Testing Relationships Between Racial-Ethnic Identity,Racial-Ethnic Discrimination,and Substance Misuse Among Black and Latinx Older Adults in a Nationally Representative Sample

Abstract

Older adults (aged 55+) comprise a rapidly growing population both in number and racial-ethnic diversity. In recent years, substance misuse prevalence among older adults has increased and is expected to continue rising, highlighting the need to understand risk and protective factors in this population. Using nationally representative data, this study examines the association of racial-ethnic identity and racial-ethnic discrimination with alcohol and illicit drug use among Black and Latinx older adults, and whether racial-ethnic identity moderates the relationship between discrimination and substance misuse. Findings show that among Latinx older adults discrimination is associated with increased substance misuse, and higher ethnic identity is associated with decreased illicit drug use. Higher racial-ethnic identity buffers the effects of discrimination on illicit drug use for Latinx, but not for Black respondents. Findings of this study highlight the complex associations between racial-ethnic identity, discrimination, and substance misuse, varying across racial-ethnic group, age, context, and other factors.

Keywords

substance misuse race/ethnicity discrimination racial-ethnic identity illicit drug use alcohol abuse

Introduction

The current health crisis of COVID-19 has made health disparities across racial groups ever more evident (Tai et al., 2020). An overwhelming amount of research finds a strong significant association between discrimination and negative health outcomes, particularly for mental health and substance use (Carter et al., 2019; Paradies et al., 2015). Stress and coping theory conceptualize substance misuse as a potential mechanism to cope with stressful events, such as experiences of discrimination and financial hardships (Lazarus & Folkman, 1984). Prior research shows that economic crises that lead to greater rates of unemployment increase illicit drug use (Ornell et al., 2020).

The prevalence of illicit drug use, which includes use of illegal drugs and misuse of prescribed drugs, is the highest it has ever been among older adults (Kuerbis, 2020). This trend is expected to continue increasing given the ongoing demographic shifts of the U.S. population. By 2030, older adults are expected to account for more than 20% of the total population, and 28% are expected to be from an ethnoracial minority group, with Latinx and African Americans being the largest non-White groups of older adults (National Academies of Sciences, Engineering, and Medicine, 2016). As the number of older adults increases, so do rates of alcohol and substance misuse (Choi et al., 2016). While alcohol abuse is the most common substance use problem among older adults, illicit drug use among adults aged 50+ is a growing public health concern that has been under reported (Yarnell et al., 2020). The proportion of SUD treatment admissions comprised of older adults increased from 3.4% to 7.0% between 2000 and 2012 (Chhatre et al., 2017), and it was estimated that in 2018, more than 1 million U.S. adults aged 65 and older would have an SUD (SAMHSA, 2019).

The literature indicates that Blacks and Latinx are more likely to suffer negative outcomes associated with substance use than non-Latinx Whites (Carter et al., 2019) and less likely to receive treatment (Priester et al., 2016). Prior research has identified discrimination and structural disadvantage as exacerbators of substance misuse and ethnic identity as both a protective and risk factor (Choi et al., 2017; Jetten et al., 2017). Yet, empirical research examining the role of ethnic identity on discrimination and substance misuse has largely focused on youth (Brittian et al., 2015; Umaña-Taylor et al., 2015)). This study expands this literature by considering the moderating effects of ethnic identity on the association between racial-ethnic discrimination and substance misuse among the growing population of Black and Latinx adults aged 55 and older.

Coping With Racial-Ethnic Discrimination

Discrimination has been primarily defined as a form of unfair treatment of marginalized individuals or groups based on personal characteristics (e.g., race, age, gender, sexual-orientation, etc.) to protect the power and privilege of the dominant group (Landrine & Klonoff, 1996). Racial and ethnic discrimination continues to be one of the most widespread forms of oppression in the United States for minority populations (Carter et al., 2019). A recent prevalence study using data from the National Longitudinal Study of Adolescent to Adult Health found that 25% of Blacks and 20% of Latinx individuals experienced some type of everyday discrimination based on race, ancestry, and/or skin color (Boutwell et al., 2017). Lazarus and Folkman (1984) described illicit drug use as an avoidant coping mechanism that may relieve negative emotions caused by stress and trauma, and that may lead to substance abuse and addiction. Prior literature has found that substance use (illicit drugs, alcohol, and tobacco) is a means for Blacks and Latinxs to manage the negative stress induced by racial discrimination (Broman, 2016; Lee and Ahn, 2012).

