J Behav Med DOI 10.1007/s10865-013-9547-0

Everyday discrimination and chronic health conditions among Latinos: the moderating role of socioeconomic position Kristine M. Molina • Yenisleidy Simon

Received: March 11, 2013 / Accepted: October 26, 2013  Springer Science+Business Media New York 2013

Abstract Emerging research has revealed that everyday discrimination and socioeconomic position may have synergistic effects on the health of racial/ethnic minorities. The present study examined the association between selfreported everyday discrimination and count of chronic health conditions, and explored the moderating role of objective and subjective socioeconomic position on the discrimination–health relation. We utilized nationally representative data of Latino adults (N = 2,554) from the National Latino and Asian American Study. Weighted negative binomial regression modeling was used to estimate the association between self-reported everyday discrimination and count of chronic health conditions, and to test whether this relation was modified by markers of socioeconomic position. Binomial regressions revealed that everyday discrimination was associated with a greater count of chronic conditions. However, moderation analyses indicated that household income moderated the discrimi-

nation–health relation, controlling for sociodemographic variables. More specifically, the adverse effects of discrimination were stronger for Latinos in middle-income tertiles compared to their lower income counterparts, such that as frequency of discrimination increased, Latinos with medium levels of household income were predicted to have greater counts of chronic conditions. This was only marginally significant among those in the high-income tertile. Our findings suggest that identifying segments of the Latino population that may be at greatest (and lowest) risk of ill health in the context of perceiving being discriminated against may prove useful for understanding Latino health ‘‘paradoxes,’’ and may have implications for tailoring prevention and intervention efforts to particular segments of the Latino population.

Portions of this manuscript were presented at the UMDNJ Robert Wood Johnson Medical School/Latino Behavioral Health Institute’s Critical Research Issues in Latino Mental Health Conference, Los Angeles, CA 2012.

Introduction

Electronic supplementary material The online version of this article (doi:10.1007/s10865-013-9547-0) contains supplementary material, which is available to authorized users. K. M. Molina (&) Community and Prevention Research Division, Department of Psychology, University of Illinois at Chicago, 1007 W. Harrison Street, Behavioral Sciences Building, Room 1050A, Chicago, IL 60607, USA e-mail: [email protected] Y. Simon Psychology Department, Smith College, Northampton, MA, USA

Keywords Hispanics
Unfair treatment
Physical health
Income
Education
Subjective social status

Everyday discrimination has been conceptualized as subtle unfair treatment that occurs within daily routine practices (Essed, 1991). For example, in a typical day, everyday forms of discrimination can range from being treated as less smart or that others are better than you, to receiving less respect than others, and/or receiving poorer service than others in restaurants or stores (Essed, 1991; Harrell, 2000). Although any individual can experience this type of unfair treatment, members of socially marginalized groups are more likely to be targets of these kinds of discriminatory experiences than are members of non-marginalized social groups (Harrell, 2000). Not surprisingly, this form of discrimination is argued to act as a chronic stressor that can

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take a toll on the health of people of color, and has been posited as a key contributor of racial/ethnic health disparities (Harrell et al., 2003; Williams & Mohammed, 2009). Biopsychosocial models of discrimination (Clark et al., 1999; Harrell, 2000) posit that heightened and prolonged psychological and physiological responses to experiences of discrimination can harm physical health, given that the cognitive, emotional, and biological costs associated with constant adjustments to stressors may lead to allostatic load (McEwen, 1998). For example, the accumulation of day-today experiences of discrimination can elicit acute physiological activation, altering heart rate and blood pressure, and the release of stress hormones such as cortisol (Harrell et al., 2003; Miller et al., 2009). Moreover, prior studies have also found that stressors are associated with immune system alterations, including chronic inflammation, which has been shown to be associated with a number of stressrelated conditions, including upper respiratory illnesses (Cohen et al., 1998), susceptibility to infectious diseases, and rheumatoid arthritis (cf. Dougall & Baum, 2012). When these responses to stress are prolonged, such pathogenic mechanisms can over time confer risk for cardiovascular, neuroendocrine and immunologic stress-related diseases (Dougall & Baum, 2012; Harrell et al., 2003). Moreover, others have shown that ethnic minorities who have experienced verbal abuse or physical attacks showed an increased risk for respiratory problems (Karlsen & Nazroo, 2002). Not surprisingly, in a recent meta-analysis, Pasco and Smart Richman (2009) found that perceived discrimination had a significant effect on physical health outcomes, including general self-reported health, diseases and chronic physical conditions (e.g., diabetes, cardiovascular disease, respiratory conditions), and indicators of illness such as pain and headaches. In sum, a substantial number of studies have demonstrated robust associations between self-reported discrimination and stress-related health outcomes among racial/ethnic minorities, even after adjusting for a number of confounders (cf. Paradies, 2006; Williams & Mohammed, 2009).

