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Abstract

Background

Multiple chronic conditions (MCCs) are of increasing public health concern. There remain significant gaps in understanding the relationship between racial discrimination as a determinant of MCC burden. This study examines the association between race-based differential treatment and MCC prevalence by race.

Methods

We analyzed data from Black and White adults who completed the South Carolina Behavioral Risk Factor Surveillance System (2016-2017) survey Reactions to Race optional module (n=18,047). MCCs were summed and categorized (0; 1; 2-3; 4+ conditions). Racial discrimination was operationalized across multiple domains: experiences of race-based differential treatment in work settings and in healthcare settings, and emotional and physical reactions to race-based differential treatment. Multinomial logistic regression models were stratified by race and adjusted for confounders.

Results

Overall, 63.7% of Black and 60.4% of White adults had ≥2 MCC. Experiences of race-based differential treatment in work and health care settings and emotional reactions to race-based differential treatment were associated with a higher risk of MCCs among Black and White adults.

Conclusions

Our findings suggest that experiences and reactions to race-based differential treatment were associated with greater MCC burden among Black and White adults. This adds to a growing literature highlighting the importance of examining racial discrimination as a key factor contributing to the MCC burden within populations. Future research should interrogate potential social mechanisms identifying high MCC risk within racial groups.

Keywords

racial discrimination multiple chronic conditions socioeconomic status racism race multimorbidity health equity

Introduction

Multiple chronic conditions (MCCs), commonly called multimorbidity, are defined as the presence of two or more concurrent chronic conditions and are a major population health and clinical priority in the United States.¹ The prevalence of MCCs has grown steadily over time among all adults and across all racial/ethnic groups.^2,3 Approximately 42% of adults report two to three chronic conditions, and nearly 19% report 4 or more chronic conditions.^1,4 Moreover, recent studies note an increasing proportion of young adults reporting MCCs and evidence of racial disparities in MCC burden earlier during the life course.⁵ This growing burden of multiple chronic conditions among the adult population, which is projected to increase,⁶ is a poignant marker of overall population health.⁷

A proxy for multisystem dysregulation and an indicator of cumulative disease burden,^8,9 MCCs are associated with reduced health-related quality of life, increased risk of disability, accelerated aging, and premature mortality.^10,11 The extant literature demonstrates heterogeneity in MCC prevalence by race.^2,12,13 For example, a recent study demonstrated that Black adults had a higher prevalence of MCC between 2008-2018 in comparison to other racial/ethnic groups across the lifespan.² While the epidemiologic evidence base on MCCs is growing, there remain significant gaps in understanding how social factors, namely racial discrimination, contribute to MCC burden and inequities.

Investigating racial discrimination as a risk factor for MCC is of great significance given its role in amplifying health risks and poor health outcomes. Experiencing racial discrimination is postulated to activate physiological and psychological stress response pathways that can lead to and contribute to maladaptive health behaviors (e.g., suboptimal diet, smoking, physical inactivity) and result in a higher risk of multiple chronic conditions.^14–17 While there is robust evidence documenting an adverse relationship between racial discrimination and single chronic conditions (e.g., hypertension, diabetes),^18,19 there are a few examples of empirical research examining the association between racial discrimination and MCC burden.^20–23 For example, using data from the National Survey of American Life, Oh et al (2020) measured everyday discrimination and major discriminatory events and found individuals who reported experiencing everyday discrimination were significantly more likely to report physical and psychiatric multimorbidity.²⁰ These studies offer critical insights that augment an emerging evidence base investigating the relationship between racial discrimination and chronic disease accumulation. However, additional research capturing this relationship across specific regions of the US can help inform the social epidemiology of MCC burden in the US.