The negative effects of discrimination on health have been well documented in the literature. Various systematic reviews have shown that experiencing racial discrimination significantly increases risks for excessive alcohol use and illicit drug use (Gilbert & Zemore, 2016; Lee and Ahn, 2012). Carter and colleagues’ (2019) meta-analytic review of the literature on racial discrimination and health compared the strength of correlations across racial groups and found that racial discrimination was more highly correlated with substance use among Latinx than any other racial-ethnic group. A study of older Puerto Ricans living in Boston found that perceived discrimination was associated with a higher probability of ever having been a drinker and worse psychological and physical health (Todorova et al., 2010). Similarly, a study using a multi-ethnic sample of adults 45–84 years of age found that for Latinx participants perceived discrimination was associated with greater odds of reporting heavy drinking. The same study found that Blacks who reported discrimination had increased odds of reporting smoking and drinking compared to those who did not report discrimination (Borrell et al., 2010). Gilbert and Zemore’s (2016) systematic review of literature measuring the association between experiences of racial-ethnic discrimination and alcohol abuse, found that anger, post-traumatic stress symptoms, and depressive symptoms were identified as the mechanism by which racial discrimination led to alcohol abuse or dependence.

Racial-Ethnic Identity

Phinney and Ong (2007) define ethnic identity as a multidimensional construct that involves aspects of both personal and group identities that is shaped over time and is context dependent. Ethnic identity theory describes how social and political contexts can support or diminish one’s sense of ethnic identity and lead to both negative and positive outcomes (Jetten et al., 2017; Phinney and Ong 2007). In addition, scholars have identified various dimensions of racial-ethnic identity. Umaña-Taylor and colleagues (2014) organizes these under content and process dimensions. Content dimensions include salience (importance of one’s race/ethnicity in a situation), centrality (importance of race/ethnicity to one’s whole identity), public regard (how others view one’s racial-ethnic identity), ideology (beliefs and values about one’s race/ethnicity), and affirmation (racial-ethnic pride). Process dimensions include exploration (learning about racial-ethnic background) and resolution (meaning making regarding one’s race/ethnicity) (Sellers et al., 1997; Umaña-Taylor et al., 2014). The multifaceted nature of racial-ethnic identity implies that, depending on multiple factors, individuals can have different responses to discrimination that can sometimes be helpful or maladaptive (Schwartz et al., 2009).

Research finds that different dimensions of racial-ethnic identity can affect experiences of racial discrimination to different extents (Brittian et al., 2015; Schwartz et al., 2009; Umaña-Taylor et al., 2015). However, these studies have largely been done with youth and college students, because ethnic identity has been primarily studied using identity development theory, which describes adolescence and young adulthood as a prominent time for identity exploration and formation (Marcia, 2002). Findings from these studies also lend evidence to the variability of racial-ethnic identity across racial and ethnic groups, age groups, dimension of racial-ethnic identity, and source of discrimination.

A study by Umaña-Taylor and colleagues (2015) found that greater levels of racial-ethnic identity resolution in the context of more discrimination by peers led to less externalizing problems among Latinx middle and high school students, but the reverse was found when the source of discrimination was teachers. On the other hand, one study found that Latinx college students who perceived greater racial-ethnic discrimination against the Latinx population also reported higher levels of ethnic exploration (Brittian et al., 2015) which has been associated with increased feelings of anxiety, depression, and impulsivity (Schwartz et al., 2009). Studies have also found that the effect of racial-ethnic identity on health outcomes and behaviors may be significant for one racial group and not for another. For example, Brittain et al. (2015) found that for Latinx, and not for Black college students, racial-ethnic identity affirmation mediated the association between discrimination and mental health, while Caldwell and colleagues (2004) found that feeling more positively about one’s racial group (affirmation) was associated with less reported alcohol use among Black youth.

In addition, these studies show that that the process of racial-ethnic identity formation is not linear. Developmental psychology theories have begun to describe the process of identity development as it continues to change and adapt throughout the life-course (Marcia, 2002). Among the limited literature examining ethnic identity among older adults, Yip and colleagues (2008) found that for adults 51 and over, a stronger sense of ethnic identity was associated with more psychological distress from discrimination, but a protective effect was found for those between the ages of 41–50. The authors explain this difference through life span research showing that in middle age individuals have more stability and are more effective in coping with stress and emotional regulation. After the age of 50, however, a restructuring of goals and priorities occurs centering on minimizing negative interactions and maximizing happiness, and therefore an experience of discrimination may provoke a stronger negative reaction as it threatens happiness. To our knowledge, this is the only study that examined ethnic identity in the context of racial discrimination among older adults but is limited by its focus on only one race and use of a single item to measure ethnic identity.

More recently Woo and colleagues (2019) examined the moderating role of ethnic identity on psychological well-being among a racially diverse sample of adults 18 and older using data from the National Epidemiological Survey on Alcohol and Related Conditions-III (NESARC-III). Results showed that ethnic identity was protective for some racial groups (Asians and Blacks) but not for others (Whites, American Indians/Alaska Natives, and Latinx). Differences across age groups were not examined, however. Similarly, prior literature consistently shows that the effect of racial-ethnic identity on the relationship between discrimination and health outcomes is not the same for all racial-ethnic groups, prompting us to compare differences by race/ethnicity. Among the many scales that have been developed and tested to measure racial-ethnic identity, this study uses the eight-item instrument that is included in the NESARC-III survey, which is adapted from other measures and has been used in prior studies (e.g., Woo et al., 2019). This scale adequately captures the principal elements of ethnic identity (e.g. self-identification as a group member, values and attitudes about one’s group, meaning about oneself as a group member, commitment to racial-ethnic group, and ethnic behaviors and practices) (Phinney & Ong, 2007).