Latinos, discrimination, and physical health Despite the surge of research on the role of discrimination on the health of racial/ethnic minorities in the last two decades, it is notable that limited research to date exists on the association between discrimination and health-related outcomes among Latinos (cf. Lee & Ahn, 2012). The demographic landscape of the US has undoubtedly shifted, with Latinos accounting for 56 % of the nation’s growth in the past decade, presently making them the nation’s largest ethnic minority group (Passel et al., 2011). Unfortunately, the increased presence of Latinos has resulted in higher

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levels of anti-Latino sentiments and discrimination targeted against this group (Forman et al., 2002). The reported prevalence rate of everyday discrimination among a national sample of Latinos was found to be 30 % (Pe´rez et al., 2008), and other studies find that Latinos report higher levels of everyday discrimination than non-Latino Whites (Otiniano & Gee, 2012). Considering the growth of the Latino population and that discrimination against them remains widespread, Latinos may be at an increased risk of experiencing the health burden associated with exposure to discrimination. Yet only a handful of studies have investigated the relation between discrimination and Latino physical health. However, available cross-sectional research in this area has identified discrimination as an important underlying determinant of poor physical health among Latino adults (Finch et al., 2001; Lee & Ferraro, 2009; Molina et al., 2013; Otiniano & Gee, 2012). For example, the bulk of empirical evidence finds that even after adjusting for acculturation and sociodemographic characteristics, discrimination is associated with higher counts of chronic health conditions (Finch et al., 2001), more physical health problems (Lee & Ferraro, 2009), poorer self-rated health (Brondolo et al., 2011; Flores et al., 2008; Molina et al., 2013), and an increased number of unhealthy and disability days (Otiniano & Gee, 2012) among Latinos. Still to date, the large corpus of research on discrimination among Latinos continues to be focused on them in the aggregate (cf. Lee & Ahn, 2012), with the assumption being that Latinos are all similarly affected by experiences of discrimination and that, results from such prior studies are generalizable across Latino subgroups. At the same time, although there are studies that have examined Latino within-group differences in the relation between discrimination and health, as of yet, much of this work has focused on nativity (Ryan et al., 2006), ethnic (Lee & Ferraro, 2009; Otiniano & Gee, 2012), or gender differences (Finch et al., 2001), while controlling for socioeconomic differences. Yet, the discrimination–health relation may also vary across the socioeconomic spectrum and by indicator of socioeconomic position. For example, past research indicates that Latinos with lower levels of education report less frequency of discrimination, but no difference exists by income after adjusting for a number of confounders (Pe´rez et al., 2008); and still, mixed findings appear by subjective social position ranking (Molina, 2011). These findings raise questions as to whether the effects of discrimination on physical health may also diverge by socioeconomic position. Hence, conclusions regarding how discrimination relates to health outcomes among Latinos in the aggregate may overlook potential intra-group differences by socioeconomic position.

J Behav Med

Latinos, socioeconomic position and physical health Research has demonstrated that individuals of low socioeconomic position have poorer health and experience higher rates of morbidity and mortality compared to their higher socioeconomic position counterparts (cf. Adler et al., 1994; Braveman et al., 2005). Such differences typically follow a gradational pattern, whereby those higher in the socioeconomic hierarchy are healthier than the group right below them, and so forth; and this pattern generally appears across different socioeconomic indicators or cut-off criteria (Adler et al., 1994). Invoked in these health differentials is that persons of lower socioeconomic means typically possess fewer financial resources, are exposed to greater levels of environmental health hazards, and have less access to health insurance and quality health care, which independently or jointly may contribute to poor health, greater morbidity as well as mortality (Williams & Collins, 1995). Regrettably, as a group, Latinos are one of the nation’s most socioeconomically disenfranchised groups in the US (Marotta & Garcia, 2003). They have higher rates of living in poverty and of being unemployed, overrepresented in low-wage jobs, and have lower rates of educational attainment compared to non-Latino Whites (Motel, 2012). Yet paradoxically, evidence from epidemiological studies show that lower socioeconomic position—whether measured objectively or subjectively—is not consistently predictive of poor health among Latinos (Acevedo-Garcia & Bates, 2008). First, regarding objective indicators of socioeconomic position, past research finds significant associations between income and education and self-rated health (Finch & Vega, 2003), with lower income and education levels associated with poorer health; whereas other studies indicate that these same objective indicators have no effect on the physical health of Latinos (Bostean, 2010; Bui et al., 2011). Second, although less examined than objective indicators of socioeconomic position, subjective social status—a psychological measure used to capture a person’s assessment about his/her position in the social hierarchy based on their past and current socioeconomic conditions—also shows inconsistent associations with health-related outcomes, independent of objective socioeconomic indicators (Franzini & Fernandez-Esquer, 2006; Molina et al., 2013; Ostrove et al., 2000). That is, similar to findings of objective socioeconomic measures, studies assessing the relation between subjective social status and physical health outcomes find that on the one hand, perceiving oneself to occupy a higher social status is associated with better self-rated physical health (Molina et al., 2013), whereas other studies find no significant association between the two variables (Franzini & Fernandez-Esquer, 2006; Ostrove et al., 2000) among Latinos.

Understanding health paradoxes: interplay of discrimination and socioeconomic position According to American standards, I‘ve been privileged with regard to education and now I‘m working toward one of the most prestigious degrees that can be awarded in the U.S., but I can‘t help but second guess myself and still feel like a second class citizen…like I don’t really belong (Latino male, ParkTaylor et al., 2008, p. 135) As illustrated from the quote above, even with high levels of educational attainment, for example, it is possible that a Latino person may still ‘‘feel like a second class citizen.’’ That is, on the one hand, some Latinos may be socioeconomically privileged, but on the other, may feel they occupy a lower social status, particularly as a function of social exclusion and marginalization (Park-Taylor et al., 2008). Indeed, emerging research points to the importance of considering the interplay between discrimination and socioeconomic position as a way of understanding noted health paradoxes among racial/ethnic minority groups. In a recent study, Hudson et al. (2012) showed that reports of everyday discrimination were associated with increased odds of depression among African American men with high-income levels when compared to their low-income counterparts. Similarly, Zhang and Hong (2012) found that the adverse effect of everyday discrimination on psychological distress was strongest among Asian Americans with higher educational attainment than for those with less than a college degree. Although not focused on physical health outcomes, still jointly, these findings suggest that for racial/ ethnic minorities, socioeconomic position and discrimination may exert their effects on health in synergistic ways, and that higher socioeconomic position may not always confer health benefits under certain situations. The diminishing returns hypothesis (Farmer & Ferraro, 2005) is useful for grounding these seemingly counterintuitive findings. According to the diminishing returns hypothesis (Farmer & Ferraro, 2005), racial/ethnic minority groups do not benefit equally from socioeconomic advantages, as do nonLatino Whites. For example, compared to non-Latino Whites, Latinos generally do not receive the same economic returns or rewards (e.g., higher income, occupational prestige) from similar levels of educational attainment, nor do they accrue the same benefits that usually result from gains in income earnings (e.g., economic stability, home ownership; Tomaskovic-Devey et al., 2005). Likewise, this theory suggests that racial/ethnic minorities with greater acquired levels of human capital may be more attuned to experiences of social and economic

