functional Health Literacy in Adults [83] or the Newest Vital Sign [84]. Seventh, our cross-sectional analysis prevented us from inferring causal relationships between perceived discrimination, social status, and health literacy. Lastly, we did not have access to medical records, which would have allowed us to control for factors related to health literacy, such as emergency department visits, hospitalizations, and health insurance status.

Despite its limitations, our study exhibited notable strengths and innovations. We analyzed a large sample size of over 5000 participants, which may have increased the statistical power to detect interaction effects within the overall sample and across different racial/ethnic groups. Additionally, the relatively large sample sizes within each racial/ethnic group enabled us to perform stratified analyses in a diverse study population. Our study also used validated measures of health literacy, subjective social status, and perceived discrimination. Notably, the life course measure of perceived discrimination demonstrated high reliability [85].

4.2 Innovation

This study is innovative in its focus on associations between health literacy and two SDOH (i.e., perceived discrimination over the life course and subjective social status). While conceptual frameworks have highlighted the importance of examining relationships between SDOH and health literacy [10], little empirical research has investigated this issue. The inclusion of a life-course measure of discrimination also adds to the innovation [[47], [48], [49], [50], [51],86]. The present study is also innovative in the health literacy literature with its grounding in psychosocial epidemiological theory [87,88], which informed the investigation of the interplay between two psychosocial mechanisms concerning health literacy. While much is known about how individual-level demographic factors are associated with health literacy [89,90], demographic data available in the electronic health record is often missing or unreliable [91]. There is a need to develop screening instruments for SDOH that are reliable predictors of health literacy and can be quickly collected in clinical settings. This work provides a key first step in determining how to measure SDOH.

Additionally, the present study's findings have potential implications for healthcare education and patient-provider communication. By demonstrating an association between lower health literacy and perceived discrimination in our sample, we highlight a critical area for improvement in healthcare delivery. These results suggest that healthcare providers may need enhanced education and re-education on implicit bias and its potential impact on patient understanding. If patients with lower health literacy are experiencing higher levels of perceived discrimination, it raises questions about how effectively healthcare professionals are conveying information to these patients. This insight could lead to innovations in medical education curricula and the development of more effective, culturally sensitive communication strategies in clinical settings. By addressing these issues, we may improve patient understanding, reduce perceived discrimination, and ultimately enhance health outcomes for minoritized populations.

4.3 Conclusion

After adjusting for potential confounders, we found that the impact of perceived discrimination on health literacy depends on subjective social status. Further research is necessary to confirm these associations, particularly studies that assess various household factors. In addition, further analyses of the relationships between perceived discrimination over the life course, subjective social status, and health literacy are needed among generalizable populations. Establishing evidence highlighting the social influences on health literacy can provide meaningful insights for interventions focused on patient engagement and communication.

CRediT authorship contribution statement

Jemar R. Bather: Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis. Adolfo G. Cuevas: Writing – review & editing, Resources, Conceptualization. Adrian Harris: Writing – review & editing, Software, Investigation, Data curation. Kimberly A. Kaphingst: Writing – review & editing, Resources, Conceptualization. Melody S. Goodman: Writing – review & editing, Validation, Resources, Project administration, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization.

Declaration of competing interest

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

Appendix A Supplementary data

Supplementary material: Sample characteristics by health literacy status

Image 1

Data availability

The data that support the findings of this study are available from the Center for Anti-racism, Social Justice & Public Health (gph.casjph@nyu.edu), upon reasonable request.

Acknowledgments

This work was supported by the Center for Anti-racism, Social Justice & Public Health at the NYU School of Global Public Health. We thank Arushi Chadha, Ridwan Nafiu, and Feng Liu for their valuable assistance with the secondary data analysis preparation. We greatly appreciate the editorial team and the anonymous reviewers for taking the time to review our manuscript and providing constructive comments.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.pecinn.2024.100334.
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References

