SEASONAL CHANGES IN MOOD AND BEHAVIOR
AMONG CHILDREN AND ADOLESCENTS
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the
Graduate School of The Ohio State University
By
Katharine Davies Smith, M.A.
*****
The Ohio State University
2005
Dissertation Committee:
Dr. Mary Fristad, Advisor Approved by
Dr. Steven Beck ______
Dr. Michael Vasey Advisor
Dr. Nancy Ryan-Wenger Department of Psychology
Copyright by
Katharine Davies Smith
2005
ABSTRACT
Seasonal mood disorder (SMD) is likely prevalent among children and
adolescents; however, there are few empirical investigations of pediatric SMD in the
literature. To contribute to the field, the current study investigates the seasonality of
mood and behavior among children and adolescents using longitudinal data collected
from 1987 to 1998. One hundred eleven youths diagnosed with depression, 369 bereaved
youths, 129 community controls, and their parents completed individual assessments at
baseline, five, thirteen, and twenty-five months later. Assessment materials include
several measures of depressive symptoms and diagnostic instruments. Multilevel
analyses were conducted using MlwiN software.
According to both parent- and child-report, youths from all three groups earn significantly higher total scores on measures of depression during the winter than the summer. On the other hand, significant seasonal effects do not arise for a measure of overall psychopathology. This implies that seasonality is limited to mood disorders.
While this seasonal effect is significant, actual changes of mean scores on measures of depression from the summer to the winter are minimal, suggesting that season may have little affect on the mood and behavior for the majority of participants from each group. It is uncertain whether a few highly seasonal participants influence the mean changes between summer and winter or whether each group as a whole experiences very mild
ii seasonal changes. In either case, the finding that depressed, bereaved, and community
controls show similar average levels of seasonality supports the dual vulnerability model
of SMD, which posits that seasonality and propensity toward depression are independent.
Data collected from the youths, themselves, suggest that seasonality shown on measures of depression arise from seasonal changes in atypical vegetative symptoms as opposed to cognitive or affective symptoms. This is consistent with the hypotheses.
However, data collected from their parents do not show an association between season
and any particular depressive symptom cluster. Therefore, it is unclear whether
seasonality of depression is attributable to the characteristics uniquely associated with
SMD; namely, atypical vegetative symptoms. Overall, the current study provides useful
information for understanding seasonality in mood and behavior among children and
adolescents.
iii
Dedicated to Gregory Richard Smith
iv
ACKNOWLEDGMENTS
This dissertation would not have been possible without the assistance of several
individuals. First and foremost, I would like to thank my advisor, Mary A. Fristad, for
encouraging me in its conception and supporting me through its completion. I feel
indebted to Dr. Fristad for helping me develop both my research and practical skills in the
field of child clinical psychology. Further, she has fostered in me an interest in and
knowledge about childhood mood disorders that will aid me in my future endeavors.
I also wish to thank Julie Cerel for sending me the data I required for completion of this project, as well as explaining to me logistics about how the data were collected and the meanings of numerous variables. I am also grateful to Nancy Briggs and Meade
Eggleston for introducing me to multilevel analysis and providing me with several resources that assisted me in learning the new statistical technique. Also, I would like to thank Nancy for allowing me to use the student office of the Quantitative Psychology
Department where the software I needed to conduct the analyses was licensed.
Finally, I am thankful for the love and support that my friends and family have shown. My parents, Alice and Tony Davies, have always inspired me to learn and fostered a belief that I may achieve whatever I aspire. My husband, Greg Smith, has provided me with the emotional support I require as well as taught me how to utilize computer programs to manipulate data and format documents as the project requires.
v
VITA
August 31, 1977 ...... Born - Fairbanks, AK
2000 ...... B.A., Harvard University
2002...... M.A., The Ohio State University
2000 – 2003...... Graduate Research Associate,
The Ohio State University
2003 – 2004...... Distinguished University Fellow,
The Ohio State University
PUBLICATIONS
1. Wang, Q., Leichtman, M.D., & Davies, K.I. (2000). Sharing memories and telling
stories: American and Chinese mothers and their 3-year-olds. Memory, 8(3), 159-
177.
2. Smith, K. D. & Fristad, M.A. (2003). Bipolar disorder. In T. Ollendick & C.
Schroeder (eds.), Encyclopedia of Pediatric and Child Psychology. New York:
Kluwer Academic/Plenum Press.
FIELDS OF STUDY
Major Field: Psychology
vi
TABLE OF CONTENTS
Page
Abstract ...... ii
Dedication...... iv
Acknowledgments...... v
Vita...... vi
List of Tables ...... x
List of Figures...... xi
Introduction...... 1
Diagnostic and clinical features ...... 2
Adults...... 2
Children and adolescents ...... 4
Assessment...... 6
Distinction from nonseasonal mood ...... 7
Epidemiology...... 11
Adults...... 11
Children and adolescents ...... 12
Distinction from nonseasonal mood ...... 14
vii Onset ...... 14
Adults...... 14
Children and adolescents ...... 15
Distinction from nonseasonal mood ...... 15
Long-term course...... 15
Adults...... 15
Children and adolescents ...... 17
Distinction from nonseasonal mood ...... 17
Etiological mechanisms ...... 18
Adults...... 18
Children and adolescents ...... 22
Distinction from nonseasonal mood ...... 24
Treatment ...... 25
Adults...... 25
Children and adolescents ...... 29
Distinction from nonseasonal mood ...... 31
Summary...... 35
The present study...... 36
Method...... 38
Participants...... 38
Materials ...... 38
Procedure ...... 40
Results...... 41
viii Data analysis ...... 41
Participant demographics...... 45
Assessment descriptives...... 46
Assessment plots...... 47
Diagnostic Interview for Depression in Children and Adolescents...... 48
Children’s Depression Rating Scale-Revised ...... 49
Children’s Depression Inventory...... 52
Behavior, Anxiety, Mood, and Other...... 54
Cognitive symptoms ...... 55
Affective symptoms...... 58
Atypical vegetative symptoms...... 59
Discussion...... 62
Interpretation of findings ...... 62
Limitations ...... 69
Summary...... 71
Future directions ...... 72
List of references ...... 74
Appendices
Appendix A: Tables ...... 82
Appendix B: Figures...... 101
ix
LIST OF TABLES
Table Page
1 Descriptions of the parameters estimated by the MLA model ...... 83
2 Participants interviewed by year...... 86
3 Participants interviewed by month...... 86
4 Multilevel regression results for child DIDCA total scores...... 87
5 Multilevel regression results for parent DIDCA total scores...... 88
6 Multilevel regression results for child CDRSR total scores ...... 89
7 Multilevel regression results for parent CDRSR total scores ...... 90
8 Multilevel regression results for child CDI total scores ...... 91
9 Multilevel regression results for parent CDI total scores ...... 92
10 Multilevel regression results for child BAMO total scores ...... 93
11 Multilevel regression results for parent BAMO total scores ...... 94
12 Multilevel regression results for child cognitive symptom scores...... 95
13 Multilevel regression results for parent cognitive symptom scores...... 96
14 Multilevel regression results for child affective symptom scores ...... 97
15 Multilevel regression results for parent affective symptom scores ...... 98
16 Multilevel regression results for child atypical vegetative symptom scores ...... 99
17 Multilevel regression results for parent atypical vegetative symptom scores ...... 100
x
LIST OF FIGURES
Figure Page
1 Hypothesized seasonal trend for outcome variables
across assessment occasions ...... 102
2 Child CDI total scores plotted by month ...... 103
3 Predicted trends in child DIDCA total scores across assessment occasions ...... 104
4 Predicted trends in parent DIDCA total scores across assessment occasions ...... 105
5 Predicted trends in child CDRSR total scores across assessment occasions...... 106
6 Predicted trends in parent CDRSR total scores across assessment occasions...... 107
7 Predicted trends in child CDI total scores across assessment occasions ...... 108
8 Predicted trends in parent CDI total scores across assessment occasions ...... 109
9 Predicted trends in child BAMO total scores across assessment occasions ...... 110
10 Predicted trends in parent BAMO total scores across assessment occasions ...... 111
11 Predicted trends in child cognitive symptom scores
across assessment occasions ...... 112
12 Predicted trends in parent cognitive symptom scores
across assessment occasions ...... 113
13 Predicted trends in child affective symptom scores
across assessment occasions ...... 114
xi 14 Predicted trends in parent affective symptom scores
across assessment occasions ...... 115
15 Predicted trends in child atypical vegetative symptom scores
across assessment occasions ...... 116
16 Predicted trends in parent atypical vegetative symptom scores
across assessment occasions ...... 117
xii
INTRODUCTION
Pediatric mood disorders are becoming an increasing area of concern for mental health professionals, who recognize both their prevalence and the level of discomfort or impairment they cause. Children and adolescents with mood disorders exhibit significant disturbances in their mood and behavior that influence their functioning at home, school, and the community. As such, professionals must gather as much information as possible about childhood mood disorders in order to strengthen prevention and treatment strategies.
One relatively untouched area of research within the pediatric mood disorder literature is seasonal affective disorder or seasonal mood disorder. (The acronym, SMD, will be used herein instead of the usual acronym, SAD, which can be confused with separation anxiety disorder in pediatric populations.) In fact, no known investigations of pediatric SMD were excluded from the following review. However, as the terse review will show, SMD is prevalent among children and adolescents. Further, youth with SMD demonstrate marked impairment in school performance and psychosocial functioning, including relationships with family and peers.
Given the fact that children and their parents are often unaware of the seasonal pattern of their mood and behavior, SMD is under-diagnosed and under-treated. This is a shame given the availability potentially effective treatments. Since SMD persists over
1 time, children and adolescents with untreated SMD likely experience unnecessary
extended suffering. Therefore, it is of utmost importance that parents, teachers,
physicians, and mental health professionals learn to recognize SMD in children and
adolescents.
There has been some debate over the diagnostic validity of SMD; therefore the
review herein will not only include descriptions of SMD in adults and youths, but also
provide data indicating how SMD may or may not be diagnostically distinct from mood
disorders in general. Following the literature review below, a study investigating the
seasonality of mood and behavior among children and adolescents diagnosed with
depression, bereaved youths, and community controls will be presented. This study
gathers information that informs the reader about the prevalence of seasonality among
youths and identifies signs or symptoms that will aid in the early identification of children and adolescents with SMD, so that these youth may receive the proper treatment they require in a timely manner.
Diagnostic and Clinical Features
Adults. While early experts, such as Kraepelin, have noted seasonal changes their patients' moods since the beginning of the 20th century, little attention was paid to such seasonal patterns until the 1980s. Sparking the movement was Rosenthal and colleagues'
(1984) seminal article describing twenty-nine patients with seasonal patterns to their mood pathology, for which they coined the phrase "seasonal affective disorder."
Specifically, SMD is "characterized by recurrent depressive episodes that occur annually"
(Rosenthal et al., 1984, p.72). While Rosenthal and colleagues included individuals who have these consecutive depressive episodes only in the fall or winter (i.e. winter-SMD),
2 others may have a seasonal pattern in which depressive episodes occur only in the spring or summer (i.e. summer-SMD).
In the DSM-IV-TR, SMD is actually a subtype of, or "course specifier" for, major depressive disorder and bipolar disorder. Diagnostic criteria for SMD include seasonally recurrent major depressive episodes, with onset and full remission occurring at characteristic times of the year. The seasonal pattern must be evident for the last two years, without any nonseasonal depressive episodes. Over the individual's lifetime, seasonal episodes must be more common than nonseasonal episodes. Lastly, seasonally related psychosocial stressors, such as seasonal unemployment, must not better explain the seasonal pattern of major depressive episodes (DSM-IV-TR).
Depressive episodes associated with winter SMD tend to begin in late fall and remit in early spring. For instance, Rosenthal and colleagues (1984) identified individuals with SMD whose episode onsets ranged from September to January and remitted from January to May. In general, "depression started most frequently between
October and December, and symptoms ended most frequently in March" (Rosenthal et al., 1984, p.73). Further, depressive episodes lasted an average of four months in
Rosenthal and colleagues' investigation.
Characteristics common to adults with SMD are atypical vegetative symptoms, including hypersomnia, overeating, and carbohydrate craving (Lam, 2002; Lee et al.,
1998; Rosenthal et al., 1984). These vegetative symptoms "have been described as
'atypical' in that they differ from the pattern of insomnia, anorexia, and weight loss typical of major depression with melancholia" (Rosenthal et al., 1984, p.76). These symptoms reportedly occur in 65-85% of individuals with SMD, while the other
3 individuals with SMD have more typical symptoms of depression occurring seasonally
(Tam et al., 1997). For example, in Rosenthal and colleagues' hallmark study (1984), the majority of participants with SMD (79%) craved carbohydrates, including sweets, chocolate, and starches.
Another course specifier for major depression and bipolar disorder is "atypical" depression, whose essential features are: mood reactivity, vegetative symptoms, and rejection sensitivity. While individuals with SMD display similar vegetative symptoms to those with atypical depression, individuals with SMD differ from those with atypical depression in that they are not particularly sensitive to rejection (Lam, 2002).
Children and adolescents. Just as Rosenthal and his research partners sparked clinical interest in SMD among adult populations, they initiated research concerning
SMD among child and adolescent populations with their hallmark case study of seven youths ages six to fourteen (1986). The children Rosenthal and colleagues studied regularly experienced mood and behavioral problems for at least two weeks during the winter months. As a whole, caregivers of these youths ranked their symptoms in the following order of severity: irritability, fatigue, school difficulties, sadness, hypersomnia, headaches, changes in appetite, carbohydrate craving, decreased activity, crying spells, anxiety, social withdrawal, and temper tantrums. Diagnostically, these children were experiencing recurrent depressive episodes during the winter. These depressive problems dated back to early childhood or infancy for several of the youths. However, Rosenthal and colleagues noted that these children "are often unaware that they have a seasonal problem" (1986, p.358). In other words, the youths (and their parents) had low insight into the temporal pattern found in their mood and behavior.
4 Since Rosenthal and colleagues first described SMD in children and adolescents,
others have also identified it. For instance, several other researchers have also written
case studies of SMD among children and adolescents (Lucas, 1991; Meesters, 1998;
Mghir & Vincent, 1991; Saha, Pariante, McArdle, & Fombonne, 2000). Perhaps the
youngest documented case involves a four year old boy in Scotland referred to as "Sam"
(Saha et al., 2000). Reportedly, Sam's parents first noted prolonged bouts of crying as
well as poor feeding and sleeping during the winter when he was seven months old. The
following winter, when he was one year six months old, he again was noted to have "a
poor appetite, poor sleep, not wanting to play, and appearing very irritable" (Saha et al.,
2000, p.136). Similar symptoms were again reported during the following winters when
he was two and three years old. Additionally, during the summer when Sam was three
years old, there were two to three days during which "he was described as being mildly overactive, over-talkative, 'too happy' and 'funny'" (Saha et al., 2000, p.136). Sam came to Saha and colleagues' attention during the winter when he was four years old.
The individual depressive episodes of children and adolescents with SMD tend to resemble those of adults with regard to onset and remission. In other words, as with adults, depressive episodes for children and adolescents with SMD tend to begin around the end of October, reaching their peak in January, and remitting around the end of
March (Rosenthal et al., 1986; Rosenthal et al., 1984; Swedo et al., 1997).
On the other hand, the symptom profile of SMD may be different among children and adolescents from adults. For instance, Sonis and colleagues (1987) studied five children and adolescents with SMD and found a symptom profile slightly different from those found in studies of adults. They found three central symptoms: sleep changes (both
5 hyper and hyposomnia), irritability, and anergia. Less significant symptoms were
sadness, crying, and anhedonia. Appetite changes or carbohydrate craving were not
mentioned. In other investigations, adolescents tend not to show the same increased
appetite common to adults with SMD (Lucas, 1991; Rosenthal et al., 1986; Swedo et al.,
1995). On the other hand, approximately half of the children Carskadon and Acebo
(1993) identified as having SMD ate more during the winter. Altogether, it seems
unclear whether there are any differences in the symptom profiles of SMD for adults,
adolescents, and children. As such, the current investigation will specifically look for seasonal effects on atypical vegetative symptoms amongst youths.
Assessment. The Seasonal Pattern Assessment Questionnaire (SPAQ) is the most
widely used measure of seasonal changes in mood and behavior among diverse
populations, including individuals with SMD. This self-report measure serves a
screening tool for the diagnosis of SMD, and consists of seven sections. First,
demographic data are collected. Second, a five-point Likert scale is used to assess level
of seasonal change in several SMD related symptoms, such as "appetite." Third,
individuals mark the months on a calendar in which they experience SMD-related symptoms such as "sleep most." Fourth, participants indicate on a Likert scale how different types of weather, such as "cold," affect their mood. Fifth, participants indicate the amount their weight fluctuates within a year. Sixth, they mark how much sleep they tend to get during different seasons. Finally, they provide a global rating of how problematic seasonal changes are for them. As such, the SPAQ is a very thorough measure of seasonality. Further, "its most important attributes are the absence of assumptions about the direction of seasonal change and the ability to register seasonal
6 change independently of illness so that it can be used in normal subjects" (Thompson,
Stinson, Fernandez, Fine, & Isaacs, 1988, p.258). In other words, both summer and
winter SMD may be identified, as well sub-syndromal seasonal change in normal populations.