There are strong regional and state-level differences when considering the patterning of chronic conditions.²⁴ South Carolina ranks in the top 15 states with the highest prevalence of MCCs of all 50 states in adults 18 years of age and older,²⁴ which reflects a substantial burden of MCC. There is a dearth of published studies identifying social characteristics influencing MCC distribution in geographically localized areas which can serve to aid public health authorities in targeting preventive resources, programs, and policies to those at highest risk. To address these gaps in the literature, this study aims to characterize the prevalence and distribution of MCC burden among non-Hispanic Black and non-Hispanic White adults and examines the association between racial discrimination and MCC prevalence by race.

Methods

This study used data from the 2016 and 2017 South Carolina Behavioral Risk Factor Surveillance System (SC-RFSS). BRFSS is a cross-sectional, telephone-based survey of non-institutionalized adults aged 18 or older, that assesses health risk behaviors, preventive health practices, and healthcare access at the state level. A complete description of SC-BRFSS methodology is published elsewhere.^25,26 Briefly, the survey is conducted by landline and cell phone, with overall response rates of 45.8 (2016) and 47.3% (2017), which are both slightly higher than average national BRFSS response rates.^25,26 The 2016 and 2017 SC-BRFSS surveyed 11,236 and 11,311, respectively, and included the optional Reactions to Race module (RTRM) that asks questions about one’s socially assigned race and experience of and reactions to race-based differential treatment. South Carolina was one of the few states with two years of consecutive administration of the RTRM module before the COVID-19 pandemic. Hispanic respondents were excluded due to the small sample size which resulted in low statistical power to estimate associations. For this analysis, the sample was restricted to those who identified as non-Hispanic Black (hereafter referred to as Black) or non-Hispanic White (hereafter referred to as White), those with complete information on key sociodemographic variables (i.e., education level, sex, race, age, and labor status participation), and those who had responses to at least one of the four RTRM questions were included, which yielded a final analytic sample of 17,570 (2016: n = 8,886 respondents; 2017: n = 8,684 respondents). All study participants provided informed consent to participate in the study. The data used in this analysis is publicly available, did not contain patient identifiers, and was therefore deemed exempt from our university’s Institutional Review Board.

This study focused on four outcomes that represented racial discrimination and were ascertained from the RTRM: 1) race-based differential treatment in a health care setting; 2) race-based differential treatment in a work setting; 3) emotional reaction to race-based differential treatment and 4) physical reaction to race-based differential treatment. The RTRM was first developed in 2001 and underwent extensive cognitive testing, field testing, and pilot testing in 2002.²⁷ The following two questions assessed experiences in the healthcare or work setting: “Within the past 12 months at work, do you feel you were treated worse than, the same as, or better than people of other races?”, “Within the past 12 months, when seeking health care, do you feel your experiences were worse than, the same as, or better than for people of other races?”. Response categories included worse than other races; the same as other races; better than other races; worse than some races, better than others; and only encountered people of the same race. For the healthcare question, an additional category was “no health care in the past 12 months” and responses to that category were coded as missing. The health care and work setting responses were dichotomized: “worse than other some races; worse than some races, better than others” or “the same as other races; better than other races; and only encountered people of the same race”. Emotional and physical reactions to race-based differential treatment were ascertained by querying respondents: “Within the past 30 days, have you felt emotionally upset, for example angry, sad, or frustrated, as a result of how you were treated based on your race?” and “Within the past 30 days, have you experienced any physical symptoms, for example, a headache, an upset stomach, tensing of your muscles, or a pounding heart, as a result of how you were treated based on your race?”. Response categories were “yes” or “no” to both questions.

The presence of multiple chronic conditions (MCCs) was assessed based on the self-reported presence of one of the following 12 chronic conditions: arthritis, asthma, cancer, chronic obstructive pulmonary disease (COPD), depression, diabetes (gestational diabetes was excluded), heart disease, hypertension, high cholesterol, kidney disease, obesity, and stroke. Respondents were asked if: ever been told by a health professional whether they have one of the aforementioned health conditions. MCC was defined as the sum of chronic conditions (0, 1, 2-3, or 4+), which is consistent with prior research.²⁸

The following sociodemographic variables were included as covariates in multivariate models: sex (male/female), age (18–34; 35–44; 45–64; and 65+); educational attainment (at least some high school, graduated high school, at least some high school, and graduated college); income (less than $15,000, $15,000 –$24,999, $25,000–$34,999, $35,000–$49,999, $50,000 or more); employment status (employed/self-employed, not employed, retired, homemaker or student; and unable to work); health insurance status (insured and uninsured) and geographic residence (urban or rural).

