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. Author manuscript; available in PMC: 2022 Aug 1.
Published in final edited form as: Addict Behav. 2021 Apr 6;119:106944. doi: 10.1016/j.addbeh.2021.106944

Risk Factors for Alcohol, Marijuana, and Cigarette Polysubstance Use During Adolescence and Young Adulthood: A 7-Year Longitudinal Study of Youth at High Risk for Smoking Escalation

Natania A Crane 1, Scott A Langenecker 1,2, Robin J Mermelstein 3
PMCID: PMC8120743  NIHMSID: NIHMS1692693  PMID: 33872847

Abstract

Introduction:

Alcohol, nicotine, and marijuana are the three most widely used substances among adolescents and young adults, with co-use of multiple substances being common. Few longitudinal studies have examined risk factors of alcohol, marijuana, and nicotine poly-substance use. We examined frequency of alcohol, marijuana, and cigarette poly-substance use over time and how key risk factors contribute to this substance use during adolescence and young adulthood.

Methods:

Participants (N=1,263 9th and 10th graders) were oversampled for ever-smoking a cigarette at baseline from 16 Chicago-area high schools between 2004-2006. Many participants progressed to heavier cigarette use, as well as alcohol and marijuana use over time. Participants completed questionnaires assessing substance use and psychosocial factors at baseline, 6-, 15-, 24-, 33-months, and 5-, 6-, and 7-years.

Results:

Longitudinal multi-level models demonstrated that at baseline and over time, more depression symptoms, more anxiety symptoms, negative mood regulation expectancies, and lower grade point average (GPA) were each associated with more poly-substance use over time. In addition, there were a number of interaction effects of gender (e.g., depression was related to substance use in males) and developmental stage moderated these relationships.

Conclusions:

Depression, anxiety, negative mood regulation expectancies, and GPA all significantly influence both initial and longitudinal levels of substance use across adolescence and young adulthood. Our findings underscore the importance of identifying and treating youth with depression and anxiety symptoms, as well as providing resources early for those struggling in school in order to help with substance use prevention and intervention efforts.

Keywords: alcohol, anxiety, cigarettes, depression, marijuana, nicotine

1. Introduction

Alcohol, nicotine, and marijuana are the three most widely used substances in the U.S. among adolescents and young adults, and poly-substance use of these three drugs is increasingly more common. Poly-substance use of alcohol, marijuana, and cigarettes is associated with increased substance use and substance-related problems1-13. Individuals who use alcohol before the age of 18 are two times more likely to develop an Alcohol Use Disorder (AUD), and individuals who use marijuana before the age of 18 are four to seven times more likely to develop a Cannabis Use Disorder (CUD)14.

Alcohol, nicotine, and marijuana use share risk factors. Several studies show psychiatric symptoms are associated with substance use15-22. Specifically, depression symptoms23-25 and high negative affect and dysregulated mood regulation25-27 are associated with AUD, Tobacco Use Disorder (TUD), and CUD. In addition, poor school achievement is a risk factor for AUD, TUD, and CUD28, 29. Several longitudinal studies have found that increased substance use (especially nicotine and marijuana use) is related to increased depressive symptoms during adolescence and young adulthood (e.g.,30-39). Many of these studies found sex/gender differences in the relationship between substance use and depression, with some finding the relationship is stronger for females than males, especially the link between alcohol use and depression in adolescence (e.g.,33,39), while others found no sex/gender differences in the relationship between substance use and depression (e.g.,37). Additionally, a few longitudinal studies have examined the association between substance use and other internalizing mental health symptoms, such as anxiety symptoms (e.g.,39-42). However, most of these studies examined substances individually, despite poly-substance use being highly prevalent43. Less attention has been paid to identifying the key internalizing mental health risk factors for poly-substance use and how these risk factors vary over time during adolescence and young adulthood and by gender.

In two previous studies, we found that more marijuana and cigarette use were related to more depression symptoms in males, but not in females44,45. Those studies were limited, though, by their lack of inclusion of alcohol use and their focus singularly on depressive symptoms, not considering other mood-related risk factors (e.g., anxiety, negative mood regulation). The goals of the current study were to extend our previous findings by evaluating other potential key mental health, mood-related risk factors and baseline academic functioning (grade point average, GPA) through later timepoints into young adulthood to better understand the longitudinal association between mood-related risk factors and poly-substance use (marijuana, cigarette, and alcohol use frequency). In addition, we examined if these mood risk factors were differentially related to frequency of cigarette, marijuana, and alcohol use. We hypothesized that 1) mood-related risk factors and lower GPAs at baseline are associated with more poly-substance use at baseline and over time and 2) increases in mood-related risk factors over time predict progression in poly-substance use. We included sex/gender and race, and ethnicity in all analyses, given a growing literature indicating that these factors are important to consider when examining substance use and risk factors for substance use (e.g.,33,39,44-46), although we did not have specific hypotheses for these variables. Further, since poly-substance use is complex, we included additional analyses on each substance individually in order to better understand if mood-related risk factors and GPA were specifically associated with use of cigarette, marijuana, and/or alcohol use.

2. Materials and Methods

2.1. Participants

The longitudinal, observational study recruited 9th and 10th graders (N=1263; mean baseline age=15 years) from 16 Chicago-area high schools, selected to reflect the area demographics, over a two-year period between 2004-2006. Participants were oversampled for ever-smoking a cigarette (83% ever-smoked), and thus at high-risk for smoking escalation. Of 3654 students invited, 1344 agreed to participate, and 1263 (94%) completed baseline measurements (see Supplement and47 for more information). Many participants progressed to heavier cigarette use, and alcohol and marijuana use over the years. Participants completed questionnaires assessing substance use and psychosocial factors at baseline, 6-, 15-, 24-, 33-months, and 5-, 6-, and 7-years. Retention at 7-years was greater than 84% and participants and non-participants at 7-years did not differ on baseline demographic or substance use measures. For the current study, participants who completed at least three timepoints and were assessed at either 5-, 6-, or 7-years (covering the young adult years) were used for analyses (n=1211; about 96% of the total sample).

