Untangling Influences in the Longitudinal Relationship Between Depressive Symptoms and Drinking Frequency in High School

Journal of Adolescent Health xxx (2019) 1e7

www.jahonline.org Original article

Untangling Influences in the Longitudinal Relationship Between Depressive Symptoms and Drinking Frequency in High School Robert J. Wellman, Ph.D. a, Michael Chaiton, Ph.D. b, Matthis Morgenstern, Ph.D. c, and Jennifer O’Loughlin, Ph.D. d, e, f, * a

Department of Population and Quantitative Health Sciences, Division of Preventive and Behavioral Medicine, University of Massachusetts Medical School, Worcester, Massachusetts b Division of Epidemiology, Office of Global Public Health Training, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada c Institute for Therapy and Health Research, Kiel, Germany d Department of Social & Preventive Medicine, School of Public Health, Université de Montréal, Montréal, Quebec, Canada e Centre de Recherche CRCHUM, Université de Montréal, Montréal, Quebec, Canada f Institut National de Santé Publique du Québec, Montréal, Quebec, Canada

Article history: Received May 16, 2019; Accepted October 2, 2019 Keywords: Alcohol drinking; Depressive symptoms; Adolescents; Teens; Youth; Longitudinal survey

A B S T R A C T

Purpose: In young people, alcohol consumption and depressive symptoms are related, but the temporal ordering of the relationship is an open question. Previous studies have not disaggregated influences of interindividual and intraindividual components affecting the relationship. We investigated whether a reciprocal relationship between frequency of alcohol use and depressive symptoms exists in the general population of adolescents after removing interindividual influences. Methods: A total of 1,293 Canadian adolescents provided data on depressive symptoms and frequency of alcohol use every 3 months from grade 7 to 11 (1999e2005) for a total of 20 cycles. We used latent curve models with structured residuals, which disaggregate interindividual and intraindividual components to assess the directionality of the relationship. Results: Both drinking frequency and depressive symptoms increased linearly and quadratically over time, with significant interindividual variation around the origin and rate of change. Intercepts and slopes for drinking frequency and depressive symptoms differed by sex and age. After controlling for sex, age, maternal education, sensation seeking, impulsivity and clustering by school, a significant positive association was observed between depressive symptoms and drinking frequency 3 months later (.032 [.004, .060]; p ¼ .024), but no association was observed between drinking frequency and subsequent depressive symptoms (.011 [.006 to .029]; p ¼ .193). Conclusions: Our data provide longitudinal evidence that changes in depressive symptoms exceeding one’s “normal” level predict increases in drinking frequency. This suggests that teaching youth to recognize and cope with mood changes would be worthwhile. Ó 2019 Society for Adolescent Health and Medicine. All rights reserved.

Conflicts of interest: The authors declare no conflicts of interest. * Address correspondence to: Jennifer O’Loughlin, Ph.D., Department of Social and Preventive Medicine, University of Montréal, 850 rue Saint-Denis, Bureau S02-370, Montréal, Québec H2X 0A9, Canada. E-mail address: [email protected] (J. O’Loughlin). 1054-139X/Ó 2019 Society for Adolescent Health and Medicine. All rights reserved. https://doi.org/10.1016/j.jadohealth.2019.10.001

IMPLICATIONS AND CONTRIBUTION

Changes in depressive symptoms exceeding one’s “normal” level predict increases in drinking frequency, suggesting that failure to cope with such fluctuations in symptoms may portend later problematic alcohol use. Teaching youth to recognize and cope with mood changes would be worthwhile, and universal, school-based interventions to increase social and emotional learning have shown promise in that regard.

Alcohol consumption and depressive symptoms are common in adolescents. Despite recent declines in underage alcohol use [1,2], the prevalence of past-year use in 2016 in the U.S. ranged from 18% in eighth graders to 56% in 12th graders and was similar

