Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Jan 5.
Published before final editing as: J Ethn Subst Abuse. 2025 Jan 5:1–25. doi: 10.1080/15332640.2024.2446739

Ecodevelopmental Influences on Latent Classes of Substance Use among Urban American Indian Adolescents

Stephen S Kulis a, Justin Jager a, Stephanie L Ayers b, Matt Ignacio b
PMCID: PMC12227693  NIHMSID: NIHMS2044321  PMID: 39755958

Background

Strong relationships between substance use and an array of negative health outcomes for youth are well established. A large body of research has linked substance use among adolescents to bullying, high risk sexual behaviors, lower educational attainment, unintentional injuries and death, substance abuse, and addiction later in life (Chawla & Sarkar, 2019; Eaton et al., 2012; Gage et al., 2022; Morales et al., 2020; Pichel et al., 2022). Compared with other racial and ethnic groups, American Indian (AI) youth are more likely to engage in substance use (Swaim & Stanley, 2018a) and other risky behaviors (Garcia, 2020), like drinking and driving (Pavkov et al., 2010), and risky sexual behaviors (Centers for Disease Control & Prevention, 2019). The damaging consequences related to substance use for AI adolescents include relatively high rates of alcohol-involved motor vehicle-related deaths, alcohol mortality, and violent victimization (Indian Health Service, 2019; Sittner & Hautala, 2016).

However, the majority of research related to AI health disparities has focused on those living on tribal lands (Yuan et al., 2014). Although more than 70% of AIs live off reservations and in cities (Indian Health Service, 2018), urban AIs are often referred to as an “invisible minority” (Rhoades et al., 2005). Knowledge related to risk and protective factors for substance use among urban AIs, particularly for youth, is relatively underrepresented and underdeveloped (Yuan et al., 2014). A distinct set of social determinants exert risk and protective influences on urban AI youth substance use. Compared to their peers living on tribal lands, urban AI youth live in communities where there is great diversity in tribal backgrounds and histories of migration to the city, where inter-marriage across tribes and racial/ethnic groups is common, and where families are spread out geographically rather than clustered in the same neighborhoods (Hartmann et al., 2014; Lobo, 2001; Moran et al., 1999; Stubben, 2001). These differences exacerbate challenges for urban AI youth to stay connected to their tribal cultural heritage, limiting opportunities and support for them to learn and practice their tribal traditions (Brown et al., 2016; Dickerson et al., 2016; Straus & Valentino, 1998). These differences may place urban AI youth at risk for using substances to cope with pressure to fit in within both Native and mainstream cultures (LaFromboise et al., 1990; Schinke et al., 1988; Weaver, 2012), to navigate cultural conflicts within the family and with peers (Garrett & Herring, 2001; Herring, 1997), and deal with the common experience of racial discrimination in their daily lives (D’Amico et al., 2019). Better understanding of the potentially unique protective and risk factors influencing urban AI youth substance use is needed given the complex social and cultural factors shaping their lives.

Previously identified risk and promotive factors for substance use among urban AI youth include an array of family and peer influences. Exposure to substance misuse and access to substances within the home, ineffective parenting practices, poor parent-child communication, and high family conflict are important family level risk factors (Eitle, 2016; Mmari et al., 2010; Silmere & Stiffman, 2006; Swaim & Stanley, 2018b; Yuan et al., 2010). Peer level risks for AI adolescents include friends engaging in delinquent behaviors, and offering, approving of, and encouraging substance use (Heavyrunner-Rioux & Hollist, 2010; King, 2014; Kulis et al., 2006; Novins & Mitchell, 1998; Tingey et al., 2017; Unger et al., 2004). Promotive factors for preventing substance use include a supportive family environment and prosocial peer affiliations (Ayers et al., 2021). However, these risk and promotive factors are not universally associated with substance use for all urban AI youth. The adolescents most at-risk are older, male, and from multiracial/multiethnic AI backgrounds (Ayers, 2021). To further understand the unique multi-level influences on substance use among urban AI youth proposed by ecodevelopmental theory, this study examined school and neighborhood influences as well as family and peer factors, and how these are associated with patterns of use of different types of substances in combination, rather than examining each type of substance separately.

Ecodevelopmental Theoretical Framework

Ecodevelopmental theory provides a comprehensive framework for understanding youth substance use by focusing on multiple, interacting social contexts that influence youth development (Bronfenbrenner, 1977). At the microsystem level, family, peers, schools, and neighborhoods directly influence adolescent behavior and health. Importantly, positive interactions and support between and within these microsystem contexts can result in pro-social, desirable outcomes, and unfortunately, if conflict arises between these levels, negative behaviors can occur, such as substance use (Coatsworth et al., 2002; Szapocznik & Coatsworth, 1999). Other components of the framework include the mesosystem, which is the set of interrelations between the microsystems that influence youth less proximally, such as interactions between parents and the school or parents’ monitoring of peers (Bronfenbrenner, 2009; Rayle et al., 2006). Next, the exosystem is a more distal system where youth do not participate in, but instead, the exosystem directly impacts members of the youth’s microsystem, for example, a parent’s reaction to a stressful work environment (Rayle et al., 2006; Lopez et al., 2010). Finally, the macrosystem consists of broader cultural, social, and structural forces that influence youth, such as residential instability or socioeconomic inequality. In this current study, we investigate the microsystem influences of family, peers, schools, and neighborhood contexts to examine how different interpersonal relationships and community factors play a role in substance use behaviors among urban AI youth.

Role of Family

Families play a key role in influencing AI youth substance use. Permissive parental norms toward alcohol use, offers of substances to youth within the family, as well as parental and sibling modeling of substance use and addiction, all significantly increase the risk for AI youth to initiate, use, and abuse substances (Howard et al., 1999; King et al., 2014; Stanley et al., 2009). AI youth are more likely to use substances if living in families with high levels of conflict, interpersonal violence, and a lack of cohesion (Brockie et al., 2015; Stanley et al., 2009). Factors reflecting problems in family functioning ― lack of stability of the home and family environments, poor parental child-rearing practices, and inadequate parent-child attachment, support, and communication ― increase the risk of substance misuse among AI youth (Woods et al., 2022).

The family can also play a critical role in increasing resilience against substance use. Parental monitoring, family connectedness, warmth, support, and cohesion decrease the likelihood of alcohol initiation, cannabis use, and other illicit drug use and misuse symptomology among AI youth (Allen et al., 2014; Boyd-Ball et al., 2014; Greenfield et al., 2017; Hautala & Sittner, 2018; Mohatt et al., 2014; Moon et al., 2014; Stanley et al., 2009; Turanovic & Pratt, 2017; Urbaeva et al., 2017; Whitesell et al., 2014; Yu & Stiffman, 2010). Importantly, familial anti-drug communication and norms, as well as negative familial attitudes towards and disapproval of substance use are key factors that contribute to protecting AI adolescents from substance use and misuse (Kelley et al., 2019; Stanley et al., 2017; Swaim & Stanley, 2016, 2019; Urbaeva et al. 2017; Whitesell et al., 2014).

Role of Peer Influences

Demonstrated risk factors for substance use among AI youth at the peer microsystem level include having a peer group with pro-substance use social norms, receiving substance use offers from peers, hanging out frequently with deviant peers, and spending excessive unsupervised free time with peers (Cheadle & Sittner Hartshorn, 2012; Cheadle & Whitbeck, 2011; Eitle & Eitle, 2018; Hagler et al., 2017; King et al., 2014; Kulis, et al., 2006; Whitesell et al., 2014; Yu & Stiffman, 2007; Yu et al., 2005). Perceptions that classmates are using substances because it is a “normal” part of adolescence increase the odds of AI substance use (Prince et al., 2017; Stanley et al., 2017). For urban AI youth specifically, associations with anti-social peers mediate the relationship between low self-worth and alcohol-related problems in younger adolescents; while in older adolescents, these associations directly impact alcohol problems later in life (Radin et al., 2006). On the other hand, prosocial peer groups who are less involved in anti-social activity and with stronger anti-drug norms serve as a protective mechanism for adolescents, lowering the likelihood of initiating alcohol, tobacco and other drug use (Kelley et al., 2019; King et al., 2014; Martinez et al, 2015; Greenfield et al., 2017; Whitesell et al., 2014). Among urban AI youth, having a racially heterogeneous supportive peer network is protective against substance use and misuse (Kulis et al., 2016).

