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Published in final edited form as: Addict Behav. 2020 Sep 28;113:106682. doi: 10.1016/j.addbeh.2020.106682

Alcohol- and Drug-Related Consequences Across Latent Classes of Substance Use Among American Indian Adolescents

Melissa R Schick 1, Silvi C Goldstein 1, Tessa Nalven 1, Nichea S Spillane 1
PMCID: PMC8392693  NIHMSID: NIHMS1633350  PMID: 33038678

Abstract

Introduction:

Substance use among American Indian (AI) adolescents is a significant public health concern, as they report greater health disparities related to substance use compared to other racial/ethnic groups. The present study examines differences across classes of substance use behaviors regarding alcohol- and drug-related consequences.

Methods:

The current study was a secondary analysis of the dataset used by Stanley & Swaim (2018). AI adolescents (n = 3,498, 47.8% female, Mage = 14.8) completed a survey including substance use and related consequences. Protocols were approved by institutional IRB, tribal authority, school boards, and parental consent/child assent were obtained.

Results:

In line with Stanley & Swaim (2018), we identified four classes of substance use: no past month substance use; marijuana and cigarette use only; alcohol, marijuana, and cigarette use only; and polysubstance use. Cross-class comparisons revealed that adolescents in classes characterized by the use of a greater number of substances also reported experiencing greater alcohol- and drug-related consequences with one exception: the class characterized by marijuana and cigarette use reported greater drug-related consequences compared to the class characterized by alcohol, marijuana, and cigarette use.

Conclusions:

AI adolescents who engage in the use of multiple substances should be provided with psychoeducation around the increased risk of associated negative consequences. Given the health disparity experienced by AI adolescents, interventions to alleviate the burden of negative consequences are necessary.

Keywords: American Indian, adolescent, substance use, alcohol-related consequences, drug-related consequences

1. Introduction

Substance use among American Indian (AI) adolescents represents a major public health concern (Whitbeck, Hoyt, Johnson, & Chen, 2006). Although there is great heterogeneity among groups, AI adolescents have been found to exhibit high rates of use for nearly all substances (Friese, Grube, Seninger, Paschall, & Moore, 2011; Stanley, Harness, Swaim, & Beauvais, 2014; Swaim & Stanley, 2018; Whitbeck et al., 2014) and have an increased likelihood of having used marijuana, inhalants, cigarettes, or having drank alcohol until intoxicated compared to their non-AI peers (Spillane, Treloar Padovano, & Schick, 2019; Stanley & Swaim, 2015a). Research has also found AI adolescents to be more likely to initiate using substances at young ages (Beauvais, 1996; Friese et al., 2011; Henry et al., 2011; Stanley & Swaim, 2015b; Whitesell et al., 2012; Yu & Stiffman, 2007). Furthermore, a strong body of literature finds AI adolescents experience disproportionate substance-related consequences, substance-related mortality and morbidity and increased likelihood of developing a substance-use disorder (Henry et al., 2011; Landen, Roeber, Naimi, Nielsen, & Sewell, 2014; CDC, 2008; Services, 2018; Stanley et al., 2014; Szlemko, Wood, & Thurman, 2006).

Given the clear substance-related health disparity with AI adolescents, it is vital to consider patterns of using two or more substances within this population; however, to date, a dearth of research has examined the use of two or more substances among AI adolescents. To begin to address this gap, Stanley & Swaim (2018) conducted a latent class analysis among a large, population-based sample of AI adolescents on or near reservations in order to examine patterns in endorsement of the use of various substances. They identified subgroups of individuals using “latent classes” —or otherwise unobservable groups— who share similar characteristics and use substances in similar combinations within this population. They found four classes of substance use for AI adolescents: no past month substance use (the largest class); marijuana and cigarette use only; alcohol, marijuana, and cigarette use only; and polysubstance use, defined as use of any other substance in addition to alcohol, marijuana, and cigarettes (the smallest class; Stanley & Swaim, 2018). Understanding these latent classes may provide valuable insight on nuanced substance use and related health disparities within this population.

