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Trajectories of Depressive Symptoms in Canadian Emerging Adults Mark A. Ferro, PhD, Jan Willem Gorter, MD, PhD, and Michael H. Boyle, PhD
The 12-month prevalence of depression is higher during emerging adulthood1 than at any other time.2 Understanding the natural course of depressive symptoms during this period and their associated risk and protective factors is important for informing prevention and intervention strategies. Evidence suggests that depressive symptoms are particularly dynamic during emerging adulthood,3---5 peak between 15 and 18 years of age, and exhibit robust associations with the following variables: female gender, poorer physical health, lower self-concept, lower socioeconomic status, and less supportive family environments.3,4,6,7 Previous cross-sectional and longitudinal analyses of our study data have reported that elevated symptoms of depression among youths are associated with maternal depression, lower self-concept, poorer physical health, tenuous peer and parent---child relations, and lower socioeconomic status.5,8,9 Despite consistent findings on the variables associated with youth depression, uncertainty continues about the shape, direction, and magnitude of trajectories. Some studies have shown depressive symptoms to follow an inverted U-shaped trajectory7; others, trajectories of change5,8; and others, multiple trajectories corresponding to subgroups with distinct patterns of change.3,10---12 We aimed to identify trajectories of depressive symptoms in an epidemiological sample of youths during emerging adulthood and to evaluate the effect of youth, parent, and family control variables on the number and shape of trajectories. We hypothesized that we would find 3 to 5 trajectory groups, representing chronically low, moderate, and high, as well as increasing and decreasing, levels of depressive symptoms. Our study design addressed the methodological shortcomings of existing research on trajectories of depressive symptoms3---5,7,10,12 by (1) using 8 occasions of measurement that encompassed all of emerging adulthood; (2) including a comprehensive measure of depressive
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Objectives. We identified courses of depressive symptoms in an epidemiological sample of emerging adults. Methods. We used latent class growth modeling to identify trajectories of depressive symptoms measured by the 12-item Center for Epidemiological Studies Depression Scale (CES-D) during a 14-year follow-up of 2825 Canadian youths aged 10 to 25 years enrolled in the National Longitudinal Survey of Children and Youth between 1994 and 2009. Results. After adjustment for youth, parent, and family factors, the 3 distinct trajectories of depressive symptoms were minimal (55%; CES-D < 6), subclinical (39%; CES-D = 9–13), and clinical (6%; CES-D > 18). All trajectories exhibited a parallel course, with peak symptoms at 15 to 17 years of age. Subclinical and clinical symptoms were more common than minimal symptoms in female youths and in respondents with lower self-concept, lower socioeconomic status, poorer interpersonal relations, and chronic health conditions (P < .01). Conclusions. Among emerging adults, trajectories of depressive symptoms do not trend upward or downward, and variables associated with identified trajectories demonstrated dose–response effects that agreed with vulnerability– stress theories of depression. (Am J Public Health. 2015;105:2322–2327. doi:10. 2105/AJPH.2015.302817)
symptoms to represent positive affect, somatic complaints, and interpersonal relations, in addition to depressed affect; (3) introducing a set of time-invariant control variables measured prior to the assessment of depressive symptoms to adjust trajectories of depression throughout emerging adulthood; and (4) including important covariates of youth depression excluded from previous studies (symptoms of childhood anxiety, chronic physical conditions, parental depression, place of residence, and immigrant status) and several indicators of socioeconomic status (parental education and employment and household income).
METHODS We obtained data from the National Longitudinal Survey of Children and Youth, which followed Canadian children from birth to early adulthood, examining influences on their social and behavioral development.13 The survey used a stratified, multistage probability design to enlist a representative sample of newborns to
children aged 11 years (n = 22 831). At each 2-year data collection cycle, youths and their corresponding most knowledgeable caregiver (hereafter parent) completed measures assessing health, psychological, and sociodemographic characteristics. Additional methodological details are provided elsewhere.13 We followed youths aged 10 to 11 years at cycle 1 (n = 3464) to cycle 8 (24---25 years of age). The response rates were 87% and 68% at cycles 1 and 8, respectively.13 We excluded 639 youths because they had completely missing depression scores or inconsistent reports of chronic health problems. The final sample size was n = 2825 (82%). We detected no sociodemographic differences between included and excluded youths. We included youths if they completed at least 1 cycle; 1639 (58%) completed all cycles. Youths who completed all cycles were more likely to have parents who were older (odds ratio [OR] =1.03; 95% confidence interval [CI] =1.02, 1.04), living with a partner (OR =1.11; 95% CI =1.11, 1.12), employed (OR = 2.60; 95%
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CI = 2.54, 2.65), living in an urban area (OR =1.39; 95% CI 1.36, 1.41), and earning a higher household income (OR =1.15; 95% CI 1.14, 1.15).
Measures Outcome. We measured self-reported youth depressive symptoms during the past week with a 12-item version of the Center for Epidemiological Studies Depression Scale (CES-D), starting when youths were 12 years old (cycle 2) and continuing for the duration of the National Longitudinal Survey of Children and Youth.14,15 The survey did not include the CES-D in cycle 1. The CES-D uses a 4-point scale to rate the frequency of symptoms. Although the 12-item CES-D has domains of depressed and positive affect, somatic complaints, and interpersonal relations, no item assesses irritability, a common symptom of youth depression. Composite scores spanned 0 to 36, with higher scores indicating greater impairment. Thresholds for categories of depressive symptoms were minimal (0---11), somewhat elevated (12---20), and very elevated (21---36).15 Internal consistency reliability was a = 0.82. Control variables. We measured symptoms of anxiety (a = 0.76) with 7 items from the Ontario Child Health Study Checklist.16 We assessed these items with a 3-point scale (0---2). Higher scores indicated more symptoms. We measured self-concept with 4 items (a = 0.82) from the General Self-Image subscale of the Self-Description Questionnaire.17 We rated items on a 5-point scale (0---4). Higher scores indicated more positive self-concept. We assessed parenting behavior with 2 youthreported assessments calibrated on a 4-point scale (0---3): a 5-item measure (a = 0.83) of parental nurturance and a 6-item measure (a = 0.64) of parental rejection.18 Higher scores indicated more parental nurturance and more parental rejection, respectively. To assess family environment, we used the same CES-D as for youths (a = 0.86) to capture symptoms of parental depression. We measured family functioning with the 12-item General Functioning subscale of the McMaster Family Assessment Device (a = 0.91).19 We rated items on a 4-point scale (0---3), with higher scores indicating poorer functioning. For sociodemographic characteristics, parents reported their age in years, gender, marital
status, educational attainment, employment status, household income, place of residence, and immigrant status. We determined youth health (presence or absence of a chronic condition) from a question to parents: Has a health professional diagnosed any of the following long-term conditions for this child [asthma, n = 489; cerebral palsy, n = 8; epilepsy, n = 23; food allergy, n = 181; heart condition, n = 75; kidney condition, n = 15; any other long-term condition, n = 128]?
Sample characteristics are shown in Table 1.
