Factors That Influence Substance Use Among American Indian/Alaskan Native Youth: A Systematic Mixed Studies Review

Abstract

Background:

The American Indian/Alaskan Native (AI/AN) mortality rate from illicit drug use was 22.7%, double that of the general population between 2007 and 2009. Fifteen percent of AI/AN youth reported receiving treatment for substance use compared with 10% of non-AI/AN peers.

Objective:

The purpose was to explore the factors that influence substance use among AI/AN youth.

Method:

We performed a systematic review using a results-based convergent synthesis design. Eight electronic databases were searched for articles published between 2014 and 2019 using the search terms “Native American youth,” “Native American adolescent,” “Native Youth,” “substance use,” “substance misuse,” and “substance abuse.” The Mixed Methods Appraisal Tool was used to appraise the studies.

Results:

Forty-seven studies met the inclusion criteria (44 quantitative, one qualitative, and two mixed-methods studies). The results were organized using the ecological systems model and included evaluation of both protective and risk factors related to AI/AN youth substance use. Three system levels were found to influence substance use: individual, micro- and macrosystems. The individual systems-level coping mechanisms played a key role in whether AI/AN youth initiated substance use. Family, school, and peer factors influence the microsystem level. At the macrosystem level, community environmental factors were influential.

Conclusion:

The major factor linking all the systems was the influence of a connected relationship with a prosocial adult who instilled future aspirations and a positive cultural identity. Findings of this systematic mixed studies review will assist in intervention development for AI/AN youth to prevent substance misuse.

Keywords

Native youth American Indian/Alaskan Native (AI/AN)adolescence substance use

Background

American Indian/Alaskan Native (AI/AN) populations in the United States and Canada have similar disparities in health status. Although AI/AN adults older than 40 years are more likely than same-age adults from other groups to quit drinking, those who consume alcohol are more likely to drink at problematic levels and develop a substance use disorder (Sittner, 2017). Problem drinking starts early among AI/ANs, for by age 18 years adolescents have nearly an 18% rate of lifetime alcohol dependence (Sittner, 2015). Additionally, the AI/AN mortality rate from illicit drug use was 22.7%, double that of the general population between 2007 and 2009 (Indian Health Service, 2014). Fifteen percent of AI/AN youth aged 12 to 17 years reported needing and receiving treatment for substance use compared with 10% of non-AI/AN peers (Substance Abuse and Mental Health Services Administration, 2012a). A recent report indicated that eighth graders living on or near a reservation reported much higher rates of getting drunk and binge drinking (18.5% and 18.3%, respectively) relative to national rates (4.9% and 7.1%, respectively; Stanley, Miller, et al., 2014).

Substance misuse can lead to several other health concerns. AI/AN youth have the highest rates of alcohol-involved motor vehicle-related deaths of any racial/ethnic group (Sittner & Hautala, 2016). AI/AN youth are more often referred for disciplinary action at earlier ages decreasing academic success (Nicholson-Crotty et al., 2009) and increasing unmonitored time with the potential of involvement in risk behaviors such as substance use (Aronowitz et al., 2020). Additionally, rates of violent victimization are higher for AI/AN youth (Sittner & Hautala, 2016). Reservation-based AI/AN youth not only have higher national rates of substance misuse but also higher rates than nonreservation AI/AN youth (Stanley, Harness, et al., 2014). Adverse childhood events have been linked with substance use disorder (Khoury et al., 2010; Mate, 2010; van der Kolk, 2015).

In a national survey of youth, those adolescents who reported adverse childhood events had three times higher rate of substance misuse as compared with youth without a history of trauma (Kilpatrick et al., 2003). In other studies, the majority of adolescents in treatment for substance misuse reported a history of trauma (Basedow et al., 2020; Deykin & Buka, 1997; Funk et al., 2003). The positive correlation between a history of trauma and substance use disorder has been well documented (Khoury et al., 2010; Mate, 2010; van der Kolk, 2015).

Beyond individual trauma that has been reported to be significantly associated with substance use disorder, impact from societal transgressions over decades has been shown to have an effect on minoritized populations. First cited in the 1990s, historical trauma was described as an intergenerational response to the loss of AI/AN’s land, people, and culture secondary to the colonization in North America (Brave Heart & DeBruyn, 1998). Intergenerational trauma, systematic discrimination, and displacement are downstream effects of colonization and racism that often define the life experiences of AI/ANs who use substances (Shahram et al., 2017). Historical trauma has been linked to substance use among AI/ANs (Tucker et al., 2016; Whitesell et al., 2012).

Some researchers have attempted to identify genetic biomarkers to explain the high rates of alcohol use disorder among AI/ANs (Enoch & Albaugh, 2017). Genetic variations have been identified to cause an alcohol-flushing response and nausea among individuals of Asian descent (Brooks et al., 2009). Additionally, alcohol use disorder has been found to have multiple genetic contributing factors (Reilly et al., 2017). However, no biomarkers have been found to explain the greater risk of alcohol use disorder among AI/ANs (Ehlers & Gizer, 2013). Biological vulnerability (BV; e.g., firewater myth [FM]) is the belief that AI/ANs are genetically different causing alcohol use dependence (LaMarr, 2003). This myth adds to the historical trauma experienced by Native people.

The Review

Aim

With the increased rates of substance misuse among AI/AN youth and the relationship of misuse and other risk behaviors, it is critical to explore what risk and protective factors could contribute to AI/AN youth’s using substances. Knowing the strategies that can help decrease substance misuse will help in the development of prevention programs. The purpose of this systematic mixed studies review was to identify factors that are either risk or protective factors influencing substance misuse in AI/AN youth. We sought to answer the following question: What factors influence substance misuse among AI/AN youth?

Method

We completed a systematic mixed studies review (Pluye & Hong, 2014) using a results-based convergent synthesis design (Hong et al., 2017). This design involves analyzing quantitative and qualitative data separately and synthesizing the findings in a third synthesis. We chose this design because it allowed us to integrate a diverse group of studies. Because AI/AN culture is based on storytelling (Dennis & Minor, 2019), we believed it to be very important that qualitative and mixed-methods studies be included in this review. The institutional review board administrator was notified and stated that an application is not required for a literature review.

Search Methods

CINAHL, EBSCO, ERIC, JSTOR, SocINDEX, PsycArticles, PsycINFO, and PubMed were searched for articles published between 2014 and 2019 using the search terms “Native American youth,” “Native American adolescent,” “Native Youth,” “substance use,” “substance misuse” and “substance abuse.” Included studies were full-texted, peer-reviewed, quantitative, or qualitative data–based studies that focused on factors that contribute to substance misuse among AI/AN youth. Inclusion criteria were AI/AN youth in the United States and Canada between the ages of 10 to 26 years. This age range, although broad, was felt to be important due to the mean age of substance initiation among Native youth. Additionally, the habits that potentiate misuse leading to addiction often occur through emerging adulthood. Research studies published in English between the years 2014 and 2019 that focused on any type of substance misuse were included. Articles were excluded if (1) the focus was on youth from South America or Australia, (2) the study included a large number of adults, (3) the focus was on treatment/intervention only, (4) the study was a systematic review, and/or (5) the focus was on mental health issues without substance misuse.

Search Outcome

The original search from databases and hand searching resulted in 5,366 articles. Of this sample, 4,937 were excluded due to not meeting the previously stated inclusion criteria. Duplicates (n = 148) were removed, and the titles and abstracts of the 281 remaining articles were manually reviewed for relevance. The final sample consisted of 47 studies (see Figure 1).