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discrimination. As a result, this awareness may generate feelings of relative deprivation and distress, and consequently, may contribute to poor health (Farmer & Ferraro, 2005). In sum, evidence from recent empirical studies (Hudson et al., 2012; Zhang & Hong, 2012) in conjunction with the aforementioned theoretical work, suggest that for racial/ethnic minorities, socioeconomic position may have stress buffering as well as exacerbating health effects in the context of perceived discrimination. Present study No studies to our knowledge, have examined the discrimination and physical health relation among a national sample of Latino adults in the US, or whether and how this relation varies across the socioeconomic spectrum. In this study, we first examine the association between everyday discrimination and count of chronic health conditions. Consistent with biopsychosocial models of discrimination (Clark et al., 1999; Harrell, 2000) and previous empirical evidence (Finch et al., 2001; Gee et al., 2007), we hypothesized that everyday discrimination would be associated with greater counts of chronic health conditions, net of controls. Second, we examine whether and to what extent different markers of socioeconomic position (i.e., income, education, and subjective social status) moderate the relation between everyday discrimination and count of chronic conditions. Based on the aforementioned theoretical and empirical work, we postulated that everyday discrimination would be associated with a higher count of chronic conditions, but high socioeconomic position was hypothesized to accentuate this relation. That is, the relation between everyday discrimination and health was expected to be more pronounced among Latinos with higher levels of educational attainment and higher household income, and for those who perceive themselves having a higher social status compared to Latinos of lower socioeconomic position.

The NLAAS data were collected between 2002 and 2003. The sampling design is briefly described in this paper (see Alegrı´a et al., 2004 for details). The sampling procedure included three components: (1) core sampling of primary and secondary sampling units; (2) high-density supplemental samplings of census block groups in which targeted ethnic groups comprised more than 5 % of the population; and (3) secondary respondent sampling to recruit participants from households where a primary respondent had been interviewed (Heeringa et al., 2004). Primary mode of data collection was by in-person interviews conducted in either English (46.76 %) or Spanish (53.24 %). The weighted response rate for the Latino sample was 77.6 %. Written informed consent was obtained from all study participants. The Internal Review Board Committees of The University of Michigan, the Cambridge Health Alliance, Harvard Medical School, and the University of Washington approved all study procedures. Measures Everyday discrimination

Method

Self-reported everyday discrimination was measured using the Everyday Discrimination Scale (EDS; Williams et al., 1997), which measures frequency of routine experiences of unfair treatment. Sample items include: ‘‘People act as if they think you are not smart’’ and ‘‘You are called names or insulted.’’ Response options range from 1 ‘‘almost every day’’ to 6 ‘‘never.’’ Following recommendations of a recent psychometric study of the 9-item EDS (Reeve et al., 2011), we dropped the first item (‘‘You are treated with less courtesy than other people’’) because of its high correlation with the second item (‘‘You are treated with less respect than other people’’). Accordingly, responses to eight items were reverse coded and summed, with higher scores reflecting greater frequency of discrimination (a = 0.89; M = 14.14; SD = 6.80; range = 4–48). The dimensionality and construct validity of the 8-item Everyday Discrimination Scale has been confirmed among the NLAAS Latino sample (Molina et al., 2013).

Sample and procedure

Socioeconomic position

We utilized data from the National Latino and Asian American Study (NLAAS), a cross-sectional, nationally stratified probability survey of non-institutionalized Asian and Latino adults 18 years of age and older residing in the United States. For our study, we focus on the 2,554 Latino respondents in the NLAAS study. The mean age was 38.02 years (SD = 15.03).

Objective socioeconomic position variables used included annual household income and highest level of education. Based on the distribution of responses, household income was categorized into tertiles: (‘‘lowest’’ [B$18,499], ‘‘medium’’ [$18,500 to $48,499], or ‘‘highest’’ [C$48,500]), with the ‘‘lowest’’ income category serving as the reference. Highest level of education completed was coded as:

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B11 years (reference), 12–15, or C16 years. Subjective social position was measured using the MacArthur Scale of Subjective Social Status (Adler et al., 2000). Respondents were shown a graphic representation of a ladder with ten rungs accompanied by the following description: Think of this ladder as representing where people stand in the United States. At the top of the ladder are the people who are the best off—those who have the most money, the most education and the most respected jobs. At the bottom are the people who are the worst off—those who have the least money, least education, and the least respected jobs or no job. The higher up you are on the ladder, the closer you are to the people at the very top; the lower you are, the closer you are to the people at the very bottom (Adler et al., 2000). Respondents were then asked: ‘‘What is the number to the right of the rung where you think you stand at this time in your life, relative to other people in the United States?’’ Respondents rated their perceived social status on a 1–10 scale, with higher values representing a higher perceived social status (M = 5.49, SD = 1.98; range = 1–10). Chronic health conditions As in prior studies (Finch et al., 2001; Gee et al., 2007), we derived and used the total count score of chronic health conditions (M = 1.22; SD = 1.50; range = 0–10). The number of chronic health conditions was based on respondents’ lifetime endorsement of any of the following: (1) cardiometabolic conditions (stroke, heart attack, heart disease, high blood pressure, and diabetes or high blood sugar); (2) pain/somatic conditions (arthritis/rheumatism, chronic back or neck problems, frequent or severe headaches, and any other chronic pain); and (3) respiratory illnesses (hay fever and other seasonal allergies, asthma, and other chronic lung disease). We based these groupings from prior studies that have found significant associations between self-reported discrimination and aforementioned chronic conditions, as well as biological theories of stress that posit stress-induced responses may increase risk for these diseases (cf. Dougall & Baum, 2012 for a review of the association between stress, health, and illness). The validity of self-reported chronic conditions has been empirically supported (Martin et al., 2000).

Controls The following demographic characteristics served as covariates: gender, Latino ethnicity (Cuban, Puerto Rican, Mexican, Other Latino), age (in years), household size

(number of children and adults residing in the respondent’s household), work status, marital status, and health insurance coverage (insured vs. not insured). Measures used to control for acculturation level included years in the US and language of interview (English vs. Spanish). Lastly, we control for potential social desirability bias using the 10-item Crowne-Marlowe scale (Crowne & Marlowe, 1960). Sample items included ‘‘I never met a person I didn’t like’’ and ‘‘I have always told the truth’’ and ‘‘It doesn’t bother me if someone takes advantage of me.’’ The number of affirmative responses to the items were summed, resulting in a total score of 0–10 (a = 0.74; M = 2.60; SD = 2.30). We controlled for response bias based on the following premises: (1) interviews were not self-administered, which increase the probability of bias in reports, especially when the interviewers are of a similar social group as respondents (i.e., the NLAAS matched Latino respondents with Latino interviewers); (2) culturally appropriate behavior or social norms (e.g., not wanting to appear as victims of discrimination) may increase the likelihood that respondents may report more biased answers (e.g., decreased reporting of discrimination); and (3) recommendations of other studies that have also included self-report measures of discrimination and health (cf. Krieger et al., 2005). Statistical analyses We first examined percentage of missing data on predictor and outcome variables, which represented less than 2 %, fewer than the recommended 5 % for imputation (Tabachnick & Fidell, 2007). Accordingly, we allowed for listwise deletion in all analyses. Further, to ensure there were no extreme interrelations between our predictor variables, we performed a formal test of multicollinearity. The Variance Inflation Factor for each of the predictors was well below 10 (range = 1.10–2.07), thus we included all selected predictor variables in multivariable models. We estimated negative binomial regression models (NBR) because the variance of the outcome was greater than its mean, and the dispersion parameter (a) had a 95 % confidence interval that did not include zero, indicating overdispersion and that the NBR model is the preferred model for our count variable, as opposed to the Poisson model. We modeled the count variable of chronic conditions by building six NBR models. In Model 1 we entered socioeconomic position indicators (subjective social status, household income, and education). In Model 2 we further added the everyday discrimination variable. To examine effect modification by socioeconomic position, Models 3–5 included the 2-way interactions between discrimination and each socioeconomic position variable separately. Lastly, in Model 6 we added the three cross-product terms

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simultaneously. Continuous variables included in interactions were mean centered to reduce multicollinearity (Aiken & West, 1991). All models adjusted for aforementioned covariates. We formally tested for differences in slopes through the regression coefficients associated with the product term, and applied the Holm test adjustment to control for the probability of a Type I error occurring across the set of contrasts. To aid in interpreting and illustrating significant interactions, we used model coefficients to calculate and plot predicted counts of chronic health conditions. Values of 1 standard deviation below and above the mean discrimination score represent ‘‘low’’ and ‘‘high’’ levels of discrimination, respectively (Aiken & West, 1991). Analyses were conducted using Stata 12 and incorporated the NLAAS weighting and design variables in order to obtain estimates that are representative and that take into account the survey’s complex random sampling method, including clustering and stratification.

Results Descriptive statistics and preliminary analyses Table 1 summarizes the weighted distribution of characteristics by socioeconomic position. Significant overall differences by socioeconomic position were noted across most selected sociodemographic characteristics. Table 1 also displays descriptive statistics of key study variables by socioeconomic position. Briefly, of the total sample, 21.27 % reported having a cardiometabolic condition; 26.11 % reported a respiratory illness; and 38.86 % reported having a pain/somatic condition. Moreover, adjusted Wald tests showed significant overall differences on mean count of chronic conditions by income tertiles and level of education. Only pairwise comparisons for household income tertiles remained significant after the Bonferroni adjustment, with those in the ‘‘middle income’’ tertile reporting significantly more chronic conditions than those in the ‘‘lower income’’ tertile. Further, adjusted Wald tests showed significant overall differences on reports of everyday discrimination across income tertiles and education level (all ps \ 0.001), although only pairwise comparisons by level of education remained significant after the Bonferroni correction, with Latino respondents with 12–15 and C16 years of education reporting significantly higher reports of discrimination (M12–15 years = 14.92, SD = 6.70 and M16 years or more = 14.50, SD = 6.17, respectively) than those with less than a high school degree (M = 13.25, SD = 6.76). We were unable to get reliable estimates by subjective social status as a continuous measure. However, when categorized into ‘‘low SSS’’ thru