TABLE 1—Characteristics of the Study Sample: National Longitudinal Survey of Children and Youth, Canada, 1994– 2009 Characteristic
No. (%) or Mean (SE)
Youths Male
1435 (51)
Chronic health condition Age, y
793 (28) 10.5 60.01
Self-concept
13.4 60.07 3.0 60.08
Anxiety symptoms
Analysis We examined trajectories of depression among youths aged 12 to 25 years through latent class growth modeling.20 This model assumes conditional independence, which implies that for each individual within a given trajectory group, the distribution of the outcome for a given period is independent of the realized level of the outcome in previous periods.20 The number of groups to be modeled was guided by a priori expectations, overall model fit calculated with the Bayesian information criterion (BIC), posterior probability scores, and odds for correct classification for each trajectory group. We selected the model with the smallest absolute BIC, optimized probability scores, and least number of groups. The modeling process followed a previously implemented strategy, whereby we specified cubic trajectories for 3 groups being examined, added additional groups to the model, and examined the change in BIC scores to determine the best model.21 To ensure model parsimony, we removed nonsignificant higher-order terms. We repeated this modeling strategy with the control variables included, allowing us to evaluate the effect of adding control variables on the number and shape of trajectories. We obtained all control variables, except self-concept, from youths and parents in cycle 1; we assessed selfconcept as a control variable from cycles 1 to 8 and obtained all CES-D data from youths in cycles 2 to 8. We compared CES-D scores across trajectory groups at each assessment with analysis of variance. We compared youth, parent, and family characteristics across trajectory groups with analysis of variance and the v2 test. We examined pairwise group contrasts for potential
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Parental nurturance
12.0 60.09 4.6 60.09
Parental rejection Parents and families Female
2644 (94)
Age, y Living with partner
38.0 60.16 2300 (81)
Postsecondary graduate Currently employed
850 (30) 2503 (89)
Family income ‡ $50 000
1452 (51)
Residence in urban area
2342 (83)
Immigrant
504 (18)
Depressive symptoms
5.0 60.17
Family functioning
8.2 60.15
Note. The sample size was n = 2825.
dose---response effects when we observed significant overall differences. Analyses implemented sampling weights to ensure comparability with the Canadian population.13 We used multiple imputation (10 data sets) for data assumed to be missing at random. This assumption was plausible because we incorporated all variables, including those predicting missingness, in the imputation model.22 We conducted analyses with SAS version 9.4 (SAS Institute, Cary, NC): PROC TRAJ for latent class modeling and PROC MI for imputation. We applied the Holm---Bonferroni correction for multiple comparisons.23
RESULTS Initially, we fit a 3-group model to the data, with a BIC of 55 143.5 and high posterior probabilities (0.85---0.94). Subsequently, we specified a 4-group model, which demonstrated worse model fit (BIC = 55 160.9), providing very strong evidence to reject this model in favor of the 3-group model.24 It also had
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lower posterior probabilities (0.82---0.89). After removing nonsignificant higher-order terms and adding youth, parent, and family control variables, the adjusted 3-group model had better fit (BIC = 54 846.9) than other models, high posterior probabilities (0.86--0.92), and strong odds for correct classification (9.4---11.5) for each trajectory group. Table 2 shows CES-D scores for each group, and Figure 1 shows the trajectories of depression. The minimal trajectory group was the largest, consisting of 55% of youths; it was best modeled as a quadratic trajectory, with CES-D scores increasing from 3.95 at 12 to 13 years of age to 5.34 at 22 to 23 years, then decreasing to 4.69 at 24 to 25 years. The subclinical trajectory included 39% of youths; we modeled it with a cubic trajectory, whereby CES-D scores increased from 9.29 at 12 to 13 years of age to 12.64 at 16 to 17 years, then decreased and leveled off to 10.31 at 24 to 25 years. The clinical symptoms group was the smallest, consisting of 6% of youths, and we modeled it with a quadratic trajectory; CES-D scores increased from 18.61 to 24.82 between 12 to 13 years and 16 to 17 years of age, then decreased to 19.38 at 24 to 25 years. CES-D scores across groups were all significantly different at each cycle (all, P < .001). Adjustment for the control variables had the largest impact on the subclinical group,
lowering the intercept and creating a more pronounced downward trajectory (Figure 1). Other than a slightly lower intercept, the control variables had little discernible impact on the minimal group. In the clinical group, the control variables led to an accelerated symptom decline at about 21 years of age. Across all variables, the minimal group had significantly more favorable individual and family characteristics than the subclinical and clinical trajectory groups, with 3 exceptions: the proportion of parents who were female, employed, or immigrants did not differ (Table 3). We observed dose---response effects for most variables (P < .01), whereby the subclinical and clinical groups had increasingly less favorable characteristics than the minimal group.
DISCUSSION We identified 3 trajectories that corresponded to depressive symptoms experienced during emerging adulthood: minimal, subclinical, and clinical. The data also provided evidence of dose---response effects consistent with the vulnerability---stress model of depression,25 whereby less favorable characteristics relating to socioeconomic and health disadvantages increased across trajectory groups from minimal to clinical. Consistent with earlier reports,
TABLE 2—Youth Depressive Symptoms Stratified by Trajectory Group: National Longitudinal Survey of Children and Youth, Canada, 1994–2009 Trajectory Group
No. (%) Posterior probability
Minimal
Subclinical
Clinical
1548 (55)
1105 (39)
172 (6)
0.86
0.88
0.92
CES-D score, mean (SE) 12–13 y
3.95 (0.11)
9.29 (0.13)
18.61 (0.34)
14–15 y 16–17 y
4.01 (0.06) 5.22 (0.12)
11.34 (0.07) 12.64 (0.15)
21.95 (0.18) 24.82 (0.37)
18–19 y
5.27 (0.12)
12.49 (0.14)
24.65 (0.34)
20–21 y
5.28 (0.10)
11.49 (0.12)
23.49 (0.31)
22–23 y
5.34 (0.09)
10.66 (0.11)
21.33 (0.28)
24–25 y
4.69 (0.10)
10.31 (0.11)
19.38 (0.29)
Note. CES-D = Center for Epidemiological Studies Depression Scale. Analysis of variance CES-D scores across trajectory groups at each measurement occasion were all statistically significant (all, P < .001). Pairwise contrasts were conducted with the post hoc Tukey test and showed significant differences in CES-D scores between each trajectory group at each measurement occasion (all, P < .001).