Figure 1.

Search Results.

Quality Appraisal

Table 1 contains concise information about each study, including authors, design, sample, setting, and factors. We used the Mixed Methods Appraisal Tool (MMAT) to evaluate the studies (Hong et al., 2018; Pluye et al., 2011). MMAT has separate questions for each type of research design as demonstrated in Table 2 and can be used even when the majority of the articles are from a quantitative worldview. The latest version of the MMAT encourages a descriptive quality appraisal rather than a numerical score. The product of this appraisal can be found in Table 2.

Table 1.

Table of Evidence.

No.	Author(s) (year)	Design	Sample	Setting	Factors
1	Allen et al. (2014)	Quantitative Descriptive Cross-sectional	N = 413 Ages 12-18 years (M = 15.31, SD = 1.61) Female (n = 223) Male (n = 190)	Boarding school Rural communities in Southeast Alaska and Southwestern Alaska, U.S.	Protective factors • Family role • Community characteristics (support) • Peer influences (discourage, disapproval) • Reflective processes • Reasons for life • Individual characteristics (family mastery) Risk factors • N/A
2	Armenta, Sittner, & Whitbeck (2016)	Quantitative Nonrandomized Longitudinal	N = 674 (M_age = 11.11, SD = 0.83) Female (50.3%)	3 AI reservations in the Upper Midwestern, U.S. 4 First Nations Reserves in the adjoining region in Canada	Protective factors • N/A Risk factors • Discrimination • Peer influence
3	Armenta et al. (2016)	Quantitative Nonrandomized 8-Year longitudinal	N = 636 (M_age = 12.09, SD = 0.86) Female (50%)	3 AI reservations in the Upper Midwestern U.S. 4 First Nations Reserves in the adjoining region in Canada	Protective factors • N/A Risk factors • Positive drinker prototypes • Past year drinking • Perceived drinking norm
4	Boyd-Ball et al. (2014)	Mixed methods Focus group Several data points collected in 3 waves	N = 281 Ages 8-16 years (M_age = 12, SD = 2) Female (54.8%) Male (45.2%)	3 Tribal communities in Pacific Northwestern, U.S.	Protective factors • Community monitoring • Parental monitoring Risk factors • Alcohol use among parents and peers • Age (older youth)
5	Brockie et al. (2015)	Quantitative Descriptive Cross-sectional	N = 288 Ages 15-19 years Female (51%) 48% attending high school or college	Reservation and tribal members Community health setting	Protective factors • N/A Risk factors • Physical abuse • Sexual abuse • Emotional neglect and emotional abuse • Exposure to domestic violence • Perceived discrimination • Any adverse childhood experience to multiple (increased risk with increased number of experiences)
6	Brown et al. (2016)	Qualitative Focus groups	N = 70 youth, parents, providers, and community advisory board members Youth ages 4-11 years 10 focus groups	2 Large urban communities in Northern and Southern California, U.S.	Protective factors • Ethnic identity • Cultural identity Risk factors • Family disconnection • Intergenerational stressors • Pantribal identity • Living on reservation • Stereotypes and harassment • AI/AN identity as a double-edged sword • AI/AN cultural disconnection
7	Clarkson et al. (2015)	Quantitative Nonrandomized cohort study	N = 605 Ages 14-31 years Mean age range (22.8-24.4)	Vancouver and Prince George, Northwest Canada	Protective factors • N/A Risk factors • Place in child welfare system
8	Cwik et al. (2017)	Quantitative Nonrandomized Cross-sectional	N = 136 Ages 10-19 years	Fort Apache Indian Reservation Northeastern Arizona, U.S.	Protective factors • Cultural identity Risk factors • Family dysfunction • Poor peer relation • High impulsivity • Stressful life events
9	Davis et al. (2019)	Quantitative Nonrandomized Cross-sectional	N = 1,934 Youth grades 7th-12th (M_age = 15.31)	Northeast, Northwest, Southeast, Southwest, Northern Plains, Southern Plains, and Upper Great Lakes schools with Grade 7 located in the continental U.S. (excluding Oklahoma)	Protective factors • Ethnic identity Risk factors • Perceived discrimination
10	Donovan et al. (2015)	Mixed methods	N = 8 from 10th-12th grade N = 23 from 9th-12th grade	Northwestern, U.S.	Protective factors • Cultural identity • Participation in cultural activities • Optimism/self-efficacy Risk factors • N/A
11	Eitle et al. (2017)	Quantitative Nonrandomized Secondary analysis of data from National Longitudinal Study of Adolescent to Adult Health (Add Health—Wave I)	N = 523 M_age = 15.23 years	U.S.	Protective factors • School connectedness • School resources (advantaged) • School drug awareness programs • Parental education (the lower, the more protective for marijuana use) Risk factors • Peer substance use • Parental alcohol use • Stressful life events • School racial composition • School positive environment • School punitiveness • Self-esteem • Gender (female)
12	Eitle & McNulty Eitle (2018)	Quantitative Nonrandomized Secondary analysis of data from the National Longitudinal Study of Adolescent to Adult Health (Add Health, in-home Waves I and II)	N = 623 with 12th grade	U.S.	Protective factors • Family structure (two-parent family) Risk factors • Gender (females) • Non–two-parent family • Depressive symptoms • Peer substance use • Stressful life event • Increased body mass index • Parental education (the more, the increased risk)
13	Fairman et al. (2019)	Quantitative Nonrandomized Secondary analysis of data from NSDUH	N = 275,559 (included multiple races) Ages 12-21 years	U.S.	Protective factors • N/A Risk factors • Gender (males) • Age (the older, the increase in risk) • Race
14	Friese et al. (2015)	Quantitative Descriptive Secondary analysis of data	N = 719 AI/NA youth Ages 10-18 years	High schools Montana, U.S.	Protective factors • School involvement • Police enforcement • Neighborhood antidrug norms Risk factors • neighborhood disorganization
15	Garrett et al. (2017)	Quantitative Descriptive Cross-sectional	N = 1,421 (M_age = 15.9 years) Male (49.8%)	Schools from rural communities, specifically Cherokee Nation in Northeast Oklahoma, U.S.	Protective factors • N/A Risk factors • Perceived racial discrimination (frequency and intensity)
16	Gonzalez & Skewes (2016)	Quantitative Descriptive Cross-sectional	N = 157 Ages 18-61 years (M_age = 27.1) Female (69.8%) Male (30.2%)	College setting in Alaska, U.S.	Protective factors • N/A Risk factors • Belief of BV to alcohol
17	Gonzalez & Skewes (2018)	Quantitative Descriptive Secondary analysis of data	N = 157 college students Ages 18-61 years (M_age = 27.11) Female (70.1%) Male (29.9%)	Colleges from Alaska, Washington, Colorado, Wyoming, Missouri, New Mexico, Virginia, Mississippi, Idaho, Nebraska, U.S.	Protective factors • Harm reduction strategies Risk factors • Belief of BV to alcohol
