Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Am Acad Child Adolesc Psychiatry. Author manuscript; available in PMC 2013 May 20.
Published in final edited form as:
PMCID: PMC3658110

Treatment-Resistant Depressed Youth Show a Higher Response Rate if Treatment Ends During Summer School Break



There is little work on the effect of school on response to treatment of depression, with available research suggesting that children and adolescents with school difficulties are less likely to respond to fluoxetine compared with those with no school difficulties.


Depressed adolescents in the Treatment of Resistant Depression in Adolescents study, who had not responded to a previous adequate selective serotonin reuptake inhibitor (SSRI) trial, were randomly assigned to one of the following: another SSRI, venlafaxine, another SSRI + cognitive behavior therapy (CBT), or venlafaxine + CBT. Participants were classified into four groups depending on whether their enrollment in the study and end of treatment was during school or summer vacation.


Controlling for baseline differences, adolescents ending their 12-week treatment during summer vacation had odds 1.7 times (95% confidence interval = 1.02-2.8, p = .04) greater to have an adequate response as those ending their treatment while being in school. In addition, adequate depression response was associated with fewer school problems at week 12 (scores <5 versus scores ≥5: odds ratio = 3.3, 95% confidence interval = 1.9-5.8, p < .001). There was a significant interaction between school difficulties and timing of treatment, with the lowest rates of response being among adolescents having school difficulties and ending their treatment during the active school year.


School problems are relevant to treatment response in depressed adolescents and should be incorporated into the treatment plan. These findings also suggest that the time of the year might need to be taken into consideration for analysis of clinical trials in school-aged youth. Clinical trial registration information—Treatment of SSRI-Resistant Depression in Adolescents (TORDIA);; NCT00018902.

Keywords: adolescent depression, therapy, school problems, time variation

Several studies have established a relation between depression among adolescents and school related factors such as poor academic performance,1,2 less connectedness to school,3 and the teacher’s involvement and depression.2,4 On the other hand, there is little work on the effect of school factors on response to treatment of depression. School problems in the 6 months before treatment with fluoxetine in depressed children and adolescents predicted a lower response rate.5 Because school difficulties may precipitate a referral for the treatment of depression, the timing when treatment is received may affect outcome. For example, if a patient began treatment while in school, and ended acute treatment during the summer, he or she may show symptomatic improvement that is in part attributable to being out of school rather than to treatment per se. In the above-noted antidepressant treatment study, patients enrolled from the beginning of March to the end of August were seven times more likely to respond to fluoxetine as compared with those enrolled at other times of the year, although timing of treatment did not have any impact on the placebo response.5 These findings suggest that school stressors, or their relief, may have a strong influence on treatment response. However, aside from this report, we did not find any assessment of the impact of school stress or school attendance on treatment outcome in adolescent depression. Therefore, we decided to examine the impact of school stress and school attendance on treatment response in the Treatment of SSRI-Resistant Depression in Adolescents study (TORDIA), a multisite randomized treatment study of adolescent depression.6

In this article, we examine the impact of the timing of treatment on treatment outcome in the TORDIA study. We hypothesize that, first, participants whose treatment ended during summer vacation would show a better response rate to treatment than those for whom their last assessment was during the active school year; second, that this seasonal difference in response rate would be particularly pronounced for those who reported facing difficulties at school; and third, that remission would be less likely to be sustained in those in whom an adequate clinical response was assessed while the participant was on summer vacation.