2.2. Demographics and Substance Use

Race/ethnicity, gender, as well as GPA, were obtained through self-report questionnaires. At each timepoint, frequency of marijuana, cigarette, and alcohol use was measured by asking participants to report the number of days they used each substance in the past month. Marijuana and alcohol use frequency was measured using single item question for each substance with 5 ordinal options coded as: 0=zero days; 1=one day a month or less; 3=more than one day a month but less than one day a week; 9=more than one day a week but less than daily; 27=every day. Cigarette use frequency was measured using a single item question with 9 ordinal options coded as: 0=zero days; 1=one day; 3= 2-3 days; 5= 4-5 days; 7= 6-7 days; 9= 8-10 days; 16= 11-20 days; 25= 21-29 days; 30= every day. We created a poly-substance use z-score variable by transforming each substance use variable into a z-score based on the baseline mean and standard deviation for each substance, to allow it to vary over time. We then averaged the z-score for each substance over time.

2.3. Mood-Related Risk Factors

2.3.1. Center for Epidemiological Scale-Depression (CES-D).

At each wave, participants completed the 20-item CES-D48. Total score was used to determine the severity of depressive symptoms49.

2.3.2. Adolescent Anxiety and Depressive Symptoms.

At each wave, participants completed 12-items from the Mood and Affect Symptom Questionnaire (MASQ50, 51). Total score was used to assess symptoms of general distress, anxious arousal, and anhedonic depression51.

2.3.3. Negative Mood Regulation Expectancies Scale (NMR).

At each wave, participants completed the 14-item NMR52. Items were averaged for a total score (higher scores indicate a strong belief one can alter negative moods).

2.4. Substance Use Measures to Characterize the Sample

2.4.1. Modified Fagerstrom Questionnaire (mFTQ).

For each wave, all participants completed the mFTQ53,54. Total score measure nicotine dependence (scores >6 indicate high level of nicotine dependence54).

2.4.2. Cannabis Use Disorder Identification Test–Revised (CUDIT-R).

At 5-, 6-, and 7-years, participants completed the CUDIT-R55 (higher values represent increased marijuana problems and dependence).

2.4.3. Marijuana Problem Scale (MPS).

At 5-, 6-, and 7-years, participants completed the MPS, about negative psychological, social, occupational, and legal consequences of marijuana use in the last 90 days56. Higher scores represent more marijuana-related problems.

2.4.4. Alcohol-Related Problems Scale (ARPS).

At baseline, 6-month, 15-month, and 24-months, participants completed the ARPS57-59 (see Supplement). At 5-, 6-, and 7-years, participants completed the Alcohol Problem Items, based on the National Institute on Alcohol Abuse and Alcoholism (NIAAA) Task Force recommendations.

2.5. General Statistical Procedures

All analyses were carried out using SPSS 24.0 (IBM) and included all participants. There were almost no missing data (< 1%) at the item/measure level at each assessment point. Variables were checked for inter- and intra-measure consistency (see Supplement), and distributions examined for unusual data points, skewness, and sphericity.

Multi-level random effects regression models examined how baseline risk factors were associated with cigarette, alcohol, and marijuana poly-substance use and how these factors varied longitudinally. These models treated observations nested within subjects, allowing for random intercepts and slopes, and incorporating covariates of gender, race, and ethnicity (Hispanic or non-Hispanic). Gender was effect coded (−1=male, 1=female), race was dummy coded (1=Caucasian, 0=non-Caucasian), ethnicity was dummy coded (1=Hispanic, 0=non-Hispanic), and time was measured continuously by year and centered at baseline. Gender, ethnicity, and race were static and based on reported values at baseline. The dependent variable was the z-score of poly-substance use; however, we also ran separate models with the frequency of each substance use over time to understand how risk factors were related to each substance individually.

Models assessing baseline risk factors related to baseline poly-substance use, mood-related risk factors, and GPA were static. Depression symptoms, anxiety, negative mood regulation, and GPA were all mean centered. We included the interaction of gender with each risk factor in the respective models.

For longitudinal models, depression, anxiety, and negative mood regulation were time-varying independent variables and were grand mean centered. Due to the fact that GPA was not assessed at all time-points, models assessing how this risk factor varies over time were not run. Interactions between each risk factor and time, as well as interactions with gender were included in each model. The interactions served as controls in the models to test whether other variables (i.e., time/developmental stage, gender) moderate the relationship between the independent variables (i.e., mood-related risk factors) and dependent variables (i.e., substance use).

For all models, non-significant covariates and interactions were removed and then reduced models were re-run. Tables 2 and 3 show full and reduced models. Analyses with gender were run separately for males and females. Analyses with time used a median split of age to capture developmental differences before age 18 (years 0-2) and after age 18 (years 5-7). Results were deemed statistically significant when p-values< .05.

3. Results

3.1. Participant Characteristics and Substance Use Frequency

Participant characteristics are shown in Table 1. Substance use frequencies over time are shown in Figure 1. Rates of poly-substance use over time are shown in Figure 2. Figure 3 shows rates of types of poly-substance use among individuals who report using two substances over time. Correlations among risk factors and substance use measures at baseline are shown in Supplemental Table 1.

