discussion 3

For the week’s topics of Mood Disorders, analyze the primary arguments presented in either one of additional articles posted on Canvas OR a relevant empirical, peer-reviewed article of your choosing.

Discuss how the author’s perspective contributes to the broader academic conversation on these subjects. Reflect on the strengths and limitations of the author’s arguments, providing specific examples from the text. Include your critical evaluation of the evidence presented and how it supports or contradicts other sources you have encountered or your current knowledge of the study of abnormal child psychology. Ensure you properly cite (APA formatting, 7th edition) the additional articles from Canvas in your discussion.

Feel free to let me know if you need any more assistance.

Effects of Family-Focused Therapy vs Enhanced Usual Care
for Symptomatic Youths at High Risk for Bipolar Disorder
A Randomized Clinical Trial
David J. Miklowitz, PhD; Christopher D. Schneck, MD; Patricia D. Walshaw, PhD; Manpreet K. Singh, MD, MS;
Aimee E. Sullivan, PhD; Robert L. Suddath, MD; Marcy Forgey Borlik, MD;
Catherine A. Sugar, MS, PhD; Kiki D. Chang, MD

IMPORTANCE Behavioral high-risk phenotypes predict the onset of bipolar disorder among
youths who have parents with bipolar disorder. Few studies have examined whether early
intervention delays new mood episodes in high-risk youths.

OBJECTIVE To determine whether family-focused therapy (FFT) for high-risk youths is more
effective than standard psychoeducation in hastening recovery and delaying emergence of
mood episodes during the 1 to 4 years after an active period of mood symptoms.

DESIGN, SETTINGS, AND PARTICIPANTS This multisite randomized clinical trial included referred
youths (aged 9-17 years) with major depressive disorder or unspecified (subthreshold) bipolar
disorder, active mood symptoms, and at least 1 first- or second-degree relative with bipolar
disorder I or II. Recruitment started from October 6, 2011, and ended on September 15, 2016.
Independent evaluators interviewed participants every 4 to 6 months to measure symptoms
for up to 4 years. Data analysis was performed from March 13 to November 3, 2019.

INTERVENTIONS High-risk youths and parents were randomly allocated to FFT (12 sessions in
4 months of psychoeducation, communication training, and problem-solving skills training;
n = 61) or enhanced care (6 sessions in 4 months of family and individual psychoeducation;
n = 66). Youths could receive medication management in either condition.

MAIN OUTCOMES AND MEASURES The coprimary outcomes, derived using weekly psychiatric
status ratings, were time to recovery from prerandomization symptoms and time to a
prospectively observed mood (depressive, manic, or hypomanic) episode after recovery.
Secondary outcomes were time to conversion to bipolar disorder I or II and longitudinal
symptom trajectories.

RESULTS All 127 participants (82 [64.6%] female; mean [SD] age, 13.2 [2.6] years) were
followed up for a median of 98 weeks (range, 0-255 weeks). No differences were detected
between treatments in time to recovery from pretreatment symptoms. High-risk youths in
the FFT group had longer intervals from recovery to the emergence of the next mood episode
(χ2 = 5.44; P = .02; hazard ratio, 0.55; 95% CI, 0.48-0.92;), and from randomization to the
next mood episode (χ2 = 4.44; P = .03; hazard ratio, 0.59; 95% CI, 0.35-0.97) than youths in
enhanced care. Specifically, FFT was associated with longer intervals to depressive episodes
(log-rank χ2 = 6.24; P = .01; hazard ratio, 0.53; 95% CI, 0.31-0.88) but did not differ from
enhanced care in time to manic or hypomanic episodes, conversions to bipolar disorder, or
symptom trajectories.

CONCLUSIONS AND RELEVANCE Family skills-training for youths at high risk for bipolar disorder
is associated with longer times between mood episodes. Clarifying the relationship between
changes in family functioning and changes in the course of high-risk syndromes merits future
investigation.

TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT01483391.

JAMA Psychiatry. 2020;77(5):455-463. doi:10.1001/jamapsychiatry.2019.452

Published online January 15, 2020.

Supplemental content

CME Quiz at
jamacmelookup.com and
CME Questions page 548

Author Affiliations: Department of
Psychiatry and Biobehavioral
Sciences, David Geffen School of
Medicine, University of California,
Los Angeles (UCLA), Los Angeles
(Miklowitz, Walshaw, Suddath,
Forgey Borlik, Sugar); Department of
Psychiatry, University of Colorado
Anschutz Medical Campus, Denver
(Schneck, Sullivan); Department of
Psychiatry and Behavioral Sciences,
Stanford University School of
Medicine, Stanford, California
(Singh); Department of Biostatistics,
Fielding School of Public Health,
UCLA, Los Angeles (Sugar);
Private practice, Menlo Park,
California (Chang).

Corresponding Author: David J.
Miklowitz, PhD, Department of
Psychiatry and Biobehavioral
Sciences, David Geffen School of
Medicine, University of California,
Los Angeles, 760 Westwood Plaza,
Room A8-256, Los Angeles, CA
90024-1759 (dmiklowitz@mednet.
ucla.edu).

Research

JAMA Psychiatry | Original Investigation

(Reprinted) 455

Downloaded from jamanetwork.com by University of Houston – Main Campus user on 07/16/2024

https://clinicaltrials.gov/ct2/show/NCT01483391

https://jamanetwork.com/journals/jama/fullarticle/10.1001/jamapsychiatry.2019.4520?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

https://jamanetwork.com/journals/psy/fullarticle/10.1001/jamapsychiatry.2019.4520/?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

https://jama.jamanetwork.com/learning/article-quiz/10.1001/jamapsychiatry.2019.4520/?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

mailto:dmiklowitz@mednet.ucla.edu

Y ouths who develop bipolar I disorder (BD-I) or bipolar
II disorder (BD-II) during late adolescence or early adult-
hood often experience subthreshold mood symptoms

in childhood.1 In the Pittsburgh Bipolar Offspring Study, youths
with depression, anxiety, mood instability, and subthreshold
manic symptoms who had a parent with childhood-onset BD
had a 49% chance of converting to BD-I or BD-II in 8 years com-
pared with 6.8% of youth without these symptom features
whose parents had childhood-onset BD.2 Onset of BD in child-
hood and delays to first treatment are associated with more
time being depressed, less time being euthymic, and poorer
functioning in adulthood.3,4 However, there is little agree-
ment on what treatments are most effective in preventing
symptom progression among high-risk children.4-

Psychosocial interventions may facilitate the high-risk
youths’ acquisition of skills for coping with stress, develop-
ing social supports, and achieving autonomy.10 In a 2-site
pilot randomized clinical trial11 of 40 youths with active symp-
toms of major depressive disorder (MDD) or unspecified (sub-
threshold) BD and a family history of BD-I or BD-II, Miklowitz
et al11 found that family-focused therapy (FFT) for high-risk
youths, consisting of 12 sessions of family psychoeducation,
communication skills training, and problem-solving skills train-
ing was associated with more rapid recovery from mood symp-
toms, more time in remission, and a more favorable trajec-
tory of hypomania symptoms during 1 year compared with brief
family education. These findings are consistent with trials
showing that FFT and pharmacotherapy are more effective
than comparison treatments and pharmacotherapy in enhanc-
ing mood stabilization and delaying mood recurrences among
adults with BD.12-15

We conducted a randomized clinical trial of the effects of
FFT compared with standard psychoeducation (enhanced care
[EC]) on time to recovery and time to prospectively observed
mood episodes among symptomatic high-risk youths. This
study expanded on the pilot randomized clinical trial11 by
including 3 sites with a larger number of participants (N = 127)
followed up for 1 to 4 years. The duration of the EC treatment
was standardized at 4 months to match the duration of FFT.
Participants received pharmacotherapy from study psychia-
trists (C.D.S., M.K.S., R.L.S., M.F-B., and K.D.C.) using algo-
rithms designed for this population.16 We hypothesized that
high-risk youths receiving FFT would have (1) shorter times
to recovery from pretreatment symptoms and longer inter-
vals until their next prospectively observed mood episode
(coprimary outcomes), and (2) lower rates of conversion to syn-
dromal BD-I or BD-II and greater improvements in symptom
severity over time (secondary outcomes) compared with youths
receiving EC.