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our findings indicate that depressive symptoms are dynamic during emerging adulthood3,5,7,8,26; symptoms tend to peak during mid-to-late adolescence6---8; the largest group consists of youths with low symptom levels3,26; in most groups, symptom levels decline during emerging adulthood5,7,8; and several variables associated with less favorable trajectories in other studies are represented in this one (female gender, lower self-concept, lower socioeconomic status, poorer interpersonal relations, and chronic health conditions).3---9 By contrast with previous reports of latent class modeling, we identified only 3 meaningful groups and none with cross-diagonal (distinctive upward or downward) trajectories,3,26 our data revealed a peak period of risk across all groups at 15 to 17 years of age, and our findings highlighted the influence on depression of several key variables. The absence of cross-diagonal trajectories and the parallel nature of the trajectories suggest that once the risk group for a youth is determined, the course of depressive symptoms during emerging adulthood is primarily a function of natural youth development. Although the number of depressive symptoms was influenced by exposure to several intrinsic (e.g., self-concept) and extrinsic (e.g., socioeconomic disadvantage) factors, trajectories were quite similar—the ages at which peaks and troughs occurred were consistent across groups. The gentle slope for the minimal trajectory was likely a basement effect—these youths were quite healthy and, because the CES-D is asymmetric, were bound to hover around zero. Differences in the number of trajectories between our analysis and previous studies are likely attributable to differences in methodologies. Our data provided a more comprehensive measure of depressive symptoms, more measurement occasions, and a substantial number of control variables. These considerations may have reduced variability among latent trajectories, resulting in the identification of 3 meaningful groups. Furthermore, by contrast with studies that may have overrepresented visible minorities and families with lower socioeconomic status,3,26 our sample contained families mirroring the sociodemographic distribution of the general population. Thus, this representative sample likely protected against the emergence of a cross-diagonal or chronically elevated trajectory—typically
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30
Minimal (55%) Subclinical (39%)
Depressive Symptoms (CES-D)
Clinical (6%)
24
18
12
6
0
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Age, Years Note. CES-D = Center for Epidemiological Studies Depression Scale. Adjusted trajectories included time-varying (youth selfconcept) and time-invariant (parental age, education, marital status, and depressive symptoms; family place of residence, income, and functioning; and youth gender, chronic health conditions, symptoms of anxiety, and perceptions of parental nurturance and rejection) control variables. Time-invariant variables were measured when youths were aged 10–11 years.
FIGURE 1—Trajectories of youth depression during emerging adulthood, estimated with latent class growth modeling (dashed lines are unadjusted, solid lines are adjusted): National Longitudinal Survey of Children and Youth, Canada, 1994–2009.
associated with sociodemographic disadvantage— resulting in a lower proportion of youths in these at-risk groups. Replication of findings with the methodologies of previous studies is warranted to determine whether differences in trajectories result from differences in methodology or unexplored contextual factors (e.g., cross-national differences). The peak of depressive symptoms at 15 to 17 years of age suggests that the transition from secondary school to postsecondary school or the labor force is a particularly vulnerable period for all groups and coincides with challenges that youths face as they absorb more demanding roles during emerging adulthood: management of changing relationships with parents from dependency to autonomy, the exploration of intimate partnerships, recognition of personal weaknesses, and the need to develop new skill sets that will ensure the attainment of long-term goals.27 Dose---response effects of control variables were consistent with the vulnerability---stress model of depression.25 Stress, particularly the
socioeconomic and physical health disadvantages observed in the subclinical and clinical groups, represents undesirable early life exposures that can interfere with emerging adults’ physiological and psychological homeostasis. Vulnerability is psychosocial factors, such as poor or strained interpersonal relations (e.g., parent---child relations, family functioning) that predispose individuals to depression. These stresses and vulnerabilities can exert their effects on one another concurrently with typical developmental challenges during emerging adulthood and can subsequently result in elevated symptoms, and, in some cases, onset of clinical depression.28 Maternal depression, childhood anxiety, and youth self-concept are additional stresses and vulnerabilities that influence depressive symptomatology among emerging adults.5,8,29 Previous studies indicate that introducing control variables can alter the shape and number of groups needed to optimize model fit.21 Although the control variables explained substantial variability in trajectory levels, they
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had no impact on the number of groups needed to satisfy fit requirements, attesting to model robustness. Within the subclinical group, the effect of control variables was relatively large and constant over time, with some widening between unadjusted and adjusted trajectories in later years. By contrast, control variable effects appeared later during emerging adulthood for the clinical group. Why the difference between these groups? We speculate that the symptoms of youths in the subclinical group were more amenable to change, as indicated by their sensitivity to the influence of socioeconomic and psychosocial exposures captured in the adjusted model. By contrast, the high symptom levels of youths in the clinical group were more consistent with clinical depression and may have been less amenable to change until a later developmental stage. These divergent effects might argue for a different public health response for the 2 groups: individual treatment for the clinical group and populationbased interventions for the subclinical group.
Implications A considerable number of youths in our sample had clinically relevant levels of symptoms corresponding to established thresholds.15 In light of the age of the cohort studied, the school system may be an effective environment to deliver targeted treatment and population-based interventions. In addition to having broad coverage,30,31 school-based interventions reduce felt and enacted stigma associated with seeking mental health services.32 However, the absence of crossdiagonal trajectories is relevant for public health initiatives because it suggests that during emerging adulthood, few individuals migrate across risk levels and no groups are well suited to classical prevention strategies. As a starting point during emerging adulthood, screening for depression is a practical option to identify youths meeting criteria for clinical depression so they can be offered treatment. In addition, implementation of general risk reduction interventions is warranted for the high proportion of youths who exhibit moderately elevated symptoms or subclinical depression. By contrast with classical public health approaches, this school-based intervention would focus on risk reduction strategies initiated when youths are most vulnerable so
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Limitations
TABLE 3—Comparisons of Sample Characteristics Across Trajectory Groups: National Longitudinal Survey of Children and Youth, Canada, 1994–2009 Trajectory Group Characteristic
Pairwise (1) Minimal, No. (%) (2) Subclinical, No. (%) (3) Clinical, No. (%) or Mean (SE) or Mean (SE) or Mean (SE) v2 or F Contrasts
Youths Male Chronic health condition Self-concept Anxiety symptoms Parental nurturance Parental rejection
943 (61) 330 (21)
448 (41) 378 (34)
47 (28) 85 (50)
74.69 1 > 2 > 3 43.49 1 < 2 < 3
13.8 60.06
12.9 60.07
12.4 60.18
2.3 60.07
3.5 60.08
5.2 60.20
12.3 60.08
11.4 60.09
11.4 60.23
32.11
1 > 2, 3
4.2 60.08
5.0 60.09
4.7 60.23
20.94
1 2, 3
125.92 1 < 2 < 3
Parents and families Female
1443 (93)
1042 (94)
165 (96)
Age, y Living with partner
38.2 60.13 1337 (86)
37.6 60.15 828 (75)
37.7 60.39 106 (62)
Postsecondary graduate
4.63 1>2 41.72 1 > 2 > 3
495 (32)
327 (30)
39 (23)
3.64
Currently employed
1379 (89)
965 (87)
147 (85)
1.59
Family income ‡ $50 000
1209 (78)
980 (89)
151 (88)
Residence in urban area
766 (50)
456 (41)
59 (34)
Immigrant
235 (15)
251 (23)
19 (11)
Depressive symptoms
4.3 60.15
5.7 60.17
7.9 60.44
Family functioning
7.8 60.13
8.2 60.15
10.5 60.38
39.66
1, 2 > 3 1 < 2, 3
13.14 1 > 2 > 3 5.74 40.62 1 < 2 < 3 23.76
1, 2 < 3
Note. All v2 and F tests were statistically significant at P < .01, except for parental gender, employment, and immigrant status, which were not statistically significant. Pairwise contrasts were conducted with the post hoc Tukey test for continuous variables and the v2 test for categorical variables.
they capitalize on the natural downward progression of symptoms to accelerate the process toward mental well-being. Because symptoms peaked when youths were aged 15 to 17 years, school-based intervention programs could be delivered prior to secondary school graduation with the aim to accelerate risk reduction among the nearly half of youths with subclinical symptoms. A systematic review of school-based interventions reported program effects that were generally small to moderate.33 Though effects may be relatively small, school-based interventions that effectively balance universal and targeted approaches have the potential to shift the distribution of elevated depressive symptoms in the population and sustain this effect over time.34,35 Such multipronged approaches are attractive from a public health standpoint because they could have a more substantial impact than targeted interventions that aim to reduce risk or treat symptoms for the subset of high-risk youths.36,37
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Previous reports of school-based interventions suggest that a combination of targeted treatment and universal risk reduction strategies is the most effective for youths,38 interventions with durations of 9 to 12 months are more effective at producing sustained effects over time,39 and, holding all things constant, interventions that have no implementation difficulties are likely to have larger effects in reducing youth mental health problems.40 This body of evidence highlights the importance of adopting a whole-school model in addressing youth mental health.33 Within such a model, a singular school ethos regarding mental health must be developed, teacher and parent education affirming this ethos is required to support school-based interventions, and integration with other systems (e.g., children and youth services, community agencies) is needed to ensure that school-based interventions for youth depression are adequately supported in the community.
