Journal of Affective Disorders 174 (2015) 594–601

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Research report

Trajectories of depressive symptoms during the transition to young adulthood: The role of chronic illness Mark A. Ferro a,b,c,d,e,n, Jan Willem Gorter b,e, Michael H. Boyle a,c,d a

Department of Psychiatry and Behavioural Neurosciences, McMaster University, Canada Department of Pediatrics, McMaster University, Canada c Department of Clinical Epidemiology and Biostatistics, McMaster University, Canada d Offord Centre for Child Studies, McMaster University, Canada e CanChild Centre for Childhood Disability Research, McMaster University, Canada b

art ic l e i nf o

a b s t r a c t

Article history: Received 3 December 2014 Accepted 4 December 2014 Available online 26 December 2014

Background: Little is known about the natural course of depressive symptoms among youth with chronic illness during their transition from adolescence to young adulthood. Methods: A representative epidemiological sample of 2825 youth aged 10–11 years from the National Longitudinal Survey of Children and Youth were followed until 24–25 years of age. Presence of chronic illness was measured using self-report and symptoms of depression were assessed using the Center for Epidemiological Studies Depression Scale. Multilevel modeling was used to investigate trajectories of depressive symptoms, adjusting for family environment and sociodemographic characteristics during the transition to young adulthood. Results: Trajectories showed cubic change over time – increasing from early to mid-adolescence, decreasing to early young adulthood, increasing again to late young adulthood. Youth with chronic illness (n ¼753) had significantly less favorable trajectories and significantly higher proportions of clinically relevant depressive symptoms over time compared to their peers without chronic illness (n ¼2072). Limitations: This study is limited by selective attrition, self-reported chronic illness and no assessment of illness severity, and mediating effects of family environment factors could not be examined. Conclusions: Findings support the diathesis-stress model; chronic illness negatively influenced depressive symptoms trajectories, such that youth with chronic illness had higher depression scores and less favorable trajectories over time. The health and school system are uniquely positioned to support youth with chronic illness navigate this developmental period in an effort to prevent declines in mental health. & 2014 Elsevier B.V. All rights reserved.

Keywords: Adolescence Chronic illness Depression Epidemiological study Longitudinal cohort Young adults

1. Introduction Improvements in the medical treatment of youth with a chronic illness have led to increased rates of survival – 90% of these youth live into adulthood (Ontario Association of Community Care Access Centres, 2013; Perrin et al., 2007). However, there are no known cures for many of these conditions and so youth with chronic illness still face considerable challenges to their psychological well-being (Moreira et al., 2013). In addition to experiencing poorer quality of life (Moreira et al., 2013), meta-analytic evidence suggests that youth with chronic illness have significantly higher rates of mental health problems compared to healthy children (Boyce et al., 2009; Pinquart

n Correspondence to: McMaster University, Department of Psychiatry and Behavioural Neurosciences, Chedoke Site, Central Building, Room 304, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4K1. Tel.: þ1 905 521 2100x74345; fax: þ1 905 521 4970. E-mail address: [email protected] (M.A. Ferro).

http://dx.doi.org/10.1016/j.jad.2014.12.014 0165-0327/& 2014 Elsevier B.V. All rights reserved.

and Shen, 2011a, b), including depression (Pinquart and Shen, 2011b, 2011c). The elevated risk for mental health problems and depression specifically among youth with chronic illness is consistent with current theories linking physical and mental health. According to cognitive-behavioral theories, negatively biased thought patterns that exaggerate risk and associated harm of illness exacerbations, as well as an underestimated ability to handle potentially threatening situations, produce symptoms of depression and anxiety (Beck et al., 1985). For example, the experience of unpredictable asthma attacks or seizures can lead to a state of learned helplessness and burden that can lead to episodes of depression (Chaney et al., 1999; Hoppe and Elger, 2011). Furthermore, the diathesis-stress model suggests that youth with chronic illness are exposed to higher allostatic load, which results in adverse effects on mental health (Bahreinian et al., 2013; McEwen, 1998; Monroe and Simons, 1991). During adolescence and young adulthood, the 12-month prevalence of depression increases from 2% in childhood to 20% (Costello et al., 2002; Kessler and Walters, 1998). Developmental

M.A. Ferro et al. / Journal of Affective Disorders 174 (2015) 594–601

processes that may explain this rise in prevalence include pubertyrelated hormonal changes, increased capacity for self-reflection and rumination associated with cognitive maturation, increased psychological stress resulting from normative developmental transitions, and changing relationships with parents and peers (Ge et al., 2001; Hankin et al., 2007; Koenig and Gladstone, 1998; Nolen-Hoeksema and Girgus, 1994). In addition, imaging studies have shown that the brain continues to organize, adapt, and change in adolescence – in fact, the changes that occur in the brain during the transitions from childhood to adolescence to young adulthood are particularly dramatic (Jetha and Segalowitz, 2012). Given the evidence that depressive episodes first appear in adolescence (Costello et al., 2005) and that early age at onset predicts longer duration (Kovacs et al., 1984), adolescence and young adulthood is a critical period for identification, prevention, and intervention. While there is strong evidence supporting the increase in risk of depression during the transition from childhood to adolescence (Costello et al., 2005, 2002), epidemiological studies examining symptoms of depression during the transition to young adulthood have produced more heterogeneous findings with respect to trajectories of change over time. Early research suggested that rates of clinical depression were low from early to middle adolescence, then increased dramatically in late adolescence, and remained high into young adulthood (Hankin et al., 1998). In contrast, other researchers reported linear declines in symptoms of depression among emerging adults aged 18–25 years (Galambos et al., 2006). Using more sophisticated analyses (i.e., growth curve modeling), researchers have shown symptoms of depression to be much more dynamic during this period of development, reporting curvilinear trajectories of symptoms that tend to peak between middle and late adolescence (Natsuaki et al., 2009; Rawana and Morgan, 2014). Extending these findings, other researchers have used latent class growth modeling to show that some heterogeneity exists in the trajectories of depressive symptoms of adolescents transitioning to young adulthood (Costello et al., 2008; Frye and Rossignol, 2011). Despite the progress made in understanding changes in depressive symptoms during the transition to young adulthood in general population samples of youth, there is virtually no information on this phenomenon among youth with chronic illness. Most longitudinal studies examining mental health trajectories among individuals with chronic illness have typically sampled adults, not youth (Hasler et al., 2005; Oga et al., 2007). Findings from a few short-term longitudinal studies ranging from one to three years in clinical samples of children and young adolescents with chronic illness, suggest that symptoms of depression fluctuate considerably, but typically display a curvilinear ‘U-shaped’ trajectory (Austin et al., 2011; Grey et al., 1995; Helgeson et al., 2007; Jaser et al., 2012). One prospective clinical study of 10 years duration followed a small sample of youth aged 8-13 years with new-onset diabetes and reported that the prevalence of depression was highest in the first year after diagnosis (Kovacs et al., 1997). Prevalence of depression followed the typical ‘U-shape’ described in the short-term studies. One epidemiological study which investigated the psychological distress of youth with asthma and epilepsy followed from 16-25 years of age showed that youth with asthma or epilepsy were at elevated risk for psychological distress compared to healthy controls during the 10-year follow-up (Ferro, 2013). Risk peaked between the ages of 18–20 years. While youth with epilepsy reported more psychological distress compared to youth with asthma, the differences were not statistically significant, supporting the view that mental health problems in youth with chronic illness are partially a result of the shared effects of having a chronic illness. Contemporary estimates suggest that over 13% of youth have a mental health problem (Waddell et al., 2014). Depression and anxiety