18	Gonzalez et al. (2019)	Quantitative Descriptive Cross-sectional	N = 137 (M_age = 21.7, SD = 4.29) Female (68.6%)	Multiple states: Alaska, Washington, Colorado, Wyoming, Missouri, New Mexico, Virginia, Mississippi, Idaho, Nebraska, U.S.	Protective factors • Harm reduction strategies Risk factors • Belief of BV to alcohol
19	Greenfield et al. (2017)	Quantitative Nonrandomized Longitudinal	N = 673 (M_age = 11.1) Female (50.3%) 85.5% lived on a reservation/reserve	3 reservations in the Northern Midwest, U.S. 4 reservations in Canada	Protective factors • Caretaker warmth • Positive school adjustment • Less discrimination • Fewer deviant peers • Caretaker with no major depression Risk factors • N/A
20	Hagler et al. (2017)	Quantitative Nonrandomized Cross-sectional	N = 393 college students (NA n = 147 and NHW n = 246) NA female (77.6%) NHW female (67.5%)	College setting in Southwestern, U.S.	Protective factors • N/A Risk factors • Peer alcohol consumption/descriptive norm of best friend
21	Hautala & Sittner (2018)	Quantitative Nonrandomized Longitudinal Secondary data analysis	N = 521 Ages 16-19 years AI/AN living within 50 miles of the reservation	Upper-Midwest, U.S. and Canada	Protective factors • Family connectedness • Warmth and support Risk factors • Violence among caretakers • Community violence exposure • Living in remote environment • Delinquent peer associations • Lifetime SUD diagnosis
22	Hautala et al. (2019)	Quantitative Nonrandomized Longitudinal Secondary data analysis	N = 744 Ages 10-18 years Mean age range 11.1-18.2 AI/AN living within 50 miles of the reservation	Reservation area in the Upper-Midwest of the U.S. and Canada	Protective factors • Increase in family income (SES) Risk factors • Age • Past year externalizing disorders (conduct disorder, oppositional defiance disorder, and attention-deficit/hyperactivity disorder) • Prior substance use • Gender in case of multiple SUD • Past year internalizing (major depressive disorder, dysthymia, and generalized anxiety disorder)
23	Kelley et al. (2019)	Quantitative Nonrandomized Cross-sectional	N = 565 Ages 12-20 years (M_age = 14.3, SD = 1.66) Female (n = 245) Male (n = 330) 92.3% of the sample lived on reservations 7.7% lived in an urban Indian community	AI community in the Great Plains Region, U.S.	Protective factors • Community connectedness including social norms and cultural values • Social support Risk factors • Age
24	King et al. (2014)	Quantitative Descriptive Cross-sectional	N = 366 AI youth 7th-12th grades Male (62.8%)	Public and private schools in the greater metropolitan area, U.S.	Protective factors • Parental injunctive norms • Peer injunctive norms • Perception of harmful effects of alcohol Risk factors • Ease of access
25	Kulis et al. (2016)	Quantitative Descriptive Secondary data analysis	N = 2,407 AI youth 8th-12th grades (M_age = 15.1) Male (51.4%)	Public or charter schools in urban metropolitan areas Arizona, U.S.	Protective factors • Age of initiation (the later, the better) • Age of initiation of antisocial behavior • Antidrug attitudes • Perceived harm • Disapproval of antisocial behavior Risk factors • Age • Grade level • Being offered substance • Antisocial behavior • Oppositional behavior • Exposure to trauma • Family history of mental health and substance use
26	Lowe, Liang, Henson, & Riggs (2016)	Quantitative Nonrandomized Quasi-experimental Longitudinal	N = 100 AI youth with 6th grade (M_age = 11.24 years, SD = 0.80)	After-school programs in Keetoowah-Cherokee, Oklahoma, U.S. 10-week intervention, two participating public schools were randomized	Protective factors • CTC substance misuse prevention intervention that is culturally focused is effective over a sustained time frame compared with standard substance misuse prevention program Risk factors • N/A
27	Martinez et al. (2015)	Quantitative Descriptive Secondary analysis of data from a longitudinal RCT study	N = 155 AI youth 7th-8th grade (M_age = 12.4, SD = 1.25) Female (51%) Male (49%)	AI youth from in the prevention program and cultural enrichment program at the 5 middle schools in a large urban Southwestern city in the U.S.	Protective factors • Grandparent injunctive norms • Parent injunctive norms • Peer injunctive norms Risk factors • Gender (females)
28	McNulty Eitle & Eitle (2014)	Quantitative Descriptive Cross-sectional	N = 568 AI youth (20.4%) Ages 12-19 years (M = 15.8, SD = 1.44)	Total 5 rural schools including 2 high schools in the U.S.	Protective factors • Using instrumental support • Religion • Active coping • Planning Risk factors • Behavioral disengagement • Self-distraction
29	Mileviciute et al. (2014)	Quantitative Descriptive Cross-sectional	N = 146 Ages 13-18 years (M_age = 14.5) Female (n = 93) Male (n = 53)	A youth summer program in North Americans Plains tribes, U.S.	Protective factors • Self-efficacy to resistant negative • Peer influence Risk factors • N/A
30	Mohatt et al. (2014)	Quantitative Nonrandomized Quasi-experimental study—2 types of intervention groups (ET and YA)	ET intervention Total sample (N = 54) (M_age = 14.24 years; SD = 1.72) Female (n = 31), Male (n = 23) YA intervention Total sample (N = 52) (M_age = 14.62 years; SD = 1.96) Female (n = 25), Male (n = 27)	Rural Yup’ik AN youth in 2 remote communities approximately 530 total population. ET intervention Total 52 sessions with youth (10 sessions), families (2), and community (10) YA intervention Total 15 prevention modules including youth (4), families (5), and community (6)	Protective factors • Individual characteristics (self-efficacy, communal mastery, wanting to be a role model) • Family characteristics (affection/praise, being treated special, clear limits and expectations, family model of sobriety) • Community characteristics (safe spaces, opportunities, role models, limits on alcohol use) Risk factors • N/A
31	Moilanen et al. (2014)	Quantitative Descriptive Secondary analysis of data from 2010 AYS	N = 5,701 Ages 12-19 years (M_age = 15.23, SD = 1.68) Male (50%) Reservation status (28.9%)	372 individual schools in Arizona, U.S.	Protective factors • School-based extracurricular activities (perception and intensity of utilization) Risk factors • Age (the older, the more use) • Living on reservations
32	Moon et al. (2014)	Quantitative Descriptive Cross-sectional Secondary analysis of data from 2010 NSDUH	N = 287 Ages 12-17 years Female (52.6%) Male (47.4%) 29.6% have had used marijuana	U.S.	Protective factors • Parental monitoring • School connectedness (on peer substance use as an indirect factor) Risk factors • Peer substance use
33	Patchell et al. (2015)	Quantitative Nonrandomized Quasi-experimental with a one group, pretest–posttest cohort design	N = 44 Majority in 10th grade (n = 30) Ages 16-18 years Female (n = 24) Male (n = 20), Substance use behavior • Alcohol (25%) • Marijuana (9%)	2 Rural Southwestern Oklahoma high schools, U.S. Native Talking Circle Intervention (school-based group intervention) 30-45min session (total 10 hours), 2-3 times per week over 8.5 weeks	Protective factors • Ethnic identity • Self-reliance (by self, referring to family and community and not just the individual) Risk factors • N/A
