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. Author manuscript; available in PMC: 2024 May 9.
Published in final edited form as: Drug Alcohol Depend. 2023 May 19;250:109936. doi: 10.1016/j.drugalcdep.2023.109936

Protective factors in the relationship between perceived discrimination and risky drinking among American Indian adolescents

Ying Guo a,*, Randall C Swaim b, W Alex Mason c
PMCID: PMC11081532  NIHMSID: NIHMS1988487  PMID: 37418800

Abstract

Introduction:

The relationship between perceived discrimination and risky drinking among American Indian (AI) youth is understudied, and the potential protective factors that may buffer this association are unknown. Therefore, the objective of this study was to examine protective factors across individual, family, school, peer, and cultural domains of the social ecology that might attenuate the relationship between perceived discrimination and risky drinking among AI adolescents.

Method:

Data were from the Substance Use Among American Indian Youth Study (Swaim and Stanley, 2018, 2021). AI youth who have used alcohol in their lifetime (n = 2516 within 62 schools) had an average age of 15.16 years (SD = 1.75) and 55.5% were female. Five sets of linear regressions were conducted. Risky drinking was regressed on demographic variables, alcohol use frequency, perceived discrimination, one protective factor (religiosity, parental monitoring, peer disapproval of alcohol use, school engagement, and ethnic identity), and one two-way interaction between perceived discrimination and the protective factor.

Results:

Prevalence of risky drinking among lifetime drinkers was 40.1%. There were positive associations between perceived discrimination and risky drinking in all models (Bs range from.20 to.23; p <.001). Parental monitoring had a negative association with risky drinking (B = −0.255, p <.001). Religiosity was the only statistically significant moderator (B = −0.08, p = 0.01), indicating that religiosity weakened the relation between perceived discrimination and risky drinking.

Conclusions:

Religiosity may represent an important protective factor that could help guide efforts to prevent risky drinking in the face of discrimination among AI adolescents.

Keywords: Alcohol use, American Indian, Adolescent, Protective factor, Religiosity

1. Introduction

Perceived discrimination (Schmitt et al., 2014) refers to the recognition and subjective experience of discrimination (e.g., racial discrimination) in the social environment (Todorova et al., 2010; Miller-Roenigk et al., 2021). It is one of the most significant and prevalent stress exposures for American Indian (AI) youth (D’Amico et al., 2021; Whitbeck et al., 2002) and can have a pervasive impact on their mental health and substance use (LaFromboise et al., 2006), including alcohol use. Most prior studies in this area have been conducted with African American and Latino youth and adults (Gilbert and Zemore, 2016); therefore, the association between perceived discrimination and drinking remains understudied among AI youth. Possibly due to the complex consequences of pervasive poverty, systemic racism, and historical trauma (Brave Heart and DeBruyn, 1998; Goodkind et al., 2010; Whitbeck et al., 2002), alcohol involvement is prevalent among AI youth (Schick et al., 2020). Compared to US adolescents nationally, AI adolescents report higher levels of use for many substances, including alcohol (Swaim and Stanley, 2018). Risky drinking behaviors, including drunkenness (Swaim and Stanley, 2018) and heavy episodic drinking (Davis et al., 2019), also are prevalent among AI adolescents.

Studies examining the relationship between perceived discrimination and risky drinking among AI adolescents are few in number and have produced mixed results. Most have reported a statistically significant positive association between perceived discrimination and alcohol-related outcomes, including heavy episodic drinking and the development of an alcohol use disorder (e.g., Cheadle and Whitbeck, 2011; Garrett et al., 2017). For some AI youth, drinking may represent a coping response to the stress elicited by perceived discrimination (Davis et al., 2019; Gilbert and Zemore, 2016; Guo et al., 2021). However, one study reported a statistically non-significant association between perceived discrimination and both alcohol use and heavy drinking (Dickerson et al., 2019). The current study fills the need for research to shed additional light on this important question and improves on the rigor of previous studies by drawing on large, nationally-representative cohorts of AI adolescents.