595

are the most common conditions, comprising nearly 70% of mental disorder diagnoses in youth (Merikangas et al., 2010; Waddell et al., 2014). Chronic illnesses affect nearly 20% of youth (van der Lee et al., 2007). Although prevalence estimates of youth with multimorbidity (i.e., youth with physical and mental comorbidity) are difficult to obtain, general population studies suggest that approximately 11% of youth have physical-mental multimorbidity (Britt et al., 2008; Ferro, Submitted for publication; Harrison et al., 2014; van den Akker et al., 1998). In a U.S. study that sampled youth from several public sectors (e.g., justice, welfare), 51% of youth with anxiety had a chronic illness, with asthma, epilepsy, diabetes, and gastrointestinal problems being the most common (Chavira et al., 2008). Data from the 1983 Ontario Child Health Study found that among children 4–16 years with a chronic illness and functional limitation, 33% had one or more mental health problems; among those with a chronic illness only, the prevalence of mental health problems was 23% (Cadman et al., 1987). Compared to youth with a mental health problems only, youth with multimorbidity were more likely to have been receiving care at a mental health clinic (11% vs. 28%) (Cadman et al., 1987). Multimorbidity has extraordinary importance not only for the general population, but also for the health care system; it is associated with increased mortality (Gijsen et al., 2001), poor functioning (Bayliss et al., 2003), lower quality of life (Fortin et al., 2006), and high health care use (Le et al., 2011; Reigada et al., 2011). In addition, mental health outcomes are poorer for those with multimorbidity. For example, individuals with diabetes are less likely to achieve remission of their depression than those without (Bryan et al., 2010) and despite consuming significantly more health resources ($19,707 vs. $11,237 per year), individuals with depression and diabetes are more likely to be diagnosed with another mental comorbidity (Le et al., 2011). Thus, examining the unique perspective of youth with chronic illness is needed to understand the natural course of symptoms of depression and to inform the coordination of health services aimed at the prevention of deteriorating mental health during the period of development from adolescence to young adulthood (Crowley et al., 2011). The objective of this epidemiological study was to prospectively assess the course of depressive symptoms in a representative sample of youth with and without chronic illness during their transition to young adulthood. Specifically, our aim was to estimate and compare trajectories of depressive symptoms between youth with and without chronic illness during the transition from adolescence (12–13 years) to young adulthood (24–25 years). Given our knowledge of current theory and previous empirical findings, we hypothesized that trajectories will be different for youth with chronic illness compared to those without, whereby youth with chronic illness would report higher initial levels of depressive symptoms and would experience a less favorable trajectory over time.

2. Methods 2.1. Data source and participants Data were obtained from the National Longitudinal Survey of Children and Youth (NLSCY; Statistics Canada, 2007). The NLSCY was a study of Canadian children from birth to early adulthood on factors influencing children's social and behavioral development. The study methods are summarized here, with details available elsewhere (Statistics Canada, 2007). Using a stratified, multistage, probability design based on Statistics Canada's Labour Force Survey, the NLSCY enlisted a representative sample of newborns to 11 year-old children (N¼ 22,831). Institutionalized youth and those living on Aboriginal settlements were excluded. At each two-year interval, youth and their corresponding person most knowledgeable caregiver (herein parent) completed a survey battery assessing sociodemographic and

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health-related constructs, including medical, psychological, and behavioral variables. Youth who were 10–11 years of age at Cycle 1 of the NLSCY (N ¼ 3434) were followed longitudinally to Cycle 8 when they were 24–25 years of age. The youth-level response rate at Cycle 1 was 86.5% and 68.0% at Cycle 8 (Statistics Canada, 2007). Youth who did not have consistent reports of their health status across backto-back cycles (e.g., chronic illness at 12–13 and 16–17 years, but not at 14–15 years) were excluded, n ¼30. A total of 609 youths were excluded due to having missing depression scores for each cycle, resulting in a final sample size of N ¼ 2825 (82%). There were no significant sociodemographic differences between youth included and excluded in the analysis. Youth were included if they completed at least one assessment; 1639 (58%) completed all seven assessments. Complete data on youth symptoms of depression was associated with youth whose mothers were older [OR¼0.97, 95% CI (0.96, 0.98)], living with a partner [OR¼1.11, 95% CI (1.11, 1.12)], employed [OR¼ 2.60, 95% CI (2.54, 2.65)], lived in an urban area [OR¼1.39, 95% CI (1.36, 1.41)], and had higher household incomes [OR¼1.15, 95% CI (1.14, 1.15)]. Participation in the NLSCY was voluntary and ethical approval for these analyses was obtained from McMaster University. 2.2. Measures 2.2.1. Chronic illness Chronic illness in youths was measured by asking parents (Cycles 2 and 3) and youth (Cycles 4 to 8), “Has a health professional diagnosed any of the following long-term conditions for [this child/ you]…? [asthma (65%); cerebral palsy (1%); epilepsy (3%); food allergy (24%); heart condition (10%); kidney condition (2%); any other long-term condition (17%)]. As required by Statistics Canada, the categories were aggregated and coded as binary (1¼present; 0¼absent) due to low case counts for some conditions. This resulted in 753 youth classified as having at least one chronic illness and 2072 healthy controls.

2.2.2. Depressive symptoms Youth depressive symptoms were measured using a reduced version of the Center for Epidemiological Studies Depression Scale (CES-D) (Radloff, 1977), a 12-item self-report questionnaire designed to assess depressive symptoms over the past week (Poulin et al., 2005). Youth reports on the CES-D were available starting at age 12 (Cycle 2) and continued for the duration of the follow-up (the CES-D for youths was not included in Cycle 1 of the NLSCY). A four-point Likert scale (0–3) was used to rate the frequency of symptoms experienced (e.g., “I felt depressed”; “I felt lonely”). Composite scores spanned 0–36 with higher scores indicating greater impairment. Established thresholds for three categories of depressive symptoms among youth are: minimal (0–11), somewhat elevated (12–20), and very elevated (21–36) (Poulin et al., 2005). The scale demonstrated good internal consistency (α¼0.82).