Our limited knowledge concerning the role of racial-ethnic identity in moderating the negative health effects of discrimination on substance misuse among older adults is particularly relevant today given the rapid changes in the demographic makeup of the U.S. population. As the discourse on racial-ethnic inequality comes to the forefront of American politics, it will be essential to understand how, for whom, and in what contexts racial-ethnic identity moderates the negative effects of discrimination on health behaviors. Although previous research has explored these relationships among the broader U.S. population, this is the first known study to focus on older adults and substance misuse. Results from this study have implications for practice and future research on protective and risk factors for substance misuse among Black and Latinx older adults.

The Present Study

Given the growth in the U.S. of adults aged 55 and older, particularly minority older adults, coupled with increasing rates of illicit drug use and alcohol misuse, this study aims to explore associations of discrimination and ethnic identity with illicit drug use and excessive alcohol use among older adults. This study uses data from the NESARC-III to examine the impact of racial-ethnic discrimination and racial-ethnic identity on substance misuse among Latinx and Black older adults, and whether racial-ethnic identity buffers against or exacerbates the deleterious effect of racial-ethnic discrimination on alcohol and substance use. Building upon prior research on the impact of racial-ethnic identity on health, this study aims to:

Examine whether racial-ethnic discrimination is associated with illicit drug use and excessive alcohol use among Latinx and Black older adults.

Examine whether racial-ethnic identity is associated with illicit drug use and excessive alcohol use among Latinx and Black older adults.

Determine if racial-ethnic identity moderates the association of discrimination with illicit drug use and excessive alcohol use among Latinx and Black older adults.

Methods

Data and Sample

This study uses data from the third wave of NESARC-III. The survey was conducted in 2012 and 2013 by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) using a cross-sectional design with a nationally representative sample of the civilian noninstitutionalized population of the United States aged 18 years and older. Multistage probability sampling was used; primary sampling units consisted of individual counties or groups of contiguous counties and secondary sampling units were groups of census-defined blocks. The third and fourth sampling stages consisted of households within secondary units and randomly selected household members, respectively. The overall survey response rate was 60.1%, resulting in a randomly selected sample of 36,309. Data were adjusted for nonresponse and oversampling, and weights were applied so the sample reflected the U.S. adult population as a whole. A detailed description of the sample and survey design is provided by Grant and colleagues (2015).

The sample used for this study includes 3,194 adults aged 55 and older, who reported their race/ethnicity as Latinx (n = 1,242) or non-Latinx Black (n = 1,952), and for whom complete data on covariates used in the analysis were available. Racial-ethnic categories were mutually exclusive and individuals who reported Latinx ethnicity were coded as Latinx regardless of their reported race. We excluded White respondents for several reasons. First this population skewed our sample because the number of White participants was disproportionately greater than Latinx and Blacks and of higher socioeconomic status (SES) than the other racial groups in the sample. Second, our focus is on racial-ethnic discrimination (i.e., racism) which rests on power differentials where the dominant group (Whites) enacts racism onto the oppressed groups (non-Whites). In addition, we excluded Asian and Native American respondents because the number of older adults in these racial-ethnic groups who reported illicit drug use was too small for our analyses. A total of 97 (3.0%) respondents were excluded due to missing data on one or more variables used in the analysis, with the greater amount of missing data in our primary variables.

Measures

The NIAAA Alcohol Use Disorder and Associated Disability Interview Schedule (AUDADIS-5; Grant et al., 2015) was used as the interview guide for the NESARC-III. This semi-structured instrument contains 18 sections that cover background and demographic information, alcohol and drug use, mental health and substance use disorders, and other areas. The measures used in this study are detailed below.

Illicit drug use

The AUDADIS-5 contains items to assess illicit drug use as well as diagnoses according to DSM-5 criteria. Illicit drug use measures include items that assess past 12-month use for the following substance categories: sedatives/tranquilizers, painkillers (e.g., oxycodone, morphine, other opioids), marijuana, cocaine/crack, stimulants (e.g., amphetamines, methamphetamines), club drugs (e.g., MDMA, ecstasy, GHB), hallucinogens/psychedelics (e.g., LSD, PCP), inhalants/solvents (e.g., nitrous oxide, cleaning fluid), heroin, and an “other” drugs category for any substances not captured by the previous categories. For this study, illicit drug use was defined as any use of illicit substances in the past 12 months, which includes use of illegal drugs as well as misuse of prescription drugs. We define any use of illicit drugs as the outcome, because even infrequent use may expose individuals to harms such as legal and health consequences. For each of the substances listed above, respondents were asked to report use of “drugs that you may have used on your own—that is, either without a doctor’s prescription; in greater amounts, more often, or longer than prescribed; or for a reason other than a doctor said you should use them” (Grant et al., 2015). Respondents who reported any use of illicit drugs in the past year were coded as 1 and those who reported no use of any substances in the past year were coded as 0. Individuals with missing data for any substances who did not indicate use of any substances were excluded from the analysis.