Role of School Factors

The school environment also exerts risk and protection on AI youth substance use. Schools with a poor school climate, like having exclusionary discipline practices towards AI and other minoritized students, increase the risk of unhealthy behaviors, including substance misuse (Armenta et al., 2016; Eitle et al., 2017; Enoch & Albaugh, 2017; Sittner, 2015;). Conversely, AI youth who feel a sense of belonging in school exhibit a lower lifetime use of alcohol and cigarettes, less frequent current substance use, and delayed initiation into substance use (Eitle et al., 2017; Friese et al., 2015; Moon et al., 2014; Napoli et al., 2003; Turanovic & Pratt, 2017). AI adolescents who are meaningfully connected to teachers and participate in after-school and in-school activities are at decreased risk for substance use (Friese et al., 2015; Moilanen et al., 2014).

Role of Neighborhood Factors

Research also suggests that urban communities that are impacted by high poverty rates, weaker antidrug norms, the presence of drug sales, greater neighborhood disorganization, high crime rates, and low levels of police enforcement increase the risk of alcohol and illicit drug use for AI youth (Freisthler et al., 2005; Friese et al., 2015; Nalls et al., 2009). One explanation for these connections between neighborhood conditions and AI youth substance use is that residing in a locality that is disorganized, unsafe, and dangerous creates vulnerability and powerlessness within adolescents, leading AI youth to use alcohol and drugs to cope with these feelings (Hautala & Sittner, 2018). However, socioeconomic conditions like neighborhood poverty may not increase vulnerability to substance use for urban AI youth to the same degree or in the same way as for non-Native youth, for multiple reasons (Yabiku et al., 2007). AI families seldom cluster together in an urban neighborhood, and AI youth may be less socially integrated into their surrounding neighborhood than their non-Native counterparts. Urban AI youth may identify more with an AI community dispersed geographically across the city than with their immediate neighborhood. This larger AI community can provide sources or resilience and protection from risk behaviors. Feeling supported by individuals in their community lowers the risk for substance use among AI youth (LaFromboise & Medoff, 2004; LaFromboise et al., 2006)

Considering Substance Use from a Person-centered Approach

Adolescents who engage in substance use rarely use only one substance (Gilreath et al., 2013; Moss et al., 2014); instead, polysubstance use is the norm. Although a majority of adolescent substance users engage in polysubstance use, among these polysubstance users there is considerable heterogeneity in the patterns or specific combinations of substances that are used (Conway et al., 2013; Kulis et al., 2016), and this heterogeneity has important short-term and long-term implications for health and well-being (Moss et al., 2014; Lee et al, 2020). Consequently, this study considers substance use from a person-centered approach and focuses on distinctive patterns of use across multiple types of substances, rather than looking at each substance type discretely via a variable-centered approach, the common approach in prior research with AI youth living in urban areas and on tribal lands. Unlike a variable-centered approach, which assumes population-level homogeneity in the relations between variables, a pattern-centered approach identifies homogenous subgroups of a larger population, distinguishes intragroup differences, and assumes heterogeneity in the data (Nurius & Macy, 2008; Reinke, Herman, Petras, & Ialongo, 2008). Taking a person-centered approach, our prior work employed a latent-class analysis of substance use among urban AI youth that focused on eight types of recent (last 30 day) substance use (Kulis et al., 2016). We found four distinct classes of substance use (Figure 1), with three characterized by some type of polysubstance use. The first class, termed “polysubstance users” (6% of the sample) had the highest rates of recent use of all 8 types of substances, with half or more reporting use of alcohol, tobacco, marijuana, and other illicit drugs, prescription drug and OTC drug misuse, and binge drinking. The second class, termed “gateway” users (17% of the sample), typically reported alcohol, tobacco, and marijuana use but seldom use of other substances. The third class, the “not alcohol” users (4% of the sample), had a pattern of tobacco and marijuana use similar to the “gateway” class, but rarely reported any alcohol use. This “not alcohol” class was more likely than the “gateway” class to report inhalant use, prescription and OTC drug misuse, and other illegal drugs. The last class, “nonusers” (73% of the sample), were youth who rarely or never reported any recent use of any substance.

Figure 1.

Figure 1

Open in a new tab

Proportion reporting recent use of substances (conditional probabilities) by latent class assignment

Overview of the current study

Although prior research has established that micro-level factors described in ecodevelopmental theory (i.e., family, peers, schools and neighborhood), play a role in substance use and abuse for AI youth, few studies demonstrate how they impact urban AI youth specifically. While AI youth residing in urban settings and on tribal lands report similar disparities in behavioral health (Castor et al., 2006), those in urban communities contend with a unique combination of challenges in their cultural and social environments, such as disruptions due to migration, loss of tribal culture, complex multi-tribal and multi-ethnic identities, and access barriers to health and social services designed to serve the non-Native majority. Building on past research, the present study aims to better understand how ecodevelopmental influences shape substance use for urban AI youth, which can inform prevention approaches for their communities and providers. While doing so, we focused on the substance use latent classes we previously identified in order to test how family, peer, school and neighborhood factors predict the substance use latent classes, both at the bivariate and multi-variate level, providing insights into how these contextual factors are associated with patterns of substance use individually, relative to each other, and in combination.

Methods

Sample

The study utilized restricted use data from the 2012 Arizona Youth Survey (AYS), an annual, state-wide, cross-sectional survey of 8th, 10th, and 12th grade students to assess risk and protective factors for substance use and other risk behaviors. The AYS sampling frame includes all public and charter middle schools and high schools in Arizona, and is designed to represent students in the state (Arizona Criminal Justice Commission [ACJC], 2012). Parents provided passive consent for their child to participate in data collection: letters were sent home informing them of the upcoming survey, where to preview the survey online, and how to exclude their child from participating. Students with passive parental consent were asked to provide active assent and complete an anonymous self-administered questionnaire during a regular class period. Assent and survey procedures were conducted by teachers. The total Arizona-wide sample included 62,817 students in 349 schools, accounting for an estimated 94% of eligible enrolled students.

The current analysis includes only students who self-identified as “American Indian or Alaskan Native” on the survey and lived in a metropolitan urban area with over 250,000 residents. We describe the study sample as urban American Indian (AI) youth because it is highly unlikely to include youth who identify as Alaska Native. Only 0.2% of Arizona residents classified as “American Indian or Alaska Native” have Alaska Native tribal backgrounds (U.S. Bureau of the Census, 2010). The restricted use data in the AYS survey included home zip codes of student respondents, which were geocoded in Google Earth and mapped against Arizona urban areas using Census Urban Area Shapefiles. The school zip code was used as a proxy if the home zip code was missing or incomplete. The final analytic sample included 2,292 AI respondents living in zip codes within the defined metropolitan area boundaries.

Measures

Survey items used in the current study included measures of recent substance use and risk and protective factors for substance use, modeled on the Communities That Care survey (Arthur et al., 2002). The phrasing of survey questions and response categories is presented in Supplemental Appendix A. We note that questions referred to ‘marijuana’ rather than ‘cannabis’; accordingly we use the term marijuana throughout.

Recent Substance Use

The questionnaire assessed eight types of last 30 day substance use: (1) alcohol (“beer, wine or hard liquor)”; (2) heavy episodic or binge drinking (“5 or more drinks in a row”); (3) tobacco (“smokeless tobacco” or cigarettes); (4) inhalants (“sniffed glue, …aerosol spray…, or other gases to get high”); (5) marijuana; (6) other illegal drugs (any use of LSD, cocaine, “crack,” methamphetamines, heroin, Ecstasy, “club” drugs, and steroids); (7) prescription drug misuse (narcotics, stimulants, sedatives); and (8) over-the-counter (OTC) drug misuse. The original response categories measured number of occasions (0, 1-2, 3-5, 6-9, 10-19, 20-39, 40+) or days (0, 1-2, 3-5, 6-9, 10-19, 20-29, all 30) of use, with distributions that were highly skewed (skewness coefficients from 3 to 24). Most respondents reported no use, and substance users tended to report infrequent use. Therefore, recent substance use measures were dichotomized into nonuse (0) and any use (1) in the latent class analyses.

