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. Author manuscript; available in PMC: 2025 Oct 1.
Published in final edited form as: J Adolesc Health. 2024 Jul 26;75(4):610–619. doi: 10.1016/j.jadohealth.2024.06.004

Generational Immigration Status Modifies the Association between Psychosocial Distress and Substance Use among Alternative High School Students

Cameron K Ormiston 1,2, James R Pike 3, Melanie D Sabado-Liwag 4, Patchareeya Kwan 5, Bin Xie 6, Alan W Stacy 6, Faustine Williams 1
PMCID: PMC12341841  NIHMSID: NIHMS2003593  PMID: 39066754

Abstract

Purpose:

Alternative high schools (AHS) are designed to provide individualized education, more flexible scheduling, and smaller class sizes for students referred out of traditional high school. AHS students report higher levels of substance use (SU) and face disproportionately higher levels of trauma and toxic stress than their traditional high school peers. We sought to examine whether generational immigration (GenIm) status modifies the association of mental health and SU among AHS students using a longitudinal study of 1,060 Southern California AHS students.

Methods:

Subscales from the Depression Anxiety Stress Scale-21 were administered. Effect modification was examined by GenIm status defined as first-generation (born outside of the US), second-generation (born in the US with a parent born outside the US), or third generation (born in the US with US-born parent(s)). Main outcomes included the number of times different substances were used in the past year over a three-year period.

Results:

Multilevel, negative binomial, covariate-adjusted latent growth curve models generated incidence rate ratios (IRR) and 95% confidence intervals (CI) of the time-varying association between depression, anxiety, or stress and the use of cigarettes, e-cigarettes, cigars, alcohol, or marijuana. Multiple group models examined effect modification by GenIm status.

Discussion:

The link between mental health and SU was stronger among first- and second-generation students than third-generation students. For example, a one-unit increase in stress relative to the average stress of students from the same school was associated with an increase in the rate of e-cigarette use among first-generation (IRR=2.03, 95% CI=1.07, 3.85), second-generation (IRR=2.25, 95% CI=1.86, 2.72), and third-generation (IRR=1.68, 95% CI=1.31, 2.16) students. Effective strategies tailored to subgroups of AHS students are needed to counter disparities between traditional and alternative school systems that may contribute to long-term trajectories of SU.

Keywords: Alternative high school, generational status, immigrant, mental health, substance use

Mental health problems among United States (US) high school students have increased in the last decade.1 Low-income and racial/ethnic minoritized students are especially impacted by this burgeoning crisis as they have higher rates of undiagnosed mental health conditions and mental health symptoms.1,2 Hispanic/Latino students are more likely to feel persistently sad or hopeless than Asian, Black, and White students; Black students are more likely to attempt suicide than their peers.1 Hispanic and Non-Hispanic Black adolescents also report higher levels of poly-victimization and trauma-related mental health symptoms than their non-Hispanic White peers.2 Despite these trends, Black and Hispanic/Latino students report lower levels of mental health service use and inadequate service quality.2,3 This is likely explained by the greater exposure to poverty and resource deprivation experienced by low-income families and adolescents, who are disproportionately comprised of racial and ethnic minoritized individuals, which in turn precludes access to mental health services.2,3 Moreover, poverty is associated with poorer mental health service quality, and youth in low-resource environments are also less likely to have sufficient social support.4,5 In place of effective treatment and protective factors, students with psychosocial distress (i.e. depression, anxiety, trauma, chronic stress) are more likely to adopt maladaptive coping behaviors, such as substance use (SU) earlier in the lifecourse.3,6

One population that is disproportionately represented by low-income and racial/ethnic minoritized adolescents, and is at particularly high risk for poor mental health and SU is alternative high school (AHS) students. Indeed, some estimates show that attempted suicide and current SU are 2x higher and 2 to 4x higher, respectively, among AHS students compared with their traditional high school (THS) peers.7,8 AHS’s aim to provide individualized support and education, more flexible scheduling, and smaller class sizes for students who may struggle with THS expectations (e.g., academics, schedule).9,10 However, there is limited oversight of AHS’s and their success.10 Thus, AHS’s may inadvertently place youth in high-risk environments which further compound poor mental health and SU issues that arose from vulnerable circumstances that exist prior to AHS referral including domestic and neighborhood violence, poor academic performance, unstable housing, pregnancy, and excessive tardiness or absenteeism.8,9 Further, AHS students are reported to respond to stress by adopting negative coping strategies (e.g. SU) more often than THS students.11 Moreover, AHS students tend to be overlooked by public policy and research. This exclusion is in essence systemic discrimination and racism given AHS’s are predominantly made up of youth who are historically disenfranchised, segregated, and oppressed by society and school systems: English learners, youth with disabilities, Black, Latino, and low-income youth.9,10,12,13 This is likely due to non-White and immigrant students being disproportionately disciplined, suspended, and arrested for behavioral and school-related issues,14,15 leading to higher AHS matriculation.16 If not mitigated, the AHS system can act as a form of educational incarceration and potential pipeline to the juvenile legal system, whereby predominantly non-White and marginalized students experience early interaction with law enforcement, are shut off from support systems and prosocial activities (i.e. sports, clubs), and placed in schools with inadequate and withheld resources that suppresses upward mobility.7,16,17