The SPAQ appears to have adequate reliability and validity. With regard to reliability, Rohan and Sigmon (2000) administered the SPAQ two months apart to college students, and found excellent overall test-retest reliability of the scale. In addition, Thompson and colleagues (1988) administered the SPAQ to adults with SMD approximately one year apart, and found adequate test-retest reliability for each section with the exception of the weather preference section. In addition, the construct validity of SPAQ is supported by studies, such as Hardin and colleagues (1991) and Magnusson
(1996), showing that the SPAQ differentiates between individuals previously diagnosed with SMD or sub-syndromal SMD and normal controls.
Distinction from nonseasonal mood. Thus far, it has been claimed that atypical vegetative symptoms are characteristic of adult SMD and possibly pediatric SMD; but the question remains whether individuals with SMD truly have different symptom profiles from people with nonseasonal mood disorders. Several researchers have found evidence supporting the distinctiveness of SMD’s symptom profile. For instance, Allan and colleagues (1993) compared individuals with SMD to those with nonseasonal mood disorders and found that hypersomnia, hyperphagia, and weight gain occur more often among individuals with SMD. Similarly, Tam and colleagues (1997) found that hyperphagia and hypersomnia occur more often among participants with SMD, while interpersonal sensitivity and other rejection avoidance occurs more often among
7 participants with nonseasonal major depressive disorder. Therefore, atypical vegetative
symptoms are associated with SMD, so I expect to find seasonal effects on atypical
vegetative symptoms in the current investigation.
While Tam and colleagues (1997) found atypical vegetative symptoms amongst
individuals with SMD, they did not find a significantly different rate of diagnosis of
"atypical" depression, another course specifier, in participants with SMD than
participants with nonseasonal major depression. In addition to reverse vegetative
symptoms, mood reactivity, rejection sensitivity, and leaden paralysis are symptoms of
atypical depression. Tam and colleagues found that these additional symptoms are not
more prevalent in participants with SMD than other participants with nonseasonal major
depression. Therefore, SMD appears to be a diagnostically distinct entity from atypical
depression, sharing similarity only in standard reverse vegetative symptoms.
Thompson and colleagues (1988) compared individuals with SMD to normal
controls and clients with bipolar disorder receiving lithium treatment. According to their
results, all groups have seasonal changes in their mood as well as vegetative symptoms
during the winter; however, SMD participants have significantly greater seasonal changes
than normal controls, with patients with bipolar disorder receiving intermediate scores.
Therefore, Thompson and colleagues conclude that SMD may be an exaggerated form of normal seasonal variation.
There appears to be mixed data on whether or not individuals with SMD resemble individuals with nonseasonal mood disorders on cognitive symptoms. Michalak and colleagues (2002) investigated the difference in symptom profiles between individuals with SMD and nonseasonal major depression. According to their results, nonseasonal
8 depression is associated with higher occupational impairment and psychiatric intervention than SMD. Further, negative attributional style, hopelessness, and weight loss were particularly predictive of nonseasonal depression as opposed to SMD. On the other hand, Hodges and Marks (1998) found that both the participants with SMD and with nonseasonal depression reported more dysfunctional attitudes and negative automatic thoughts, including thoughts about personal maladjustment, negative self- concept, low self-esteem, and hopelessness, than a control group but similar amounts to each other. Perhaps, some of the negative automatic thoughts exhibited by individuals with SMD are specifically winter-related, contributing to the seasonal nature of their mood episodes. Levitan and colleagues (1998) found that participants with SMD do not differ from participants with nonseasonal depression on negative attributional style.
However, Levitan and colleagues did find that negative attributional style predicted poor response to pharmocotherapy in nonseasonal participants while it did not predict response to light therapy in SMD participants.
While studies concentrating on negative thinking and attributional styles amongst individuals with SMD tend to indicate that they resemble individuals with nonseasonal depression, memory studies tend not to do so. For instance, Dalgleish and colleagues
(2001) conducted an investigation where participants with SMD and controls were required to generate autobiographic memories in response to emotional cue words. In contrast to findings with nonseasonally depressed individuals, the SMD participants provided very specific memories. Dagleish and their colleagues explained their results in terms of the differing etiologies of SMD and nonseasonal depression, indicating that
SMD primarily arises from a biological response to light while nonseasonal depression is
9 associated with childhood trauma. As a result, Dagleish and colleagues argue that
individuals with SMD resemble those with nonseasonal depression on cognitive tasks
that are based on their current mood states (e.g., negative attributional style), but not on
cognitive tasks that arise from the etiology of the illness (e.g., autobiographical
memories).
In a pair of subsequent studies, Dagleish and colleagues (2004) found further
support for the distinction between different categories of cognitive correlates of mood.
In both experiments, they found that individuals with SMD endorse more negative and
less positive words with reference to themselves than controls, indicating negative
thought processes relating to their current mood states. Additionally, participants with
SMD had stronger negative attributional styles than controls. On the other hand, both experiments failed to show that participants with SMD recall more negative self-referent material than controls, indicating a lack of the memory bias previously shown in studies of nonseasonal depression. Dagleish and colleagues propose a process of “automatic mood repair” or a schema-based account or a combination of the two as an explanation of their results.
Altogether, it appears that the profile of cognitive processing in SMD may be different from nonseasonal mood disorders. On memory-related tasks, individuals with
SMD do not resemble individuals with nonseasonal mood disorders. However, on
cognitive tasks related to current mood state (such as negative attributional style and
automatic thoughts) individuals with SMD resemble those with nonseasonal depression.
Since the measures included in the present study only cover cognitions related to one’s
10 current mood state, it is unlikely that seasonal effects on cognitive depressive symptoms
will be found in the current investigation.
Overall, the investigations comparing the symptom profile of SMD against other
variants of mood disorders indicate that SMD is a diagnostically distinct entity. In other
words, patients with SMD can easily be differentiated from individuals with other mood
disorders or course specifiers. Atypical vegetative symptoms without mood reactivity,
rejection sensitivity, and leaden paralysis are particularly distinctive of SMD.
Epidemiology
Adults. While published prevalence estimates of SMD have varied greatly
(Eagles, Naji, Gray, Christie, & Beattie, 1998), the prevalence of winter SMD is
estimated to be 5% worldwide (Lee et al., 1998). In the United States, it is estimated to
be between 4.0% and 6.3% (Swedo et al., 1995). Overall, the prevalence of winter SMD in North America is estimated to be approximately twice that of Europe (Lam, 2002;
Mersch, Middendorp, Bouhuys, Beersma, & van den Hoofdakker, 1999). Interestingly, summer SMD (6.19%) is more prevalent than winter SMD (1.03%) in the north tropics
(Srisurapanont & Intaprasert, 1999). Many factors, including climatic variation and/or cultural differences, may contribute to variations in prevalence rates.
While many do not have seasonal changes of sufficient clinical severity to warrant diagnosis of SMD, general seasonal variation in mood and behavior may be common.
For instance, Lee and colleagues (1998) cite several investigations showing that 20% or more of the general population report significant but non-clinical seasonal mood changes.
The prevalence of SMD appears to vary with sex. In other words, SMD has primarily been observed in females (Goel, Terman, & Terman, 2002; Rosenthal et al.,
11 1984). Based on Alaskan data, Lee and colleagues (1998) suggest that there is a 4:1 ratio of SMD in females versus males. In addition, the prevalence of SMD may vary with its associated mood disorder. As aforementioned, SMD is a course descriptor for mood disorders, such as bipolar disorder and major depressive disorder. The majority of SMD patients described by Rosenthal and colleagues (1984) had been diagnosed with bipolar disorder (93%), particularly bipolar II (76%). Therefore, SMD may be more prevalent among individuals who experience depressive episodes and hypomania seasonally. Goel and colleagues (2002) investigated whether the symptom presentation of SMD differs between individuals diagnosed with bipolar or unipolar depression. According to their results, bipolar patients with SMD are more depressed and exhibit more psychomotor agitation and social withdrawal than unipolar patients with SMD.
Children and adolescents. To date, there are only two published studies on the epidemiology of SMD in children and adolescents (Carskadon & Acebo, 1993; Swedo et al., 1995). Carskadon and Acebo (1993) surveyed over a thousand parents across the
United States about seasonal changes in sleeping, eating, irritability, energy, withdrawal, and sadness among fourth through sixth graders. In their parental survey, Carskadon and
Acebo (1993) found that 48.5% of parents reported that their children had at least one recurring symptom during winters. Thus, some seasonal changes in mood or behavior appear common among children. Specifically, the most commonly reported symptoms were increased appetite, hypersomnia, and tiredness, each reported in approximately 24% of the population. As such, similar seasonal changes in mood and behavior will likely be found in all groups of the current investigation.
12 While seasonality is common, SMD is not. According to Carskadon and Acebo’s results, seasonal changes may reflect SMD in 4.2% percent of the children, who had increased sadness as well as at least two other symptoms during winter. While this rate is roughly equivalent to that found in adult samples, it likely over-estimates the prevalence given Carskadon and Acebo’s lenient requirement of only two additional symptoms rather than the DSM-IV’s requirement of four symptoms in addition to sadness or anhedonia. Further, seasonal changes were noted equally between males and females, except that girls were more likely to feel tired. In addition, more winter symptoms were identified in children living farther north, possibly indicating the role of sunlight or other climatic variables in seasonal mood changes. However, interpretations of Carskadon and
Acebo's findings are hindered by a low response rate and the fact that children were not directly questioned themselves.
In order to determine the prevalence of SMD among older children and adolescents, Swedo and colleagues (1995) administered a modified version of the SPAQ to over a thousand middle and high school students in the Washington, D.C. area. The modifications were meant to cover information more pertinent to childhood-onset SMD, including questions about school performance, irritability, and conduct problems.
According to their results, the prevalence of SMD is conservatively estimated to be 3.3% in children and adolescents older than nine; thereby supporting the findings of Carskadon and Acebo (1993). Again, the proportion of males and females with SMD was approximately equal (45.0% and 55.0%, respectively). However, SMD was more common in post-pubertal girls than pre-pubertal girls. Thus, while the rate of SMD among pre-pubertal and post-pubertal males remained 3.1%, it increased from 1.7% to
13 4.5% with puberty in females. Girls and boys combined, the proportion of cases of SMD
increased with age from 1.7% in sixth grade to 5.5% in twelfth grade. In contrast to
Carskadon and Acebo's (1993) finding that children eat and sleep more in winter, Swedo
and colleagues (1995) did not find increased sleep and appetite in winter. As Swedo and
colleagues note, teenagers tend to sleep more in summer because they do not have to get
up early for school. Overall, the epidemiological data indicate that approximately 3-4%
of the child and adolescent population in the United States have SMD.
Distinction from nonseasonal mood. SMD resembles general mood disorders in
its childhood ratio of female to male prevalence. In other words, as with children with
SMD, the proportion of males and females with depression, seasonal and nonseasonal
inclusive, are equal amongst children (Fristad, Shaver, & Holderle, 2002). Gender differences in depression tend to emerge as the incidence rate among females grows during adolescence, producing the 2:1 ratio of females to males common amongst adults
(Nolen-Hoeksema & Girgus, 1994). However, the 4:1 ratio of SMD in adult females versus males is twice as high, suggesting an additional association of gender with seasonality.
Onset
Adults. Lee and colleagues (1998) report that studies, such as Rosenthal and colleagues' (1984) initial investigation, show that the onset of SMD typically occurs in the second and third decades of life. Specifically, 10% of Rosenthal and colleagues' patients reported seasonal patterns of mood beginning in childhood, while others identified adolescent and adult onsets. The average age of onset was calculated to be
14 26.9 years old (Rosenthal et al., 1984). In addition, Rosenthal and colleagues (1984) indicate:
Many patients were unable to pinpoint exactly when the cycles began because (1)
mood changes frequently were mild at first and became more severe with
increasing age, and (2) it took several years to recognize the recurrent annual
pattern of the cycle. (p.73)
Therefore, the typical SMD age of onset is difficult to establish, although most participants are able to indicate that it was during early adulthood.
Children and adolescents. It is hardly surprising that clinical researchers and practitioners have been identifying SMD among pediatric populations. After all, 10% of the adult patients in Rosenthal and colleagues' initial investigation (1984) reported the onset of SMD before age eleven and 33% before age nineteen. In other words, early onset of SMD is not uncommon. In fact, there have been rare occasions where pediatric- onset of SMD has been traced back to infancy (Rosenthal et al., 1986; Saha et al., 2000).
Distinction from nonseasonal mood. According to Rosenthal and colleagues
(1984), the average age of onset of SMD is approximately twenty-seven years old. This is comparable to the onset of unipolar and bipolar mood disorders, which each typically onsets in the late twenties. Hence, the timing of SMD onset does not appear to distinguish the phenomenon from other mood disorders.
Long-Term Course
Adults. Over the long term, SMD does not have a particularly clear clinical course. In other words, while many consistently have a seasonal pattern of depression over multiple years, many do not. For instance, Sakamoto and colleagues (1995)
15 investigated the long-term stability of SMD among Japanese patients. They found that approximately 22% of SMD clients consistently exhibited a fall-winter pattern of
depression over a decade. On the other hand, approximately 41% of participants with
SMD shifted seasons or showed less seasonality in their depression over time. Of note,
this alteration in seasonality may be associated with antidepressant medications or
adverse life events. Sakamoto and colleagues suggest "a switch from depression to
mania or hypomania caused by acute antidepressant treatment, with or without lithium,
during the fall-winter period may contribute to the increased frequency of affective
episodes, resulting in the alteration of a seasonal pattern of recurrence to a nonseasonal
pattern" (1995, p.866). Furthermore, atypical vegetative symptoms were predictive of
maintaining stable patterns of SMD.
Following Sakamoto’s Japanese study, Schwartz and colleagues (1996) followed-
up American individuals diagnosed with SMD over an average of nine years. According
to the results, 42% continued to only have seasonal episodes, 44% had varying degrees of
nonseasonal episodes, and 14% had full remission. On the other hand, in a literary debate
with proponents of SMD (Grof, 2002a, 2002b; Michalak & Lam, 2002a) , Grof claims ‘I
have collected lifetime data on the clinical course of nearly 2000 patients with mood
disorders, and I have approached 2 colleagues who claim to specialize in [SMD], yet I
still have not come across a single patient with several recurrences limited mostly to a
particular season’ (Grof, 2002b). Therefore, Grof believes that SMD does not have a
long-term course that distinguishes it from other mood disorders.
Pooling the results from Schwartz and colleagues (1996) and Sakamoto and
colleagues (1995) as well as two other studies, Lam and colleagues (2001) claim that
16 approximately one-third of individuals with SMD maintain their seasonal patterns, one-
third develop a more nonseasonal pattern, and one-third have partial or complete
remission. A year later, Lam revises his estimates, indicating that studies following
individuals with SMD up to ten years show "about 30% of patients continued to have
seasonal episodes, about 20% were in remission (some because of treatment) and the
remaining 50% had complex patterns that were not strictly seasonal" (2002, para. 3).
Overall, this suggests that SMD maintains its seasonality at a rate higher than chance.
Children and adolescents. Six out of the seven initial cases of pediatric SMD
identified by Rosenthal and colleagues (1984) were followed seven years (Giedd, Swedo,
Lowe, & Rosenthal, 1998). According to their results, "all of the subjects continued to have persistent seasonal symptoms which remained relatively stable in severity, although lessened by light therapy. Compliance with light therapy was generally good, with minimal side effects, and out-of-season occurrences were rare." Therefore, children and adolescents with SMD are likely to continue having seasonal problems in mood and behavior as adults, especially when they lack proper treatment.
Distinction from nonseasonal mood. These investigations of SMD indicate that it has a long-term outcome similar to unipolar and bipolar disorders, involving several recurrent mood episodes. More importantly, approximately 30% of individuals diagnosed with SMD continue to have depressive episodes in the winter only. While the probability that their second episode occurs during the winter is 25% by chance, the probability that their second and third episodes both recur in winter is 6.4%, and so forth.
Therefore, the maintenance of a seasonal pattern of mood episodes in 30% of the individuals provides support for the distinction of SMD.
17 Moreover, the fact that 30-50% of clients diagnosed with SMD lose the seasonality of their mood episodes should not negate the diagnostic existence of SMD.
As Sakamoto and colleagues (1995) argue, pharmacological antidepressant treatment of
SMD may produce a switch from depression to hypomania or mania and, thereby, increase the frequency of affective episodes, causing nonseasonal patterns. Further, seasonal mood episodes experienced by individuals with SMD may produce long- standing changes in their brain chemistry and psychosocial environments (e.g., loss of friendships). These changes likely extend beyond their seasonal mood episodes and may logically induce the onset of nonseasonal mood disorders.
Etiological Mechanisms
Adults. There have been several theories or models proposed to explain the causation of SMD. Most models describe the potential role of environmental factors, such as sunlight, along with individual differences in susceptibility in the etiology of
SMD. These individual differences may be genetically heritable.
The photoperiod model of SMD speculates that it “is caused by abnormal body response to seasonal changes in day length [or hours of sunshine]" (Lee et al., 1998, p.277). However, treatment studies suggest that bright light therapy does not need to extend the length of the day to produce therapeutic effects (Lee, Blashko, Janzen,
Paterson, & Chan, 1997; Lee et al., 1998).
On the other hand, day length is inversely correlated with latitude in the winter; therefore, the photoperiod model would assume that SMD would be more prevalent at higher latitudes. In Rosenthal and colleagues' seminal study (1984), 83% of the
Americans who traveled during the winter reported improved mood after traveling south
18 and worsened mood after traveling north. However, latitude or photoperiod may not be
the primary influence; for example, there are socio-cultural (e.g., vacation resort) and
climatic variables (e.g., temperature) that may contribute to Rosenthal and colleagues' findings. Further, climatic variables rather than photoperiod may better explain the existence of summer-SMD in tropical areas.