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by sex [1]. In Canada, in 2015, 45% of grade 7e12 students reported past-year use, with a similar prevalence by sex [2]. Reports of depressive symptoms, such as feeling sad or losing interest in previously engaging pursuits, are also common. Depressive symptoms in the past 2 weeks were reported by 10%, 20%, and 25% of sixth, eighth, and 10th graders in the U.S., respectively, with more girls than boys in these grades reporting symptoms [3]. There is considerable evidence of an association between alcohol consumption and depressive symptoms. Adolescents who drink alcohol have a higher risk of concurrent depressive symptoms than their nondrinking peers [3], whereas experiencing more depressive symptoms is associated with more frequent concurrent consumption and intoxication, as well as earlier onset of alcohol use [4]. Co-occurrence of problem drinking and depression in adolescence poses greater risks for negative outcomes (e.g., alcohol use disorder, severity of depression, and suicide attempt) in young adulthood than either problem alone [5,6]. Longitudinally, the presence of a depressive disorder or subclinical depressive symptoms predicts initiation of alcohol use among abstainers, earlier age of drinking onset, future drinking, and harmful drinking [7], whereas frequency of alcohol use predicts higher levels of subsequent depressive symptoms [8], suggesting a reciprocal relationship. Results are mixed in studies exploring the temporal ordering of depression and drinking, with several reporting bidirectionality [9e14] and others finding no relationship [15,16] or a unidirectional association only [17,18]. Several studies have also explored factors such as sex [9,19], age [9], or the presence of delinquent behavior [20], as possible moderators of a bidirectional relationship. Pedrelli et al. [21] suggest that inconsistencies in findings may be attributable to the specific behavior considered (i.e., drinking vs. heavy drinking vs. alcohol use disorder) and the severity of depression (i.e., depressive symptoms vs. depressive disorder). Other factors that might contribute to contradictory findings are study design (e.g., length of time between data collection points) and the analytic approach. We posit that a limitation in previous studies is the inability of the analytic techniques to tease apart interindividual and intraindividual influences in the relationship. As an example, blood pressure tends to follow a course over time that is, in part, predictable by interindividual differences (e.g., sex, age, height, weight, parental history, etc.). However, at each measurement occasion, blood pressure may differ from that “average” course because of factors, such as stress level or coffee consumption, before the measurement (i.e., intraindividual influences). Analogously, the time course of depressive symptoms and drinking frequency might also depend on both interindividual and intraindividual influences. If depressive symptoms and drinking frequency are reciprocally related, then it is conceivable that when depressive symptoms exceed an individual’s “predictable” level, inclination to drink might increase. Similarly, when drinking frequency falls below an individual’s “expectable” level, feelings of depression might lessen. Given equivocal findings in the extant literature, the objective of the present study was to investigate whether a reciprocal relationship between frequency of alcohol use and depressive symptoms exists in the general population of adolescents after accounting for interindividual influences. We used a new analytic technique, the latent curve model with structured residuals (LCM-SR [22]), that disaggregates interindividual and

intraindividual components of developmental change. Once potential confounders of the association are taken into account (as is traditional), this approach allows us to examine how variation within an individual (e.g., short-term fluctuations in mood or drinking) influence the association. Clarification of our understanding of the temporal ordering in the association between drinking and depression may inform intervention efforts by suggesting whether or when discouraging underage drinking might also prevent future depression or whether or when addressing depressive symptoms might prevent escalation of drinking. Methods Participants in the Nicotine Dependence in Teens (NDIT) Study [23] were recruited during seventh grade in 1999e2000, in a purposive sample of 10 high schools in Montréal, Canada. Sampling was designed to assure inclusion of a mix of urban, suburban, and rural schools; schools located in high, moderate, and low socioeconomic status neighborhoods; and French- and English-language schools. The baseline characteristics of the sample (n ¼ 1,293) resembled those of 13-year-olds from the population-based, provincially representative sample of the 1999 Québec Child and Adolescent Health and Social Survey [24]. Self-report questionnaires were administered at school every 3 months from grades 7 to 11, for a total of 20 data collection cycles during the 5 years of secondary school (1999e2000 to 2004e 2005). Parents and guardians provided written informed consent, and all students provided assent. NDIT was approved by the Institutional Review Board of the Ethics Research Committee of the Centre de Recherche du Centre Hospitalier de l’Université de Montréal. Study variables Drinking frequency was assessed in each cycle with “During the past three months, how often did you drink alcohol (beer, wine, hard liquor)?”, with response options (never, a bit to try, once or a couple of times a month, once or a couple of times a week, and usually every day) on an ordinal scale ranging from 1 to 5. Depressive symptoms were assessed in each cycle with the Depressive Symptoms Scale [25], which includes six items: “During the past three months, how often have you.felt too tired to do things?.had trouble going to sleep or staying asleep?.felt unhappy, sad, or depressed?.felt hopeless about the future?.felt nervous or tense?.worried too much about things?” Response options, never, rarely, sometimes, and often, were scored 0 to 3, summed and averaged, yielding a scale ranging from 0 to 3. The inventory has adequate psychometric properties, assesses a single latent factor, correlated highly with the depression subscale of the 90-item Symptom Checklist, and showed a higher correlation than the 90-item Symptom Checklist with a diagnosis of major depressive disorder diagnosis [25]. Demographic covariates included sex, age at baseline, and mother university-educated (no/yes) as an indicator of socioeconomic status. To account for the fact that individual participants entered NDIT at different cycles (1,267 at cycle 1 and the remaining 26 between cycles 2 and 11), we drew “baseline” data on age, drinking frequency, and depressive symptom score at the cycle of entry. In addition, based on Hussong et al.’s [26] suggestion that the unique contribution of internalizing symptoms (e.g., depression) to alcohol involvement could be discerned only