2.2.3. Covariates: family environment Factors that could potentially influence trajectories of depressive symptoms were assessed at Cycle 1 when youth were 10–11 years of age. Covariates were adjusted for in the current study to present unbiased trajectories of youth depressive symptoms. Parent-reported family functioning was measured using the 12item General Functioning subscale of the McMaster Family Assessment Device (FAD), providing an overall measure of the health/ pathology of the family (Byles et al., 1988). Items are rated on a four-point Likert scale (0–3) with higher scores indicating more dysfunction. The scale had excellent internal consistency (α¼0.91).

Parenting behavior was measured using two youth-reported assessments calibrated on a four-point (0–3) Likert scale (Lempers et al., 1989) – a five-item measure of parental nurturance (e.g., “My parents smile at me”; “My parents listen to my ideas and opinions”) and a six-item measure of parental rejection (e.g., “My parents nag me about little things”; “My parents get angry and yell at me”). Higher scores indicate more parental nurturance and more parental rejection, respectively. The nurturance and rejection scales demonstrated good and somewhat low internal consistency (α¼0.83 and α¼0.64, respectively). Symptoms of parental depression were measured using the 12item CES-D (Poulin et al., 2005; Radloff, 1977); the same scale completed by youth. The scale demonstrated good internal consistency (α¼0.86). 2.2.4. Covariates: sociodemographics For sociodemographic characteristics, parents reported their age in years (parent and youth), sex (parent and youth), marital status [living with a partner (includes married and common-law) or not living with a partner], education attainment (less than secondary, secondary school graduate, beyond secondary school, postsecondary graduate), employment status (currently working, not currently working), place of residence (urban or rural), and annual household income (categorized by $10,000 intervals, rangingo$10,000 toZ$80,000). 2.3. Statistical analysis Multilevel modeling was conducted with SAS 9.4 using PROC MIXED (SAS Institute Inc.) to examine trajectories of depressive symptoms for youth with and without chronic illness during their transition to young adulthood, as well as the impact of earlier childhood exposures on these trajectories. The models were built following published guidelines using full information maximum likelihood estimation (Singer, 2002; Singer and Willett, 2003). The model intercept and slope were specified as random effects (i.e., differing for each individual in the sample). An unstructured variance-covariance matrix was specified, which is the most heterogeneous type and requires estimation of several parameters, thus additional degrees of freedom, but does not constrain any pairwise comparisons within the matrix, allowing for additional flexibility (Weiss, 2005). Youth age (in years) was specified as the unit of time and centered such that age 12 corresponded to time ¼0. After modeling the unconditional means and growth models (linear, quadratic, and cubic time) to assess the average pattern of change in depressive symptoms over time, time-invariant variables were added to the model in a hierarchical fashion. In the first conditional growth model (CGM), health status (presence or absence of chronic illness) was added as a main effect to examine its impact on the intercept, as well as an interaction term with youth age to examine its impact on depressive symptoms over time. In the second CGM, family environment covariates were added to the model to account for their confounding effects. The covariates included were: family functioning, parental nurturance and rejection, and parent depressive symptoms. Covariate main effects and interactions with time were specified in the model. In the third CGM, sociodemographic covariates were added to the model. These included parent age and sex, child sex, marital status, education, family income, and geography. All variables were grand-mean centered to create a meaningful intercept (time ¼0) and improve interpretation of the modeling results. Model fit was assessed by comparing Bayesian information criterion (BIC) values during model building, with lower values indicative of better fit. Grades of evidence corresponding to ΔBIC followed published guidelines (Raftery, 1995).

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Analyses conducted in this study implemented sampling weights based on the probabilities of selection and participation, developed by Statistics Canada to ensure comparability between the NLSCY sample and Canadian population (Canada, 2007). Multiple imputation using the expectation-maximum likelihood algorithm was conducted to account for data assumed to missing at random (10 datasets generated).

3. Results 3.1. Sample characteristics The mean age of youth at Cycle 1 was 10.5 (SE 0.14) years and 51% were male. Youth reported mean anxiety/emotional disorder and parental nurturance and rejection scores of 3.0 (0.08), 12.0 (0.09), and 4.5 (0.09), respectively. Parents were on average 38.0 (0.16) years at Cycle 1 and 94% were female. Eighty-one percent of parents were living with a partner, 30% were postsecondary graduates,

Table 1 Characteristics of the study sample at Cycle 1. Characteristic

Child factors Male, % Age, years Emotional symptoms OCHS Parent/family factors Female, % Age, years Living with partner, % Postsecondary graduate, % Employed, % Household income Z $50,000, % Urban area, % Depressive symptoms, CES-D Family functioning, FAD Parental nurturance Parental rejection

Chronic illness (N¼ 753)

Healthy (N¼ 2072)

χ2/t

48.8 10.5 (0.03) 3.5 (0.19)

51.6 10.5 (0.02) 2.8 (0.09)

0.77 0.379  0.48 0.633  5.78 o 0.001

94.6 38.1 (0.32) 79.5 31.0

93.2 37.9 (0.18) 82.1 29.8

1.05  1.18 10.78 1.94

0.305 0.236 0.095 0.586

87.3 49.9

89.1 52.0

0.61 5.71

0.434 0.680

84.2 5.1 (0.37)

82.4 4.9 (0.19)

0.99 0.319  0.95 0.340

8.4 (0.17)

7.6 (0.29)

 3.87 o 0.001

12.2 (0.17) 4.5 (0.18)

11.9 (0.11) 4.6 (0.10)

 1.96 0.051 0.86 0.390

Values denote mean (standard error), unless indicated otherwise.

P-value

597

89% were employed, and 51% had annual household incomes ofZ $50,000. The majority of families lived in an urban area (83%). Parents reported a mean CES-D score of 5.0 (0.17) and FAD score of 8.2 (0.15), indicating relatively low symptoms of depression and families that were functioning generally well. Fourteen percent of parents had elevated depression scores (CES-D411). Table 1 describes the sample characteristics by health status. The chronic illness and healthy groups were similar, with the exceptions that youth with chronic illness reported more symptoms of anxiety/emotional disorder (3.5 vs. 2.8; p o0.001) and parents of youth with chronic illness reported worse family functioning (8.4 vs. 7.6; p o0.001).