1 Institute of Medicine Vital Signs: Core Metrics for Health and Health Care Progress, Washington, DC 2015 The National Academies Press
2 Berkman N.D. Sheridan S.L. Donahue K.E. Halpern D.J. Crotty K. Low health literacy and health outcomes: an updated systematic review Ann. Intern. Med. 155 2 2011 97 107 [PMID:21768583] 21768583
3 Fan J.H. Lyons S.A. Goodman M.S. Blanchard M.S. Kaphingst K.A. Relationship between health literacy and unintentional and intentional medication nonadherence in medically underserved patients with type 2 diabetes Diabetes Educ. 42 2 2016 199 208 10.1177/0145721715624969 26763625
4 Hibbard J.H. Peters E. Dixon A. Tusler M. Consumer competencies and the use of comparative quality information: it Isn’t just about literacy Med. Care Res. Rev. 64 4 2007 379 394 [PMID:17684108] 17684108
5 Schillinger D. Grumbach K. Piette J. Wang F. Osmond D. Daher C. Association of health literacy with diabetes outcomes JAMA 288 4 2002 475 482 [PMID:12132978] 12132978
6 Paasche-Orlow M.K. Parker R.M. Gazmararian J.A. Nielsen-Bohlman L.T. Rudd R.R. The prevalence of limited health literacy J. Gen. Intern. Med. 20 2 2005 175 184 [PMID:15836552] 15836552
7 Berkman N.D. Sheridan S.L. Donahue K.E. Halpern D.J. Viera A. Crotty K. Health literacy interventions and outcomes: an updated systematic review Evid. ReportTechnol. Assess 199 2011 1 941 [PMID:23126607]
8 Griffey R.T. Kennedy S.K. D’Agostino McGowan L. Goodman M. Kaphingst K.A. Is low health literacy associated with increased emergency department utilization and recidivism? Acad. Emerg. Med. 21 10 2014 1109 1115 [PMID:25308133] 25308133
9 US Department of Health and Human Services Office of Disease Prevention and Health Promotion. Healthy People 2030 Available from https://health.gov/healthypeople/objectives-and-data/browse-objectives 2024 accessed Jun 8, 2023
10 Schillinger D. Social determinants, health literacy, and disparities: intersections and controversies Health Lit. Res. Pract. 5 3 2021 e234 e243 [PMID:34379549] 34379549
11 Schillinger D. The intersections between social determinants of health, health literacy, and health disparities Stud. Health Technol. Inform. 25 269 2020 22 41 [PMID:32593981]
12 Krieger N. Sidney S. Racial discrimination and blood pressure: the CARDIA study of young black and white adults Am. J. Public Health 86 10 1996 1370 1378 [PMID:8876504] 8876504
13 Mustillo S. Krieger N. Gunderson E.P. Sidney S. McCreath H. Kiefe C.I. Self-reported experiences of racial discrimination and black-White differences in preterm and low-birthweight deliveries: the CARDIA study Am. J. Public Health 94 12 2004 2125 2131 [PMID:15569964] 15569964
14 Chae D.H. Martz C.D. Fuller-Rowell T.E. Spears E.C. Smith T.T.G. Hunter E.A. Racial discrimination, disease activity, and organ damage: the black Women’s experiences living with lupus (BeWELL) study Am. J. Epidemiol. 188 8 2019 1434 1443 [PMID:31062841] 31062841
15 Singh-Manoux A. Marmot M.G. Adler N.E. Does subjective social status predict health and change in health status better than objective status? Psychosom. Med. 67 6 2005 855 861 [PMID:16314589] 16314589
16 Tan J.J.X. Kraus M.W. Carpenter N.C. Adler N.E. The association between objective and subjective socioeconomic status and subjective well-being: a Meta-analytic review Psychol. Bull. 146 11 2020 970 1020 [PMID:33090862] 33090862
17 Garza J.R. Glenn B.A. Mistry R.S. Ponce N.A. Zimmerman F.J. Subjective social status and self-reported health among US-born and immigrant Latinos J. Immigr. Minor. Health 19 1 2017 108 119 [PMID:26895151] 26895151
18 Williams D.R. Yu Y. Jackson J.S. Anderson N.B. Racial differences in physical and mental health: socio-economic status, stress and discrimination J. Health Psychol. 2 3 1997 335 351 10.1177/135910539700200305 22013026