Excessive alcohol use

The NESARC-III includes a dichotomous variable indicating past 12-month alcohol use that exceeds NIAAA low-risk drinking guidelines (NIAAA, 2020). We focus on excessive alcohol use because prior studies have demonstrated that alcohol use below the thresholds defined by NIAAA (2020) are unlikely to cause harm. Respondents were positive for excessive alcohol use if they exceeded weekly limits (no more than 14 drinks for men and seven for women or individuals aged 65 or older) or daily limits (no more than four drinks in a day for men and three for women or individuals aged 65 and older). Weekly limits were calculated by averaging daily ethanol intake (standardized across different types of alcohol based on ethanol content), and exceeding daily limits was measured with items measuring frequency of consumption of more than four drinks in a day for men and three for women or individuals 65 and older.

Racial-ethnic discrimination

Discrimination due to respondents’ race/ethnicity in the past 12 months was assessed in the NESARC-III using six items from the AUDADIS-5 that were adapted from the Experiences with Discrimination scale (Ruan et al., 2008). All items referred to discrimination experienced because of one’s race or ethnicity and therefore do not capture discrimination based on other personal characteristics (e.g., age, sexual orientation). Sample items include “During the past 12 months, about how often did you experience discrimination in public, like on the street, in stores, or in restaurants, because of your race or ethnicity?” and “During the past 12 months, about how often were you called a racist name because of your race or ethnicity?” Separate questions were used for Latinx and Black respondents, but these only differed in that the questions administered to Latinx respondents specified discrimination due to Latinx ethnicity. Cronbach’s α for the scales administered to Latinx and Black respondents were .85 and .81, respectively. A 5-point Likert-type response scale with options ranging from Never to Very Often was used. The mean of the six items was calculated among individuals who responded to at least five items; those who responded to four or fewer items were excluded from the analysis. Higher scores on the scale correspond to a greater degree of racial-ethnic discrimination.

Racial-ethnic identity

Racial-ethnic identity was assessed with eight items included in the NESARC-III (Ruan et al., 2008). This scale was designed to measure race-ethnic identification (centrality), pride (affirmation), shared values, attitudes, and behaviors (ideology), importance of racial-ethnic heritage (salience), and the role it plays in interactions with others (public regard) (Ruan et al., 2008). The items included “You identify with other people from your race/ethnic group” and “You are proud of your race/ethnic group.” Separate scales were administered for Latinx and Black respondents, but these differed only in that the scale administered to Latinx respondents specifically referred to Latinx ethnicity. Cronbach’s Alpha for the scales administered to Latinx and Black respondents were .93 and .80, respectively. A 6-point Likert response format was used, ranging from Strongly Disagree to Strongly Agree. The mean of the eight items was calculated among individuals who responded to at least seven items; those who responded to six or fewer items were excluded from the analysis. Higher scores reflect a greater degree of racial-ethnic identification.

Control Variables

We included a set of control variables to adjust for individual characteristics associated with alcohol and substance use. Sociodemographic characteristics adjusted for in models included gender, age, marital status, (married or cohabitating; widowed, divorced, or separated; never married), and education level (less than high school, high school or GED, some college, college degree or higher). A dichotomous variable representing employment or other occupation was created, with a value of 1 for respondents who reported full- or part-time employment or school attendance, full-time homemaker, or some other occupation. We included a categorical variable for household income, divided into quartiles based on the U.S. household income distribution in 2012, the same year as the survey (Elwell, 2014).

To control for respondents’ health status, which can affect alcohol and drug use, we included a single item indicating perceived health status as well as a comorbidity index comprised of 32 items. The single item asked, “In general, would you say your health is excellent, very good, good, fair or poor?” and we dichotomized the five responses to create a variable with a value of 1 indicating good, very good, or excellent health. The comorbidity index captures 32 self-reported medical conditions experienced in the past 12 months, such as diabetes, hypertension, high cholesterol, liver disease, and others included in the AUDADIS-5 instrument (Grant et al., 2015). Items were summed to create a continuous index representing the number of medical conditions endorsed. Social support was measured using the 12-item Interpersonal Support Evaluation List (Cohen et al., 1985) included in the AUDADIS-5. Sample items include “There is someone I can turn to for advice about handling problems with my family” and “If I were stranded 10 miles from home, someone I know would come and get me.” A 4-point Likert-type scale with responses ranging from Definitely False to Definitely True was used, with higher values representing greater levels of social support. The 12 items had an adequate internal consistency (α = .82). A mean was calculated for the scale for individuals who responded to at least 10 items; individuals who answered nine or fewer items were excluded from the analysis. Higher scores indicate greater levels of social support.