Methods
This randomized clinical trial was approved by medical insti-
tutional review boards of the University of California, Los An-
geles (UCLA), the University of Colorado, Boulder, the Univer-
sity of Colorado Anschultz Medical Center, Aurora, and
Stanford University, Stanford, California. After receiving an ex-

planation of the procedures, participants and parents gave writ-
ten informed assent and consent to participate. The trial pro-
tocol is available in Supplement 1.

Participants
Recruitment of participants occurred from October 6, 2011, to
September 15, 2016. Data were analyzed from March 13, to
November 3, 2019. Participants were clinically referred or
learned of the study through online, radio, or print advertise-
ments or public presentations. Eligibility criteria included
(1) age between 9 years 0 months and 17 years 11 months;
(2) meeting lifetime DSM-IV and, later, DSM-5 criteria17,18 for
unspecified BD or major depressive disorder (MDD) (eMethods
in Supplement 2); (3) having at least 1 first- or second-degree
relative with a lifetime history of BD-I or BD-II; and (4) a prior
week Young Mania Rating Scale (YMRS)19 score more than 11
or a 2-week Children’s Depression Rating Scale, Revised
(CDRS-R)20 score more than 29, indicating at least moderate
current mood symptoms. Unspecified BD (formerly BD, not
otherwise specified) was defined as distinct periods of abnor-
mally elevated, expansive, or irritable mood and 2 (3, if irri-
table mood only) symptoms of mania that caused a change in
functioning, lasted 1 to 3 days, and occurred for at least 10 days
in the child’s lifetime.21,22

Baseline Assessments
Study diagnosticians administered the Kiddie Schedule for
Affective Disorders and Schizophrenia, Present and Lifetime
Version (KSADS-PL)23,24 with the youth and at least 1 parent,
with final item ratings based on consensus judgments. Inter-
rater reliability for KSADS Depression and Mania Rating
scales23,25 had means of 0.74 and 0.84 (intraclass correla-
tions) across sites. A trained research assistant interviewed each
parent about their own psychiatric history using the MINI
International Neuropsychiatric Interview26 and about psychi-
atric illnesses in the youth’s other first- and second-degree rela-
tives using the Family History Screening instrument.27

Study Design and Procedures
Before the study, the independent data core at UCLA created
a dynamic random allocation procedure28 that assigned par-
ticipants to FFT (n = 61) or EC (n = 66). Assignments were made

Key Points
Question Is family-focused therapy for youths at high risk for
bipolar disorder effective in delaying mood disorder episodes?

Findings This randomized clinical trial included 127 youths (aged
9-17 years) with symptomatic mood disorder and a family history
of bipolar disorder. For a mean of 2 years, youths at high risk for
bipolar disorder who received 12 sessions of family-focused
therapy (psychoeducation, communication, and problem-solving
skills training) with their families had longer well intervals between
mood episodes compared with youths who received less intensive
family and individual psychoeducation.

Meaning The findings suggest that family-focused therapy is
associated with longer times between mood episodes among
youths at high risk for bipolar disorder.

Research Original Investigation Family-Focused Therapy vs Enhanced Care for Symptomatic Youths at High Risk for Bipolar Disorder

456 JAMA Psychiatry May 2020 Volume 77, Number 5 (Reprinted) jamapsychiatry.com

Downloaded from jamanetwork.com by University of Houston – Main Campus user on 07/16/2024

https://jamanetwork.com/journals/jama/fullarticle/10.1001/jamapsychiatry.2019.4520?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

http://www.jamapsychiatry.com/?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

separately by site. After a participant was determined to be eli-
gible, the site’s principal investigator entered a diagnosis (un-
specified BD or MDD), age (<13 years or ≥13 years), and initial medications (mood stabilizers or antipsychotics vs neither) into the algorithm, which then randomly allocated a treatment as- signment to minimize imbalances between study arms across these variables.

Pharmacotherapy
At baseline, a study psychiatrist conducted a separate medi-
cal evaluation of the youth. Participants were offered main-
tenance pharmacologic care (biweekly and then monthly meet-
ings) when clinically indicated or requested by the youth or
parents. Physicians who were unaware of psychosocial assign-
ments followed a pharmacotherapy algorithm for high-risk
youths that described medication choices, starting doses, dose
ranges, and clinical adjustments to manage mood or comor-
bid conditions and control adverse effects (trial protocol in
Supplement 1 and eResults in Supplement 2).16,22

Psychosocial Treatments
All therapists administered both psychosocial treatments. Fam-
ily-focused therapy involved the high-risk child, parents or
stepparents, and when possible, siblings. The protocol con-
sisted of 12 sixty-minute sessions (8 weekly, 4 biweekly) in 4
months of psychoeducation, communication enhancement
training (eg, practicing active listening or expressing positive
or negative feelings), and problem-solving skills training. The
4-month EC treatment consisted of 3 weekly 60-minute fam-
ily psychoeducation sessions followed by 3-monthly youth-
only sessions that focused on implementing a mood manage-
ment plan (eMethods and eTable in Supplement 2). Family
clinicians were trained in the FFT and EC protocols during a
study launch meeting and supervised in monthly teleconfer-
ences throughout the study. Clinician fidelity ratings on the
Therapist Competence and Adherence Scales29 indicated high
levels of adherence and skill (mean [SD], 5.04 [0.96] on a
7-point scale) in administering both treatments (eMethods in
Supplement 2).

Outcome Assessments
Independent evaluators blinded to treatment condition inter-
viewed the youth and at least 1 parent (regarding the youth) at
baseline (covering the previous 4 months), every 4 months af-
ter randomization in year 1, and every 6 months for up to 4 years.
At each assessment, the evaluators administered the Adoles-
cent Longitudinal Interval Follow-up Evaluation (A-LIFE) and
associated Psychiatric Status Ratings (PSRs),30 defined as
1 (asymptomatic) to 6 (fully syndromal, severe) point scales of
depression, mania, and hypomania rated for every week of the
interval. Interrater reliabilities for 6-point depression PSR was
0.79 (intraclass r) and for 6-point mania PSR was 0.76 (intra-
class r) calculated across evaluators at each study site.

Statistical Analysis
All participants had at least subthreshold mood symptoms (PSR
scales ≥3) in the 2 weeks before randomization. The primary
analysis was a 2-stage survival model of the coprimary out-

comes. Using conventions for the A-LIFE PSRs, we first com-
pared the FFT and EC groups on the number of weeks from
treatment assignment to the beginning of a recovery period (all
PSR mood scales rated 1 [asymptomatic] or 2 [mildly sympto-
matic] for ≥8 consecutive weeks).21,30 For those who recov-
ered from prerandomization symptoms, we next compared
treatment arms on time to a new mood episode, defined as
either at least 2 weeks with PSR depression ratings of 4 (syn-
dromal with moderately severe), 5 (severe), or 6 (extremely se-
vere symptoms or impairment) or at least 1 week with PSR hy-
pomania or mania ratings of 5 (syndromal with full intensity)
or 6 (severe intensity). Reliability between raters for estimat-
ing time to recovery was 0.93 and for time to mood episodes
was 0.89. Secondarily, we fit individual survival models for
time to depressive episodes, time to manic or hypomanic epi-
sodes, and time to diagnostic conversion, defined as onset of
mood symptoms that changed the diagnosis from MDD or un-
specified BD to BD-I or BD-II (eMethods in Supplement 2).

For the time-to-event analyses, we obtained Kaplan-
Meier estimates of the survival curves for each study arm and
used the log-rank procedure (PROC LIFETEST in SAS, version
9.4 [SAS Institute Inc]31) to test for overall treatment effects.
In follow-up analyses, we used Cox proportional hazards re-
gression models (PROC PHREG in SAS31) to quantify the treat-
ment effects (via hazard ratio estimates) and to explore the in-
dependent effects of specific baseline covariates (site, age, sex,
primary and comorbid diagnoses, family history [first- vs sec-
ond-degree affected relatives], YMRS and CDRS-R scores, and
medication regimens) beyond treatment effects.