34	Philip et al. (2016)	Quantitative Nonrandomized Quasi-experimental design without a control group	N = 57 Ages 12-19 years (M_age = 15.25) Female (n = 27) Male (n = 30)	AN youth from the Yup’ik AN community in Southwest Alaska	Protective factors • Social networks (family characteristics, community characteristics, individual characteristics) Risk factors • N/A
35	Prince et al. (2017)	Quantitative Descriptive Cross-sectional	N = 3,050 AI youth from 7th-12th grades (M_age = 14.76, SD = 1.70) Female (49.5%) Male (50.5%) Substance use behavior • Ever being drunk (44%) • Used any drugs (including illegal) (49%)	46 Sampled schools from 6 geographical regions (Northwest, Northern Plains, Southeast, Southern Great Plains, & Southwest), U.S. Region Northern Plains 51% Southwest 30.7% Upper Great Lakes 11.4% Southeast 3.6% Northwest 3.3%	Protective factors • Injunctive norms (only among males) • Perceived harm Risk factors • Descriptive norms: peer drug use
36	Sittner (2015)	Quantitative Descriptive Longitudinal	N = 619 Ages 10-13 years (M_age = 11) Female (50.4%)	5 Reservations in the U.S. 4 Reservations in Canada	Protective factors • N/A Risk factors • Early onset (gender-females, conduct disorder, expelled from school, deviant peers, low average per capita family income)
37	Sittner (2017)	Quantitative Descriptive Longitudinal	N = 745 Ages 10-17 years	5 Reservations in the U.S. 4 Reservations in Canada	Protective factors • Caretaker monitoring • School adjustment (positive attitude toward school) • Living remote Risk factors • Age (the older, the more difficult to abstain) • Prior smoking • Peer substance use
38	Spillane et al. (2017)	Quantitative Nonrandomized Cross-sectional	N = 3,489 ≥20% AI students on or near reservations	33 Schools were surveyed in 11 states with reservations where 6 geographic regions including, Northwest, Northern Plains, Northeast, Southeast, Southern Great Plains, and Southwest, U.S.	Protective factors • Parental monitoring Risk factors • Peer substance use • Lifetime prescription of stimulant by a physician • Age (increased age, increased risk) • Perception of peer substance use
39	Stanley, Miller, et al. (2014)	Quantitative Non-randomized Longitudinal Secondary analysis of data from AIR data (9 data points)	N = 281 Ages 11-12 years at baseline	Schools from urban Indian clinic in Seattle, U.S.	Protective factors • School connectedness Risk factors • Family conflict • Gender—females are more likely to have AUD • Externalizing behavior
40	Stanley, Harness, et al. (2014)	Quantitative Descriptive Cross-sectional	N = 1,399 AI youth with 8th-12th grades 20% AI students on or near reservations	33 Schools in 11 states with reservations, where six different geographic regions in which reservation-based AIs reside (Northwest, Northern Plains, Northeast, Southeast, Southern Great Plains, and Southwest), U.S.	Protective factors • N/A Risk factors • Grade level, the higher the grade, the increased prevalence of substance use
41	Stanley & Swaim (2015)	Quantitative Descriptive Secondary analysis of data	N = 3,480 Ages 11-17 years (M_age = 14.8) Female (47.4%) Male (49.4%)	32 Schools in reservations that have at least 20% AI youth enrolled from 7 geographic regions (Northwest, Northern Plains, Upper Great Lakes, Northeast, Southeast, Southern Great Plains, and Southwest), U.S.	Protective factors • N/A Risk factors • Residency on or near reservation • Ethnicity • Age
42	Stanley et al. (2017)	Quantitative Descriptive Cross-sectional	N = 3,446 AI youth with 7th-12th grades Female (49.5%)	45 Schools surveyed across four academic years (2009-2012) American Indian reservations across six regions (Northeast, Northwest, Northern Plains, Southeast, Southwest, and Upper Great Lakes), U.S.	Protective factors • Family injunctive roles • Student injunctive norms Risk factors • School descriptive norms • Descriptive norms • Anticipatory socialization
43	Swaim & Stanley (2016)	Quantitative Descriptive Cross-sectional	N = 4,942 with AI youth (68.4%) Female (49.2%)	35 Middle schools and 17 high schools on or near reservations, having at least 20% AI students from different seven geographic regions (Northwest, Upper Great Lakes, Southeast, Southern Great Plains, Southwest)	Protective factors • Family participation in school events • Family structure-living with both parents • Parental monitoring • Family sanctions against substance use Risk factors • Family conflict
44	Swaim & Stanley (2019)	Quantitative Nonrandomized Longitudinal	N = 1,503 AI youth 7th-12th grade	Schools on or near reservations in Colorado, U.S.	Protective factors • Family attitudes (family injunctive norms) • Perceived harm Risk factors • Peer factor • Perceived availability • Early initiation • Grade level (the higher, the increased involvement)
45	Turanovic & Pratt (2017)	Quantitative Descriptive Secondary analysis of data from Add Health	N = 558 Ages 12-19 years (Wave I), 19-26 years (Wave III) (M_age = 15.4, SD = 1.64) Females (49.8%) Males (50.8%)	80 High schools and 52 feeder middle and junior high schools in U.S.	Protective factors • Family connectedness • School connectedness Risk factors • Verbal intelligence • Parent public assistance • Childhood victimization
46	Urbaeva et al. (2017)	Quantitative Nonrandomized Secondary analysis of data from cross-sectional survey between 2009 and 2013	N = 2,638 Ages 11-16 years (M_age = 14.77, SD = 1.7) Female (49%) Male (51%)	Random selection of public, tribal schools from 8 geographic regions of the country: the Northwest, Northern Plains, Upper Great Lakes, Northeast, Southeast, Southwest, Oklahoma, and the Southern Great Plains, U.S.	Protective factors • Family connectedness • Family communication about alcohol • Parental monitoring • Cultural identification on parental monitoring Risk factors • Mood-enhancing benefits • Peer pressure • Cultural identification on communication about alcohol • Cultural identification on family involvement in school • Age (11-16 years) • Alcohol availability
47	Whitesell et al. (2014)	Quantitative Nonrandomized Longitudinal Secondary data analysis	N = 381 (Wave 1) N = 375 (Wave 2) N = 276 (Wave 3) N = 246 (Wave 4) (M_age = 12.77 years; SD = 1.04 at Wave 1) Male (54.4%) 60% reported currently receiving food stamps	Middle schools of AI reservations in Northern Plains, U.S.	Protective factors • N/A Risk factors • High exposure to stressful events (including early puberty and deviant peer relationships) • Living on reservations • Gender (female) • Age • Early puberty • Deviant peer influence • Poor parent–child relationship