To date, only one study has investigated whether SU differs by generational immigration (GenIm) status among AHS students, despite research showing GenIm status may play a role in complex mental health issues among adolescents.18–21 While some suggest immigrant or first-generation status is protective relative to subsequent generations due to their lower levels of acculturation and possible cultural protective factors,19–21 first-generation youth may more frequently internalize poor mental health symptoms than latter-generation youth.22,23 Additionally, the potential protective effect of first-generation status may attenuate over time, with longer residence in the US being associated with increased risk of poor mental health and SU.21,24 Given the prevalence of psychosocial distress and SU among AHS students and the significant immigrant population within the AHS system,18 investigating how GenIm may affect the link between poor mental health and SU among AHS students is urgently important. We thus present the first study to explore whether GenIm status modifies25 the association of mental health and SU among AHS students. Based on prior literature,18,19,24,26,27 we hypothesize that GenIm will modify the relationship between mental health and SU, and that first-generation students (individuals born outside the US) will be more impacted by increases in adverse mental health symptoms relative to their latter generation peers.

Methods

Sampling

The California Department of Education (CDE) documented 183 AHS with at least 100 students within 100 miles of the program offices in Claremont, California. Available demographics reported by the CDE indicated that among the 73,134 students attending these schools 40.3% were female, 71.0% were of Hispanic/Latino ethnicity, and 47.7% were enrolled in a free lunch program. In February 2014, research staff began contacting each AHS in a randomly selected order. All schools were invited to participate in accordance with a protocol approved by the Claremont Graduate University Institutional Review Board (#2214). Schools were accepted on a first-come, first-served basis until 29 sites enrolled.

Between October 2014 and May 2015, research staff visited each school and distributed interest forms. Parental consent and youth assent was obtained from interested students. Consented students were given a link to a web-based, confidential survey. A total of 1,060 students completed the survey before enrollment concluded on September 1st, 2015. Each student received a $45 gift card as compensation for their time.

One-year follow-up assessments were conducted between September 2015 and September 2016. The average length of time between the baseline assessment and the one-year follow-up was 330 days (SD = 26.6). The retention rate was 87.1% (923/1060). Among the 137 participants who did not complete a one-year follow-up assessment, 93.5% failed to respond to repeated contact attempts, 5.8% withdrew from the study, and 0.7% were incarcerated.

Two-year follow-up assessments were administered between September 2016, and September 2017. Average length of time between the baseline assessment and the two-year follow-up assessment was 695 days (SD=33.7) and the retention rate was 81.0% (859/1060). Among the 201 participants who did not complete a two-year assessment, 93.0% failed to respond to repeated contact attempts, 4.5% withdrew from the study, 1.5% had died, 0.5% were incarcerated, and 0.5% were deployed overseas after enlisting in the military.

Measures

Mental Health.

The mental health of each student was assessed annually using the 21-item Depression Anxiety Stress Scale (DASS-21), which has shown excellent construct validity28 and psychometric properties in adolescent samples.29 Each student was asked to read seven statements describing feelings of depression (‘I felt that I had nothing to look forward to’), anxiety (‘I felt I was close to panic’), or stress (‘I felt that I was using a lot of nervous energy’) that they may have experienced in the past week. For each statement, the student chose from four response options (0=‘Did not apply to me at all’ to 3=‘Applied to me very much, or most of the time’). The mean of each 7-item subscale was computed for each student. The subscales of stress (α=.85), anxiety (α=.82), and depression (α=.89) demonstrated good internal reliability.

Substance use.

A validated scale30 employed in prior studies of SU among AHS students31 quantified how often each student used cigarettes, e-cigarettes, cigars, alcohol, or marijuana in the past year. To assess concurrent SU, two additional questions asked how often the student used nicotine (cigarettes, e-cigarettes, or cigars) and alcohol at the same time or nicotine and marijuana at the same time. The student selected a response from eleven options (0 times, 1–10 times, 11–20 times, 21–30 times, 31–40 times, 41–50 times, 51–60 times, 61–70 times, 71–80 times, 81–90 times, or 91+ times).

Generational immigration (GenIm).

Prior definitions32,33 of GenIm were applied to classify students as first-generation (students born outside of the US), second-generation (born in the US with at least one parent born outside the US), or third generation (born in the US with US-born parents).

Socio-demographics.

Prior meta-analyses and systematic reviews have documented an association between youth SU and socio-demographic characteristics including age,34 gender,34,35 ethnicity,34,35 parental education,34,35 socioeconomic status,34,35 and personal income.34,35 To mitigate bias from these confounders, students were asked a series of questions. The first question asked students to provide their date of birth. Age in years at the baseline assessment was calculated by subtracting the date of birth from the date the survey was completed. Students were then asked to identify their gender (Female, Male) and ethnicity (Hispanic/Latino, non-Hispanic). The next set of questions ascertained the level of education attained by the student’s parent(s). The highest level attained by either parent was incorporated into a single variable comprised of five categories (Did not finish high school, Completed high school, Some college, Completed college, Completed graduate school). Socioeconomic status was determined by asking each student if they received a free lunch at school or if their family received a welfare check or food stamps. Responses were aggregated into a dichotomous variable (No nutritional or financial assistance, Nutritional or financial assistance). Weekly income was assessed utilizing three items adapted from the Monitoring the Future survey36 that inquired how much money students received from a job, their family, or other sources. For each source of income, students selected from ten response options ($0, $1–5, $6–10, $11–20, $21–35, $36–50, $51–75, $76–125, $126–175, $176+). The mean of all three sources was computed for each student.