3.2. Trajectories of youth depressive symptoms Exploratory analyses of a random sample of youths suggested non-linear changes in CES-D scores from age 12–25 years (data not shown). After fitting the unconditional means model (UMM), unconditional linear, quadratic, and cubic growth models (UGM) were specified as shown in Table 2. The UMM provided the mean CES-D score across all ages considered; that is, independent of age, the mean CES-D score during the transition to young adulthood was 6.2 (0.06). Intraindividual variance was statistically significant indicating significant variability in depressive symptoms over time. In a hierarchical fashion, linear, quadratic, and cubic age effects were added to the model. With each step in the modeling process, model fit improved significantly, as did the amount of intraindividual variability. All parameter estimates were statistically significant at p o0.001 and the ΔBIC between the UGM-quadratic and UGM-cubic provided “very strong” evidence to support the UGM-cubic as the best fitting model. After confirming the cubic pattern of change in depressive symptoms, a CGM was specified to examine the impact of chronic illness on the trajectories of depressive symptoms during the transition to young adulthood (CGM-1, Table 3). Adjusting for family environment and sociodemographic covariates, modeling results showed that chronic illness did not have a significant impact on CES-D scores at 12 years of age (β¼0.16, p ¼0.319). However, chronic illness did influence the change in CES-D scores during the transition to young adulthood, such that youth with chronic illness has a less favorable trajectory over time (Fig. 1). Youth with chronic illness had a faster rate of increasing depressive symptoms from age 12 to 16 years (β¼0.38, p o0.01), then a slower rate of decreasing depressive symptoms from age 17 to 23

Table 2 Unconditional multilevel models of depressive symptoms during the transition to young adulthood. Parameters Fixed effects Initial status Intercept Rate of change Age Age2 Age3 Variance components Intraindividual Intercept Linear Quadratic Cubic Goodness-of-fit BIC

UMM

UGM-linear

UGM-quadratic

UGM-cubic

6.23 (0.06)a

7.80 (0.08)a

6.35 (0.09)a

5.49 (0.11)a

 0.24 (0.01)a

0.49 (0.03)a  0.06 (0.00)a

1.42 (0.07)a  0.24 (0.01)a 0.01 (0.00)a

17.98 (0.20)a 9.24 (0.32)a

16.58 (0.20)a 11.68 (0.55)a 0.02 (0.00)a

14.58 (0.19)a 10.34 (0.66)a 0.68 (0.07)a 0.01 (0.00)a

14.13 (0.19)a 11.69 (0.83)a 2.64 (0.21)a 0.03 (0.00)a 0.00 (0.00)

117586.0

116502.0

115434.8

115018.1

Values denote mean (standard error), unless indicated otherwise. UMM, unconditional means model; UGM, unconditional growth model. a

p o 0.001.

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M.A. Ferro et al. / Journal of Affective Disorders 174 (2015) 594–601

Table 3 Conditional multilevel models of depressive symptoms during the transition to young adulthood. Parameters Fixed effects Initial status Intercept Health status (HS) Parent depression (PD) Family functioning (FF) Parental rejection (PR) Parental nurturance (PN) Parent age (PA) Parent sex (PS) Parent education (PE) Marital status (MS) Family income (FI) Urban residence (UR) Rate of change Age Age2 Age3 HS  Age HS  Age2 HS  Age3 PD  Age PD  Age2 PD  Age3 FF  Age FF  Age2 FF  Age3 PR  Age PR  Age2 PR  Age3 PN  Age PN  Age2 PN  Age3 PA  Age PA  Age2 PA  Age3 PS  Age PS  Age2 PS  Age3 PE  Age PE  Age2 PE  Age3 MS  Age MS  Age2 MS  Age3 FI  Age FI  Age2 FI  Age3 UR  Age UR  Age2 UR  Age3 Variance components Intraindividual Intercept Linear Quadratic Cubic Goodness-of-fit BIC

CGM-1

CGM-2

CGM-3

5.66 (0.23)c 0.19 (0.24)

5.49 (0.10)c 0.17 (0.22) 0.08 (0.02)c 0.01 (0.02) 0.05 (0.04)  0.16 (0.04)c

5.72 (0.23)c 0.16 (0.24) 0.08 (0.02)c 0.00 (0.02) 0.06 (0.04)  0.15 (0.04)c  0.02 (0.02)  1.32 (0.42)b  0.14 (0.11) 0.01 (0.07)  0.04 (0.06)  0.35 (0.16)a

1.34 (0.07)c  0.22 (0.01)c 0.01 (0.00)c 0.44 (0.15)b  0.04 (0.02)a 0.01 (0.00)a  0.01 (0.01) 0.01 (0.00)  0.00 (0.00) 0.02 (0.01)  0.01 (0.00) 0.00 (0.00) 0.09 (0.02)c  0.02 (0.00)c 0.01 (0.00)c  0.02 (0.02) 0.01 (0.00)a  0.01 (0.00)b

1.10 (0.15)c  0.18 (0.03)c 0.01 (0.00)c 0.38 (0.15)b  0.04 (0.02)a 0.01 (0.00)a  0.01 (0.01) 0.00 (0.00)  0.00 (0.00) 0.02 (0.01)  0.01 (0.00) 0.00 (0.00) 0.08 (0.02)c  0.02 (0.00)c 0.00 (0.00)c  0.03 (0.02) 0.01 (0.00)a  0.00 (0.00)b  0.01 (0.01) 0.00 (0.00)  0.00 (0.00) 1.09 (0.26)c  0.18 (0.04)c 0.01 (0.00)c  0.02 (0.07) 0.01 (0.01)  0.00 (0.00) 0.13 (0.04)b  0.02 (0.01)b 0.00 (0.00)b 0.10 (0.04)a  0.02 (0.01)b 0.00 (0.00)a 0.38 (0.16)a  0.07 (0.03)b 0.00 (0.00)b

14.11 (0.19)c 11.71 (0.83)c 2.61 (0.20)c 0.03 (0.00)c 0.00 (0.00)c

13.99 (0.18)c 11.05 (0.81)c 2.11 (0.19)c 0.02 (0.00)c 0.00 (0.00)c

13.94 (0.18)c 11.06 (0.81)c 2.08 (0.19)c 0.02 (0.00)c 0.00 (0.00)c

114986.8

114629.4

114593.1

1.18 (0.14)c  0.20 (0.02)c 0.01 (0.00)c 0.50 (0.15)c  0.07 (0.03)b 0.01 (0.00)b

Values denote mean (standard error), unless indicated otherwise. CGM, conditional growth model; FF, family functioning; FI, family income; HS, health status; MS, marital status; PA, parent age; PD, parent depression; PE, parent education; PN, parental nurturance; PR, parental rejection; PS, parent sex; UR, urban residence. a b c

p o 0.05. p o0.01. po 0.001.

years (β¼ -0.04, po 0.05), finally a faster rate of increasing depressive symptoms from 24 to 25 years (β¼ 0.01, p o0.05). Symptoms of parental depression, as well youth reports of parental rejection and nurturance were shown to influence trajectories of depressive symptoms negatively. Likewise, with respect to

sociodemographic characteristics, less favorable trajectories were associated with having fathers as the primary caregiver, parents who were not in a partnered relationship, lower family income, and living in an urban area. Model fit improved substantially from CGM1 to CGM-3 (ΔBIC¼393.7). 3.3. Clinically relevant symptoms of depression The prevalence of youth with clinically relevant levels of depressive symptoms (CES-D411) was compared between groups over time. As shown in Table 4, prevalence was generally highest between the ages of 16–19 years and peaked at 38.8% and 32.1% at 16– 17 years for youth with and without chronic illness, respectively. Trends in clinically relevant levels of depressive symptoms were similar to CES-D scores over time with larger differences between groups as youth approached late adolescence and young adulthood.