In secondary analyses examining the differential effects
of FFT vs enhanced care on the trajectory of mood symptoms
over time, we computed a maximum PSR mood (depression,
mania, or hypomania) severity score for each week of fol-
low-up and then averaged these weekly maximum scores
(range, 1-6) in each 4- to 6-month study interval for up to 48
months. We fit a mixed effect regression model (in PROC
MIXED in SAS31) with mean maximum PSR scores as the out-
come, treatment as the between-persons effect, time as the
within-persons effect, and treatment-by-time interaction
terms. We used a piecewise linear segmentation of time,
allowing for a change in slope at 8 months because we ex-
pected faster improvements during and immediately after
the acute treatment period followed by a leveling after treat-
ment as the corresponding skills learned in treatment were
consolidated.

For all analyses, we initially included site and its interac-
tions with group and time to ensure that differential imple-
mentations of the interventions were not affecting observed
results. Because there was no evidence of any site effects, we
present the final results for models with site terms removed.
Statistical significance was set at 2-sided P < .05.

Results
Participants
Participants were 127 youths (82 female [64.6%]; mean [SD]
age, 13.2 [2.6] years; range, 9.0-17.9 years.), including 75 youths

Family-Focused Therapy vs Enhanced Care for Symptomatic Youths at High Risk for Bipolar Disorder Original Investigation Research

jamapsychiatry.com (Reprinted) JAMA Psychiatry May 2020 Volume 77, Number 5 457

Downloaded from jamanetwork.com by University of Houston – Main Campus user on 07/16/2024

https://jamanetwork.com/journals/jama/fullarticle/10.1001/jamapsychiatry.2019.4520?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

http://www.jamapsychiatry.com/?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

with MDD (59.1%) and 52 youths with unspecified BD (40.9%).
The FFT participants did not differ from the EC participants
on any baseline characteristic overall or by site (Table). The fi-
nal sample of 127 participants did not differ in sex, age, or race/

ethnicity from 154 candidates who were screened and found
ineligible or who refused to participate in the study (Figure 1).

Participants were in the study for a median of 98 weeks
(range, 0-255 weeks); 14 (11.0%) were lost to follow-up (10 in

Table. Demographic and Illness History Features of High-risk Youths Receiving Family-Focused Therapy
or Enhanced Carea

Variable

Family-Focused
Therapy
(n = 61)

Enhanced Care
(n = 66)

Total
(N = 127)

Age, mean (SD), y 13.2 (2.7) 13.3 (2.5) 13.2 (2.6)

Socioeconomic status, mean (SD)b 3.7 (0.8) 4.1 (0.8) 3.9 (0.8)

Young Mania Rating Scale at baseline, mean (SD) 12.8 (6.8) 12.5 (7.7) 12.6 (7.3)

Children’s Depression Rating Scale–Revised
at baseline, mean (SD)

46.3 (13.5) 48.3 (15.5) 47.3 (14.5)

Children’s Global Assessment Scale in the last 2 wk
at baseline, mean (SD)

52.7 (9.8) 52.2 (22.5) 52.5 (10.6)

Children’s Global Assessment Scale, most severe
past episode, mean (SD)

44.5 (7.6) 42.8 (8.5) 43.6 (8.1)

Female 37 (60.7) 45 (68.2) 82 (64.6)

Nonwhite race 12 (19.7) 10 (15.2) 22 (17.3)

Hispanic ethnicity 15 (24.6) 8 (12.1) 23 (18.1)

Primary diagnosis

Major depressive disorder 37 (60.7) 38 (57.6) 75 (59.1)

Bipolar disorder, not otherwise specified 24 (39.3) 28 (42.4) 52 (40.9)

Mood polarity at study entry

Depression, no mania or hypomania 27 (44.3) 31 (47.0) 58 (45.7)

Hypomania, no depression 0 1 (1.5) 1 (0.8)

Depression, subthreshold mania or hypomania 24 (39.3) 26 (39.4) 50 (39.4)

Hypomania, subthreshold depression 3 (4.9) 3 (4.5) 6 (4.7)

Subthreshold depression and mania
or hypomania

7 (11.5) 5 (7.6) 12 (9.4)

Comorbid disordersc

None 6 (9.8) 11 (16.7) 17 (13.4)

Internalizing disorders only 21 (34.4) 26 (39.4) 47 (37.0)

Externalizing disorders 13 (21.3) 14 (21.2) 27 (21.3)

Internalizing and externalizing disorders 21 (34.4) 15 (22.7) 36 (28.4)

Baseline medications

None 23 (37.7) 33 (50.0) 56 (44.1)

Lithium 1 (1.6) 0 1 (0.8)

Antipsychotic 13 (21.3) 17 (25.8) 30 (23.6)

Anticonvulsant 10 (16.4) 8 (12.1) 18 (14.2)

Antidepressant 27 (44.3) 20 (30.3) 47 (37.0)

Anxiolytic 2 (3.3) 2 (3.0) 4 (3.1)

Psychostimulant or other ADHD agent 12 (19.7) 14 (21.2) 26 (20.5)

Family composition

Both biological parents, intact family 32 (52.5) 30 (45.5) 62 (48.8)

Both biological parents, joint custody 6 (9.8) 5 (7.6) 11 (8.7)

1 Biological parent without stepparent 7 (11.5) 14 (21.2) 21 (16.5)

1 Biological parent plus stepparent 9 (14.8) 11 (16.7) 20 (15.7)

Grandparent 2 (3.3) 1 (1.6) 3 (2.4)

Other relative 5 (8.2) 5 (7.6) 10 (7.9)

Family history of bipolar disorder

Youths with first-degree relatives only 35 (57.4) 47 (71.2) 82 (64.6)

Youths with second-degree relatives 10 (16.4) 9 (13.6) 19 (15.0)

Youths with first- and second-degree relatives 16 (26.2) 10 (15.2) 26 (20.5)

Abbreviation: ADHD,
attention-deficit/hyperactivity
disorder.
a Data are presented as number

(percentage) of participants unless
otherwise indicated.

b Higher values for socioeconomic
status indicate higher educational
level and occupation.

c Internalizing disorders include all
anxiety disorders and eating
disorders. Externalizing disorders
include ADHD, conduct disorder,
oppositional defiant disorder, and
disruptive mood dysregulation
disorder.

Research Original Investigation Family-Focused Therapy vs Enhanced Care for Symptomatic Youths at High Risk for Bipolar Disorder

458 JAMA Psychiatry May 2020 Volume 77, Number 5 (Reprinted) jamapsychiatry.com

Downloaded from jamanetwork.com by University of Houston – Main Campus user on 07/16/2024

http://www.jamapsychiatry.com/?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

EC and 4 in FFT) shortly after randomization (Figure 1).
Duration of follow-up did not differ significantly across psy-
chosocial treatments (FFT: median, 114 weeks; range, 0-255
weeks; EC: median, 92.5 weeks, range, 0-221 weeks; survival
analysis log-rank χ2 = 2.78; P = .10) nor as a function of base-
line depression (CDRS-R) or mania or hypomania (YMRS)
scores, study site, sex, age, family history, or primary or
comorbid diagnoses. Patients in the FFT and EC groups
attended the same proportion (91.7%) of protocol therapy
sessions (FFT: mean [SD], 11.0 [3.4] of 12.0; EC: mean [SD],
5.5 [2.4] of 6.0), and the proportion of participants who
dropped out during the 4-month treatment period did not
differ significantly across groups (8.2% vs 16.7%; χ2 = 2.07;
P = .15). Additional checks of the potential impact of
follow-up duration on the primary outcome results are pre-
sented in the eResults in Supplement 2.