Analysis

The analytic sample was restricted to 1034 students from 29 schools who provided enough information to be classified as first-generation (N=112), second-generation (N=575), or third-generation (N=347). Descriptive statistics of participant characteristics stratified by GenIm were generated in SAS 9.4 (SAS Institute, Cary, NC). A series of latent growth curve (LGC) models with a negative binomial link function were fit in Mplus 8.6 (Muthén & Muthén, Los Angeles, CA) to quantify changes in past year SUs over time. Time was defined as a continuous measure documenting the time in years from the baseline assessment. A random effect was added to account for individual variation in the timing of follow-up assessments and a correlation among students from the same school was specified to account for geographic differences. Standard errors robust to non-normality and non-independence of observations were computed.

A comparison between students with complete data versus those with missing responses at either the one-year or two-year follow-up assessment revealed no statistically significant differences by first-generation status (34.9% vs 31.6%, p = .30), second-generation status (55.8% vs 55.3%, p = .91), third-generation status (9.3% vs 13.0%, p = .17), Hispanic/Latino ethnicity (75.2% vs 75.6%, p = .89), or age at the baseline assessment (17.5 vs 17.5, p = .83). Similarly, the differences between baseline measures of stress (0.4 vs 0.5, p = .37), anxiety (0.4 vs 0.4, p = .33), depression (0.5 vs 0.5, p = .81), cigarette use (25.3% vs 28.4%, p = .28), e-cigarette use (32.9% vs 33.1%, p = .96), cigar use (18.8% vs 21.4%, p = .30), and alcohol use (49.7% vs 52.0%, p = .37) were not statistically significant. However, students with missing data were more likely to be male (45.4% vs 57.5%, p < .001) and more likely at the baseline assessment to report marijuana use (46.2% vs 55.0%, p = .007), concurrent use of nicotine and alcohol (24.0% vs 31.0%, p = .004), or concurrent use of nicotine and marijuana (26.0% vs 32.3%, p = .03). To minimize bias caused by informative attrition, full-information maximum likelihood was employed.37

Annual measurements of depression, anxiety, and stress were centered38 to the school-specific subscale mean at the baseline assessment and integrated into the LGC models as time-varying exposures. Baseline measurements of age, parental education, and weekly income were similarly group-mean centered. Age, gender, ethnicity, parental education, socioeconomic status, and weekly income at the baseline assessment were included in the LGC models as time-invariant covariates that affected the intercept and slope of SU over time.

Multilevel, negative binomial, covariate-adjusted LGC models generated incidence rate ratios (IRR) and 95% confidence intervals (95% CIs) of the relationship between (1) depression, (2) anxiety, and (3) stress and the past year use of (1) cigarettes, (2) e-cigarettes, (3) cigars, (4) alcohol, (5) marijuana, (6) concurrent use of nicotine and alcohol, and (7) concurrent use of nicotine and marijuana. An equivalent series of multiple group models assessed whether parameter estimates varied across GenIm subgroups. In a sensitivity analysis, the same models were modified to examine the prospective association of mental health with SU in the following year. All levels of statistical significance were set at a two-tailed p<0.05.

Results

The sample was 49.6% female, 75.3% Hispanic/Latino, 10.8% first-generation, 55.6% second-generation, and 33.6% third-generation (Table 1). Students varied in age from 14.7 years to 20.8 years with a mean age of 17.5 years (SD=0.9). Differences in self-reported mental health between GenIm subgroups were minimal at the baseline. During the one-year and two-year follow-up assessments, first- and second-generation immigrant students tended to report slightly lower levels of depression, anxiety, and stress than third-generation students. First- and second-generation immigrant students typically reported lower levels of SU.

Table 1.