Ecodevelopmental Influences

We measured family, peer, school and neighborhood influences with multi-item scales.

Positive Family Influences.

Four scales assessed the presence of a supportive or promotive family environment. Attachment to Mother included four questions asking how connected the adolescent felt to his/her mother in terms of feeling close, sharing thoughts and feelings, enjoying spending time together, and being able to ask the mother about a personal problem (α=.88). A parallel set of questions created Attachment to Father (α=.89). The scale for Positive Parent-Child Communication included two items, whether parents noticed when the adolescent did a good job, and how often they told the adolescent they were proud of something the s/he did (α=.88). Six questions formed the Parental Monitoring scale: whether parents know where the adolescent is and who they are with when away from home; whether parents would know if the adolescent used alcohol or drugs, skipped school, or did not come home on time; and whether the parents have clear rules for the adolescent about substance use (α=.80).

Negative Family Influences (Familial Substance Use).

Two scales measured exposure to permissive family environments regarding substance use attitudes, norms, and behaviors. The Parent Permissive Substance Use Norms scale included three questions, “How wrong do your parents feel it would be for you to …drink beer, wine or hard liquor regularly?…smoke cigarettes?…smoke marijuana?” (α=.78). Family Substance Use Offers assessed whether the adolescent used alcohol or marijuana in the last 30 days that they got from a parent or from another adult family member. A “yes” to any of these questions was coded as (1) Yes, received a substance use offer from a family member; otherwise coded (0) No family offers.

Positive Peer Influences.

Two scales assessed peer relationships promoting pro-social behavior. Pro-social Behavior by Best Friends combined three questions asking how many of the adolescent’s best friends: participate in school clubs, organizations or activities; try to do well in school; and have committed to staying drug-free (α=.58). Peer Approval of Pro-social Behavior combined three questions asking whether the adolescent would be viewed as “cool” if s/he worked hard at school, did volunteer work, or defended a student being verbally abused (α=.72).

Negative Peer Influences (Anti-Social Peer Affiliation).

Three mean scales assessed risky associations with peers. Substance Use by Best Friends was a four-item scale that asked how many best friends had used alcohol, cigarettes, marijuana, and other illegal drugs in the last year (α=.82). A similar set of seven questions created the Anti-social Behavior by Best Friends scale, which included how many best friends had been suspended from school, dropped out of school, been arrested, carried a handgun, sold illegal drugs, stolen a vehicle (or attempted to), or belonged to a gang (α=.81). Peer Approval of Substance Use combined three questions: whether the adolescent would be seen as “cool” if s/he used alcohol regularly, smoked cigarettes, and used marijuana (α=.85).

Positive School Influences.

Three scales gauged the adolescent’s Commitment to and Enjoyment of School, Prosocial Opportunities in School, and Positive Interactions with Teachers. The first included 6 items: how often in the last year the adolescent enjoyed being in school, tried to do their best work, did not hate school, and felt that school work was meaningful, that most courses were interesting, and that what they were learning would be important in later life (α=.79). The scale for prosocial opportunities combined four items: having lots of chances to be part of class discussions/activities, to talk to a teacher one-on-one, and to get involved in extracurricular activities, and feeling safe (α=.67). Two items assessed positive interactions with teachers: how often a teacher noticed when the adolescent did a good job, and gave praise when the adolescent worked hard (α=.70).

Positive Neighborhood Influences.

Two scales measured a positive neighborhood environment. Neighborhood Attachment combined four items: whether the adolescent liked the neighborhood, would miss it if s/he had to move, felt safe in it, and did not want to get out of it (α=.81). Prosocial Neighborhood Involvement combined four items: whether the adolescent feels safe in the neighborhood, and whether neighbors notice when the adolescent does a good job, provide praise when the adolescent does something well, and are available for the adolescent to talk to about something important (α=.89).

Negative Neighborhood Influence.

Four scales assessed negative neighborhood environments. Neighborhood Substance Use Availability combined four items, how easy it would be to get alcohol, cigarettes, marijuana, and illegal drugs (α=.88). Permissive Drug Norms included three items, how accepting are most adults in the neighborhood of adolescent alcohol, cigarette and marijuana use (α=.87). Neighborhood Social Control over Substance Use combined two questions: whether youth used marijuana in the neighborhood and would be caught by police if they drank alcohol (α=.86).

Ecodevelopmental Factor Scores.

To simplify computational demands in estimating final models, and for ease in interpreting results, we conducted a second order principal components factor analysis of the 20 scales described above. Our final analyses employed the resulting seven observed factor scores: positive family influences, positive peer influences, positive school influences, positive neighborhood influences, negative family influences, negative peer influences, and negative neighborhood influences. Each of these formed single factors with factor loadings between 0.58 and 0.89 for individual component scales; all except three of the scales had loadings above 0.70 (see supplemental Appendix A, available online for details).

Demographic Controls.

Analyses controlled for whether the adolescent self-identified as female (0) or male (1), and for age (in whole years), grade level (8th, 10th, 12th), family type (two parent household=1, else=0), receipt of a free or reduced price school lunch (no=0, yes=1), mother’s educational attainment (1=grade school or less to 7=graduate degree), and number of people living in the household. In addition, two dummy variables controlled for the adolescent’s racial/ethnic identity. The first designated students as multiracial (0=no, 1=yes) if they checked “Asian,” Black or African American,” “Hawaiian or Other Pacific Islander” or “White” in addition to “American Indian or Alaska Native (AI/AN)” on a racial identity question allowing multiple selections. The second dummy variable designated students as multi-ethnic (0=no, 1=yes) if they indicated they were “Hispanic or Latino,” independent of the racial categories they selected. The omitted reference category for these dummy variables was students identifying as AI/AN only

Analysis Plan

A previously reported LCA analysis determined the optimal number of latent classes needed to describe combinations of recent use of the 8 types of substances (alcohol, heavy episodic drinking, tobacco, marijuana, inhalants, illegal drugs, prescription drug misuse, OTC misuse) in this sample (Kulis et al., 2016), using established criteria (Lo et al., 2001; Muthén & Muthén, 2012; Nylund et al., 2007). That LCA analysis showed there were four distinctive latent class patterns of substance use, and we use the same latent class solution in the current analysis. Utilizing the same data set and building from the optimal four-class LCA model solution, the present analyses used the R3STEP command in Mplus (Asparouhov & Muthén, 2014; Vermunt, 2010) to model predictors of latent class membership. While accounting for classification error in the most likely class membership (Asparouhov & Muthen, 2014), the R3STEP procedure executes a series of multinomial logistic regressions that assess whether an increase in a predictor results in a higher probability that a person belongs to one latent class over another latent class. In a first set of analyses, each of the predictors were specified individually to determine total effects of each predictor on latent class membership. In a second set of analyses, the full set of predictors were entered simultaneously to determine the unique effects of each predictor on latent class membership. Both sets of analyses controlled for demographic characteristics. We used multiple imputation (Rubin, 2004) to adjust for item level missing data. Following recommended practice (Graham et al., 2007; Graham, 2009), we imputed 100 data sets using the Mplus “Data imputation” command, and executed analyses of the multiply imputed data using the “Type = Imputation” command.