Like most prospective epidemiological studies, sample attrition was associated with socioeconomic disadvantage. This selective loss may have resulted in an underestimation of distribution of youths in the elevated trajectory groups, as well as muted estimates of effects. This limitation is tempered by the fact that we used sampling weights developed to account for attrition. The reliability of trajectory estimates for the clinical group may be reduced. This group composed only 6% of the sample, and attrition was likely to have a greater impact in the latter years of the study. As a result, the standard errors associated with these estimates, especially during young adulthood, would be inflated. Control variables used in modeling trajectories of depression were limited to the constructs included in the National Longitudinal Survey of Children and Youth, as well as their measurement schedule. The survey included only youth self-concept as a time-varying control variable; the remaining variables were time invariant. The survey assessed symptoms of anxiety, parental behavior (nurturance, rejection), parental depression, and family functioning on 3 occasions, when youths were aged 10 to 15 years. Because these constructs were only measured in the first 3 cycles, it was not possible to include them as timevarying control variables in the model. In addition, because of the robust intercorrelations among these variables,41 it is reasonable to suggest that self-concept captured most of the influence of these variables during the follow-up and that their inclusion would not have resulted in meaningful changes to the trajectories.
Conclusions Youths follow 3 distinct trajectories of depressive symptoms during emerging adulthood. School-based intervention strategies may be useful in identifying at-risk youths for whom targeted intervention may be warranted and subclinical youths who may be responsive to interventions designed to accelerate the natural downward trajectories of depressive symptoms that appear to begin in midadolescence and continue to young adulthood. j
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About the Authors Mark A. Ferro and Jan Willem Gorter are with the Department of Pediatrics, and Mark A. Ferro and Michael H. Boyle are with the Departments of Psychiatry and Behavioural Neurosciences and Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada. Correspondence should be sent to Mark Ferro, Dept of Psychiatry and Behavioural Neurosciences, 1280 Main St West, MIP 201A, Hamilton, Ontario, Canada, L8S 4K1 (e-mail: [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted June 14, 2015.
Contributors M. A. Ferro had full access to all the data in the study; was responsible for the integrity of the data and the accuracy of the data analysis; conceptualized, designed, and supervised the study; performed the statistical analysis; and provided administrative, technical, and material support. All authors obtained funding; acquired, analyzed, and interpreted data; and drafted and revised the article.
Acknowledgments This study was funded by a new investigator grant from Hamilton Health Sciences (NIF-14355). Mark A. Ferro is supported by the Hamilton Health Sciences Early Career Award. Note. Statistics Canada collected and provided the data for academic purposes, but the analyses are the sole responsibility of the authors. The opinions expressed do not necessarily represent the views of Statistics Canada.
Human Participant Protection Participants enrolled in the National Longitudinal Survey of Children and Youth provided informed consent to Statistics Canada, and their privacy is guaranteed by the Statistics Act. Our analyses were approved by the Hamilton Integrated Research Ethics Board at McMaster University.
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adulthood: the effects of pubertal timing and adolescent dating. J Res Adolesc. 2009;19(1):47---74. 8. Ferro MA, Gorter JW, Boyle MH. Trajectories of depressive symptoms during the transition to young adulthood: the role of chronic illness. J Affect Disord. 2015;174:594---601. 9. MacPhee AR, Andrews JJ. Risk factors for depression in early adolescence. Adolescence. 2006;41(163):435---466. 10. Chaiton M, Contreras G, Brunet J, et al. Heterogeneity of depressive symptom trajectories through adolescence: predicting outcomes in young adulthood. J Can Acad Child Adolesc Psychiatry. 2013;22(2):96---105. 11. Fernandez Castelao C, Kröner-Herwig B. Different trajectories of depressive symptoms in children and adolescents: predictors and differences in girls and boys. J Youth Adolesc. 2013;42(8):1169---1182. 12. Rodriguez D, Moss HB, Audrain-McGovern J. Developmental heterogeneity in adolescent depressive symptoms: associations with smoking behavior. Psychosom Med. 2005;67(2):200---210. 13. Statistics Canada. Microdata User Guide: National Longitudinal Survey of Children and Youth, Cycle 8 September 2008 to July 2009. Ottawa, Ontario: Statistics Canada; 2007. 14. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385---401. 15. Poulin C, Hand D, Boudreau B. Validity of a 12-item version of the CES-D used in the National Longitudinal Study of Children and Youth. Chronic Dis Can. 2005;26 (2---3):65---72. 16. Boyle MH, Offord DR, Racine Y, Fleming JE, Szatmari P, Sanford M. Evaluation of the revised Ontario Child Health Study scales. J Child Psychol Psychiatry. 1993;34(2):189---213. 17. Marsh HW. Self Description Questionnaire (SDQ) II: A Theoretical and Empirical Basis for the Measurement of Multiple Dimensions of Adolescent Self-Concept. A Test Manual and Research Monograph. Parramatta, Australia: University of Western Sydney; 1992. 18. Lempers JD, Clark-Lempers D, Simons RL. Economic hardship, parenting, and distress in adolescence. Child Dev. 1989;60(1):25---39. 19. Byles J, Byrne C, Boyle MH, Offord DR. Ontario Child Health Study: reliability and validity of the General Functioning subscale of the McMaster Family Assessment Device. Fam Process. 1988;27(1):97---104. 20. Nagin DS. Group-Based Modeling of Development. Cambridge, MA: Harvard University Press; 2005. 21. Ferro MA, Speechley KN. Depressive symptoms in mothers of children with epilepsy: a comparison of growth curve and latent class modeling. Health Serv Outcomes Res Methodol. 2012;12(1):44---61. 22. Sterne JA, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. 23. Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat. 1979;6(2):65---70. 24. Raftery AE. Bayesian model selection in social research. Sociol Methodol. 1995;25:111---163. 25. Ingram RE, Luxton DD. Vulnerability-stress models. In: Hankin BL, Abela JRZ, eds. Development of Psychopathology. A Vulnerability-Stress Perspective. Thousand Oaks, CA: Sage Publications; 2005:32---46.