Note. AI/AN = American Indian/Alaskan Native; NA = Native American; U.S. = the United States; N/A = not applicable; NSDUH = National Survey on Drug Use and Health; BV = biological vulnerability; NHW = non-Hispanic White; SUD = substance use disorder; SES = socioeconomic status; CTC = Cherokee Talking Circle; RCT = randomized controlled trial; AYS = Arizona Youth Survey; AIR = American Indian Research.

Table 2.

Mixed Methods Appraisal Tool for Quantitative, Qualitative, and Mixed-Methods Studies.

Quantitative nonrandomized	Are there clear research questions?	Do the collected data allow to address the research questions?	Are the participants representative of the target population?	Are measurements appropriate regarding both the outcome and intervention (or exposure)?	Are there complete outcome data?	Are the confounders accounted for the design and analysis?	During the study period, is the intervention administered (or exposure occurred) as intended?
Armenta, Sittner, & Whitbeck (2016)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Armenta et al. (2016)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Clarkson et al. (2015)	Yes	Yes	Yes	Can’t tell	Yes	Yes	Yes
Cwik et al. (2017)	Yes	Yes	No (total n = 123; case n = 68; control n = 55)	Yes	Yes	Yes	Yes
Davis et al. (2019)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Eitle et al. (2017)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Eitle & McNulty Eitle (2018)	No	Yes	Yes	Yes	Yes	Yes	Yes
Fairman et al. (2019)	No	Yes	Yes	Yes	Yes	Yes	Yes
Greenfield et al. (2017)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Hagler et al. (2017)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Hautala & Sittner (2018)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Hautala et al. (2019)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Kelley et al. (2019)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Lowe, Liang, Henson, & Riggs (2019)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Patchell et al. (2015)	Yes	Yes	Yes, but small sample	Yes	Can’t tell	Yes	Yes
Sittner (2017)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Spillane et al. (2017)	Yes	yes	Yes	Yes	Yes	Yes	Yes
Stanley, Miller, et al. (2014)	Yes	Yes	No	Yes	Yes	Can’t tell	Yes
Stanley & Swaim, (2015)	No	Yes	Yes	Can’t tell (It tells they used a famous survey but also should refer to other study)	Yes	Yes	Yes
Stanley et al. (2017)	Yes	Yes	Yes	Yes	Yes	Can’t tell	Yes
Swaim & Stanley (2016)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Swaim & Stanley (2019)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Turanovic & Pratt (2017)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Urbaeva et al. (2017)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Whitesell et al. (2014)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Quantitative descriptive	Are there clear research questions?	Do the collected data allow to address the research questions?	Is the sampling strategy relevant to address the research questions?	Is the sample representative of the target population?	Are the measurements appropriate?	Is the risk of nonresponse bias low?	Is the statistical analysis appropriate to answer the research questions?
Allen et al. (2014)	Yes	Yes	Yes	No, only 4% AI	Yes	Yes	Yes
Brockie et al. (2015)	Yes	Yes	Yes	No (The team exceeded the preestablished goal to recruit at least 60 participants from each of the two larger communities, but did not meet the goal of at least 30 from each of the four smaller communities.)	Yes, although one measure not at all discussed (HLAS)	Yes	Yes
Friese et al. (2015)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Garrett et al. (2017)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Gonzalez & Skewes (2016)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Gonzalez & Skewes (2018)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Gonzalez et al. (2019)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
King et al. (2014)	Yes	Yes	Yes	Yes	Yes	Can’t tell	Yes
Kulis et al. (2016)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Martinez et al. (2015)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
McNulty Eitle & Eitle (2014)	Yes	Yes	Yes	Yes	Mostly, outcome variable was a dichotomous question about use of marijuana/alcohol in last year	Yes	Yes
Mileviciute et al. (2014)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Mohatt et al. (2014)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Moilanen et al. (2014)	Yes	Yes	Yes	Yes	One item about their perceptions of availability of extracurricular activities (ECA) at their school and one item for participation in ECA; the major IV	Yes	Yes
Moon et al. (2014)	Yes	Yes	Yes	Yes	Yes	Can’t tell	Yes
Philip et al. (2016)	Yes	Yes	Yes	No	Yes	Yes	Yes
Prince et al. (2017)	Yes	Yes	Yes	Yes	Can’t tell (measurements are clearly presented but lacking discussion about validity . . .)	Yes	Yes
Sittner (2015)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Stanley, Harness, et al. (2014)	No	Yes	Yes	Yes	Yes	Yes	Yes
Qualitative methods study	Are there clear research questions?	Do the collected data allow addressing the research questions?	Is the qualitative appropriate to answer the research question?	Are the qualitative data collection methods adequate to address the research question?	Are the findings adequately derived from the data?	Is the interpretation of results sufficiently substantiated by data?	Is there coherence between data source, collection, analysis, and interpretation?
Brown et al. (2016)	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Mixed-methods studies	Are there clear research questions?	Do the collected data allow for addressing the research questions?	Is there an adequate rationale for using a mixed-methods design to address the research questions?	Are the different components of the study effectively integrated to answer the research question?	Are the results adequately brought together into overall interpretation?	Are divergences and inconsistencies between quantitative and qualitative results adequately addressed?	Do the different components of the study adhere to the quality criteria of each tradition of the methods involved?
Boyd-Ball et al. (2014)	Yes	Yes	No (no information to describe why they planned to conduct in-person interview)	Yes	No (no description about qualitative data and results)	Can’t tell	Yes
Donovan et al. (2015)	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Data Abstraction and Synthesis

We extracted numerical data from quantitative studies and synthesized these data narratively. We performed qualitative thematic synthesis to abstract and synthesize data from the one qualitative study (Hong et al., 2017; Thomas & Harden, 2008). To form themes, studies were read in their entirety, and findings from all included studies were coded (Hong et al., 2017). Codes were then grouped together to develop themes (Pluye & Hong, 2014; Thomas & Harden, 2008). We then performed a third synthesis to compare quantitative and qualitative findings and derived additional themes from the combination of these findings. Themes emerged represented the different levels of the ecological systems model (Bronfenbrenner, 1974, 1994), which was then employed for thematic analysis of the extracted data consistent with the method of qualitative descriptive analysis described by Sandelowski (2000).

Ecological Systems Model

The ecological systems model provides a useful framework for examining the protective and risk factors for substance misuse among AI/AN youth. Past research has focused on identifying individual, family, peers, school, community, and cultural characteristics that foster or inhibit substance misuse behaviors among AI/AN youth (Friese et al., 2015; Sittner, 2017). The ecological systems model is composed of subsystems affecting human growth and development. It has been described using the analogy of a Russian doll and includes the individual, micro-, and macrosystems (Bronfenbrenner, 1994). Individual system level is characterized by the individual’s sociodemographic characteristics. Microsystem is described as the pattern of roles and relations in a given face-to-face setting within an immediate environment, including family, school, and peer group, whereas macrosystem is the overarching beliefs and values from the community and neighborhood environment that affects AI/AN youth.

Results

Sample

A total of 47 studies met the inclusion criteria. Employing MMAT (Hong et al., 2018) critical appraisal tool’s definition of study design, the majority of the studies were quantitative design (24 quantitative nonrandomized; 20 quantitative descriptive; one qualitative; and two mixed-methods studies). There was only one qualitative study that met the inclusion criteria, and the researchers employed focus group methodology (see Table 1). The focus of this review was to identify factors that contribute to substance misuse among AI/AN youth.

Many of the studies took a systems conceptual approach to examine factors that protect AI/AN youth against substance misuse. The emerging themes were categorized based on the ecological systems model subsystems of individual, microsystem (family, peer group, and school), and macrosystem (community).

The studies included in this review had a sample size ranging from 31 (Donovan et al., 2015) to 275,559 (Fairman et al., 2019). Twenty-seven studies had a sample size of less than 100, while six studies had a sample size between 100 and 1,000, and finally, 14 studies had a sample of more than 1,000 AI/AN youth. As outlined in the setting within Table 1, Canada was represented in five studies; however, the majority of the studies took place in the United States. The majority of the included studies (40/47) reported that more than 50% of their sample identified as female between the ages 10 to 21 years. As shown in Table 1, the represented areas in the included studies varied between rural, urban, and national, where six studies focused on the rural areas, six on urban areas, and five studies were national, using national data sets. AI/AN youth included in these studies were living on or near reservations. Schools were an important place for recruitment in the majority of the studies, including public, private, and boarding schools, ranging between middle to high school. University-based recruitment was also employed in 11 studies. Only one study focused on a community health setting for recruitment of the AI/AN sample (Brockie et al., 2015).

Ecological Systems Model

Extensive research supports the use of the ecological systems model to understand adolescent substance use, highlighting characteristics of the individual, as well as those of the family, school, peer, and community domains and their influence on the likelihood of such involvement (Friese et al., 2015; Sittner, 2017). Emerging from this review of the literature included individual, micro-, and macro-systems–level themes. The use of Bronfenbrenner’s (1994) model illustrates how substance initiation and misuse among AI/AN youth is influenced by a complex set of both protective and risk factors at these three levels of the ecological model.