Results

Table 1presents a demographic profile of urban AI respondents. The sample was nearly gender balanced between female and male students and students were 15 years old on average. About half the youth were in 8th grade, 30% in 10th grade and 20% in 12th grade. Most (63%) lived with both their mother and father or with one biological and one stepparent. The remainder lived with their mother only (26%), with their father only (5%), and 7% with neither parent, mostly with grandparents or aunts. Slightly more than half of the families were low income as indicated by the youth’s participation in the Federal school lunch program. Their mothers’ educational attainments varied: about a third had a 2- or 4-year college degree (36%), 40% had a high school or GED degree, and the remainder had not completed high school (23%). Most students indicated they had a non-Native ethnic or racial background in addition to their AI heritage: 28% were American Indian only; 26% had both AI and Latinx/Hispanic heritage; 28% were AI and another race (African American, Asian, non-Hispanic White); and 17% claimed a combination of AI, other racial, and Latinx/Hispanic heritage. The prevalence of substance use in the last month ranged from 28% for any alcohol use, 17% for binge drinking, tobacco and marijuana use, 10% for prescription drug misuse, and around 5% for other illegal drugs, inhalants and OTC misuse.

Table 1.

Descriptive Statistics for Analysis Variables, Urban American Indian Sample

Variables % M SD Range
% male versus female 51.4%
Age (years) 15.1 1.68 12-19
Grade level 9.39 1.78 8-12
 8th 50.6%
 10th 29.5%
 12th 19.9%
% in two-parent home 63.3%
% in Federal school lunch program 51.6%
Mother’s educational attainment 3.34 0.74 1-7
Household size 4.12 1.39 2-8
% Mixed AI and other racial heritage 46.0%
% Mixed AI & Latinx heritage 43.6%
Last 30 day substance use (% any use):
 Alcohol 28.5%
 Heavy episodic drinking 17.2%
 Tobacco 17.3%
 Inhalants 4.6%
 Marijuana 17.3%
 Other illicit drugs 5.5%
 Prescription drug misuse 10.2%
 Over-the-counter drug misuse 5.6%
Substance Use Latent Classes
 Polysubstance  5.6%
 “Gateway” (alcohol, tobacco, marijuana) 17.4%
 Not alcohol  4.0%
 Nonuser 73.0%
Open in a new tab

Note: N = 2,292

Table 2 shows how each of the ecodevelopmental factor scores predict the bivariate odds of membership in each class relative to another class. With one exception, students with higher scores on the four measures of positive ecodevelopmental influences (family, peer, school and neighborhood) were significantly more likely to be in the nonuser class than in each of the three classes of substance users. The exception was that positive neighborhood influences did not significantly predict the odds of membership in the nonuser class relative to the polysubstance user class. Only one positive ecodevelopment influence was significant in predicting membership among the three substance-using classes: positive neighborhood influences increased the odds of being in the “gateway” class rather than the “not alcohol” class.

Table 2.

Ecodevelopmental Contexts Predicting Recent Substance Use Latent Class Membership: Bivariate Odds Ratios

Reference→ Poly-substance
 Gateway
 Not Alcohol
 Nonuser
Gateway Not Alcohol Nonuser Poly-substance Not Alcohol Nonuser Poly-substance Gateway Nonuser Poly- substance Gateway Not Alcohol
Family + 1.255 0.926 2.050** 0.758 0.737 1.634** 1.080 1.357 2.212** 0.488** 0.612** 0.452**
Peer + 1.055 1.157 1.490** 0.947 1.096 1.412** 0.864 0.912 1.289** 0.671** 0.708** 0.776**
School + 1.314 1.182 1.948** 0.761 0.899 1.483** 0.846 1.112 1.647** 0.513** 0.674** 0.607**
Neighborhood + 1.033 0.683 1.297 0.969 0.661* 1.255** 1.464 1.513** 1.898** 0.771 0.797** 0.527**
Family − 0.685** 0.589** 0.260** 1.460** 0.859 0.380** 1.699** 1.164 0.442** 3.846** 2.639** 2.262**
Peer − 0.432** 0.529** 0.088** 2.315** 1.227 0.204** 1.891** 0.815 0.166** 11.364** 4.902** 6.024**
Neighborhood − 0.612* 0.623* 0.224** 1.634* 1.017 0.366** 1.606* 0.983 0.360** 4.464** 2.732** 2.778**
Open in a new tab

Notes: Ecodevelomental contexts are measured with observed factor scores. Estimates are from multiply imputed data and control for student gender, age, grade level, family type, receipt of a free or reduced price school lunch, mother’s educational attainment, number of people in household, and multi-racial and multi-ethnic background.

*

p < .05.

**

p < .01.

Negative ecodevelopmental influences were more consistent bivariate predictors of class membership. Each standard deviation increase in negative family, peer and neighborhood influence raised the odds of being in the polysubstance, “gateway” or “not alcohol” class rather than the nonuser class by a factor of at least two and up to 11. Each of the three negative ecodevelopmental influences also raised the odds of being in the polysubstance user rather than the “gateway” or “not alcohol” user classes. Negative ecodevelopmental influences, however, did not distinguish “gateway” from the “not alcohol” users.

In multivariate models entering all seven ecodevelopmental predictors simultaneously (Table 3), only one of the positive influences remained significant: positive family influences increased the odds of membership in the nonuser rather than polysubstance use class. However, nearly all the bivariate relationships between class membership and negative family and negative peer influences were reproduced in multivariate tests, with similar or higher odds ratios, with only two exceptions: (1) negative family influences failed to distinguish nonusers and “not alcohol” users, and (2) negative neighborhood experiences failed to distinguish nonusers from both “not alcohol” users and poly-substance users .

Table 3.

Ecodevelopmental Contexts Predicting Recent Substance Use Latent Class Membership: Multivariate Odds Ratios

Reference→ Poly-substance
 Gateway
 Not Alcohol
 Nonuser
Gateway Not Alcohol Nonuser Poly-substance Not Alcohol Nonuser Poly-substance Gateway Nonuser Poly- substance Gateway Not Alcohol
Family + 1.397 1.190 1.680* 0.716 0.852 1.203 0.840 1.174 1.412 0.595* 0.831 0.708
Peer + 0.835 1.037 0.943 1.197 1.241 1.129 0.964 0.806 0.909 1.061 0.886 1.100
School + 1.076 0.947 1.110 0.930 0.881 1.030 1.056 1.135 1.171 0.901 0.970 0.854
Neighborhood + 0.963 0.705 0.940 1.039 0.733 0.976 1.418 1.364 1.332 1.064 1.024 0.751
Family − 0.674* 0.463** 0.348** 1.484** 0.687 0.516** 2.160** 1.455 0.751 2.874** 1.938** 1.332
Peer − 0.393** 0.497** 0.110** 2.545** 1.264 0.281** 2.012** 0.791 0.222** 9.091** 3.559** 4.505**
Neighborhood − 0.930 0.852 0.624 1.074 0.916 0.671** 1.174 1.092 0.733 1.603 1.490** 1.364
Open in a new tab

Notes: Ecodevelomental contexts are measured with observed factor scores. Estimates are from multiply imputed data and control for student gender, age, grade level, family type, receipt of a free or reduced price school lunch, mother’s educational attainment, number of people in household, and racial and ethnic background.

*

p < .05.

**

p < .01.

Discussion

The study provides evidence that all the ecodevelopmental microsystems—family, peer, school and neighborhood—are associated with patterns of substance use by urban AI adolescents, and in the expected directions. Positive influences predicted being drug free rather than engaging in one of three distinctive types of substance use in this sample, while negative influences predicted recent use of some substances and, among users, polysubstance use rather than other patterns of use. But when tested together and compared to positive ecodevelopmental influences, the negative influences—in the family and among peers especially—emerged as stronger and more consistent predictors of substance use and of polysubstance use in particular.

These findings align with ecodevelopmental theory, which posits that family, peer, school, and neighborhood microsystems shape adolescent behavior in concerted, overlapping and interacting ways (Coatsworth et al., 2002). Families occupy a central role in socializing children but not in isolation from other microsystems in which the child participates directly. The microsystems can line up in promoting or inhibiting healthy youth development, or may contain a mix of positive and negative influences, both within and across microsystems. Interrelationships among these microsystems may account for the overall finding that positive family, peer, school and neighborhood influences did not persist as significant predictors of substance use latent classes in multivariate models, which points to fruitful avenues for further research. One possibility is that the positive influences operating in different microsystems are so highly aligned as to cause problems of multi-collinearity in statistical models that attempt to separate out their influences. However, the measures used in the analysis were only modestly or moderately correlated with one another (r < |0.48|), indicating that their influences remained distinct from one another. A second possibility is that positive influences reinforce each other in cumulative ways, operating not in isolation but in concerted fashion or through diffusion across microsystems. Different analytic approaches are needed to explore these avenues in future research. A third possibility is that positive ecodevelopmental influences play an important role but only in interaction with other influences, for example by countering the effects of negative contextual influences. This study did not directly investigate all the mesosystems where microsystems come into contact (Gorman-Smith et al., 2002), although some of these dynamics are represented in the measures, e.g. the parental monitoring scale that is part of the measure of positive family influences addresses interactions across the family and peer realms.