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after accounting for co-occurring externalizing (i.e., behavioral disinhibition) symptoms or tendencies (p. 393), we included as covariates sensation seeking and impulsivity (which are directly related to alcohol use during early adolescence [27] and which predict later antisocial behavior [28]). Both were measured in cycles 14 (10th grade, mean age ¼ 15.8 years) and 18 (11th grade, mean age ¼ 16.7 years). The sensation seeking scale comprised nine items (e.g., “I often try new things just for fun or thrills, even if most people think it is a waste of time”) and the impulsivity scale comprised six items (e.g., “I often do things without stopping to think”). Response options for both ranged from 1 (not at all true) to 5 (very true) [29]. Item scores at each cycle were summed and averaged, yielding a scale from 1 to 5, with higher scores indicating greater sensation seeking or impulsivity. Cronbach’s alpha for sensation seeking was .83 in cycle 14 and .82 in cycle 18; for impulsivity, it was .88 in cycle 14 and .89 in cycle 18. We then created a composite score for each trait using the average score across cycles for participants who provided data in both and the score in the available cycle for those with data in only one. Sensation seeking and impulsivity were moderately-to-highly correlated (r ¼ .70), and for analyses, sensation seeking and impulsivity were entered as continuous scores and considered time invariant. Statistical analyses Preliminary analyses were conducted in 2019 with Stata version 14.2, revision 29 (2018; Stata Corp LLC, College Station), and structural equation modeling was performed with the lavaan package (version 0.6, September 3, 2018) for the R statistical system (version 3.5.1, July 2018). Because autoregressive and cross-lagged relationships cannot be examined with fewer than two adjacent time points, we restricted the analyses to participants who provided data on depressive symptoms and drinking in at least one pair of cycles separated by 3 months. We first compared participants retained for analyses with those not retained, then examined missing data at baseline and across data collection waves. Statistical references (A to F) are available in the online Appendix. Our analytic strategy was based on the LCM-SR, which assesses both stability and change in reciprocal relationships between two constructs. The primary advantage of the LCM-SR approach is that it allows for complete separation of interindividual and intraindividual components of change by assessing interindividual variations among the observed variables via a random intercept and slope (as well as exogenous covariates) and intraindividual variations (i.e., autoregressive and crosslagged parameters) among their latent residuals [22]. We included school (1e10) in all models as a predictor of intercepts and slopes to account for possible clustering. Full information maximum likelihood estimation was used to account for missing data (A), and robust standard errors were calculated (B). In previous studies, data were collected at 1-year [10,15,17,18], 2-year [12], 3-year [11], or longer intervals [16] or in some combination of intervals [9,13,14]. To capitalize on the density of measurement occasions in NDIT, we examined associations between drinking frequency and depressive symptoms at 3-month intervals. We used a three-phase iterative modelebuilding strategy (refer to Iterative Model Fitting Strategy, Supplementary Data) adapted from Curran et al. [22]. Briefly, in phase 1, we examined interindividual influences by first investigating the rate of change