The work of Stanley & Swaim (2018) is crucial given that little research has examined polysubstance use in this vulnerable population. Polysubstance use among adolescents has distinct harmful substance-related consequences, including greater likelihood of engagement in risky sexual behavior, greater psychological distress (e.g., anxiety, depression), and greater likelihood of not completing school (Connell, Gilreath, & Hansen, 2009; Kelly, Chan, Mason, & Williams, 2015a; Kelly et al., 2015b; Morley, Lynskey, Moran, Borschmann, & Winstock, 2015; Stanley & Swaim, 2018). Although a strong body of literature shows AI adolescents experience disproportionate substance-related consequences (Henry et al., 2011; Landen et al., 2014; Prevention, 2008; IHS, 2018; Stanley et al., 2014; Szlemko et al., 2006), we know very little about how these consequences differ based on patterns of endorsement of the use of various substances, and in particular, how polysubstance use contributes to this health disparity. Understanding patterns of endorsing use of various substances is imperative in order to gain insight into AI adolescent substance-related consequences. Thus, the purpose of the present study is to expand upon the work of Stanley & Swaim (2018) by examining differences in alcohol- and drug-related consequences across the four classes of substance use identified by Stanley & Swaim (2018): no past month substance use; marijuana and cigarette use only; alcohol, marijuana, and cigarette use only; and polysubstance use. Given the clear health disparity among AI adolescents who use substances and experience disproportionate substance-related harm, we hypothesize that adolescents in classes characterized by use of a greater number of substances would also endorse experiencing the greatest alcohol- and other drug-related consequences.

2. Materials and Methods

2.1. Participants and Procedures

Data used for the present study were collected between 2009 and 2013 as part of a larger study examining rates and correlates of substance use among adolescents attending schools on or near AI reservations. Schools were invited to participate if they were on or near an AI reservation and if at least 20% of their student body identified as AI. The schools invited were stratified into six geographic regions in which AIs live based upon the 2000 United States Census (Snipp, 2005), thereby making this sample reasonably representative of non-urban AI youth. Within those regions, appropriate tribal and/or school board authority approval was obtained. Parents were able to opt their children out of participating by contacting the school, and students could decline to participate by leaving their surveys blank. However, less than 1% of children declined to participate or were opted out by their parents. Self-report surveys were administered in classrooms by school staff at 33 identified schools (Stanley et al., 2014). Participants included in the present study were a subsample of 7–12th graders who identified as AI (n = 3,498) drawn from the larger sample of students (N = 5,744, 47.0% female), which included non-AI participants. Four individuals were excluded from further analyses because they were missing data for all substance use variables (remaining n = 3,494).

2.2. Measures

2.2.1. Survey.

Participants were administered the American Drug and Alcohol Survey (ADAS), a well-validated measure of child and adolescent substance use (Oetting, Edwards, & Beauvais, 1985). The ADAS includes questions regarding types, frequencies, and levels of substance use as well as questions assessing consequences related to substance use, normative influences of substance use, outcome expectancies related to substance use, and participant psychosocial characteristics.

Substance use was assessed using the six variables derived from the ADAS used by Stanley & Swaim (2018).

Alcohol use.

Two variables assessed alcohol use: frequency of past month alcohol use and frequency of past month heavy drinking (i.e., consuming five drinks or more in a two-hour period), with the following response options: 0 = 0 times, 1 = 1 – 2 times, and 2 = 3 or more times.

Marijuana use was assessed by one item assessing the frequency of past month marijuana use, with the following response options: 0 = 0 times, 1 = 1 – 2 times, and 2 = 3 or more times.

Inhalant use was assessed by one item assessing the frequency of past month inhalant use, with the following response options: 0 = 0 times, 1 = 1 – 2 times, and 2 = 3 or more times.

Cigarette smoking was assessed with one item assessing the frequency of cigarette smoking, with the following response options: 0 = not at all, 1 = once in a while, 2 = one cigarette or more per day.

Other drug use was assessed with one item assessing the use of any other drugs (i.e., tranquilizers without a prescription, cocaine, crack, other amphetamines, Oxycontin, other narcotics, LSD, ecstasy, heroin, or methamphetamine), with the following response options: 0 = no use of any of these drugs in the last month, and 1 = any use of any of these drugs in the last month.