In a review of prevalence studies across Europe and North America, Mersch and colleagues (1999) did not find a significant correlation between prevalence and latitude overall. However, when they analyzed data for the two continents separately, Mersch and colleagues found a significant correlation between prevalence of SMD and latitude in
North America and a borderline significant correlation in Europe. The authors conclude that if a relationship between SMD and latitude exists, “its impact on prevalence is smaller than (the combined effect of) a number of other factors of which climatological, social and cultural influences and genetic factors are most prominent” (Mersch et al.,
1999, p.46). Following Mersch and colleagues’ investigation focusing on Europe and
North American, Michalak and Lam (2002b) conducted their own analysis including other areas of the world. According to Michalak and Lam, their results show a global correlation between SMD and latitude. However, Michalak and Lam’s results appeared as a letter to the editor, and thus, their methodology has not been elucidated, and has not been subject to the level of critique possible for a peer-reviewed article. Altogether, investigations of light therapy and latitude provide mixed support for the photoperiod hypothesis.
Another model of SMD focuses on human biological or circadian rhythms.
According to this model, SMD is related to a phase-delay in circadian rhythms (Lam,
19 2002; Lee et al., 1997; Lee et al., 1998). In other words, the timing of one's circadian rhythm is the primary causative disturbance. Light reportedly synchronizes circadian rhythms; therefore, bright light exposure in the early morning, which would correct a phase-delay, should be more therapeutic than bright light exposure at other times of the day. Across studies, inconsistent findings have been obtained concerning the superiority of morning exposure (Lam, 2002; Lee et al., 1997; Lee et al., 1998); thus, the circadian rhythms or phase-shifting model is not conclusively supported.
Melatonin is a natural hormone secreted by the pineal gland nocturnally. Not only does it facilitate sleep, but it is also believed to help regulate circadian rhythms.
Bright light (>2500 lux) can suppress pineal melatonin secretion, but dim light cannot
(Lee et al., 1997; Lee et al., 1998). Across studies, inconsistent findings have been obtained concerning the superiority of bright light over dim light or dawn stimulation
(Lee et al., 1997; Lee et al., 1998); hence, the melatonin hypothesis is not conclusively supported either.
An additional model of SMD suggests the presence of neurochemical dysregulation and neurotransmitter abnormalities (Lam, 2002; Lee et al., 1998). In particular, low levels of serotonin may contribute to SMD. Supporting evidence includes the knowledge that serotonergic mechanisms are associated with the atypical vegetative symptoms common in SMD. In addition, experimental depletion of tryptophan, which is involved in serotonin synthesis, induces relapse of depressive symptoms in SMD patients
(Glod & Baisden, 1999; Lam, 2002). Lastly, selective serotonin reuptake inhibiters are effective medications in the treatment of SMD.
20 Young and colleagues (1991) proposed a dual vulnerability model of SMD, which
states that SMD arises when an individual has significant seasonal vegetative symptoms
(such as hypersomnia) combined with a vulnerability to other depressive symptoms (such
as low self-esteem). In other words, seasonality and depression are separate continuous
dimensions interacting with each other in an additive manner to form SMD. Lam and
colleagues (2002; 2001) elaborate farther on this model to show how varying levels of
seasonality and of depression contribute to different symptom expressions resulting in
four diagnostic categories: sub-syndromal SMD, SMD, seasonal episodes with
incomplete summer remission, and nonseasonal mood disorder. Data showing
significantly greater seasonality scores on the SPAQ for SMD participants, while
nonseasonally depressed patients do not differ significantly from normal controls in their
degree of seasonality (Hardin et al., 1991) supports the dual vulnerability model. This
shows that levels of seasonality are distinct from levels of depression. Further, it
suggests that similar degrees of seasonality between depressed, bereaved, and community
controls in the current investigation will be found.
Both susceptibility to depression and seasonality may be heritable traits.
Specifically, there are three types of data examining the genetic basis of SMD: familiality studies, heritability studies, and molecular genetic research (Sher, Goldman, Ozaki, &
Rosenthal, 1999). Regarding familiality, family studies show that SMD and subsyndromal SMD are more prevalent in individuals of non-Icelandic decent than
Icelandic decent (Axelsson, Stefansson, Magnusson, Sigvaldason, & Karlsson, 2002).
Furthermore, Sher and colleagues summarize the following from their review of the literature: ‘Between 13 and 17% of first-degree relatives appeared to be affected by
21 [SMD] and between 25 and 67% of the relatives had nonseasonal mood disorders….
This compares to population prevalences between 1.4 and 9.7% for [SMD] … and between 8 and 20% for non-seasonal mood disorders’ (Sher et al., 1999). Thus, family studies indicate strong familiality of mood disorders in general, not just SMD, amongst relatives of individuals with SMD. This, however, does not disprove the existence of
SMD any more than the increased rate of unipolar disorder found in relatives of probands with bipolar disorder negates the existence of bipolar disorder.
With regards to heritability, an Australian twin study (Madden, Heath, Rosenthal,
& Martin, 1996) is the only investigation conducted to date. Madden and colleagues found that genetic effects account for at least 29% of variance in seasonality amongst the general population. However, their study did not directly evaluate the heritability of
SMD itself.
Concerning molecular genetics, two genetic variants related to serotonergic transmission (5-HTTLPR and 5-HT2A-1488G/A) are associated with SMD and one (5-
HTTLPR) is also related to seasonality (Sher et al., 1999). More recently, Johansson and colleagues (2003) pooled all of the data from studies investigative 5-HTTLPR and found that it is not associated with SMD but with seasonality.
Children and adolescents. The research of Glod and colleagues (1997) focuses on the etiological model of SMD that suggests disruptions in human biological or circadian rhythms are associated with SMD. Glod and colleagues (1997) were interested in learning whether childhood SMD more closely resembles adult SMD or childhood nonseasonal depression in terms of circadian rest-activity. Previous research has shown that nonseasonally depressed children have blunted circadian rhythms (i.e., smaller
22 activity fluctuations during the day), while adults with SMD have delayed or poorly
entrained rhythms (i.e., activity rhythms deviating from the twenty-four-hour day).
According to Glod and colleagues’ results (1997), in comparison to normal controls,
children and adolescents with SMD had blunted circadian amplitudes; however, there
was no difference in circadian rhythm or level of entrainment. In other words, children
with SMD were less active than controls during the day, although timing of the activity
patterns were similar to controls. Additionally, "children with [SMD] had significantly
less robust circadian rhythms, which accounted for 30 percent of the variability in the
activity profile" (Glod, 1997). In conclusion, "these differences lend support to the
hypothesis that neurochemical disturbances associated with depression may be different
in children than adults" (Glod et al., 1997, p.193). Children and adolescents with SMD more closely resemble nonseasonally depressed children than adults with SMD in terms
of the severity and timing of their circadian rhythms.
Another popular etiological model of SMD in the adult literature is the
photoperiod model, which hypothesizes that SMD results from abnormal responses to
seasonal changes in day length or amount of sunshine. Since latitude is inversely
correlated with day length, the photoperiod model suggests that SMD should be more
prevalent at higher latitudes. Sourander and colleagues (1999) aimed to investigate
whether latitude has an effect on SMD in adolescents. They administered the pediatric
version of the SPAQ (Swedo et al., 1995) to seventh and ninth grade students in two
Finnish cities located in the 60th and 67th northern latitudes. According to their results,
Finnish adolescents have high rates of seasonal mood changes. In fact, Sourander and
colleagues suggest that seasonality almost seems normative at such latitudes. The
23 adolescents reportedly felt worst during the winter months, particularly January. Further, the researchers found that females have higher symptomatology in all areas except for school performance. They posit that this sex difference may be the result of "sex hormones in modulating the brain's vulnerability to changing light levels" (Sourander et al., 1999); although it may also just be indicative of the usual sex differences in mood disorders among adolescents. Most importantly, Sourander and colleagues did not find a significant difference in global seasonality for youths in the 60th and 67th northern latitudes. Therefore, their research with adolescents fails to support the notion that SMD is primarily caused by seasonal changes in day length.
Distinction from nonseasonal mood. At the current juncture, most models for the etiology of SMD have mixed evidence. In support of the photoperiod model, latitude may be associated with adult SMD. Conversely, phototherapy need not extend the length of daylight to produce therapeutic effects. Regarding the biological rhythms model, inconsistent results concerning the superiority of morning light exposure have been obtained. Concerning the melatonin hypothesis, several investigations have failed to show the superiority of bright light over dim light or dawn simulation. Meanwhile, there appears to be evidence supporting the independence of seasonality and depression, as described in the dual vulnerability model of SMD. Therefore, SMD probably arises from a combination of general etiological factors for mood disorders and specific etiological factors for seasonality.
While there is clearly a familial contribution to SMD, it is not yet known whether this contribution is distinct from the genetics of nonseasonal mood disorders. As Sher and colleagues state, “In order to answer this question, it would be necessary to perform
24 family studies [and/or heritability studies and/or molecular genetics research] on [SMD] patients and non-seasonal depressives, looking for rates of both seasonal and non- seasonal depressives among relatives. To date, such a study has not yet been performed”
(1999, p.208). For instance, there are not any studies indicating the prevalence of SMD amongst relatives of individuals with nonseasonal mood disorders nor studies indicating the association of 5-HTTLPR and 5-HT2A-1488G/A with nonseasonal mood disorders at present.
Treatment
Adults. Given the widely held belief that sunlight may contribute to the etiology of SMD, it should not be surprising that, from the very conception of SMD, researchers have attempted to treat it using light therapy. For instance, in their seminal study,
Rosenthal and colleagues (1984) administered light treatment to eleven SMD patients.
Patients were randomly assigned to 2,500 lux of bright, white full-spectrum fluorescent light or 100 lux of dim, yellow fluorescent light and were instructed to sit in front of these lights for three hours before dawn and three hours after dusk. After three to four days of treatment, significant improvement was discernible. However, sleep deprivation may have contributed to the antidepressant effect in Rosenthal and colleagues' study, given that participants awakened well before dawn.
Since Rosenthal and colleagues' hallmark study, a plethora of investigations have supported the efficacy of bright light treatment. These studies tend to show that response to light therapy occurs rapidly, often within one week (Lam, 2002; Lee et al., 1998).
Further, if patients stop treatment before the time of their natural spring remission, relapse tends to occur (Lam, 2002; Lee et al., 1998). For instance, Ghadirian and
25 colleagues (1998) found significant improvement in depressive symptoms amongst participants with SMD following two weeks of phototherapy that rapidly returned during a one-week washout period.
Nonetheless, critics note that most of these investigations of phototherapy are small, uncontrolled studies. Possible confounding variables that need better control include amount of sleep (or sleep deprivation due to early morning therapy), medications, psychotherapy, and other forms of therapy (Lee et al., 1998). Furthermore, bright light therapy has been widely publicized as a treatment for SMD, making it difficult to recruit naïve subjects. Despite these problems, it seems implausible that light therapy is really causing subjects to improve due to a placebo effect. First, studies tend to show "a time lag of approximately 2-4 days for both remission after initiating light treatment and relapse following light withdrawal" (Lee et al., 1998, p.280). Both Rosenthal and colleagues (1984) and Lee and colleagues (1998) argue, if light treatment were a placebo, one would expect a more rapid and variable response. Furthermore, "the effect of light therapy tended to be long-lasting" (Lee et al., 1998, p.280). However, Lee and colleagues indicate it should lessen with time if light treatment were merely a placebo. In addition, two recent randomized control studies with large sample sizes show the superiority of bright light treatment to a credible placebo control: negative ions (Eastman,
Young, Fogg, Liu, & Meaden, 1998; Terman, Terman, & Ross, 1998). Along with several supporting meta-analyses (Lam, 2002), these studies attest to the efficacy of light therapy above and beyond any possible placebo effect.
Since it seems clear that light therapy is effective at treating SMD, a logical step would be to determine the best specifications for light treatment. Terman, Terman, and
26 colleagues (1989) conducted a meta-analysis of twenty-nine studies in an attempt to
identify the most effective means of light therapy. According to their findings, bright
light is more effective than dim light, and morning light is more effective than evening
light. Further, Lam (2002) suggests that light therapy consisting of 10,000 lux administered for at least thirty minutes each morning should be considered the standard treatment for SMD since less intense lights will require longer duration of exposure.
Reportedly, following Lam's suggestion leads to a 67% clinical response rate. Lee and
colleagues (1998) indicate that it is still unknown what kind of light is best with regard to
its spectral properties. For instance, it is unknown whether full-spectrum light (i.e., all
wavelength bands) is more or less effective than other types of light, such as red light or
white fluorescent light.
While light therapy is often advisable for individuals with SMD, there may be
some negative consequences of treatment. Lam (2002) identifies several mild and time-
limited side effects of bright light therapy, including headaches, eyestrain, nausea,
agitation, sedation, and dizziness. Additional side effects of bright light therapy are
indicative of mania or hypomania, include hypomanic irritability and hyperactivity (Lam,
2002; Rosenthal et al., 1984). Therefore, if an individual has a history of mania or
hypomania, it may be advisable to administer mood stabilizers while treating SMD with
bright light therapy.
Given these possible side effects to bright light treatment, as well as the added hassle imposed by finding time to sit by the light, Terman, Schlager, and colleagues
(1989) developed an alternative light treatment called “dawn simulation.” Dawn simulation involves a low intensity light administered to individuals while they sleep
27 during the early morning. The light gradually increases in illuminance before the
individual awakens to simulate natural dawn. Avery and colleagues (2001) conducted a
recent study comparing dawn simulation (i.e., 1.5 hour dawn signal peaking at 250 lux) to bright light therapy (i.e., 10,000 lux for 30 minutes) and placebo (i.e., 1.5 hour red signal peaking at .5 lux). This study is superior to other controlled studies in that it contains a
larger sample size (N=95) and has a longer duration of treatment (six weeks).
According to Avery and colleagues' results, dawn simulation is associated with
greater remission and response rates than both placebo and bright light therapy.
However, the authors caution that the bright light treatment in their study was less
effective than shown in previous investigations. Therefore, they conclude that dawn
simulation is superior to placebo, and "probably as effective as bright light therapy” in
the treatment of SMD (Avery et al., 2001, p. 214). In fact, Avery and colleagues write,
"The response rate of 84% and the remission rate of 61% seen in the dawn simulation
group in this study are similar to those in previous studies of bright light therapy” (2001,
p.214). Therefore, dawn simulation appears to be an effective alternative to bright light therapy in the treatment of SMD.
Another logical alternative to bright light therapy may be to administer
antidepressant medications during the winter months. While Lam (2002) indicates that
research studying the efficacy of antidepressant medication among individuals with SMD
is limited, he indicates that selective serotonin reuptake inhibitors have the best-
demonstrated efficacy. Further, Lam reports that only one investigation has directly
compared light treatment to antidepressant medication. According to Lam (2002),
Rurhman and colleagues found no significant differences between bright light therapy
28 and fluoxetine (Prozac). However, these null results may also be attributable to the study's small sample size. Therefore, it is not yet clear whether medications work as well as (or even better than) bright light therapy with SMD patients. Further, it may be possible that the combination of the two is the most effective means of treatment.
In summary, both bright light therapy and antidepressant medications appear to be effective treatments for SMD. While treatment type may be selected on an individual basis according to preferences, "light therapy remains for some clinicians a potentially less expensive and less invasive form of treatment" (Michalak, Wilkinson, Dowrick, &
Wilkinson, 2001, p.33). Hence, bright light treatment has been widely popularized.
Children and adolescents. Like adults, children and adolescents diagnosed with
SMD respond well to light therapy. For instance, in Rosenthal and colleagues' initial case study (1986), they followed seven children with SMD over two years, treating their winter symptoms with light when the symptoms were severe enough to interfere with functioning. The researchers found that light therapy for two to four hours a day improved mood, reversed other symptoms of SMD, and increased psychosocial functioning among children and adolescents with SMD. However, these changes may have also occurred due to regression toward the mean, given that they only treated the children when their symptoms were severe. Rosenthal and colleagues also noted that "a few of the children appeared to do better on the dim yellow light (300 lux) than on the bright full-spectrum light (2,500 lux), which some seemed to experiences as overactivating and disruptive of sleep" (1984, p.358). It appears logical that dim light may work well with children, who have more transparent corneas than adults (Glod et al.,
29 1997). However, Rosenthal and colleagues’ (1986) investigation involves too few participants to conclude whether or not dim light is more effective or not.
In the case of the youngest documented child with SMD, Saha and colleagues
(2000) provided two weeks of light therapy (10,000 lux) for several hours twice a day to the four-year-old and his "behaviour improved within the next three to four days. His appearance, appetite and play were back to normal. His speech was clearer, he was more animated, and the 'pale-look' went away" (Saha et al., 2000, p.136). When the light therapy ended after two weeks, Sam relapsed within three days. Similar results to treatment were again noted the following winter when Sam was five years old.
In another case study, Rosenthal (1995) found that administration of bright light
(2,500 lux) for 30-45 minutes in the morning improved the level of energy, concentration, and ability to fall asleep and wake up at conventional times for a nine-year-old girl with
SMD and attention-deficit hyperactivity disorder. Similarly, Meesters (1998) investigated then effectiveness of treating a nine-year-old boy with dawn simulation
(100-300 lux). Reportedly, the boy had been responsive to bright light treatment (10,000 lux) during previous winters; however, he refused to use the light box again. Therefore,
Meesters had the child wake up to dawn simulation therapy starting in October, which reportedly was very effective. However, twice during the winter the boy stopped therapy for a few days and experienced relapses each time. In conclusion, Meesters notes that dawn simulation was an effective treatment; but, perhaps, not as powerful an intervention as bright light therapy. On the other hand, dawn simulation requires less effort on the part of the patient than bright light therapy.