TORDIA was a six-site, National Institute of Mental Health–funded study conducted between 2000 and 2006. All 334 participants had clinically significant major depression by DSM-IV criteria,7 despite a treatment trial with a selective serotonin reuptake inhibitor (SSRI) for at least 8 weeks, the last 4 weeks of which were at a dosage that is the equivalent of 40 mg of fluoxetine. Significant depression was defined as a total score ≥40 on the Children’s Depression Rating Scale—Revised (CDRS-R)8; and a score ≥4 on the Clinical Global Impressions—Severity Subscale (CGI-S).9

Exclusion criteria were as follows: completion of two or more adequate SSRI trials; history of nonresponse to an adequate trial of venlafaxine; prior trial of cognitive behavioral therapy (CBT), with seven or more sessions; currently taking medication with psychoactive properties, excluding some study-allowed medications at stable doses (≥6 weeks’ duration); diagnosis of bipolar I or II disorder, psychosis, autism, eating disorder, or substance abuse or dependence; hypertension (diastolic blood pressure ≥90 mm Hg); and, for females, pregnancy, breast-feeding, or not reliably using contraception.

The study was approved by each site’s local institutional review board. All participants gave informed and parents gave informed consent in accordance with local institutional review board regulations.

As per our hypotheses, we grouped participants based on whether they ended their treatment during the school year (S; September to May), or during the summer vacation (V); those 14 youth whose entire treatment took place over the summer vacation were excluded from further analyses. Of those whose treatment ended during the school year, 60 participants were enrolled when out of school, and 162 participants had their entire treatment during the school year. Because these two subgroups’ baseline characteristics, treatment assignment, and response rates did not differ (50.0% versus 41.4%, χ2 = 1.3, df = 1, p = .25), they were combined. In addition, 98 participants were enrolled during the school year but finished treatment during the school vacation (V).

Randomization and Treatment

Participants were randomly assigned to one of four treatments after the failed SSRI treatment: switching to a second SSRI; switching to venlafaxine; switching to a second SSRI combined with CBT; or switching to venlafaxine combined with CBT. Randomization was balanced both within and across sites on incoming treatment medication, comorbid anxiety, chronic depression (duration ≥24 months), and suicidal ideation (Beck Depression Inventory [BDI] item 9 ≥2).10

Cognitive Behavioral Therapy

Therapists who provided CBT had at least a master’s degree in a mental health field, and had at least 1 year of prior experience in using this treatment modality. CBT drew upon the manuals that emphasize cognitive restructuring and behavior activation, emotion regulation, social skills, and problem solving for participants, and that also emphasize parent-child sessions to decrease criticism and to improve support, family communication, and problem solving.6 The protocol consisted of 12 weekly sessions (60-90 minutes each) of CBT, three to six of which were to be family sessions. A median of nine CBT sessions were delivered across the treatment groups. Therapy audiotapes were reviewed using the Cognitive Therapy Rating Scale11 by on-site supervisors, supervisors in Pittsburgh, and one external consultant, with a high proportion (>93.9%) rated as acceptable.


Participants in the SSRI switch groups who were initially treated with citalopram, sertraline, or fluvoxamine were randomized to receive either fluoxetine or paroxetine. If they were initially treated with fluoxetine, they were switched to receive paroxetine and vice versa. After the Food and Drug Administration (FDA) warnings on paroxetine, citalopram was used instead. The SSRI dosage was 10 mg per day for the first week and 20 mg per day for weeks 2 to 6, with an option to increase to 40 mg per day if there was insufficient clinical improvement (CGI-I ≥3). The venlafaxine dosages for weeks 1 to 4 were 37.5, 75, 112.5, and 150 mg, respectively, with an option to increase to 225 mg at week 6. If intolerable adverse effects developed after a medication increase, the participant’s dosage was lowered to either 20 mg of an SSRI or to 150 mg of venlafaxine.

Pharmacotherapists were either psychiatrists or master’s degree–nurses working with the supervision of a psychiatrist. The study psychiatrist examined participants at entry, 6 weeks, and 12 weeks. Medication sessions were 30 to 60 minutes in duration and included assessment of vital signs, adverse effects, safety, and symptomatic response, and occurred weekly for the first 4 weeks and every other week thereafter.