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26. Wickrama T, Wickrama KA. Heterogeneity in adolescent depressive symptom trajectories: implications for young adults’ risky lifestyle. J Adolesc Health. 2010;47(4):407---413. 27. Eccles JS, Templeton J, Barber B, Stone M. Adolescence and emerging adulthood: the critical passage ways to adulthood. In: Bornstein MH, Davidson L, Keyes C, Moore KA, eds. Positive Development Across the Life Course. Mahwah, NJ: Erlbaum; 2003:383---406. 28. Braet C, Van Vlierberghe L, Vandevivere E, Theuwis L, Bosmans G. Depression in early, middle and late adolescence: differential evidence for the cognitive diathesis-stress model. Clin Psychol Psychother. 2013;20(5):369---383. 29. Kim-Cohen J, Caspi A, Moffitt TE, Harrington H, Milne BJ, Poulton R. Prior juvenile diagnoses in adults with mental disorder: developmental follow-back of a prospectivelongitudinal cohort. Arch Gen Psychiatry. 2003;60(7):709-- 717. 30. Kratochwill TR, Shernoff ES. Evidence-based practice: promoting evidence-based interventions in school psychology. Sch Psychol Q. 2004;18(4):389---408. 31. National Research Council and Institute of Medicine. Preventing Mental, Emotional, and Behaviorial Disorders Among Young People: Progress and Possibilities. Committee on Prevention of Mental Disorders and Substance Abuse Among Children, Youth, and Young Adults: Research Advances and Promising Interventions. Washington, DC: National Academies Press; 2009. 32. Lowry-Webster HM, Barrett PM, Dadds MR. A universal prevention trial of anxiety and depressive symptomatology in childhood: preliminary data from an Australian study. Behav Change. 2001;18(1):36---50. 33. Weare K, Nind M. Mental health promotion and problem prevention in schools: what does the evidence say? Health Promot Int. 2011;26(suppl 1):i29---i69. 34. Clarke GN, Hawkins W, Murphy M, Sheeber LB, Lewinsohn PM, Seeley JR. Targeted prevention of unipolar depressive disorder in an at-risk sample of high school adolescents: a randomized trial of a group cognitive intervention. J Am Acad Child Adolesc Psychiatry. 1995;34(3):312---321. 35. Cuijpers P, Beekman AT, Reynolds CF 3rd. Preventing depression: a global priority. JAMA. 2012;307 (10):1033---1034. 36. Rowling L, Weist MD. Promoting the growth, improvement, and sustainability of school mental health programs worldwide. Int J Ment Health Promot. 2004;6(2):3---11. 37. Rose G. Sick individuals and sick populations. Int J Epidemiol. 2001;30(3):427---432, discussion 433---434. 38. Adi Y, Killoran A, Janmohamed K, Stewart-Brown S. Systematic Review of the Effectiveness of Interventions to Promote Mental Wellbeing in Primary Schools: Universal Approaches Which Do Not Focus on Violence or Bullying. London, UK: National Institute for Clinical Excellence; 2007. 39. Greenberg MT, Domitrovich C, Bumbarger B. Preventing Mental Disorders in School Aged Children. A review of the Effectiveness of Prevention Programmes. State College, PA: Prevention Research Center for the Promotion of Human Development, Pennsylvania State University; 2001. 40. Durlak JA, Weissberg RP, Dymnicki AB, Taylor RD, Schellinger K. The impact of enhancing students’ social and emotional learning: a meta-analysis of school-based universal interventions. Child Dev. 2011;82(1):405---432. 41. Ferro MA, Boyle MH. The impact of chronic physical illness, maternal depressive symptoms, family functioning, and self-esteem on symptoms of anxiety and depression in children. J Abnorm Child Psychol. 2015;43(1):177---187.
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Trajectories of Depressive Symptoms in Canadian Emerging Adults Mark A. Ferro, PhD, Jan Willem Gorter, MD, PhD, and Michael H. Boyle, PhD
The 12-month prevalence of depression is higher during emerging adulthood1 than at any other time.2 Understanding the natural course of depressive symptoms during this period and their associated risk and protective factors is important for informing prevention and intervention strategies. Evidence suggests that depressive symptoms are particularly dynamic during emerging adulthood,3---5 peak between 15 and 18 years of age, and exhibit robust associations with the following variables: female gender, poorer physical health, lower self-concept, lower socioeconomic status, and less supportive family environments.3,4,6,7 Previous cross-sectional and longitudinal analyses of our study data have reported that elevated symptoms of depression among youths are associated with maternal depression, lower self-concept, poorer physical health, tenuous peer and parent---child relations, and lower socioeconomic status.5,8,9 Despite consistent findings on the variables associated with youth depression, uncertainty continues about the shape, direction, and magnitude of trajectories. Some studies have shown depressive symptoms to follow an inverted U-shaped trajectory7; others, trajectories of change5,8; and others, multiple trajectories corresponding to subgroups with distinct patterns of change.3,10---12 We aimed to identify trajectories of depressive symptoms in an epidemiological sample of youths during emerging adulthood and to evaluate the effect of youth, parent, and family control variables on the number and shape of trajectories. We hypothesized that we would find 3 to 5 trajectory groups, representing chronically low, moderate, and high, as well as increasing and decreasing, levels of depressive symptoms. Our study design addressed the methodological shortcomings of existing research on trajectories of depressive symptoms3---5,7,10,12 by (1) using 8 occasions of measurement that encompassed all of emerging adulthood; (2) including a comprehensive measure of depressive
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Objectives. We identified courses of depressive symptoms in an epidemiological sample of emerging adults. Methods. We used latent class growth modeling to identify trajectories of depressive symptoms measured by the 12-item Center for Epidemiological Studies Depression Scale (CES-D) during a 14-year follow-up of 2825 Canadian youths aged 10 to 25 years enrolled in the National Longitudinal Survey of Children and Youth between 1994 and 2009. Results. After adjustment for youth, parent, and family factors, the 3 distinct trajectories of depressive symptoms were minimal (55%; CES-D < 6), subclinical (39%; CES-D = 9–13), and clinical (6%; CES-D > 18). All trajectories exhibited a parallel course, with peak symptoms at 15 to 17 years of age. Subclinical and clinical symptoms were more common than minimal symptoms in female youths and in respondents with lower self-concept, lower socioeconomic status, poorer interpersonal relations, and chronic health conditions (P < .01). Conclusions. Among emerging adults, trajectories of depressive symptoms do not trend upward or downward, and variables associated with identified trajectories demonstrated dose–response effects that agreed with vulnerability– stress theories of depression. (Am J Public Health. 2015;105:2322–2327. doi:10. 2105/AJPH.2015.302817)
symptoms to represent positive affect, somatic complaints, and interpersonal relations, in addition to depressed affect; (3) introducing a set of time-invariant control variables measured prior to the assessment of depressive symptoms to adjust trajectories of depression throughout emerging adulthood; and (4) including important covariates of youth depression excluded from previous studies (symptoms of childhood anxiety, chronic physical conditions, parental depression, place of residence, and immigrant status) and several indicators of socioeconomic status (parental education and employment and household income).