30 Mghir and Vincent (1991) performed an additional case study of the effects of phototherapy on an adolescent female who had a three-year history of depressive episodes starting in October and remitting in May. Mghir and Vincent found that the adolescent responded very well to one hour of bright light treatment every morning.
Further, when treatment was discontinued before spring, the adolescent suffered a full relapse. Reinstating treatment again produced full recovery. Thus, Mghir and Vincent's study supports the use of bright light therapy with adolescents who have SMD.
Swedo and colleagues (1997) conducted a much larger double-blind crossover study of phototherapy of children and adolescents with SMD. In their study, conducted at two geographically distinct sites, Swedo and colleagues initially required participants to wear dark glasses for an hour a day for one week (i.e. baseline). Then, they randomly assigned participants to one hour of bright light therapy plus two hours of dawn simulation or one hour of clear goggles plus five minutes dawn simulation (i.e. the placebo). After one week of treatment, participants wore dark glasses again for approximately one to two weeks (i.e. the washout period). Then, the participants received the alternate treatment. According to Swedo and colleagues’ results, participants receiving light therapy had a significantly greater decrease in depression scores from baseline than participants receiving placebo did. In addition, approximately
80% of the parents and the youths indicated that the children "felt best" when they were receiving light therapy. Overall, these case studies and experiments show that phototherapy is an effective treatment for SMD in children and adolescents.
Distinction from nonseasonal mood. Many believe that phototherapy has specificity in treating SMD. Evidence to support this theory includes the finding that the
31 atypical vegetative symptoms common in SMD are predictive of good response to light therapy. For instance, Terman and colleagues (1996) found that the ratio of atypical vegetative symptoms to traditional symptoms of depression best predict treatment outcome for individuals with SMD. Responders to phototherapy are characterized by hypersomnia, afternoon or evening slumps, and carbohydrate craving; whereas, nonresponders are characterized by insomnia, morning slumps, and appetite loss among other melancholic symptoms.
Further evidence to support the specificity of phototherapy in treating SMD comes from two investigations comparing individuals with SMD to other individuals who exhibit winter depression but incomplete summer remission (Lam et al., 2001; Lingjaerde
& Foreland, 1999). While Lingjaerde and Foreland found that the two groups share similar winter symptomatology, Lam and colleagues found that participants with SMD were more likely to endorse carbohydrate craving and participants with incomplete summer remission were more likely to endorse panic attacks and past suicide attempts.
However, both groups of researchers found that participants with incomplete summer remission responded less well to light therapy. It is unclear, however, whether individuals with incomplete summer remissions are less responsive to phototherapy than persons with SMD because of the difference in seasonality between the two groups or, perhaps, the difference in severity (e.g., suicidality) and chronicity of the two conditions.
While studies suggest that light therapy is more effective at treating SMD than winter depression with incomplete summer remissions, other data suggest that it may be just as effective at treating other nonseasonal mood disorders. Kripke (1998) reviewed the research on phototherapy for nonseasonal major depression and found that light
32 treatment produces decreases of 12-35% in depressive symptoms, often within one week.
This response to phototherapy is comparable to that of individuals with SMD (Kripke,
1998). Further, it is comparable to nonseasonal depressives’ response to antidepressant
medications (Kripke, 1998). Kripke advocates using the treatments in combination for
nonseasonal depression, given light therapy’s speed of efficacy and pharmacotherapy’s
delayed but long-term efficacy.
Since Kripke’s review, investigators have continued to study the effects of
phototherapy for nonseasonal depression, particularly amongst pregnant and postpartum
women. Light therapy provides an especially valuable alternative for these women
because of the possible deleterious effects of antidepressant medications on their
offspring, in the womb and while breast-feeding. One investigation of phototherapy for
pregnant women found significant improvements in depression ratings within three
weeks, which were maintained through a five-week follow-up (Oren et al., 2002). A case report involving two women with postpartum depression similarly shows significant improvements within four weeks (Corral, Kuan, & Kostaras, 2000).
On the other hand, a direct comparison of the effects of phototherapy on children
with SMD and others with nonseasonal depression, which was not included in Kripke’s
review, indicates that phototherapy is more effective at treating the SMD. Sonis and
colleagues (1987) conducted a small double-blind crossover study using relaxation
treatment as an alternative to phototherapy. While cognitive-behavioral therapy is
probably more efficacious at treating childhood depression (Wood, Harrington, & Moore,
1996), relaxation therapy is effective at treating children and adolescents with non-
seasonal depression (Kahn, Kehle, Jenson, & Clarke, 1990; Reynolds & Coats, 1986).
33 Participants in Sonis and colleagues’ investigation included nine youth with nonseasonal depression and five youth with SMD. Some participants were randomly assigned to bright light treatment (2,500 lux) for two hours each day; while other participants were randomly assigned to listen to a fifteen-minute imagery relaxation tape and then read or did homework for an hour and forty-five minutes each day. After six days of treatment, the participants had two-day washout period before they switched to the alternative treatment. According to the results, children and adolescents with SMD responded significantly better to the light treatment than children and adolescents with nonseasonal depression. In other words, light therapy produced major improvements in neuro- vegetative symptoms such as sleep, appetite, speech, fatigue, and energy for youth with
SMD. On the other hand, children and adolescents with nonseasonal depression responded significantly better to relaxation therapy than children and adolescents with
SMD. In other words, relaxation therapy produced major improvements in cognitive symptoms such as guilt, suicidal ideation, and self-esteem for youths with nonseasonal depression.
Overall, phototherapy appears to be an effective treatment for nonseasonal mood disorders. In fact, some investigators suggest that it works just as well for nonseasonal mood disorders as SMD. This would imply phototherapy works on properties common to both SMD and non-seasonal mood disorder, such as regulating sleep, or that there is little meaningful difference between the conditions. However, the only study directly comparing phototherapy’s efficacy between seasonal and nonseasonal participants (Sonis et al., 1987) indicates that it is significantly better at improving the symptoms of children
34 with SMD. These results suggest that light has some universal antidepressant properties,
as well as specific properties for the reduction of SMD.
Summary
SMD is an important subtype or “course specifier” for mood disorders.
Similarities between SMD and other mood disorders in terms of epidemiology, age of
onset, genetic heritability, and response to treatments indicate that it ought to be
classified amongst these other conditions. However, its distinctive symptom profile,
stronger prevalence differential amongst females versus males, and recurrent seasonal nature sets it apart from other mood disorders, suggesting a dual vulnerability to depression and seasonality.
SMD is found among children and adolescents, as well as adults. In the United
States, the prevalence of SMD is estimated to be around 3-4% for youth and 4-6% for adults. For individuals with SMD of all ages, seasonally recurrent depressive episodes tend to begin in late October and remit by late March. Hence, the current investigation splits the year into two seasons: winter (October through March) and summer (April through September).
Amongst adults, SMD may be distinguished from nonseasonal depression by its symptom profile. In other words, individuals with SMD have increased rates of atypical vegetative symptoms, but not the other symptoms of “atypical” depression. On the other hand, it is unclear whether these same characteristics are prevalent in pediatric SMD.
Therefore, atypical vegetative, cognitive, and affective symptoms of depression are analyzed separately in the present investigation.
35 The Present Study
To further the literature investigating SMD in children and adolescents, a study
examining seasonality in mood and behavior among children and adolescents using
longitudinal data collected from 1987 to 1998 during an investigation of childhood
bereavement was performed. The participants of the investigation include youths with
depression, bereaved youths, and community controls. Data from the investigation have
been used to: examine depressive symptoms in bereaved children (Weller, Weller,
Fristad, & Bowes, 1990); investigate the relation between bereavement rituals and childhood adjustment (Fristad, Cerel, Goldman, Weller, & Weller, 2001); and compare suicide-bereaved children and adolescents to other bereaved youth (Cerel, Fristad,
Weller, & Weller, 1999; Cerel, Fristad, Weller, & Weller, 2000).
In light of the epidemiological literature showing that sub-clinical seasonal variation in mood and behavior is common (Carskadon & Acebo, 1993; Lee et al., 1998;
Sourander et al., 1999), my primary hypothesis is that seasonal changes in mood and behavior exist among all three participant groups. Within these groups, individual participants will vary significantly from each other on how much seasonality they experience.
While seasonality will be shown on overall measures of depression, it may not be evident in all depressive symptom clusters. In other words, I expect to find significant seasonal changes in atypical vegetative symptoms (such as hypersomnia and hyperphagia), but not necessarily in cognitive or affective symptoms. Seasonal changes should be observable in atypical vegetative symptoms because these are distinctive characteristics of SMD (Allen et al., 1993; Tam et al., 1997). On the other hand, seasonal
36 changes should not be found in cognitive symptoms relating to negative attributional style and automatic thoughts, for example, because individuals with SMD and nonseasonal depression both display the same level of these types of cognitive symptoms
(Dalgleish et al., 2004; Hodges & Marks, 1998; Levitan et al., 1998). Therefore, state- related depressive cognitions, such as feelings of worthlessness or guilt, probably do not vary with season.
In summary, the current project investigates the following hypotheses: 1) seasonality in depressive symptoms exists amongst depressed, bereaved, and community control youths; 2) within all three sub-samples, individual youths vary from each other in their levels of seasonality; and 3) seasonality will be associated with atypical vegetative symptoms as opposed to cognitive and affective symptoms.
37
METHOD
Participants
The sample of the present study consists of 609 children (ages 6-18) and their
parents who took part in a longitudinal investigation of childhood bereavement from
1987 to 1998. Participants are divided into three groups. The first group consists of 369
children who experienced the death of a parent within two months of study entry. These
participants were recruited from the community by reading obituaries and death
announcements. The second group consists of 111 children with depressive diagnoses
(55% major depressive disorder, 23% dysthymic disorder, 20% major depressive and dysthmic disorder, and 2% bipolar disorder – most recent episode depressed) and no history of parental death. These participants were recruited from an outpatient child and adolescent psychiatry clinic. The third group consists of 129 community control children who have no history ever of parental death and no mental health treatment in the past two years. These participants were recruited from the community through schools and churches. All parents provided consent and children provided assent to participate in the study.
Materials
The present study utilizes several assessment instruments from the full interview battery of the grief study. Firstly, the Diagnostic Interview for Depression in Children and Adolescents (DIDCA) is a structured interview designed to assess depressive
38 symptoms in children and adolescents (Weller & Weller, 1979b). The DIDCA has been
shown to have excellent sensitivity and specificity (Fristad, Weller, & Weller, 1995).
The Diagnostic Interview for Children and Adolescents-Revised (DICA-R) is a structured
interview designed to assess DSM-III-R symptoms of twenty disorders in children and adolescents (Reich & Welner, 1988). The DICA-R has been shown to have good test- retest reliability, agreement with chart diagnoses, and parent-child agreement (Welner,
Reich, Herjanic, & Jung, 1987). Using the DICA-R structured interview data, Cerel and
Fristad (2001) created a Behavior, Anxiety, Mood, and Other (BAMO) scale to measure overall psychopathology rather than individual disorders. BAMO was designed to treat all major disorders equally (except cigarette smoking, enuresis, encopresis, and phobias, which are given half-weight). To do this, the number of symptoms endorsed for each disorder is divided by the total number of symptoms for that disorder. These disorder scores are summed to create a BAMO score, ranging from 0 (i.e., no symptoms endorsements) to 18 (i.e., every symptom endorsed for all twenty disorders). BAMO has been shown to have excellent convergent and discriminant validity (Cerel & Fristad,
2001).
In addition to the structured interview data, a clinical rating scale was used for the present study. The Children's Depression Rating Scale-Revised (CDRS-R) is a 21-item measure that assesses current severity of depressive symptomatology in children and adolescents (Poznaski et al., 1984). Scores range from 17 to 113, with higher scores indicating more severe depressive symptomatology. The CDRS-R has been shown to have good inter-rater and test-retest reliability (Poznaski et al., 1984).
39 Lastly, child and parent versions of the Children's Depression Inventory (CDI) was used in the present study (Kovacs, 1981, 1985, 1992; Weller & Weller, 1979a). The
CDI is a 27-item self-report inventory assessing depressive symptoms in children and adolescents. Both versions have been shown to have good reliability and validity
(Fristad, Weller, Weller, Teare, & Preskorn, 1991; Kovacs, 1992).
Procedure
Children and their parents completed individual assessments at baseline and five, thirteen, and twenty-five months later. These assessments were conducted as part of a longitudinal childhood bereavement study between 1987 and 1998. Interviews were preformed by highly trained staff, graduate students, and undergraduate students who were required to achieve inter-rater reliability ratings greater than .90 before interviewing independently. Interviews each lasted approximately one to four hours.
40
RESULTS
Data Analysis
A dataset containing dates of interviews, demographic information, and responses to the aforementioned assessment measures was created. Given research showing that
SMD episodes typically last from October through March (Rosenthal et al., 1986;
Rosenthal et al., 1984; Swedo et al., 1997), interviews conducted during that six-month time period were coded as "winter;" while interviews conducted during the other six months of the year were coded as "summer." This method of defining seasons was selected as a logical scheme based on a lack of precedence among previous studies.
Data were analyzed using multilevel analysis (MLA), which allows one to consider repeated assessments as nested within individuals (Hox, 2002; MacCallum,
Kim, Malarkey, & Kiecolt-Glaser, 1997; Snijders & Bosker, 2002). Therefore, multilevel modeling allows the investigator to partition variance both over time and over participants. It resembles a repeated measures approach to analysis based on regression equations. Benefits of MLA over repeated measures analysis of variance are its allowance for: (1) longitudinal assessments that occur at varying intervals (i.e., it is permissible for some participants to be reassessed four months after baseline, while others are reassessed six months after baseline); (2) missing data or participant dropout by assuming that the available data are representative of an individual’s deviation from
41 the average trend line and using that information to estimate the individual’s trend across
time (Hedeker & Gibbons, 1997); (3) analyzing multiple independent variables, such as
assessment time and group membership, at the same time; (4) non-linear functions; (5)
and, most importantly, utilizing predictors that change over time, such as season (i.e.
"winter" versus "summer"). Therefore, I am permitted to investigate whether scores for
each of the aforementioned measures vary according to season for bereaved, depressed,
and community control participants. Given the current sample size and number of
observations, MLA allows for a powerful test of the hypotheses.
Multilevel analyses were conducted using MlwiN software (Rasbash et al., 2000).
For each dependent variable, I began with the following full model:
yij ~ N(XB, O) yij = ß0ij + ß1j (time ij) + ß2j (season ij) + ß3 (bereaved j) + ß4 (community j) +
ß5 (time*season ij) + ß6 (time*bereaved ij) + ß7 (time*community ij) +
ß8 (season*bereaved ij) + ß9 (season*community ij) + ß10 (time^2 ij) +
ß11 (time^2*bereaved ij) + ß12 (time^2*community ij) +
ß13 (time^2*season ij) + ß14 (time^3 ij) + ß15 (time^3*bereaved ij) +
ß16 (time^3*community ij) + ß17 (time^3*season ij)
ß0ij = ß0 + u0j + e0ij
ß1j = ß1 + u1j
ß2j = ß2 + u2j
42
This full model assumes that each participant’s score on the outcome variable is
dependent on assessment time (baseline through the twenty-five-month follow-up), season (summer vs. winter), group status (depressed, bereaved, or community), and the interactions between these variables. The subscript “i” indicates whether each variable varies over the first or lower level: assessment occasion. The subscript “j” indicates whether each variable varies over the second or higher level: study participant. For instance, the explanatory variable “season” has both subscripts because it varies across each assessment occasion and each participant. Meanwhile, the explanatory variable
“bereaved” only has the “j” subscript because participants vary on whether or not they are members of the bereaved group, but their bereavement status does not change over the assessment occasions. Additionally, the regression coefficients for “time since baseline” and “season” have the subscript “j” because I believe the effects of assessment occasion and season will vary across participants. In other words, I allowed the rate of change between assessments and seasons to differ between participants. Seasonality was allowed to differ across participants in accord with the dual vulnerability theory of SMD (Lam,
2002; Lam et al., 2001; Young et al., 1991), which assumes that people vary in their level of seasonality independent of their propensity toward depression.
The reader will also note that quadratic and cubic functions of "time since baseline” are included in the full model equation. This was done because the season at each interview occasion should follow either of these two patterns for the typical participant: summer-winter-summer-summer (S-W-S-S) or winter-summer-winter-winter
(W-S-W-W), and I expected the outcome variables to be higher for winter assessments. 43 This assessment strategy results in approximately a cubic pattern across occasions or time
(see Figure 1). By introducing quadratic and cubic functions of "time since baseline” into the model, I may account for nonlinear changes over assessment occasions and still allow the model to be linear with respect to its parameters (Hox, 2002; MacCallum et al.,
1997). Not all participants followed the anticipated S-W-S-S or W-S-W-W patterns, but the model was able to include them since time was measured in actual months since baseline and season was coded separately by actual date of assessment. Lastly, group status was dummy coded because it is a categorical explanatory variable. Descriptions of the meanings of each parameter included in the MLA results are included in Table 1.