To the extent that a positive relationship does exist, there is a need to identify potential protective factors (Priest et al., 2013) that may buffer the association between perceived discrimination and risky drinking among AI youth. Numerous studies have examined protective factors against alcohol use among adolescents in general population samples (Connell et al., 2010; Ennett et al., 2008; Hawkins et al., 1992; Trucco et al., 2014; Trucco, 2020; Van Ryzin et al., 2012). Again, however, similar studies among AI adolescents are few in number (e.g., Garrett et al., 2017), although extant studies suggest that AI youth share similar protective (and risk) profiles with other youth (Swaim and Stanley, 2018). Still, prior studies of protective factors against alcohol-related outcomes among AI adolescents have displayed considerable measurement heterogeneity, sometimes examining alcohol use together with other substances (e.g., Whitesell et al., 2014; Yu and Stiffman, 2010), sometimes as one of multiple health outcomes, and sometimes as specific alcohol-related risk behaviors (e.g., driving after drinking, Moilanen et al., 2014; suicide, Allen et al., 2014, 2021). In addition, few studies have used a social ecological framework to examine multiple factors associated with AI adolescent alcohol use (for notable exceptions, see Guo et al., 2021 and Nuño and Herrera, 2020). The current study fills a key gap by examining protective factors across individual, family, school, peer, and cultural domains of the social ecology that might buffer the positive association between perceived discrimination and risky drinking among AI adolescents. The knowledge gained from this study can guide the development of culturally adapted prevention and treatment interventions that build resilience, or positive adaptation in the face of risk factors (Masten, 2019) in this underrepresented and underserved population (Swaim and Stanley, 2018) by identifying factors that reduce or eliminate the positive association between perceived discrimination and risky drinking among AI adolescents.

In the individual domain, studies have shown that religiosity has a statistically significant negative association with adolescents’ alcohol use (e.g., Russell et al., 2020), for example, due to proscriptions against alcohol and drug use. Religiosity is a multidimensional construct represented by beliefs, attitudes, commitments, practices, and behaviors (Kelly et al., 2015; Kendler et al., 2003; Russell et al., 2020; Yeung et al., 2009). It typically has been operationalized as perceived religious importance/salience and religious activity/involvement (e.g., church affiliation; Mason and Spoth, 2011). The few existing studies of the relationship between religiosity and AI alcohol use commonly have emphasized these two aspects of religiosity. Kulis et al. (2012) found a statistically significant negative relationship between Native American church affiliation, which in their study context represented Christian churches, and AI alcohol use. Yu and Stiffman (2007) found that church affiliations functioned as a significant protective factor that moderated associations of peers’ misbehaviors and family members’ substance use problems with alcohol abuse/dependence symptoms among AI adolescents. However, no study has examined religiosity as a moderator of the association between perceived discrimination and risky drinking among AI adolescents.

In the family domain, parental monitoring, which refers to parents’ awareness or knowledge of children’s whereabouts and activities, has a statistically significant negative association with alcohol use (Dishion and McMahon, 1998; Yap et al., 2017; Ryan et al., 2010). This is important because parents play a particularly important role in AI family life and culture (Szlemko et al., 2006). Indeed, parental monitoring has been shown to have negative associations with early-age onset of drinking (Boyd-Ball et al., 2014) and alcohol use (King et al., 2014) among AI adolescents. Parental monitoring has been shown to serve as a protective factor by buffering the influence of depressed mood on alcohol use in a sample of predominantly Caucasian adolescents (Kelly et al., 2017), but tests of parental monitoring as a moderator of the positive association between perceived discrimination and risky drinking among AI adolescents have not been conducted.

In the school domain, school engagement (e.g., bonding) has a negative association with adolescent substance use (LaFromboise et al., 2006), including heavy drinking (Tingey et al., 2016), among AI youth. Similar findings have been reported in studies of adolescents representative of other racial/ethnic groups (Catalano et al., 2004; Henry and Slater, 2007; Henry et al., 2012; Weatherson et al., 2018). School attendance, which is related to school bonding, has been shown to be associated with lower odds of heavy drinking among Apache AI youth (aged 10–19 years) accessing emergency health services (Tingey et al., 2016). Dickens et al. (2012) reported that school bonding is a protective factor that buffers the relationship between peer alcohol use and lifetime alcohol use among AI adolescents, but tests of the extent to which school engagement moderates the positive association between perceived discrimination and risky drinking among AI adolescents have not been conducted.