Bivariate and multivariate analyses were stratified by race. Analyses are stratified by race given the conceptual and theoretical interest in understanding factors within racial groups and not between racial groups. First, we calculated descriptive statistics to examine the distribution of MCC categories across respondent sociodemographic characteristics by race. The distribution of characteristics across MCC categories was compared using chi-square tests for independence. Additionally, the distribution of the four variables representing experiences and reactions to race-based differential treatment was calculated for Black and White adults and stratified by educational attainment and income. Multinomial logistic regression models were used to estimate relative risk ratios and 95% confidence intervals for the association between each RTRM variable and MCC, stratified by race. Models of the four RTRM (exposure) variables were fit separately. To evaluate the association between the experiences of and reactions to race-based differential treatment, we fit an unadjusted multinomial logistic regression model with zero chronic conditions as the referent group. We fit an additional, fully adjusted model which included all sociodemographic variables. Survey sampling weights were applied to analyses to account for the complex survey design, and analyses were conducted with the statistical software STATA, version 17.0 (StataCorp, College Station, Texas). Institutional review board approval was not required for this study since the SC-BRFSS is a public-use dataset and does not meet the criteria of human subject research.

Results

The distribution of sociodemographic factors by the number of MCCs among Black and White adults residing in SC is presented (Table 1). Overall, 63.7% of Black adults and 60.4% of White adults reported having two or more MCCs. Specifically, 38% of Black adults and 37.5% of White adults reported having two to three chronic, while approximately one-quarter of Black adults and 22.9% of White adults reported living with four or more chronic conditions. Among both Black and White adults, those with a higher burden of MCC’s were more likely to be female, 65 years of age or older, have lower levels of educational attainment and income, be unable to work, and reside in rural areas.

Table 1.

Distribution of sociodemographic factors by number of multiple chronic conditions among black and white SC adults, SC-BRFSS 2016-2017.^a,b