Individual System–Level Factors

Individual system characteristics of the AI/AN youth play an important role in shaping individual decisions concerning whether they will initiate and/or misuse substances.

Protective Factors

AI/AN youth’s perceptions of the harmful effects of substances were seen as a protective factor, increasing resilience against substance misuse (Kulis et al., 2016; Prince et al., 2017; Swaim & Stanley, 2016). In one study, AI/AN youth who acknowledged high harmful effects of drinking alcohol were 89% less likely to drink alcohol in the past month than those who did not believe there were harmful effects from drinking alcohol (odds ratio [OR] = 0.11, p < .001, 95% CI [0.06, 0.50]; King et al., 2014). In addition, antidrug attitudes and disapproval of antisocial behavior by the AI/AN youth were related to increased resilience against initiation. The use of reflective processes on self and Native way of life was seen as important individual-level protective factors (Allen et al., 2014). These processes are similar to self-efficacy, optimism, and hope that were also leading protective factors in the individual subsystem.

Self-efficacy, which is an individual’s ability to control their behavior, was a main protective factor with respect to peer influence regarding substance use (Donovan et al., 2015; Mileviciute et al., 2014; Mohatt et al., 2014). Increased self-efficacy, defined by relying not only on self but also on family and community was seen to enhance resilience against substance initiation among AI/AN youth (Patchell et al., 2015). Increased optimism and hope toward one’s future were associated with a decrease in substance misuse (Donovan et al., 2015). An additional protective factor against substance misuse for AI/AN youth was a mentored internship opportunity to work with and role model for younger AI/AN adolescents (Mohatt et al., 2014).

Risk Factors

On the individual level, a variety of factors were found to increase the risk of substance misuse among AI/AN youth. Demographic differences, such as age and gender, were seen to be associated with risk factors. Older youth (Boyd-Ball et al., 2014; Fairman et al., 2019; Hautala et al., 2019; Lowe et al., 2019) and AI/AN youth in higher grades (King et al., 2014; Stanley, Harness, et al., 2014) were more likely to initiate and misuse substances. Additionally, the early age of initiation was associated with an increased risk of later substance misuse (Stanley, Harness, et al., 2014; Swaim & Stanley, 2019). Gender was seen to be another individual-level risk factor concerning substance misuse among the AI/AN youth. The relationship between gender and substance misuse was seen to be relative to the nature and the number of substances used. Both males and females were at significant risk for substance use, with multiple substances (Fairman et al., 2019; Hautala et al., 2019; Martinez et al., 2015; Stanley, Miller, et al., 2014). Males had a 69% higher relative risk of using marijuana than females as opposed to those youth not using substances (adjusted relative risk ratio [aRRR] = 1.69, p < .001, 95% CI [1.61, 1.78]; Fairman et al., 2019). Female AI/AN youth were 49% less likely to use illicit substances compared with their male counterparts (OR = 0.51, p < 0.05, 95% CI [0.27, 0.97]; Stanley, Miller, et al., 2014) but were 39 times more likely to have heavy alcohol use compared with males (incidence rate ratio [IRR] = 0.39, p < .01, 95% CI [0.20, 0.79]; Eitle & McNulty Eitle, 2018). Additionally, female AI/AN youth had a 38% higher risk of developing multiple substance use disorders including cigarettes, alcohol, and marijuana (OR = 1.38, p < .05, 95% CI [1.01, 1.87]; Hautala et al., 2019) whereas males had lower intentions of using multiple substances (β = −0.23, p < .05; Martinez et al., 2015).

AI/AN youth beliefs present an important individual-level risk factor regarding substance misuse. Perceived drinking norms, characterized by the recognition of the AI/AN youth of their peer’s substance use in addition to the belief in the positive drinking prototype, also known as BV (Armenta, Sittner, & Whitbeck, 2016; Gonzalez & Skewes, 2016, 2018) places AI/AN youth at an increased risk of substance misuse. AI/AN youth who believe in BV as a cause of alcohol use disorder were positively associated with heavier drinking and a lack of self-efficacy to drink moderately as well as a decreased belief that harm reduction strategies are effective (Gonzalez & Skewes, 2018). In fact, greater belief in BV was associated with less use of protective behavioral strategies (Gonzalez et al., 2019). Another perceived norm is that substance use is a means of socialization and has been positively associated with substance misuse among AI/AN youth (Stanley et al., 2017).

Additionally, AI/AN youth with behavioral problems, both externalizing behaviors (Cwik et al., 2017; Hautala et al., 2019; Kulis et al., 2016; Sittner, 2015; Stanley, Harness, et al., 2014) and internalizing behaviors (Hautala et al., 2019; McNulty Eitle, & Eitle, 2014) were at increased odds of substance misuse. Drinking alcohol (Armenta et al., 2016) or smoking (Sittner, 2017) was associated with future substance misuse among AI/AN youth. AI/AN youth who had drank alcohol during the past year were more likely to drink at 3 (r = .24, p < .01) and 5 years later (r = .19, p < .01) (Armenta et al., 2016).

Summary of Individual System–Level Factors

The younger someone initiates substance use the greater likelihood of future issues with a substance use disorder. However, older youth were more likely to report substance use within the last month. Males were more likely to initiate marijuana use as compared with females, but if females initiated substance use, they were more likely than males to use multiple substances. Youth who knew the adverse effects of substance use were less likely to initiate use. Additionally, youth whose social norms aligned with attitudes against substance use in youth were less likely to initiate substance use. The literature suggests that both knowledge and attitudes affect AI/AN youth resilience against substance use.

Microsystem-Level Factors

The three components of the microsystem that emerged from a review of the included studies were family, school, and peer group.

Protective Factors

Factors that increase resilience against substance use will be presented from the family, school, and peer groups.

Family

Connectedness to family increases resilience (Hautala & Sittner, 2018) with parental monitoring key to prevention (Boyd-Ball et al., 2014). Family connectedness, also defined as family warmth and support, was shown to have negative associations with AI/AN youth substance misuse in several of the included studies (Allen et al., 2014; Greenfield et al., 2017; Hautala et al., 2018; Mohatt et al., 2014; Turanovic & Pratt, 2017; Urbaeva et al., 2017; Whitesell et al., 2014). Even elder family members such as grandparents’ monitoring was significantly associated with reducing Native youth’s substance misuse including the use of alcohol (Martinez et al., 2015), reflecting AI/AN culture of respect for elders and their leadership role in the household (Martinez et al., 2015). It was found that relationships with family and community influenced behavior, more than peers in AI/AN youth (Allen et al., 2014). In fact, when family members monitor and nurture friendships with peers from families with similar values against youth substance misuse increase resilience in their youth against substance misuse (Allen et al., 2014).

The role of the family in the resilience building of AI/AN youth was also found to be depicted by family structure and family income. Two studies highlighted the significant association of AI/AN youth living with both parents in respect to the decreased risk of substance misuse (Eitle & McNulty Eitle, 2018; Swaim & Stanley, 2016). Living with both parents decreased the risk of current marijuana use for AI/AN middle school youth (OR = −0.58, p < .001, 95% CI [0.46, 0.73]; Swaim & Stanley, 2016).