Iterative generalized least squares (IGLS) was used to estimate all parameters of the full model. The iterative process stopped when convergence was achieved. In other words, all the parameters changed by less than the tolerance I set, 10-2, between iterations. Next, I used the Wald Test (Hox, 2002) and its chi-square approximate [?2(
Estimate2/SE, 1)] to evaluate the significance of the regression coefficients as well as the variance and covariance components for the model. If the covariance components were insignificant, I took them out of the model and ran the IGLS again. Next, I used the deviance statistic, –2*log(likelihood), to compare the alternative models. For nested models, the difference between deviance statistics follows a chi-square distribution, with the degrees of freedom being the difference between the number of parameters (Hox,
2002); therefore, I used the chi-square test to see whether the more elaborate model fit the data better than the more parsimonious model. As long as the p-value from the chi- square difference tests was non-significant, I kept the more parsimonious model.
44 I repeated this process of calculating the significance of the regression
coefficients and variance components, removing insignificant parameters from the model,
and checking the difference in the fit between the two models until I reached a final model that best fit the data for each outcome variable. Note that I removed higher-order
parameters before lower-order ones. Thus, the order in which insignificant parameters
were removed from the regression equation was: covariances, then variances, then time-
cubed interactions, then time-cubed, then time-squared interactions, then time-squared,
then primary level interactions, and finally main effects. Removal of insignificant
parameters was conducted in this way because higher-order parameters depend upon
lower-order parameters. As such, main effects could not be dropped from the model if
their interaction effects were significant. For example, even if the regression coefficients
for "time" and "season" were nonsignificant, those main effects needed to be kept in the
equation when "time^3*season" was significant. Note that the main effect of season and
its variance were never removed, even when they were insignificant, because the purpose
of the study is to determine whether seasonality exists and whether people vary on their
level of seasonality, not to best predict the outcome variable.
Following completion of the MLA, I used the regression equations from the final
models to calculate the average predicted values for the outcome variables at different
assessment times and seasons for the three groups. The resulting plots aid in interpreting
the effects of these explanatory variables on the outcome variables.
Participant Demographics
Demographic data were similar across the three participant groups: depressed,
bereaved, and community. Therefore, overall descriptives are herein reported. The
45 majority (94.2%) of the children and adolescents in this sample are Caucasian. About
half of the youths are male (50.91%). Their ages at baseline ranged from six to eighteen
years old, varying along a normal distribution (M = 11.30, SD = 3.19).
The youths tend to come from small to medium families, with a mean of 2.76
siblings (SD = 1.16, range 0-8). The majority of participants are the second or third child
in their families (78.1%). Their families' socioeconomic status ranged from upper (I) to lower class (V) on the Hollingshead Index (Hollingshead & Redlich, 1958), varying along a normal distribution. On average, participants were middle class (M = 2.75, SD =
1.16). Parent informants are primarily female (78.2%). Their ages at baseline ranged from twenty-three to fifty-eight years old, varying along a normal distribution. The majority of which were between the ages of thirty and forty-five (M = 39.89, SD = 5.92).
Assessment Descriptives
Out of 2,436 possible interviews, the children attended 1,922 (78.9%) and parents attended 1,992 (81.8%). For each of the bereaved participants, baseline assessments were conducted anywhere from half of a month to five months after the death of one of their parents (M = 2.22, SD = .90). For any participant, the second interview was conducted anywhere from two to ten months after baseline (M = 5.35, SD 1.39). The third assessment was performed between nine and eighteen months after baseline (M = 12.50,
SD = 1.52). And the fourth interview was conducted twenty-one to thirty-two months after baseline (M = 24.67, SD = 1.77). All four assessment occasions were evenly divided between winter and summer months. Overall, 51.5% of the interviews were conducted in the summer half of the year.
46 Assessment Plots
Before conducting MLA, the data were visually inspected. Originally, I planned to plot outcome variables, such as CDRSR total score, for each group across the months separately for each year. However, only 1993, 1995, and 1996 had enough participants in each group to possibly represent each month with a few informants (Table 2). In fact,
month by year cross-tabulations suggest that only 1995 has sufficient data. Therefore, instead of plotting each year separately and using insufficient data for all but one plot, all years were plotted together. Likewise, it was determined unnecessary to collect meteorological data for specific months and years.
Plans were also made to plot the baseline, five-, thirteen-, and twenty-five-month assessments separately. However, there were too few participants to sufficiently represent each month when plotting the occasions separately. For instance, only one community control child had a baseline assessment in each November and December.
On the other hand, there were sufficient numbers of participants representing each month when plotting all assessment occasions together. Occasions combined, the fewest number representing any month were nineteen depressed children in the month of
December and nineteen community parents in the month of May (Table 3).
Graphing total scores on each of the measures, DIDCA, CDRSR, CDI, and
BAMO, in this manner did not show a discernable pattern of mood and behavior over the seasons. The only plot that appeared to possibly have a pattern for any group was the
CDI plot based on child report (Figure 2). In this case, the depressed children appear to
have endorsed higher levels of depressive symptoms in the winter and right around the
47 start of the school year than at other times of year. On the other hand, bereaved and
community controls do not show monthly patterns in their CDI scores.
Diagnostic Interview for Depression in Children and Adolescents
The DIDCA assesses nine symptoms of depression: dysphoria, anhedonia,
appetite changes, sleep changes, fatigue, psychomotor changes, low self-esteem or guilt,
concentration problems, and suicidal ideation; accordingly, total scores on the DIDCA range from zero to nine. Children in the current study reported a mean total DIDCA score of 1.82 (SD = 2.27, range 0-9), while parents in the study reported a mean of 1.59 (SD =
2.15, range 0-9). The distributions of total DIDCA scores based on each the child and the
parent report were skewed toward zero because many of the children and parents in the
bereaved and community control groups reported no symptoms. Therefore, a natural logarithm transformation was performed on the child and parent DIDCA scores to bring their distributions closer to normal before conducting MLA.
Results from the MLA model for the child data (Table 4, Figure 3) show
significant group effects. Depressed participants received the highest DIDCA scores,
followed by bereaved participants (p < .01), followed by community controls (p < .01).
Additionally, there are significant effects for assessment time, time-squared, and time-
cubed (p < .01 for each). For both the depressed and bereaved participants, DIDCA
scores decrease over time. However, the significant time by community and time-
squared by community interactions (p < .01 for each) suggest a dissimilar pattern for the
community controls. As Figure 3 shows, community DIDCA scores remain relatively
stable over time. Most importantly, the model shows a significant effect of season on
DIDCA scores (p < .01). In other words, across all time periods and groups, children
48 scored significant higher on the DIDCA during the winter than the summer. While season effects were statistically significant, DIDCA scores differed by less than a point
between seasons. Lastly, the season-time covariance is significant (p = .03), suggesting
that participants whose DIDCA scores change the most over time also experience the
highest levels of seasonality.
The model for the parent data (Table 5, Figure 4) also shows significant group effects. At baseline, depressed participants received the highest DIDCA scores based on
parent report, followed by bereaved participants (p < .01), followed by community
controls (p < .01). However, the group pattern changes over time. As the significant
time, time-squared, and time-cubed effects (p < .01 for each) suggest, both the depressed
and bereaved participants' total DIDCA scores decrease over time. However, as the
significant time by community and time-squared by community effects (p < .01 for each)
suggest, community controls' parent DIDCA scores do not decrease over time. In fact, by
the thirteen-month follow-up, bereaved and community control participants receive
similar DIDCA scores based on parent report. Finally, the parent total DIDCA model
shows a non-significant effect for season (p = .08). While summer and winter DIDCA
scores do not differ significantly at each assessment time, Figure 4 shows a consistent
trend toward higher DIDCA scores during the winter.
Children’s Depression Rating Scale-Revised
Using 5- and 7-point Likert scales, the CDRSR assesses the severity of seventeen
depressive symptoms: impaired schoolwork, capacity to have fun, social withdrawal,
sleep changes, appetite changes, fatigue, physical complaints, irritability, guilt, low self-
esteem, dysphoric feelings, morbid ideation, suicidal ideation, crying, dysphoric affect,
49 tempo of speech, and psychomotor retardation. Meaningful total scores on the CDRSR range from seventeen to 113. When item(s) are unable to be rated, scores lower than seventeen are possible. Since scores for the impaired schoolwork item were systematically missing (i.e., the value was zero) during the summer, zeroes on that item were recoded as equal to the mean of the other seven-point items to avoid an artificial season effect. Additionally, total scores below sixteen were removed from the analysis to reduce the impact of missing data. Children in the current investigation reported a mean score of 24.25 (SD = 10.87, range 16-90), while parents reported a mean score of 27.86
(SD = 13.69, range 16-91). The distribution of CDRSR total scores for both the children and parents were skewed toward seventeen because many participants in the bereaved and community groups reported no symptoms. Thus, a natural logarithm transformation was performed on the child and parent CDRSR total scores to bring their distributions closer to normal before conducting MLA.
The final MLA model for the child data (Table 6, Figure 5) shows significant group effects: depressed participants received the highest CDRSR scores, followed by bereaved participants (p < .01), followed by community controls (p < .01). However, the groups become more similar over time. As the significant time, time-squared, and time- cubed effects (p < .01, p < .01, and p = .05, respectively) indicate, the depressed group’s
CDRSR scores decrease over time, with diminishing rate. On the other hand, the significant time by bereaved and time-squared by bereaved interactions (p < .01 for each) and Figure 5 show that CDRSR scores decrease at a lower rate for the bereaved group than the depressed group. The significant time by community and time-squared by community interactions (p < .01 for each) also suggest a different pattern for the
50 community group. As Figure 5 illustrates, CDRSR scores remain relatively stable for the
community group. In addition, the model includes a significant effect for season (p <
.01). Across all time periods and groups, children report higher CDRSR scores during
the winter than the summer. Despite reaching statistical significance, CDRSR scores for
the winter and summer differed only by a couple of points. Further, the season-time
covariance is significant (p < .01), which indicates that subjects whose CDRSR scores
change the most over time also experience the highest levels of seasonal change.
The MLA model for the parent CDRSR data (Table 7, Figure 6) shows very
similar results. There are significant group effects: parents of depressed participants
report the highest CDRSR scores, followed by bereaved participants (p < .01), and then
followed by community controls (p < .01). Moreover, the effects of time differ between groups. As the significant time and time-squared effects (p < .01 for each) suggest,
CDRSR scores decrease over time for the depressed group, reaching a plateau after thirteen months. The significant time by bereaved and time-squared by bereaved interactions (p < .01 and p = .01, respectively) suggest a different rate of change for the bereaved group. As Figure 6 shows, CDRSR scores decrease less over time for the bereaved group than the depressed group, but similarly reach a plateau around thirteen months. Additionally, the significant time by community and time-squared by community effects (p < .01 for each) and Figure 6 show that community controls’ parent
CDRSR scores hardly change over time. Furthermore, the model shows a significant
effect of season (p < .01). For each group, winter CDRSR scores are higher than summer scores at all time periods. While this effect was statistically significant, summer and winter CDRSR scores based on parent report differed only by a couple points. In
51 addition, the season variance is significant (p = .01), indicating that participants differed
significantly on how much seasons affected their CDRSR scores from parental report.
Children’s Depression Inventory
The CDI gathers responses to five categories of symptoms for depression:
negative mood, interpersonal problems, ineffectiveness, anhedonia, and negative self-
esteem. Total scores range from zero to fifty-four. Children in the current investigation reported a mean CDI total score of 4.72 (SD = 6.19, range 0-48), while parents reported a mean total score of 5.86 (SD = 7.63, range 0-42). The distributions of CDI total scores for each the child and parent data were skewed toward zero because many of the participants in the bereaved and community control groups reported no depressive symptoms. As such, a natural logarithm transformation was performed on the child and parent CDI total scores to bring their distributions closer to normal prior to performing
MLA.
Results from the MLA of the child data (Table 8, Figure 7) show that significant group effects: depressed participants received the highest CDI total scores, followed by bereaved participants (p < .01), followed by community controls (p < .01). The effects of
time differ between groups. As the significant time and time-squared effects (p < .01 for
each) suggest, CDI total scores decrease over time for the depressed group, reaching a
plateau around thirteen months. The significant time-cubed by bereaved interaction (p =
.01) suggests a different rate of change for the bereaved group. As Figure 7 shows, CDI
total scores based on child report decrease less over time for the bereaved group than the
depressed group, but similarly reach a plateau around thirteen months. Additionally, the
significant time by community and time-cubed by community effects (p < .01 for each)
52 suggest yet another pattern for the community controls. According to Figure 7,
community controls’ self-reported depressive symptoms appear to decrease even less
over time than the bereaved group. Finally, the child MLA model shows a significant
effect for season (p < .01). For all three groups, children reported higher CDI total scores
during the winter than the summer at all assessment points. While the main effect of
season was significant, CDI total scores differed by a point or less between seasons.
The parent MLA model (Table 9, Figure 8) again shows significant group effects
whereby depressed participants receive the highest CDI total scores, followed by bereaved participants (p < .01), and then by community controls (p < .01). Additionally,
there is a significant effect of time that varies by group. For the depressed group, the
significant time, time-squared, and time-cubed effects (p < .01, p = .02, and p = .05,
respectively) produce lower CDI scores over subsequent assessment periods (Figure 8).
On the other hand, the significant time by bereaved and time by community interactions
(p < .01 each) show that these groups’ CDI scores based on parent data decease less over
time than the depressed group’s scores. In fact, Figure 8 suggests that parent report on
the CDI remains relatively stable over time for the bereaved participants and community
controls. Finally, while there is not a significant main effect for season, there are
significant time by season, time-squared by season, and time-cubed by season interaction
effects (p = .01, p < .01, and p < .01, respectively). Figure 8 suggests that winter and
summer parent CDI total scores do not differ at baseline, thirteen-month, and twenty-
five-month assessments. On the other hand, winter scores are significantly higher than
summer scores at the five-month assessment for all groups.
53 Behavior, Anxiety, Mood, and Other
The BAMO summarizes overall psychopathology among youths. The scale encompasses six externalizing disorders (attention-deficit/hyperactivity, oppositional defiant disorder, conduct disorder, cigarette smoking, drug abuse, and alcoholism), six anxiety disorders (overanxious disorder, separation anxiety, avoidant disorder, phobia, obsessive-compulsive disorder, and posttraumatic stress disorder), two mood disorders
(depression and mania), and six other disorders (anorexia, bulimia, enuresis, encopresis, psychosis, and somatization disorder). Total scores may range from zero to eighteen.
Children in the present study’s responses to the DICA-R earned a mean BAMO score of
.48 (SD = .72, range 0-7.09), while their parents responses earned a mean BAMO score of .57 (SD = .80, range 0-5.82). As expected, the distributions of BAMO scores for each children and parents were skewed toward zero because the overall sample was relatively free of psychopathology. Hence, a square-root transformation was performed on the
BAMO scores to draw their distributions closer to normal before performing MLA.
The MLA model for BAMO scores based on child report show significant group effects (Table 10, Figure 9). As with the other scales, depressed participants received the highest BAMO scores, followed by bereaved participants (p < .01), and then community controls (p < .01). Furthermore, significant effects for time, time-squared, and time- cubed exist (p < .01, p < .01, and p = .01, respectively). As Figure 9 illustrates, these effects mean that BAMO scores decrease over time for both the depressed and bereaved participants. Alternatively, the significant time by community and time-squared by community interactions (p < .01 for each) suggest a different pattern for the community group. According to Figure 9, child BAMO score remain relatively stable over time for
54 the community controls. Last of all, the BAMO model based on child report shows a
non-significant effect for season (p = .09). This trend suggests that BAMO scores may
be very mildly elevated in the winter as opposed to the summer.
MLA for the parent data (Table 11, Figure 10) produces similar results. Again,
depressed participants received the highest BAMO scores, followed by the bereaved
participants (p < .01), then closely followed by the community controls (p < .01). As for
changes across time, there are significant effects for time, time-squared, and time-cubed
(p < .01 for each), which produce lower scores on the BAMO at a decreasing rate of
change over time for the depressed group. On the other hand, the significant time by
bereaved interaction (p < .01) suggests a slightly different pattern for the bereaved
participants. As Figure 10 shows, their parents report lower BAMO scores at the five-
month follow-up than initially; afterward, their BAMO scores appear to steady. More
noticeably, parents of community controls appear to report relatively stable BAMO score over all assessment points. This alternative pattern may be explained by the significant
time by community and time-squared by community interactions (p < .01 each). Finally,
the effect for season is nonsignificant (p = .14). In other words, while it may appear that
winter BAMO scores based on parent report are ever so slightly higher than summer scores, there essentially is no difference between summer and winter.
Cognitive Symptoms
While the DIDCA assesses only nine types of symptoms, within these nine
categories several questions are asked in order to assess each. Specifically, eight sub-
items inquire about cognitive symptoms of depression, such as excessive guilt, low self-
esteem, and hopelessness. Hence, a cognitive symptom scale ranging from zero to eight
55 can be created from these items. On this cognitive scale, children in the current
investigation reported a mean of .43 (SD = 1.24, range 0-8), while their parents reported a
mean of .60 (SD = 1.45, range 0-8). There is a strong skew in each of the distributions of
cognitive symptoms toward zero because many children and parents reported no cognitive symptoms. As a result, a natural logarithm transformation was used to bring the distributions closer to normal before conducting MLA.