Presence of a mental health diagnosis was included as a control in this analysis. The following diagnoses were assessed as part of the AUDADIS-5: major depressive disorder, dysthymia, bipolar I disorder, specific phobia, social phobia, panic disorder, agoraphobia, generalized anxiety disorder, post-traumatic stress disorder, anorexia nervosa, bulimia nervosa, binge-eating disorder, schizotypal personality disorder, borderline personality disorder, conduct disorder, and antisocial personality disorder. For schizotypal and borderline personality disorders, at least two distress/social-occupational dysfunction criteria had to be met. For conduct disorder or antisocial personality disorder, at least one distress/social-occupational dysfunction criteria had to be met. Respondents who met criteria for any of the above diagnoses were coded as 1 while those who did not were coded as 0.

Analysis

Analyses were conducted using Stata/MP 15.0. Stata’s svy function and the subpop option account for the complex survey design and to generate the appropriate variance and standard error estimates. In the first step, weighted means, frequencies, and 95% confidence intervals were calculated for all continuous and categorical variables included in subsequent analyses, both for the full sample and subsamples comprised of Latinx and Black respondents. Next, a series of binary logistic regression models were fit to test the association of ethnic identity and racial-ethnic discrimination with illicit drug use and excessive alcohol use. Models were fit separately for Latinx and Black respondents to examine whether these associations differ between the two groups. Variables were added to models hierarchically; for each of the two dependent variables (illicit drug use and excessive alcohol use), we initially only included ethnic identity and discrimination as predictors, followed by a second set of models that adjusted for control variables. For our aim to examine whether ethnic identity moderates the association of discrimination with illicit drug use and excessive alcohol use, we fit models that included interactions between these variables. Results of logistic regression models are shown as odds ratios (OR) and 95% confidence intervals for these estimates. All study procedures were approved with exempt status by the Rutgers University Institutional Review Board and the research team entered into a data use agreement with the NIAAA for permission to use the data.

Results

Characteristics of the full sample and subsamples comprised of Latinx and Black individuals are presented in Table 1. Weighted means or percentages are shown and reflect the population of U.S. older adults who meet inclusion criteria for the study. Approximately 8.2% of the study population used illicit substances in the past year, with the most frequently used substances being painkillers (4.2%), marijuana (3.7%), and sedatives (1.4%). Among those who used any illicit substances in the past year, 24.5% used more than one substance. Approximately one-fifth of the population consumed alcohol in excess of NIAAA recommendations in the past year. Mean scores on the discrimination and ethnic identity scales were 1.3 and 5.1, respectively. The study population was 46.9% Latinx, 53.1% Black, 56.5% female, and averaged 65.2 years old. The average value on the social support scale was 3.2 and 31.5% of the population had less than a high school education. The population averaged 2.8 medical comorbidities (of 32) and 62.1% reported good, very good, or excellent health. Approximately 40% were employed or otherwise occupied and 21.8% met criteria for a mental health disorder. Approximately half of the population was married or cohabitating and 45.6% had household incomes below $24,000.

Table 1.

Sample Characteristics.

	Weighted % or mean
Variable	Latinx(N = 1,242)	Non-Latinx Black(N = 1,952)	Total(N = 3,194)
Past 12-Month Substance Use	5.6	10.5	8.2
Past 12-Month Excessive Alcohol Use	20.1	18.3	19.1
Discrimination Mean	1.2	1.3	1.3
Ethnic Identity Mean	5.0	5.1	5.1
Ethnicity
Black	—	100.0	53.1
Latinx	100.0	—	46.9
Female	55.0	57.7	56.5
Age Mean	65.2	65.2	65.2
Social Support Mean	3.2	3.3	3.2
Education Level
Less than HS	38.8	25.1	31.5
HS Diploma/GED	26.7	28.9	27.9
Some College	19.4	28.4	24.2
College or Higher	15.1	17.6	16.4
Medical Comorbidities Mean	2.7	3.0	2.8
Good Health	63.3	61.1	62.1
Marital Status
Married/Cohabitating	56.2	42.4	48.9
Widowed/Divorced/Separated	35.9	44.7	40.6
Never Married	7.9	12.9	10.5
Employed/Occupied	45.7	34.8	39.9
Mental Health Diagnosis	22.0	21.6	21.8
Household income
Less than $24,000	42.8	48.0	45.6
$24,000–$49,999	29.3	26.7	27.9
$50,000–$89,999	16.8	17.6	17.3
$90,000 or more	11.2	7.7	9.3

Note: Values are weighted to reflect the full population of 13,798,732 Americans meeting study criteria.

Table 2 shows logistic regression models of illicit drug use on ethnic identity and discrimination. In models without adjustment for control variables for both Latinx (Model 1a) and Black older adults (Model 2a), higher levels of discrimination were associated with higher odds of illicit drug use, and greater ethnic identity was associated with lower odds of illicit drug use, but the associations were stronger for Latinx older adults. When control variables were added in Model 1b and Model 2b, the effects of discrimination and ethnic identity on illicit drug use remained for Latinx older adults, but not Black older adults. For Latinx older adults, a unit increase in discrimination was associated with twice the odds in illicit drug use (OR = 2.01, 95% CI = 1.31–3.09), and a unit increase in ethnic identity was associated with a 33% decrease in the odds of illicit drug use (OR = .67, 95% CI = .55–.83).