19 Adler N.E. Epel E.S. Castellazzo G. Ickovics J.R. Relationship of subjective and objective social status with psychological and physiological functioning: preliminary data in healthy White women Health Psychol. 19 6 2000 586 592 [PMID:11129362] 11129362
20 Finch K.A. Ramo D.E. Delucchi K.L. Liu H. Prochaska J.J. Subjective social status and substance use severity in a young adult sample J. Soc. Psychol. Addict. Behav. 27 3 2013 901 908 [PMID:23915371]
21 Manuck S.B. Phillips J.E. Gianaros P.J. Flory J.D. Muldoon M.F. Subjective socioeconomic status and presence of the metabolic syndrome in midlife community volunteers Psychosom. Med. 72 1 2010 35 45 [PMID:19933505] 19933505
22 Bather J.R. McSorley A.-M.M. Rhodes-Bratton B. Cuevas A.G. Rouhani S. Nafiu R.T. Love after lockup: examining the role of marriage, social status, and financial stress among formerly incarcerated individuals Health Justice 12 1 2024 7 10.1186/s40352-024-00264-x 38400934
23 Bather J.R. Robinson T.J. Goodman M.S. Bayesian kernel machine regression for social epidemiologic research Epidemiology 2024 10.1097/EDE.0000000000001777
24 Chew L.D. Griffin J.M. Partin M.R. Noorbaloochi S. Grill J.P. Snyder A. Validation of screening questions for limited health literacy in a large VA outpatient population J. Gen. Intern. Med. 23 5 2008 561 566 [PMID:18335281] 18335281
25 Goodman M.S. Griffey R.T. Carpenter C.R. Blanchard M. Kaphingst K.A. Do subjective measures improve the ability to identify limited health literacy in a clinical setting? J am board fam med American board of Fam. Med. 28 5 2015 584 594 [PMID:26355130]
26 Carpenter C.R. Kaphingst K.A. Goodman M.S. Lin M.J. Melson A.T. Griffey R.T. Feasibility and diagnostic accuracy of brief health literacy and numeracy screening instruments in an urban emergency department Acad. Emerg. Med. 21 2 2014 137 146 [PMID:24673669] 24673669
27 Bullock H.E. Limbert W.M. Scaling the socioeconomic ladder: low-income women’s perceptions of class status and opportunity J. Soc. Issues 59 4 2003 693 709 10.1046/j.0022-4537.2003.00085.x
28 Franzini L. Fernandez-Esquer M.E. The association of subjective social status and health in low-income Mexican-origin individuals in Texas Soc. Sci. Med. 63 3 2006 788 804 [PMID:16580107] 16580107
29 Ostrove J.M. Adler N.E. Kuppermann M. Washington A.E. Objective and subjective assessments of socioeconomic status and their relationship to self-rated health in an ethnically diverse sample of pregnant women Health Psychol. 19 6 2000 613 618 [PMID:11129365] 11129365
30 Odumegwu J.N. Chavez-Yenter D. Goodman M.S. Kaphingst K.A. Associations between subjective social status and predictors of interest in genetic testing among women diagnosed with breast Cancer at a young age Cancer Causes Control 2024 10.1007/s10552-024-01878-0
31 US Department of Agriculture Rural-Urban Commuting Area Codes Available from https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes.aspx 2024 accessed Jun 12, 2023
32 Goodman M.S. Gaskin D.J. Si X. Stafford J.D. Lachance C. Kaphingst K.A. Self-reported segregation experience throughout the life course and its association with adequate health literacy Health Place 18 5 2012 1115 1121 [PMID:22658579] 22658579
33 Sentell T. Zhang W. Davis J. Baker K.K. Braun K.L. The influence of community and individual health literacy on self-reported health status J. Gen. Intern. Med. 29 2 2014 298 304 [PMID:24096723] 24096723
34 Smith S.G. Curtis L.M. O’Conor R. Federman A.D. Wolf M.S. ABCs or 123s? The independent contributions of literacy and numeracy skills on health task performance among older adults Patient Educ. Couns. 98 8 2015 991 997 [PMID:25936579] 25936579