Another key factor in the family microsystem in increasing resilience against substance misuse in AI/AN youth is seen through the perspective of family attitudes and communication about substance misuse. Four studies show the negative association between family disapproval of substance use and AI/AN youth substance misuse (Kelley et al., 2019; Stanley et al., 2017; Swaim & Stanley, 2016; Swaim & Stanley, 2019), which can also be described as negative family social norms toward substance use among youth (Kelley et al., 2019; Stanley et al., 2017). Furthermore, AI/AN family communication and discussion about substance misuse was seen as an important factor in increasing resilience among AI/AN youth against substance misuse (Urbaeva et al. 2017; Whitesell et al., 2014). Moreover, the family’s role as a protective factor against AI/AN substance misuse was seen through the interaction between family connectedness and school performance (Stanley et al., 2017), where better family connections resulted in better performance, leading to increased resilience.

School

Connectedness to school increased resilience against substance misuse (Eitle et al., 2017; Friese et al., 2015; Moon et al., 2014; Turanovic & Pratt, 2017). Positive school adjustment, highlighted by AI/AN youth’s positive attitude regarding school, had increased resilience against substance misuse (Greenfield et al., 2017; Sittner, 2017). Several of the included studies showed the importance of school resources to protect AI/AN youth against substance misuse. Schools with more resources were seen to be concomitant with lower substance misuse among AI/AN youth (Eitle et al., 2017). The more the AI/AN youth were connected to the school by both in-school and after-school activities, including involvement with teachers, the more resilient against substance misuse they were. This is especially true when activities were culturally sensitive (Friese et al., 2015; Moilanen et al., 2014) and included substance misuse awareness (Eitle et al., 2017).

Peers

Peers play an important role in substance misuse among AI/AN youth. Peer attitude toward substance use was seen as a major contributing factor in building resilience. Peer social norms are the perceptions by AI/AN youth of what is approved or disapproved by their peers. When the perception that peers disapproved of substance initiation and use, youth were less likely to initiate substance use (Kelley et al., 2019; King et al., 2014; Martinez et al, 2015). Peer group structure, including supportive peer network (Kulis et al., 2016) and less deviant peers (Greenfield et al., 2017), was also seen to promote resilience among AI/AN youth. Moreover, having racially heterogeneous (Kulis et al., 2016) and prosocial peer networks (Whitesell et al., 2014) created a protective factor for the AI/AN youth against substance misuse.

Risk Factors

Factors that increase the risk of substance misuse among AI/AN youth will be presented within the three microsystems as was discussed above.

Family

Exposure to interpersonal violence (IPV) is associated with substance misuse and mental health concerns. In one study, AI/AN youth who had experienced three to six adverse childhood events were more likely to have polysubstance misuse (OR = 4.17, p < .001, 95% CI [2.22, 8.79]; Brockie et al., 2015). Violence among caregivers and family conflict were other factors associated with a decrease in resilience among AI/AN youth (Hautala & Sittner, 2018; Stanley, Miller, et al., 2014). Additionally, parental social norms that are permissive toward alcohol use increased the risk of all substance misuse (King et al., 2014).

School

Higher perception of classmate use of substance was associated with higher levels of substance misuse among AI/AN youth (Prince et al., 2017; Stanley et al., 2017). In fact, when AI/AN youth believed that their peers were using substance, and it was a normal part of adolescent development to use substances, they were 83% more likely to be currently using (p < .001, 95% CI [0.74, 0.92]; Stanley et al., 2017). Peer social norms can be expressed as peer influence or peer pressure, seen in many studies, reflecting a positive association with substance use and peer social norms that are positive toward substance use among AI/AN youth (Eitle et al., 2017; Eitle & McNulty Eitle, 2018; Hagler et al., 2017; Hautala & Sittner, 2018; Moon et al., 2014; Spillane et al., 2017; Stanley et al., 2017; Swaim & Stanley, 2019; Urbaeva et al., 2017; Whitesell et al., 2014). Racism, including in schools, decreases resilience to delaying initiation and misuse of substances (Garrett et al., 2017). Two studies found that exclusionary discipline practices, which are more often employed with minoritized youth, increase risk behaviors (Eitle et al., 2017; Sittner, 2015). These disciplinary practices can make youth feel less connected to school and increase feelings of stigmatization that could decrease resilience against substance misuse (Armenta, Sittner, & Whitbeck, 2016).

Peers

Peer social norms that are positive for drinking decreases resilience against substance misuse (Eitle & McNulty Eitle, 2018; Hagler et al., 2017); social norms perpetuate the myth about a genetic link for AI/AN and alcohol misuse decreases resilience (Gonzalez et al., 2019; Gonzalez & Skewes, 2018). Youth who believe that most adolescents engage in substance misuse are less likely to look unfavorably on youth that drink and have a higher behavioral intent associated with the amount of drinking (Armenta et al., 2016). In one study, AI/AN youth who had higher perceived discrimination and more positive drinker prototype peers were more likely to develop an alcohol use disorder (Armenta, Sittner, & Whitbeck, 2016). The older the youth become, the less negative peer social norms there were against substance misuse (King et al., 2014). Peer social norms that were positive for drinking decreased resilience against substance misuse (Eitle & McNulty Eitle, 2018; Hagler et al., 2017). Social norms that perpetuate the myth about a genetic link for AI/AN and alcohol misuse decreased resilience (Gonzalez et al., 2019; Gonzalez & Skewes, 2018).

Summary of Microsystem-Level Factors

AI/AN youth that experienced/witnessed interpersonal violence were more likely to initiate substance use. AI/AN youth that went to school that had a high percentage of minoritized youth were more likely to initiate substance use. They were also more likely to use substances if they had peers who had positive attitudes toward substance use. However, when AI/AN youth were connected to family and school they were less likely to initiate substance use. When the family had negative attitudes toward adolescent substance use, they were more likely to communicate these attitudes with their youth, leading to less substance initiation. Social norms of both family and peers that were against substance use in adolescents were more likely to increase resilience against substance use.

Macrosystem-Level Factors

The community is the key influencer in the macrosystem. Both protective and risk factors will be discussed.

Protective factors

Communities with antidrug social norms increased resilience against substance misuse (Eitle et al., 2017; Friese et al., 2015; Moon et al., 2014; Turanovic & Pratt, 2017).

Community

Positive ethnic identity increased resilience against substance misuse (Brown et al., 2016) and the negative effects of racism (Cwik et al., 2017; Davis et al., 2019; Lowe et al., 2019). Promoting connectedness of the AI/AN youth to their ethnic community increased resilience (Kelley et al., 2019). Community monitoring was found to be an important factor contributing to resilience against substance misuse. Even as youth become emerging adults, AI/AN communities have high kinship affiliations (Boyd-Ball et al., 2014), and this community connectedness increases resilience against substance misuse. Youth that are more connected to their cultural community are less likely to be influenced by negative peer relationships (Cwik et al., 2017). Instilling future aspirations by increasing the youth’s optimism and self-efficacy was seen as important in preventing substance misuse in AI/AN youth (Donovan et al., 2015).

Risk factors

Several risks were discussed and focused on the long history of trauma that has increased the risk of substance misuse within tribal communities (Brockie et al., 2015).