Alcohol-related consequences were assessed using thirteen items asking whether or not respondents have ever experienced specific consequences as a result of their alcohol use with the following response options: 0 = no, and 1 = 1–2 times, 2 = 3–9 times, and 3 = 10 or more times. Such consequences included fighting with friends or family members, being arrested, and inability to remember what happened while drinking, among others. For the purposes of the present study, item responses were dichotomized such that 0 = have not experienced this problem and 1 = have ever experienced this problem to allow for the creation of a count variable reflecting how many alcohol-related consequences participants have experienced (Range: 0 – 13).

Drug-related consequences were assessed using twelve items asking whether or not respondents have ever experienced specific consequences as a result of their use of drugs other than alcohol with the following response options: 0 = no, and 1 = 1–2 times, 2 = 3–9 times, and 3 = 10 or more times. Examples of consequences included getting into trouble at school and hurting schoolwork, damaging a friendship, and breaking something. For the purposes of the present study, item responses were recoded such that 0 = have not experienced this problem and 1 = have ever experienced this problem to allow for the creation of a count variable reflecting how many drug-related consequences participants have experienced (Range: 0 –12).

2.3. Data Analytic Approach

Following the approach set forth by Stanley & Swaim (2018), we conducted a latent class analysis in Mplus 7 to identify groups of participants with qualitatively distinct patterns of substance use. We accounted for the nested structure of the data, wherein participants (level 1) were nested within communities (level 2) by using a sandwich estimator to adjust standard errors (Muthén & Muthén, 1998; White, 1980); one- through five-class models were analyzed. According to recommended fit indices, the optimal class solution had the lowest Bayesian Information Criterion (BIC) values, lowest sample-size adjusted BIC (SSABIC), an adjusted Lo-Mendell-Rubin (LMR) Likelihood Ratio Test p < .05, higher entropy values, parsimony, and conceptual meaning (DiStefano & Kamphaus, 2006; Nylund et al., 2007a; Nylund et al., 2007b). A model with a 10-point lower BIC value has a 150:1 likelihood of being the better fitting model (Raftery, 1995). Following determination of a final model configuration, cross-class comparisons on demographic variables and alcohol- and drug-related consequences were conducted using the Mplus DCAT and Bolck, Croons, and Hagenaars (BCH) approach for categorical (Lanza et al., 2013) and continuous distal outcomes (Bakk and Vermunt, 2016), respectively.

3. Results

See Table 1 for LCA results. We examined solutions with one- to five-classes and selected the four-class solution as optimal based on established guidelines (DiStefano & Kamphaus, 2006; Nylund et al., 2007a). Although a two-class solution had a higher entropy value and significant LMR Likelihood Ratio Test, we selected the four-class solution on the basis of the SSABIC. The bootstrap likelihood ratio test is a superior method for determining the correct number of classes, but is not available for complex samples (Nylund et al., 2007a). Therefore, we used the SSABIC, which has been found to perform similarly to the bootstrap likelihood ratio test (Tein et al., 2013). We also selected the four-class solution to align with the results reported by Stanley & Swaim (2018). Consistent with the findings of Stanley & Swaim (2018), these four classes were characterized by no past month use (n = 2,079, 59.5%), cigarette and marijuana use (n = 869, 24.9%), cigarette, marijuana, and alcohol use (n = 321, 9.2%), and polysubstance use (n = 225, 6.4%; i.e., use of any other substance in addition to alcohol, marijuana, and cigarettes; Stanley & Swaim, 2018). Demographic characteristics of the full sample and the subsamples comprising each class are presented in Table 2.

Table 1.

Fit Indices of Latent Class Models

Model AIC BIC SSABIC Entropy Lo-Mendell-Rubin Adjusted Likelihood Ratio Test (p)
1 class 37127.53 37207.60 37166.29 - -
2 class 20657.86 20799.52 20726.44 0.80 2873.99 (p < .001)
3 class 20239.83 20455.39 20344.18 0.74 437.56 (p = .13)
4 class 20065.06 20354.53 20205.18 0.75 196.76 (p = .34)
5 class 20039.15 20402.52 20215.05 0.82 49.46 (p = .55)
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Note. AIC = Akaike Information Criteria; BIC = Bayesian Information Criteria; SSABIC = sample size adjusted BIC

Table 2.