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‘‘high SSS,’’ results showed there was an overall significant difference on mean levels of everyday discrimination (F(2, 52) = 2,117.79, p \ .001) and count of chronic conditions (F(2, 52) = 270.85, p \ .001), but pairwise comparisons were non-significant. Appendix 1 (Supplementary Material) shows the intercorrelation matrix between socioeconomic position indicators and everyday discrimination (not with our health measure given correlations are not suitable for count variables). Bivariate correlations between objective and subjective socioeconomic position variables were positive and statistically significant. Further, objective socioeconomic position variables were significantly correlated with everyday discrimination, yet subjective social status did not correlate with everyday discrimination. Negative binomial regression models Results of negative binomial regression models of the count of chronic conditions are reported in Table 2. Model 1 showed that neither subjective social status nor any of two objective socioeconomic position variables were associated with count of chronic conditions. Model 2 added the everyday discrimination measure; these analyses revealed that greater levels of discrimination were associated with a higher count of chronic conditions. Estimates for objective socioeconomic position variables remained virtually the same and nonsignificant. Model 3 added the two-way interaction between everyday discrimination and subjective social status; the interaction term was non-significant, F(1, 53) = 1.25, p = .27. The association between discrimination and count of chronic conditions remained significant. Further, Models 4 and 5 added the interaction terms between everyday discrimination and objective socioeconomic position indicators (i.e., education and household income, respectively). Overall, the interaction for education (in Model 4) was non-significant, F(2, 52) = 1.07, p = .35. In contrast, Model 5 revealed a significant moderating effect of household income on the relation between everyday discrimination and count of chronic conditions, F (2, 52) = 4.70, p \ .05. Finally, Model 6 simultaneously included the three cross-product terms for everyday discrimination by socioeconomic position variables. Results from this model revealed that neither subjective social status nor education was significantly associated with count of chronic conditions, nor did they moderate the relation between everyday discrimination and chronic conditions. However, household income remained a consistent moderator, F(2, 52) = 4.37, p \ .05, even after controlling for sociodemographic factors and social desirability (see Fig. 1).

J Behav Med Table 1 Selected sociodemographic characteristics and descriptive statistics by subjective and objective socioeconomic position Subjective social status

Household income tertiles

(n = 2,521)

Lowest (n = 864) % or M

% or M Age (in years) Ethnicity Cuban Puerto Rican Mexican Other Latinos Employment Employed Unemployed/OLF Marital status Married/ cohabitating W/D/S Never married Language of interview English Spanish Years in the US Less than 5 years US-born/5 years and over Insurance status No Yes Household size Everyday discrimination Number of chronic conditions

p

Middle (n = 840) % or M a

N/A

Highest level of education completed Highest (n = 850) % or M b

39.15 (17.73)

36.82 (13.60)

38.06 (13.03)

3.74 8.92 63.22 24.12

4.27 8.67 58.33 28.73

5.94 12.66 47.90 33.49

.152 5.68 5.60 5.37 5.66

(4.35) (2.60) (1.55) (1.79)

% % % %

% % % %

% % % %

0.005 5.58 (1.97) 4.93 (2.10)

44.19 % 55.81 %

68.69 % 31.31 %

77.49 % 22.51 %

5.58 (1.90)

49.84 %

68.45 %

75.01 %

5.33 (2.55) 5.34 (1.84)

21.44 % 28.72 %

13.70 % 17.85 %

7.81 % 17.18 %

0.000

.000 5.82 (1.72) 5.20 (2.17)

35.10 % 64.90 %

41.12 % 58.88 %

64.91 % 35.09 %

14.94 % 85.06 %

10.01 % 89.99 %

3.61 % 96.39 %

N/A

a

C16 years (n = 360) % or M b

15.85 18.35 3.02 13.62

11.91 21.44 3.06 14.16

6.70 25.75 3.11 14.69

(15.68)

34.08 (13.08)

39.32 (15.63)

% % % %

5.45 12.46 49.25 32.84

11.39 10.61 39.94 38.05

% %

70.69 % 29.31 %

74.26 % 25.74 %

%

61.12 %

63.54 %

16.81 % 15.66 %

12.11 % 26.77 %

14.66 % 21.80 %

% % % %

1.35 (1.60)a

% % (1.64) (7.02)

1.09 (1.40)b

% % (1.64) (6.49)

1.20 (1.47)

0.000

0.000

0.000 25.60 % 74.40 %

62.08 % 37.92 %

69.63 % 30.37 %

12.55 % 87.45 %

6.94 % 93.06 %

9.03 % 90.97 %

12.95 32.69 2.97 14.92

1.62 8.69 2.50 14.50

0.001

.000

19.90 24.15 .560 3.29 .000 13.25 .000

.000 .000

% % % %

.000 % % (1.77) (6.81)

p a

0.000

.000 5.02(1.91) 5.75(1.95) N/A N/A

.036 41.05 .007 2.20 7.42 68.14 22.23 0.000 52.77 47.23 0.000 67.53

12–15 years (n = 1,200) % or M

.000

0.048 5.13 (2.39) 5.54 (1.93)

p

B11 years (n = 994) % or M

% % (1.71)a (6.76)a

1.30 (1.51)

% % (1.61)b (6.70)b

1.14 (1.39)

% % (1.57)c (6.17)b

1.27 (1.83)