In the peer domain, having friends who use substances, including alcohol, is a proximal predictor of early onset substance use among all youth (Oetting and Beauvais, 1987), including among AI adolescents (Boyd-Ball et al., 2014). In prior research, peer alcohol use (Dickens et al., 2012) and peer favorable attitudes toward drinking (Szlemko et al., 2006) have been positively associated with alcohol use among AI youth. Friends can also have a salutary influence on adolescents. King et al. (2014) found that perception of peer disapproval of alcohol use was associated with lower alcohol use among AI middle school (7–8th graders) and high school (9–12th graders) students. Peers also can have a moderating influence on other risk factors. For example, Glaser et al. (2010) reported that the positive relationship between conduct problems and alcohol use problems among twins (aged 11–18 years) from the Cardiff Study was only present when youth had close friends who used substances. However, to date, there have been no tests of the degree to which peer disapproval of alcohol use moderates the positive association between perceived discrimination and alcohol involvement among AI youth.

AI youth may benefit from cultural connectedness (e.g., ethnic identity; Henson et al., 2017), which refers to connections with traditional and cultural heritage in the AI community (D’Amico et al., 2021). In the cultural domain, this study focused on ethnic identity, defined as “the degree to which one identifies with a particular ethnicity/culture” (Davis et al., 2019). Previous studies have shown little or no direct relationship (main effect) between ethnic identity and alcohol use among AI adolescents (Bates et al., 1997; Davis et al., 2019; Whitesell et al., 2014). Regarding tests of moderation, one recent study showed that high ethnic identity significantly weakened the relationship between high historical trauma-related thoughts and drug use days but not alcohol use days (Gameon and Skewes, 2021). However, the extent to which ethnic identity moderates the positive association between perceived discrimination and risky drinking is unknown.

In summary, guided by social ecological theory (Bronfenbrenner and Morris, 1998; Lounsbury and Mitchell, 2009), the objective of this study was to examine protective factors across the social ecology (individual, family, school, peer, and cultural domains) that might attenuate the positive relationship between perceived discrimination and risky drinking among AI youth. A series of linear regressions was conducted, examining each protective factor (religiosity, parental monitoring, school engagement, peer disapproval of alcohol use, and ethnic identity) as a potential moderator, while controlling for sex, grade, and alcohol involvement. Based on the reviewed literature, it was expected that each protective factor would significantly attenuate the relationship between discrimination and risky drinking among AI youth.

2. Methods

2.1. Participants and procedures

This study conducted secondary analyses of existing cross-sectional cohort data collected under the Substance Use Among American Indian Youth Study (Stanley and Swaim, 2015; Swaim and Stanley, 2018, 2021), an annual surveillance study of youth living on or near American Indian reservations with schools enrolling at least 20% American Indian students. This study used the data collected from annual in-school, self-report surveys during eight school semesters between the years 2016–2020 on 14,340 students who identify as AI in Grades 7–12 in 6 regions (Northern Plains, Upper Great Lakes, Southeast, Southwest, Oklahoma, California) of the United States. Because our objective was to study the relation between perceived discrimination and risky drinking among alcohol users, the analysis sample represented the subset of AI adolescents who reported ever using alcohol in their lifetime (n = 2516 within 62 schools). AI lifetime alcohol users had an average age of 15.16 years (SD = 1.75) and were 55.5% female.

2.2. Measures

Substance use data were collected using the American Drug and Alcohol Survey (Oetting et al., 1985). Data on social ecological factors were collected using the Prevention Planning Survey (Beauvais and Swaim, 2013). Internal consistency (Cronbach’s Alpha [α]) is reported below for all multi-item scales based on the subsample of 2016–2018 AI adolescent lifetime alcohol users (n=2516).

2.2.1. Alcohol involvement

Alcohol initiation was measured by one question “How many times (if any) have you had any ALCOHOL to drink – more than just a few sips. IN YOUR LIFETIME?,” which was used to select lifetime alcohol users as the analysis sample. Responses (number of times) were collapsed into a dichotomous variable coded 0=never, and 1=one or more times.

Alcohol use was the sum of two items tapping into “How many times (if any) have you had any ALCOHOL to drink – more than just a few sips. DURING THE LAST 12 MONTHS?,” “How many times (if any) have you had any ALCOHOL to drink – more than just a few sips. DURING THE LAST 30 DAYS?,” (response options for the two items: 1=0 times to 7=40 or more times). Items were summed to create an overall scale (α =.83). This scale was used as covariate to adjust for alcohol use frequency in the models.