Descriptive Information on Demographics and Alcohol- and Drug-Related Consequences

Full Sample (N = 3,494) No Past Month Use (n = 2,079) Cigarette & Marijuana Use (n = 869) Cigarette, Marijuana & Alcohol Use (n = 321) Polysubstance Use (n = 225)
M (SD)
Age 14.76 (1.70) 14.55 (0.17) 14.82 (0.16) 15.52 (0.29) 15.10 (0.23)
Alcohol-related consequences1 2.15 (3.27) 0.79 (0.15) 3.25 (0.24) 4.08 (0.34) 6.02 (0.33)
Drug-related consequences1 1.74 (3.37) 0.88 (0.15) 2.80 (0.28) 2.11 (0.20) 4.16 (0.33)
%
Sex
 Male 48.8% 51.0% 47.9% 42.8% 43.5%
 Female 47.9% 45.8% 48.1% 54.5% 52.0%
Grade in School
 7th grade 22.1% 26.2% 20.6% 11.6% 12.6%
 8th grade 20.8% 20.4% 22.2% 18.2% 22.6%
 9th grade 17.2% 16.6% 19.9% 12.6% 19.5%
 10th grade 14.9% 14.3% 12.9% 22.4% 18.9%
 11th grade 14.5% 13.1% 14.7% 18.7% 18.6%
 12th grade 10.4% 9.5% 10.2% 18.0% 7.4%
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Note.

1

Alcohol- and drug-related consequences reflect a count of how many consequences participants report having experienced in their lifetime (Alcohol: minimum = 0 maximum = 13; Drug: minimum = 0, maximum = 12).

Cross-class comparisons revealed significant differences across classes with respect to alcohol, χ2(3) = 3095.48, p < .001, and drug-related consequences, χ2(3) = 227.41, p < .001. Findings regarding cross-class comparisons are summarized in Table 3. Regarding alcohol-related consequences, significant differences were identified between all classes, with those classes characterized by use of a higher number of substances endorsing the greatest extent of alcohol-related consequences. That is, those in the class characterized by no past month substance use (M = 0.79, SD = 0.15) reported significantly lower alcohol-related consequences compared to those in the class characterized by cigarette and marijuana use (M = 3.25, SD = 0.24; χ2(1) = 153.52, p < .001). Those in the class characterized by cigarette and marijuana use reported significantly lower alcohol-related consequences compared to those in the class characterized by cigarette, marijuana, and alcohol use (M = 4.08, SD = 0.34; χ2(1) = 4.41, p = .04). Finally, those in the class characterized by cigarette, marijuana, and alcohol use reported significantly lower alcohol-related consequences compared to those in the polysubstance use class (M = 6.02, SD = 0.33; χ2(1) = 16.38, p < .001).

Table 3.

Cross-Class Comparisons Regarding Alcohol- and Drug-Related Consequences and Demographic Characteristics

Class 1 vs. 2 Class 1 vs. 3 Class 1 vs. 4 Class 2 vs. 3 Class 2 vs. 4 Class 3 vs. 4
Age χ2 = 10.90** χ2 = 26.13*** χ2 = 7.56** χ2 = 10.86** χ2 = 167 χ2 = 2.45
Alcohol-Related Consequences χ2 = 153.52*** χ2 = 106.78*** χ2 = 476.03*** χ2 = 4.41* χ2 = 52.87*** χ2 = 16.38***
Drug-Related Consequences χ2 = 62.76*** χ2 = 36.48*** χ2 = 95.35*** χ2 = 5.01* χ2 = 9.20** χ2 = 26.95***
Sex
 Male χ2 = 103 χ2 = 4.90* χ2 = 103 χ2 = 107 χ2 = 0.92 χ2 = 3.26
 Female χ2 = 0.54 χ2 = 5.36* χ2 = 2.16 χ2 = 165 χ2 = 0.71 χ2 = 0.17
Grade
 7th grade χ2 = 4.82* χ2 = 36.66*** χ2 = 22.46*** χ2 = 8.06*** χ2 = 6.16* χ2 = 0.07
 8th grade χ2 = 0.63 χ2 = 0.15 χ2 = 0.43 χ2 = 0.36 χ2 = 0.02 χ2 = 0.36
 9th grade χ2 = 2.60 χ2 = 103 χ2 = 0.87 χ2 = 6.22* χ2 = 0.01 χ2 = 2.85
 10th grade χ2 = 0.57 χ2 = 5.07* χ2 = 2.02 χ2 = 6.64* χ2 = 2.94 χ2 = 0.46
 11th grade χ2 = 0.69 χ2 = 4.26* χ2 = 2.96 χ2 = 145 χ2 = 126 χ2 = 0.001
 12th grade χ2 = 0.17 χ2 = 8.40** χ2 = 0.74 χ2 = 6.09* χ2 = 1.06 χ2 = 0.74
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Note: Class 1 = no past month use, Class 2 = cigarette and marijuana use, Class 3 = cigarette, marijuana, and alcohol use, Class 4 = polysubstance use;