METHODS We obtained data from the National Longitudinal Survey of Children and Youth, which followed Canadian children from birth to early adulthood, examining influences on their social and behavioral development.13 The survey used a stratified, multistage probability design to enlist a representative sample of newborns to
children aged 11 years (n = 22 831). At each 2-year data collection cycle, youths and their corresponding most knowledgeable caregiver (hereafter parent) completed measures assessing health, psychological, and sociodemographic characteristics. Additional methodological details are provided elsewhere.13 We followed youths aged 10 to 11 years at cycle 1 (n = 3464) to cycle 8 (24---25 years of age). The response rates were 87% and 68% at cycles 1 and 8, respectively.13 We excluded 639 youths because they had completely missing depression scores or inconsistent reports of chronic health problems. The final sample size was n = 2825 (82%). We detected no sociodemographic differences between included and excluded youths. We included youths if they completed at least 1 cycle; 1639 (58%) completed all cycles. Youths who completed all cycles were more likely to have parents who were older (odds ratio [OR] =1.03; 95% confidence interval [CI] =1.02, 1.04), living with a partner (OR =1.11; 95% CI =1.11, 1.12), employed (OR = 2.60; 95%
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CI = 2.54, 2.65), living in an urban area (OR =1.39; 95% CI 1.36, 1.41), and earning a higher household income (OR =1.15; 95% CI 1.14, 1.15).
Measures Outcome. We measured self-reported youth depressive symptoms during the past week with a 12-item version of the Center for Epidemiological Studies Depression Scale (CES-D), starting when youths were 12 years old (cycle 2) and continuing for the duration of the National Longitudinal Survey of Children and Youth.14,15 The survey did not include the CES-D in cycle 1. The CES-D uses a 4-point scale to rate the frequency of symptoms. Although the 12-item CES-D has domains of depressed and positive affect, somatic complaints, and interpersonal relations, no item assesses irritability, a common symptom of youth depression. Composite scores spanned 0 to 36, with higher scores indicating greater impairment. Thresholds for categories of depressive symptoms were minimal (0---11), somewhat elevated (12---20), and very elevated (21---36).15 Internal consistency reliability was a = 0.82. Control variables. We measured symptoms of anxiety (a = 0.76) with 7 items from the Ontario Child Health Study Checklist.16 We assessed these items with a 3-point scale (0---2). Higher scores indicated more symptoms. We measured self-concept with 4 items (a = 0.82) from the General Self-Image subscale of the Self-Description Questionnaire.17 We rated items on a 5-point scale (0---4). Higher scores indicated more positive self-concept. We assessed parenting behavior with 2 youthreported assessments calibrated on a 4-point scale (0---3): a 5-item measure (a = 0.83) of parental nurturance and a 6-item measure (a = 0.64) of parental rejection.18 Higher scores indicated more parental nurturance and more parental rejection, respectively. To assess family environment, we used the same CES-D as for youths (a = 0.86) to capture symptoms of parental depression. We measured family functioning with the 12-item General Functioning subscale of the McMaster Family Assessment Device (a = 0.91).19 We rated items on a 4-point scale (0---3), with higher scores indicating poorer functioning. For sociodemographic characteristics, parents reported their age in years, gender, marital
status, educational attainment, employment status, household income, place of residence, and immigrant status. We determined youth health (presence or absence of a chronic condition) from a question to parents: Has a health professional diagnosed any of the following long-term conditions for this child [asthma, n = 489; cerebral palsy, n = 8; epilepsy, n = 23; food allergy, n = 181; heart condition, n = 75; kidney condition, n = 15; any other long-term condition, n = 128]?
Sample characteristics are shown in Table 1.
TABLE 1—Characteristics of the Study Sample: National Longitudinal Survey of Children and Youth, Canada, 1994– 2009 Characteristic
No. (%) or Mean (SE)
Youths Male
1435 (51)
Chronic health condition Age, y
793 (28) 10.5 60.01
Self-concept
13.4 60.07 3.0 60.08
Anxiety symptoms
Analysis We examined trajectories of depression among youths aged 12 to 25 years through latent class growth modeling.20 This model assumes conditional independence, which implies that for each individual within a given trajectory group, the distribution of the outcome for a given period is independent of the realized level of the outcome in previous periods.20 The number of groups to be modeled was guided by a priori expectations, overall model fit calculated with the Bayesian information criterion (BIC), posterior probability scores, and odds for correct classification for each trajectory group. We selected the model with the smallest absolute BIC, optimized probability scores, and least number of groups. The modeling process followed a previously implemented strategy, whereby we specified cubic trajectories for 3 groups being examined, added additional groups to the model, and examined the change in BIC scores to determine the best model.21 To ensure model parsimony, we removed nonsignificant higher-order terms. We repeated this modeling strategy with the control variables included, allowing us to evaluate the effect of adding control variables on the number and shape of trajectories. We obtained all control variables, except self-concept, from youths and parents in cycle 1; we assessed selfconcept as a control variable from cycles 1 to 8 and obtained all CES-D data from youths in cycles 2 to 8. We compared CES-D scores across trajectory groups at each assessment with analysis of variance. We compared youth, parent, and family characteristics across trajectory groups with analysis of variance and the v2 test. We examined pairwise group contrasts for potential
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Parental nurturance
12.0 60.09 4.6 60.09
Parental rejection Parents and families Female
2644 (94)
Age, y Living with partner
38.0 60.16 2300 (81)
Postsecondary graduate Currently employed
850 (30) 2503 (89)
Family income ‡ $50 000
1452 (51)
Residence in urban area
2342 (83)
Immigrant
504 (18)
Depressive symptoms
5.0 60.17
Family functioning
8.2 60.15
Note. The sample size was n = 2825.
dose---response effects when we observed significant overall differences. Analyses implemented sampling weights to ensure comparability with the Canadian population.13 We used multiple imputation (10 data sets) for data assumed to be missing at random. This assumption was plausible because we incorporated all variables, including those predicting missingness, in the imputation model.22 We conducted analyses with SAS version 9.4 (SAS Institute, Cary, NC): PROC TRAJ for latent class modeling and PROC MI for imputation. We applied the Holm---Bonferroni correction for multiple comparisons.23
RESULTS Initially, we fit a 3-group model to the data, with a BIC of 55 143.5 and high posterior probabilities (0.85---0.94). Subsequently, we specified a 4-group model, which demonstrated worse model fit (BIC = 55 160.9), providing very strong evidence to reject this model in favor of the 3-group model.24 It also had
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lower posterior probabilities (0.82---0.89). After removing nonsignificant higher-order terms and adding youth, parent, and family control variables, the adjusted 3-group model had better fit (BIC = 54 846.9) than other models, high posterior probabilities (0.86--0.92), and strong odds for correct classification (9.4---11.5) for each trajectory group. Table 2 shows CES-D scores for each group, and Figure 1 shows the trajectories of depression. The minimal trajectory group was the largest, consisting of 55% of youths; it was best modeled as a quadratic trajectory, with CES-D scores increasing from 3.95 at 12 to 13 years of age to 5.34 at 22 to 23 years, then decreasing to 4.69 at 24 to 25 years. The subclinical trajectory included 39% of youths; we modeled it with a cubic trajectory, whereby CES-D scores increased from 9.29 at 12 to 13 years of age to 12.64 at 16 to 17 years, then decreased and leveled off to 10.31 at 24 to 25 years. The clinical symptoms group was the smallest, consisting of 6% of youths, and we modeled it with a quadratic trajectory; CES-D scores increased from 18.61 to 24.82 between 12 to 13 years and 16 to 17 years of age, then decreased to 19.38 at 24 to 25 years. CES-D scores across groups were all significantly different at each cycle (all, P < .001). Adjustment for the control variables had the largest impact on the subclinical group,
lowering the intercept and creating a more pronounced downward trajectory (Figure 1). Other than a slightly lower intercept, the control variables had little discernible impact on the minimal group. In the clinical group, the control variables led to an accelerated symptom decline at about 21 years of age. Across all variables, the minimal group had significantly more favorable individual and family characteristics than the subclinical and clinical trajectory groups, with 3 exceptions: the proportion of parents who were female, employed, or immigrants did not differ (Table 3). We observed dose---response effects for most variables (P < .01), whereby the subclinical and clinical groups had increasingly less favorable characteristics than the minimal group.