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(i.e., the shape of the slope) in each outcome and then determining the demographic and personal characteristics (covariates) that were associated with the intercept and the slope(s) of the growth curves. Studies of drinking trajectories typically find linear and quadratic trends [30], and, using NDIT data, Chaiton et al. [31] found three trajectories of depressive symptoms with linear and quadratic growth. Hence, our unidirectional models examined both linear and quadratic slopes. In phase 2, we addressed the question of how stable each outcome was over time by testing unidirectional models with autoregressive parameters. In phase 3, we addressed the question of crossoutcome influences by testing bidirectional models with crosslagged (i.e., intraindividual) parameters. In all models, the scores for depressive symptoms and drinking frequency were rescaled to the same metric by mean centering and dividing by twice the standard deviation, a method recommended for input variables regardless of their distribution (C). We tested changes in model fit between nested models with a likelihood ratio test using a scaled difference chisquare (D). Goodness-of-fit for all models was assessed with four statistics: (1) relative/normed chi-square (c2/df) close to 2, (2) root mean square error of approximation and the lower bound of its 90% confidence interval (CI) < .05, (3) Comparative Fit Index .95, (4) Tucker-Lewis Index .95 (E). All fit estimates were scaled to account for robust estimation (F).

Results Table 1 presents demographic and other characteristics of participants retained for analysis (n ¼ 1,262) and those not retained (n ¼ 31). Participants retained were younger and less likely to drink at least monthly than those not retained. Table A1 presents missing data for key variables and covariates. The proportion of missing values for drinking frequency ranged from approximately 6% in cycle 1 to 57% in cycle 4 when six schools were not surveyed, tending to increase over time (median [IQR] ¼ 24.8% [17.1e30] with cycle 4 excluded). Similarly, the proportion of missing values for depressive symptoms ranged from approximately 8% in cycle 1 to 57.8% in cycle 4, tending to increase over time (median [IQR] ¼ 24.5% [17.8e30.2] with cycle 4 excluded). Drinking frequency escalated sharply over follow-up. The proportion of participants who drank at least monthly rose from

Table 1 Characteristics of participants retained and not-retained for analyses

Sex, % male Age, mean (SD)a Primary language, French/bilingual (%) Maternal education, university educated (%) Depressive symptomsa, mean (SD) Alcohol usea, monthly (%) Sensation seeking, mean (SD) Impulsivity, mean (SD)

Retained (n ¼ 1,262)

Not retained (n ¼ 31)

47.9 12.8 (.6) 40.9 44.6 1.1 (.6) 12.5 2.9 (.8) 2.3 (.9)

58.1 13.3 (1.0)* 51.6 25.0 1.1 (.7) 28.6* db db

SD ¼ standard deviation. *p  .01. a Measured in the cycle in which the participant joined the Nicotine Dependence in Teens cohort. b Nonretained participants left the cohort before the cycle in which sensation seeking and impulsivity were measured.

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12% in cycle 1 to 60% in cycle 20. In contrast, depressive symptoms appeared to increase slightly over time (Table 2). Table 3 presents a comparison of the fit indices for all models. Unidirectional models with quadratic slopes and freely estimated autoregressive parameters fit both outcomes well, and a bidirectional model with constrained cross-lagged associations between outcomes fit best (Figure 1). Interindividual Influences Drinking frequency. The mean (95% CI) (2.07 [2.78 to 1.35]) and variance (.12 [.10e.14]) of the intercept for drinking frequency indicated significant variation around the origin, and the mean (.09 [.04e.14]) and variance (.001 [.001.001], truncated by rounding) of the linear slope indicated that drinking frequency increased linearly, with significant variation around the rate of linear change. Finally, the mean of the quadratic slope (.03 [.68 to .62]) was not significant, but the variance (.05 [.04e.07]) indicated significant variation around the rate of quadratic growth. Participants who drank more frequently at the outset increased their drinking less rapidly (r with linear slope ¼ .009 [.012 to .006]; r with quadratic slope ¼ .006 [.008 to .004]), likely reflecting a ceiling effect. The linear and quadratic slopes of drinking frequency were inversely related (r ¼ .011 [.015 to .008]). Girls drank less frequently than boys at the outset (.058 [.113 to .002]) but showed a greater linear increase in frequency (.005 [.001e.009]). Older participants drank more frequently at the outset (.15 [.09e.20]) and increased their drinking less rapidly (.008 [.012 to .004]) than their younger peers. School was significantly related to both the intercept and linear slope of drinking frequency, indicting clustering. No other covariates were related to drinking frequency. Depressive symptoms. The mean (1.23 [1.94 to .52]) and variance (.15 [.02 to .17]) of the intercept for depressive