*

p < .05,

**

p < .01,

***

p < .001

Regarding drug-related consequences, significant differences were again identified between all classes, though with a slightly different pattern of differences compared to alcohol-related problems. That is, those in the class characterized by no past month substance use (M = 0.88, SD = 0.15) reported significantly lower drug-related consequences compared to those in the class characterized by cigarette, marijuana and alcohol use (M = 2.11, SD = 0.20; χ2(1) = 36.48, p < .001). Those in the class characterized by cigarette, marijuana, and alcohol use reported significantly lower drug-related consequences compared to those in the class characterized by cigarette and marijuana use (M = 2.80, SD = 0.28; χ2(1) = 5.01, p = .03). Finally, those in the class characterized by cigarette and marijuana use reported significantly lower drug-related consequences compared to those in the polysubstance use class (M = 4.16, SD = 0.33; χ2(1) = 9.20, p < .001).

4. Discussion

The present study aimed to expand on the work of Stanley & Swaim (2018) by examining alcohol- and drug-related consequences across latent classes of substance use in a large, population-based sample of American Indian adolescents living on or near reservations. Consistent with our expectations, we found that adolescents in classes characterized by the use of a greater number of substances also reported experiencing greater alcohol- and drug-related consequences with one exception. Specifically, the class characterized by use of cigarette and marijuana use had significantly higher drug-related consequences than did the class characterized by cigarette, marijuana, and alcohol use. Importantly, we also found that the largest identified class was characterized by no past month substance use (59.5% of the total sample). This rate of substance use is similar to nationally representative data from Monitoring the Future, which suggests that approximately one-third of adolescents in the 12th grade report past-month use of any substance (NIDA, 2020). Our finding is important given the context of previous literature regarding disparities in substance use among American Indian youth and in contrast to stereotypes which portray AI youth as using substances at higher rates than their peers (Duran, 2018; Quintero, 2001). Additionally, this result supports previous research that finds high rates of abstinence among AI communities (Beals et al., 2003; Mitchell et al., 2003), but also disproportionately high consequences among those who use substances (Meyer et al., 2013; Spillane et al, 2015). Investigations, such as the present study, are vital in order to more clearly understand the substance-related health disparities experienced by AI communities as an important step to ameliorate those disparities.

Consistent with expectations, our finding that use of a greater number of substances was associated with having experienced a greater number of substance-related consequences is well-supported by literature in non-AI communities, including among adolescents (Briere, Fallu, Descheneaux, & Janosz, 2011; Midanik, Tam, & Weisner, 2007). Perhaps this finding reflects that individuals who use a greater number of substances may be more likely to use simultaneously. Previous research has shown that simultaneous use of multiple substances is associated with more frequent substance use (McCabe, Cranford, Morales, & Young, 2006; Midanik et al., 2007), which may in turn confer greater risk for experiencing consequences of substance use. Further, it is well established that concomitant use of multiple substances confers risk to the individual given potential for synergistic drug interactions which can lead to each substance potentiating the effects of the other (Belgrave et al., 1979; Lamers & Ramaekers, 2001; Robbe, 1998). For example, using alcohol and cocaine together can be much more dangerous than use of either substance alone because cocaine is a central nervous system (CNS) stimulant, which can increase metabolism and cause alcohol to go to the brain more quickly. Further, because cocaine is a CNS stimulant while alcohol is a CNS depressant, each can dampen the subjective effects of the other, leading to increased risk of overdose (Pennings, Leccese, & Wolff, 2002).