DISCUSSION We identified 3 trajectories that corresponded to depressive symptoms experienced during emerging adulthood: minimal, subclinical, and clinical. The data also provided evidence of dose---response effects consistent with the vulnerability---stress model of depression,25 whereby less favorable characteristics relating to socioeconomic and health disadvantages increased across trajectory groups from minimal to clinical. Consistent with earlier reports,
TABLE 2—Youth Depressive Symptoms Stratified by Trajectory Group: National Longitudinal Survey of Children and Youth, Canada, 1994–2009 Trajectory Group
No. (%) Posterior probability
Minimal
Subclinical
Clinical
1548 (55)
1105 (39)
172 (6)
0.86
0.88
0.92
CES-D score, mean (SE) 12–13 y
3.95 (0.11)
9.29 (0.13)
18.61 (0.34)
14–15 y 16–17 y
4.01 (0.06) 5.22 (0.12)
11.34 (0.07) 12.64 (0.15)
21.95 (0.18) 24.82 (0.37)
18–19 y
5.27 (0.12)
12.49 (0.14)
24.65 (0.34)
20–21 y
5.28 (0.10)
11.49 (0.12)
23.49 (0.31)
22–23 y
5.34 (0.09)
10.66 (0.11)
21.33 (0.28)
24–25 y
4.69 (0.10)
10.31 (0.11)
19.38 (0.29)
Note. CES-D = Center for Epidemiological Studies Depression Scale. Analysis of variance CES-D scores across trajectory groups at each measurement occasion were all statistically significant (all, P < .001). Pairwise contrasts were conducted with the post hoc Tukey test and showed significant differences in CES-D scores between each trajectory group at each measurement occasion (all, P < .001).
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our findings indicate that depressive symptoms are dynamic during emerging adulthood3,5,7,8,26; symptoms tend to peak during mid-to-late adolescence6---8; the largest group consists of youths with low symptom levels3,26; in most groups, symptom levels decline during emerging adulthood5,7,8; and several variables associated with less favorable trajectories in other studies are represented in this one (female gender, lower self-concept, lower socioeconomic status, poorer interpersonal relations, and chronic health conditions).3---9 By contrast with previous reports of latent class modeling, we identified only 3 meaningful groups and none with cross-diagonal (distinctive upward or downward) trajectories,3,26 our data revealed a peak period of risk across all groups at 15 to 17 years of age, and our findings highlighted the influence on depression of several key variables. The absence of cross-diagonal trajectories and the parallel nature of the trajectories suggest that once the risk group for a youth is determined, the course of depressive symptoms during emerging adulthood is primarily a function of natural youth development. Although the number of depressive symptoms was influenced by exposure to several intrinsic (e.g., self-concept) and extrinsic (e.g., socioeconomic disadvantage) factors, trajectories were quite similar—the ages at which peaks and troughs occurred were consistent across groups. The gentle slope for the minimal trajectory was likely a basement effect—these youths were quite healthy and, because the CES-D is asymmetric, were bound to hover around zero. Differences in the number of trajectories between our analysis and previous studies are likely attributable to differences in methodologies. Our data provided a more comprehensive measure of depressive symptoms, more measurement occasions, and a substantial number of control variables. These considerations may have reduced variability among latent trajectories, resulting in the identification of 3 meaningful groups. Furthermore, by contrast with studies that may have overrepresented visible minorities and families with lower socioeconomic status,3,26 our sample contained families mirroring the sociodemographic distribution of the general population. Thus, this representative sample likely protected against the emergence of a cross-diagonal or chronically elevated trajectory—typically
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30
Minimal (55%) Subclinical (39%)
Depressive Symptoms (CES-D)
Clinical (6%)
24
18
12
6
0
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Age, Years Note. CES-D = Center for Epidemiological Studies Depression Scale. Adjusted trajectories included time-varying (youth selfconcept) and time-invariant (parental age, education, marital status, and depressive symptoms; family place of residence, income, and functioning; and youth gender, chronic health conditions, symptoms of anxiety, and perceptions of parental nurturance and rejection) control variables. Time-invariant variables were measured when youths were aged 10–11 years.
FIGURE 1—Trajectories of youth depression during emerging adulthood, estimated with latent class growth modeling (dashed lines are unadjusted, solid lines are adjusted): National Longitudinal Survey of Children and Youth, Canada, 1994–2009.
associated with sociodemographic disadvantage— resulting in a lower proportion of youths in these at-risk groups. Replication of findings with the methodologies of previous studies is warranted to determine whether differences in trajectories result from differences in methodology or unexplored contextual factors (e.g., cross-national differences). The peak of depressive symptoms at 15 to 17 years of age suggests that the transition from secondary school to postsecondary school or the labor force is a particularly vulnerable period for all groups and coincides with challenges that youths face as they absorb more demanding roles during emerging adulthood: management of changing relationships with parents from dependency to autonomy, the exploration of intimate partnerships, recognition of personal weaknesses, and the need to develop new skill sets that will ensure the attainment of long-term goals.27 Dose---response effects of control variables were consistent with the vulnerability---stress model of depression.25 Stress, particularly the
socioeconomic and physical health disadvantages observed in the subclinical and clinical groups, represents undesirable early life exposures that can interfere with emerging adults’ physiological and psychological homeostasis. Vulnerability is psychosocial factors, such as poor or strained interpersonal relations (e.g., parent---child relations, family functioning) that predispose individuals to depression. These stresses and vulnerabilities can exert their effects on one another concurrently with typical developmental challenges during emerging adulthood and can subsequently result in elevated symptoms, and, in some cases, onset of clinical depression.28 Maternal depression, childhood anxiety, and youth self-concept are additional stresses and vulnerabilities that influence depressive symptomatology among emerging adults.5,8,29 Previous studies indicate that introducing control variables can alter the shape and number of groups needed to optimize model fit.21 Although the control variables explained substantial variability in trajectory levels, they
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had no impact on the number of groups needed to satisfy fit requirements, attesting to model robustness. Within the subclinical group, the effect of control variables was relatively large and constant over time, with some widening between unadjusted and adjusted trajectories in later years. By contrast, control variable effects appeared later during emerging adulthood for the clinical group. Why the difference between these groups? We speculate that the symptoms of youths in the subclinical group were more amenable to change, as indicated by their sensitivity to the influence of socioeconomic and psychosocial exposures captured in the adjusted model. By contrast, the high symptom levels of youths in the clinical group were more consistent with clinical depression and may have been less amenable to change until a later developmental stage. These divergent effects might argue for a different public health response for the 2 groups: individual treatment for the clinical group and populationbased interventions for the subclinical group.