Characteristics of longitudinal cohort of 1034 alternative high school students

All First-generation Second-generation Third-generation
N N = 1034 N=112 N=575 N=347 p
Baseline
Age in years, mean (SD)		 1034 17.5 (0.9) 17.6 (1.0) 17.5 (0.9) 17.4 (0.9) .03
Female sex, No. (%) 1030 511 (49.6) 45 (40.2) 290 (50.5) 176 (51.2) .10
Hispanic/Latino ethnicity, No. (%) 1030 776 (75.3) 103 (92) 526 (91.8) 147 (42.6) <.001
Socioeconomic Status, No. (%)
 Family receives welfare check		 1027 172 (16.7) 9 (8.2) 98 (17.2) 65 (18.8) .03
 Family receives food stamps 1029 323 (31.4) 32 (28.6) 184 (32.2) 107 (30.9) .73
 Student receives free lunch at school 1032 755 (73.2) 87 (77.7) 448 (78.0) 220 (63.6) <.001
Highest Education Level of Parent(s), No. (%)
 Did not complete high school		 929 275 (29.6) 48 (52.7) 192 (37.9) 35 (10.6) <.001
 Completed high school 305 (32.8) 20 (22.0) 159 (31.4) 126 (38.1)
 Attended but did not complete college 182 (19.6) 9 (9.9) 88 (17.4) 85 (25.7)
 Completed college 109 (11.7) 8 (8.8) 42 (8.3) 59 (17.8)
 Completed graduate school 58 (6.2) 6 (6.6) 26 (5.1) 26 (7.9)
Weekly Income, No. (%)
 $0		 973 94 (9.7) 8 (7.6) 53 (9.9) 33 (10.0) .21
 $1 to $100 577 (59.3) 56 (53.3) 332 (61.8) 189 (57.1)
 More than $100 302 (31.0) 41 (39.0) 152 (28.3) 109 (32.9)
DASS-21, mean (SD)
 Depression		 986 0.5 (0.6) 0.4 (0.6) 0.4 (0.6) 0.5 (0.7) .86
 Anxiety 986 0.4 (0.5) 0.4 (0.6) 0.4 (0.5) 0.4 (0.5) .65
 Stress 985 0.4 (0.6) 0.4 (0.6) 0.4 (0.6) 0.5 (0.6) .15
Substance Use in the Past Year, No. (%)
 Cigarettes		 986 262 (26.6) 22 (20.6) 136 (24.4) 104 (32.4) .01
 E-Cigarettes 988 326 (33.0) 28 (26.7) 177 (31.7) 121 (37.3) .08
 Cigars 973 193 (19.8) 18 (16.8) 90 (16.3) 85 (27.0) .001
 Alcohol 989 501 (50.7) 45 (42.1) 281 (50.2) 175 (54.3) .08
 Marijuana 956 475 (49.7) 37 (36.6) 267 (49.3) 171 (54.6) .007
 Concurrent nicotine & alcohol 964 258 (26.8) 24 (23.8) 136 (24.9) 98 (30.9) .12
 Concurrent nicotine & marijuana 964 275 (28.5) 24 (23.8) 156 (28.5) 95 (30.1) .47
One-Year Follow-Up
DASS-21, mean (SD)
 Depression		 829 0.3 (0.6) 0.4 (0.6) 0.3 (0.6) 0.4 (0.6) .07
 Anxiety 829 0.3 (0.5) 0.3 (0.5) 0.3 (0.5) 0.3 (0.5) .08
 Stress 829 0.4 (0.6) 0.3 (0.5) 0.3 (0.5) 0.5 (0.6) .001
Substance Use in the Past Year, No. (%)
 Cigarettes		 862 217 (25.2) 26 (28.6) 105 (21.3) 86 (30.9) .009
 E-Cigarettes 857 236 (27.5) 24 (26.7) 117 (23.9) 95 (34.2) .009
 Cigars 858 164 (19.1) 18 (19.8) 69 (14.1) 77 (27.9) <.001
 Alcohol 835 370 (44.3) 38 (42.2) 199 (41.9) 133 (49.3) .14
 Marijuana 818 336 (41.1) 28 (32.6) 183 (39.2) 125 (47.2) .03
 Concurrent nicotine & alcohol 823 183 (22.2) 20 (22.7) 99 (21.1) 64 (24.1) .65
 Concurrent nicotine & marijuana 822 157 (19.1) 20 (23.0) 79 (16.8) 58 (21.9) .15
Two-Year Follow-Up
DASS-21, mean (SD)
 Depression		 783 0.4 (0.6) 0.3 (0.4) 0.4 (0.6) 0.5 (0.7) .02
 Anxiety 784 0.3 (0.5) 0.3 (0.5) 0.3 (0.5) 0.4 (0.6) .07
 Stress 784 0.4 (0.6) 0.3 (0.4) 0.3 (0.6) 0.5 (0.7) <.001
Substance Use in the Past Year, No. (%)
 Cigarettes		 802 171 (21.3) 17 (20.0) 74 (16.5) 80 (29.9) <.001
 E-Cigarettes 801 163 (20.3) 18 (20.9) 63 (14.1) 82 (30.7) <.001
 Cigars 799 139 (17.4) 14 (16.5) 57 (12.7) 68 (25.8) <.001
 Alcohol 786 353 (44.9) 38 (46.3) 190 (43.4) 125 (47.0) .62
 Marijuana 774 296 (38.2) 24 (30.4) 145 (33.5) 127 (48.5) <.001
 Concurrent nicotine & alcohol 774 154 (19.9) 19 (23.8) 72 (16.7) 63 (24.0) .04
 Concurrent nicotine & marijuana 773 156 (20.2) 14 (17.5) 82 (19.1) 60 (22.8) .40
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Note: Students were permitted to skip survey questions they did not want to answer. Consequently, the number of complete cases varied for each measure. Univariate differences in study variables were assessed using Pearson chi-square tests, Cochran-Mantel-Haenszel trend tests, and analysis of variance F-tests as appropriate.

Parameter estimates presented in Table 2 suggest that after accounting for confounders (Supplemental Tables 1-6c) most students used substances less than ten times per year at baseline and SU declined over time. However, these estimates assume a normal level of depression, anxiety, or stress.39 The association between each DASS-21 subscale and each substance reveals how changes in mental health impacts SU. The smallest effect observed was for a one-unit increase in depression relative to the average level of depression among students from the same school, which was associated with a 53% increase in the number of times a student consumed alcohol (IRR=1.53, 95% CI=1.36, 1.72). The largest effect was for a one-unit increase in anxiety relative to the school-specific average which was associated with a 223% increase in the number of times a student concurrently used both nicotine and alcohol (IRR=3.23, 95% CI =2.46, 4.24).

Table 2.