4. Discussion 4.1. Summary of findings Findings from this study demonstrated that symptoms of depression are dynamic during the developmental period from early adolescence to young adulthood – increasing from early to late adolescence, decreasing from late adolescence to early young

Fig. 1. Trajectories of depressive symptoms during the transition to young adulthood. Prototypical trajectories based on the multilevel modeling results from CGM2 (Table 3) are illustrated for individuals with chronic illness and those without.

Table 4 Frequencies of elevated symptoms of depression. Age

Chronic illness

Healthy

χ2

P  value

12–13 14–15 16–17 18–19 20–21 22–23 24–25

17.9 27.2 38.8 33.1 18.6 13.5 13.9

15.7 25.3 32.1 27.6 14.0 8.6 7.8

2.00 1.03 10.85 7.99 9.04 14.85 24.22

0.157 0.311 0.001 0.005 0.003 o0.001 o0.001

Values reported as percentages of somewhat elevated or very elevated symptoms of depression based on the CES-D.

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adulthood, and then increasing again in the latter part of young adulthood. Chronic illness negatively influenced depressive symptoms trajectories, such that youth with chronic illness had higher depression scores and less favorable trajectories over time. Similar cubic trajectories of depressive symptoms have been observed in other epidemiological studies during the transition to adulthood (Natsuaki et al., 2009; Rawana and Morgan, 2014), as well as the general trend of overall decline in depressive symptoms from late adolescence to young adulthood (Galambos et al., 2006). However, epidemiological studies that have specifically considered the unique perspective of youth with chronic illness are rare. One study showed that individuals with asthma or epilepsy had similar trajectories of psychological distress that peaked during late adolescence and early adulthood (Ferro, 2013). Prevalence of clinically relevant levels of depressive symptoms were similar, albeit slightly higher, than contemporary estimates of major depressive episodes among an epidemiological sample of emerging adults as measured using the Composite International Diagnostic Interview 3.0 (Ferro, Submitted for publication). Findings support the diathesis-stress model (Ingram and Luxton, 2005) – emerging adulthood is a time when youth are predisposed to the develop of elevated symptoms of depression and this relationship is augmented by having a chronic illness. The mental health declines resulting from the interaction of the diathesis (i.e., chronic illness) and stress (i.e., transition to young adulthood) can be explained by the concepts of allostatic load – the state of exhaustion after long-term stress exposure in the presence of a particular vulnerability, whereby the normal allostatic response becomes dysfunctional leading to poorer health outcomes (Juster et al., 2010; McEwen, 1998). Evidence of compounding stressors in childhood resulting in poorer mental health outcomes in adolescence and young adulthood have been reported by other researchers (Colman et al., 2013; Copeland et al., 2013; Gorter et al., 2014; Weeks et al., 2014) and highlight the dynamic processes influencing the mental health of youth. 4.2. Implications Findings from the current study show that while the shape of trajectories is generally similar between youth with and without chronic illness, those with chronic illness do have less favorable trajectories of depressive symptoms. The transition to adulthood for youth with chronic illness is also the transition from the pediatric to the adult health system, which can be, as Gorter describes, a “crisis event” for youth with chronic illness and their families (Gorter, 2009). Often, this is a migration from a health care system that is family-centered, nurturing, and strongly coordinated with supportive health services to an adult care system that can be limiting and fragmented. This lack of coherence within the broader health system is not new and over the past two decades progress has been made in the development of guidelines and calls to action for successful transition of youth with chronic illness (American Academy of Pediatrics, 1996, 2002; Fernandes et al., 2014; Rosen et al., 2003; Sharma et al., 2014). Unfortunately, transition programs are few, youth are commonly “transferred” to adult care, and mental health is often overshadowed by physical health (Gorter et al., 2011; Grant and Pan, 2011). Obstacles and challenges relating to independence, education and vocations, psychosocial well-being, and participation during the transition to young adulthood can contribute negatively to mental health, particularly symptoms of depression during this critical developmental period. More often than not, health professionals in the adult setting are highly specialized to treat chronic illness, but lack the training to address these other important challenges (Sharma et al., 2014). Professional societies for child and adolescent health argue in favor of transition programs

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to be included in the care strategy for youth; specifically the transition process should start as early as 12 years of age and in partnership with youth and their families, recognize the challenges and develop realistic solutions to promote the best possible mental and physical health outcomes for youth with chronic illness (American Academy of Pediatrics, 2002; Rosen et al., 2003). Complementing strategies to improve mental health trajectories of youth during transition to young adulthood in the health system, the school setting is an important venue for prevention and promotion activities for youth with and without chronic illness. Theoretical and empirically-supported advantages to targeting youth via the school system are numerous: most youth spend a substantial part of their day within the school setting (National Research Council and Institute of Medicine, 2009); school programs can reach youth who would not access mental health services (Kratochwill and Shernoff, 2004); students are more likely than clinic-referred youth to receive and adhere to an intervention (Kazdin et al., 1997); school programs can facilitate early identification and maximize positive mental health (Middlebrooks and Audage, 2008; Rowling and Weist, 2004); improved functioning and academic performance and cost-reductions (Durlak et al., 2011; Weist and Murray, 2007). A systematic review of school-based mental health programs showed that such preventive interventions have a wide range of beneficial effects on youth, families, and communities (Weare and Nind, 2011). School programs provide the opportunity for high-risk youth, including those with chronic illness, to benefit from observing emotionallyskilled peers without experiencing the stigma associated with seeking mental health services (Lowry-Webster et al., 2001).