Significant effects between the groups on cognitive symptoms are shown in the final child MLA model (Table 12, Figure 11). As with previous scales, depressed
participants received the highest scores, followed by bereaved participants (p < .01) and community controls (p < .01). Both time and time-squared also have significant effects on the cognitive symptoms children reported. These effects explain the sharp decrease in cognitive symptoms endorsed by depressed participants over the first thirteen months,
followed by a small increase by twenty-five months (Figure 11). The bereaved group
follows a slightly different pattern whereby their cognitive symptoms mildly decrease
over the first thirteen months and then remain steadily low. This alternative pattern is represented in the equation by the significant time by bereaved and time-squared by bereaved interactions (p < .01 for each). The significant time by community and time- squared by community interactions (p < .01 for each) suggest yet another pattern for the community controls. As Figure 11 shows, the community controls report an extremely low and stable number of cognitive symptoms of depression over time. Most importantly, season does not have a significant effect (p = .14) on cognitive symptoms.
On the other hand, a significant season-time covariance exists (p < .01). This covariance
56 suggests that participants whose cognitive symptoms change most drastically over time also have the most seasonality in their cognitive symptoms.
Similarly, parent report provides evidence of significant group effects on cognitive symptoms (Table 13, Figure 12). Yet again, parents of depressed participants report the most cognitive symptoms for their children, followed by parents of bereaved participants (p < .01), and then parents of community controls (p < .01). Substantial changes in cognitive symptoms over time for the depressed group are depicted by the significant time, time-squared, and time-cubed effects (p < .01, p < .01, and p = .05, respectively). Figure 12 shows that parents of depressed youths report dropping numbers of cognitive symptoms over the first thirteen months. The significant time by bereaved and time-squared by bereaved interactions (p < .01 for each) suggest an alternative pattern for the bereaved group. Additionally, the significant time by community and time-squared by community interactions (p < .01 for each) suggest that the community controls also differ in their pattern over time from the depressed group. As Figure 12 shows, the bereaved and community control groups share a very similar pattern of mild decrease in cognitive symptoms over time, having both started with very few cognitive symptoms at baseline. Importantly, the MLA model for parent reported cognitive symptoms does not show a significant seasonal effect (p = .85). Further, season by time interactions dropped out of the model due to their respective insignificances. On the other hand, the significant season variance (p=.03) suggests that participants vary significantly in how much their score changes across seasons.
57 Affective Symptoms
Within the DIDCA several questions are asked in order to assess dysphoria.
Specifically, nine sub-items inquire about the affective symptoms of depression,
including unhappiness, discouragement, nervousness, and irritability. These sub-items
may be used to create an affective scale, on which children in this study reported a mean
score of 1.38 (SD = 2.35, range 0-9); their parents also reported a mean score of 1.38 (SD
= 2.40, range 0-9). Since most participants were not feeling low or down at each of the
assessments, the distributions for child and parent reported affective symptoms were
skewed toward zero. Consequently, a natural logarithm transformation was used to bring
each distribution closer to normal before conducting MLA.
The MLA model for affective symptoms based on child report illustrates
significant differences in the amount of affective distress experienced between the groups
(Table 14, Figure 13). As in previous cases, children with depression received the
highest levels of affective symptoms, followed by the bereaved participants (p < .01), and
then by the community controls (p < .01). Both the depressed and the bereaved groups
had similar decreases in affective symptoms over time (Figure 13). These trends across
the assessment periods are represented in the model by the significant time and time-
squared effects (p < .01 for each). On the other hand, the community controls follow
their own course over time, as indicated by the significant time by community and time-
squared by community interactions (p < .01 and p = .02, respectively). As Figure 13 shows, the community controls report relatively stable and extremely low affective symptoms of depression at all assessment occasions. Interestingly, there is a non- significant seasonal effect on affective symptoms (p = .08). This suggests that, although
58 levels of affective symptoms are likely to be the same in summer and winter, participants
may report slightly higher levels of affective symptoms during the winter.
Data from parent report produced a very similar MLA model (Table 15, Figure
14). Again, the depressed group received the highest levels of affective symptoms,
followed by the bereaved participants (p < .01) and subsequently the community controls
(p < .01). The parents of youths with depressive syndromes report significant decreases in their children’s affective symptoms over time as demonstrated by the significant time, time-squared, and time-cubed effects (p < .01 for each). On the other hand, the significant time by bereaved and time-squared by bereaved interaction effects (p < .01 for each) represent the less significant drop in affective symptoms experienced by this group.
Further, the significant time by community and time-squared by community interactions
(p < .01 and p = .02, respectively) illustrate yet another pattern over time for the community controls. While both the depressed and bereaved groups have reduced affective symptoms over time, community controls appear to have relatively stable and extremely low levels of affective symptoms. Finally, the MLA model for parent reported affective symptoms does not show a significant seasonal effect (p = .15).
Atypical Vegetative Symptoms
While only hyperphagia and hypersomnia are atypical vegetative symptoms, excessive fatigue and psychomotor retardation as reported on either the DIDCA or the
DICA-R were also included in the atypical vegetative symptom scale, giving it a range of zero to four and making MLA feasible. On this scale, children in this study reported a mean score of .42 (SD = .85, range 0-4, a = .64), while their parents reported a mean score of .35 (SD = .78, range 0-4, a = .65). Since few participants experienced atypical
59 vegetative symptoms at any occasion, the distributions for child and parent reported symptoms were both skewed toward zero. Consequently, a natural logarithm transformation was used to bring both distributions closer to normal before conducting the MLA on the atypical vegetative symptom data.
MLA on the child reported atypical vegetative symptom scale produces several significant results (Table 16, Figure 15). Firstly, the three groups vary greatly in the levels of atypical vegetative symptoms they endorse: the children with depressive syndromes received the highest levels, followed by the bereaved youths (p < .01), and then by the community controls (p < .01). Next, the pattern of change over time also varies between all three groups. The significant time, time-squared, and time-cubed effects (p < .01, p < .01, and p = .02, respectively) describe the depressed group’s diminishing decrease in atypical vegetative symptoms, which plateaus around thirteen months. The significant time by bereaved and time by bereaved-squared interactions indicate that the bereaved group’s pattern differs slightly from the depressed group’s pattern. According to Figure 15, levels of atypical symptoms decrease at a more gradual, steadier rate for the bereaved participants. Furthermore, the significant time by community and time-squared by community interactions (p < .01 for each) suggest yet another pattern. As Figure 15 illustrates, the community controls report relatively stable and extremely low atypical vegetative symptoms across assessments. Most importantly, there is a significant seasonal effect (p < .01), whereby study participants endorse higher levels of atypical vegetative symptoms during the winter than the summer at all assessment points. Visually, this seasonal effect has never been as prominent in the community control group as shown in Figure 15. Lastly, there is significant seasonal
60 variance (p < .01) in the model, which means that individual youths vary greatly from
each other in how much season affects their scores on the atypical vegetative symptom scale.
In agreement, the MLA model for atypical vegetative symptoms based on parent
report reveals significant group effects (Table 17, Figure 16). According to their parents,
depressed participants have the most atypical vegetative symptoms, followed by the
bereaved participants (p < .01), and thirdly the community controls (p < .01). Like the
depressed youths themselves, their parents report significant diminishing decreases in
atypical vegetative symptoms over the first thirteen months that gradually increase over
the following year. This pattern over time is represented in the model by the significant
time, time-squared, and time-cubed effects (p < .01 for each). There is a similar but more
gradual change for the bereaved group. The difference between the depressed and
bereaved groups’ patterns of change are depicted by the significant time by bereaved,
time-squared by bereaved, and time-cubed by bereaved interactions (p < .01, p < .01, and
p = .01, respectively). On the other hand, parents of community control subjects report
relatively stable and extremely low levels of atypical vegetative symptoms across the
assessment occasions. This alternative pattern is represented in the model by the
significant time by community, time-squared by community, and time-cubed by
community interactions (p < .01 for each). While the model for atypical vegetative
symptoms based on child report produces a very impressive seasonal effect, the parent
model unfortunately does not (p = .24). Despite the insignificance of the difference
between summer and winter scores on atypical vegetative scores based on parent report,
Figure 16 does show a possible trend toward winter scores being higher.
61
DISCUSSION
Interpretation of Findings
The main purpose of the current investigation was to test whether one could
identify seasonality of mood and behavior among a diverse group of participants using
standard measures of depressions administered on multiple occasions. It was
hypothesized that youths with depressions, bereaved youths, and community controls
would all have higher mean total depressive scores during the winter than during the
summer. According to the results of this study, children reported significantly higher
total scores on the DIDCA, CDRSR, and CDI in the winter than the summer. Further,
their parents reported significantly higher total scores on the CDRSR during the winter at
all assessment points. Alternatively, parents reported similar levels of depressive
symptoms on the CDI during the summer and winter at baseline, thirteen-month, and twenty-five-month assessments; however, at five-months, total CDI scores were
significantly higher during the winter than during the summer. Despite inconsistencies in
the parent data, the results support the hypothesis that seasonal changes in depressive mood and behavior exists within the sample overall.
While season has a significant effect on the DIDCA, CDRSR, and CDI, at least
via child report, changes in actual point values of the mean scores are minimal. For instance, there is a significant seasonal main effect on the CDRSR according to parent
62 report; however, the predicted CDRSR total score at baseline is 50.78 during the winter
and 48.68 during the summer. The difference between these scores is trivial. This
suggests that season may play a very small role in the mood and behavior of the vast majority of participants in the study.
On the other hand, a small minority within each participant group may be highly seasonal; pulling the summer and winter averages apart ever so slightly. This influential
minority is assumed to incorporate outliers representing individuals with SMD in the
depressed group and other highly seasonally individuals in the bereaved and community
control groups. However, if these outliers exist in the sample, then significant season
variances and covariances ought to have been found since these measures of spread are
‘even more sensitive to a few extreme observations than is the mean’ (Moore &
McCabe, 1993). While there is a significant season variance for the CDRSR as reported
by parents, there are not significant season variances for the other depressive measures.
On the other hand, the children’s data produce significant season by time covariances for
the CDRSR and DIDCA, but the parent data do not. Altogether, it is unclear whether a
few highly seasonal participants are influencing the group means or whether each group
as a whole experiences mild seasonal changes.
The BAMO, on the other hand, is a scale encompassing overall psychopathology
in children and adolescents. Therefore, its score includes the presence of symptoms of
disorders, such as generalized anxiety disorder, that are not theorized to vary with season.
Neither the report of children nor parents showed significant effects of season on the
BAMO. This validates the overall results by showing that the analyses discriminate
between the three depressive measures and a measure of overall psychopathology. Note,
63 however, that the BAMO does have an insignificant trend toward higher scores during the winter than during the summer presumably because mood spectrum disorders are one component of the scale.
None of the MLA models include any significant season by group interactions. In other words, depressed, bereaved, and community control youths show the same average amounts of seasonality. This result is not surprising given the dual vulnerability model of
SMD, which posits that seasonality is entirely independent of propensity toward depression. Therefore, even though bereaved and community control participants have lower average depressive symptom scores than depressed participants do, their scores may vary from season to season just as much. In other words, while some members of the depressed group may actually have SMD, members of the other groups may have equivalent seasonal changes in their mood or behavior without having as severe depressive symptomology.
Although no significant season by group interactions exist, the main of effects of group are significant for all measures. In simpler terms, there is a clear and consistent pattern across all analyses whereby the depressed group received the highest scores, the bereaved group received intermediary scores, and the community controls received the lowest scores. This pattern conforms to expectation. After all, one should expect elevated levels of distress amongst youths who have been bereaved of a parent, but most should not develop a true depressive episode as a result of the stressor.
The main effect of time is also significant for all measures, generally indicating that the participants’ scores on each measure decreases across the assessment occasions.
Initially, I had not expected youths in the depressed group to exhibit such a pattern. On
64 reflection, however, the depressed participants were recruited from an outpatient child
psychiatric clinic, which means that, at the time of recruitment, these individuals had
severe enough levels of depression that their guardians were actively seeking treatment.
These participants were likely to have received the pharmaceutical and psychological services they required from the outpatient clinic, resulting in lower depressive scores over time. Regardless of treatment, depressive episodes last seven to nine months on average
(Fristad et al., 2002). Hence, it is not surprising that the depressed group got better over the two-year period.
Since the bereaved group’s elevated levels of depressive mood and behavior at baseline are likely part of their grief response to parental death, I had expected them to recover more quickly than the depressed group. According to the results, there are significant time by bereaved interactions for many of the measures. These interactions
indicate that the pattern over time for the bereaved group is not the same as for the depressed group. Contrary to expectation, their depressive scores tend to decrease at a lower rate than the depressed group’s depressive scores. Of course, looking at the graphs, this lower rate makes sense. Initial scores for the bereaved group are not as high as for the depressed group meaning that they have less room to drop due to a floor effect.
There are significant time by community interactions for every measure, signifying that the pattern over time for the community group is never the same as for the depressed group. In fact, the community group’s scores hardly change over time. But, of course, there is no reason to presume that they would change over time. After all, these are youths have no history of parental death ever or mental health treatment in the past two years. As the data show, they are healthy with regards to mood and behavior and
65 they remain healthy. Basically, these youths score as low as possible on each of the
measures. Perhaps, this also explains why the seasonal effect is not as graphically visible
for the community group. While the community group is just as seasonal as the
depressed group as a whole, their scores on the measures are extremely low even in
winter. Due to a floor effect, they cannot score lower on the scales during the summer
because the measures are not calibrated to distinguish between healthy states. Thus,
seasonal changes are not quite as visible for the community group.
Another principal hypothesis of the current investigation was that seasonal changes would be most evident in the depressive characteristics uniquely associated with
SMD; namely, atypical vegetative symptoms. Information provided by the children supports this hypothesis. In other words, the children report significantly higher levels of hypersomnia, hyperphagia, fatigue, and psychomotor retardation in the winter than in the summer. Further, they do not report significant seasonal differences in their levels of cognitive and affective symptoms. Therefore, the child data suggests that elevated total scores on measures of depression during the winter may be due to seasonal changes in atypical vegetative symptoms, rather than changes in cognitive or affective symptoms.
On the other hand, parents do not report significant seasonal differences in cognitive,
affective, or atypical vegetative symptoms. Thus, it is unclear which constellation(s) of
depressive symptoms vary according to season.
A very interesting cross-informant pattern arises from the data. Revisiting all of
the analyses based on the child data, one is pleased to see that all hypotheses are
supported. In other words, according to child report, there are seasonal changes in each
of the depressive scales, as well as atypical vegetative symptoms; but not in the measure
66 of overall psychopathology, as well as affective and cognitive symptoms. On the other
hand, the results based on the parent data are not so convincing. In other words, while
most expectations are supported, three are not: first, the seasonal effect on the DIDCA is
non-significant; second, a seasonal effect on the CDI is only discovered at the five-month follow-up, and there does not appear to be a significant seasonal effect for atypical vegetative symptoms.
These minor differences in child and parent report are to be expected. After all, the average correlation between parent and child report on emotional and behavioral problems is .25 (Achenbach, McConaughy, & Howell, 1987). Furthermore, Achenbach and colleagues (1987) found that this modest rate of agreement exists for both clinical and nonclinical samples. Hence, it is not surprising that the results from the children and parents are not identical for any of the groups.
This difference, however, underscores the importance of including children in the assessment of their own mood and behavior. After all, ‘the child is privy to his or her own views, judgments, thoughts, and feelings across a variety of situations’ (Tarullo,
Richardson, Radke-Yarrow, & Martinez, 1995). For the present investigation, it appears as though the children are skilled at reporting how they currently are feeling and behaving. Meanwhile, their parents may have difficulty distinguishing recent past mood and behavior from current mood and behavior; thereby, blending the seasons.
In addition, the inconsistency between the results from the MLA and simply plotting mean scores over the months must be addressed. As argued above, the results from the MLA support the notion that seasonal changes in mood and behavior exist amongst depressed, bereaved, and community control youths. Unfortunately, simple
67 graphs plotting each measure across the months do not show this seasonal pattern.
However, one must remember that the preliminary descriptive graphs collapse all four assessment occasions together. Therefore, assessment occasion and/or time since baseline is not accounted for in the initial assessment plots. MLA, on the other hand, provides a stronger test of seasonality by also allowing the investigator to model the effects of other important predictor variables, such as time since baseline, and their interactions at the same time as he or she looks at seasonal effects.
Finally, an epidemiological study of SMD (Swedo et al., 1995) suggests that
SMD is more common in adolescence than childhood, particularly amongst females.
However, the current investigation gathers a broad age range of children and adolescents into the same analyses. Follow-up analyses were conducted by splitting the sample into two groups: under age twelve and equal to or above age twelve. The groups were not further split by sex because the resulting sample sizes would have been too small. As such, the sample in the children-only analyses had a mean age of 8.71 years (range 6-11).
They included 51 individuals with depression, 179 bereaved participants, and 87 controls.
The sample in the adolescents-only analyses had a mean age of 14.13 years (range 12-18) at onset. They included 60 participants with depression, 188 bereaved individuals, and
42 community controls.
Follow-up analyses were initiated using the atypical vegetative symptom scale,
DIDCA, and CDRSR. Across these three scales, neither parent- nor youth-report produced a clear child versus adolescent pattern. More specifically, only two of the six sets of analyses appeared to conform to the notion that adolescents are more seasonal
68 than children. In fact, one set of analyses suggested the exact opposite. Altogether, these
follow-up analyses were terminated due to inconsistency.