Table 1.

Participant Characteristics

Demographics Baseline
(n=1211)		 6 months
(n=1144)		 15 months
(n=1128)		 2 years
(n=1144)		 5 years
(n=1026)		 6 years
(n=1068)		 7 years
(n=1066)
Age 15.63 (0.61) 16.14 (0.62) 16.89 (0.62) 17.59 (0.60) 21.38 (0.81) 22.41 (0.83) 23.41 (0.83)
Gender (% female) 57% 57% 58% 58% 59% 59% 59%
GPA 2.72 (0.75) 2.88 (0.71) 2.82 (0.74) 2.85 (0.75) -- -- --
Ethnicity/Race
  Caucasian 57% 57% 57% 57% 58% 57% 57%
  Black 17% 17% 17% 17% 17% 17% 17%
  Hispanic 17% 16% 17% 17% 16% 16% 16%
  Asian 4% 4% 4% 4% 4% 4% 4%
  Other 5% 6% 5% 5% 5% 6% 6%
CES-D Total Score 16.85 (9.83) 16.37 (9.93) 14.64 (9.27) 15.06 (9.47) 13.42 (9.85) 12.78 (9.69) 12.44 (9.43)
Modified MASQ Total Score 28.40 (7.85) 27.60 (8.11) 26.48 (7.57) 26.08 (7.26) 25.08 (7.49) 24.59 (7.30) 24.29 (7.47)
NMR Total Score 3.49 (0.69) 3.52 (0.71) 3.60 (0.70) 3.62 (0.69) 3.81 (0.72) 3.88 (0.74) 3.92 (0.71)
Substance Use
Cigarettes
 Frequency of cigarette use in past month (days) 3.74 (7.61) 4.36 (8.41) 5.57 (9.98) 6.48 (10.64) 3.32 (5.12) 10.07 (12.67) 9.78 (12.76)
 Nicotine Dependence (mFTQ) 1.38 (1.18) 1.48 (1.32) 1.64 (1.42) 1.71 (1.46) 2.57 (1.53) 2.67 (1.59) 2.73 (1.65)
 Cigarette Use Z-score- based on baseline 0.00 (1.00) 0.08 (1.11) 0.24 (1.31) 0.36 (1.39) −0.05 (0.67) 0.83 (1.66) 0.79 (1.68)
Marijuana
 Frequency of marijuana use in past month (days) 2.01 (5.12) 2.42 (5.79) 2.94 (6.45) 3.84 (7.55) 5.35 (8.93) 5.20 (9.01) 5.32 (9.27)
 Marijuana-Related Problems (CUDIT-R) -- -- -- -- 5.91 (7.28) 5.40 (6.77) 5.10 (6.78)
 Marijuana Use Z-score- based on baseline 0.00 (1.00) 0.08 (1.13) 0.18 (1.26) 0.36 (1.48) 0.65 (1.74) 0.62 (1.76) 0.65 (1.81)
Alcohol
 Frequency of alcohol use in past month (days) 2.28 (2.84) 2.42 (3.19) 2.82 (3.48) 3.11 (3.46) 5.79 (4.56) 5.97 (4.38) 6.10 (4.52)
 Alcohol Problem Scale 3.62 (1.67) 3.82 (1.74) 4.05 (1.74) 4.31 (1.72) -- -- --
 Alcohol Problem Items -- -- -- -- 2.77 (2.10) 3.47 (1.47) 3.41 (1.42)
 Alcohol Use Z-score- based on baseline 0.00 (1.00) 0.05 (1.12) 0.19 (1.22) 0.29 (1.22) 1.24 (1.61) 1.30 (1.54) 1.35 (1.59)
Poly-substance Use Z-score- based on baseline 0.00 (0.73) 0.70 (0.84) 0.20 (0.95) 0.33 (1.00) 0.61 (0.94) 0.92 (1.22) 0.92 (1.12)
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Note: all values are means or standard deviations unless otherwise noted; GPA, Grade-Point Average; CES-D, Center for Epidemiological Scale-Depression; MASQ, Mood and Affect Symptom Questionnaire; NMR, Negative Mood Regulation Expectancies Scale; mFTQ, modified Fagerstrom Questionnaire; CUDIT-R, Cannabis Use Disorders Identification Test-Revised.

Figure 1.

Figure 1

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Frequency of Substance Use Over Time

Figure 2.

Figure 2

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Percent of Individuals with Poly-Substance Use

Figure 3.

Figure 3

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Percent of Individuals Reporting Using Two Substances

3.2. Key Baseline Risk Factors of Poly-Substance Use

Multi-level models demonstrated that at baseline, more depression symptoms (CES-D), more anxiety symptoms (MASQ), weaker mood regulation expectancies (NMR), and lower GPA were each associated with more poly-substance use over time (Table 2). When examining the relationships between these risk factors and each substance individually, higher baseline depression symptoms (similar to our previous findings with marijuana and cigarettes) and weaker mood regulation expectancies were each related to more marijuana and cigarette use, but not to alcohol use, over time. On the other hand, more baseline anxiety symptoms and lower GPA were associated with increased use of each substance over time (Table 2).

Table 2.