Table 2.

Logistic Regression Models of Illicit Drug use on Ethnic Identity and Discrimination.

	Latinx				Non-Latinx Black
	Model 1a		Model 1b		Model 2a		Model 2b
Variable	OR	(95% CI)	OR	(95% CI)	OR	(95% CI)	OR	(95% CI)
Discrimination	2.49***	(1.79–3.45)	2.01**	(1.31–3.09)	1.60**	(1.20-2.13)	1.24	(.91–1.69)
Ethnic Identity	.64**	(.50-.82)	.67***	(.55-.83)	.74*	(.57-.95)	.81	(.61–1.07)
Female			.45**	(.25–.80)			.60*	(.38–.94)
Age			.96*	(.93–.99)			.94***	(.92–.97)
Social Support			.67	(.44–1.02)			.91	(.69–1.19)
Education Level
HS Diploma/GED			3.07**	(1.59–5.92)			1.12	(.69–1.83)
Some College			2.42*	(1.05–5.57)			1.62	(1.04–2.51)
College or Higher			1.40	(.49–4.03)			1.41	(.77–2.58)
(Less than HS)
Medical Comorbidities			1.07	(.91–1.25)			1.06	(.97–1.16)
Good Health			.50	(.24–1.06)			.82	(.54–1.26)
Marital Status
Widow/Divorced/Separated			1.76*	(1.05–2.95)			1.02	(.62–1.70)
Never Married			.97	(.40–2.38)			.50*	(.27–.93)
(Married/Cohabitating)
Employed/Occupied			1.18	(.69–2.01)			.52**	(.33–.84)
Mental Health Diagnosis			1.55	(.71–3.42)			1.48	(.94–2.34)
Household income
$24,000–$49,999			.78	(.42–1.45)			.71	(.45–1.10)
$50,000–$89,999			1.25	(.58–2.69)			.43*	(.21–.91)
$90,000 or more			1.38	(.47–4.08)			.27*	(.08–.92)
(Less than $24,000)

Note: Reference category is in parentheses for categorical variables.

* p < .05. ** p < .01. *** p < .001.

Table 3 shows logistic regression models of excessive alcohol use on ethnic identity and discrimination. In models without adjustment for control variables for both Latinx (Model 3a) and Black older adults (Model 4a), higher levels of discrimination, but not ethnic identity, were associated with greater odds of excessive alcohol use. When adjusting for control variables in Model 3b and Model 4b, the association between discrimination and excessive alcohol use remained for Latinx but not for Black older adults. For Latinx older adults, when controlling for other variables in the model, a unit increase in discrimination was associated with 46% greater odds of past-year excessive alcohol use (OR = 1.46, 95% CI = 1.20–1.78).

Table 3.

Logistic Regression Models of Excessive Alcohol use on Ethnic Identity and Discrimination.

	Latinx				Non-Latinx Black
	Model 3a		Model 3b		Model 4a		Model 4b
Variable	OR	(95% CI)	OR	(95% CI)	OR	(95% CI)	OR	(95% CI)
Discrimination	1.60**	(1.23–2.10)	1.43*	(1.03 –1.96)	1.46***	(1.20 –1.78)	1.19	(.94 –1.51)
Ethnic Identity	.89	(.76 –1.04)	.92	(.75 –1.12)	1.02	(.82 –1.27)	1.06	(.85 –1.33)
Female			.30***	(.21–.43)			.35***	(.27–.47)
Age			.97**	(.95–.99)			.93***	(.91–.95)
Social Support			1.27	(.98 –1.65)			.93	(.77 –1.14)
Education Level
HS Diploma/GED			1.33	(.84–2.10)			.85	(.60 –1.21)
Some College			1.13	(.70 –1.83)			.85	(.53 –1.37)
College or Higher			1.09	(.62 –1.93)			.65	(.36 –1.19)
(Less than HS)
Medical Comorbidities			1.01	(.95 –1.08)			.97	(.91 –1.03)
Good Health			1.26	(.80 –1.99)			1.05	(.72 –1.53)
Marital Status
Widow/Divorced/Separated			.95	(.68 –1.33)			1.44	(.98–2.10)
Never Married			1.12	(.61–2.07)			1.34	(.88–2.06)
(Married/Cohabitating)
Employed/Occupied			.91	(.63 –1.31)			.97	(.68 –1.38)
Mental Health Diagnosis			1.40	(.50–2.01)			1.50*	(1.08–2.07)
Household income
$24,000–$49,999			1.19	(.82 –1.74)			1.25	(.88 –1.78)
$50,000–$89,999			1.04	(.65 –1.67)			1.00	(.60 –1.65)
$90,000 or more			1.01	(.50–2.01)			.94	(.49 –1.77)
(Less than $24,000)

Note: Reference category is in parentheses for categorical variables.