Multilevel, covariate-adjusted, negative binomial, latent growth curve models examining the concurrent relationship between mental health and past year substance use among 1034 alternative high school students assessed over a three-year period

Cigarettes E-Cigarettes Cigars Alcohol Marijuana Concurrent Nicotine & Alcohol Concurrent Nicotine & Marijuana
IRR (95% CI) IRR (95% CI) IRR (95% CI) IRR (95% CI) IRR (95% CI) IRR (95% CI) IRR (95% CI)
Intercept 0.24 (0.10, 0.59)* 0.39 (0.20, 0.78)* 0.16 (0.05, 0.46)* 0.75 (0.48, 1.18) 0.76 (0.40, 1.44) 0.29 (0.17, 0.48)* 0.25 (0.13, 0.48)*
Slope 1.00 (0.62, 1.62) 0.61 (0.42, 0.88)* 0.83 (0.42, 1.63) 0.98 (0.75, 1.29) 0.93 (0.72, 1.21) 0.60 (0.40, 0.91)* 0.61 (0.37, 0.98)*
Depression 1.85 (1.52, 2.27)* 1.57 (1.31, 1.88)* 2.05 (1.71, 2.45)* 1.53 (1.36, 1.72)* 1.53 (1.37, 1.72)* 2.09 (1.78, 2.45)* 2.32 (1.93, 2.78)*
Intercept 0.24 (0.06, 1.02) 0.24 (0.07, 0.85)* 0.16 (0.06, 0.49)* 0.77 (0.49, 1.21) 0.79 (0.43, 1.45) 0.30 (0.18, 0.49)* 0.25 (0.13, 0.48)*
Slope 1.01 (0.02, 1.65) 1.01 (0.05, 21.05) 0.86 (0.42, 1.78) 0.98 (0.74, 1.29) 0.92 (0.71, 1.19) 0.59 (0.41, 0.87)* 0.64 (0.40, 1.02)
Anxiety 2.57 (1.50, 4.40)* 2.57 (1.64, 4.01)* 3.03 (2.29, 4.02)* 1.72 (1.51, 1.96)* 1.80 (1.58, 2.05)* 2.69 (2.26, 3.21)* 3.23 (2.46, 4.24)*
Intercept 0.24 (0.10, 0.59)* 0.39 (0.15, 1.04) 0.16 (0.06, 0.42)* 0.74 (0.48, 1.15) 0.78 (0.42, 1.44) 0.30 (0.18, 0.48)* 0.26 (0.14, 0.49)*
Slope 0.94 (0.61, 1.46) 0.58 (0.35, 0.97)* 0.78 (0.49, 1.25) 0.95 (0.72, 1.26) 0.90 (0.70, 1.15) 0.56 (0.38, 0.81)* 0.57 (0.36, 0.89)*
Stress 2.27 (1.76, 2.92)* 1.98 (1.58, 2.47)* 2.54 (2.01, 3.21)* 1.63 (1.43, 1.85)* 1.61 (1.42, 1.84)* 2.28 (1.89, 2.75)* 2.42 (1.92, 3.06)*
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Abbreviations: CI, confidence intervals; IRR, incidence rate ratios.

*

p<0.05

Note: Models estimated concurrent associations between mental health and substance use measured in the same year. Nicotine use was defined as the use of cigarettes, e-cigarettes, or cigars. The effect of age, gender, ethnicity, parental education, socioeconomic status, and weekly income at the baseline assessment on the intercept and slope were incorporated into each model.

Multiple group models that explored effect modification across GenIm subgroups provided a more nuanced portrait of mental health and SU (Table 3). Associations were generally greater among first- and second-generation immigrant students. For example, a one-unit increase in stress was associated with an increase in the frequency of e-cigarette use of 103% (IRR=2.03, 95% CI=1.07, 3.85) among first-generation, 125% (IRR =2.25, 95% CI =1.86, 2.72) among second-generation, and 68% (IRR=1.68, 95% CI=1.31, 2.16) among third-generation adolescents. Using previously defined39 levels for normal, moderate, and severe stress, the impact of these differences across subgroups can be viewed in Figure 1. Comparable patterns are observed across all substances including concurrent use of nicotine and either alcohol or marijuana which implies that students may use multiple substances in a given year when they have higher levels of depression, anxiety, or stress. These patterns persisted when the prospective association between mental health and substance use in the following year was estimated (Supplemental Tables 7 and 8), although the size of these associations decreased.

Table 3.

Multiple group, multilevel, covariate-adjusted, negative binomial, latent growth curve models examining the concurrent relationship between mental health and past year substance use among 1034 alternative high school students assessed over a three-year period