Effects of Treatment on Time to Recovery
Of the 127 participants, 90 (70.9%) met the 8-week mood re-
covery criteria at some point during follow-up, 23 (18.1%) did
not, and 14 (11.0%) withdrew at baseline. In the FFT group, 47
of 61 participants (77.0%) recovered in a median of 24 weeks
(95% CI, 17-33 weeks) compared with 43 of 66 (65.2%) in the
EC group in 23 weeks (95% CI, 17-29 weeks) (log-rank χ2 = 0.01;
P = .93; unadjusted hazard ratio [HR] for FFT vs EC, 1.02; 95%
CI, 0.67-1.54). In a Cox proportional hazards regression model
that examined baseline covariates, lower CDRS-R depression
scores (Wald χ2 = 7.59; P = .006; HR, 0.98; 95% CI, 0.96-
0.99) and male sex (Wald χ2 = 5.57; P = .02; HR, 1.81; 95% CI,

1.11-2.96) were independently associated with shorter time to
mood recovery.

Effects of Treatment on Prospectively Observed
Mood Episodes
Among the 90 participants who recovered, new mood epi-
sodes were observed in 71 (78.9%) during follow-up; 70 of 90
participants (77.8%) had new episodes of major depression and
12 (13.3%) had new episodes of mania (n = 7; 3 with mixed epi-
sodes) or hypomania (n = 5) at follow-up. In the FFT group, new
mood episodes occurred in 37 of 47 recovered participants
(78.7%) compared with 34 of 43 (79.1%) in the EC group. The
survival analysis of time from recovery to recurrence indi-
cated that FFT participants experienced longer times with-
out a new mood episode than EC participants (log-rank
χ2 = 5.44; P = .02; HR, 0.55; 95% CI, 0.48-0.92).

This conditional analysis included only participants who
recovered (n = 90) and was therefore not randomized. Be-
cause the 2 groups did not differ on time to recovery, we con-
ducted an intent-to-treat analysis of time from randomiza-
tion until the first observed mood episode to assess whether
participants in FFT had longer periods of remission. The esti-
mated median time from randomization to a new mood epi-
sode was 73 weeks (95% CI, 55-82 weeks) in the intent-to-
treat sample (n = 127), with a median of 81 weeks (95% CI, 56-
123 weeks) for those in the FFT group and 63 weeks (95% CI,
44-78 weeks) for those in the EC group. Patients in the FFT
group had longer intervals of wellness before new mood
episodes than patients in the EC group (χ2 = 4.44; P = .03;

Figure 1. CONSORT Diagram

281 Assessed for eligibility

154 Excluded
69 Declined to participate

51 Did not meet diagnostic criteria
20 Excluded by investigators
12 No family member with bipolar disorder

2 Abuse or domestic violence

5 Transportation issues

43 No reason given
21 Pursued treatment elsewhere

127 Randomized

56 Analyzed with at least 1 follow-up

66 Randomized to enhanced care
55 Received intervention as randomized

11 Did not receive intervention

50 Received >75% of intervention
5 Received 50%-75% of intervention

61 Randomized to family-focused therapy
56 Received intervention as randomized

5 Did not receive intervention

48 Received >75% of intervention
8 Received 50%-75% of intervention

57 Analyzed with at least 1 follow-up

66 Follow-up
24 For ≥25 months

10 Lost to follow-up
7 No longer interested
3 Staff unable to contact

16 For 13-24 months
16 For 4-12 months

61 Follow-up
32 For ≥25 months

4 Lost to follow-up
2 No longer interested
2 Staff unable to contact

14 For 13-24 months
11 For 4-12 months

The participants allocated to
interventions were enrolled at UCLA
(n = 56), University of Colorado
(n = 44), or Stanford University
(n = 27) Schools of Medicine.

Family-Focused Therapy vs Enhanced Care for Symptomatic Youths at High Risk for Bipolar Disorder Original Investigation Research

jamapsychiatry.com (Reprinted) JAMA Psychiatry May 2020 Volume 77, Number 5 459

Downloaded from jamanetwork.com by University of Houston – Main Campus user on 07/16/2024

https://jamanetwork.com/journals/jama/fullarticle/10.1001/jamapsychiatry.2019.4520?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

http://www.jamapsychiatry.com/?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

HR, 0.59; 95% CI, 0.35-0.97) (Figure 2). In a Cox proportional
hazards regression model, there were no independent effects
of baseline covariates on time to mood episodes, whereas the
effect of treatment group in this analysis remained robust (Wald
χ2 = 8.58; P = .003; HR, 0.39; 95% CI, 0.21-0.74).

Because of the large proportion of participants lost to follow-
up at the Stanford University site (eResults in Supplement 2), we
also constrained the survival models to the UCLA and Colorado
sites only (n = 100). In the 2-site subsample, we observed a stron-
ger effect of FFT vs EC on time to new mood episodes (log-rank
χ2 = 6.08; P = .01; HR, 0.50; 95% CI, 0.28-0.88), suggesting that
the 3-site comparison was more conservative.

Of 61 participants in the FFT group, 36 (59.0%) experi-
enced recovery and then had new depressive episodes in a me-
dian of 87 weeks (95% CI, 73-127 weeks) compared with 34 of
66 EC participants (51.5%) in 63 weeks (95% CI, 44-78 weeks),
indicating longer well intervals before recurrences of depres-
sion in the FFT group (log-rank χ2 = 6.24; P = .01; HR, 0.53; 95%
CI, 0.31-0.88). Base rates of hypomanic and manic episodes af-
ter recovery were lower. Of 61 youths in the FFT group who
recovered, 9 had manic or hypomanic episodes in a mean (SE)
of 140.6 (5.7) weeks (median not estimable because of num-
ber of events). Of 66 EC participants, 3 had manic or hypo-
manic episodes in a mean (SE) of 133.6 (2.9) weeks (log-rank
χ2 = 2.43; P = .12).

Conversion to BP-I or BP-II Disorder
Of 127 participants, 9 (7.1%) had manic or mixed episodes at
follow-up, resulting in a change from unspecified BD (n = 6)
or MDD (n = 3) to BD-I; 9 (7.1%) had hypomanic episodes re-
sulting in a change from unspecified BD (n = 4) or MDD (n = 5)
to BD-II. One participant progressed from unspecified BD to

schizoaffective disorder, depressed type (eMethods in Supple-
ment 2). In the FFT group, 11 participants converted in a mean
(SE) of 135.5 (6.6) weeks, whereas in the EC group, 8 con-
verted in a mean (SE) of 91.4 (4.0) weeks (medians not esti-
mable because of low number of events) (log-rank χ2 = 0.17;
P = .68). Only baseline YMRS scores were independently
associated with risk of conversion (Wald χ2 = 3.84; P = .05;
HR, 1.08; 95% CI, 1.00-1.16).

Effects of Treatment on Symptom Trajectories
In secondary analyses, we examined whether youths in FFT
had a more favorable trajectory of mood symptom scores
than youths in EC in up to 48 months of follow-up. In mixed
effect regression models, with time treated as piecewise
linear, the longitudinal patterns of mean maximum PSR
mood scores did not differ by group (likelihood-ratio test
comparing models with and without the group-by-time
interaction terms, χ2 = 0.50; P = .78). However, each of the
time components was statistically significant (P < .001), with FFT participants and EC participants showing a decline in symptoms during the first 8 months, followed by a sub- stantial leveling off during the follow-up period (eFigure in Supplement 2).

Effects of Pharmacotherapy
We detected no differences between treatment arms in the
frequency of antipsychotic, mood stabilizer, antidepressant,
anxiolytic, or psychostimulant use at baseline (Table) or at
any follow-up point. In Cox proportional hazards regression
models, there were no relationships between baseline medi-
cations and time to recovery or time to diagnostic conver-
sions, nor any effects of medications on time to mood recur-

Figure 2. Family-Focused Therapy vs Enhanced Care for Youths at High Risk for Bipolar Disorder

1.0

0.8

0.4

0.2

Cu
m

ul
at

iv
e

Pr
op

or
tio

n
Su

rv
iv

in
g

W
ith

ou
t N

ew
M

oo
d

Ep
is

od
e

Time From Recovery to First Mood Episode, wk
0

61
66

5 10

61
66

15 20

53
51

25 30

47
42

35 40

40
32

45 50

33
24

55 60

28
18

65 70

26
15

75 80

21
11

85 90

16
7

95 100

16
6

105 110

16
6

115 120

16
1

126 131

136

141

146

2
No. at risk

Family-focused therapy

Enhanced care

Effect of treatment condition on time to mood episode (χ2 = 4.44, P = .03;
hazard ratio, 0.59 [95% CI, 0.35-0.97]). All patients (N = 127) began with at
least subthreshold symptoms. Time to episode was calculated from the date of

randomization to the beginning of the first prospectively observed mood
episode. Dashed vertical lines indicate group medians.