Characteristic	Black					White
	Multiple chronic conditions					Multiple chronic conditions
	0 (n=724)	1 (n=1,049)	2-3 (n=1,857)	≥4 (n=1,255)	p-value^c	0 (n=2,203)	1 (n=2,815)	2-3 (n=4,759)	≥4 (n=2,908)	p-value^c
Overall	14.8	21.5	38	25.7	<.0001	17.4	22.2	37.5	22.9	<.0001
Sex
Male	18.3	23.1	36.3	22.4	<.0001	18.5	21.8	37.1	22.7	0.036
Female	13.1	20.7	38.9	27.4		16.5	22.5	37.9	23.1
Age
18 - 34	41.0	34.2	21.9	2.9	<.0001	43.4	34.0	20.7	1.9	<.0001
35 - 44	25.8	28.7	34.5	11.0		33.1	32.0	26.5	8.4
45 - 64	12.1	21.4	38.3	28.3		19.9	23.9	36.1	20.1
65+	4.8	14.5	44.9	35.8		7.7	16.9	43.8	31.6
Education
At least some HS	9.8	16.3	36.7	37.2	<.0001	8.9	14.8	35.2	41.2	<.0001
Graduated HS	14.4	20.2	38.8	26.6		14.3	19.9	39.2	26.6
At least some college	16.8	22.1	37.8	23.3		16.0	21.3	38.2	24.4
Graduated college	16.9	26.5	37.9	18.7		22.3	26.0	36.2	15.5
Income (per year)
Less than $15,000	14.4	15.5	34.7	40.5	<.0001	7.0	15.2	34.3	43.6	<.0001
$15,000-$24,999	14.4	21.5	38.8	26.2		9.9	15.4	37.4	37.3
$25,000-$34,999	16.1	24.1	38.9	20.9		12.9	18.1	42.7	26.3
$35,000-$49,999	15.8	26.0	39.9	18.3		14.5	21.1	40.1	24.3
$50,000 or more	19.8	25.7	37.6	17.0		23.5	26.7	35.4	14.4
Missing	14.4	18.3	39.3	26.7		15.9	21.4	39.6	23.1
Employment status
Employed	23.7	30.0	36.3	10.0	<.0001	28.1	29.0	33.0	10.0	<.0001
Not employed	21.4	26.0	31.7	21.0		18.4	24.4	35.7	21.5
Retired	5.4	16.0	46.4	32.2		7.9	17.6	43.9	30.6
Homemaker/student	29.9	23.9	33.3	12.9		25.4	24.9	34.4	15.4
Unable to work	3.3	8.0	31.7	57.0		1.8	6.8	32.0	59.4
Health insurance	3.3	8.0	31.7	57.0		1.8	6.8	32.0	59.4
Insured	13.8	20.9	38.5	26.8	<.0001	16.9	21.9	38.0	23.3	<.0001
Uninsured	23.0	26.4	34.4	16.3		24.9	26.7	30.8	17.6
Geographic residence
Urban	16.8	21.4	37.3	24.4		18.5	22.9	36.8	21.8
Rural	12.6	21.6	38.8	27.1	<.0001	15.4	21.0	38.7	24.9	<.0001

Abbreviations: CI = confidence intervals; HS = high school.

^aValues were weighted according to BRFSS methodology.

^bN is the weighted sample size; % is the weighted percentage.

^cP values are from χ2 tests.

The prevalence of experiences and reactions to race-based differential treatment among Black and White SC adults are presented by educational attainment and income (Figure 1). Among black SC adults, those with higher educational attainment were more likely to report experiences of differential treatment in work settings and greater emotional reactions when compared to those with lower educational attainment. Reports of experiencing differential treatment in healthcare settings were relatively stable across educational attainment. For White SC adults, educational attainment was inversely and significantly associated with each of the four measures of racial discrimination. Black SC adults with low income were more likely to report differential treatment in work settings and experiencing a physical reaction to differential treatment in comparison to those with the highest income. Among White SC adults, lower income was significantly associated with greater reports of experiencing and having a reaction to race-based differential treatment.

Figure 1.

Experiences of and reactions to race-based differential treatment among black and white SC adults by educational attainment and income, SC-BRFSS 2016-2017.

Estimates of the association between experiences and reactions to race-based differential treatment and MCC among Black adults are presented (Table 2). In adjusted models, the expected risk of having 2-3 MCCs (RRR: 1.81; 1.06, 3.10) and 4+ MCCs (RRR: 2.34; 95% CI: 1.35, 4.07) was higher among those reporting experiencing race-based differential treatment in healthcare settings. Among those who experienced worse treatment due to race in work settings, the expected risk of 4+ MCCs was higher (RRR: 2.06; 95% CI: 1.17, 3.63). The expected risk of having 2-3 MCCs (RRR: 1.53; 95% CI: 1.04, 2.26) and 4+ MCCs (RRR: 2.42; 95% CI: 1.58, 3.70) was higher among those who reported an emotional reaction to race-based differential treatment.

Table 2.

Estimates of experiences of and reactions to race-based differential treatment and MCCs among black adults, SC-BRFSS 2016-2017.