Table 2 Mean (SD) depressive symptoms and frequency (%) of participants who drank  monthly per survey cycle, Nicotine Dependence in Teens (1999e2005) Cycle

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Depressive symptomsa

Drank  monthly

N

Mean (SD)

1,160 1,155 1,146 532 1,075 1,077 930 955 999 975 953 956 899 894 889 875 862 835 828 833

1.09 1.03 .92 .90 .94 .93 .90 .90 1.02 1.01 .98 .96 1.02 1.16 1.03 1.07 1.09 1.25 1.12 1.13

SD ¼ standard deviation. a Range ¼ 0e3.

(.61) (.65) (.68) (.69) (.70) (.73) (.73) (.76) (.78) (.80) (.78) (.77) (.76) (.81) (.79) (.81) (.80) (.83) (.79) (.81)

n

n (%)

1,189 1,185 1,166 538 1,089 1,086 943 965 1,006 982 949 971 904 895 894 874 851 846 832 828

143 221 208 95 272 358 271 300 366 378 372 360 425 460 446 433 512 505 524 494

(12.0) (18.7) (17.8) (17.7) (25.0) (33.0) (28.8) (31.1) (36.4) (38.5) (38.2) (37.1) (47.0) (51.4) (49.9) (49.5) (60.2) (59.7) (62.3) (59.7)

symptoms indicated significant variation around the origin. The means of the linear (.02 [.07 to .04]) and quadratic (.20 [.94 to .55]) slopes and the variance of the quadratic slope (.04 [.11 to .18]) were not significant, but the variance of the linear slope (.001 [.000 to .001], truncated by rounding) indicated significant variation around the rate of linear change. Participants who reported more depressive symptoms at the outset showed less growth (r with linear slope ¼ .012 [.014 to .001]; r with quadratic slope ¼ .008 [.015 to .001]), likely reflecting a ceiling effect. The linear and quadratic slopes of depressive symptoms were unrelated (r ¼ .00 [.023 to .024]). Girls reported more depressive symptoms at the outset (.19 [.13 to .24]) and showed more linear growth in symptoms (.011 [.006 to .017]) than boys. Older (.07 [.01 to .12]) and more impulsive (.09 [.04 to .15]) participants reported more depressive symptoms at the outset. No other covariates, including school, were related to depressive symptoms. Cross-outcome relationships. The intercept of drinking frequency was inversely related to the intercept (r ¼ .005 [.006 to .003]), linear slope (r ¼ .009 [.012 to .006]), and quadratic slope (r ¼ .006 [.011 to .001]) of depressive symptoms, indicating that participants who drank more frequently at the outset reported fewer and less rapid growth in depressive symptoms. Finally, the linear slope of drinking frequency was inversely related to the linear slope of depressive symptoms (r ¼ .06 [.08 to .04]), indicating that participants whose linear increase in drinking was faster reported less linear growth in depressive symptoms. There was no relationship between the linear slope of drinking frequency and the quadratic slope of depressive symptoms (r ¼ .01 [.04 to .01]), between the linear slope of depressive symptoms and the quadratic slope of drinking frequency (r ¼ .00 [.02 to .02]), or between the two quadratic slopes (r ¼ .003 [.02 to .03]). Intraindividual influences Drinking frequency exhibited moderate stability (mean autoregressive parameter ¼ .22 [mean lower bound ¼ .11, mean upper bound ¼ .34], range: .01e.32), and 17 of 19 parameters were statistically significant. Depressive symptoms also exhibited moderate stability (mean autoregressive parameter ¼ .21 [mean lower bound ¼ .08, mean upper bound ¼ .33], range: .01e .44), and 14 of 19 parameters were statistically significant (Table A2). Depressive symptoms were positively related to subsequent drinking frequency (.032 [.004 to .060], p ¼ .024), but drinking frequency was unrelated to subsequent depressive symptoms (.011 [.006 to .029], p ¼ .193). Discussion We investigated the longitudinal relationship between depressive symptoms and frequency of alcohol use in adolescents from grades 7 to 11. Our findings extend the existing literature because our analytic technique allowed us to separate interindividual and intraindividual components of change in both constructs. This is the first study to demonstrate a relationship between depressive symptoms and drinking frequency after accounting for associations with interindividual factors (i.e., sex, age, mother’s education, sensation seeking, and impulsivity). Our findings suggest that when adolescents experience more