Limitations
The current project utilizes archival data from a previous longitudinal study,
which provides limitations in its sample, measures, and design. Given that the original
investigation focused on childhood bereavement, the majority of participants were
bereaved youth. However, the current investigation’s focus on seasonality in mood and
behavior would suggest that the majority of participants should have been depressed
youths. In fact, the bereaved group was the least important with regards to the purposes
of this study. Nonetheless, there were more than enough depressed and community
control participants to observe seasonal effects in these groups as well as the bereaved
group. Another problem with the sample selection may be its generalizability to other
depressed, bereaved, and community control youths. Participants in the longitudinal
study represent a homogenous group with regard to ethnicity or race. In other words, the
Midwestern sample predominantly includes Caucasian-Americans. While the results may not be generalizable to the population at large, central Ohio is an ideal location to conduct a site study of seasonality, however. Columbus, OH is located at 40.1 degrees latitude and ranks 26 out of 174 U.S. cities (i.e. bottom 15th percentile) for mean
percentage of possible sunshine annually (National Oceanic and Atmospheric
Administration, 2004).
Assessment of seasonality is limited by the measures included in the original study. Firstly, the original investigation was conducted under the DSM-IIIR diagnostic system. Since diagnostic criteria for depression did not change between the DSM-IIIR
69 and DSM-IV, the only measure in the current investigation affected by noncontemporary
diagnostic criteria is the BAMO. Further, measures directly assessing seasonality, such
the childhood version of the Seasonal Pattern Assessment Questionnaire (Swedo et al.,
1995), were not included. Similarly, information necessary for the diagnosis of SMD,
such as a two-year history of depressive episodes only occurring at a particular time of year, had not been gathered. Therefore, the current investigation cannot identify children and adolescents with SMD; however, it can and does provide results suggesting that depressed, bereaved, and control participants have seasonal changes in mood and behavior captured by repeated administration of typical measures of depression.
With regards to the study’s design, the original investigators were not thinking about the effects of season when they enrolled their participants and scheduled their follow-up interviews. Thus, they did not ensure that each of the months was equally represented by members of each participant group at each time period, which limited my ability to visually inspect the data. Further, they did not ensure that multiple assessments representing each season were taken from each participant. In fact, as the study was designed, only the five-month follow-up interview was in a different season from the other assessments. A better design, for example, would be to assess each participant every three months regarding the last three months for a two year period. That way, the data for each participant would include two summer measurements, two winter measurements, and four measurements in the changing of the seasons. The proposed design would also provide the information necessary to determine whether there is a two- year history of depressive episodes only occurring during a certain season.
70 The study’s original design largely limits the statistical methods one can use to
conduct subsequent analyses. Given the nature of the data, MLA was the only way to
statistically examine my hypotheses. For instance, the data involves longitudinal
assessments taken at varying occasions with much missing data. MLA is able to account for such complications. However, MLA does exact the same price as many other statistical methods: it assumes that each dependent variable follows a normal distribution
(Hox, 2002). As none of the dependent variables were normally distributed, I had to use
transformations, such as the natural logarithm and square root, to adjust the spread of the
data. The resulting scales for the DIDCA, CDRSR, CDI, and BAMO were fairly normal.
The resulting scales for atypical symptoms, cognitive symptoms, and affective symptoms
were much closer to normal but still skewed. As such, the results from these latter scales
must be interpreted with extreme caution. Further, the actual values of the coefficients in
all of the MLA tables are not directly interpretable. What are interpretable are their significances, and the predicted outcome values they produce when converted back into the original scales for the creation of the figures.
Summary
The current study contributes to the existing literature by further exploring whether seasonal changes in mood and behavior are prevalent among children and adolescents. It serves as a precursor to studies of SMD in children and adolescents given a dearth of large-scale studies investigating SMD in this population. Analyses were
conducted on archival longitudinal data to investigate whether seasonality exists amongst
depressed, bereaved, and community control youths. The findings indicate that all three
groups show more depressive mood and behavior during the winter than during the
71 summer. However, the actual degree of change is so small that many may not be aware
of it. Interestingly, the effects of season appear to be similar for all three groups. In
other words, depressed, bereaved, and community control participants’ mood and
behavior change by approximately the same amount between summer and winter on
average. It is unclear, however, whether or not each group has a few highly seasonal
individuals amongst them.
Similarly, the findings do not provide a clear answer to what changes in mood
and/or behavior result in the overall decrease in depressive symptoms over the summer.
The child data support the hypothesis that seasonal changes are associated with atypical vegetative symptoms, such as hypersomnia, and not cognitive or affective symptoms.
Meanwhile, the parent data do not attribute the seasonal change to a specific symptom cluster, suggesting that minor changes in each cluster results in a statistically significant
change in overall depressive symptoms.
Future Directions
The current investigation provides useful information for understanding the
prevalence of seasonality in mood-related symptoms among children and adolescents
with depressive syndromes as well as other populations. Given its limitations, however,
interpretation should be made cautiously and follow-up studies conducted. In a future
study not utilizing archival data, I would suggest including questions or instruments that
may determine whether diagnostic criteria are met for SMD, thereby allowing the
investigator to obtain true epidemiological data and to compare children and adolescents
with SMD to nonseasonal controls. Additionally, measures fleshing out clusters of depressive symptoms, including affective, cognitive, typical vegetative, and atypical
72 vegetative symptoms may be utilized to further determine which symptoms vary seasonally. It is important that follow-up studies determine whether SMD among children and adolescents is taxometrically distinct. In other words, these studies need to show whether children with SMD have clear distinguishing features worthy of classifying them separately from youths with non-seasonal mood disorders.
73
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81
APPENDIX A
TABLES
82 Parameter Meaning of Estimate
Fixed part Average baseline score for the depressed group during the Intercept (ß0) summer
Time (ß1) Average change in score per month since baseline
Season (ß2) Average change in score from summer to winter
Average change in score for the bereaved group versus the Bereaved (ß3) depressed group Average change in score for the community group versus the
83 Community (ß4) depressed group
Average change in score due to an interaction between months Time x Season (ß5) since baseline and season Average change in score due to an interaction between months Time x Bereaved (ß6) since baseline and bereaved group membership Average change in score due to an interaction between months Time x Community (ß7) since baseline and community group membership Average change in score due to an interaction between season Season x Bereaved (ß8) and bereaved group membership Table 1 (Continues)
Table 1. Descriptions of the parameters estimated by the MLA model
Table 1 (Continued)
Parameter Meaning of Estimate Average change in score due to an interaction between season Season x Community (ß9) and community group membership
^2 Time (ß10) Average change in score per months since baseline squared
^2 Average change in score due to an interaction between time- Time x Bereaved (ß11) squared and bereaved group membership
^2 Average change in score due to an interaction between time- Time x Community (ß12) squared and community group membership
^2 Average change in score due to an interaction between time-
84 Time x Season (ß13) squared and season
^3 Time (ß14) Average change in score per months since baseline cubed
^3 Average change in score due to an interaction between time- Time x Bereaved (ß15) cubed and bereaved group membership
^3 Average change in score due to an interaction between time- Time x Community (ß16) cubed and community group membership
^3 Average change in score due to an interaction between time- Time x Season (ß17) cubed and season Table (1 Continues)
Table 1 (Continued)
Parameter Meaning of Estimate
Random part
2 Intercept/Between Participants Variance (s u0) Differences between individuals at baseline
2 Differences between individuals on how much months since Time Variance (s u1) baseline affects their scores
2 Differences between individuals on how much season affects Season Variance (s u2) their scores
85 2 Differences between individuals on how much changes over Intercept-Time Covariance (s u10)
time depends on baseline scores
2 Differences between individuals on how much changes over Season-Time Covariance (s u21) time relate to seasonality
2 Differences between scores taken at multiple occasions within Error/Within Participants Variance (s e0) individuals due to random effects
Deviance statistic representing how well the model fits the -2*loglikelihood (IGLS) data
Group Depressed Bereaved Community Year Child Parent Child Parent Child Parent 1987 0 0 74 78 0 0 1988 0 0 89 93 0 0 1989 2 2 106 108 15 15 1990 20 20 137 139 62 64 1991 24 24 129 128 75 78 1992 27 27 126 126 61 65 1993 45 50 126 126 64 63 1994 40 43 149 157 26 26 1995 72 83 114 127 93 89 1996 58 69 49 54 54 53 1997 28 30 9 9 41 40 1998 5 4 0 0 2 2
Table 2. Participants interviewed by year
Group Depressed Bereaved Community Month Child Parent Child Parent Child Parent January 28 36 110 119 26 26 February 26 26 94 92 31 35 March 29 33 110 112 49 43 April 33 38 113 109 65 67 May 31 33 85 98 25 19 June 30 33 90 89 35 40 July 26 25 70 72 52 52 August 21 23 97 97 72 73 September 20 23 82 84 43 44 October 27 29 92 95 37 36 November 31 31 91 99 34 36 December 19 22 74 79 24 24
Table 3. Participants interviewed by month
86
Child DIDCA Total Parameter Estimate SE p Fixed part
Intercept (ß0) 1.65288 0.05978 0.00000
Time (ß1) -0.11408 0.01314 0.00000
Season (ß2) 0.07524 0.02504 0.00266
Bereaved (ß3) -0.47952 0.06717 0.00000
Community (ß4) -1.28255 0.07843 0.00000
Time x Season (ß5)
Time x Bereaved (ß6) 0.00691 0.01179 0.55782
Time x Community (ß7) 0.06332 0.01294 0.00000
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00582 0.00106 0.00000 ^2 Time x Bereaved (ß11) -0.00034 0.00045 0.44992 ^2 Time x Community (ß12) -0.00166 0.00049 0.00070 ^2 Time x Season (ß13) ^3 Time (ß14) -0.00009 0.00003 0.00270 ^3 Time x Bereaved (ß15) ^3 Time x Community (ß16) ^3 Time x Season (ß17)
Random part 2 Intercept/Between Participants Variance (s u0) 0.15736 0.01912 0.00000 2 Time Variance (s u1) 0.00009 0.00006 0.13361 2 Season Variance (s u2) 0.00550 0.01724 0.74971 2 Intercept-Time Covariance (s u10) -0.00178 0.00089 0.04550 2 Season-Time Covariance (s u21) 0.00125 0.00057 0.02831 2 Error/Within Participants Variance (s e0) 0.21553 0.01150 0.00000
-2*loglikelihood (IGLS) 3047.23600
Table 4. Multilevel regression results for child DIDCA total scores
87 Parent DIDCATotal Parameter Estimate SE p Fixed part
Intercept (ß0) 1.86320 0.05165 0.00000
Time (ß1) -0.11911 0.01175 0.00000
Season (ß2) 0.04049 0.02338 0.08331
Bereaved (ß3) -0.91590 0.05831 0.00000
Community (ß4) -1.54384 0.06860 0.00000
Time x Season (ß5)
Time x Bereaved (ß6) 0.01853 0.01027 0.07119
Time x Community (ß7) 0.08446 0.01138 0.00000
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00547 0.00096 0.00000 ^2 Time x Bereaved (ß11) -0.00029 0.00039 0.45712 ^2 Time x Community (ß12) -0.00218 0.00049 0.00001 ^2 Time x Season (ß13) ^3 Time (ß14) -0.00008 0.00002 0.00006 ^3 Time x Bereaved (ß15) ^3 Time x Community (ß16) ^3 Time x Season (ß17)
Random part 2 Intercept/Between Participants Variance (s u0) 0.10945 0.01147 0.00000 2 Time Variance (s u1) 0.00008 0.00004 0.04550 2 Season Variance (s u2) 0.02046 0.01409 0.14648 2 Intercept-Time Covariance (s u10) 2 Season-Time Covariance (s u21) 2 Error/Within Participants Variance (s e0) 0.18403 0.00866 0.00000
-2*loglikelihood (IGLS) 2907.31400
Table 5. Multilevel regression results for parent DIDCA total scores
88 Child CDRSR Total Parameter Estimate SE p Fixed part
Intercept (ß0) 3.70203 0.02774 0.00000
Time (ß1) -0.06000 0.00552 0.00000
Season (ß2) 0.04235 0.01068 0.00007
Bereaved (ß3) -0.50545 0.03132 0.00000
Community (ß4) -0.78819 0.03658 0.00000
Time x Season (ß5)
Time x Bereaved (ß6) 0.03169 0.00490 0.00000
Time x Community (ß7) 0.05102 0.00538 0.00000
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00232 0.00045 0.00000 ^2 Time x Bereaved (ß11) -0.00100 0.00019 0.00000 ^2 Time x Community (ß12) -0.00148 0.00020 0.00000 ^2 Time x Season (ß13) ^3 Time (ß14) -0.00002 0.00001 0.04550 ^3 Time x Bereaved (ß15) ^3 Time x Community (ß16) ^3 Time x Season (ß17)
Random part 2 Intercept/Between Participants Variance (s u0) 0.04381 0.00421 0.00000 2 Time Variance (s u1) 0.00006 0.00001 0.00000 2 Season Variance (s u2) 0.00296 0.00330 0.36974 2 Intercept-Time Covariance (s u10) -0.00108 0.00020 0.00000 2 Season-Time Covariance (s u21) 0.00033 0.00011 0.00270 2 Error/Within Participants Variance (s e0) 0.03488 0.00194 0.00000
-2*loglikelihood (IGLS) 22.26048
Table 6. Multilevel regression results for child CDRSR total scores
89 Parent CDRSR Total Parameter Estimate SE p Fixed part
Intercept (ß0) 3.88526 0.03344 0.00000
Time (ß1) -0.05215 0.00488 0.00000
Season (ß2) 0.04221 0.01617 0.00905
Bereaved (ß3) -0.54606 0.04081 0.00000
Community (ß4) -0.92738 0.04917 0.00000
Time x Season (ß5)
Time x Bereaved (ß6) 0.02031 0.00628 0.00123
Time x Community (ß7) 0.04804 0.00700 0.00000
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00142 0.00018 0.00000 ^2 Time x Bereaved (ß11) -0.00061 0.00024 0.01018 ^2 Time x Community (ß12) -0.00132 0.00026 0.00000 ^2 Time x Season (ß13) ^3 Time (ß14) ^3 Time x Bereaved (ß15) ^3 Time x Community (ß16) ^3 Time x Season (ß17)
Random part 2 Intercept/Between Participants Variance (s u0) 0.04847 0.00672 0.00000 2 Time Variance (s u1) 0.00009 0.00002 0.00004 2 Season Variance (s u2) 0.01310 0.00537 0.01479 2 Intercept-Time Covariance (s u10) -0.00107 0.00031 0.00064 2 Season-Time Covariance (s u21) 2 Error/Within Participants Variance (s e0) 0.03615 0.00287 0.00000