Research Original Investigation Family-Focused Therapy vs Enhanced Care for Symptomatic Youths at High Risk for Bipolar Disorder

460 JAMA Psychiatry May 2020 Volume 77, Number 5 (Reprinted) jamapsychiatry.com

Downloaded from jamanetwork.com by University of Houston – Main Campus user on 07/16/2024

https://jamanetwork.com/journals/jama/fullarticle/10.1001/jamapsychiatry.2019.4520?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

http://www.jamapsychiatry.com/?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

rences beyond psychosocial treatment (eResults in
Supplement 2).

Discussion
In a prior 2-site randomized clinical trial,11 youths at clinical
and familial risk for BD who received 4 months of FFT had more
favorable mood trajectories (faster episode recovery, more time
in remission, and lower mania or hypomania scores) during
1 year compared with youths in a 1 to 2 session EC treatment.
In the present study, a randomized clinical trial with a larger
sample, 3 sites, a more intensive EC comparator (6 sessions dur-
ing 4 months), and a longer follow-up period (average of
2 years), we observed no difference between FFT and EC in
recovery times. However, FFT was associated with longer
well intervals from randomization to new mood episodes
(median, 81; 95% CI, 56-123 weeks) than EC (median, 63; 95%
CI, 44-78 weeks), suggesting that FFT may have uniquely en-
during effects that extend into the maintenance phase of treat-
ment. This study extends the results of other randomized clini-
cal trials indicating effects of family psychoeducation and skill
training on the long-term trajectory of depressive symptoms
in pediatric mood disorders (eDiscussion in Supplement 2)32-34

as well as trials indicating enduring effects of cognitive behav-
ioral therapy (given acutely) on recurrence among adult pa-
tients with depression.35,36

Of 7 randomized clinical trials of adult and pediatric BD,
5 indicated stronger effects of FFT on depressive symptoms
than manic or hypomanic symptoms,11,12,14,37,38 whereas 2 in-
dicated stronger effects of FFT on manic or hypomanic
symptoms.13,39 Because the FFT protocols in these 7 trials were
similar, we suspect that differences between study popula-
tions in the polarity of baseline symptoms influenced whether
treatment effects were specific to one pole vs the other. Of note,
85% of youths in the present study enrolled while in a de-
pressed state, and treatment effects were primarily for time
to depressive episodes.

Contrary to one of our hypotheses, the treatment groups
did not differ on the trajectory of mood severity scores dur-
ing 1 to 4 years of follow-up. Of interest, both groups showed
significant mood improvement during the treatment period
and 4 months after treatment, followed by a leveling of symp-
toms (with intermittent fluctuations) for the remainder of the
follow-up period (eFigure in Supplement 2). This pattern of im-
mediate symptom improvement followed by a leveling of
symptoms has been observed in previous trials of FFT in
BD.12,37-39 The longer-term period of follow-up may be the time
when the relevant behavioral skills learned in treatment are
consolidated.

The FFT and EC groups did not differ in the rate of con-
versions to syndromal BD. In secondary analyses, baseline lev-
els of mania and hypomania emerged as the only factors as-
sociated with diagnostic conversion. Subthreshold mania
symptoms are a key component of risk calculation algo-
rithms for onset of BD in high-risk youths, especially when
combined with early indicators of depression, anxiety, and
mood instability.2,7,40-42 In clinical practice, measuring sub-

threshold manic symptoms can be accomplished with child-
and parent-report questionnaires.43

Limitations
This trial has limitations. First, the EC condition was matched
to the FFT condition in duration (4 months) but not number
of sessions (12 vs 6 sessions). Thus, group differences in symp-
tom outcomes could have been attributable to more opportu-
nities for FFT clinicians to observe symptom changes in
patients and arrange preventative interventions. Second, al-
though the treatment groups did not differ significantly on time
in study, the estimated median follow-up time in the FFT group
(114.0 weeks) was numerically longer than that in the EC group
(92.5 weeks). We did not find any effects of study site, sex, age,
baseline symptoms, or other covariates on time in study, and
the treatment effects on time to new mood episode remained
significant after controlling for these factors. Moreover, there
were no indications that participants in the EC group who were
doing well initially were more likely to drop out early. Thus,
the pattern of data loss did not appear to be informative in a
way that would bias the results in favor of FFT (eResults in
Supplement 2).

Third, families in the FFT and EC groups completed the
same proportion of protocol therapy sessions (91.7%) and
did not differ significantly in rates of treatment discontinua-
tion, reflecting the substantial outreach to families by clini-
cal and research staff. We did not, however, examine
whether youths and families in the FFT group developed
stronger relationships with their assigned study staff than
those in the EC group, leading to longer study participation
and perhaps better outcomes. Examining this question
would require measuring therapeutic alliance as an interven-
ing variable in the relationship between treatment and clini-
cal outcomes. Such a study is currently underway.44 Partici-
pant attrition should also be examined in community care
settings where treatment costs are higher, travel to clinics
more expensive, and socioeconomic status more variable
than in this study.

In addition, previous trials have shown that FFT is asso-
ciated with increases in constructive family communication
and decreases in criticism or conflict compared with com-
parison treatments.45-48 The present study’s design did not
enable us to examine the temporal relationship between
changes in family communication and symptom changes in
patients, such as whether (1) incorporating communication
skills reduces adversity in family interactions and contrib-
utes to symptom regulation in patients or (2) stabilization of
symptoms enables patients to downregulate their reactions
to critical comments by family members. These questions
are important to elucidating the mechanisms by which fam-
ily interventions are associated with clinical improvements
among patients.

Conclusions
Among youths with a family history of BD who show early signs
of depression or subthreshold mania or hypomania, mood dis-

Family-Focused Therapy vs Enhanced Care for Symptomatic Youths at High Risk for Bipolar Disorder Original Investigation Research

jamapsychiatry.com (Reprinted) JAMA Psychiatry May 2020 Volume 77, Number 5 461

Downloaded from jamanetwork.com by University of Houston – Main Campus user on 07/16/2024

https://jamanetwork.com/journals/jama/fullarticle/10.1001/jamapsychiatry.2019.4520?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

http://www.jamapsychiatry.com/?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

order episodes may be delayed through participation in a
4-month program of FFT. Delaying or preventing episodes of
mood disorder may have enduring effects on psychosocial

functioning for youths with high-risk syndromes, as well as
among parents in terms of the considerable burden of care-
giving for a young person with early-onset BD.4,49

ARTICLE INFORMATION

Accepted for Publication: November 8, 2019.

Published Online: January 15, 2020.
doi:10.1001/jamapsychiatry.2019.4520

Author Contributions: Drs Miklowitz and Schneck
had full access to all the data in the study and take
responsibility for the integrity of the data and the
accuracy of the data analysis.
Concept and design: Miklowitz, Schneck, Singh,
Chang.
Acquisition, analysis, or interpretation of data:
All authors.
Drafting of the manuscript: Miklowitz, Schneck,
Singh, Sugar, Chang.
Critical revision of the manuscript for important
intellectual content: All authors.
Statistical analysis: Miklowitz, Sugar.
Obtained funding: Miklowitz, Chang.
Administrative, technical, or material support:
Miklowitz, Schneck, Walshaw, Sullivan, Suddath,
Forgey Borlik, Chang.
Supervision: Miklowitz, Schneck, Walshaw, Singh,
Sullivan, Suddath, Chang.