Cigarettes E-Cigarettes Cigars Alcohol Marijuana Concurrent Nicotine & Alcohol Concurrent Nicotine & Marijuana
IRR (95% CI) IRR (95% CI) IRR (95% CI) IRR (95% CI) IRR (95% CI) IRR (95% CI) IRR (95% CI)
First-generation (N=112)
Intercept 0.08 (0.00, 2.03) 0.40 (0.07, 2.47) 0.16 (0.00, 8.13) 0.40 (0.09, 1.78) 0.21 (0.01, 3.40) 0.37 (0.09, 1.51) 0.29 (0.03, 2.63)
Slope 1.76 (0.31, 10.05) 0.55 (0.16, 1.86) 0.55 (0.15, 2.06) 2.27 (1.10, 4.70)* 2.46 (0.81, 7.46) 0.45 (0.36, 0.55)* 0.50 (0.10, 2.51)
Depression 2.04 (1.29, 3.22)* 1.54 (1.05, 2.26)* 2.09 (1.02, 4.28)* 1.44 (1.04, 2.00)* 1.59 (1.11, 2.28)* 1.61 (1.01, 2.57)* 2.09 (1.26, 3.45)*
Intercept 0.04 (0.00, 0.59) 0.32 (0.02, 5.17) 0.13 (0.00, 8.00) 0.36 (0.09, 1.41) 0.19 (0.01, 2.66) 0.21 (0.02, 2.02) 0.08 (0.00, 2.29)
Slope 3.29 (1.02, 10.55)* 0.71 (0.06, 8.04) 0.78 (0.12, 5.30) 2.52 (1.06, 6.02)* 2.85 (1.02, 8.00)* 1.24 (0.34, 4.50) 0.70 (0.05, 6.87)
Anxiety 4.66 (2.05, 10.58)* 2.83 (1.64, 4.90)* 4.46 (2.45, 8.09)* 1.94 (1.17, 3.24)* 1.97 (1.28, 3.03)* 2.49 (1.53, 4.08)* 4.08 (0.53, 31.25)
Intercept 0.03 (0.00, 0.51) 0.34 (0.04, 2.57) 0.13 (0.00, 15.12) 0.36 (0.07, 1.77) 0.19 (0.01, 3.49) 0.23 (0.03, 1.94) 0.23 (0.02, 2.88)
Slope 1.86 (0.08, 20.29) 0.63 (0.12, 3.32) 0.75 (0.04, 12.54) 2.50 (0.95, 6.55) 2.75 (0.87, 8.72) 1.02 (0.25, 4.10) 0.66 (0.13, 3.22)
Stress 3.41 (1.12, 10.41)* 2.03 (1.07, 3.85)* 2.86 (1.22, 6.74)* 1.78 (1.13, 2.80)* 1.74 (1.21, 2.50)* 1.76 (0.95, 3.27) 2.49 (1.36, 4.59)*
Second-generation (N=575)
Intercept 0.17 (0.06, 0.47)* 0.27 (0.12, 0.62)* 0.06 (0.01, 0.47)* 0.45 (0.23, 0.91)* 0.29 (0.11, 0.75)* 0.23 (0.06, 0.93)* 0.18 (0.05, 0.62)*
Slope 1.08 (0.58, 2.04) 0.52 (0.27, 1.00) 0.63 (0.06, 6.44) 1.04 (0.67, 1.62) 0.75 (0.51, 1.10) 0.86 (0.27, 2.73) 0.50 (0.13, 1.91)
Depression 2.06 (1.48, 2.87)* 1.55 (1.26, 1.90)* 2.52 (2.06, 3.08)* 1.63 (1.42, 1.88)* 1.60 (1.39, 1.84)* 2.19 (1.82, 2.64)* 2.60 (2.08, 3.23)*
Intercept 0.15 (0.05, 0.46)* 0.29 (0.14, 0.58)* 0.07 (0.01, 0.35)* 0.47 (0.26, 0.88)* 0.32 (0.14, 0.74)* 0.24 (0.08, 0.76)* 0.20 (0.07, 0.58)*
Slope 1.10 (0.50, 2.34) 0.50 (0.30, 0.85)* 0.66 (0.12, 3.59) 1.02 (0.68, 1.51) 0.71 (0.48, 1.04) 0.80 (0.34, 1.87) 0.48 (0.16, 1.44)
Anxiety 2.84 (1.74, 4.65)* 2.16 (1.74, 2.68)* 3.47 (2.43, 4.95)* 1.72 (1.48, 2.01)* 1.93 (1.58, 2.35)* 2.79 (2.29, 3.39)* 3.51 (2.50, 4.93)*
Intercept 0.17 (0.03, 0.98)* 0.25 (0.11, 0.57)* 0.07 (0.02, 0.21)* 0.45 (0.24, 0.86)* 0.30 (0.12, 0.72)* 0.24 (0.08, 0.74)* 0.19 (0.07, 0.56)*
Slope 1.05 (0.19, 5.94) 0.55 (0.33, 0.92)* 0.64 (0.20, 2.01) 1.04 (0.70, 1.55) 0.72 (0.51, 1.03) 0.69 (0.31, 1.52) 0.46 (0.18, 1.15)
Stress 2.75 (1.73, 4.35)* 2.25 (1.86, 2.72)* 3.32 (2.50, 4.43)* 1.70 (1.45, 1.99)* 1.78 (1.52, 2.09)* 2.46 (2.04, 2.98)* 2.76 (2.12, 3.61)*
Third-generation (N=347)
Intercept 0.31 (0.04, 2.25) 0.38 (0.16, 0.87)* 0.22 (0.01, 3.40) 1.06 (0.63, 1.77) 1.27 (0.68, 2.37) 0.21 (0.13, 0.33)* 0.29 (0.12, 0.67)*
Slope 0.99 (0.15, 6.57) 0.86 (0.67, 1.12) 0.84 (0.12, 6.01) 0.88 (0.62, 1.24) 0.96 (0.62, 1.47) 0.85 (0.73, 1.01) 0.64 (0.33, 1.26)
Depression 1.61 (1.26, 2.07)* 1.54 (1.22, 1.94)* 1.62 (1.18, 2.23)* 1.41 (1.14, 1.74)* 1.41 (1.16, 1.71)* 2.15 (1.51, 3.06)* 1.89 (1.40, 2.55)*
Intercept 0.32 (0.12, 0.83)* 0.40 (0.17, 0.91)* 0.23 (0.01, 10.07) 1.06 (0.61, 1.84) 1.31 (0.71, 2.41) 0.33 (0.13, 0.85)* 0.29 (0.10, 0.82)*
Slope 0.96 (0.57, 1.61) 0.83 (0.59, 1.16) 0.87 (0.05, 15.97) 0.88 (0.60, 1.28) 0.95 (0.61, 1.49) 0.44 (0.21, 0.93)* 0.69 (0.29, 1.66)
Anxiety 1.92 (1.29, 2.86)* 1.62 (1.21, 2.16)* 2.33 (1.51, 3.60)* 1.60 (1.29, 1.99)* 1.58 (1.27, 1.96)* 2.39 (1.71, 3.33)* 2.48 (1.71, 3.61)*
Intercept 0.31 (0.05, 1.87) 0.40 (0.17, 0.94)* 0.22 (0.11, 0.44)* 1.04 (0.62, 1.75) 1.29 (0.70, 2.36) 0.31 (0.15, 0.65)* 0.30 (0.13, 0.67)*
Slope 0.94 (0.22, 4.07) 0.79 (0.49, 1.29) 0.81 (0.54, 1.21) 0.85 (0.59, 1.24) 0.94 (0.61, 1.46) 0.42 (0.21, 0.84)* 0.61 (0.30, 1.23)
Stress 1.70 (1.19, 2.41)* 1.68 (1.31, 2.16)* 1.85 (1.27, 2.69)* 1.52 (1.25, 1.85)* 1.37 (1.13, 1.68)* 2.07 (1.42, 3.01)* 1.93 (1.39, 2.69)*
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Abbreviations: CI, confidence intervals; IRR, incidence rate ratios.