	Black
	1 CC VS 0 CC		2-3 CC VS 0 CC		4+ CC VS 0 CC
	UnadjustedRRR (95%CI)	Adjusted^aRRR (95%CI)	UnadjustedRRR (95%CI)	Adjusted^aRRR (95%CI)	UnadjustedRRR (95%CI)	Adjusted^aRRR (95%CI)
Experiences of differential treatment
Health care
Same or better than other	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Worse than other races	1.39 (0.83, 2.31)	1.37 (0.79, 2.36)	1.72 (1.07, 2.77)	1.81 (1.06, 3.10)	2.65 (1.65,4.24)	2.34 (1.35, 4.07)
Work setting
Same or better than other	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Worse than other races	1.22 (0.81, 1.86)	1.19 (0.77, 1.83)	1.09 (0.72, 1.66)	1.11 (0.72, 1.71)	1.85 (1.08, 3.15)	2.06 (1.17, 3.63)
Reactions to differential treatment
Physical reaction
No	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Yes	1.13 (0.70, 1.82)	1.07 (0.66, 1.74)	1.44 (0.93, 2.24)	1.32 (0.81, 2.15)	2.02(1.28, 3.19)	1.48 (0.86, 2.57)
Emotional reaction
No	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Yes	1.27 (0.87, 1.87)	1.35 (0.91, 2.00)	1.30 (0.91, 1.85)	1.53 (1.04, 2.26)	1.80 (1.25, 2.59)	2.42 (1.58, 3.70)

Abbreviations: CI = confidence intervals; CC = chronic conditions; RRR= relative risk ratio; HS = high school.

^aModels adjusted for sex, age, education, income, employment status, health insurance status, and geographic residence.

Table 3 depicts the results of the multinomial regression estimating the association between experiences and reactions to race-based treatment and MCC among White adults. In the fully adjusted models, the expected risk of having 4+ MCCs was higher among those who reported race-based differential treatment in health care settings (RRR = 2.35; 95% CI: 1.24, 4.47) and in work settings (RRR = 2.77; 95% CI: 1.45, 5.28) settings and among those reported experiencing an emotional (RRR = 3.91; 95% CI: 2.50, 6.11) or physical (RRR = 7.96; 95% CI: 3.86, 16.43) response to race based differential treatment. Similar results were also observed among White adults who had 2-3 MCCs, with a higher risk observed among those who reported race-based differential treatment in work settings (RRR = 2.34; 95% CI: 1.32, 4.14), experiencing a physical reaction (RRR = 4.24; 95% CI: 2.13, 8.42), and an emotional reaction (RRR = 2.41; 95% CI: 1.61, 3.59) in comparison to those who did not report differential treatment.

Table 3.

Estimates of experiences of and reactions to race-based differential treatment and MCC among white SC adults, SC-BRFSS 2016-2017.

	White
	1 CC VS 0 CC		2-3 CC VS 0 CC		4+ CC VS 0 CC
	UnadjustedRRR (95%CI)	Adjusted^aRRR (95%CI)	UnadjustedRRR (95%CI)	Adjusted^aRRR (95%CI)	UnadjustedRRR (95%CI)	Adjusted^aRRR (95%CI)
Experiences of differential treatment
Health care
Same or better than other	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Worse than other races	1.25 (0.69, 2.24)	1.29 (0.71, 2.34)	1.39 (0.79, 2.43)	1.36 (0.73, 2.54)	2.76 (1.61, 4.73)	2.35 (1.24, 4.47)
Work setting
Same or better than other	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Worse than other races	1.29 (0.70, 2.35)	1.24 (0.69, 2.22)	2.54 (1.47, 4.40)	2.34 (1.32, 4.14)	3.03 (1.62, 5.66)	2.77 (1.45, 5.28)
Reactions to differential treatment
Physical reaction
No	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Yes	2.88 (1.47, 5.65)	3.13 (1.57, 6.24)	3.26 (1.73, 6.14)	4.24 (2.13, 8.42)	6.82 (3.66, 12.71)	7.96 (3.86, 16.43)
Emotional reaction
No	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Yes	1.39 (0.93, 2.08)	1.58 (1.05, 2.37)	1.64 (1.14, 2.37)	2.41 (1.61, 3.59)	2.31 (1.58, 3.37)	3.91 (2.50, 6.11)

Abbreviations: CI = confidence intervals; CC = chronic conditions; RRR= relative risk ratio; HS = high school.