Blinding Procedure

The intent was for study participants and clinicians to be blinded to medication treatment assignment and for an independent evaluator (IE) to be blinded to both medication and CBT assignment. Blinding for medication was maintained by use of three encapsulated pills daily for all prescriptions, some of which might be placebo to mask drug type and dose. The blinding to CBT for IE was maintained by scheduling the IE’s assessments at a time not contiguous with CBT sessions and by asking participants and staff not to discuss CBT treatment assignment when the IE was present. In 64 cases, the blinding of the IE was compromised, most commonly because of participant disclosure of receiving CBT, although results were similar after controlling for the effects of unblinding.6

Outcome and Measures

The primary outcome, “adequate clinical response” at week 12, was defined as a 50% reduction in CDRS-R score and Clinical Global Impressions—Improvement Subscale (CGI-I) score of 2 or less. The CDRS-R, a measure of depression symptom severity based on separate interviews of the child and parent, is a 17-item scale, which results in total scores ranging from 17 to 113, with a total score of 40 or greater indicating significant depression.12 The CGI-I is a measure of clinical improvement, on a scale of 1 (very much improved) to 7 (very much worse9). Both the CDRS and CGI-I were completed by an independent evaluator. A total of 33 adolescents (14.9%) in the S group and 14 adolescents (12.5%) in the V group did not have week 12 assessment (χ2 = 0.34, df = 1, p = .56), so missing data on outcome were not associated with the timing of the end of treatment.

The adolescents were also assessed for the following: hopelessness using the Beck Hopelessness Scale (BHS)13; severity of suicidal ideation by the Suicide Ideation Questionnaire—Jr. (SIQ-Jr.)14; substance use–related impairment by Drug Use Screening Inventory (DUSI)15; family conflict using the Conflict Behavior Questionnaire—Adolescent version (CBQ-A)16; social functioning by the Social Adjustment Scale—Self Report (SAS-SR)17; anxiety by the Screen for Child Anxiety Related Disorders—Child Version (SCARED); and other comorbidities using the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime version.18

Parental depression and anxiety were assessed using the Beck Depression Inventory (BDI)10 and Beck Anxiety Inventory (BAI),19 respectively.

School problems were assessed using six items from the Social Adjustment Scale—Self Report (SAS-SR).17 The items assessed days missing classes, being able to keep up with class work, being ashamed of how school work was done, having arguments with other students at school, feeling unhappy at school, and finding school work interesting in the 2 weeks before the assessment. The score for each item ranged from 0 (no problem) to 4 (having the problem always or most of the time). A total score was calculated with a possible score ranging from 0 to 24 (mean = 5.6, median = 5.0, range = 0-21). The total score was missing for 56 participants (16.8%) at week 0 and for 102 participants (30.5%) at week 12. Both week 0 and week 12 scores were available for 192 participants, with a significant correlation between the two scores (r = 0.41, p < .001).

Participants completing their SAS-SR in from June to August were more likely to have missing answers on the school-related items. The missing rate for SAS-SR at enrollment was 19.8% for the S group and 10.2% for the V group (χ2 = 4.5, df = 1, p = .03), whereas the missing rate at week 12 was 23.4% in the S group and 42.9% in the V group (χ2 = 12.4, df = 1, p < .001).

Having missing SAS-SR school problem scores at week 0 was not associated with either age (p = .43), gender (p = .35), race (p = .54), site (p = .32), treatment assignment (p = .73), type of antidepressant (p = .73), or CDRS (p = .09), CGAS (p = .37), and CGI-S (p = .14) baseline scores. In addition, there was no statistically significant difference between those with missing scores and those with available scores with respect to adequate response at week 12 (41.1% versus 48.9%, χ2 = 1.1, df = 1, p = .28). Among the V group, having missing SAS-SR scores at week 0 was not associated with response rates at week 12 (40.0% versus 61.4%, χ2 = 1.7, df = 1, p = .20).