*

p<0.05

Note: Multiple group models estimated concurrent associations between mental health and substance use measured in the same year. Each association and the trends over time were freely estimated within the subgroups of first-, second-, and third-generation adolescents. Nicotine use was defined as the use of cigarettes, e-cigarettes, or cigars. The effect of age, gender, ethnicity, parental education, socioeconomic status, and weekly income at the baseline assessment on the intercept and slope were incorporated into each model.

Figure 1. Trends over time in the annual use of e-cigarettes for an average, Hispanic/Latino, male adolescent with differing levels of stress.

Figure 1.

Open in a new tab

Note: Parameter estimates from a multiple group, multilevel, covariate-adjusted, negative binomial, latent growth curve model were used to generate trends over time for a Hispanic/Latino, male adolescent from the most prevalent socioeconomic status whose age, parental education, and weekly income were equal to the baseline mean. Based on prior research49, stress was defined as normal (0.00–1.00), moderate (1.36–1.79), or severe (1.86–2.36) on a scale that ranged from 0 to 3.

Discussion

We present the first longitudinal analysis of GenIm status as an effect modifier25 of the relationship between mental health and SU among AHS students. Findings highlight the detrimental effects of adolescent mental health issues on SU. Notably, we found that GenIm status modifies the link between mental health and SU, with SU rates among first- and second-generation students being more impacted by increases in all three categories of psychosocial distress (depression, anxiety, and stress) relative to their third-generation peers.

Baseline SU, particularly alcohol (50.7% vs. 23%), marijuana (49.7% vs. 16%), and e-cigarette (33.0% vs. 18%) use, was demonstrably higher among the overall AHS student sample and across GenIm statuses when compared with estimates among THS students.1 High rates of SU among AHS students are likely a result of social norms, availability of substances, and acceptability of SU among high school aged youth layered on top of the disproportionately high exposure of socioeconomic and neighborhood deprivation, abuse, law enforcement, and violence among AHS youth—which are historically linked to greater SU rates.9,40,41

Rising levels of SU with each successive generation is consistent with findings from prior studies, but we are the first to show such a trend among AHS youth.19,42 However, SU rates among first- and second-generation youth increased at a faster rate throughout the study period relative to third-generation youth, which may be reflective of acculturation processes and its harmful effects on health behaviors among immigrant youth.43 For instance, as adolescents acculturate, they may be more susceptible to peer influence and experience acculturative stress, leading to attenuation of their social and cultural ties to protective factors.43,44 Moreover, AHS’s are high-risk environment for risky behaviors and may result in greater exposure to pro-SU peers.43 As such, pressures to assimilate and fit in may manifest as rapidly rising SU rates for earlier GenIm status AHS students.43

We also found higher levels of psychosocial distress were associated with greater SU among first- and second-generation youth compared to their third-generation peers. Given that first- and second-generation respondents reported higher levels of poor mental health at the one- and two-year follow-up, they may be manifesting depressive, anxiety, or stress symptoms more strongly over time. Prior research is inconsistent. One study of Hispanic young adults found first-generation youth had less exposure to social stress and thus lower rates of SU relative to their second- and third-generation peers, suggesting an immigrant health advantage.45 It should be noted, however, that this study was cross-sectional and comprised of Miami-based immigrant youth, a distinctly different population from Southern California AHS students, who are mostly Hispanics/Latinos of Mexican heritage with other varying characteristics (e.g., built environments, immigration history). Conversely, another study found first- and second-generation youth reported more stressors and greater stress appraisal than third-generation youth, indicating they may be more affected by increases in stress.46 Moreover, studies show first- and second-generation youth are more likely to experience acculturative stress, bullying, discrimination, and victimization, which are linked to pro-SU attitudes and participation among immigrant youth.20,46,47 Greater stress experiences and appraisal may translate to steeper increases in greater SU rates among first- and second-generation youth; however, direct causal determinants driving the present study’s findings are unclear and require further exploration.