^aModels adjusted for sex, age, education, income, employment status, health insurance status, and geographic residence.

Discussion

To our knowledge, this study is the first to examine experiences of and reactions to race-based differential treatment and its association with the prevalence of MCC. Our findings reveal several notable patterns. First, we observed variations in the distribution of race-based differential treatment by race and across socioeconomic status. Second, the results suggest that individuals, regardless of race, reporting experiencing differential race-based treatment in health care and work settings and emotional reactions to race-based treatment have a higher expected risk of reporting MCCs.

Both Black and White respondents residing in SC reported experiencing racial discrimination, although the prevalence was differentially patterned. Consistent with prior literature, experiences and reactions to race-based differential treatment were highest among Black adults.^20,29 We also observed variations in the prevalence of the four measures of race-based differential treatment by educational attainment and income. Among Black adults, there was a less prominent SES-racial discrimination gradient. However, among White adults, there was a stronger, dose-response SES-racial discrimination gradient observed. Scholars have postulated that the relationship between SES and experiences of discrimination operates qualitatively differently for Black and White adults.³⁰ Black adults with higher educational attainment, that is at least some college or higher, were more likely to report unfair treatment in work settings and report physical and emotional reactions to race in comparison to those with a high school education or lower. In contrast, having a college degree or higher was unrelated to reporting experiencing unfair treatment in health care settings. Black adults with lower income reported greater levels of unfair treatment in health care settings and more emotional reactions to race-based treatment in comparison to those with higher income. The literature is mixed regarding the relationships between racial discrimination and SES. Some prior studies show significant limited or very weak heterogeneity in reports of racial discrimination by education and income.²⁹ However, these relationships may be a function of the type of measure of racial discrimination operationalized. For example, Bleich et al (2019) found no variation in levels of education or income when measuring racial discrimination in healthcare settings or related to employment.²⁹ Explanations for the weak and limited variation in experiences of racial discrimination among Black adults by SES have centered on the fact that greater resources in the form of a higher income and college degree or professional degree are not protective factors against experiencing racial discrimination.²⁹ In contrast, there is evidence to suggest that Black adults with higher SES report more racial discrimination in comparison to Black adults with lower SES and that this may be a function of these resources and access that may result in more exposure to racial discrimination as well as greater recognition, identification, and acknowledgment of covert forms of racial discrimination.³¹

In our sample, the prevalence of reporting race-based differential treatment was most prominent among White adults with lower SES. Specifically, we observed that White respondents with lower educational attainment and income were more likely to report experiences of and reactions to race-based differential treatment compared to White respondents with higher income and educational attainment. This is congruent with several prior studies that have shown variations in reports of racial discrimination among White adults by SES and Southern region of residence.^30,32,33 Such findings are partly explained by status dissonance theory, which offers a positional lens to explain discrepancies in perceptions of positioning in a social hierarchy relative to other groups.³⁴ It has been demonstrated that white individuals with lower education and income are more likely to perceive anti-White bias and some reports suggest that this is increasing.^30,34 Status dissonance theory is a useful framework to understand the processes of anti-White bias and how it may arise from perceptions that the social and economic “advancements” of Black and Latino adults result in the loss of economic opportunities and therefore status among White adults.³⁴