Longitudinal Multilevel Regression Models with Substance Use Measures as the Dependent Variables and Baseline Risk Factor Measures as the Independent Variables

Z-Score of Poly-substance Use Cigarette Use Frequency Marijuana Use Frequency Alcohol Use Frequency
Predictor Estimate
(SE)		 df t Sig. Estimate
(SE)		 df t Sig. Estimate
(SE)		 df t Sig. Estimate
(SE)		 df t Sig.
 CESD
Intercept −0.15 (0.04) 1336.79 −3.71 <.001 3.04 (0.42) 1335.87 7.31 <.001 2.50 (0.15) 1206.47 16.82 <.001 1.30 (0.14) 1340.78 9.59 <.001
Time (linear) 0.13 (0.00) 1150.41 27.66 <.001 0.70 (0.05) 1152.69 13.77 <.001 0.49 (0.04) 1143.33 11.56 <.001 0.62 (0.02) 1129.88 31.70 <.001
Baseline CES-D 0.01 (0.00) 1202.27 4.25 <.001 0.11 (0.02) 1196.97 4.97 <.001 0.04 (0.01) 1198.93 2.99 <.001 0.01 (0.01) 1189.55 1.78 .075
 Gender −0.14 (0.02) 1209.71 −7.02 <.001 −1.06 (0.21) 1204.53 −5.01 <.001 −0.97 (0.14) 1207.09 −6.95 <.001 −0.25 (0.07) 1199.98 −3.55 <.001
 Race 0.26 (0.04) 1222.02 5.86 <.001 1.97 (0.46) 1214.53 4.27 <.001 0.51 (0.30) + 1211.38 1.67 .095 1.21 (0.15) 1206.99 7.99 <.001
 Ethnicity −0.06 (0.05) + 1221.06 −1.11 .266 −1.28 (0.54) 1216.15 −2.35 .019 0.08 (0.36) + 1216.72 0.23 .822 0.02 (0.18) + 1216.51 0.09 .93
Interactions
  Gender*CES-D 0.00 (0.00) + 1204.48 −1.18 .238 −0.02 (0.02) + 1199.18 −0.68 .495 −0.02 (0.01) + 1199.23 −1.49 .136 −0.01 (0.01) + 1192.98 −0.94 .349
 MASQ
Intercept −0.13 (0.04) 1337.10 −3.30 <.001 3.19 (0.42) 1334.48 7.61 <.001 2.50 (0.15) 1207.58 16.85 <.001 1.32 (0.14) 1341.40 9.79 <.001
Time (linear) 0.13 (0.00) 1152.40 27.70 <.001 0.71 (0.05) 1153.81 13.83 <.001 0.49 (0.04) 1145.28 11.56 <.001 0.62 (0.02) 1131.98 31.74 <.001
Baseline MASQ 0.01 (0.00) 1211.03 4.03 <.001 0.11 (0.03) 1206.08 3.94 <.001 0.06 (0.02) 1210.00 3.26 <.001 0.02 (0.01) 1201.83 2.24 .025
 Gender −0.14 (0.02) 1211.26 −6.92 <.001 −0.99 (0.21) 1206.24 −4.66 <.001 −0.97 (0.14) 1208.34 −6.99 <.001 −0.25 (0.07) 1201.40 −3.67 <.001
 Race 0.24 (0.04) 1222.78 5.36 <.001 1.74 (0.46) 1215.98 3.75 <.001 0.39 (0.30) + 1212.77 1.27 .204 1.17 (0.15) 1207.62 7.74 <.001
 Ethnicity −0.05 (0.05) + 1222.55 −0.88 .377 −1.18 (0.55) 1217.80 −2.16 .031 0.16 (0.36) + 1218.67 0.43 .664 0.04 (0.18) + 1218.26 0.22 0.827
Interactions
  Gender*MASQ 0.00 (0.00) + 1210.92 −0.40 .691 −0.01 (0.03) + 1205.73 −0.39 .693 −0.02 (0.02) + 1207.46 −0.85 .397 0.00 (0.01) + 1202.21 0.04 .969
 NMR
Intercept −0.14 (0.04) 1339.65 −3.59 <.001 3.09 (0.42) 1335.84 7.37 <.001 2.51 (0.15) 1208.32 16.84 <.001 1.30 (0.14) 1343.23 9.64 <.001
Time (linear) 0.13 (0.00) 1152.55 27.71 <.001 0.71 (0.05) 1153.71 13.83 <.001 0.49 (0.04) 1145.60 11.57 <.001 0.62 (0.02) 1132.04 31.74 <.001
Baseline NMR −0.09 (0.03) 1208.75 −3.07 <.001 −0.84 (0.31) 1204.50 −2.72 .007 −0.66 (0.20) 1208.04 −3.30 <.001 −0.10 (0.10) 1199.51 −0.99 .322
 Gender −0.14 (0.02) 1210.96 −6.75 <.001 −0.95 (0.21) 1206.37 −4.43 <.001 −0.99 (0.14) 1208.42 −7.06 <.001 −0.24 (0.07) 1200.77 −3.39 <.001
 Race 0.25 (0.04) 1223.73 5.70 <.001 1.89 (0.46) 1217.05 4.08 <.001 0.46 (0.30) + 1213.55 1.54 .125 1.20 (0.15) 1208.57 7.91 <.001
 Ethnicity −0.06 (0.05) + 1222.76 −1.13 .257 −1.30 (0.55) 1218.18 −2.36 .018 0.07 (0.36) + 1219.05 0.19 .851 0.02 (0.18) + 1218.08 0.12 .903
Interactions
  Gender*NMR 0.01 (0.03) + 1208.59 0.28 .782 0.07 (0.32) + 1205.19 0.23 .818 0.00 (0.21) + 1205.96 −0.02 .987 0.05 (0.10) + 1199.71 0.51 .611
 GPA
Intercept −0.16 (0.04) 1318.10 −3.84 <.001 2.90 (0.42) 1332.87 6.98 <.001 2.44 (0.15) 1192.45 16.42 <.001 1.29 (0.14) 1323.28 9.38 <.001
Time (linear) 0.13 (0.00) 1137.06 27.47 <.001 0.70 (0.05) 1140.43 13.59 <.001 0.49 (0.04) 1130.25 11.54 <.001 0.62 (0.02) 1116.25 31.61 <.001
Baseline GPA −0.17 (0.03) 1191.66 −6.20 <.001 −2.30 (0.27) 1186.12 −8.38 <.001 −0.84 (0.18) 1192.40 −4.70 <.001 −0.21 (0.07) 1187.14 −3.12 <.001
 Gender −0.11 (0.02) 1193.64 −5.35 <.001 −0.67 (0.20) 1189.62 −3.26 <.001 −0.76 (0.14) 1192.79 −5.61 <.001 1.20 (0.15) 1193.65 7.84 <.001
 Race 0.28 (0.05) 1205.86 6.17 <.001 2.20 (0.46) 1198.63 4.80 <.001 0.57 (0.30) + 1197.57 1.89 .059 −0.06 (0.09) 1184.40 −0.61 .543
 Ethnicity −0.11 (0.05) 1206.29 −1.99 .047 −2.06 (0.55) 1199.78 −3.77 <.001 −0.13 (0.36)+ 1201.32 −0.35 .728 0.00 (0.18) + 1201.53 −0.02 .982
Interactions
  Gender*GPA 0.05 (0.03) 1192.80 2.01 .044 0.40 (0.27) + 1187.14 1.47 .141 0.33 (0.18) + 1189.78 1.87 .062 0.11 (0.09) + 1183.53 1.21 .225
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Note. CES-D, Center for Epidemiological Scale-Depression; MASQ, Mood and Affect Symptom Questionnaire; NMR, Negative Mood Regulation Expectancies Scale; GPA, Grade-Point Average