Conflict of Interest Disclosures: Dr Miklowitz
reported receiving research support from the
National Institute of Mental Health (NIMH) during
the conduct of the study; receiving grants from the
Danny Alberts Foundation, the Attias Family
Foundation, the Carl and Roberta Deutsch
Foundation, the Kayne Family Foundation, AIM for
Mental Health, the American Foundation for Suicide
Prevention, and the Max Gray Fund; and book
royalties from Guilford Press and John Wiley and
Sons. Dr Schneck reported receiving research
support from the NIMH during the conduct of the
study and receiving grants from the Ryan White
Foundation outside the submitted work.
Dr Walshaw reported receiving grants from NIMH
during the conduct of the study. Dr Singh reported
receiving grants from the NIMH during the conduct
of the study; receiving grants from Allergan, the
Brain and Behavior Foundation, Johnson &
Johnson, the National Institutes of Health, the
Patient-Centered Outcomes Research Institute, the
Stanford Maternal Child Health Research Institute,
and the Stanford Department of Psychiatry and
Behavioral Sciences; serving on the advisory board
for Sunovion; being a consultant for Google X and
Limbix; and receiving royalties from the American
Psychiatric Association Publishing outside the
submitted work. Dr Sullivan reported receiving
grants from the NIMH during the conduct of the
study; reported receiving a grant from the Caring
for Colorado Foundation to provide clinician
trainings in the family-focused therapy model,
covering approximately 50% of time, outside the
submitted work. Dr Forgey Borlik reported
receiving grants from the NIMH during the conduct
of the study. Dr Sugar reported receiving grants
from the National Institutes of Health during the
conduct of the study. Dr Chang reported receiving
personal fees from Sunovion and Allergan outside
the submitted work. No other disclosures were
reported.

Funding/Support. This study was supported in
part by grants R01MH093676 (Drs Miklowitz and

Schneck) and R01MH093666 (Dr Chang) from the
NIMH.

Role of the Funder/Sponsor: The funding sources
had no role in the design or conduct of the study:
collection, management, analysis and
interpretation of data; preparation, review, or
approval of the manuscript; or decision to submit
the manuscript for publication.

Data Sharing Statement: See Supplement 3.

Additional Contributions. The following
individuals provided administrative support and
study diagnostic or follow-up evaluations: Casey
Armstrong, MA, Samantha Frey, BA, Brittany
Matkevich, BA, and Natalie Witt, BA (UCLA School
of Medicine); Tenah Acquaye, BA, Daniella
DeGeorge, BA, Kathryn Goffin, BA, Jennifer
Pearlstein, MA, and Aimee-Noelle Swanson, PhD
(Stanford University); and Laura Anderson, MA,
Addie Bortz, MA, Amethyst Brandin, BA, Anna Frye,
BA, Luciana Massaro, MA BA, Zachary Millman,
PhD, Izaskun Ripoll, MD, Rochelle Rofrano, BA, and
Meagan Whitney, MA (University of Colorado,
Boulder). The following clinicians provided study
pharmacological or psychosocial treatments: Alissa
Ellis PhD, Danielle Keenan-Miller, PhD, Eunice Kim,
PhD, Sarah Marvin, PhD, and Jennifer Podell PhD
(UCLA School of Medicine); Victoria Cosgrove, PhD,
Priyanka Doshi, PsyD, Claire Dowdle, PsyD, Meghan
Howe, LCSW MSW, Amy Friedman, LCSW, Jake
Kelman, PsyD, Catherine Naclerio, PsyD, Casey
O’Brien, PsyD, Donna Roybal, MD, Salena Schapp,
PsyD, and Katherine Woicicki, PsyD (Stanford
University); and Melissa Batt, MD, Emily Carol, PhD,
Jasmine Fayeghi, PsyD, Elizabeth George, PhD,
Christopher Hawkey, PhD, Daniel Johnson, PhD,
Barbara Kessel, DO, Mikaela Kinnear, PhD, Daniel
Leopold, MA, Jessica Lunsford-Avery, PhD, Susan
Lurie, MD, Ryan Moroze, MD, MA, Dan Nguyen, MD,
Andrea Pelletier-Baldelli, PhD, Christopher Rogers,
MD, Lela Ross, MD and Dawn O. Taylor, PhD
(University of Colorado, Boulder). Independent
fidelity ratings of therapy sessions were provided
by Eunice Y. Kim, PhD, UCLA School of Medicine.
The following individuals provided consultation on
study procedures, pharmacotherapy protocols, and
statistical analyses: David Axelson, MD, Boris
Birmaher, MD, and John Merranko, MA (University
of Pittsburgh Medical Center, Pittsburgh, PA);
Melissa DelBello, MD, MS (University of Cincinnati
School of Medicine, Cincinnati, OH); Amy Garrett,
PhD and Antonio Hardan, MD (Stanford University);
Michael Gitlin, MD and Gerhard Helleman, PhD
(UCLA School of Medicine); and Judy Garber, PhD
(Vanderbilt University). The members of the data
safety monitoring board included Howard
Markman, PhD (University of Denver, Denver CO),
Frederick Wamboldt, MD (University of Colorado
Anschutz Medical Campus, Denver, CO), and
Charles Judd, PhD (University of Colorado, Boulder,
CO). Members of the board were financially
compensated annually for their contributions. The
other contributors did not receive compensation.

REFERENCES

1. Axelson DA, Birmaher B, Strober MA, et al.
Course of subthreshold bipolar disorder in youth:

diagnostic progression from bipolar disorder not
otherwise specified. J Am Acad Child Adolesc
Psychiatry. 2011;50(10):1001-16.e3. doi:10.1016/j.jaac.
2011.07.005

2. Hafeman DM, Merranko J, Axelson D, et al.
Toward the definition of a bipolar prodrome:
dimensional predictors of bipolar spectrum
disorders in at-risk youths. Am J Psychiatry. 2016;
173(7):695-704. doi:10.1176/appi.ajp.2015.15040414

3. Perlis RH, Dennehy EB, Miklowitz DJ, et al.
Retrospective age at onset of bipolar disorder and
outcome during two-year follow-up: results from
the STEP-BD study. Bipolar Disord. 2009;11(4):391-
400. doi:10.1111/j.1399-5618.2009.00686.x

4. Post RM, Leverich GS, Kupka RW, et al.
Early-onset bipolar disorder and treatment delay
are risk factors for poor outcome in adulthood.
J Clin Psychiatry. 2010;71(7):864-872. doi:10.4088/
JCP.08m04994yel

5. Axelson D, Birmaher B, Strober M, et al.
Phenomenology of children and adolescents with
bipolar spectrum disorders. Arch Gen Psychiatry.
2006;63(10):1139-1148. doi:10.1001/archpsyc.
63.10.1139

6. Duffy A, Horrocks J, Doucette S,
Keown-Stoneman C, McCloskey S, Grof P. The
developmental trajectory of bipolar disorder. Br J
Psychiatry. 2014;204(2):122-128. doi:10.1192/bjp.bp.
113.126706

7. Axelson D, Goldstein B, Goldstein T, et al.
Diagnostic precursors to bipolar disorder in
offspring of parents with bipolar disorder:
a longitudinal study. Am J Psychiatry. 2015;172(7):
638-646. doi:10.1176/appi.ajp.2014.14010035

8. Malhi GS, Morris G, Hamilton A, Outhred T,
Mannie Z. Is “early intervention” in bipolar disorder
what it claims to be? Bipolar Disord. 2017;19(8):627-
636. doi:10.1111/bdi.12576

9. Vallarino M, Henry C, Etain B, et al. An evidence
map of psychosocial interventions for the earliest
stages of bipolar disorder. Lancet Psychiatry. 2015;2
(6):548-563. doi:10.1016/S2215-0366(15)00156-X

10. Miklowitz DJ, Chang KD, Taylor DO, et al. Early
psychosocial intervention for youth at risk for
bipolar I or II disorder: a one-year treatment
development trial. Bipolar Disord. 2011;13(1):67-75.
doi:10.1111/j.1399-5618.2011.00890.x

11. Miklowitz DJ, Schneck CD, Singh MK, et al. Early
intervention for symptomatic youth at risk for
bipolar disorder: a randomized trial of
family-focused therapy. J Am Acad Child Adolesc
Psychiatry. 2013;52(2):121-131. doi:10.1016/j.jaac.2012.
10.007

12. Miklowitz DJ, George EL, Richards JA,
Simoneau TL, Suddath RL. A randomized study of
family-focused psychoeducation and
pharmacotherapy in the outpatient management of
bipolar disorder. Arch Gen Psychiatry. 2003;60(9):
904-912. doi:10.1001/archpsyc.60.9.904