The findings add to the strong body of evidence that negative peer influences play a central role in promoting substance use in the general adolescent population (Henneberger et al., 2021; Leung et al., 2014), a role that accelerates throughout adolescence and often supersedes family influences (Gardner & Steinberg, 2005). In this study, negative peer influences were the strongest predictors of urban AI adolescent substance use, especially the most harmful form-polysubstance use of multiple licit and illicit substances. Prior research suggests that parental and community monitoring and strong AI cultural identity may mediate or buffer the negative peer influences promoting AI youth substance use (Baldwin et al., 2011; Boyd-Ball et al., 2014). However, family and peer influences in AI communities may be interrelated in complex ways, such as having a monitoring role for grandparents, aunts and uncles that is distinct from that of parents (Martinez et al., 2015), suggesting a need for further research to identify more targeted ways to minimize the deleterious effects of strong negative influences increasing substance use risk for urban AI youth.

The delineation of substantively meaningful latent classes of substance in an urban AI youth sample is a strength and novel contribution of this study, recognizing that youth substance use typically involves use of more than one substance. Although the nonuser, “gateway” (alcohol, cigarettes, marijuana), and polysubstance (licit and illicit) use latent classes have been reported in non-AI samples, the urban AI youth using only substances other than alcohol form an unusual latent class. Their pattern of use may be connected to the heightened impact and concern about alcohol misuse in many AI communities. Although the ecodevelopmental predictors did not distinguish the “not alcohol” class from the “gateway” class of substance users, the “not alcohol” class was distinctive from nonusers on one predictor, greater negative peer influences. A more in-depth study of the peer influences shaping the “not alcohol” class of substance users may reveal distinctive dynamics of peer selection and socialization. Similarly, the greater negative family influences that distinguished “gateway” and polysubstance users from nonusers point to a need to explore the complexities of family relationships in urban AI communities.

Despite the notable strengths of the study, particularly the large sample of urban AI youth and ability to assess polysubstance use comprehensively, interpretations of findings are limited in generalizability, causal inference, and statistical precision. Data come from in-school surveys, underrepresenting students who are frequently absent and excluding those who have dropped out of school, both of which are groups of youth more at risk of problematic substance use than those typically present for a survey administration. The data also come from one state, in the southwestern U.S. Although it includes numerous large and diverse urban AI communities, in other regions tribal compositions and urban migration histories may differ in ways that impact patterns of youth substance use and the nature of peer, family and neighborhood influences on youth risk behaviors. Second, we are unable to draw causal inferences from the study’s cross-sectional data. Finally, the research design resulted in individual student data being clustered within schools. However, the research team was granted only restricted access by the primary data collectors, excluding any school identifiers or information that could be triangulated to pinpoint individual schools. Thus, the standard errors and significance levels of findings have not been adjusted for possible random effects at the school level. Although it is possible that these adjustments could change key findings, large scale investigations show that the variance in student reports of substance use that is due to differences between schools generally is very small (O’Malley et al., 2006).

In conclusion, family, peer, school and neighborhood influences shape urban AI adolescent substance use in ways that largely mirror these relationships in the general population. However, urban AI youth report distinctive patterns of use of multiple substances as well as a particularly dominant role for negative family and peer influences as predictors of use.

Supplementary Material

Supp 1

Acknowledgements

Data collection and analysis for this study was supported by the National Institutes of Health (awards P20-MD002316, R01-MD006110 and R01-DA056417). The content is solely the responsibility of the authors and does not necessarily represent the views of the National Institutes of Health.

Footnotes

Declaration of Interest

The authors declare no potential conflicts of interest with respect to the research, authorship and/or publication of this article. The research grants providing support for the study also provided release time for authors SK, JJ, and SA.