Our finding that the class characterized by cigarette and marijuana use reported greater drug-related consequences than did the class characterized by alcohol, cigarette and marijuana use was somewhat surprising. It may be that adolescents who are experiencing negative consequences when using alcohol and other substances concurrently are more likely to attribute those negative consequences to alcohol rather than cigarettes, marijuana, or other substances. Perhaps then, this leads those adolescents to report experiencing greater alcohol-related consequences and fewer drug-related consequences. This finding may be because the subjective effects of alcohol are biphasic in nature, with the second phase being characterized by negative, unpleasant effects (Earleywine & Martin, 1993), and these unpleasant effects may be highly salient given their recency and, perhaps, may be more likely to be remembered because of their unpleasantness. Alternatively, this may reflect differing motivations to use various substances, as well as individual beliefs about the effects of alcohol versus other substances. For instance, marijuana is often perceived as the least risky illicit substance (Gaher & Simons, 2007), and may be more likely to be used to attain a sense of calmness or relaxation (Kristjansson et al., 2012). Further, there are commonly held positive expectancies regarding marijuana use, such as the belief that marijuana can heighten awareness and concentration (Lenné, 2001). Cigarettes may also be seen as more acceptable because it is a form of tobacco, which has traditionally played a role in spirituality and ceremonies (Unger et al., 2006). On the other hand, alcohol may be seen as less acceptable and therefore more likely to cause problems because alcohol was not a traditional part of Indigenous culture, as AI communities did not have distilled, potent forms of alcohol prior to European contact (Beauvais, 1998). Work specifically examining beliefs regarding effects of alcohol and other substance use on driving found that participants endorsed driving following alcohol consumption as significantly higher risk than driving following marijuana use (McCarthy, Lynch, & Pederson, 2007), and that drivers who had been in car accidents following simultaneous use of multiple substances were more likely to attribute the accident to their use of alcohol than other substances (Albery, Strang, Gossop, & Griffiths, 2000). Future work should aim to further understand perception of consequences when using various substances simultaneously versus at different points in time.

While findings of the present study provide important knowledge regarding patterns of substance-related consequences across classes of substance use, they should be considered within the context of the study’s limitations. First, the correlational and cross-sectional nature of the data precludes the ability to examine temporal ordering of variables of interest. For instance, there is no way to ascertain whether substances are being used concurrently, and these types of temporal patterns may contribute to heightened risk of substance-related consequences. Further, while the variables assessing substance use behaviors represent past month use, variables assessing alcohol- and drug-related consequences represent lifetime experiences. It may be that past consequences are influencing current patterns of use, rather than the patterns observed in the current study leading to subsequent consequences. To address each of these concerns, prospective designs should be utilized to explicate these temporal, causal patterns. Next, while the ADAS has good empirical support for assessing adolescent substance use (Wills & Cleary, 1997; Winters, Stinchfield, Henly, & Schwartz, 1990), data are based solely on self-report measures that may underestimate actual rates of substance use and substance-related consequences among adolescents. Next, the nature of school-based samples precludes our ability to examine patterns of substance-related consequences among adolescents who have dropped out of school and are therefore not reflected in this data. It is possible that having dropped out of school represents a severe consequence of substance use. Finally, there may be differences in rates of experiencing negative alcohol- and drug-related by age and sex; these differences should be investigated in future research.

Despite these limitations, findings of the present study underscore the importance of considering patterns of endorsing use of various substances. Adolescents who engage in the use of multiple substances should be provided with psychoeducation around the increased risk of associated negative consequences. Given the pressing health disparity experienced by AI communities, such interventions to alleviate the burden of negative consequences are crucially needed.

Supplementary Material

1

Highlights.

  • Nearly one-third of adolescents reported any past month substance use.

  • Adolescents had experienced an average of 2–3 substance-related consequences.

  • Use of a greater number of substances was associated with greater consequences.

Footnotes

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Declarations of interest: This work was supported by the National Institute on Drug Abuse (NIDA) grant R01DA003371.

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