.000 .000 .000

W/D/S widowed/divorced/or separated, OLF out of labor force. N/A = Not applicable to get reliable estimates because both measures are continuous and results into many categories. The Rao-Scott statistic for the Pearson Chi square test for contingency tables was computed for categorical variables. Adjusted Wald tests of differences were computed for continuous variables. Values in parentheses are standard deviations. Groups with different superscripts differed significantly from each other after the Bonferroni correction was applied. Data are weighted

Additionally, post hoc simple slope analyses revealed that at high levels of discrimination, Latinos in the middleincome tertile (compared to those in the lowest income tertile) showed significantly higher predicted counts of chronic conditions (F(1, 53) = 4.56, p \ .05), and marginally significant increased rate for number of chronic conditions at moderate levels of discrimination (F(1, 53) = 3.39, p = .07). There were no significant differences in predicted counts of chronic conditions between individuals in the high income tertile and those in the low and middle income tertiles across levels of everyday discrimination (all ps [ 0.05). Moreover, we conducted supplementary logistic regression analyses to examine the sensitivity of our results

when chronic conditions were separated by type of chronic condition. Results for the model including discrimination and socioeconomic position variables (Model 2) showed that higher levels of everyday discrimination were associated with a significantly increased risk of cardiometabolic conditions (OR = 1.03, 95 % CI = 1.01, 1.06); respiratory conditions (OR = 1.02; 95 % CI = 1.00, 1.04); and pain/somatic conditions (OR = 1.03; 95 % CI = 1.01, 1.05). Once the interaction terms were included in the models, none of the interaction terms between socioeconomic position variables and discrimination were statistically significant for any of the chronic conditions. However, somewhat similar to the results for total count of

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J Behav Med Table 2 Negative binomial regressions of chronic condition count on subjective social status, income, education, and discrimination Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Estimate SE

Estimate SE

Estimate SE

Estimate SE

Estimate SE

Estimate SE

Main effects Subjective social status in US

-0.01

0.01 -0.01

0.01 -0.01

0.01 -0.01

0.01 -0.01

0.01 -0.01

0.01

Education level (years) B11

Ref

12–15

-0.03

0.08 -0.03

Ref

0.08 -0.04

Ref

0.08 -0.03

Ref

0.08 -0.02

Ref

0.08 -0.03

Ref 0.08

C16

-0.09

0.11 -0.09

0.12 -0.10

0.11 -0.10

0.11 -0.08

0.11 -0.10

0.11

Household income tertiles Lowest

Ref

Middle

-0.08

0.06 -0.09

0.06 -0.09

0.06 -0.09

0.06 -0.09

0.06 -0.08

Highest

-0.09

0.05 -0.10

0.05 -0.10

0.05 -0.09

0.05 -0.10

0.10 -0.09

0.06

–

–

0.02*** 0.00 0.02*** 0.00 0.02 

0.01 0.01

0.01 0.00

0.01

–

–

–

–

–

0.00

Everyday discrimination Interactions Subjective social status in US 9 discrimination

Ref

Ref

–

0.00

Ref

0.00 –

Ref

–

Ref

0.00

0.05

Education level 9 discrimination B11 years 9 discrimination

–

–

–

–

–

–

Ref

–

–

Ref

12–15 years 9 discrimination

–

–

–

–

–

–

0.01

0.01 –

–

0.01

0.01

C 16 years 9 discrimination

–

–

–

–

–

–

0.02

0.02 –

–

0.01

0.01

Household income tertiles 9 discrimination Lowest 9 discrimination

–

–

–

–

–

–

–

–

Ref

Middle 9 discrimination

–

–

–

–

–

–

–

–

0.03**

0.01 0.03**

0.01

Highest 9 discrimination

–

–

–

–

–

–

–

–

0.02*

0.01 0.02 

0.01

Constant

-0.83

-0.97

-0.99

-1.32

-1.02

-0.96

Ref

All models adjusted for gender, Latino ethnicity, age, household size, years in the US, language of interview, marital status, work status, health insurance coverage, and social desirability. Coefficients represent unstandardized values. Estimates are weighted. Count of chronic conditions (0–10)  

p \ .10; * p \ .05; ** p \ .01; *** p \ .001

chronic conditions, the slope for respondents in the middle income tertile was marginally significantly different from those in the low income tertile for the pain/somatic model (F(1, 53) = 3.91, p = .053).

Discussion

Fig. 1 Predicted number of chronic conditions as a function of selfreported everyday discrimination and household income. Note Slope for respondents in the middle income tertile differed from slope of respondents in the lowest income tertile at p \ .05 after Holm test correction

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Results demonstrated that among a national sample of Latinos, everyday discrimination has a significant impact on count of chronic health conditions, net of social desirability and sociodemographic factors. Our findings corroborate previous research: discrimination is crosssectionally associated with greater counts of chronic conditions (Finch et al., 2001; Gee et al., 2007) and with specific chronic conditions, including cardiometabolic, pain/somatic, and respiratory-related conditions (Pasco and Smart Richman 2009). They are also in line with conceptualizations of everyday discrimination as a form of social stress that racial/ethnic minorities experience dispropor-