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Table 3 Comparison of models of the relationship between depressive symptoms and drinking frequency, Nicotine Dependence in Teens (1999e2005) Modelb

Model fit indicesa

c2

df

c2/df

Phase 1: unidirectional models examining between-person influences Drinking frequency 1.1 998*** 223 4.5 1.2 562*** 200 2.8 1.3 714*** 295 2.4 Depressive symptoms 1.1 1,220*** 223 5.5 1.2 650*** 200 3.3 1.3 820*** 290 2.8 Phase 2: unidirectional models examining within-person influences Drinking frequency 2.1 478*** 286 1.7 2.2 427*** 268 1.6 Depressive symptoms 2.1 492*** 286 1.7 2.2 414*** 268 1.5 Bidirectional models 3.1 1,385*** 934 1.5 3.2 1,379*** 933 1.5 3.3 1,370*** 915 1.5 3.4 1,384*** 933 1.5 3.5 1,368*** 915 1.5 Final 1,377*** 932 1.5

Dc2

RMSEA (95% CI)

CFI

TLI

BIC

.052 (.050e.055) .038 (.035e.041) .034 (.031e.036)

.91 .96 .96

.91 .96 .96

23,627 23,181 32,451

.060 (.057e.062) .042 (.039e.045) .038 (.035e.041)

.93 .97 .97

.93 .97 .96

17,822 17,239 26,351

42.4***

.023 (.020e.026) .022 (.018e.025)

.98 .99

.98 .98

32,219 32,264

68.4***

.024 (.021e.027) .021 (.017e.024)

.99 .99

.99 .99

25,978 26,008

.020 .019 .020 .020 .020 .019

.98 .98 .98 .98 .98 .98

.98 .98 .98 .98 .98 .98

42,511 42,512 42,626 42,518 42,623 42,517

11.2 17.7

(.018e.022) (.017e.021) (.018e.022) (.018e.022) (.018e.022) (.017e.021)

Boldface indicates the selected model within each category. ***p < .001. a All fit measures are scaled/robust to account for the use of robust estimation. c2/df ¼ the relative/normed c2 (values  2:1 are considered better); Dc2 is assessed by the likelihood ratio test between nested models (statistically significant finding indicates that the more complex model [i.e., with fewer df] fits better); root mean square error of approximation (RMSEA; values < .05 indicate acceptable fit); Comparative Fit Index (CFI; values > .90 indicate acceptable fit and >.95 good fit); Tucker-Lewis Index (TLI; values > .90 indicate acceptable fit and >.95 good fit); Bayesian Information Criterion (BIC; lower values indicate a more parsimonious model). b To account for clustering, all models included school as a predictor of intercepts and slopes.  Unidirectional models: Model 1.1 ¼ intercept and linear slope; Model 1.2 ¼ intercept, linear, and quadratic slopes; Model 1.3 ¼ intercept, linear, and quadratic slopes plus covariates (age, sex, mother’s education, sensation seeking, and impulsivity).  Unidirectional models: Model 2.1 ¼ Model 1.3 plus constrained autoregressive parameters; Model 2.2 ¼ Model 1.3 plus freely estimated autoregressive parameters.  Bidirectional models: Model 3.1 ¼ best fitting of Model 2.1 or 2.2 for each outcome, combined; Model 3.2 ¼ Model 3.1 plus constrained cross-lagged parameters from depressive symptoms to drinking frequency (cross-lags for drinking to depression at 0); Model 3.3 ¼ Model 3.1 plus freely estimated cross-lagged parameters from depressive symptoms to drinking frequency (cross-lags for drinking to depression at 0); Model 3.4 ¼ Model 3.1 plus constrained cross-lagged parameters from drinking frequency to depressive symptoms (cross-lags for depression to drinking at 0); Model 3.5 ¼ Model 3.1 plus freely estimated cross-lagged parameters from drinking frequency to depressive symptoms (cross-lags for depression to drinking at 0); Final model ¼ best-fitting of 3.2 or 3.3 and 3.4 or 3.5. All models included age, sex, mother’s education, sensation seeking, and impulsivity as predictors of intercepts and slopes. The final model is presented in Figure 1.