35 McNaughton C.D. Cawthon C. Kripalani S. Liu D. Storrow A.B. Roumie C.L. Health literacy and mortality: a cohort study of patients hospitalized for acute heart failure J. Am. Heart Assoc. 4 5 2015 e001799 [PMID:25926328]
36 R Core Team R: A Language and Environment for Statistical Computing, Vienna, Austria 2024 R Foundation for Statistical Computing Available from: https://www.R-project.org/
37 Forde A.T. Crookes D.M. Suglia S.F. Demmer R.T. The weathering hypothesis as an explanation for racial disparities in health: a systematic review Ann. Epidemiol. 33 2019 1 18.e3 [PMID:30987864] 30987864
38 Geronimus A.T. The weathering hypothesis and the health of African-American women and infants: evidence and speculations Ethn. Dis. 2 3 1992 207 221 [PMID:1467758] 1467758
39 Hickey K.T. Masterson Creber R.M. Reading M. Sciacca R.R. Riga T.C. Frulla A.P. Low health literacy: implications for managing cardiac patients in practice Nurs. Pract. 43 8 2018 49 55 [PMID:30028773]
40 Fan Z.-Y. Yang Y. Zhang F. Association between health literacy and mortality: a systematic review and Meta-analysis Arch. Public Health 79 1 2021 119 [PMID:34210353] 34210353
41 King A. Poor health literacy: a “hidden” risk factor Nat. Rev. Cardiol. 7 9 2010 473 474 [PMID:20725102] 20725102
42 Yin H.S. Jay M. Maness L. Zabar S. Kalet A. Health literacy: an educationally sensitive patient outcome J. Gen. Intern. Med. 30 9 2015 1363 1368 [PMID:26173523] 26173523
43 Dewalt D.A. Berkman N.D. Sheridan S. Lohr K.N. Pignone M.P. Literacy and health outcomes: a systematic review of the literature J. Gen. Intern. Med. 19 12 2004 1228 1239 [PMID:15610334] 15610334
44 Berkman N.D. Davis T.C. McCormack L. Health literacy: what is it? J. Health Commun. 15 Suppl. 2 2010 9 19 [PMID:20845189]
45 Paasche-Orlow M.K. Wolf M.S. The causal pathways linking health literacy to health outcomes Am. J. Health Behav. 31 Suppl. 1 2007 S19 S26 [PMID:17931132] 17931132
46 Rudd R.E. Improving Americans’ health literacy N. Engl. J. Med. 363 24 2010 2283 2285 [PMID:21142532] 21142532
47 White K. Borrell L.N. Racial/ethnic residential segregation: framing the context of health risk and health disparities Health Place 17 2 2011 438 448 [PMID:21236721] 21236721
48 Williams D.R. Lawrence J.A. Davis B.A. Racism and health: evidence and needed research Annu. Rev. Public Health 40 1 2019 105 125 10.1146/annurev-publhealth-040218-043750 30601726
49 Gilbert K.L. Elder K. Lyons S. Kaphingst K. Blanchard M. Goodman M. Racial composition over the life course: examining separate and unequal environments and the risk for heart disease for African American men Ethn. Dis. 25 3 2015 295 304 [PMID:26673460] 26673460
50 Gee G.C. Ford C.L. Structural racism and health inequities: old issues, new directions Bois. Rev. Soc. Sci. Res. Race 8 1 2011 115 132 [PMID:25632292]
51 Gee G.C. Walsemann K.M. Brondolo E. A life course perspective on how racism may be related to health inequities Am. J. Public Health 102 5 2012 967 974 [PMID:22420802] 22420802
52 Willis H.A. Sosoo E.E. Bernard D.L. Neal A. Neblett E.W. The associations between internalized racism, racial identity, and psychological distress Emerg. Adulthood 9 4 2021 384 400 10.1177/21676968211005598 34395061
53 Adkins-Jackson P.B. Chantarat T. Bailey Z.D. Ponce N.A. Measuring structural racism: a guide for epidemiologists and other health researchers Am. J. Epidemiol. 191 4 2022 539 547 [PMID:34564723] 34564723
54 Braveman P. Parker Dominguez T. Abandon “race.” Focus on racism Front. Public Health 9 2021 10.3389/fpubh.2021.689462
55 Groos M. Wallace M. Hardeman R. Theall K. Measuring inequity: a systematic review of methods used to quantify structural racism J. Health Dispar. Res. Pract. 11 2 2018 Available from: https://digitalscholarship.unlv.edu/jhdrp/vol11/iss2/13