On the other hand, participants with missing SAS-SR school scores at week 12, compared with those with available scores, were older (16.2 ± 1.6 versus 15.7 ± 1.5 years, t = 2.4, df = 332, p = .02), had higher baseline CDRS (61.2 ± 10.4 versus 57.7 ± 10.2, t = 2.7, df = 332, p = .01) and CGI-S scores (4.6 ± 0.7 versus 4.4 ± 0.7, t = 2.2, df = 174.2, p = .04), and had lower baseline CGAS scores (49.2 ± 8.3 versus 51.2 ± 7.4, t = −2.1, df = 172.4, p = .04). In addition, they were more likely to be assigned to an SSRI switch rather than to venlafaxine (63.7% versus 44.4%, χ2 = 10.6, df = 1, p = .001); there was no difference with respect to CBT assignment (p = .08). There was no statistically significant association between missing SAS-SR school problem scores at week 12 with gender (p = .76), race (p = 0.16), or site (p = .06). There was no difference between the two groups regarding response rate at week 12 (41.2% versus 50.4%, χ2 = 2.4, df = 1, p = .12). Finally, among the V group, missing SAS-SR scores at week 12 were not associated with response rates (54.8% versus 62.5%, χ2 = 0.6, df = 1, p = .44).

Statistical Analysis

The Statistical Package for Social Sciences (SPSS 14.0) was used to conduct statistical analysis. Patients were classified into two treatment time periods depending on whether their enrollment upon entry and at the end of acute treatment was during school (S; September-May) or out of school, during summer vacation (V; June-August). Pearson χ2, independent-sample t test, and nonparametric tests were conducted to examine the baseline variables associated with timing of treatment. The Mann-Whitney U test was conducted to examine differences regarding ordinal variables such as number of CBT and pharmacotherapy sessions; however, because the median for pharmacotherapy session did not differ between groups, the mean and standard deviation are presented as well. Baseline variables significantly associated with timing of treatment were controlled for in a multivariable binary logistic regression with response to treatment being the outcome.

An independent-sample t test was conducted to examine whether school difficulty scores, at initiation and end of treatment, were significantly associated with timing of the treatment and adequate response at week 12. Because of the high missing rate on those scores, a separate multivariable binary logistic regression was conducted to examine the relation between timing of treatment and school problems, on one hand, and adequate response, on the other hand, while controlling to type of treatment. In this model, the school problems variable was categorized into two groups using median split. Values of p < .05 were considered to be statistically significant.


Timing of Treatment

The V group had a higher rate of adequate response as compared with the S group (59.2% versus 40.8%, odds ratio [OR] = 1.9, 95% confidence interval [CI] = 1.2-3.0, p = .01). Combination therapy, compared with monotherapy, was significantly associated with higher rates of adequate response among the V group (70.0% versus 47.9%, χ2 = 4.9, df = 1, p = .03) but not in the S group (49.1% versus 38.6%, χ2 = 2.5, df = 1. p = .12).

Controlling for Site and Baseline Differences

There was no significant association between the time period of the treatment and any demographic characteristics (Table 1). The V group, compared with the S group, had higher SCARED total score (32.2 ± 14.9 versus 28.4 ± 16.2, t = −2.0, df = 314, p = .05). In particular, the V group had higher scores on the following subscales: generalized anxiety disorder (9.9 ± 4.1 versus 8.8 ± 5.0, t =−2.1, df = 225, p = .04), separation anxiety disorder (4.2 ± 3.6 versus 3.1 ± 3.0, t =−2.6, df = 160.8, p = .01), and social anxiety disorder (7.3 ± 4.2 versus 6.2 ± 4.2, t = −2.1, df = 313, p = .04). There was no difference between the two groups with respect to any of the other clinical characteristics. Moreover, time of treatment was not associated with either that parental psychopathology (depression and anxiety) or family environment. In addition, there was no difference between the two groups with respect to being switched to an SSRI or venlafaxine (p = .47), or the type of SSRI they were switched to (p = .80). However, there was a significant difference with respect to number of pharmacotherapy visits (median, nine for both groups, Mann-Whitney z =−2.5, p = .01; mean ± SD, S = 7.7 ± 2.3 versus V = 8.3 ± 1.8) but not number of CBT sessions (p = .30). Controlling for demographic characteristics (age, gender, race, and family income), site, treatment assignment, SCARED total scores, and number of pharmacotherapy visits, the V group was 1.8 times (95% CI = 1.03-3.31, p = .04) as likely to show adequate response as the S group. Upon adding interaction terms to the logistic regression model, timing of treatment had no significant interaction with assignment to combination therapy (p = .21), type of antidepressant the participants were switched to (p = .11), or SCARED scores (p = .81).