+

, variable removed from final model.

In general, poly-substance use, as well as use of each substance, increased over time (Table 2, Figure 1, Figure 2). Caucasians had a higher frequency of poly-substance use over time, but ethnicity (Hispanic/not Hispanic) was not related to poly-substance use (Table 2). Caucasians and non-Hispanic individuals had a higher frequency of cigarette use over time, and Caucasians had a higher frequency of alcohol use over time, but ethnicity was not related to alcohol use (Table 2). Frequency of marijuana use over time was not related to race or ethnicity (Table 2). Overall, males had a higher frequency of poly-substance use, and use of each substance individually, over time than females (Table 2; Supplemental Table 2).

3.2.1. Follow-up of Gender Interactions.

The interaction between gender and baseline GPA was significant for poly-substance use, but not for each substance individually (Table 2). Follow-up revealed a lower baseline GPA was more strongly associated with more poly-substance use for males (β=−.14, t(3173)=−8.12, p<.001), than for females, β=−.06, t(4404)=−4.00, p<.001. No other 2-way interactions with gender were significant.

3.3. How Risk Factors Vary Over Adolescence with Poly-Substance Use

Multi-level models demonstrated that in general, similar to what is reported above, more depression symptoms, more anxiety symptoms, and weaker mood regulation expectancies were each associated with more poly-substance use over time (Table 3). For the individual substances, more depression symptoms and more anxiety symptoms over time were each related to more cigarette use, more marijuana use, and more alcohol use over time (Table 3). On the other hand, stronger mood regulation expectancies were associated with less marijuana use and less alcohol over time, but was not related to cigarette use (Table 3). It is important to note that overall, females, compared to males, had significantly higher GPAs at baseline, depressive symptoms from baseline through 2-years, and anxiety symptoms from baseline through 7-years compared to males, and weaker mood regulation expectancies from baseline through 7-years (see Supplemental Table 3).

Table 3.

Longitudinal Multilevel Regression Models with Substance Use Measures as the Dependent Variables and Time-Varying Risk Factor Measures as the Independent Variables