56 LaVeist T.A. Beyond dummy variables and sample selection: what health services researchers ought to know about race as a variable Health Serv. Res. 29 1 1994 1 16 [PMID:8163376] 8163376
57 Lett E. Asabor E. Beltrán S. Cannon A.M. Arah O.A. Conceptualizing, contextualizing, and operationalizing race in quantitative health sciences research Ann. Fam. Med. 20 2 2022 157 163 [PMID:35045967] 35045967
58 Martinez R.A.M. Andrabi N. Goodwin A.N. Wilbur R.E. Smith N.R. Zivich P.N. Conceptualization, operationalization, and utilization of race and ethnicity in major epidemiology journals, 1995-2018: a systematic review Am. J. Epidemiol. 192 3 2023 483 496 [PMID:35938872] 35938872
59 Swilley-Martinez M.E. Coles S.A. Miller V.E. Alam I.Z. Fitch K.V. Cruz T.H. “We adjusted for race”: now what? A systematic review of utilization and reporting of race in American journal of epidemiology and epidemiology, 2020-2021 Epidemiol. Rev. 45 1 2023 15 31 [PMID:37789703] 37789703
60 Wizentier M.M. Stephenson B.J.K. Goodman M.S. The measurement of racism in health inequities research Epidemiol. Rev. 45 1 2023 32 43 [PMID:37147182] 37147182
61 White K. Lawrence J.A. Tchangalova N. Huang S.J. Cummings J.L. Socially-assigned race and health: a scoping review with global implications for population health equity Int. J. Equity Health BioMed Central 19 1 2020 1 14 10.1186/s12939-020-1137-5
62 Borrell L.N. Kiefe C.I. Williams D.R. Diez-Roux A.V. Gordon-Larsen P. Self-reported health, perceived racial discrimination, and skin color in African Americans in the CARDIA study Soc. Sci. Med. 63 6 2006 1415 1427 [PMID:16750286] 16750286
63 Dixon A.R. Telles E.E. Skin color and colorism: global research, concepts, and measurement Annu. Rev. Soc. 31 43 2017 405 424 10.1146/annurev-soc-060116-053315
64 Hochschild J.L. Weaver V. The skin color paradox and the American racial order Soc. Forces 86 2 2007 643 670 10.1093/sf/86.2.643
65 Krieger N. Sidney S. Coakley E. Racial discrimination and skin color in the CARDIA study: implications for public Health Research. Coronary artery risk development in young adults Am. J. Public Health 88 9 1998 1308 1313 [PMID:9736868] 9736868
66 Lawrence J.A. Kawachi I. White K. Bassett M.T. Williams D.R. Instrumental variable analysis of racial discrimination and blood pressure in a sample of young adults Am. J. Epidemiol. 192 12 2023 1971 1980 [PMID:37401004] 37401004
67 Louie P. Revisiting the cost of skin color: discrimination, mastery, and mental health among black adolescents Soc. Ment. Health 10 1 2020 1 19 10.1177/2156869318820092
68 Monk E.P. The cost of color: skin color, discrimination, and health among African-Americans Am. J. Sociol. 121 2 2015 396 444 10.1086/682162
69 Perreira K.M. Wassink J. Harris K.M. Beyond race/ethnicity: skin color, gender, and the health of young adults in the United States Popul. Res. Policy Rev. 38 2 2019 271 299 [PMID:31595099] 31595099
70 Zou H. Chen Y. Fang W. Zhang Y. Fan X. The mediation effect of health literacy between subjective social status and depressive symptoms in patients with heart failure J. Psychosom. Res. 91 2016 33 39 [PMID:27894460] 27894460
71 Coleman C. Birk S. DeVoe J. Health literacy and systemic racism-using clear communication to reduce health care inequities JAMA Intern. Med. 183 8 2023 753 754 [PMID:37358860] 37358860
72 Chen K.A. Kapadia M.R. Health literacy disparities: communication strategies to narrow the gap Am. J. Surg. 223 6 2022 1046 [PMID:34887018] 34887018
73 Santana S. Brach C. Harris L. Ochiai E. Blakey C. Bevington F. Updating health literacy for healthy people 2030: defining its importance for a new decade in public health J. Public Health Manag. Pract. 27 Suppl. 6 2021 S258 S264 [PMID:33729194] 33729194