* p < .05. ** p < .01. *** p < .001.

Table 4 shows models that add terms for the interaction between ethnic identity and discrimination to the previous fully-adjusted models. The full set of control variables are included in these models but are not shown in the table. Model 1c shows that for Latinx older adults, ethnic identity moderates the association of discrimination with illicit drug use, such that higher ethnic identity reduces the positive association between discrimination and illicit drug use (OR = .68, 95% CI = .47–.99). Put differently, each additional point on the 6-point racial-ethnic identity scale reduces the effect of discrimination on illicit drug use by a factor of 0.68. We found no significant interaction between ethnic identity and discrimination for excessive alcohol use among Latinx older adults (Model 3c), or for illicit drug use (Model 2c) or excessive alcohol use (Model 4c) among Black older adults.

Table 4.

Logistic Regression Models of Illicit Drug Use and Excessive Alcohol Use on Ethnic Identity and Discrimination, With Interactions.

	Illicit Drug Use		Excessive Alcohol Use
Variable	Model 1cLatinx	Model 2cNon-Latinx Black	Model 3cLatinx	Model 4cNon-Latinx Black
Discrimination	12.95* (1.96–85.39)	.34 (.04–2.66)	3.27 (.56–19.17)	.83 (.12–5.70)
Ethnic Identity	1.09 (.67 –1.79)	.56 (.30 –1.06)	1.11 (.69 –1.77)	.96 (.55 –1.67)
Discrimination x Ethnic Identity	.68* (.47–.99)	1.29 (.86 –1.92)	.85 (.60 –1.21)	1.07 (.74 –1.56)

Note: Models control for all variables in Table 2 and Table 3 (i.e., gender, age, social support, education level, medical comorbidities, self-reported health status, marital status, employment, mental health diagnosis, and household income). Values in table are odds ratios and (95% CIs).

Discussion and Limitations

Racial-ethnic identity has traditionally been studied among adolescent and young adult populations (Umaña-Taylor et al., 2014). The present study uses a nationally representative sample of older adults to expand the literature on racial-ethnic identity in later life. This study examines the association of racial-ethnic discrimination with illicit drug use and excessive alcohol use and the moderating effect of racial-ethnic identity among Latinx and Black older adults in the US. No study to date has examined this moderating effect among different racial-ethnic groups of adults aged 55 and older. Guided by theory on racial-identity which explains that the multifaceted nature of this construct can have a positive and negative moderating effect on discrimination we hypothesized that ethnic identity would be a potential moderator on the effects of discrimination on excessive drug use among older adults (Schwartz et al., 2009). In addition to ethnic-racial identity theory, ample literature also affirmed that the moderation effect behaves differently across racial groups. Therefore, we hypothesized that the moderating effects would be different between Latinx and Black older adults.

Results from this study show that discrimination has a negative effect for Latinx older adults’ substance misuse, but no significant effect was found for Black older adults. These findings are consistent with prior research that shows a stronger correlation between racial-ethnic discrimination and substance use for Latinx than any other racial-ethnic group (Carter et al., 2019). While studies examining this association among Blacks has been mixed, with some studies showing a relationship and others showing no relationship (Borrell et al., 2010). Although we did not find a significant association of discrimination with illicit substance use among Black older adults in fully-adjusted models, this may partially reflect power constraints given the sample size and multiple covariates included in models. Indeed, there was a strong association between discrimination and illicit substance use in the unadjusted model.

The second aim of our study was to examine the association between racial-ethnic identity, illicit drug use and excessive alcohol use among Latinx and Black older adults. Our results show that only for Latinx older adults was this association significant. Finally, our study is the first to test if racial-ethnic identity moderates the association of racial-ethnic discrimination with illicit drug use and excessive alcohol use among Latinx and Black older adults. Findings from this study show that racial-ethnic identity is a significant protective factor in reducing the negative impact of racial-ethnic discrimination on illicit drug use and excessive alcohol use among Latinx older adults. However, no moderating effect was found for Black older adults. This finding is supported by prior literature that has found variability in the moderating effect of racial-ethnic identity across racial-ethnic groups. An earlier study using NESARC-III data did find support for the exacerbating effects of ethnic identity for some racial groups, but also found it was a protective factor for others (Woo et al., 2019). Woo and colleagues’ (2019) study found that for Asians and Blacks ethnic identity was found to be a protective factor, with a stronger effect among U.S born individuals, but not for Whites, American Indians/Alaska Natives, and Latinx. Though our results show a different trend, it is important to note that their study did not focus on older adults or substance misuse. This provides further evidence of the contextual nature of ethnic identity that has been found in previous literature (Acevedo-Polakovich et al., 2014; Umaña-Taylor et al., 2015).