4.3. Limitations This study has some limitations that are noteworthy. First, like most longitudinal epidemiological studies, attrition and missing data were associated with socioeconomic disadvantage. While multiple imputation was used to account for missing data, it is possible that effects are underestimated due to this selective loss in the sample. Second, with regards to assessing chronic illness, chronic illness was parent and self-reported and could not be validated with medical records; illness-specific trajectories or dose-response effects of having more than one chronic illness trajectories of depressive symptoms could not be examined; and severity of chronic illness was not measured in the study. Third, potential mediating effects of parental depression and family functioning could not be examined due to the ages at which these contextual factors were assessed in the study (i.e., these measures were not available for youth aged 12 years and older). Previous work in younger child samples suggested that youth chronic illness, parental depression, and family functioning form an intricate web of causal paths influencing youth mental health (Ferro and Boyle, 2014). More research is needed to examine these causal paths during adolescence and young adulthood, in particular at times of transitions, for example leaving secondary school or transfer from the pediatric to adult health care system, where both the person and the environment are in a state of change.

5. Conclusions Chronic illness has a strong effect on symptoms of depression during the transition from adolescence to young adulthood. Because growing up, from adolescence to young adulthood, can be particularly stressful for youth with chronic illness, manifesting itself through elevated symptoms of depression, both the health and school system are uniquely positioned to provide supportive resources and preventive interventions in which declines in mental health can be muted during this critical developmental period.

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Conflict of interest None of the authors has any conflicts of interest to disclose.

Role of funding source Hamilton Health Sciences had no involvement in the conduct of the research or the preparation of the manuscript. While the research and analyses are based on data from Statistics Canada, the opinions expressed do not represent the views of Statistics Canada.

Acknowledgments This study was funded by a Hamilton Health Science New Investigator Grant (NIF-14355) awarded to Dr. Ferro. Dr. Ferro is supported by the Hamilton Health Sciences Research Early Career Award, Dr. Gorter holds the Scotiabank Chair in Child Health Research, and Dr. Boyle holds a Canada Research Chair in the Social Determinants of Health.

References American Academy of Pediatrics, 1996. Transition of care provided for adolescents with special health care needs. American Academy of Pediatrics Committee on Children with Disabilities and Committee on Adolescence. Pediatrics ,98, 1203– 1206. American Academy of Pediatrics, 2002. American Academy of Family Physicians, American College of Physicians-American Society of Internal MedicineA consensus statement on health care transitions for young adults with special health care needs. Pediatrics 110, 1304–1306. Austin, J.K., Perkins, S.M., Johnson, C.S., Fastenau, P.S., Byars, A.W., deGrauw, T.J., Dunn, D.W., 2011. Behavior problems in children at time of first recognized seizure and changes over the following 3 years. Epilepsy Behav. 21, 373–381. Bahreinian, S., Ball, G.D., Vander Leek, T.K., Colman, I., McNeil, B.J., Becker, A.B., Kozyrskyj, A.L., 2013. Allostatic load biomarkers and asthma in adolescents. Am. J. Respir. Crit. Care Med. 187, 144–152. Bayliss, E.A., Steiner, J.F., Fernald, D.H., Crane, L.A., Main, D.S., 2003. Descriptions of barriers to self-care by persons with comorbid chronic diseases. Ann. Fam. Med. 1, 15–21. Beck, A.T., Emery, G., Greenberg, R.L., 1985. Anxiety Disorders and Phobias: A Cognitive Perspective. Guildford Press, New York. Boyce, W.F., Davies, D., Raman, S.R., Tynjala, J., Valimaa, R., King, M., Gallupe, O., Kannas, L., 2009. Emotional health of Canadian and Finnish students with disabilities or chronic conditions. Int. J. Rehabil. Res. 32, 154–161. Britt, H.C., Harrison, C.M., Miller, G.C., Knox, S.A., 2008. Prevalence and patterns of multimorbidity in Australia. Med. J. Aust. 189, 72–77. Bryan, C., Songer, T., Brooks, M.M., Rush, A.J., Thase, M.E., Gaynes, B., Balasubramani, G.K., Trivedi, M.H., Fava, M., Wisniewski, S.R., 2010. The impact of diabetes on depression treatment outcomes. Gen. Hosp. Psychiatry 32, 33–41. Byles, J., Byrne, C., Boyle, M.H., Offord, D.R., 1988. Ontario Child Health Study: reliability and validity of the general functioning subscale of the McMaster Family Assessment Device. Fam. Process 27, 97–104. Cadman, D., Boyle, M., Szatmari, P., Offord, D.R., 1987. Chronic illness, disability, and mental and social well-being: findings of the Ontario Child Health Study. Pediatrics 79, 805–813. Chaney, J.M., Mullins, L.L., Uretsky, D.L., Pace, T.M., Werden, D., Hartman, V.L., 1999. An experimental examination of learned helplessness in older adolescents and young adults with long-standing asthma. J. Pediatr. Psychol. 24, 259–270. Chavira, D.A., Garland, A.F., Daley, S., Hough, R., 2008. The impact of medical comorbidity on mental health and functional health outcomes among children with anxiety disorders. J. Dev. Behav. Pediatr. 29, 394–402. Colman, I., Garad, Y., Zeng, Y., Naicker, K., Weeks, M., Patten, S.B., Jones, P.B., Thompson, A.H., Wild, T.C., 2013. Stress and development of depression and heavy drinking in adulthood: moderating effects of childhood trauma. Soc. Psychiatry Psychiatr. Epidemiol. 48, 265–274. Copeland, W.E., Adair, C.E., Smetanin, P., Stiff, D., Briante, C., Colman, I., Fergusson, D., Horwood, J., Poulton, R., Costello, E.J., Angold, A., 2013. Diagnostic transitions from childhood to adolescence to early adulthood. J. Child Psychol. Psychiatry 54, 791–799. Costello, D.M., Swendsen, J., Rose, J.S., Dierker, L.C., 2008. Risk and protective factors associated with trajectories of depressed mood from adolescence to early adulthood. J. Consult. Clin. Psychol. 76, 173–183. Costello, E.J., Egger, H., Angold, A., 2005. 10-year research update review: the epidemiology of child and adolescent psychiatric disorders: I. Methods and public health burden. J. Am. Acad. Child Adolesc. Psychiatry 44, 972–986. Costello, E.J., Pine, D.S., Hammen, C., March, J.S., Plotsky, P.M., Weissman, M.M., Biederman, J., Goldsmith, H.H., Kaufman, J., Lewinsohn, P.M., Hellander, M., Hoagwood, K., Koretz, D.S., Nelson, C.A., Leckman, J.F., 2002. Development and natural history of mood disorders. Biol. Psychiatry 52, 529–542. Crowley, R., Wolfe, I., Lock, K., McKee, M., 2011. Improving the transition between paediatric and adult healthcare: a systematic review. Arch. Dis. Child. 96, 548–553. Durlak, J.A., Weissberg, R.P., Dymnicki, A.B., Taylor, R.D., Schellinger, K.B., 2011. The impact of enhancing students' social and emotional learning: a meta-analysis of school-based universal interventions. Child Dev. 82, 405–432.