References

  1. Allen J, Mohatt GV, Fok CCT, Henry D, Burkett R, & People Awakening Team. (2014). A protective factors model for alcohol abuse and suicide prevention among Alaska Native youth. American Journal of Community Psychology, 54, 125–139. 10.1007/s10464-014-9661-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Armenta BE, Sittner KJ, & Whitbeck LB (2016). Predicting the onset of alcohol use and the development of alcohol use disorder among indigenous adolescents. Child Development, 87(3), 870–882. 10.1111/cdev.12506 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Arthur MW, Hawkins JD, Pollard JA, Catalano RF, & Baglioni AJ (2002). Measuring risk and protective factors for use, delinquency, and other adolescent problem behaviors the communities that care youth survey. Evaluation Review, 26, 575–601. [DOI] [PubMed] [Google Scholar]
  4. Arizona Criminal Justice Commission. (2012). Arizona Youth Survey State Report 2012. Arizona Criminal Justice Commission, Phoenix, AZ. [Google Scholar]
  5. Asparouhov T, & Muthén B (2014). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 329–341. 10.1080/10705511.2014.915181 [DOI] [Google Scholar]
  6. Ayers S, Jager J, & Kulis SS (2021). Variations in risk and promotive factors on substance use among urban American Indian youth. Journal of Ethnicity in Substance Abuse, 20(2), 187–210. 10.1080/15332640.2019.1598907 [DOI] [PubMed] [Google Scholar]
  7. Baldwin JA, Brown BG, Wayment HA, Nez RA, & Brelsford KM (2011). Culture and context: Buffering the relationship between stressful life events and risky behaviors in American Indian youth. Substance use & misuse, 46(11), 1380–1394. 10.3109/10826084.2011.592432 [DOI] [PubMed] [Google Scholar]
  8. Boyd-Ball AJ, Véronneau MH, Dishion TJ, & Kavanagh K (2014). Monitoring and peer influences as predictors of increases in alcohol use among American Indian youth. Prevention Science, 15, 526–535. 10.1007/s11121-013-0399-1 [DOI] [PubMed] [Google Scholar]
  9. Brockie TN, Dana-Sacco G, Wallen GR, Wilcox HC, & Campbell JC (2015). The relationship of adverse childhood experience to PTSD, depression, poly-drug use and suicide attempt in reservation-based Native American adolescents and young adults. American Journal of Community Psychology, 55(3-4), 411–421. 10.1007/s10464-015-9721-3 [DOI] [PubMed] [Google Scholar]
  10. Bronfenbrenner U. (1977). Toward an experimental ecology of human development. American Psychologist, 32(7), 513–531. 10.1037/0003-066X.32.7.513 [DOI] [Google Scholar]
  11. Bronfenbrenner U. (2009). The ecology of human development: Experiments by nature and design. Harvard University Press. [Google Scholar]
  12. Brown RA, Dickerson DL, & D’Amico EJ (2016). Cultural identity among urban American Indian/Alaska Native youth: Implications for alcohol and drug use. Prevention Science, 17, 852–861.57. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dickerson DL, Brown RA, Johnson CL, Schweigman K, & D’Amico EJ (2016). Integrating motivational interviewing and traditional practices to address alcohol and drug use among urban American Indian/Alaska Native youth. Journal of Substance Abuse Treatment, 65, 26–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Castor ML, Smyser MS, Taualii MM, Park AN, Lawson SA, & Forquera RA (2006). A nationwide population-based study identifying health disparities between American Indians/Alaska Natives and the general populations living in select urban counties. American Journal of Public Health, 96(8), 1478–1484. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Centers for Disease Control and Prevention (2019). Sexually transmitted disease surveillance 2018. https://www.cdc.gov/std/stats18/STDSurveillance2018-full-report.pdf
  16. Chawla N, & Sarkar S (2019). Defining “High-risk Sexual Behavior” in the Context of Substance Use. Journal of Psychosexual Health, 1(1), 26–31. 10.1177/2631831818822015 [DOI] [Google Scholar]
  17. Cheadle JE, & Sittner Hartshorn KJ (2012). Marijuana use development over the course of adolescence among North American indigenous youth. Social Science Research, 41, 1227–1240. 10.1016/j.ssresearch.2012.03.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Cheadle JE, & Whitbeck LB (2011). Alcohol use trajectories and problem drinking over the course of adolescence: A study of North American indigenous youth and their caretakers. Journal of Health and Social Behavior, 52(2), 228–245. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Coatsworth JD, Pantin H, McBride C, Briones E, Kurtines W, & Szapocznik J (2002). Ecodevelopmental correlates of behavior problems in young Hispanic females. Applied Developmental Science, 6(3), 126–143. 10.1207/S1532480XADS0603_3 [DOI] [Google Scholar]
  20. Conway KP, Vullo GC, Nichter B, Wang J, Compton WM, Iannotti RJ, & Simons-Morton B (2013). Prevalence and patterns of polysubstance use in a nationally representative sample of 10th graders in the United States. Journal of Adolescent Health, 52(6), 716–723. 10.1016/j.jadohealth.2012.12.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. D’Amico EJ, Dickerson DL, Brown RA, Klein DJ, Agniel D, & Johnson C (2021). Unveiling an ‘invisible population’: health, substance use, sexual behavior, culture, and discrimination among urban American Indian/Alaska Native adolescents in California. Ethnicity & Health, 26(6), 845–862. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Eaton DK, Kann L, Kinchen S, Shanklin S, Flint KH, Hawkins J, Harris WA, Lowry R, McManus T, Chyen D, Whittle L, Lim C, & Wechsler H (2012). Youth Risk Behavior Surveillance — United States, 2011. Morbidity and Mortality Weekly Report: Surveillance Summaries, 61(4), 1–162. http://www.jstor.org/stable/24806047 [PubMed] [Google Scholar]
  23. Eitle TME,D. (2016). Explaining the association between gender and substance use among American Indian adolescents: An application of power-control theory. Sociological Perspectives, 58(4), 686–710. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Eitle D, & Eitle TM (2018). Weight status and substance use among urban American Indian adolescents. Journal of Alcohol & Drug Education, 62(3), 23–42. https://www.jstor.org/stable/48517539 [Google Scholar]
  25. Enoch MA, & Albaugh BJ (2017). Review: Genetic and environmental risk factors for alcohol use disorders in American Indians and Alaskan Natives. The American Journal on Addictions, 26(5), 461–468. 10.1111/ajad.12420 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Friese B, Grube JW, & Seninger S (2015). Drinking among Native American and White youths: The role of perceived neighborhood and school environment. Journal of Ethnicity in Substance Abuse, 14(3), 287–307. 10.1080/15332640.2014.994723 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Freisthler B, Needell B, & Gruenewald PJ (2005). Is the physical availability of alcohol and illicit drugs related to neighborhood rates of child maltreatment? Child Abuse & Neglect, 29(9), 1049–1060. 10.1016/j.chiabu.2004.12.014 [DOI] [PubMed] [Google Scholar]
  28. Gage SH, Sallis HM, Lassi G, Wootton RE, Mokrysz C, Smith GD, & Munafò MR (2022). Does smoking cause lower educational attainment and general cognitive ability? Triangulation of causal evidence using multiple study designs. Psychological Medicine, 52(8), 1578–1586. 10.1017/S0033291720003402 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Garcia JL (2020). Historical trauma and American Indian/Alaska Native youth mental health development and delinquency. New Directions for Child and Adolescent Development, 2020(169), 41–58. 10.1002/cad.20332 [DOI] [PubMed] [Google Scholar]
  30. Gardner M, & Steinberg L (2005). Peer influence on risk taking, risk preference, and risky decision making in adolescence and adulthood: An experimental study. Developmental Psychology, 41(4), 625–635. https://psycnet.apa.org/doi/10.1037/a0026993 [DOI] [PubMed] [Google Scholar]
  31. Garrett MT, & Herring RD (2001). Honoring the power of relation: Counseling Native adults. The Journal for Humanistic Counseling, Education, and Development, 40(2), 139–160. 10.1002/j.2164-490X.2001.tb00113.x [DOI] [Google Scholar]
  32. Gilreath TD, Astor RA, Estrada JN, Johnson RM, Benbenishty R, & Unger JB (2014). Substance use among adolescents in California: A latent class analysis. Substance Use & Misuse, 49(1-2), 116–123. 10.3109/10826084.2013.824468 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Gorman-Smith D, Henry DB, & Tolan PH (2004). Exposure to community violence and violence perpetration: The protective effects of family functioning. Journal of Clinical Child and Adolescent Psychology, 33(3), 439–449. [DOI] [PubMed] [Google Scholar]
  34. Graham JW (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576. [DOI] [PubMed] [Google Scholar]
  35. Graham JW, Olchowski AE & Gilreath TD (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science, 8, 206–213. 10.1007/s11121-007-0070-9 [DOI] [PubMed] [Google Scholar]