Demographic and Clinical Characteristics of Study Participants, by Season

To examine the possible effect of seasonal affective disorders, which might interact with latitude, sites were split into northern (Pittsburgh, Brown, and Portland) and southern (Dallas, Los Angeles, and Galveston) sites. Controlling for site, SCARED, and number of pharmacotherapy visits, the V group was 1.7 times (95% CI = 1.03-2.8, p = .04) as likely to show adequate response as the S group. Moreover, there was no significant interaction between timing of treatment and site in predicting outcome (p = .22), indicating that timing of treatment had same effect in the northern and southern sites. In addition, participants were screened for seasonal affective disorder at baseline and subsequent time points, and only two participants were found to have this disorder.

School problems at week 0 were not associated with either response at week 12 (responders: 9.1 ± 4.6 versus non responders: 9.0 ± 4.3, t = −0.2, df = 276, p = .81) or timing of treatment (S: 9.0 ± 4.5 versus V: 9.4 ± 4.4, t =−0.7, df = 264, p = .50). However, responders, as compared with nonresponders, had significantly fewer school problems at week 12 (4.0 ± 3.2 versus 7.2 ± 4.7, t = 5.8, df = 204.1, p < .001). Also, V participants reported fewer school problems than S participants (4.4 ± 3.9 versus 6.1 ± 4.4, t = 2.5, df = 224, p = .01).

In a multivariable logistic regression, adequate response at week 12 was associated with lower school problem scores at week 12 (SAS-SR school difficulty scores <5 versus scores ≥5: OR = 3.2, 95% CI = 1.7-5.8, p < .001) and being assigned to CBT (CBT versus no CBT: OR = 1.8, 95% CI = 1.03-3.3, p = .04), but not to type of pharmacotherapy (SSRI versus venlafaxine: OR = 0.9, 95% CI = 0.5-1.7, p = .83) or timing of treatment (V versus S: OR = 1.5, 95% CI = 0.7-2.9, p = .29). There was no significant interaction between school problems and being assigned to CBT (p = .63) or type of pharmacotherapy (p = .81); however, there was significant interaction between school problems and timing of treatment (p = .01). Upon adding the interaction term between school problems and timing of treatment to the model, the following were significantly associated with adequate response: being assigned to CBT (OR = 1.9, 95% CI = 1.1-3.5, p = .03), lower school problem scores (OR = 4.9, 95% CI = 2.4-10.2, p < .001), V group membership (OR = 3.6, 95% CI = 1.3-10.0, p = .01), and the interaction term of timing by school problems at week 12 (OR = 0.2, 95% CI = 0.05-0.7, p = .01).

To understand this interaction, we examined the relationship between school difficulties in response in both the S and V groups. Among the S youth, those with lower school difficulty scores (scores <5) were more likely to show response at week 12 as compared with participants with higher school difficulty scores, i.e., SAS-SR school difficulty scores ≥5 (68.0% versus 30.5%, χ2 = 23.6, df = 1, p < .001; Figure 1). On the other hand, among the V youth, who ended their treatment while out of school, there was no significant association between school problem scores and response to treatment (SAS-SR school difficulty scores <5: 61.8% versus scores ≥5: 63.6%, χ2 = 0.02, df = 1, p = .89; Figure 1). In other words, although there was no significant association between timing of treatment and response among participants with low school difficulty (S versus V, 68% versus 61.8%; χ2 = 0.2, df = 1, p = .52), among participants with higher school difficulty (scores ≥5), ending treatment, while in school was associated with lower rates of response as compared with ending treatment while being out of school (S versus V, 30.5% versus 63.3%, χ2 = 8.4, df = 1, p = .004).