Fernandes, S.M., O'Sullivan-Oliveira, J., Landzberg, M.J., Khairy, P., Melvin, P., Sawicki, G.S., Ziniel, S., Kenney, L.B., Garvey, K.C., Sobota, A., O'Brien, R., Nigrovic, P.A., Sharma, N., Fishman, L.N., 2014. Transition and transfer of adolescents and young adults with pediatric onset chronic disease: the patient and parent perspective. J. Pediatr. Rehabil. Med. 7, 43–51. Ferro, M.A., 2013. Adolescents and young adults with physical illness: a comparative study of psychological distress. Acta Paediatr. 103, e32–e37. Ferro, M.A., Mental and substance use disorder among emerging adults with chronic physical health conditions. Manuscript submitted for publication. Ferro, M.A., Boyle, M.H., 2014. 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. http://dx.doi.org/ 10.1007/s10802-014-9893-6 (online). Fortin, M., Bravo, G., Hudon, C., Lapointe, L., Almirall, J., Dubois, M.F., Vanasse, A., 2006. Relationship between multimorbidity and health-related quality of life of patients in primary care. Qual. Life Res. 15, 83–91. Frye, R.E., Rossignol, D.A., 2011. Mitochondrial dysfunction can connect the diverse medical symptoms associated with autism spectrum disorders. Pediatr. Res. 69, 41R–47R. Galambos, N.L., Barker, E.T., Krahn, H.J., 2006. Depression, self-esteem, and anger in emerging adulthood: seven-year trajectories. Dev. Psychol. 42, 350–365. Ge, X., Conger, R.D., Elder Jr., G.H., 2001. Pubertal transition, stressful life events, and the emergence of gender differences in adolescent depressive symptoms. Dev. Psychol. 37, 404–417. Gijsen, R., Hoeymans, N., Schellevis, F.G., Ruwaard, D., Satariano, W.A., van den Bos, G.A., 2001. Causes and consequences of comorbidity: a review. J. Clin. Epidemiol. 54, 661–674. Gorter, J.W., 2009. Transition to adult-oriented health care: perspectives of youth and adults with complex physical disabilities. Phys. Occup. Ther. Pediatr. 29, 362–366. Gorter, J.W., Stewart, D., Woodbury-Smith, M., 2011. Youth in transition: care, health and development. Child Care Health Dev. 37, 757–763. Gorter, J.W., Stewart, D., Woodbury-Smith, M., King, G., Wright, M., Nguyen, T., Freeman, M., Swinton, M., 2014. Pathways toward positive psychosocial outcomes and mental health for youth with disabilities: a knowledge synthesis of information on developmental trajectories. Can. J. Commun. Ment. Health 33, 45–61. Grant, C., Pan, J., 2011. A comparison of five transition programmes for youth with chronic illness in Canada. Child Care Health Dev. 37, 815–820. Grey, M., Cameron, M.E., Lipman, T.H., Thurber, F.W., 1995. Psychosocial status of children with diabetes in the first 2 years after diagnosis. Diabetes Care 18, 1330–1336. Hankin, B.L., Abramson, L.Y., Moffitt, T.E., Silva, P.A., McGee, R., Angell, K.E., 1998. Development of depression from preadolescence to young adulthood: emerging gender differences in a 10-year longitudinal study. J. Abnorm. Psychol. 107, 128–140. Hankin, B.L., Mermelstein, R., Roesch, L., 2007. Sex differences in adolescent depression: stress exposure and reactivity models. Child Dev. 78, 279–295. Harrison, C., Britt, H., Miller, G., Henderson, J., 2014. Examining different measures of multimorbidity, using a large prospective cross-sectional study in Australian general practice. BMJ Open 4, e004694. Hasler, G., Gergen, P.J., Kleinbaum, D.G., Ajdacic, V., Gamma, A., Eich, D., Rossler, W., Angst, J., 2005. Asthma and panic in young adults: a 20-year prospective community study. Am. J. Respir. Crit. Care Med. 171, 1224–1230. Helgeson, V.S., Snyder, P.R., Escobar, O., Siminerio, L., Becker, D., 2007. Comparison of adolescents with and without diabetes on indices of psychosocial functioning for three years. J. Pediatr. Psychol. 32, 794–806. Hoppe, C., Elger, C.E., 2011. Depression in epilepsy: a critical review from a clinical perspective. Nature reviews. Neurology 7, 462–472. Ingram, R.E., Luxton, D.D., 2005. Vulnerability-stress models. In: Hankin, B.L., Abela, J.R.Z. (Eds.), Development of Psychopathology. A Vulnerability-Stress Perspective. Sage Publications Inc, Thousand Oaks. Jaser, S.S., Dumser, S., Liberti, L., Hunter, N., Whittemore, R., Grey, M., TeenCope Research, G., 2012. Seasonal trends in depressive symptoms in adolescents with type 1 diabetes. Diabetes Res. Clin. Pract. 96, e33–35. Jetha, M.K., Segalowitz, S.J., 2012. Adolescent Brain Development: Implications for Behavior. Academic Press, San Diego. Juster, R.P., McEwen, B.S., Lupien, S.J., 2010. Allostatic load biomarkers of chronic stress and impact on health and cognition. Neurosci. Biobehav. Rev. 35, 2–16. Kazdin, A.E., Holland, L., Crowley, M., 1997. Family experience of barriers to treatment and premature termination from child therapy. J. Consult. Clin. Psychol. 65, 453–463. Kessler, R.C., Walters, E.E., 1998. Epidemiology of DSM-III-R major depression and minor depression among adolescents and young adults in the National Comorbidity Survey. Depress. Anxiety 7, 3–14. Koenig, L., Gladstone, T., 1998. Pubertal development and school transitions: joint influences on depressive symptoms in middle and late adolescents. Behav. Modif. 22, 335–357. Kovacs, M., Feinberg, T.L., Crouse-Novak, M.A., Paulauskas, S.L., Finkelstein, R., 1984. Depressive disorders in childhood. I. A longitudinal prospective study of characteristics and recovery. Arch. Gen. Psychiatry 41, 229–237. Kovacs, M., Goldston, D., Obrosky, D.S., Bonar, L.K., 1997. Psychiatric disorders in youths with IDDM: rates and risk factors. Diabetes Care 20, 36–44. Kratochwill, T.R., Shernoff, E.S., 2004. Evidence-based practice: promoting evidence-based interventions in school psychology. Sch. Psychol. Quat. 18, 389–408.