J Behav Med

tionately, and which therefore may contribute unevenly to risk of ill health among people of color (Clark et al., 1999; Harrell, 2000). However, results from our moderation analyses provide additional insight as to how this relationship varies across the socioeconomic spectrum. Household income When the interactive effects of discrimination and socioeconomic position were considered, analyses showed that household income modified the discrimination and chronic health condition relation, such that the effects of discrimination on physical health were most detrimental for those with medium levels of income, partially paralleling findings reported by Hudson et al. (2012). Interestingly however, Latinos across income levels did not differ on predicted counts of chronic conditions at low levels of discrimination. Instead, for Latinos in the medium income tertile, the predicted count of chronic conditions was significantly higher (compared to Latinos in the lowest income tertile) at high levels of discrimination, and was marginally significantly higher for those in the highincome tertile. One potential explanation for these findings is that perhaps as Latinos ‘‘move up’’ the socioeconomic ladder, their threshold for tolerating discrimination decreases. Drawing from a life span theory of control, which posits that individuals strive for primary control when confronted with challenges (Heckhausen & Schulz, 1995), it is plausible that greater intolerance to repeated exposure to discrimination may have negative health costs, especially if these individuals feel they are unable to control or stop these experiences from occurring (Ruggiero & Taylor, 1995). At the same time, consistent with the diminished returns hypothesis, perhaps Latinos experience status incongruence, such that as they gain increases in income, their expectations for a higher standard of living may also increase (Dressler, 1996), yet may be confronted with frustrations with disproportionate unfair treatment, barriers and/or diminished returns, which in combination may prevent them from successfully attaining expected goals (Cole & Omari, 2003). This may be more pronounced for those in the ‘‘medium’’ income bracket (and not necessarily for those already in the high income bracket), who may feel they still have ‘‘ways to go’’ to reach a high socioeconomic status, whereas those already in the high income bracket may not necessarily encounter. Against this backdrop, we speculate that the juxtaposition of being in a relatively higher income bracket and yet routinely discriminated against, in conjunction with inability to meet expectations or discrepancy between one’s income level and current lifestyle perhaps results in a host of cognitive, emotional, and psychological responses that when cumulative and

prolonged, may result in poor health. Given that a trend was apparent for those in the high-income tertile, it may also be that we did not have sufficient power to detect significant associations for this group compared to those in the low-income tertile. Nonetheless, altogether, higher levels of income could be a necessary, although certainly not sufficient, condition for deriving health benefits, especially when confronted with uncontrollable, chronic social stressors. Conversely, the findings for Latinos in the lowest income bracket can be framed within the shift-and-persist hypothesis (Chen & Miller, 2012), which postulates that lower socioeconomic status does not always translate into poorer health, given that individuals in such positions may utilize coping strategies that buffer them from adverse health effects when faced with multiple adversity (e.g., low social status, discrimination). That is, it is theorized that individuals of low socioeconomic status develop a ‘‘shiftand-persist’’ strategy in response to stressful environmental situations, whereby they positively reframe appraisals to stressful situations (through emotion regulation) in tandem with persisting through life with optimism and meaning. It is posited that together, shift-and-persist strategies may both help reduce the emotional impact that uncontrollable situations may have on these individuals, and the biological responses that could potentially lead to chronic health conditions (Chen & Miller, 2012). Education level In contrast to results for income, we did not find support for effect modification of education, although we found a trend for a stronger association between discrimination and chronic conditions for those with higher levels of education before including the interaction for income. Our findings are somewhat consistent with previous studies that find the education gradient on health (more so than the one based on income) appears to be less consistent among Latinos (Braveman et al., 2010; Kimbro et al., 2008). At the same time, it may be that the moderating effects of education are dependent on both the type of discrimination and health outcome measured. For example, Hudson et al. (2012) found that education moderated the relation between major discrimination and depression, but not for everyday discrimination. Perhaps highly educated individuals have a more concrete understanding of the racial/ethnic patterning of inequalities, such as the structural barriers that exist for people of color as they try and achieve upward social mobility through educational achievement. In turn, for highly educated people of color, interpersonal discrimination may still be stressful and come to affect their physical health, but perhaps not directly or to the same extent as structural discrimination, since it may not necessarily

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create blocked opportunities that systematically prevent them from ‘‘getting ahead.’’ Indeed, as Cole and Omari (2003) noted, both the social as well as the structural aspects of social class may frame the experience of upward social mobility. Subjective social status Although we found that subjective social status was not directly associated with count of chronic conditions, these findings are similar to those of other studies that show that after controlling for objective socioeconomic position, subjective social status is not associated with physical health measures among Latino groups (Franzini & Fernandez-Esquer, 2006; Ostrove et al., 2000). At the same time, subjective social status did not modify the relationship between discrimination and chronic health conditions. Because no other study to date has examined the moderating role of subjective social status on the discriminationphysical health relation, these findings should be taken as tentative. Thus, drawing from social psychological theories of social comparison and relative deprivation, which suggest individuals who perceive themselves as relatively deprived or discriminated against tend to compare themselves to individuals who are more similar to them (Crocker et al., 1998; Fiske, 2010), we conducted a set of supplementary analyses (data not shown) using ‘‘other people in one’s community’’ as the reference group measure. The results did not appear to be driven by differences in the reference group used for comparison. Interestingly however, a recent study found that subjective social status mediated the effects of everyday discrimination on selfrated physical health among Latinos (Molina et al., 2013), suggesting that rather than having stress buffering or exacerbating effects on physical health, a lower subjective social status that may result from higher levels of discrimination may serve as a mechanism through which discrimination contributes to poor health among Latinos. Study limitations and future directions Despite that our study had several strengths, our results should be interpreted in light of a number of limitations. First, we relied on self-report measures of chronic conditions, which restrict common variance. Moreover, there may be individuals who have undiagnosed chronic conditions; for example, prior studies have found that a high proportion of Latinos and younger individuals with diabetes have gone undiagnosed with the disease (Boltri et al., 2005). Thus, future studies should also include objective measures of health, including reports of medication use and biological assessments.