than their usual level of depressive symptoms, they are likely to increase their drinking frequency 3 months later, and provide support for the self-medication hypothesis, whereby some adolescents use alcohol to cope with negative affect [32]. However, the lack of a reciprocal relationship does not support the converse hypothesis that drinking can lead to depression [8]. Self-reported drinking to alleviate mood symptoms was associated with increased odds of both incident alcohol dependence and the persistence of dependence among adults [33]. Given that 12% of our participants had already begun drinking at least monthly by age 12 years and the general trend in our sample was a linear increase in drinking frequency throughout high school, this should be cause for concern, as both the behavior and its effects may persist. Separating the interindividual and intraindividual differences may also shed light on contradictory findings in previous studies regarding sex differences in the relationship between drinking and depression [21]. In concert with other studies ([3]), girls in NDIT had more baseline depressive symptoms and drank less frequently at the outset. Moreover, both girls’ symptoms and their drinking increased more rapidly than did boys’, suggesting

that girls might be at particular risk for drinking to cope with negative affect. Although some studies report a doseeresponse relationship between frequency of alcohol and other drug use and internalizing symptoms [26,34], others indicate that the relationship is stable across levels of alcohol consumption [35,36]. Our study suggests that apparent contradictory findings in the literature are not incompatible, but rather that there are separate interindividual and intraindividual influences that are associated with the relationship between drinking and depression. If this is so, unless the analyses control for interindividual variation, we would expect to see variations in the relationship of alcohol and depression over time and between populations. It is conceivable that demographic, social, and cultural (i.e., interindividual) factors might determine a person’s choice to use alcohol to selfmedicate and might underlie the shape of that person’s longterm trajectories of drinking and depression, whereas intrapersonal biological or psychological mechanisms might affect the short-term relationship between drinking and depression. In an experience sampling study of college students, Gottfredson and Hussong found that students whose affect varied more widely

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School

-0.006 -0.009 Int DRK

-0.011 Lslp DRK

Qslp DRK

2

X

2

1 1 0

1

1

2

X

-0.012 0

0.15

-0.008 -0.06

0.005

0.22 (0.11, 0.34)

a

εDRK1

εDRK2

Mother's Education -0.005

-0.057

Age

εDRK3

0.032 (0.004, 0.060) 0.007

0.007

Sex

0.007

0.011 (-0.006, 0.029)

Sensation-seeking

εDEP1

εDEP2 0.21 (0.08, 0.33)

εDEP3

a

-0.008 Impulsivity

0.07

0.19

-0.009

-0.006

0.09

0

0.01

2

0

1

X

1

1

1

2 2

X

Int DEP

Lslp DEP

Qslp DEP

-0.005

-0.008

Figure 1. Conditional latent curve model with structured residuals for the relationship between drinking frequency (DRK) and depressive symptoms (DEP) showing 3 of 20 data collection cycles at 3-month intervals. Autoregressive coefficients are freely estimated over time; they are reported as mean coefficient plus mean lower and upper bounds of the 95% confidence intervals (refer to Table A2 for autoregressive coefficients by cycle). Covariances between DRK and DEP residuals at each cycle and cross-lagged coefficients are constrained to equality. Unstandardized regression coefficients are reported in hexagons, correlations in octagons, and covariances in trapezoids. With the exception of the cross-lagged relationship between DRK and DEP, only statistically significant paths are shown.

than others’ (an interindividual difference) were more likely to drink, and that students were more likely to drink on days when they experienced levels of affect fluctuation greater than their personal “average” (an intraindividual difference) [37]. Limitations This article examines a population-based sample of adolescents so that depressive symptoms are likely within the normal range of experience; the relationship of clinically significant levels of depressive symptom or alcohol abuse may be different. Although the period between follow-up surveys was relatively short (i.e., 3 months), we cannot rule out directional changes that occur on a shorter timescale. Data based on self-report are

subject to misclassification. There was considerable missing data attributable to fluctuations in the number of participants providing data in each cycle. Our use of full information maximum likelihood estimation (A) should have mitigated any bias in estimates. In addition, bias may have been introduced by our treating sensation seeking and impulsivity as time invariant, as both have been observed to change over the course of adolescence and young adulthood [38,39]. Given the 1-year span between measurement occasions in this study, as contrasted with the 14- and 20-year spans in which those changes were observed, any bias is likely minimal. Our findings are consistent with a process whereby adolescents increase their drinking in response to short-term increases in their “normal” level of depressive symptoms. The primary