M.A. Ferro et al. / Journal of Affective Disorders 174 (2015) 594–601

Le, T.K., Curtis, B., Kahle-Wrobleski, K., Johnston, J., Haldane, D., Melfi, C., 2011. Treatment patterns and resource use among patients with comorbid diabetes mellitus and major depressive disorder. J. Med. Econ. 14, 440–447. Lempers, J.D., Clark-Lempers, D., Simons, R.L., 1989. Economic hardship, parenting, and distress in adolescence. Child Dev. 60, 25–39. Lowry-Webster, H.M., Barrett, P.M., Dadds, M.R., 2001. A universal prevention trial of anxiety and depressive symptomatology in childhood: preliminary data from an Australian study. Behav. Change 18, 36–50. McEwen, B.S., 1998. Stress, adaptation, and disease. Allostasis and allostatic load. Ann. N.Y. Acad. Sci. 840, 33–44. Merikangas, K.R., He, J.P., Burstein, M., Swanson, S.A., Avenevoli, S., Cui, L., Benjet, C., Georgiades, K., Swendsen, J., 2010. Lifetime prevalence of mental disorders in U. S. adolescents: results from the National Comorbidity Survey Replication– Adolescent Supplement (NCS-A). J. Am. Acad. Child Adolesc. Psychiatry 49, 980–989. Middlebrooks, J.S., Audage, N.C., 2008. The Effects of Childhood Stress on Health Across the Lifespan. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Atlanta. Monroe, S.M., Simons, A.D., 1991. Diathesis-stress theories in the context of life stress research: implications for the depressive disorders. Psychol. Bull. 110, 406–425. Moreira, H., Carona, C., Silva, N., Frontini, R., Bullinger, M., Canavarro, M.C., 2013. Psychological and quality of life outcomes in pediatric populations: a parentchild perspective. J. Pediatr. 163, 1471–1478. National Research Council and Institute of Medicine, 2009. 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. The National Academies Press, Washington, D.C. Natsuaki, M.N., Biehl, M.C., Ge, X., 2009. Trajectories of depressed mood from early adolescence to young adulthood: the effects of pubertal timing and adolescent dating. J. Res. Adolesc. 19, 47–74. Nolen-Hoeksema, S., Girgus, J.S., 1994. The emergence of gender differences in depression during adolescence. Psychol. Bull. 115, 424–443. Oga, T., Nishimura, K., Tsukino, M., Sato, S., Hajiro, T., Mishima, M., 2007. Analysis of longitudinal changes in the psychological status of patients with asthma. Respir. Med. 101, 2133–2138. Ontario Association of Community Care Access Centres, 2013. Health Comes Home: A Conversation About Children with Complex Halth Needs Part 3, Health Comes Home. Perrin, J.M., Bloom, S.R., Gortmaker, S.L., 2007. The increase of childhood chronic conditions in the United States. JAMA 297, 2755–2759. Pinquart, M., Shen, Y., 2011a. Anxiety in children and adolescents with chronic physical illnesses: a meta-analysis. Acta Paediatr. 100, 1069–1076. Pinquart, M., Shen, Y., 2011b. Behavior problems in children and adolescents with chronic physical illness: a meta-analysis. J Pediatr. Psychol. 36, 1003–1016. Pinquart, M., Shen, Y., 2011c. Depressive symptoms in children and adolescents with chronic physical illness: an updated meta-analysis. J. Pediatr. Psychol. 36, 375–384. Poulin, C., Hand, D., Boudreau, B., 2005. Validity of a 12-item version of the CES-D used in the National Longitudinal Study of Children and Youth. Chronic Dis. Can. 26, 65–72.

601

Radloff, L.S., 1977. The CES-D scale: a self-report depression scale for research in the general population. Appl. Psychol. Meas. 1, 385–401. Raftery, A.E., 1995. Bayesian model selection in social research. Sociol. Methodol. 25, 111–164. Rawana, J.S., Morgan, A.S., 2014. Trajectories of depressive symptoms from adolescence to young adulthood: the role of self-esteem and body-related predictors. J. Youth Adolesc. 43, 597–611. Reigada, L.C., Bruzzese, J.M., Benkov, K.J., Levy, J., Waxman, A.R., Petkova, E., Warner, C.M., 2011. Illness-specific anxiety: implications for functioning and utilization of medical services in adolescents with inflammatory bowel disease. J. Spec. Pediatr. Nurs. 16, 207–215. Rosen, D.S., Blum, R.W., Britto, M., Sawyer, S.M., Siegel, D.M., Society for Adolescent, M., 2003. Transition to adult health care for adolescents and young adults with chronic conditions: position paper of the Society for Adolescent Medicine. J. Adolesc. Health 33, 309–311. Rowling, L., Weist, M.D., 2004. Promoting the growth, improvement, and sustainability of school mental health programs worldwide. Int. J. Ment. Health Promot. 6, 2–11. Sharma, N., O'Hare, K., Antonelli, R.C., Sawicki, G.S., 2014. Transition care: future directions in education, health policy, and outcomes research. Acad. Pediatr.14, 120–127. Singer, J.D., 2002. Fitting individual growth models using SAS PROC MIXED. In: Moskowitz, D.S., Hershberger, S.L. (Eds.), Modeling Intraindividual Variability with Repeated Measures Data. Methods and Applications. Lawrence Erlbaum Associates, Mahwah. Singer, J.D., Willett, J.B., 2003. Applied Longitudinal Data Analysis: Modeling Change and Event OccurrenceOxford University Press, New York. Statistics Canada, 2007. Microdata user guide: National Longitudinal Survey of Children and Youth, Cycle 8 September 2008 to July 2009. Statistics Canada. van den Akker, M., Buntinx, F., Metsemakers, J.F., Roos, S., Knottnerus, J.A., 1998. Multimorbidity in general practice: prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases. J. Clin. Epidemiol. 51, 367–375. van der Lee, J.H., Mokkink, L.B., Grootenhuis, M.A., Heymans, H.S., Offringa, M., 2007. Definitions and measurement of chronic health conditions in childhood: a systematic review. JAMA 297, 2741–2751. Waddell, C., Shepherd, C., Schwartz, C., Barican, J., 2014. Child and Youth Mental Disorders: Prevalence and Evidence-Based Interventions. British Columbia Ministry of Children and Family Development, Vancouver. Weare, K., Nind, M., 2011. Mental health promotion and problem prevention in schools: what does the evidence say? Health Promot. Int. 26 (Suppl 1), i29–i69. Weeks, M., Wild, T.C., Ploubidis, G.B., Naicker, K., Cairney, J., North, C.R., Colman, I., 2014. Childhood cognitive ability and its relationship with anxiety and depression in adolescence. J. Affect. Disord. 152–154, 139–145. Weiss, R.E., 2005. Modeling Longitudinal Data. Springer, New York. Weist, M.D., Murray, M., 2007. Advancing school mental health promotion globally. Adv. Sch. Ment. Health Promot. 1, 2–12.