The outcome variable, risky drinking, was the sum of three items tapping into two drunkenness questions and one heavy episodic drinking question: “How many times (if any) have you gotten DRUNK.DURING THE LAST 12 MONTHS/30 DAYS?,” (Response options for the two items: 1=0 times to 7=40 or more times) and “During the LAST TWO WEEKS, how many times (if any) did you have 5 OR MORE drinks in a row?” (Response options for this item: 1=none to 6=10 or more times). Items were standardized and then summed to create an overall scale (α =.84).

2.2.2. Perceived discrimination

The measure of perceived discrimination was adapted from a perceived discrimination scale validated in a sample of AI early adolescents by Whitbeck et al. (2001), which assessed the frequency of race/ethnicity-based perceived discrimination (Davis et al., 2019). The measure has twelve items with responses recorded on a 4-point Likert scale Items such as, “How often have other kids said something bad or insulting to you because of your race/ethnicity?,” “How often has a store owner, sales clerk, or person working at a place of business treated you in a disrespectful way because of your race/ethnicity?,” “How often have you had a teacher be surprised that you did something really well?,” “How often do you feel school staff members (e.g. secretaries, teachers’ aides) treat you different from non-Native (non-Indian) kids?” (Response options: 1=not at all, 2=not much, 3=some, 1= a lot). The items were summed to create a scale (α =.94).

2.2.3. Protective factors from the Social Ecology

2.2.3.1. Individual domain.

Religiosity was measured by two items “How often do you attend religious services?” (Response options: 1=never, 2=rarely, 3=once or twice a month, 4=about once a week or more), and “How important is religion in your life?” (Response options: 1=not important, 2=a little important, 3=pretty important, 4=very important). Items were standardized and then summed to create a scale (α =.72).

2.2.3.2. Family domain.

Parental monitoring was measured by four questions on a 5-point Likert scale, “My parents know where I am after school,” “When I go out at night, my parents know who I am with,” “When I go out at night my parents know where I am,” “When I go out on weekend nights, I have to be home by a set time.” (Response options: 1=never to 5=always). Items were summed to create a scale (α =.85).

2.2.3.3. Peer domain.

Peer disapproval of alcohol use was measured by two items shared with the same question stem for alcohol and drunkenness on a 4-point Likert scale, “How much would your friends try to stop you from drinking alcohol/getting drunk?” (Response options: 1=not at all, 2=not much, 3=some, 1= a lot). The items were summed to create a scale (α =.97).

2.2.3.4. School domain.

School engagement was measured by four items on a 4-point Likert scale that asked students to “think about the past year in school, how often did you.,” “Enjoy being in school,” “Look forward to going to school,” “Try to do your best work in school,” “Find your school work interesting.” (Response options: 1=never, 2=not often, 3=sometimes, 1= almost always). The items were summed to create a scale (α =.83).

2.2.3.5. Cultural domain.

Ethnic identity was measured by six items that assess the internalization of American Indian Identification based on a scale developed by Oetting et al. (1998) validated with American Indian youth, and used in prior project research (Guo et al., 2021; Swaim and Stanley, 2019). Sample items include “How many of these special activities or traditions does your family have that are based on the American Indian culture,” and “Does your family live by or follow the American Indian way of life.,” The responses were made on a 4-point scale (1=no, 2=not much, 3=some, 4=a lot). The items were summed to create a scale (α =.92).

2.2.4. Demographic covariates

In addition to the measure of alcohol use frequency, covariates included two demographic variables: biological sex (male=1, female=2) and school grade level.

2.3. Analytic strategy

Before conducting the primary analyses, several steps were taken as preliminary data preparation procedures. First, the data was examined for patterns of missingness. Overall, there was 10.7% missing data, and no robust patterns in the associations of various measured variables in relation to missingness. Second, skewness and kurtosis of the variable distributions were checked. Skewness ranged from 0.004 to 2.05, whereas kurtosis ranged from 0.044 to 4.31. The Kolmogorov-Smirnov and Shapiro-Wilk tests of some variables (e.g., risky drinking) were significant in SPSS (version 26), which indicated that the data violated the multivariate normality assumption. Thus, the maximum likelihood-robust (MLR) estimator with robust standard errors was implemented using Mplus version 8.6 (Muthén and Muthén, 1998–2017) for the primary analyses. The MLR estimator is robust to non-normality in the data, ensuring accurate estimation of standard errors, and implements maximum likelihood missing data estimation under the assumption that the data are missing at random (MAR) (Wang and Wang, 2012).