Community

Perceived discrimination and historical loss associated symptoms were highly associated with substance misuse and mental health concerns (Brockie et al., 2015). Historical trauma such as loss of biological parents and parents’ history of substance misuse decreased AI/AN youth resilience due to lack of AI/AN cultural identity (Brown et al., 2016). Intergenerational stressors related to historical trauma continue to have an effect on youth. Tribal leaders feel that youth who do not have a strong ethnic identity or cultural connection have an increased possibility of substance misuse especially among urban AI/AN youth, where communities are fragmented (Brown et al., 2016). One study examined how the child welfare system in Canada was supplanted for the residential school system as a means of dismantling AI/AN families and their way of life. These emerging adults were more likely to have a parent that attended residential school, experienced sexual abuse, had been involved in sex work, had a substance use disorder, and were HIV positive (Clarkson et al., 2015). The stress of frequent and intense experiences with racial discrimination may increase a youth’s desire to dull the negative effect of discrimination (Garrett et al., 2017). It may be that heavy drinking is a coping mechanism used by AI/AN youth to help cope with discrimination (Davis et al., 2019). AI/AN youth that have perceived racial discrimination are less resilient to substance misuse especially highly addictive substances.

BV (e.g. FW) can add to the trauma of discrimination and historical trauma. AI/AN youth that believe in BV as a cause of alcohol use disorder were positively associated with heavier drinking and a lack of self-efficacy to drink moderately (Gonzalez & Skewes, 2016). A lack of self-esteem also decreases AI/AN youth’s belief that harm reduction strategies are effective (Gonzalez & Skewes, 2018). In fact, greater belief in BV is associated with fewer uses of protective behavioral strategies (Gonzalez et al., 2019). Providing culturally specific normative feedback could have the benefit of fostering a sense of ethnic pride and help correct erroneous stereotypical beliefs regarding AI/AN drinking and the influence of peer norms (Hagler et al., 2017).

Summary of Macrosystem-Level Factors

There is a long history of societally induced trauma that has contributed to the increased rates of substance misuse within tribal communities. However, increasing self-efficacy and ethnic pride is protective against risk behaviors in youth. Community social norms that are antidrug use were increased resilience in AI/AN youth. Enhancing ethnic identity and dispelling BV increases resilience against substance use and the effects of racism. AI/AN communities have high kinship affiliations, and this increased connectedness is protective against substance initiation.

Discussion

This systematic review focused on the factors influencing substance initiation and misuse among Native youth. The emerging themes aligned with three systems within the ecological systems model, specifically individual, micro-, and macrosystems (Bronfenbenner, 1992). Focusing on the protective and risk factors of substance initiation among Native youth could assist in developing prevention programs for youth. There is evidence that knowledge alone is not enough to support behavior change. In addition to knowledge, understanding motivations and providing skills to be successful are required (Fisher & Fisher, 1992).

Individual System–Level Factors

Findings from the reviewed studies suggest that sex does have some influence on the substance that AI/AN youth initiate (Eitle & McNulty Eitle, 2018; Fairman et al., 2019; Stanley, Miller, et al., 2014); however, the findings were not consistently in agreement (Hautala et al., 2019; Martinez et al., 2015). Having the knowledge about the harm substance use can cause can increase resilience against substance initiation, but it is not enough for sustained resilience (Kulis et al., 2016; Prince et al., 2017; Swaim & Stanley, 2019). Other studies discussed concepts that have been attributed to personality traits, such as optimism and hope (Allen et al, 2014; Donovan et al., 2015). Supporting optimism and hope regarding the future can best be accomplished within a mentored relationship, which emphasizes the importance of a systems approach.

Microsystem-Level Factors

Family

Although the adolescent theory has discussed the importance of peers in this development stage (Jessor, 1991), it was found that a supportive relationship with family influenced behavior more than peers in AI/AN youth (Allen et al., 2014). However, exposure to IPV can increase the risk of substance misuse in AI/AN youth (Brockie et al., 2015). It is important to connect AI/AN youth whose families are struggling with potential mentors in school or the tribal community, because adolescents exposed to domestic violence may be parented in ways that then put them at risk for problematic behaviors, including substance initiation and misuse (Foshee et al., 2016). Being connected to a responsible adult and prosocial institutions was discussed as critical to resilience against substance misuse. Previous research has shown that if a youth has a connected prosocial relationship with an adult that instills future aspirations (either in family or school), they have an increased resilience against risk behaviors (Donovan et al., 2015). Since Native communities have extended kinship relationships family needs to be defined as more inclusive than just the nuclear family.

School

As has been shown in previous studies (Gracia-Moya et al., 2018; Steiner et al., 2019), school connectedness is important for healthy adolescent development and resilience against risk behaviors. Several studies included in this review demonstrated the association between school connectedness and decreased substance initiation and misuse among AI/AN youth (Eitle et al., 2017; Friese et al., 2015; Moon et al., 2014; Turanovic & Pratt, 2017). Involvement with teachers both during school and during extracurricular activities increases resilience in AI/AN youth (Moilanen et al., 2014).

However, school disciplinary policies such as suspension and expulsion disrupt academic achievement and lead to students feeling less connected to and invested in course work (Gregory et al., 2010). AI/AN students are subject to higher rates of suspensions and expulsions, even though there is no evidence that they offend at higher rates than White students (Gregory et al. 2010). Therefore, interventions that increase school connectedness by involving the family in the school community are important (Milne & Wotherspoon, 2020).

Peers

Studies have shown that peer social norms related to substance use influence initiation and misuse (Amialchuk et al., 2019; Davis et al., 2019). Adolescents who believe that their peers are against substance use are more likely to remain resilient against using substances. This was found to be true among AI/AN youth as well (Kelley et al., 2019; King et al., 2014; Martinez et al., 2015). However, when youth believe that most youth engage in substance misuse, they have a higher intent to use (Armenta et al., 2016; Eitle & McNulty Eitle, 2018). Additionally, peer social norms that are positive for drinking alcohol perpetuate the BV and decrease resilience against alcohol misuse (Gonzalez et al., 2019; Gonzalez & Skewes, 2018).

Macrosystem-Level Factors

Community

Native communities are collective societies with a community focus. Tribal groups are very connected and are more likely to rely on each other than the overall society (Champagne & Goldberg, 2005). In fact, it may be that a prosocial mentor from the community could be a positive influence in instilling future aspirations and affect the youth’s level of optimism and hope. In addition to having information about the risk of substance use and addressing the motivational aspects of prevention work, providers also need to make sure that youth have the skills to complete the health promotion activity. Several study findings highlighted the importance of self-efficacy in remaining resilient against substance initiation (Donovan et al., 2015; Mileviciute et al., 2014; Mohatt et al., 2014). Mentoring was shown to be important (Mohatt et al., 2014) as well as instilling future aspirations was key to resilience against substance use (Donovan et al., 2015). As has been shown in other studies with minoritized youth, having a connected relationship with a prosocial adult that instills future aspirations promotes healthy adolescent development (Aronowitz et al., 2015; Aronowitz & Morrison-Beedy, 2005).

Conclusion

Awareness of the protective and risk factors that contribute to substance initiation among AI/AN youth is key to developing prevention programming. Community-based participatory methods (Wallerstein & Duran, 2010) are key to matching the needs of the tribal communities and substance misuse prevention and should include multimethod, multi-informant methodology (Boyd-Ball et al., 2014). The importance of flexible interventions with an appeal to a broad range of AI/AN identities is also important (Brown et al., 2016). Providing knowledge to AI/AN youth about how substances can affect their health and offering mentored experiences with tribal members that can instill future aspirations is key to increasing resilience against substance initiation and misuse.

Footnotes

Author Roles

TA, BK, and CW conceived the study and determined the methodology under the guidance of TA. All authors analyzed the data. TA and CW were the lead authors with substantial contributions and editing of all sections. BK, KG, TN, and ST compiled and assisted in the review of studies. All authors reviewed the final manuscript before submitting it for publication.

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Teri Aronowitz

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