  36. Greenfield BL, Sitnner KJ, Forbes MK, Walls ML, & Whitbeck LB (2017). Conduct disorder and alcohol use disorder trajectories, predictors, and outcomes for Indigenous youth. Journal of the American Academy of Child & Adolescent Psychiatry, 56(2), 133–139. 10.1016/j.jaac.2016.11.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Hagler KJ, Pearson MR, Venner KL, & Greenfield BL (2017). Descriptive drinking norms in Native American and non-Hispanic White college students. Addictive Behaviors, 72, 45–50. 10.1016/j.addbeh.2017.03.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Hartmann WE, Wendt DC, Saftner MA, Marcus J, & Momper SL (2014). Advancing community-based research with urban American Indian populations: Multidisciplinary perspectives. American Journal of Community Psychology, 54, 72–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Heavyrunner-Rioux AR, & Hollist DR (2010). Community, family, and peer influences on alcohol, marijuana, and illicit drug use among a sample of Native American youth: An analysis of predictive factors. Journal of Ethnicity in Substance Abuse, 9(4), 260–283. [DOI] [PubMed] [Google Scholar]
  40. Hautala D, & Sittner K (2018). Moderators of the association between exposure to violence in community, family, and dating contexts and substance use disorder risk among North American Indigenous adolescents. Journal of Interpersonal Violence, 36(9-10), 4615–4640. 10.1177/0886260518792255 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Henneberger AK, Mushonga DR, & Preston AM (2021). Peer influence and adolescent substance use: A systematic review of dynamic social network research. Adolescent Research Review, 6, 57–73. 10.1007/s40894-019-00130-0 [DOI] [Google Scholar]
  42. Herring RD (1997). Counseling indigenous American youth. In Lee CC(Ed.), Multicultural issues in counseling: New approaches to diversity (2nd ed., pp. 53–70). Alexandria, VA: American Counseling Association. [Google Scholar]
  43. Howard MO, Walker RD, Walker PS, Cottler LB, & Compton WM (1999). Inhalant use among urban American Indian youth. Addiction, 94(1), 83–95. 10.1046/j.1360-0443.1999.941835.x [DOI] [PubMed] [Google Scholar]
  44. Indian Health Service. (2018, October). Urban Indian Health Program. https://www.ihs.gov/sites/newsroom/themes/responsive2017/display_objects/documents/factsheets/UrbanIndianHealthProgram_FactSheet.pdf
  45. Indian Health Service (2019, October). Indian Health Disparities. https://www.ihs.gov/sites/newsroom/themes/responsive2017/display_objects/documents/factsheets/Disparities.pdf [Google Scholar]
  46. Kelley A, Witzel M, & Fatupaito B (2019). Preventing substance use in American Indian youth: The case for social support and community connections. Substance Use & Misuse, 54(5), 787–795. 10.1080/10826084.2018.1536724 [DOI] [PubMed] [Google Scholar]
  47. King KA, Vidourek RA, & Hill MK (2014). Recent alcohol use and episodic heavy drinking among American Indian youths. Journal of Child & Adolescents Substance Abuse, 23(5), 334–346. 10.1080/1067828X.2014.928117 [DOI] [Google Scholar]
  48. Kulis SS, Jager J, Ayers SL, Lateef H, & Kiehne E (2016). Substance use profiles of urban American Indian adolescents: A latent class analysis. Substance Use & Misuse, 51(9), 1159–1173. 10.3109/10826084.2016.1160125 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Kulis SS, Okamoto SK, Rayle AD, & Sen S (2006). Social contexts of drug offers among American Indian Youth and their relationship to substance use: An exploratory study. Journal of Cultural Diversity and Ethnic Minority Psychology, 12(1), 30–44. 10.1037/1099-9809.12.1.30 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. LaFromboise TD, Hoyt DR, Oliver L, & Whitbeck LB (2006). Family, community, and school influences on resilience among American Indian adolescents in the upper Midwest. Journal of Community Psychology, 34(2), 193–209. 10.1002/jcop.20090 [DOI] [Google Scholar]
  51. LaFromboise T, & Medoff L (2004). Sacred spaces. In Clauss-Ehlers CS & Weist MD (Eds.) Community planning to foster resilience in children (pp. 45–63). Springer, Boston, MA. [Google Scholar]
  52. LaFromboise TD, Trimble JE, & Mohatt GV (1990). Counseling Intervention and American Indian Tradition: An Integrative Approach. The Counseling Psychologist, 18(4), 628–654. 10.1177/0011000090184006 [DOI] [Google Scholar]
  53. Lee H, Yang K, Palmer J, Kameg B, Clark L, & Greene B (2020). Substance use patterns among adolescents: a latent class analysis. Journal of the American Psychiatric Nurses Association, 26(6), 586–594. 10.1177/1078390319858658 [DOI] [PubMed] [Google Scholar]
  54. Leung RK, Toumbourou JW, & Hemphill SA (2014). The effect of peer influence and selection processes on adolescent alcohol use: a systematic review of longitudinal studies. Health Psychology Review, 8(4), 426–457. 10.1080/17437199.2011.587961 [DOI] [PubMed] [Google Scholar]
  55. Lo Y, Mendell NR, & Rubin DB (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767–778. 10.1093/biomet/88.3.767 [DOI] [Google Scholar]
  56. Lobo S. (2001). Is urban a person or a place? Characteristics of urban Indian country. In Lobo S & Peters K (Eds.), American Indians and the urban experience (pp. 73–84). AltaMira Press. [Google Scholar]
  57. Lopez B, Schwartz SJ, Prado G, Huang S, Rothe EM, Wang W, & Pantin H (2008). Correlates of early alcohol and drug use in Hispanic adolescents: examining the role of ADHD with comorbid conduct disorder, family, school, and peers. Journal of Clinical Child & Adolescent Psychology, 37(4), 820–832. 10.1080/15374410802359676 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Mmari K, Blum R, & Teufel-Shone N (2010). What increases risk and protection for delinquent behaviors among American Indian youth? Findings from three tribal communities. Youth and Society, 41(3), 382–413. [Google Scholar]
  59. Martinez MJ, Ayers SL, Kulis S, & Brown E (2015). The relationship between peer, parent, and grandparent norms and intentions to use substances for urban American Indian youths. Journal of Child & Adolescent Substance Abuse, 24(4), 220–227. 10.1080/1067828x.2013.812529 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Mohatt GV, Fok CCT, Henry D, & Allen J (2014). Feasibility of a community intervention for the prevention of suicide and alcohol abuse with Yup’ik Alaska Native youth: The Elluam Tungiinun and Yupiucimta Asvaortuumallerkaa studies. American Journal of Community Psychology, 54(1-2), 153–169. 10.1007/s10464-014-9646-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Moilanen KL, Markstrom CA, & Jones E (2014). Extracurricular activity availability and participation and substance use among American Indian adolescents. Journal of Youth Adolescence, 43(3), 454–469. 10.1007/s10964-013-0088-1 [DOI] [PubMed] [Google Scholar]
  62. Moon SS, Blakey JM, Boyas J, Horton K, & Kim YJ (2014). The influence of parental, peer, and school factors on marijuana use among Native American adolescents. Journal of Social Service Research, 40(2), 147–159. 10.1080/01488376.2013.865578 [DOI] [Google Scholar]
  63. Morales AM, Jones SA, Kliamovich D, Harman G, & Nagel BJ (2020). Identifying Early Risk Factors for Addiction Later in Life: A Review of Prospective Longitudinal Studies. Current Addiction Reports, 7(1), 89–98. 10.1007/s40429-019-00282-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Moran JR, Fleming CM, Somervell P & Manson SM (1999) Measuring bicultural ethnic identity among American Indian adolescents: a factor analysis study. Journal of Adolescent Research. 14(4), 405–426. 10.1177/0743558499144002 [DOI] [Google Scholar]
  65. Moss HB, Chen CM, & Yi HY (2014). Early adolescent patterns of alcohol, cigarettes, and marijuana polysubstance use and young adult substance use outcomes in a nationally representative sample. Drug and Alcohol Dependence, 136, 51–62. 10.1016/j.drugalcdep.2013.12.011 [DOI] [PubMed] [Google Scholar]
  66. Muthén B, & Muthén L (2012). Mplus user’s guide. 7th ed. Los Angeles. Muthén & Muthén. [Google Scholar]
  67. Nalls AM, Mullis RL, & Mullis AK (2009). American Indian youths’ perceptions of their environment and their reports of depressive symptoms and alcohol/marijuana use. Adolescence, 44(176), 965–978. [PubMed] [Google Scholar]
  68. Napoli M, Marsiglia FF, & Kulis S (2003). Sense of belonging in school as a protective factor against drug abuse among Native American urban adolescents. Journal of Social Work Practice in the Addictions, 3(2), 25–41. 10.1300/J160v03n02_03 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Novins DK, & Mitchell CM (1998). Factors associated with marijuana use among American Indian adolescents. Addiction, 93(11), 1693–1702. [DOI] [PubMed] [Google Scholar]
  70. Nylund KL, Asparouhov T, & Muthén BO (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14, 535–569. 10.1080/10705510701575396 [DOI] [Google Scholar]
  71. O’Malley PM, Johnston LD, Bachman JG, Schulenberg JE, & Kumar R (2006). How substance use differs among American secondary schools. Prevention Science, 7, 409–420. 10.1007/s11121-006-0050-5 [DOI] [PubMed] [Google Scholar]
  72. Pavkov TW, Travis L, Fox KA, King CB, & Cross TL (2010). Tribal youth victimization and delinquency: Analysis of youth risk behavior surveillance survey data. Cultural Diversity and Ethnic Minority Psychology, 16(2), 123–134. 10.1037/a0018664 [DOI] [PubMed] [Google Scholar]