Response rate by treatment timing stratified by school problems at week 12. School problems assessed with six items from the Social Adjustment Scale—Self Report.

Sensitivity analyses were conducted to determine whether SAS-SR missing values could have affected our results. The V group had a higher rate of adequate response as compared with the S group regardless of whether the week 12 SAS-SR scores were missing (54.8% versus 32.7%, χ2 = 4.6, df = 1, p = .03) or not missing (62.5% versus 47.1%, χ2 = 4.0, df = 1, p = .047).

Remission at Week 24

The overall 24-week remission rate for S and V were 36.0% and 46.9%, respectively (χ2 = 3.4,df = 1, p = .07). Among those who showed an adequate response to treatment at week 12, the 24-week remission rates for S versus V were 58.8% and 63.8%, respectively (χ2 = 0.4, df = 1, p = .54).


In this study, we confirmed our hypotheses as follows: first, response rates to treatment were higher if treatment began during the school year, but ended during vacation, compared with rates in those participants whose treatment ended during the school year; second, school problems interacted with treatment timing with respect to outcome, so that the timing of treatment favored ending treatment out of school only in those participants who reported greater school problems at the end of treatment; and third, among those participants who showed an adequate response at week 12, the remission rate at 24 weeks was similar for both groups.

These results should be considered within the context of limitations and strengths of this study. First, this study reports on “post hoc” hypotheses that were not part of the original study design. This raises the possibility of having false-positive results because of multiple hypothesis testing. Therefore, these findings should be validated by a study specifically designed to test them. Second, we did not document the exact time when participants’ schooling ended but instead assumed that, on average, participants were out of school during the summer months. Some of these students may have been attending summer school or been in school districts that began school toward the end of August. Third, patients were not randomized based on time of start of treatment. However, the few baseline differences between the S and V groups were taken into consideration. Fourth, academic performance, intelligence quotient scores, and other school environment variables, such as being bullied in school or the quality of relationships with teachers, were not collected for this study. Fifth, school problems scores were missing for approximately one-third of the sample, although missingness of this variable did not appear to be a confounder for our findings. Sixth, the causal direction of the relationship between poor response and school problems cannot be determined. On the other hand, this is only the second study that has examined the timing of treatment with respect to school attendance, as well as the relationship of reported school difficulties and response to treatment for youth mental health problems.

The effect of timing of treatment on response to treatment is most likely related to school stressors that were not present at the time of the final assessment for those participants who were out of school. This includes such school stressors as poor academic performance,1,2 low connected-ness to school,3 and teacher performance,2,4 all of which have been found to be associated with depression and its severity among adolescents. Such stressors could affect the course of the treatment if they had not been addressed by the therapist. For those adolescents whose treatment ended in the summer, those stressors could have had less impact. Consistent with this viewpoint, the relationship between the timing of the ending of treatment and treatment response was significant only in those participants who had higher numbers of school problems, and that overall, higher numbers of school problems at the end of treatment was associated with a poorer response rate. These findings are mostly consistent with those of Kowatch et al., who reported that patients with school difficulties in the 6 months before enrollment in the study were five times less likely to respond to fluoxetine as those without school difficulties.5 However, our ratings of school problems at the beginning of treatment, 3 months before assessment of response, were not related to treatment outcome.

School difficulties could be confounded with comorbid diagnoses such as conduct, oppositional disorders, or anxiety disorders. In TORDIA, neither adequate response to treatment nor timing of the treatment was associated with conduct disorder or oppositional defiant disorder. In addition, even after controlling for anxiety, adolescents who were enrolled in the study during school and ended treatment while out of school did better than those who had the whole course of treatment during school or were enrolled while they were out of school and ended treatment during school. Moreover, an anxiety disorder diagnosis did not interact with timing of treatment in predicting adequate response at week 12, which indicates that effect of timing of end of treatment on response was the same among those with and without anxiety disorders. Therefore, although school refusal and other school difficulties are associated with depression, anxiety, conduct disorder, and oppositional defi-ant disorder, the variation in response reported here does not seem to be associated with any of these conditions.20