-2*loglikelihood (IGLS) 183.75090
Table 7. Multilevel regression results for parent CDRSR total scores
90 Child CDI Total Parameter Estimate SE p Fixed part
Intercept (ß0) 2.25821 0.08634 0.00000
Time (ß1) -0.09500 0.01389 0.00000
Season (ß2) 0.11213 0.03046 0.00023
Bereaved (ß3) -0.70038 0.09752 0.00000
Community (ß4) -1.15335 0.11333 0.00000
Time x Season (ß5)
Time x Bereaved (ß6) 0.01922 0.01014 0.05803
Time x Community (ß7) 0.03383 0.01102 0.00214
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00336 0.00123 0.00630 ^2 Time x Bereaved (ß11) ^2 Time x Community (ß12) ^2 Time x Season (ß13) ^3 Time (ß14) -0.00002 0.00003 0.50499 ^3 Time x Bereaved (ß15) -0.00003 0.00001 0.00270 ^3 Time x Community (ß16) -0.00005 0.00002 0.01242 ^3 Time x Season (ß17)
Random part 2 Intercept/Between Participants Variance (s u0) 0.48264 0.04149 0.00000 2 Time Variance (s u1) 0.00042 0.00010 0.00003 2 Season Variance (s u2) 0.00000 0.00000 ns 2 Intercept-Time Covariance (s u10) -0.00525 0.00164 0.00137 2 Season-Time Covariance (s u21) 2 Error/Within Participants Variance (s e0) 0.28117 0.01396 0.00000
-2*loglikelihood (IGLS) 3859.87000
Table 8. Multilevel regression results for child CDI total scores
91 Parent CDI Total Parameter Estimate SE p Fixed part
Intercept (ß0) 2.78436 0.10416 0.00000
Time (ß1) -0.08637 0.02305 0.00018
Season (ß2) 0.01385 0.06361 0.82764
Bereaved (ß3) -1.30879 0.11283 0.00000
Community (ß4) -1.92854 0.12727 0.00000
Time x Season (ß5) 0.08119 0.03244 0.01232
Time x Bereaved (ß6) 0.01641 0.00478 0.00060
Time x Community (ß7) 0.01880 0.00503 0.00019
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00589 0.00259 0.02296 ^2 Time x Bereaved (ß11) ^2 Time x Community (ß12) ^2 Time x Season (ß13) -0.00954 0.00353 0.00688 ^3 Time (ß14) -0.00014 0.00007 0.04550 ^3 Time x Bereaved (ß15) ^3 Time x Community (ß16) ^3 Time x Season (ß17) 0.00025 0.00009 0.00547
Random part 2 Intercept/Between Participants Variance (s u0) 0.55442 0.04429 0.00000 2 Time Variance (s u1) 2 Season Variance (s u2) 0.00000 0.00000 ns 2 Intercept-Time Covariance (s u10) 2 Season-Time Covariance (s u21) 2 Error/Within Participants Variance (s e0) 0.24899 0.01257 0.00000
-2*loglikelihood (IGLS) 2648.70300
Table 9. Multilevel regression results for parent CDI total scores
92 Child BAMO Parameter Estimate SE p Fixed part
Intercept (ß0) 1.12319 0.03959 0.00000
Time (ß1) -0.05393 0.00765 0.00000
Season (ß2) 0.02502 0.01494 0.09399
Bereaved (ß3) -0.48305 0.04478 0.00000
Community (ß4) -0.79573 0.05219 0.00000
Time x Season (ß5)
Time x Bereaved (ß6) 0.01149 0.00680 0.09108
Time x Community (ß7) 0.02777 0.00744 0.00019
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00279 0.00063 0.00001 ^2 Time x Bereaved (ß11) -0.00033 0.00026 0.20436 ^2 Time x Community (ß12) -0.00080 0.00028 0.00427 ^2 Time x Season (ß13) ^3 Time (ß14) -0.00005 0.00002 0.01242 ^3 Time x Bereaved (ß15) ^3 Time x Community (ß16) ^3 Time x Season (ß17)
Random part 2 Intercept/Between Participants Variance (s u0) 0.09239 0.00862 0.00000 2 Time Variance (s u1) 0.00010 0.00002 0.00000 2 Season Variance (s u2) 0.00553 0.00607 0.36228 2 Intercept-Time Covariance (s u10) 2 Season-Time Covariance (s u21) 2 Error/Within Participants Variance (s e0) 0.06829 0.00373 0.00000
-2*loglikelihood (IGLS) 1278.59700
Table 10. Multilevel regression results for child BAMO scores
93 Parent BAMO Parameter Estimate SE p Fixed part
Intercept (ß0) 1.35504 0.03719 0.00000
Time (ß1) -0.05416 0.00707 0.00000
Season (ß2) 0.02073 0.01411 0.14179
Bereaved (ß3) -0.78285 0.04233 0.00000
Community (ß4) -1.00653 0.04975 0.00000
Time x Season (ß5)
Time x Bereaved (ß6) 0.01766 0.00620 0.00439
Time x Community (ß7) 0.03065 0.00684 0.00001
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00291 0.00058 0.00000 ^2 Time x Bereaved (ß11) -0.00041 0.00024 0.08757 ^2 Time x Community (ß12) -0.00079 0.00025 0.00158 ^2 Time x Season (ß13) ^3 Time (ß14) -0.00005 0.00001 0.00000 ^3 Time x Bereaved (ß15) ^3 Time x Community (ß16) ^3 Time x Season (ß17)
Random part 2 Intercept/Between Participants Variance (s u0) 0.08696 0.00692 0.00000 2 Time Variance (s u1) 0.00003 0.00001 0.00270 2 Season Variance (s u2) 0.00298 0.00571 0.60175 2 Intercept-Time Covariance (s u10) 2 Season-Time Covariance (s u21) 2 Error/Within Participants Variance (s e0) 0.06621 0.00331 0.00000
-2*loglikelihood (IGLS) 1249.78000
Table 11. Multilevel regression results for parent BAMO scores
94 Child Cognitive Symptoms Parameter Estimate SE p Fixed part
Intercept (ß0) 0.80370 0.04678 0.00000
Time (ß1) -0.07000 0.00758 0.00000
Season (ß2) 0.02640 0.01793 0.14091
Bereaved (ß3) -0.51204 0.05284 0.00000
Community (ß4) -0.71675 0.06161 0.00000
Time x Season (ß5)
Time x Bereaved (ß6) 0.03932 0.00871 0.00001
Time x Community (ß7) 0.06043 0.00959 0.00000
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00206 0.00029 0.00000 ^2 Time x Bereaved (ß11) -0.00121 0.00033 0.00025 ^2 Time x Community (ß12) -0.00179 0.00036 0.00000 ^2 Time x Season (ß13) ^3 Time (ß14) ^3 Time x Bereaved (ß15) ^3 Time x Community (ß16) ^3 Time x Season (ß17)
Random part 2 Intercept/Between Participants Variance (s u0) 0.10862 0.01182 0.00000 2 Time Variance (s u1) 0.00007 0.00003 0.01963 2 Season Variance (s u2) 0.00169 0.00917 0.85378 2 Intercept-Time Covariance (s u10) -0.00330 0.00054 0.00000 2 Season-Time Covariance (s u21) 0.00085 0.00025 0.00067 2 Error/Within Participants Variance (s e0) 0.12059 0.00633 0.00000
-2*loglikelihood (IGLS) 1854.04800
Table 12. Multilevel regression results for child cognitive symptom scores
95 Parent Cognitive Symptoms Parameter Estimate SE p Fixed part
Intercept (ß0) 1.33633 0.04384 0.00000
Time (ß1) -0.12001 0.00926 0.00000
Season (ß2) 0.00351 0.01886 0.85236
Bereaved (ß3) -1.11949 0.04978 0.00000
Community (ß4) -1.18366 0.05842 0.00000
Time x Season (ß5)
Time x Bereaved (ß6) 0.09962 0.00813 0.00000
Time x Community (ß7) 0.09688 0.00899 0.00000
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00433 0.00074 0.00000 ^2 Time x Bereaved (ß11) -0.00277 0.00031 0.00000 ^2 Time x Community (ß12) -0.00266 0.00034 0.00000 ^2 Time x Season (ß13) ^3 Time (ß14) -0.00004 0.00002 0.04550 ^3 Time x Bereaved (ß15) ^3 Time x Community (ß16) ^3 Time x Season (ß17)
Random part 2 Intercept/Between Participants Variance (s u0) 0.09332 0.01093 0.00000 2 Time Variance (s u1) 0.00001 0.00003 0.73888 2 Season Variance (s u2) 0.02031 0.00930 0.02897 2 Intercept-Time Covariance (s u10) -0.00082 0.00046 0.07465 2 Season-Time Covariance (s u21) 2 Error/Within Participants Variance (s e0) 0.11647 0.00606 0.00000
-2*loglikelihood (IGLS) 2044.91500
Table 13. Multilevel regression results for parent cognitive symptom scores
96 Child Affective Symptoms Parameter Estimate SE p Fixed part
Intercept (ß0) 1.40892 0.06576 0.00000
Time (ß1) -0.10249 0.01529 0.00000
Season (ß2) 0.05212 0.02955 0.07775
Bereaved (ß3) -0.43805 0.07390 0.00000
Community (ß4) -1.25179 0.08606 0.00000
Time x Season (ß5)
Time x Bereaved (ß6) -0.01300 0.01371 0.34317
Time x Community (ß7) 0.06269 0.01509 0.00003
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00484 0.00124 0.00009 ^2 Time x Bereaved (ß11) 0.00062 0.00053 0.24284 ^2 Time x Community (ß12) -0.00132 0.00057 0.02092 ^2 Time x Season (ß13) ^3 Time (ß14) -0.00008 0.00003 0.01082 ^3 Time x Bereaved (ß15) ^3 Time x Community (ß16) ^3 Time x Season (ß17)
Random part 2 Intercept/Between Participants Variance (s u0) 0.14926 0.02286 0.00000 2 Time Variance (s u1) 0.00020 0.00009 0.02198 2 Season Variance (s u2) 0.03859 0.01992 0.05267 2 Intercept-Time Covariance (s u10) -0.00402 0.00119 0.00072 2 Season-Time Covariance (s u21) 2 Error/Within Participants Variance (s e0) 0.29349 0.01526 0.00000
-2*loglikelihood (IGLS) 3454.30200
Table 14. Multilevel regression results for child affective symptom scores
97 Parent Affective Symptoms Parameter Estimate SE p Fixed part
Intercept (ß0) 1.85870 0.05796 0.00000
Time (ß1) -0.14360 0.01413 0.00000
Season (ß2) 0.03990 0.02766 0.14915
Bereaved (ß3) -1.14526 0.06532 0.00000
Community (ß4) -1.68699 0.07673 0.00000
Time x Season (ß5)
Time x Bereaved (ß6) 0.04907 0.01238 0.00007
Time x Community (ß7) 0.11020 0.01376 0.00000
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00641 0.00115 0.00000 ^2 Time x Bereaved (ß11) -0.00108 0.00047 0.02254 ^2 Time x Community (ß12) -0.00276 0.00051 0.00000 ^2 Time x Season (ß13) ^3 Time (ß14) -0.00009 0.00003 0.00090 ^3 Time x Bereaved (ß15) ^3 Time x Community (ß16) ^3 Time x Season (ß17)
Random part 2 Intercept/Between Participants Variance (s u0) 0.09790 0.01852 0.00000 2 Time Variance (s u1) 0.00017 0.00008 0.03110 2 Season Variance (s u2) 0.03030 0.01729 0.07967 2 Intercept-Time Covariance (s u10) -0.00223 0.00100 0.02531 2 Season-Time Covariance (s u21) 2 Error/Within Participants Variance (s e0) 0.27247 0.01374 0.00000
-2*loglikelihood (IGLS) 3403.32500
Table 15. Multilevel regression results for parent affective symptom scores
98 Child Atypical Vegetative Symptoms Parameter Estimate SE p Fixed part
Intercept (ß0) 0.68969 0.04214 0.00000
Time (ß1) -0.06768 0.00884 0.00000
Season (ß2) 0.05072 0.01752 0.00380
Bereaved (ß3) -0.31202 0.04760 0.00000
Community (ß4) -0.63311 0.05549 0.00000
Time x Season (ß5)
Time x Bereaved (ß6) 0.02462 0.00798 0.00205
Time x Community (ß7) 0.05504 0.00880 0.00000
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00311 0.00071 0.00001 ^2 Time x Bereaved (ß11) -0.00084 0.00031 0.00645 ^2 Time x Community (ß12) -0.00165 0.00033 0.00000 ^2 Time x Season (ß13) ^3 Time (ß14) -0.00004 0.00002 0.02178 ^3 Time x Bereaved (ß15) ^3 Time x Community (ß16) ^3 Time x Season (ß17)
Random part 2 Intercept/Between Participants Variance (s u0) 0.08149 0.00963 0.00000 2 Time Variance (s u1) 0.00007 0.00003 0.01963 2 Season Variance (s u2) 0.01808 0.00720 0.01204 2 Intercept-Time Covariance (s u10) -0.00239 0.00046 0.00000 2 Season-Time Covariance (s u21) 2 Error/Within Participants Variance (s e0) 0.10098 0.00526 0.00000
-2*loglikelihood (IGLS) 1601.99100
Table 16. Multilevel regression results for child atypical vegetative symptom scores
99 Parent Atypical Vegetative Symptoms Parameter Estimate SE p Fixed part
Intercept (ß0) 0.83465 0.03668 0.00000
Time (ß1) -0.11282 0.01115 0.00000
Season (ß2) 0.01743 0.01493 0.24305
Bereaved (ß3) -0.51177 0.04181 0.00000
Community (ß4) -0.79452 0.04930 0.00000
Time x Season (ß5)
Time x Bereaved (ß6) 0.06118 0.01332 0.00000
Time x Community (ß7) 0.10213 0.01513 0.00000
Season x Bereaved (ß8)
Season x Community (ß9) ^2 Time (ß10) 0.00735 0.00108 0.00000 ^2 Time x Bereaved (ß11) -0.00413 0.00132 0.00175 ^2 Time x Community (ß12) -0.00634 0.00150 0.00002 ^2 Time x Season (ß13) ^3 Time (ß14) -0.00014 0.00003 0.00000 ^3 Time x Bereaved (ß15) 0.00008 0.00003 0.01464 ^3 Time x Community (ß16) 0.00012 0.00004 0.00159 ^3 Time x Season (ß17)
Random part 2 Intercept/Between Participants Variance (s u0) 0.06204 0.00733 0.00000 2 Time Variance (s u1) 0.00002 0.00002 0.36559 2 Season Variance (s u2) 0.00000 0.00000 ns 2 Intercept-Time Covariance (s u10) -0.00146 0.00034 0.00002 2 Season-Time Covariance (s u21) 2 Error/Within Participants Variance (s e0) 0.08615 0.00399 0.00000
-2*loglikelihood (IGLS) 1209.10200
Table 17. Multilevel regression results for parent atypical vegetative symptom scores
100
APPENDIX B
FIGURES
101 S-W-S-S W-S-W-W 102
Outcome Variable
0 5 13 25 Time since Baseline (months)
Figure 1. Hypothesized seasonal trend for outcome variables across assessment occasions
14
12
10
8 Bereaved Community 6 Depressed
Child CDI Total 4 103
2
0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month
Figure 2: Child CDI total scores plotted by month
6 5 4 W-S-W-W Depressed W-S-W-W Bereaved W-S-W-W Community 3 S-W-S-S Depressed S-W-S-S Bereaved 2 S-W-S-S Community 104
Child DIDCA Total 1 0 0 5 13 25 Time since Baseline (months)
Figure 3. Predicted trends in child DIDCA total scores across assessment occasions
6 5
4 W-S-W-W Depressed W-S-W-W Bereaved 3 W-S-W-W Community S-W-S-S Depressed S-W-S-S Bereaved 2 S-W-S-S Community 105
1 Parent DIDCA Total 0 0 5 13 25 Time since Baseline (months)
Figure 4. Predicted trends in parent DIDCA total score across assessment occasions
55 50 45 W-S-W-W Depressed 40 W-S-W-W Bereaved W-S-W-W Community 35 S-W-S-S Depressed 30 S-W-S-S Bereaved S-W-S-S Community 106 25
Child CDRSR Total 20 15 0 5 13 25 Time since Baseline (months)
Figure 5. Predicted trends in child CDRSR total scores across assessment occasions
55 50 45 W-S-W-W Depressed 40 W-S-W-W Bereaved W-S-W-W Community 35 S-W-S-S Depressed 30 S-W-S-S Bereaved S-W-S-S Community 107 25
Parent CDRSR Total 20 15 0 5 13 25 Time since Baseline (months)
Figure 6. Predicted trends in parent CDRSR total score across assessment occasions
18 16 14 12 W-S-W-W Depressed W-S-W-W Bereaved 10 W-S-W-W Community 8 S-W-S-S Depressed S-W-S-S Bereaved 6 S-W-S-S Community 108
Child CDI Total 4
2 0 0 5 13 25 Time since Baseline (months)
Figure 7. Predicted trends in child CDI total scores across assessment occasions
18 16 14 12 W-S-W-W Depressed W-S-W-W Bereaved 10 W-S-W-W Community 8 S-W-S-S Depressed S-W-S-S Bereaved 6 S-W-S-S Community 109 4 Parent CDI Total
2 0 0 5 13 25 Time since Baseline (months)
Figure 8. Predicted trends in parent CDI total scores across assessment occasions
2 1.8 1.6
1.4 W-S-W-W Depressed 1.2 W-S-W-W Bereaved W-S-W-W Community 1 S-W-S-S Depressed 0.8 S-W-S-S Bereaved S-W-S-S Community 110
Child BAMO 0.6
0.4 0.2 0 0 5 13 25 Time since Baseline (months)
Figure 9. Predicted trends in child BAMO scores across assessment occasions
2 1.8 1.6 1.4 W-S-W-W Depressed 1.2 W-S-W-W Bereaved W-S-W-W Community 1 S-W-S-S Depressed 0.8 S-W-S-S Bereaved 0.6 S-W-S-S Community 111 Parent BAMO
0.4 0.2 0 0 5 13 25 Time since Baseline (months)
Figure 10. Predicted trends in parent BAMO scores across assessment occasions
3
2.5
2 W-S-W-W Depressed W-S-W-W Bereaved W-S-W-W Community 1.5 S-W-S-S Depressed S-W-S-S Bereaved 1 S-W-S-S Community 112
Child Cognitive Sx 0.5
0 0 5 13 25 Time since Baseline (months)
Figure 11. Predicted trends in child cognitive symptom scores across assessment occasions
3 2.5
2 W-S-W-W Depressed W-S-W-W Bereaved W-S-W-W Community 1.5 S-W-S-S Depressed S-W-S-S Bereaved 1 S-W-S-S Community 113
0.5 Parent Cognitive Sx 0 0 5 13 25 Time since Baseline (months)
Figure 12. Predicted trends in parent cognitive scores across assessment occasions
6 5 4 W-S-W-W Depressed W-S-W-W Bereaved W-S-W-W Community 3 S-W-S-S Depressed S-W-S-S Bereaved 2 S-W-S-S Community 114
Child Affective Sx 1 0 0 5 13 25 Time since Baseline (months)
Figure 13. Predicted trends in child affective symptom scores across assessment occasions
6
5
4 W-S-W-W Depressed W-S-W-W Bereaved W-S-W-W Community 3 S-W-S-S Depressed S-W-S-S Bereaved 2 S-W-S-S Community 115 1 Parent Affective Sx 0 0 5 13 25 Time since Baseline (months)
Figure 14. Predicted trends in parent affective symptom scores across assessment occasions
1.4 1.2 1 W-S-W-W Depressed W-S-W-W Bereaved 0.8 W-S-W-W Community S-W-S-S Depressed 0.6 S-W-S-S Bereaved S-W-S-S Community
116 0.4
0.2 0 Child Atypical Vegetative Sx 0 5 13 25 Time since Baseline (months)
Figure 15. Predicted trends in child atypical vegetative symptom scores across assessment occasions
1.4 1.2
1 W-S-W-W Depressed W-S-W-W Bereaved 0.8 W-S-W-W Community S-W-S-S Depressed 0.6 S-W-S-S Bereaved S-W-S-S Community
117 0.4 0.2 0 Parent Atypical Vegative Sx 0 5 13 25 Time since Baseline (months)
Figure 16. Predicted trends in parent atypical vegetative symptom scores across assessment occasions