  73. Pichel R, Feijóo S, Isorna M, Varela J, & Rial A (2022). Analysis of the relationship between school bullying, cyberbullying, and substance use. Children and Youth Services Review, 134, 106369. 10.1016/j.childyouth.2022.106369 [DOI] [Google Scholar]
  74. Prince MA, Swaim RC, Stanley LR, & Conner BT (2017). Perceived harm as a mediator of the relationship between social norms and marijuana use and related consequences among American Indian youth. Drug and Alcohol Dependence, 181, 102–107. 10.1016/j.drugalcdep.2017.09.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Radin SM, Neighbors C, Walker PS, Walker RD, Marlatt GA, & Larimer M (2006). The changing influences of self-worth and peer deviance on drinking problems in urban American Indian adolescents. Psychology of Addictive Behaviors, 20(2), 161–170. 10.1037/0893-164X.20.2.161 [DOI] [PubMed] [Google Scholar]
  76. Rayle AD, Kulis S, Okamoto SK, Tann SS, Lecroy CW, Dustman P, & Burke AM (2006). Who is Offering and How Often? Gender Differences in Drug Offers Among American Indian Adolescents of the Southwest. The Journal of Early Adolescence, 26(3), 296–317. 10.1177/0272431606288551 [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Reinke WM, Herman KC, Petras H, & Ialongo NS (2008). Empirically derived subtypes of child academic and behavior problems: Co-occurrence and distal outcomes. Journal of Abnormal Child Psychology, 36, 759–770. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Rhoades DA, Manson SM, Noonan C, & Buchwald D (2005). Characteristics associated with reservation travel among urban Native American outpatients. Journal of Health Care for the Poor and Underserved, 16(3), 464–474. 10.1353/hpu.2005.0059 [DOI] [PubMed] [Google Scholar]
  79. Rubin DB (2004). Multiple imputation for nonresponse in surveys (Vol. 81). John Wiley & Sons. [Google Scholar]
  80. Schinke SP, Orlandi MA, Botvin GJ, Gilchrist LD, Trimble JE, & Locklear VS (1988). Preventing substance abuse among American Indian adolescents: A bicultural competence skills approach. Journal of Counseling Psychology, 35(1), 87–90. https://psycnet.apa.org/doi/10.1037/0022-0167.35.1.87 [PMC free article] [PubMed] [Google Scholar]
  81. Silmere H, & Stiffman AR (2006). Factors associated with successful functioning in American Indian youths. American Indian and Alaska Native Mental Health Research, 13(3), 23–47. [DOI] [PubMed] [Google Scholar]
  82. Sittner KJ (2015). Trajectories of substance use: Onset and adverse outcomes among North American Indigenous Adolescents. Journal of Research on Adolescence, 26(4), 830–844. 10.1111/jora.12233 [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Sittner KJ, & Hautala D (2016). Aggressive delinquency among North American Indigenous adolescents: Trajectories and predictors. Aggressive Behavior, 42(3), 274–286. 10.1002/ab/21622 [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Stanley LR, Beauvais F, Walker PS, & Walker RD (2009). Initiation of alcohol use among urban American Indian youth: A discrete time hazards model. Journal of Ethnicity in Substance Abuse, 8(4), 359–377. 10.1080/15332640903327310 [DOI] [PubMed] [Google Scholar]
  85. Stanley LR, Swaim RC, & Dieterich SE (2017). The role of norms in marijuana use among American Indian adolescents. Prevention Science, 18(4), 406–415. . 10.1007/s11121-017-0768-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Straus T, & Valentino D (1998). Retribalization in urban Indian communities. American Indian Culture and Research Journal, 22(4): 103–115. 10.17953/aicr.22.4.g4g7u036414w26m2 [DOI] [Google Scholar]
  87. Stubben JD (2001). Working with and conducting research among American Indian families. American Behavioral Scientist, 44(9), 1466–1481. 10.1177/0002764201044009004 [DOI] [Google Scholar]
  88. Swaim RC, & Stanley LR (2016). Multivariate family factors in lifetime and current marijuana use among American Indian and White adolescents residing on or near reservations. Drug and Alcohol Dependence, 169, 92–100. 10.1016/j.drugalcdep.2016.09.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Swaim RC, & Stanley LR (2018a). Substance use among American Indian youths on reservations compared with a national sample of US adolescents. JAMA Network Open, 1(1), e180382. 10.1001/jamanetworkopen.2018.0382 [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Swaim RC, & Stanley LR (2018b). Effects of family conflict and anger on alcohol use among American Indian students: Mediating effects of outcome expectancies. Journal of Studies on Alcohol and Drugs, 79(1), 102–110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Swaim RC, & Stanley LR (2019). Self-esteem, cultural identification, and substance use among American Indian youth. Journal of Community Psychology, 47(7), 1700–1713. 10.1002/jcop.22225 [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Szapocznik J, & Coatsworth JD (1999). An ecodevelopmental framework for organizing the influences on drug abuse: A developmental model of risk and protection. In Glantz MD & Hartel CR (Eds.), Drug abuse: Origins & interventions (pp. 331–366). American Psychological Association. 10.1037/10341-014 [DOI] [Google Scholar]
  93. Tingey L, Cwik M, Chambers R, Goklish N, Larzelere-Hinton F, Suttle R, … Barlow A. (2017). Motivators and influences on American Indian adolescent alcohol use and binge behavior: A qualitative exploration. Journal of Child & Adolescent Substance Abuse, 26(1), 75–85. [Google Scholar]
  94. Turanovic JJ, & Pratt TC (2017). Consequences of violent victimization for Native American youth in early adulthood. Journal of Youth and Adolescence, 46, 1333–1350. 10.1007/s10964-016-0587-y [DOI] [PubMed] [Google Scholar]
  95. Unger JB, Baezconde-Garbanati L, & Soto C (2004). Family-and peer-related risk and protective factors for tobacco use among American Indian adolescents in California. Journal of Ethnicity in Substance Abuse, 3(4), 1–15.29019292 [Google Scholar]
  96. Urbaeva Z, Booth JM, & Wei K (2017). The relationship between cultural identification, family socialization and adolescent alcohol use among Native American families. Journal of Child and Family Studies, 26(10), 2681–2693. 10.1007/s10826-017-0789-2 [DOI] [Google Scholar]
  97. U.S. Bureau of the Census. (2010). Race and Latino Origin, 2010, State of Arizona. Summary File 1, Tables P5, P8, PCT4, PCT5, PCT8, and PCT11. Washington, D.C. http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?fpt=table [Google Scholar]
  98. Vermunt JK (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18(4), 450–469. 10.1093/pan/mpq025 [DOI] [Google Scholar]
  99. Whitesell NR, Asdigian NL, Kaufman CE, Big Crow C, Shangreau C, Keane EM, Mousseau AC, & Mitchell CM (2014). Trajectories of substance use among young American Indian adolescents: Patterns and predictors. Journal of Youth and Adolescence, 43(3), 437–453. 10.1007/s10964-013-0026-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Weaver HN (2012). Urban and indigenous: The challenges of being a Native American in the city. Journal of Community Practice, 20(4), 470–488. [Google Scholar]
  101. Woods C, Kim B, Guo K, Nyguen T, Taplayan S, & Aronowitz T (2022). Factors That Influence Substance Use Among American Indian/Alaskan Native Youth: A Systematic Mixed Studies Review. Journal of the American Psychiatric Nurses Association, 28(1), 37–57. 10.1177/10783903211038050 [DOI] [PubMed] [Google Scholar]
  102. Yabiku ST, Rayle AD, Okamoto SK, Marsiglia FF, & Kulis S (2007). The effect of neighborhood context on the drug use of American Indian youth of the Southwest. Journal of Ethnicity in Substance Abuse, 6(2), 181–204. 10.1300/J233v06n02_11 [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Yu M, & Stiffman AR (2007). Culture and environment as predictors of alcohol abuse/dependence symptoms in American Indian youths. Addictive Behaviors, 32(10), 2253–2259. 10.1016/j.addbeh.2007.01.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  104. Yu M, & Stiffman AR (2010). Positive family relationships and religious affiliation as mediators between negative environment and illicit drug symptoms in American Indian adolescents. Addictive Behaviors, 35(7), 694–699. 10.1016/j.addbeh.2010.03.005 [DOI] [PubMed] [Google Scholar]
  105. Yu M, Stiffman AR, & Freedenthal S (2005). Factors affecting American Indian adolescent tobacco use. Addictive Behaviors, 30(5), 889−904. 10.1016/j.addbeh.2004.08.029 [DOI] [PubMed] [Google Scholar]
  106. Yuan NP, Bartgis J, & Demers D (2014). Promoting ethical research with American Indian and Alaska Native people living in urban areas. American Journal of Public Health, 104(11), 2085–2091. 10.2105/AJPH.2014.302027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  107. Yuan NP, Eaves ER, Koss MP, Polacca M, Bletzer K, & Goldman D (2010). “Alcohol is something that been with us like a common cold”: Community perceptions of American Indian drinking. Substance Use & Misuse, 45(12), 1909–1929. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supp 1
NIHMS2044321-supplement-Supp_1.docx (30KB, docx)

RESOURCES