Next, six sets of general linear regressions were conducted, one set for each protective factor. Each set incorporated 2 steps. In the first step, risky drinking was regressed on demographic variables (i.e., sex, grade), the measure of alcohol use, perceived discrimination, one protective factor, and one two-way interaction between perceived discrimination and the protective factor. If there was no evidence of statistically significant moderation, then the subsequent step was to drop the interaction term and rerun the model with only main effects. If there was evidence of statistically significant moderation, then the subsequent step was to probe the interaction at one standard deviation below the mean, at the mean, and one standard deviation above the mean per standard procedures (Aiken and West, 1991). All scales were standardized (i.e., subtract the mean and divide by the standard deviation) prior to creation of the interaction terms. To account for the nesting of students within schools, a school indicator (ULOCATION) that does not include grade was specified as a clustering variable using Mplus’ Cluster option and Type=Complex.

3. Results

Prevalence of risky drinking among lifetime drinkers was 40.1%. Table 1 provides correlations, means, and standard deviations for all variables in the analysis sample of AI lifetime drinkers from Spring 2016 to Spring 2018 during which the perceived discrimination questions were collected. Alcohol use and perceived discrimination had statistically significant positive correlations with risky drinking. Parental monitoring had statistically significant negative correlations with risky drinking. Sex, grade, religiosity, peer disapproval of alcohol use, school engagement, and ethnic identity had statistically non-significant correlations with risky drinking.

Table 1.

Correlation matrix for American Indian lifetime adolescent drinkers (n=2516).

1 2 3 4 5 6 7 8 9 10
1.Sex
2. Grade −.003
3.AU −.01 .20
4.RO .05 .03 −.02
5.PM .12 .01 −.25 .14
6.PDU .06 −.03 −.11 .11 .18
7.SC .05 .08 −.10 .17 .29 .24
8.EI .09 .04 −.01 .35 .16 .12 .22
9.PD −.05 −.01 .13 .10 −.07 .02 −.01 .17
10.RD −.02 .15 .79 −.03 −.28 −.10 −.09 .01 .17
Mean 1.55 9.61 4.66 .006 15.38 4.86 11.36 16.94 20.78 1.21
SD .50 1.69 2.89 1.69 4.37 2.20 3.03 5.58 9.04 3.46

Note. Bold=p<.05; AU=alcohol use; RO=religiosity; PM= parental monitoring; PDU= peer disapproval of alcohol use; SC=school engagement; EI=ethnic identity; PD=perceived discrimination; RD=risky drinking; SD=standard deviation.

4. Models

Estimates from the series of linear regression models are reported in Table 2. Results showed that both alcohol use (Bs = 2.3, p <.001) and perceived discrimination (Bs range from 0.20 to 0.23; p <.001) had statistically significant positive association with risky drinking in each model. The interaction terms in sets 2–5 (examining parental monitoring, peer disapproval of alcohol use, and school engagement, ethnic identity, respectively, as the moderators) were statistically non-significant; therefore, the interaction terms were dropped, and the main effects models were estimated. In set 2, it is noteworthy that parental monitoring had a statistically significant negative association with risky drinking (B = −0.255, p <.001).

Table 2.

Regression models of protective factors, perceived discrimination and risky drinking.