74 Persky S. Kaphingst K.A. Allen V.C. Senay I. Effects of patient-provider race concordance and smoking status on lung Cancer risk perception accuracy among African-Americans Ann. Behav. Med. 45 3 2013 308 317 [PMID:23389688] 23389688
75 Shen M.J. Peterson E.B. Costas-Muñiz R. Hernandez M.H. Jewell S.T. Matsoukas K. The effects of race and racial concordance on patient-physician communication: a systematic review of the literature J. Racial Ethn. Health Disparities 5 1 2018 117 140 [PMID:28275996] 28275996
76 Strumpf E.C. Racial/ethnic disparities in primary care: the role of physician-patient concordance Med. Care 49 5 2011 496 503 [PMID:21430577] 21430577
77 Takeshita J. Wang S. Loren A.W. Mitra N. Shults J. Shin D.B. Association of Racial/ethnic and gender concordance between patients and physicians with patient experience ratings JAMA Netw. Open 3 11 2020 e2024583 [PMID:33165609]
78 Traylor A.H. Schmittdiel J.A. Uratsu C.S. Mangione C.M. Subramanian U. Adherence to cardiovascular disease medications: does patient-provider race/ethnicity and language concordance matter? J. Gen. Intern. Med. 25 11 2010 1172 1177 [PMID:20571929] 20571929
79 Bigelow M. Schwarz R.L. Adult English Language Learners With Limited Literacy 2010 National Institute for Literacy Available from: https://eric.ed.gov/?id=ED512297 accessed Jun 17, 2024
80 Heckman J. Varieties of selection Bias Am. Econ. Rev. 80 2 1990 313 318
81 United States Census Bureau QuickFacts: United States Available from: https://www.census.gov/quickfacts/fact/table/US/PST045223 2020 accessed May 19, 2024
82 Groves R.M. Fowler F.J. Jr. Couper M.P. Lepkowski J.M. Singer E. Tourangeau R. Survey Methodology 2nd ed. 2009 Wiley Hoboken, NJ ISBN:978–0–470-46546-2
83 Parker R.M. Baker D.W. Williams M.V. Nurss J.R. The test of functional health literacy in adults: a new instrument for measuring Patients’ literacy skills J. Gen. Intern. Med. 10 10 1995 537 541 [PMID:8576769] 8576769
84 Weiss B.D. Mays M.Z. Martz W. Castro K.M. DeWalt D.A. Pignone M.P. Quick assessment of literacy in primary care: the newest vital sign Ann. Fam. Med. 3 6 2005 514 522 [PMID:16338915] 16338915
85 Tavakol M. Dennick R. Making sense of Cronbach’s alpha Int. J. Med. Educ. 2 2011 53 55 [PMID:28029643] 28029643
86 Bather J.R. Goodman M.S. Kaphingst K.A. Racial segregation and genomics-related knowledge, self-efficacy, perceived importance, and communication among medically underserved patients Genet. Med. Open 2 2024 100844 10.1016/j.gimo.2023.100844
87 Krieger N. Theories for social epidemiology in the 21st century: an ecosocial perspective Int. J. Epidemiol. 30 4 2001 668 677 [PMID:11511581] 11511581
88 Cassel J. The contribution of the social environment to host resistance: the fourth Wade Hampton frost lecture Am. J. Epidemiol. 104 2 1976 107 123 [PMID:782233] 782233
89 Keene Woods N. Ali U. Medina M. Reyes J. Chesser A.K. Health literacy, health outcomes and equity: a trend analysis based on a population survey J. Prim. Care Community Health 14 2023 21501319231156132 [PMID:36852725]
90 Stormacq C. Van den Broucke S. Wosinski J. Does health literacy mediate the relationship between socioeconomic status and health disparities? Integr. Rev. Health Promot. Int. 34 5 2019 e1 e17 [PMID:30107564]
91 Chavez-Yenter D. Goodman M.S. Chen Y. Chu X. Bradshaw R.L. Lorenz Chambers R. Association of disparities in family history and family cancer history in the electronic health record with sex, race, hispanic or latino ethnicity, and language preference in 2 large US health care systems JAMA Netw. Open 5 10 2022 e2234574 10.1001/jamanetworkopen.2022.34574