Z-Score of Poly-substance Use Cigarette Use Frequency Marijuana Use Frequency Alcohol Use Frequency
Predictor Estimate
(SE)		 df t Sig. Estimate
(SE)		 df t Sig. Estimate
(SE)		 df t Sig. Estimate
(SE)		 df t Sig.
 CESD
Intercept 0.15 (0.04) 1347.76 −3.78 0.00 2.99 (0.42) 1336.26 7.10 0.00 2.47 (0.15) 1238.17 16.58 0.00 1.27 (0.14) 1355.41 9.40 0.00
Time (linear) 0.14 (0.00) 1187.55 28.05 0.00 0.71 (0.05) 1183.48 13.85 0.00 0.51 (0.04) 1178.44 11.95 0.00 0.62 (0.02) 1172.68 31.69 0.00
CES-D 0.01 (0.00) 7405.82 6.03 0.00 0.04 (0.01) 7437.74 3.29 0.00 0.05 (0.01) 7367.64 6.21 0.00 0.02 (0.01) 5213.22 3.78 0.00
 Gender −0.13 (0.02) 1222.21 −6.58 0.00 −0.86 (0.21) 1214.71 −4.12 0.00 −0.92 (0.13) 1229.36 −6.81 0.00 −0.25 (0.07) 1229.33 −3.62 0.00
 Race 0.26 (0.04) 1223.18 5.83 0.00 1.95 (0.46) 1214.66 4.21 0.00 0.51 (0.30) 1216.17 1.70 0.09 1.21 (0.15) 1213.03 7.98 0.00
 Ethnicity −0.06 (0.05) + 1223.21 −1.11 0.27 −1.29 (0.55) 1216.11 −2.34 0.02 0.06 (0.35) + 1221.90 0.16 0.87 0.01 (0.18) + 1222.11 0.08 0.94
Interactions
 Year*CES-D 0.00 (0.00) + 6103.01 −0.55 0.58 0.00 (0.00) + 5994.23 0.94 0.35 0.00 (0.00) + 6275.33 0.45 0.66 −0.01 (0.00) 5224.13 −3.02 0.00
 Gender*CES-D 0.00 (0.00) 7456.99 −3.59 0.00 −0.02 (0.01) + 7514.69 −1.49 0.14 −0.04 (0.01) 7418.53 −4.23 0.00 −0.01 (0.00) + 7441.67 −1.18 0.24
 MASQ
Intercept −0.14 (0.04) 1338.42 −3.61 0.00 3.04 (0.42) 1330.01 7.24 0.00 2.47 (0.15) 1233.91 16.46 0.00 1.29 (0.13) 1340.73 9.56 0.00
Time (linear) 0.14 (0.00) 1191.80 28.18 0.00 0.70 (0.05) 1187.21 13.81 0.00 0.52 (0.04) 1185.33 12.19 0.00 0.63 (0.02) 1182.71 31.83 0.00
MASQ 0.01 (0.00) 7375.65 5.93 0.00 0.03 (0.01) 7438.77 2.42 0.02 0.03 (0.01) 5625.11 2.24 0.03 0.04 (0.01) 4905.61 4.52 0.00
 Gender −0.13 (0.02) 1225.15 −6.61 0.00 −0.84 (0.21) 1220.60 −4.04 0.00 −0.90 (0.14) 1233.64 −6.66 0.00 −0.26 (0.07) 1227.88 −3.77 0.00
 Race 0.25 (0.04) 1223.73 5.52 0.00 1.87 (0.47) 1217.60 4.03 0.00 0.42 (0.30) + 1215.82 1.40 0.16 1.17 (0.15) 1210.86 7.74 0.00
 Ethnicity 0.06 (0.05) + 1223.08 −1.05 0.29 −1.25 (0.55) 1219.23 −2.28 0.02 0.08 (0.36) + 1219.99 0.22 0.82 0.03 (0.18) + 1220.70 0.15 0.88
Interactions
 Year*MASQ 0.00 (0.00) + 6045.54 0.74 0.46 0.00 (0.01) + 5919.69 0.03 0.98 0.01 (0.00) 6232.80 3.11 0.00 −0.01 (0.00) 5190.70 −2.56 0.01
 Gender*MASQ 0.00 (0.00) 7448.82 −2.75 0.01 −0.03 (0.01) 7510.90 −1.80 0.07 −0.03 (0.01) 7274.86 −2.52 0.01 0.00 (0.01) + 7220.75 −0.73 0.46
 NMR
Intercept 0.15 (0.04) 1350.50 −3.72 0.00 3.03 (0.42) 1343.90 7.19 0.00 2.47 (0.15) 1260.41 16.35 0.00 1.28 (0.14) 1361.82 9.42 0.00
Time (linear) 0.14 (0.00) 1230.41 27.66 0.00 0.70 (0.05) 1231.03 13.54 0.00 0.51 (0.04) 1214.26 11.82 0.00 0.62 (0.02) 1228.89 31.03 0.00
NMR −0.04 (0.01) 7526.36 −2.38 0.02 −0.22 (0.16) 7571.41 −1.38 0.17 −0.43 (0.12) 7165.46 −3.48 0.00 −0.20 (0.09) 4534.48 −2.18 0.03
 Gender −0.13 (0.02) 1234.67 −6.38 0.00 −0.84 (0.21) 1230.36 −4.00 0.00 −0.90 (0.14) 1242.98 −6.62 0.00 −0.24 (0.07) 1238.85 −3.48 0.00
 Race 0.25 (0.04) 1224.76 5.63 0.00 1.91 (0.47) 1218.76 4.11 0.00 0.47 (0.30) 1215.22 1.55 0.12 1.20 (0.15) 1210.22 7.95 0.00
 Ethnicity −0.06 (0.05) + 1225.55 −1.07 0.29 −1.27 (0.55) 1222.56 −2.30 0.02 0.07 (0.36) + 1222.62 0.21 0.84 0.02 (0.18) + 1220.95 0.09 0.93
Interactions
 Year*NMR 0.00 (0.01) + 5411.12 0.74 0.46 −0.03 (0.06) + 5272.34 −0.49 0.62 0.01 (0.04) + 5587.19 0.22 0.83 0.05 (0.02) 4401.58 2.07 0.04
 Gender*NMR 0.02 (0.02) + 7614.70 1.28 0.20 0.15 (0.16) + 7648.67 0.93 0.35 0.32 (0.12) 7302.18 2.60 0.01 −0.06 (0.07) + 7051.34 −0.95 0.34
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Note. CES-D, Center for Epidemiological Scale-Depression; MASQ, Mood and Affect Symptom Questionnaire; NMR, Negative Mood Regulation Expectancies Scale

+

, variable removed from final model.