13. Rea MM, Tompson MC, Miklowitz DJ, Goldstein
MJ, Hwang S, Mintz J. Family-focused treatment
versus individual treatment for bipolar disorder:
results of a randomized clinical trial. J Consult Clin

Research Original Investigation Family-Focused Therapy vs Enhanced Care for Symptomatic Youths at High Risk for Bipolar Disorder

462 JAMA Psychiatry May 2020 Volume 77, Number 5 (Reprinted) jamapsychiatry.com

Downloaded from jamanetwork.com by University of Houston – Main Campus user on 07/16/2024

https://jamanetwork.com/journals/jama/fullarticle/10.1001/jamapsychiatry.2019.4520?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

https://dx.doi.org/10.1016/j.jaac.2011.07.005

https://dx.doi.org/10.1176/appi.ajp.2015.15040414

https://dx.doi.org/10.1111/j.1399-5618.2009.00686.x

https://dx.doi.org/10.4088/JCP.08m04994yel

https://jamanetwork.com/journals/jama/fullarticle/10.1001/archpsyc.63.10.1139?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

https://dx.doi.org/10.1192/bjp.bp.113.126706

https://dx.doi.org/10.1176/appi.ajp.2014.14010035

https://dx.doi.org/10.1111/bdi.12576

https://dx.doi.org/10.1016/S2215-0366(15)00156-X

https://dx.doi.org/10.1111/j.1399-5618.2011.00890.x

https://dx.doi.org/10.1016/j.jaac.2012.10.007

https://jamanetwork.com/journals/jama/fullarticle/10.1001/archpsyc.60.9.904?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

http://www.jamapsychiatry.com/?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

Psychol. 2003;71(3):482-492. doi:10.1037/0022-
006X.71.3.482

14. Miklowitz DJ, Otto MW, Frank E, et al.
Psychosocial treatments for bipolar depression:
a 1-year randomized trial from the Systematic
Treatment Enhancement Program. Arch Gen
Psychiatry. 2007;64(4):419-426. doi:10.1001/
archpsyc.64.4.419

15. Miklowitz DJ, Chung B. Family-focused therapy
for bipolar disorder: reflections on 30 years of
research. Fam Process. 2016;55(3):483-499.
doi:10.1111/famp.12237

16. Schneck CD, Chang KD, Singh MK, DelBello MP,
Miklowitz DJ. A pharmacologic algorithm for youth
who are at high risk for bipolar disorder. J Child
Adolesc Psychopharmacol. 2017;27(9):796-805.
doi:10.1089/cap.2017.0035

17. American Psychiatric Association. Diagnostic
and Statistical Manual of Mental Disorders. 4th ed,
text revision. Washington DC: American Psychiatric
Press; 2000.

18. American Psychiatric Association. Diagnostic
and Statistical Manual of Mental Disorders. 5th ed.
Arlington, VA: American Psychiatric Press; 2013.

19. Young RC, Biggs JT, Ziegler VE, Meyer DA.
A rating scale for mania: reliability, validity and
sensitivity. Br J Psychiatry. 1978;133:429-435.
doi:10.1192/bjp.133.5.429

20. Poznanski EO, Mokros HB. Children’s
Depression Rating Scale, Revised. Los Angeles, CA:
Western Psychological Services; 1999.

21. Birmaher B, Axelson D, Goldstein B, et al.
Four-year longitudinal course of children and
adolescents with bipolar spectrum disorders: the
Course and Outcome of Bipolar Youth (COBY)
study. Am J Psychiatry. 2009;166(7):795-804.
doi:10.1176/appi.ajp.2009.08101569

22. Miklowitz DJ, Schneck CD, Walshaw PD, et al.
Early intervention for youth at high risk for bipolar
disorder: a multisite randomized trial of
family-focused treatment. Early Interv Psychiatry.
2019;13(2):208-216. doi:10.1111/eip.12463

23. Chambers WJ, Puig-Antich J, Hirsch M, et al.
The assessment of affective disorders in children
and adolescents by semistructured interview:
test-retest reliability of the schedule for affective
disorders and schizophrenia for school-age
children, present episode version. Arch Gen
Psychiatry. 1985;42(7):696-702. doi:10.1001/
archpsyc.1985.01790300064008

24. 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(7):980-988.
doi:10.1097/00004583-199707000-00021

25. Axelson D, Birmaher BJ, Brent D, et al.
A preliminary study of the Kiddie Schedule for
Affective Disorders and Schizophrenia for
School-Age Children mania rating scale for children
and adolescents. J Child Adolesc Psychopharmacol.
2003;13(4):463-470. doi:10.1089/
104454603322724850

26. Sheehan DV, Lecrubier Y, Sheehan KH, et al.
The Mini-International Neuropsychiatric Interview
(M.I.N.I.): the development and validation of a
structured diagnostic psychiatric interview for
DSM-IV and ICD-10. J Clin Psychiatry. 1998;59(suppl
20):22-33.

27. Weissman MM, Wickramaratne P, Adams P,
Wolk S, Verdeli H, Olfson M. Brief screening for
family psychiatric history: the family history screen.
Arch Gen Psychiatry. 2000;57(7):675-682. doi:10.
1001/archpsyc.57.7.675

28. Begg CB, Iglewicz B. A treatment allocation
procedure for sequential clinical trials. Biometrics.
1980;36(1):81-90. doi:10.2307/2530497

29. Marvin SE, Miklowitz DJ, O’Brien MP, Cannon
TD. Family-focused therapy for individuals at clinical
high risk for psychosis: treatment fidelity within a
multisite randomized trial. Early Interv Psychiatry.
2016;10(2):137-143. doi:10.1111/eip.12144

30. Keller MB, Lavori PW, Friedman B, et al.
The Longitudinal Interval Follow-up Evaluation. a
comprehensive method for assessing outcome in
prospective longitudinal studies. Arch Gen Psychiatry.
1987;44(6):540-548. doi:10.1001/archpsyc.1987.
01800180050009

31. Ger D, Everitt BS. Handbook of Statistical
Analyses Using SAS. 3nd ed. Boca Raton, FL:
Chapman and Hall/CRC Press; 2009.

32. Nadkarni RB, Fristad MA. Clinical course of
children with a depressive spectrum disorder and
transient manic symptoms. Bipolar Disord. 2010;12
(5):494-503. doi:10.1111/j.1399-5618.2010.00847.x

33. West AE, Weinstein SM, Peters AT, et al. Child-
and family-focused cognitive-behavioral therapy for
pediatric bipolar disorder: a randomized clinical
trial. J Am Acad Child Adolesc Psychiatry. 2014;53
(11):1168-1178.e1. doi:10.1016/j.jaac.2014.08.013

34. Tompson MC, Sugar CA, Langer DA,
Asarnow JR. A randomized clinical trial comparing
family-focused treatment and individual supportive
therapy for depression in childhood and early
adolescence. J Am Acad Child Adolesc Psychiatry.
2017;56(6):515-523. doi:10.1016/j.jaac.2017.03.018

35. Cuijpers P, Hollon SD, van Straten A, Bockting
C, Berking M, Andersson G. Does cognitive
behaviour therapy have an enduring effect that is
superior to keeping patients on continuation
pharmacotherapy? a meta-analysis. BMJ Open.
2013;3(4):e002542. doi:10.1136/bmjopen-2012-
002542

36. Brent DA, Brunwasser SM, Hollon SD, et al.
Effect of a cognitive-behavioral prevention program
on depression 6 years after implementation among
at-risk adolescents: a randomized clinical trial.
JAMA Psychiatry. 2015;72(11):1110-1118. doi:10.1001/
jamapsychiatry.2015.1559

37. Miklowitz DJ, Richards JA, George EL, et al.
Integrated family and individual therapy for bipolar
disorder: results of a treatment development study.
J Clin Psychiatry. 2003;64(2):182-191. doi:10.
4088/JCP.v64n0211

38. Miklowitz DJ, Axelson DA, Birmaher B, et al.
Family-focused treatment for adolescents with

bipolar disorder: results of a 2-year randomized
trial. Arch Gen Psychiatry. 2008;65(9):1053-1061.
doi:10.1001/archpsyc.65.9.1053