On the other hand, these findings could be attributable to a biologic effect mediated by seasonal variation in available sunlight. For example, in post mortem and in vivo studies, winter and low amount of sun exposure are associated with lower levels and lower turnover of serotonin.21,22 However, in TORDIA, participants were screened for seasonal affective disorder, and only two participants met criteria at baseline. Moreover, the latitude of the site did not interact with timing of the end of treatment with respect to response.

Whether this relation is caused by school or seasonal factors, these findings have both clinical and research implications. School difficulties are relevant to treatment response, and these findings suggest that clinicians should carefully assess and monitor reported school difficulties and performance as part of treatment. Intervention with schools, to diminish school stressors, to obtain accommodation to the patient’s clinical status, and to optimize performance should be part of the treatment plan of every depressed adolescent. In clinical settings, when an adolescent’s course of treatment ends while the patient is out of school, the clinician should make sure to follow up the patient during the next school year, even if the patient has shown an adequate response to therapy. Regarding research, these findings suggest that the time of the year might need to be taken into consideration in the design and analyses of clinical trials of the treatment of depression in school-aged youth. &


This work was supported by National Institute of Mental Health (NIMH) grants MH61835 (Pittsburgh); MH61856 (Galveston); MH61864 (UCLA); MH61869 (Portland); MH61958 (Dallas); and MH62014 (Brown), and MH66371 (Pittsburgh).

The NIMH program staff participated in the design, implementation, analysis, and preparation of reports of the study. The opinions and assertions contained in this report are the private views of the authors and are not to be construed as official or as reflecting the views of the Department of Health and Human Services, the National Institutes of Health, or the NIMH.


Disclosure: Dr. Asarnow receives research grants from the National Institute of Mental Health. She has received honoraria from the California Institute of Mental Health, Hathaways-Sycamores, and the Melissa Institute. She has served as a consultant to Pfizer. She has received unrestricted funding Philip Morris. Dr. Wagner has received research support from NIMH, and has served on an advisory board for Forest. Dr. Birmaher has received research funding from NIMH. He has served as a consultant for Schering Plough. He has received royalties from Random House and Lippincott Williams and Wilkins. Dr. Emslie has received research support from Biobehaviorial Diagnostics, Eli Lilly and Co., Forest, GlaxoSmithKline, and Somerset. He has served as a consultant for Biobehaviorial Diagnostics, Eli Lilly and Co., GlaxoSmithKline, Pfizer, and Wyeth. Dr. Keller has served as a consultant and received honoraria from CENEREX, Medtronic, and Sierra Neuropharmaceuticals. He has received a grant from Pfizer. Dr. Brent has received research support from NIMH. He has received royalties from Guilford Press. He serves as editor for UpToDate Psychiatry. Drs. Shamseddeen, Clarke, and Ryan, and Ms. Porta, and Ms. Mayes report no biomedical financial interests or potential conflicts of interest.

Contributor Information

Wael Shamseddeen, Rosalind Franklin University of Medicine and Sciences, North Chicago, IL.

Gregory Clarke, Kaiser Permanente Center for Health Research, Portland, OR.

Karen Dineen Wagner, The University of Texas Medical Branch, Galveston.

Neal D. Ryan, University of Pittsburgh Medical Center and Western Psychiatric Institute and Clinic.

Boris Birmaher, University of Pittsburgh Medical Center and Western Psychiatric Institute and Clinic.

Graham Emslie, University of Texas Southwestern Medical Center at Dallas.

Joan Rosenbaum Asarnow, University of California, Los Angeles.

Giovanna Porta, University of Pittsburgh Medical Center and Western Psychiatric Institute and Clinic.

Taryn Mayes, University of Texas Southwestern Medical Center at Dallas.

Martin B. Keller, Brown University School of Medicine.