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Second, the data utilized were cross-sectional; thus it is not clear whether discrimination or socioeconomic position precede or follow our health outcome. For example, it may be that persons with poor health are more likely to perceive they experience more discrimination or that they have a lower social status. Likewise, it is possible that poorer health is associated with lower socioeconomic position, perhaps as a function of the greater likelihood of ill persons being unemployed or out of the labor force, rather than the converse. Findings from past longitudinal studies, however, have shown that discrimination (Pavalko et al., 2003) and socioeconomic position (Braveman et al., 2005) are prospectively associated with health-related outcomes. Nevertheless, prospective studies are necessary to establish the causal direction and dynamic relations between variables of interest. Third and relatedly, the NLAAS was conducted in 2002–2003. Reports of discrimination and perceptions of social status measured during this time frame may possibly differ if they were assessed during the current sociopolitical context. It will therefore be important, in future research, to examine the stability of reports of discrimination and changes in socioeconomic position across time and contexts. Likewise, qualitative studies may provide a unique methodological avenue for research in this area, as this approach can be used to tap into the phenomenological experience of discrimination and socioeconomic position at different stages of a respondent’s life. Fourth, our study did not account for different types of discrimination, although past work (Hudson et al., 2012) has shown different sources of discrimination relate differently to health outcomes. Considering ethnic and language-based, structural (e.g., racial segregation), as well as domain-specific discrimination (e.g., workplace discrimination) may provide greater insight into the specific contexts and conditions under which discrimination may be most detrimental to health. Similarly, although the construct validity of the EDS scale has been established for use with Latinos and has been found to be a useful tool for assessing discrimination among this group, we do note that a psychometric study of the EDS found that if an item for accent-based discrimination were included, discrimination reports for Latinos would be higher than without the item (cf. Reeve et al., 2011). Further despite the use of different indicators of socioeconomic position in our study—our operationalization of the construct is limited, since socioeconomic position is argued to be multidimensional, encompassing multiple related indicators (Krieger et al., 1997). Finally, although we examined within-group variation across the socioeconomic spectrum, we were unable to explore intersections with other social identities (e.g., nativity, ethnic subgroup). Given the racial heterogeneity that characterizes the Latino population, future research

J Behav Med

should account for this type of demographic diversity. Aside from these concerns being beyond the scope of our study, we also either would not have had sufficient cases within each category to estimate regression coefficients reliably, or the data were unavailable (e.g., race). Lastly, there are arguably many ways in which individuals of different social locations cope with multiple forms of adversity, which we were unable to account for in our study. Bearing this in mind, future studies should target resilience factors (e.g., optimism), resources (e.g., support networks), and coping mechanisms and strategies (e.g., positive reappraisal, emotion regulation) that Latinos of different socioeconomic positions tap into and/or employ that enable them to weather discriminatory experiences, and that help reduce or mitigate vulnerability to deleterious effects. This may better help us understand what contributes to the health advantage of Latinos (and other racial/ ethnic minorities) of low socioeconomic position relative to their counterparts with higher levels of human capital, and may be an important approach for improving the health of persons who appear most at risk of the pernicious effects associated with greater exposure to discrimination (cf. Chen, 2012).

Conclusion Despite the aforementioned limitations, our study contributes to the burgeoning literature on discrimination and socioecononomic position on ethnic minority health more generally, and Latino health more specifically. In addition to replicating prior studies that find discrimination is inversely associated with poor health outcomes, the most important conclusion to be drawn from this study is the importance of accounting for how experiences of everyday discrimination interact with socioeconomic position to shape the health of Latinos in complex ways. Indeed, a central conclusion from our findings is that a higher socioeconomic position may not necessarily buffer Latinos against experiencing discrimination or its potential toxic effects. Our more detailed description of the interplay between discrimination and socioeconomic position helps to explain some of the health paradoxes associated with socioeconomic position among Latinos. Moreover, our findings point to the significance of simultaneous consideration of different markers of socioeconomic position, as they may have differential independent and/or synergistic effects on health-related outcomes among Latinos (Molina & Alca´ntara, 2013). The rapid growth of the Latino population and their marginalized status in the US offer us a compelling reason for a continued focus on research examining the underlying social determinants of chronic disease among this segment

of the population, which should have significant population health and policy implications. Indeed, greater attention should be paid to the role of public policy in addressing the psychosocial conditions and stresses tied to minority status at the intersection of other social identities (Alegrı´a et al., 2003). Further, it no longer makes sense to treat Latinos as one homogeneous group. Identifying segments of the Latino population that may be at greatest (and lowest) risk of ill health in the context of perceiving being discriminated against may prove useful for understanding Latino health ‘‘paradoxes,’’ and can have implications for responding to group-specific needs and tailoring prevention and intervention efforts to particular segments of the Latino population. In summary, identifying effective ways, at the individual and policy level, of addressing and reducing the different forms of discrimination faced by Latinos and other marginalized groups may prove to be a viable avenue for promoting health. Acknowledgments Kristine M. Molina was partially supported by Grant #T32HL007426 from the National Heart, Lung and Blood Institute and by the NIH Loan Repayment Award (National Institute on Minority Health and Health Disparities Grant # L60MD007288). Yenisleidy Simon was supported by a Praxis Summer Internship grant provided through Smith College’s Lazarus Center for Career Development. We are grateful to Jeanne Miranda, Ph.D. for her helpful comments on a previous draft. Conflict of interest The authors have no conflict of interest to disclose.

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