3.3.1. Follow-up of Time Interactions.

The interaction between time and depression symptoms was significant for alcohol use, but was not significant for poly-substance use, cigarette use, or marijuana use (Table 3). Follow-up of simple slopes for the interaction for alcohol use found that in adolescence (years 0-2) depression was not related to alcohol use (β=.03, t(4588)=1.80, p=.07); however, in young adulthood (years 5-7) more depression symptoms were associated with less alcohol use, β=−.04, t(3101)=−2.18, p=.03. The interaction between time and anxiety symptoms was significant for marijuana use and alcohol use, but was not significant for poly-substance use or cigarette use (Table 3). Follow-up of simple slopes for the interaction for marijuana use showed that in adolescence (years 0-2) more anxiety was related to increased marijuana use (β=.05, t(4579)=3.31, p=.001) and this effect was slightly larger in young adulthood (years 5-7), β= 07, t(3106)=4.01, p<.001. Follow-up of simple slopes for the interaction for alcohol use found in adolescence (years 0-2) more anxiety symptoms was related to increased alcohol use (β=.07, t(4586)=4.77, p<.001), but in young adulthood (years 5-7) anxiety symptoms were not associated with alcohol use, β=−.01, t(3106)=−0.31, p=.76. The interaction between time and negative mood regulation was significant for alcohol use, but was not significant for poly-substance use, cigarette use, or marijuana use (Table 3). Follow-up of the simple slopes for the interaction for alcohol use showed that in adolescence (years 0-2) mood regulation expectancies were not related to alcohol use (β=−.01, t(4579)=−0.79, p=.43); however, in young adulthood (years 5-7) stronger mood regulation expectancies were associated with more alcohol use, β=.04, t(3109)=2.01, p=.045.

3.3.2. Follow-up of Gender Interactions.

The interaction between gender and depression symptoms was significant for poly-substance use and for marijuana use, but not for cigarette or alcohol use (Table 3). Follow-up of simple slopes for the interaction for poly-substance use found more depression symptoms were associated with increased poly-substance use for males (β=.11, t(3196)=6.35, p<.001), but not females, β=−.03, t(4458)=−1.70, p=.09. Similarly, follow-up of simple slopes for the interaction for marijuana use found more depression symptoms were associated with increased marijuana use for males (β=.13, t(3218)=7.61, p<.001), but not females, β=.00, t(4464)=−0.04, p=.97. The interaction between gender and anxiety symptoms was significant for poly-substance use and for marijuana use, but not for cigarette use or alcohol use (Table 3). Follow-up of simple slopes for the interaction for poly-substance use showed more anxiety symptoms were associated with increased poly-substance use for males (β=.08, t(3196)=4.36, p<.001), but not for females, β=−.02, t(4461)=−0.13, p=.90. Similarly, follow-up of \simple slopes for the interaction for marijuana use showed that more anxiety symptoms were associated with increased marijuana use for males (β=.09, t(3218)=5.08, p<.001), but not for females, β=.02, t(4467)=1.35, p=.18. The interactions between gender and weaker mood regulation expectancies were significant for marijuana use, but was not significant for polysubstance use, cigarette use, or alcohol use (Table 3). Follow-up of simple slopes for the interaction for marijuana use showed weaker mood regulation expectancies were associated with more marijuana use for males (β=−.05, t(3214)=−3.06, p=.002), but for females stronger mood regulation expectancies were related to more marijuana use, β=.03, t(4467)=2.15, p=.03.

4. Discussion

This is one of the few multi-wave longitudinal studies to examine frequency of cigarette, marijuana, and alcohol poly-substance use over time and how mood-related risk factors, as well as GPA, contribute to this substance use during adolescence and young adulthood among a large, well-characterized sample of high-risk adolescents. At baseline and over time, more depression symptoms, more anxiety symptoms, weaker mood regulation expectancies, and lower GPA were each associated with more poly-substance use, consistent with the hypotheses and previous studies with this sample and other samples23-29,44,60-62.

There were important gender differences. Males, compared to females, had a higher frequency of poly-substance use over time, as well as use of each substance individually, in line with national data63. Furthermore, lower baseline GPA was more strongly associated with more poly-substance use for males than for females. In line with our previous findings of earlier timepoints in the study44,45, more poly-substance use and more marijuana use were related to more depression and anxiety symptoms in males, but not in females. Weaker mood regulation expectancies were associated with more marijuana use for males; however, stronger mood regulation expectancies were related to more marijuana use for females. These findings support the hypothesis that males with higher negative affect, less perceived ability to regulate their negative affect, and poorer academic achievement may use marijuana as a way to regulate their affect44, while females use marijuana for other reasons that are not yet fully captured by our variables under study. In fact, it is intriguing that while females (both normatively and in the current study) have higher symptoms and rates of both depression and anxiety that emerge during childhood and adolescence (e.g.64) and males (both normatively and in the current study) have higher substance use frequency63, it is only for males that depressive and anxiety symptoms were related to substance use frequency, especially marijuana frequency. Our findings differ from a recent longitudinal study of the Add Health nationally representative sample of 7th-12th graders (recruited in 1994-1995) followed until ages 24-32, that found increased frequency of marijuana use frequency and binge drinking were associated with increased depressive symptoms (CES-D) in adolescence, especially for females47. This discrepancy in findings may be due to differences in samples, as the current study was comprised of adolescents at high-risk for cigarette escalation and were not a nationally-representative sample and/or may reflect generational/cohort differences, as the current sample was recruited about 10 years after the Add Health sample. Our results highlight the importance of diversion intervention for males who may have poorer academic achievement, at an early age, prior to substance initiation. Future and ongoing longitudinal studies that start at an earlier age, such as the Adolescent Brain Cognitive Development Study (ABCD)65 may be able to more closely disentangle the relationship between substance use and depression and how it varies by sex/gender.