While poor mental health and SU are growing problems among adolescents, AHS students are disproportionately affected. Further discourse and realistic solutions must pay close attention to the effects of racism, discriminatory policies, and systemic determinants of health on mental health and SU. Given the social contexts of many AHS students, the steady growth of AHS’s without accountability41 could disproportionately harm marginalized communities and c widen disparities in mental health and SU. For instance, the continued overrepresentation of low-income youth, English learners, youth with disabilities, and racial/ethnic minorities in the AHS system highlights the over-policing of marginalized populations and endorses the use of AHS’s as a form of early ostracism.14,15,17 These exclusionary practices identify non-traditional, truant youth as marginalized citizens of society upon entering adulthood, particularly when considering biases against students in school punishments that are well-documented.14,15,17 In fact, neighborhoods with a higher proportion of racial/ethnic minoritized individuals and people living in poverty are more likely to have AHS’s.17,48 Further, racial/ethnic minoritized AHS student enrollment is associated with strong partnerships between AHS’s and police departments.17 These upstream barriers to well-being for AHS students point to necessary interventions targeting poverty alleviation, safer environments inside and outside of school, better support for at-risk students both before and after AHS referral, and improving relationships between institutions (e.g., education, law enforcement) and marginalized communities. One suggestion is ending zero-tolerance policies in schools, as they disproportionately harms marginalized youth and are widely cited as a driving factor for increasing enrollment in AHS’s and the school-to-prison pipeline.9,41

There are several evidence-based methods which can be employed to potentially reduce the number of students entering the AHS system, such as applying a Positive Behavior Support Model (PBS) paired with school-based counseling. A more efficacious alternative to traditional school disciplinary practices, PBS recognizes the impacts of environment and social determinants on adolescent behavior, whereby adverse behaviors are a product of youth interacting with their environment.48 The use of PBS has proved effective in THS and AHS’s, reducing suspensions and punitive actions while improving community engagement such as voting and volunteering among students.48 Moreover, coupling PBS with counseling has been shown to improve mental health and academic performance in both THS and AHS settings.48

Limitations

While the current findings offer insights pertinent to future reforms, several factors that limit generalizability should be noted. First, a comparison between the analytic sample and the demographics of eligible schools revealed statistically significant differences by sex (49.6% female vs 40.3% female, p < .001), Hispanic/Latino ethnicity (75.3% vs 71.0%, p = .002), and enrollment in a free lunch program at school (73.2% vs 47.7%, p < .001). Although each of these potential confounders were included in covariate-adjusted models, generalizability may have nonetheless been impacted. Second, small sample sizes among subgroups prevented further disaggregation by race, ethnicity, sex, and socioeconomic status which likely obscured the heterogeneity of each GenIm group.18 For example, while some participants were born in or had parents from Africa, Asia, and the Pacific Islands, there were insufficient numbers or limited cell counts to estimate differences. Third, the majority of the Hispanic/Latino youth in our sample were of Mexican heritage. A rich area for future research is disentangling the false monolith of Hispanic/Latino as a subgroup. Fourth, GenIm is a relatively crude measure. More comprehensive measures should be explored in the future including documentation status, number of years each family member has lived in the US, etc. Fifth, although English proficiency was a requirement for attendance at each school, variability in English fluency may have impacted the accuracy of measures such as the DASS-21. Likewise, intellectual and neurodiversity may similarly impact measures of mental health. Sixth, the decrease in effect size in models that estimated the prospective association between mental health and SU suggests that the relationship may not be one-directional. Although prior studies characterize SU as a maladaptive coping behavior,3,6,11 there may, in fact, be a reciprocal relationship that evolves over time. Finally, it is important to consider the timing of our study is an important limitation, given the significant and changing sociopolitical landscape in the US, including the COVID-19 pandemic and the pending status of Deferred Action for Childhood Arrivals (DACA ) program, which protects young adults who were brought to the US as children from deportation and allows them to request work authorization. As such, replication of the study in a more current and ethnically diverse cohort is warranted.

Conclusions

The association between mental health and SU was persistent among a sample of Southern California AHS students. SU among first- and second-generation AHS students may be more affected by increases in poor mental health symptoms, potentially due to intersecting social factors including immigrant stress, acculturation, discrimination, domestic issues, poorly resourced neighborhoods, and peer influence Given the continued overrepresentation of marginalized youth in the AHS system and the continued use of AHS’s to warehouse struggling and racial/ethnically minoritized youth, public attention and comprehensive interventions are needed.

Supplementary Material

1

Implications and Contribution.

This study demonstrates a link between adverse mental health symptoms and substance use among alternative high school students, a neglected population in policy and research. Multi-group models found the association between mental health and substance use was stronger among first- and second-generation students compared to third-generation students.

Acknowledgments

The content is solely the responsibility of the authors and does not necessarily reflect the views of the National Institutes of Health or the Food and Drug Administration. The authors also wish to thank Sandy Asad, Sara J Asad, Melissa Garrido, Sarah Z Gonzalez, and Brenda Lisa Lucero for their tireless efforts recruiting and tracking alternative high school students. Additional thanks to Jerry Grenard for critical help designing this longitudinal investigation.

Funding

Research reported in this publication was supported by the National Institute of Child Health and Human Development and the Food and Drug Administration Center for Tobacco Products (R01HD077560). CKO and FW efforts were supported by the Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health (ZIA MD000015). The content is solely the responsibility of the authors and does not necessarily reflect the views of the National Institutes of Health or the Food and Drug Administration.

Abbreviations

AHS

alternative high school

CI

confidence interval

DASS-21

Depression Anxiety Stress Scale

GenIm

Generational immigration

IRR

incidence rate ratios

LGC

latent growth curve

PBS

Positive Behavior Support Model

SU

substance use

THS

traditional high school

US

United States

Footnotes

Disclosures

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

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