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implication for public health is that because continual universal monitoring of depressive symptoms would be impossible, interventions designed to help children and adolescents identify and cope with negative affect might forestall increases in drinking frequency. Universal school-based programs to increase social and emotional learning have shown great promise in improving students’ emotional and social skills, academic performance, behavior, and attitudes [40]. Funding Sources The Nicotine Dependence in Teens study was supported by the Canadian Cancer Society (grant numbers 010271, 017435). J.O. holds a Canada Research Chair in the Early Determinants of Adult Chronic Disease. The funders were not involved in the design or conduct of the study; collection, management, analysis, or interpretation of the data or preparation, review, or approval of the article. Supplementary Data Supplementary data related to this article can be found at http://doi.org/10.1016/j.jadohealth.2019.10.001. References [1] Miech RA, Johnston LD, O’Malley PM, et al. Monitoring the future national survey results on drug use, 1975e2016: Volume I, secondary school students. Ann Arbor: Institute for Social Research, The University of Michigan; 2017. Available at: http://monitoringthefuture.org/pubs.html#monographs. Accessed November 4, 2019. [2] Canadian Centre on Substance Use and Addiction. Canadian drug summary: Alcohol, fall 2017. Available at: https://www.ccsa.ca/sites/default/ files/2019-04/CCSA-Canadian-Drug-Summary-Alcohol-2017-en.pdf. Accessed November 4, 2019. [3] Saluja G, Iachan R, Scheidt PC, et al. Prevalence and risk factors for depressive symptoms among young adolescents. Arch Pediatr Adolesc Med 2004;158:760e5. [4] Johannessen EL, Andersson HW, Bjørngaard JH, Pape K. Anxiety and depression symptoms and alcohol use among adolescents - a crosssectional study of Norwegian secondary school students. BMC Public Health 2017;17:494. [5] Archie S, Kazemi AD, Akhtar-Danesh N. Concurrent binge drinking and depression among Canadian youth: Prevalence, patterns, and suicidality. Alcohol 2012;46:165e72. [6] Briére FN, Rohde P, Seeley JR, et al. Comorbidity between major depression and alcohol use disorder from adolescence to adulthood. Compr Psychiatry 2014;55:526e33. [7] Hussong AM, Ennett ST, Cox MJ, Haroon M. A systematic review of the unique prospective association of negative affect symptoms and adolescent substance use controlling for externalizing symptoms. Psychol Addict Behav 2017;31:137e47. [8] Cairns EK, Yap MBH, Pilkington PD, Jorm AF. Risk and protective factors for depression that adolescents can modify: A systematic review and metaanalysis of longitudinal studies. J Affect Disord 2014;169:61e75. [9] Marmorstein NR. Longitudinal associations between alcohol problems and depressive symptoms: Early adolescence through early adulthood. Alcohol Clin Exp Res 2009;33:49e59. [10] Wolitsky-Taylor K, Bobova L, Zinbarg RE, et al. Longitudinal investigation of the impact of anxiety and mood disorders in adolescence on subsequent substance use disorder onset and vice versa. Addict Behav 2012;37:982e5. [11] Jun H-Y, Sacco P, Bright CL, Camlin EAS. Relations among internalizing and externalizing symptoms and drinking frequency during adolescence. Subst Use Misuse 2015;50:1814e25. [12] Parrish KH, Atherton OE, Quintana A, et al. Reciprocal relations between internalizing symptoms and frequency of alcohol use: Findings from a longitudinal study of Mexican-origin youth. Psychol Addict Behav 2017;30: 202e8. [13] Needham B. Gender differences in trajectories of depressive symptomatology and substance use during the transition from adolescence to young adulthood. Soc Sci Med 2007;65:1166e79.

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