Racial-ethnic identity theory has also discussed how social, political, and economic position of the group in society and the developmental life stage of the individual may influence the moderation (Acevedo-Polakovich et al., 2014; Umaña-Taylor et al., 2015). Our sample of older adults tended to have higher levels of education and the Latinx population was primarily of Mexican and Caribbean heritage. More education has been associated with increased experiences of everyday discrimination among Latinx and African Americans and worse health outcomes among minority older adults (Assari et al., 2019). However, previous literature on discrimination among Latinx has discussed a strong sense of ethnic identity as a coping mechanism that provides psychological and social resources to draw from in the event of discrimination (Neblett et al., 2012). Though Latinx are a heterogenous group, census surveys show that they also find identity within the broader concept of Latinx/Hispanic. For example, a Pew Research Center (2015) survey of 2,438 Latinx adults age 18 and older found that 67% of all survey respondents identified with Latinx as part of their racial-ethnic background in addition to their separate racial-ethnic groups. Some literature shows that Latinx culture is family-oriented and collectivistic (Min & Barrio, 2009). Therefore, it is not surprising to find that older adults who identify with a collectivistic culture described as having “affiliative emotional bonds, traditions, rules, and expectations of mutual obligation” (Min & Barrio, 2009, p. 227) experience a protective effect that attenuates the effects of discrimination on illicit drug use and problematic alcohol use. These results affirm previous findings from research with Latinx older adults that show evidence that ethnic identity affirmation is associated with fewer depressive symptoms (Chavez-Korell et al., 2014).

Several limitations should be considered when interpreting the findings of this study. First, our study excluded respondents from non-White racial-ethnic groups other than Black and Latinx, due to statistical power issues given the small number of older adults reporting illicit drug use in other groups. As our study shows, the roles of ethnic identity, discrimination, and their interaction differ according to racial-ethnic background, and these relationships should be explored in the racial-ethnic groups not included in the present study. In addition, while the scale we used for racial-ethnic identity captures all content dimensions as described by Umaña-Taylor and colleagues (2014) it does not measure process dimensions of exploration and resolution. These dimensions of ethnic-identity have been found to potentially ameliorate and exacerbate the effects of stressful experiences, such as discrimination (Brittian et al., 2015; Umaña-Taylor et al., 2015).

Furthermore, findings may not be generalizable to specific substances or differing substance use severity because dichotomous outcome variables were used given low rates of substance misuse in the study population. The potential for omitted variable bias may impact the results of this study. In particular, respondents’ geographic location was not available in the NESARC-III dataset, and it would be informative to assess whether the relationships explored here differed by state or region. Although the data were weighted to reflect the U.S. population, weights did not incorporate certain important variables such as income and health, and it is likely that older adults with lower income, worse health, and other characteristics are underrepresented. The cross-sectional design of this study precludes making causal claims about the relationships explored in this study. Although our findings show associations between ethnic identity, discrimination, race/ethnicity, and substance misuse, causality cannot be determined since each construct was measured in the same time period and experimental or quasi-experimental procedures were not used to rule out confounding factors.

Conclusion

Despite these limitations, the current study makes substantial contributions to the literature on racial-ethnic discrimination and racial-ethnic identity as they relate to substance use among older adults. As the older adult population continues to grow both in relative size and racial-ethnic diversity, with a concurrent rise in illicit drug use among this population, it is important for policy and program developers to be aware of the risk and protective factors for illicit drug use and other health outcomes. Our findings show that racial-ethnic identity may serve as a buffer against the harmful effects of racial-ethnic discrimination on illicit drug use for Latinx older adults. Additionally, results provide further evidence of the damaging effects of discrimination on illicit drug use and alcohol abuse and imply that policymakers and program administrators should take steps to eliminate racial-ethnic discrimination. Furthermore, given that this study is the first to explore the interaction effect of racial-ethnic identity and discrimination across racial-ethnic groups of older adults and in the context of substance use literature, more longitudinal studies are needed to develop a deeper knowledge of the effects of these complex relationships and their relevance for older adults’ well-being.

These findings have implications in the wake of COVID-19 and the economic crisis that is underway, which is affecting housing, employment, and early education and at higher rates among Black and Latinx families (Tai et al., 2020). Attending to these social issues is essential for closing the growing health inequality gap that has plagued the United States for generations. In addition, the increase in police brutality creates a political climate of anti-Black and anti-Latinx sentiments that produces heightened fear and mistrust of law enforcement (Becerra et al., 2017). Results from this study and previous literature find that as racial-ethnic groups continue to be persecuted and systematically rejected from American society, use of illicit drugs and alcohol use continues to increase (Tai et al, 2020). While racial-ethnic identity presents a potential protective effect, the variability of findings across racial-ethnic groups, age groups, and other factors suggests that our efforts are best spent in developing interventions that address the need to eradicate structural and everyday discrimination through improving access to quality housing, employment, education, and healthcare.

Footnotes

Acknowledgments

This manuscript was prepared using a limited access dataset obtained from the National Institute on Alcohol Abuse and Alcoholism and does not reflect the opinions or views of NIAAA or the U.S. Government.

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Laurent Reyes

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