Model Predictors B SE p
Model1: Religiosity Covariates
Main Effects Sex −0.036 0.076 0.642
Grade −0.023 0.035 0.520
Alcohol Use 2.334 0.073 0.000
Predictors
Religiosity −0.012 0.024 0.626
Perceived discrimination 0.231 0.044 0.000
Interaction Effects Religiosity Moderator
Religiosity × Perceived discrimination 0.082 0.032 0.010
Model2: Family Covariates
Main Effects Sex 0.021 0.072 0.775
Grade −0.014 0.033 0.667
Alcohol Use 2.281 0.075 0.000
Predictors
Parental monitoring −0.255 0.057 0.000
Perceived discrimination 0.203 0.040 0.000
Interaction Effects Parental monitoring Moderator
Parental monitoring × Perceived discrimination −0.054 0.054 0.315
Model3: Peer Covariates
Main Effects Sex −0.035 0.079 0.657
Grade −0.023 0.035 0.518
Alcohol Use 2.330 0.073 0.000
Predictors
Peer disapprove of use −0.073 0.044 0.099
Perceived discrimination 0.213 0.040 0.000
Interaction Effects Peer disapprove of use
Moderator
Peer disapprove of use × Perceived discrimination −0.040 0.065 0.540
Model4: School Covariates
Main Effects Sex −0.041 0.079 0.605
Grade −0.021 0.034 0.545
Alcohol Use 2.334 0.074 0.000
Predictors
School engagement −0.028 0.050 0.575
Perceived discrimination 0.211 0.040 0.000
Interaction Effects School engagement Moderator
School engagement × Perceived discrimination 0.019 0.045 0.667
Model5: Ethnic Identity Covariates
Main Effects Sex −0.050 0.082 0.539
Grade −0.024 0.035 0.497
Alcohol Use 2.338 0.073 0.000
Predictors
Ethnic Identity 0.044 0.066 0.502
Perceived discrimination 0.205 0.043 0.000
Interaction Effects Ethnic Identity Moderator
Ethnic Identity × Perceived discrimination −0.088 0.066 0.182

In set 1, examining religiosity as the moderator, the interaction term was statistically significant (B = −0.08, p = 0.01). A plot of the interaction is depicted in Fig. 1. Tests of simple slopes at the mean, one standard deviation below the mean, and one standard deviation above the mean of religiosity were all statistically significant and indicated that higher levels of religiosity progressively weakened the association between perceived discrimination and risky drinking.

Fig. 1.

Fig. 1.

Plot of the interaction of religiosity with perceived discrimination in relation to risky drinking.

5. Discussion

This study used data collected from nationally representative cohorts of AI adolescents to examine protective factors drawn from the individual, family, school, peer, and community domains of the social ecology as moderators that might buffer the positive relationship between discrimination and risky drinking in this underrepresented population. Risky drinking is prevalent among AI adolescents in the U.S., perhaps as a consequence of the complex consequences of pervasive poverty, systemic racism, and historical trauma (Brave Heart and DeBruyn, 1998; Goodkind et al., 2010; Whitbeck et al., 2002). For example, in the current study, drunkenness in the past 30 days and binge drinking in the past two weeks among AI adolescents was 12.8% and 13.5%, respectively, compared to 7.7% and 7%, respectively, among general 8th to 12th graders nationally (Miech et al., 2022). In addition, perceived discrimination is a significant stress exposure for AI youth (D’Amico et al., 2021; Whitbeck, et al., 2002), and our analyses showed it had a statistically significant positive association with risky drinking, as expected. It is possible that some AI adolescents drink as a coping response to the stress elicited by perceived discrimination (Davis et al., 2019; Gilbert and Zemore, 2016; Guo et al., 2021). Further research examining coping processes is needed. Importantly, most prior studies of the relationship between discrimination and adolescent substance use have been conducted with African American and Latino participants (e. g., see review by Gilbert and Zemore, 2016); therefore, this study extends the literature by examining this relationship among AI adolescents.

Furthermore, this is among the first studies to examine both the main and interactive effects of social ecological variables in relation to AI adolescents risky drinking. Variables were selected to represent prominent protective factors within the individual, family, school, peer, and cultural domains of influence. Of note, parental monitoring showed a statistically significant negative relationship with risky drinking. A higher level of parental awareness and knowledge of their children’s whereabouts and activities was associated with a lower level of risky drinking. This finding confirms that parental monitoring is a robust negative predictor of alcohol use among all adolescents (Dishion and McMahon, 1998; Yap et al., 2017; Ryan et al., 2010), including American Indians (Boyd-Ball et al., 2014). AI adolescents may share similar protective (and risk) profiles with general adolescents’ alcohol use. Profiles also may operate differently to some degree due to the unique historical experiences and world of views of American Indians (Szlemko et al., 2006). Here, the emergence of parental monitoring as a robust predictor of risky drinking provides additional confirmation about the important role that parents and family play in the AI culture (Szlemko et al., 2006), which is characterized by respect for elders (e.g., parents) as well as close kinship and community networks that provide opportunities for the effective monitoring and supervision of youth.