Our results largely suggest an independent association between experiencing and reporting racial discrimination and MCC burden. Specifically, Black and White adults who reported differential race-based treatment in work and health care settings, and those who experienced reactions to race-based treatment had a higher MCC burden when compared to those who did not report these experiences. This finding is consistent with and adds to a limited, but growing body of literature, demonstrating the impact of racial discrimination, using multiple scales, on MCC burden.^20–23 Oh et al measured racial discrimination using the everyday discrimination scale and major lifetime discriminatory events and found that these measures were associated with greater odds of reporting multimorbidity among Black adults in the National Survey of American Life study.²⁰ In a study conducted among a national cross-section of older adults in Colombia, Reyes-Ortiz et al investigated the relationship between racial discrimination, measured as having ever experienced racial discrimination recently and during childhood, on multimorbidity.²¹ The findings revealed that those who experienced greater discrimination had higher odds of multimorbidity.²¹ Mechanisms that may help to explain the association between differential race-based treatment and MCC burden may operate via the stress response system as well as impact engagement or disengagement in healthy lifestyle behaviors.²⁰ However, future studies to confirm these results in other datasets that include more diverse racial/ethnic samples, consider exploring gender differences within racial/ethnic groups, and more importantly use alternative measures of multimorbidity that are captured in administrative claims data and electronic health records to shed additional light on who racial discrimination impacts accumulated disease burden.

Overall, we observed an association between experiences of and reactions to race-based differential treatment and MCC burden among Black and White adults. For White adults, this relationship persisted across all four domains of the RTRM measures. Several prior studies have shown that perceived racial discrimination can negatively impact the health of White adults.^33,35 For example, Fujishiro (2009) used data from BRFSS to show that both Black and White adults who reported workplace racial discrimination, using similar measures to our study, were more likely to rate their health as poor in comparison to those who did not report racial discrimination.³⁵ The finding of similar or even stronger associations of poorer health among White adults in comparison to Black adults in race-stratified analyses is consistent with several other studies.^30,32 For example Mattingly et al found that experiencing racial discrimination was associated with higher odds of tobacco use disorder and that this association was strongest for White respondents in comparison to respondents self-identifying as a member of another racial/ethnic group.³² Explaining the strength of the association observed among White adults may be a function of SES and status theory. In our sample, it is possible that White adults with lower educational attainment and income may be driving the observed relationship. This is consistent with a body of work documenting the inverse association between lower SES and multimorbidity prevalence.³⁶ Status threat theory is an emerging area of study in public health that may also help to explain these results.³⁷ Efird et al’s scoping review discusses how the health of White Americans may be impacted by perceived threats to interpersonal social experiences and individual social standing.³⁷ This present study adds to a body of research demonstrating that perceptions of racial discrimination among White adults may have negative consequences for health and provides an impetus for future work to further contextualize how status threat helps to elucidate this association, particularly in a highly divisive political climate.³⁷

Characterizing the distribution of MCC and understanding how psychosocial stressors contribute to MCC burden is particularly salient given the destabilizing impact of the unprecedented coronavirus disease-2019 (COVID-19) pandemic on healthcare and public health. SARS-CoV-2 infections have been shown to impact multiple organ systems that have implications for MCCs. While several studies show that chronic conditions such as existing hypertension, type 2 diabetes, and obesity were critical risk factors for severe COVID-19 and post-acute sequelae of COVID, there are significant knowledge gaps regarding the interplay of psychosocial drivers of adverse COVID, which are likely to be socially patterned among racially/ethnically diverse populations. This data provides a snapshot of the MCC landscape pre-COVID-19 pandemic and illustrates the critical need for sustained data collection of the RTRM variables. Quantifying pre-pandemic levels of the relationship between racial discrimination and MCC burden will provide a baseline that can further our understanding of psychosocial factors that amplify the risk associated with COVID-19. Future studies should compare changes from pre COVID-19 through COVID-19 to post-COVID-19 to elucidate MCC burden and risk.