Developmental stage also played an important role in the relationship between risk factors and substance use. A higher frequency of marijuana use was associated with more anxiety symptoms in adolescence (ages 14-16), but this effect was even larger in young adulthood (ages 22-24). Thus, anxiety seems to play an important role in both the initiation and continued use of marijuana. It is also possible that marijuana use exacerbates anxiety over time. While alcohol use was not related to depression symptoms or mood regulation expectancies in adolescence, more alcohol use was associated with fewer depression symptoms in young adulthood and stronger mood regulation expectancies. It is not clear what is driving these relationships in young adulthood, although it may be that young adults who have more depression symptoms and have weaker mood regulation expectancies are isolating themselves and not engaging in social situations that facilitate drinking alcohol. On the other hand, more alcohol use was related to more anxiety symptoms in adolescence, but not in young adulthood, indicating anxiety may be involved in early initiation of alcohol use, but may not play a role in continued alcohol use during the young adult years, when alcohol use is legal and normative in the U.S. Therefore, alcohol use in young adulthood may be driven more by social motives than by anxiety-reduction motives.

These findings have important implications for prevention and intervention efforts. Given the strong link between substance use, depression, anxiety, and GPA, it is important to target these as inter-related and co-contributing factors, instead of separate factors for early intervention and prevention. For example, prevention efforts to teach and promote mood regulation techniques (e.g., mindfulness, calm down space) starting in elementary school may help reduce substance use, depression, and anxiety in adolescence, especially among males. It is important that future research better understand the risk correlates of adolescent female substance use, especially marijuana use, to better inform future prevention and intervention efforts. Screening for substance use, depression, and/or anxiety among adolescents with lower GPAs could provide a targeted just-in-time intervention for adolescents at high-risk for substance use escalation overall (and increased depression and anxiety symptoms). Importantly, treatments need to address the inter-relationships among factors instead of targeting one specific factor (e.g. substance use). Further, it may be that depression prevention or mood regulation interventions have broader impacts into young adulthood.

Although the study has a large, diverse sample, the findings should be considered in the context of several limitations. First, although the sample captured individuals at different levels of cigarette, marijuana, and alcohol use, most of the participants were selected for having ever smoked a cigarette at baseline, and thus at high-risk for smoking escalation. The sampling strategy successfully identified individuals who increased cigarette, marijuana, and alcohol use over time; however, the sampling strategy also limits the generalizability of our findings if one wants to consider population prevalence. Indeed, 83% of the sample reported ever-smoking a cigarette at 15-16 years old, compared to 36% of 15-16 year-olds in a nationally-representative sample in 2006, around the same time period66. Additionally, at 7-years 94% of the sample used alcohol in the past month (compared to 64% of 21-25 year-olds nationally) and 48% of the sample used marijuana in the past month (compared to 19% of 18-25 year-olds nationally)67. Therefore, the sample’s lifetime and current use of cigarettes, alcohol, and marijuana were much higher than national rates. Second, the participants were enrolled in the study prior to the widespread use of e-cigarettes, and it is unclear how our findings extend to use of e-cigarettes or other nicotine products. Third, the study only examined frequency of substance use (i.e., days used). It is possible that mood-related risk factors and GPA have different associations with quantity of substance use during adolescence and young adulthood. Future and ongoing studies should examine these relationships. Fourth, we included continuous measures of nicotine dependence and of problematic cannabis and alcohol use in Table 1 to help characterize levels of dependence and problematic use among the sample, so the values are not meant to be interpreted as clinical cut-points. Fifth, we included sex and race as variables within the analysis, even though existing literature did suggest specific directionality and relevance of these variables. There is increasing awareness that these variables are relevant to substance use, and NIH is encouraging analyses that appropriately probe for effects of these variables. Despite these limitations, this study, because of its sampling of high-risk youth, was better poised to address more efficiently and strongly the association between polysubstance use and risk factors over time.

5. Conclusions

This study expanded upon previous findings to show that more depression and anxiety, poorer negative mood regulation, and lower GPA are stable risk factors for poly-substance use of alcohol, cigarettes, and marijuana into young adulthood. There were important substance specific effects in these relationships. While baseline GPA and anxiety were related to more use of all three substances over time, depressive symptoms and mood regulation expectancies in adolescence were only related to cigarette and marijuana use, but not to alcohol use, throughout adolescence and young adulthood. Further, gender and developmental stage moderated the relationships between risk factors and substance use measures. Males with higher negative affect, less perceived ability to regulate negative affect, and poorer academic achievement may use marijuana as a way to regulate their affect44, while females use marijuana for other reasons that are not yet fully understood. Across males and females, anxiety played an important role in both the initiation and continued use of marijuana. It is also possible that marijuana use exacerbates anxiety over time. Further, anxiety may be involved in early initiation of alcohol use, but may not play a role in continued alcohol use during young adulthood. Overall, our findings underscore the importance of identifying and treating youth with depression and anxiety symptoms, as well as providing resources to those struggling in school in order to help with substance use prevention and intervention efforts.

Supplementary Material

1

Highlights.

  • Co-use of alcohol, nicotine, & marijuana is highly prevalent

  • We examined risk factors of co-use over 7 years in adolescence to young adulthood

  • At baseline & over 7 years, mental health symptoms were related to co-use

  • Lower baseline grade point average (GPA) was related to more co-use over time

  • Findings stress the sustained role of mental health symptoms & GPA in substance use

Acknowledgments

Role of Funding Sources

This publication was made possible by the National Cancer Institute (NCI) (P01CA098262, PI: RM) and the National Institute on Drug Abuse (NIDA) (F31DA038388, PI: NAC). NAC was supported by NIDA (K23DA048132, PI: NAC). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NCI, NIDA, or the National Institutes of Health.

Footnotes

Conflicts of Interest

None.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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