Implications A considerable number of youths in our sample had clinically relevant levels of symptoms corresponding to established thresholds.15 In light of the age of the cohort studied, the school system may be an effective environment to deliver targeted treatment and population-based interventions. In addition to having broad coverage,30,31 school-based interventions reduce felt and enacted stigma associated with seeking mental health services.32 However, the absence of crossdiagonal trajectories is relevant for public health initiatives because it suggests that during emerging adulthood, few individuals migrate across risk levels and no groups are well suited to classical prevention strategies. As a starting point during emerging adulthood, screening for depression is a practical option to identify youths meeting criteria for clinical depression so they can be offered treatment. In addition, implementation of general risk reduction interventions is warranted for the high proportion of youths who exhibit moderately elevated symptoms or subclinical depression. By contrast with classical public health approaches, this school-based intervention would focus on risk reduction strategies initiated when youths are most vulnerable so
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Limitations
TABLE 3—Comparisons of Sample Characteristics Across Trajectory Groups: National Longitudinal Survey of Children and Youth, Canada, 1994–2009 Trajectory Group Characteristic
Pairwise (1) Minimal, No. (%) (2) Subclinical, No. (%) (3) Clinical, No. (%) or Mean (SE) or Mean (SE) or Mean (SE) v2 or F Contrasts
Youths Male Chronic health condition Self-concept Anxiety symptoms Parental nurturance Parental rejection
943 (61) 330 (21)
448 (41) 378 (34)
47 (28) 85 (50)
74.69 1 > 2 > 3 43.49 1 < 2 < 3
13.8 60.06
12.9 60.07
12.4 60.18
2.3 60.07
3.5 60.08
5.2 60.20
12.3 60.08
11.4 60.09
11.4 60.23
32.11
1 > 2, 3
4.2 60.08
5.0 60.09
4.7 60.23
20.94
1 2, 3
125.92 1 < 2 < 3
Parents and families Female
1443 (93)
1042 (94)
165 (96)
Age, y Living with partner
38.2 60.13 1337 (86)
37.6 60.15 828 (75)
37.7 60.39 106 (62)
Postsecondary graduate
4.63 1>2 41.72 1 > 2 > 3
495 (32)
327 (30)
39 (23)
3.64
Currently employed
1379 (89)
965 (87)
147 (85)
1.59
Family income ‡ $50 000
1209 (78)
980 (89)
151 (88)
Residence in urban area
766 (50)
456 (41)
59 (34)
Immigrant
235 (15)
251 (23)
19 (11)
Depressive symptoms
4.3 60.15
5.7 60.17
7.9 60.44
Family functioning
7.8 60.13
8.2 60.15
10.5 60.38
39.66
1, 2 > 3 1 < 2, 3
13.14 1 > 2 > 3 5.74 40.62 1 < 2 < 3 23.76
1, 2 < 3
Note. All v2 and F tests were statistically significant at P < .01, except for parental gender, employment, and immigrant status, which were not statistically significant. Pairwise contrasts were conducted with the post hoc Tukey test for continuous variables and the v2 test for categorical variables.
they capitalize on the natural downward progression of symptoms to accelerate the process toward mental well-being. Because symptoms peaked when youths were aged 15 to 17 years, school-based intervention programs could be delivered prior to secondary school graduation with the aim to accelerate risk reduction among the nearly half of youths with subclinical symptoms. A systematic review of school-based interventions reported program effects that were generally small to moderate.33 Though effects may be relatively small, school-based interventions that effectively balance universal and targeted approaches have the potential to shift the distribution of elevated depressive symptoms in the population and sustain this effect over time.34,35 Such multipronged approaches are attractive from a public health standpoint because they could have a more substantial impact than targeted interventions that aim to reduce risk or treat symptoms for the subset of high-risk youths.36,37
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Previous reports of school-based interventions suggest that a combination of targeted treatment and universal risk reduction strategies is the most effective for youths,38 interventions with durations of 9 to 12 months are more effective at producing sustained effects over time,39 and, holding all things constant, interventions that have no implementation difficulties are likely to have larger effects in reducing youth mental health problems.40 This body of evidence highlights the importance of adopting a whole-school model in addressing youth mental health.33 Within such a model, a singular school ethos regarding mental health must be developed, teacher and parent education affirming this ethos is required to support school-based interventions, and integration with other systems (e.g., children and youth services, community agencies) is needed to ensure that school-based interventions for youth depression are adequately supported in the community.
Like most prospective epidemiological studies, sample attrition was associated with socioeconomic disadvantage. This selective loss may have resulted in an underestimation of distribution of youths in the elevated trajectory groups, as well as muted estimates of effects. This limitation is tempered by the fact that we used sampling weights developed to account for attrition. The reliability of trajectory estimates for the clinical group may be reduced. This group composed only 6% of the sample, and attrition was likely to have a greater impact in the latter years of the study. As a result, the standard errors associated with these estimates, especially during young adulthood, would be inflated. Control variables used in modeling trajectories of depression were limited to the constructs included in the National Longitudinal Survey of Children and Youth, as well as their measurement schedule. The survey included only youth self-concept as a time-varying control variable; the remaining variables were time invariant. The survey assessed symptoms of anxiety, parental behavior (nurturance, rejection), parental depression, and family functioning on 3 occasions, when youths were aged 10 to 15 years. Because these constructs were only measured in the first 3 cycles, it was not possible to include them as timevarying control variables in the model. In addition, because of the robust intercorrelations among these variables,41 it is reasonable to suggest that self-concept captured most of the influence of these variables during the follow-up and that their inclusion would not have resulted in meaningful changes to the trajectories.
Conclusions Youths follow 3 distinct trajectories of depressive symptoms during emerging adulthood. School-based intervention strategies may be useful in identifying at-risk youths for whom targeted intervention may be warranted and subclinical youths who may be responsive to interventions designed to accelerate the natural downward trajectories of depressive symptoms that appear to begin in midadolescence and continue to young adulthood. j
American Journal of Public Health | November 2015, Vol 105, No. 11
RESEARCH AND PRACTICE
About the Authors Mark A. Ferro and Jan Willem Gorter are with the Department of Pediatrics, and Mark A. Ferro and Michael H. Boyle are with the Departments of Psychiatry and Behavioural Neurosciences and Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada. Correspondence should be sent to Mark Ferro, Dept of Psychiatry and Behavioural Neurosciences, 1280 Main St West, MIP 201A, Hamilton, Ontario, Canada, L8S 4K1 (e-mail: [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted June 14, 2015.
Contributors M. A. Ferro had full access to all the data in the study; was responsible for the integrity of the data and the accuracy of the data analysis; conceptualized, designed, and supervised the study; performed the statistical analysis; and provided administrative, technical, and material support. All authors obtained funding; acquired, analyzed, and interpreted data; and drafted and revised the article.
Acknowledgments This study was funded by a new investigator grant from Hamilton Health Sciences (NIF-14355). Mark A. Ferro is supported by the Hamilton Health Sciences Early Career Award. Note. Statistics Canada collected and provided the data for academic purposes, but the analyses are the sole responsibility of the authors. The opinions expressed do not necessarily represent the views of Statistics Canada.
Human Participant Protection Participants enrolled in the National Longitudinal Survey of Children and Youth provided informed consent to Statistics Canada, and their privacy is guaranteed by the Statistics Act. Our analyses were approved by the Hamilton Integrated Research Ethics Board at McMaster University.
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