This study has numerous strengths. The measure of racial discrimination captures multiple domains inquiring about unfair treatment in multiple locations and the responses to unfair treatment with some precision. Our findings are likely generalizable to Black and White adults living in southern states; however, given the sociopolitical climate, may not apply to other regions of the US. Although this analysis is based on data collected in 2016 and 2017, South Carolina represents one of few states that administered and consecutively collected data from the Reactions to Race module in the period before the SARS COVID-19 pandemic. More importantly, this data was collected during a distinct period of increasing political, social, and cultural polarization. However, these findings need to be interpreted in the context of several limitations. First, this study employed a cross-sectional design, and we cannot draw any causal inferences from the analyses. Our measure of racial discrimination captures only a limited time frame (e.g., 30 days and 12 months) for which these experiences or reactions may have occurred. Further, our measures of self-reported chronic diseases provide a mere snapshot of disease status and do not provide information on the duration of the chronic conditions. Considering these limitations, our study may be susceptible to reverse causation since the temporal ordering of racial discrimination and the self-reported MCCs cannot be established with certitude. Nonetheless, considering the knowledge gap in the literature, we believe this study can still offer valuable information and inform future hypothesis generation for research investigating the relationship between racial discrimination and MCCs. Second, the racial discrimination measures reflect only an individual-level experience and do not reflect structural and institutional racism. There is currently no broad consensus on how to measure and operationalize multimorbidity.³⁸ In the present study, MCCs were measured as a count of 12 chronic conditions, most of which are risk factors for the leading causes of death. However, the survey questions did not query respondents about the severity or duration of conditions. Although there is a bevy of research that considers specific combinations of chronic conditions and considers this more relevant than just a count of the number of chronic conditions,^39,40 identifying the number of MCCs can help to inform the development of and target the timing of chronic disease prevention programs.⁴¹ Another limitation of the BRFSS population-based survey instrument is that it is subject to recall bias due to the self-reported nature of data collection. Further, we are unable to capture undiagnosed and rare conditions. Relatedly, responses to the questions about self-reported conditions may be dependent on one’s access to health care to receive a diagnosis from a healthcare provider.²⁰ While we adjusted for health insurance status, our analysis did not capture frequency and contact with the health care system, which may impact whether a respondent had an opportunity to be diagnosed with a chronic condition. Lastly, although our study was not sufficiently powered to explore interactions by educational attainment and income for the relationship between racial discrimination and MCC, it will be important for future longitudinal studies to interrogate SES mechanisms underlying high risk of multimorbidity within racial groups.

Identifying factors that contribute to disparities in MCC burden is of practical, clinical, and methodological relevance, given the escalating cost of health care, particularly among groups that are disproportionately burdened.⁴² This study adds to the growing body of literature showing that experiencing racial discrimination is associated with greater MCC burden and this burden is further shaped by socioeconomic status, particularly among White adults. Greater attention to the intersection of race, ethnicity, discrimination, and SES is needed to further our understanding of the development, progression, management, and sequelae of MCC. Further work is needed to address the structural determinants that increase MCC risk across the lifespan, which is a major gap in current research. Understanding the factors that shape the prevalence and distribution of MCCs will advance our knowledge about the epidemiology of MCCs in hopes that we can improve the quality of care for those population subgroups at increased risk.

Footnotes

Acknowledgments

The research was conducted without financial support from a funding body. The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. No copyrighted materials were used in this article.

ORCID iD

Kellee White Whilby

Ethical statement

Author contributions

KWW: conceptualized the study and wrote original draft; KRE analyzed the data; KWW and KRE interpreted the findings; made critical revisions of the manuscript, reviewed and approved the final version of the manuscript.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The author declared that the research was conducted in the absence of any financial or commercial relationships that could be construed as a potential conflict of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

A publicly available dataset was analyzed in this study. The data can be found at: . Please contact the corresponding author for any research material requests.

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Experiences of and reactions to race-based differential treatment with multiple chronic conditions by race: Evidence from the South Carolina behavioral risk factor surveillance systems surveys prior to the COVID-19 pandemic

Abstract

Background

Methods

Results

Conclusions

Keywords

Introduction

Methods

Results

Discussion

Footnotes

Acknowledgments

ORCID iD

Ethical statement

Author contributions

Funding

Declaration of conflicting interests

Data Availability Statement

References