David A. Brent, University of Pittsburgh Medical Center and Western Psychiatric Institute and Clinic.


1. Field T, Diego M, Sanders C. Adolescent depression and risk factors. Adolescence. 2001;36:491–498. [PubMed]
2. Undheim AM, Sund AM. School factors and the emergence of depressive symptoms among young Norwegian adolescents. Eur Child Adolesc Psychiatry. 2005;14:446–453. [PubMed]
3. Lin HC, Tang TC, Yen JY, et al. Depression and its association with self-esteem, family, peer and school factors in a population of 9586 adolescents in southern Taiwan. Psychiatry Clin Neurosci. 2008;62:412–420. [PubMed]
4. Huang FF, Wu CC, Hu CY, Yang SS. Teacher overinvolvement and student depression among junior high school students in Taiwan. Sci World J. 2006;6:834–846. [PubMed]
5. Kowatch RA, Carmody TJ, Emslie GJ, Rintelmann JW, Hughes CW, Rush AJ. Prediction of response to fluoxetine and placebo in children and adolescents with major depression: a hypothesis generating study. J Affect Disord. 1999;54:269–276. [PubMed]
6. Brent D, Emslie G, Clarke G, et al. Switching to another SSRI or to venlafaxine with or without cognitive behavioral therapy for adolescents with SSRI-resistant depression: the TORDIA randomized controlled trial. JAMA. 2008;299:901–913. [PMC free article] [PubMed]
7. Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Association; Washington, DC: 2000. 4th text revised ed.
8. Poznanski EO, Grossman JA, Buchsbaum Y, Banegas M, Freeman L, Gibbons R. Preliminary studies of the reliability and validity of the Children’s Depression Rating Scale. J Am Acad Child Psychiatry. 1984;23:191–197. [PubMed]
9. Guy E. ECDEU Assessment Manual for Psychopharmacology—Revised. National Institutes of Mental Health; Rockville, MD: 1976.
10. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatry. 1961;4:561–571. [PubMed]
11. Vallis TM, Shaw BF, Dobson KS. The Cognitive Therapy Scale: psychometric properties. J Consult Clin Psychol. 1986;54:381–385. [PubMed]
12. Poznanski EO, Mokros HB. Children’s Depression Rating Scale—Revised. Western Psychological Services; Los Angeles, CA: 1996.
13. Beck AT, Steer RA. Manual for the Beck Hopelessness Scale. Psychological Corporation; San Antonio, TX: 1988.
14. Reynolds WM. Suicidal Ideation Questionnaire: Professional Manual. Psychological Assessment Resources; Odessa, FL: 1988.
15. Kirisci L, Mezzich A, Tarter R. Norms and sensitivity of the adolescent version of the drug use screening inventory. Addict Behav. 1995;20:149–157. [PubMed]
16. Robin AL, Foster SL. Negotiating Parent-Adolescent Conflict. Guilford Press; New York: 1989.
17. Weissman MM, Bothwell S. Assessment of social adjustment by patient self-report. Arch Gen Psychiatry. 1976;33:1111–1115. [PubMed]
18. Kaufman J, Birmaher B, Brent D, et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry. 1997;36:980–988. [PubMed]
19. Beck AT, Epstein N, Brown G, Steer RA. An inventory for measuring clinical anxiety: psychometric properties. J Consult Clin Psychol. 1988;56:893–897. [PubMed]
20. Egger HL, Costello EJ, Angold A. School refusal and psychiatric disorders: a community study. J Am Acad Child Adolesc Psychiatry. 2003;42:797–807. [PubMed]
21. Lambert GW, Reid C, Kaye DM, Jennings GL, Esler MD. Effect of sunlight and season on serotonin turnover in the brain. Lancet. 2002;360:1840–1842. [PubMed]
22. Carlsson A, Svennerholm L, Winblad B. Seasonal and circadian monoamine variations in human brains examined post mortem. Acta Psychiatr Scand Suppl. 1980;280:75–85. [PubMed]