In tests of buffering moderation, we found that religiosity functions as a protective factor that attenuates the relationship between perceived discrimination and risky drinking. Previous studies have shown that indicators of religiosity, such as church affiliation, are associated negatively with alcohol use and alcohol-related problems among AI adolescents (Kulis et al., 2012; Yu and Stiffman, 2007). This study extends prior research by further testing religiosity as a moderator in the relationship between discrimination and risky drinking. As one potential explanation for this finding, it is possible that church affiliation and religious activity bolster coping processes within a supportive social environment to help mitigate the adverse effects of discrimination. Our study did not address coping, therefore, this possible explanation needs to be tested in future research.

Neither the peer, school, nor cultural variables had statistically significant main or interactive effects on risky drinking in the adjusted analyses of this study. This is in contrast to other studies showing that some of these factors (e.g., peer influences) are robust predictors of adolescent alcohol use (Nawi et al., 2021). Measurement characteristics across studies may help explain these differences. For example, in this study, peer influence was measured as a prosocial factor (i.e., the degree that friends try to stop adolescents from using alcohol or getting drunk). It is possible that peer risk factors play a stronger role in risky drinking among AI alcohol users and that perceived peer prosociality does not interrupt the negative coping process for participants who had already started using alcohol to cope with discrimination. Likewise, it is possible that AI risky drinkers already have disengaged from prosocial peers as well as from school and potentially protective cultural supports. Of note, in prior research, ethnic identity did not predict alcohol involvement (Bates et al., 1997) or moderate the relationship between historical trauma-related thoughts and alcohol use days among AI adolescents (Gameon and Skewes, 2021). Therefore, ethnic identity may not relate strongly to risky drinking or function as a protective factor under the context of discrimination. These factors likely continue to play important roles in regard to other indicators of health and well-being among AI adolescents.

There are some noteworthy study limitations. The selection of measures was limited to the data on hand. For instance, SES was not included in the data and could not be incorporated into the analyses. Additionally, while religiosity buffered the relationship between perceived discrimination and risky alcohol use, our measure was limited to religious participation and importance. It did not distinguish between various types of religious involvement such as Christianity, the Native American Church, or other traditional forms of spirituality. Also, the measure of parental monitoring was limited to parental knowledge. Other domains of monitoring (e.g., child disclosure), as noted by Stattin and Kerr (2000), may also be informative. Moreover, our cultural measure only assessed individual’s perceived ethnic identification. Also, the school-based recruitment of students does not include school dropouts; therefore, the generalizability of findings is limited to students who are enrolled in school. Finally, data were collected as part of a cross-sectional cohort study, and longitudinal analyses could not be conducted to help establish potential causal relationships or to examine changes in relationships over time.

6. Conclusions

Despite these limitations, there are several strengths to this study, including the important research question, understudied population, nationally representative cohorts of AI adolescents, and social ecological framework. Findings may have implications for practice. Specifically, religiosity may represent a protective factor that builds resilience in AI youth who face discrimination by attenuating the link between perceived discrimination and risky drinking. This knowledge could help guide the development of prevention efforts that are culturally adaptive and accessible to AI adolescents who are at high risk of risky alcohol use.

Funding

The current study conducted secondary analyses of existing, publicly available, de-identified cross-sectional cohort data collected under the Substance Use Among American Indian Youth Study. Funding for the parent study has come from the United States Department of Health and Human Services and the National Institutes of Health, specifically the National Institute on Drug Abuse (R01DA003371). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agency. The funder played no role in the research design, data collection, analysis, or writing and submission process.

Footnotes

Declaration of Competing Interest

The authors have no conflicts of interest to declare.

CRediT authorship contribution statement

Ying Guo and W. Alex Mason contributed to formulation of the research question and conducted the analyses. Randall C. Swaim obtained funding for the collection of the data, assisted with data management, and edited the final manuscript. With support from the grant, all authors provided substantive knowledge and expertize to the study reported herein and contributed to the writing and internal review.

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