39. Miklowitz DJ, Schneck CD, George EL, et al.
Pharmacotherapy and family-focused treatment for
adolescents with bipolar I and II disorders: a 2-year
randomized trial. Am J Psychiatry. 2014;171(6):658-
667. doi:10.1176/appi.ajp.2014.13081130

40. Birmaher B, Axelson D, Strober M, et al. Clinical
course of children and adolescents with bipolar
spectrum disorders. Arch Gen Psychiatry. 2006;63
(2):175-183. doi:10.1001/archpsyc.63.2.175

41. Birmaher B, Merranko JA, Goldstein TR, et al.
A risk calculator to predict the individual risk of
conversion from subthreshold bipolar symptoms to
bipolar disorder I or II in youth. J Am Acad Child
Adolesc Psychiatry. 2018;57(10):755-763.e4. doi:10.
1016/j.jaac.2018.05.023

42. Van Meter AR, Burke C, Youngstrom EA,
Faedda GL, Correll CU. The bipolar prodrome:
meta-analysis of symptom prevalence prior to initial
or recurrent mood episodes. J Am Acad Child
Adolesc Psychiatry. 2016;55(7):543-555. doi:10.
1016/j.jaac.2016.04.017

43. Youngstrom EA, Van Meter A, Frazier TW,
Youngstrom JK, Findling RL. Developing and
validating short forms of the Parent General
Behavior Inventory mania and depression scales for
rating youth mood symptoms. J Clin Child Adolesc
Psychol. 2018:1-16. doi:10.1080/15374416.2018.
1491006

44. Miklowitz DJ, Arevian A, Walshaw P.
Technology-enhanced family intervention for
adolescents at high risk for mood disorders. Bipolar
Disord. 2019;(21)(suppl 1):52.

45. Simoneau TL, Miklowitz DJ, Richards JA,
Saleem R, George EL. Bipolar disorder and family
communication: effects of a psychoeducational
treatment program. J Abnorm Psychol. 1999;108
(4):588-597. doi:10.1037/0021-843X.108.4.588

46. Sullivan AE, Judd CM, Axelson DA, Miklowitz
DJ. Family functioning and the course of adolescent
bipolar disorder. Behav Ther. 2012;43(4):837-847.
doi:10.1016/j.beth.2012.04.005

47. O’Brien MP, Miklowitz DJ, Candan KA, et al.
A randomized trial of family focused therapy with
populations at clinical high risk for psychosis:
effects on interactional behavior. J Consult Clin
Psychol. 2014;82(1):90-101. doi:10.1037/a0034667

48. O’Brien MP, Miklowitz DJ, Cannon TD.
Decreases in perceived maternal criticism predict
improvement in subthreshold psychotic symptoms
in a randomized trial of family-focused therapy for
individuals at clinical high risk for psychosis. J Fam
Psychol. 2015;29(6):945-951. doi:10.1037/
fam0000123

49. Perlick DA, Jackson C, Grier S, et al.
Randomized trial comparing caregiver-only
family-focused treatment to standard health
education on the 6-month outcome of bipolar
disorder. Bipolar Disord. 2018;20(7):622-633.
doi:10.1111/bdi.12621

Family-Focused Therapy vs Enhanced Care for Symptomatic Youths at High Risk for Bipolar Disorder Original Investigation Research

jamapsychiatry.com (Reprinted) JAMA Psychiatry May 2020 Volume 77, Number 5 463

Downloaded from jamanetwork.com by University of Houston – Main Campus user on 07/16/2024

https://dx.doi.org/10.1037/0022-006X.71.3.482

https://jamanetwork.com/journals/jama/fullarticle/10.1001/archpsyc.64.4.419?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

https://dx.doi.org/10.1111/famp.12237

https://dx.doi.org/10.1089/cap.2017.0035

https://dx.doi.org/10.1192/bjp.133.5.429

https://dx.doi.org/10.1176/appi.ajp.2009.08101569

https://dx.doi.org/10.1111/eip.12463

https://jamanetwork.com/journals/jama/fullarticle/10.1001/archpsyc.1985.01790300064008?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

https://dx.doi.org/10.1097/00004583-199707000-00021

https://dx.doi.org/10.1089/104454603322724850

https://www.ncbi.nlm.nih.gov/pubmed/9881538

https://jamanetwork.com/journals/jama/fullarticle/10.1001/archpsyc.57.7.675?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

https://dx.doi.org/10.2307/2530497

https://dx.doi.org/10.1111/eip.12144

https://jamanetwork.com/journals/jama/fullarticle/10.1001/archpsyc.1987.01800180050009?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

https://dx.doi.org/10.1111/j.1399-5618.2010.00847.x

https://dx.doi.org/10.1016/j.jaac.2014.08.013

https://dx.doi.org/10.1016/j.jaac.2017.03.018

https://dx.doi.org/10.1136/bmjopen-2012-002542

https://jamanetwork.com/journals/jama/fullarticle/10.1001/jamapsychiatry.2015.1559?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

https://dx.doi.org/10.4088/JCP.v64n0211

https://jamanetwork.com/journals/jama/fullarticle/10.1001/archpsyc.65.9.1053?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

https://dx.doi.org/10.1176/appi.ajp.2014.13081130

https://jamanetwork.com/journals/jama/fullarticle/10.1001/archpsyc.63.2.175?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

https://dx.doi.org/10.1016/j.jaac.2018.05.023

https://dx.doi.org/10.1016/j.jaac.2016.04.017

https://dx.doi.org/10.1080/15374416.2018.1491006

https://dx.doi.org/10.1037/0021-843X.108.4.588

https://dx.doi.org/10.1016/j.beth.2012.04.005

https://dx.doi.org/10.1037/a0034667

https://dx.doi.org/10.1037/fam0000123

https://dx.doi.org/10.1111/bdi.12621

http://www.jamapsychiatry.com/?utm_campaign=articlePDF%26utm_medium=articlePDFlink%26utm_source=articlePDF%26utm_content=jamapsychiatry.2019.4520

Contents lists available at ScienceDirect

Journal of Affective Disorders

journal homepage: www.elsevier.com/locate/jad

Research paper

Are youths with disruptive mood dysregulation disorder different from
youths with major depressive disorder or persistent depressive disorder?
Xavier Benarousa,b,c, Johanne Renaudd,e, Jean Jacques Bretonf, David Cohenc,g, Réal Labellef,h,i,j,
Jean-Marc Guiléa,b,e,⁎

a Child and Adolescent Psychopathology Services, Amiens University Hospital, Amiens, France
b INSERM Unit U1105 Research Group for Analysis of the Multimodal Cerebral Function, University of Picardy Jules Verne (UPJV), Amiens, France
c Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
dManulife Centre for Breakthroughs in

een Depression and Suicide Prevention, Douglas Mental Health University Institute, McGill University, Montreal, Canada
e Department of psychiatry, McGill University, Montreal, Canada
fDepartment of psychiatry, University of Montreal, Montreal, Canada
g CNRS UMR 7222, Institute for Intelligent Systems and Robotics, Sorbonne Universités, UPMC, Paris, France
hDépartement de psychologie, Université du Québec à Montréal, Montréal, Canada
i Centre de recherche, Hôpital en santé mentale Rivière-des-Prairies, CIUSSS du Nord-de-l’Île-de-Montréal, Canada
j Centre for Research and Intervention on Suicide, Ethical Issues and End-of-life practices, (CRISE), Montreal, Canada

A R T I C L E I N F O

Keywords:
Disruptive mood dysregulation disorder
Major depressive disorder
Persistent depressive disorder
Suicidal behavior
Adolescent
Retrospective chart review

A B S T R A C T

Background: Although the disruptive mood dysregulation disorder (DMDD) was included in the depressive
disorders (DD) section of the DSM-5, common and distinctive features between DMDD and the pre-existing DD
(i.e., major depressive disorder, MDD, and persistent depressive disorder, PDD) received little scrutiny. Methods:
Youths consecutively assessed as outpatients at two Canadian mood clinics over four years were included in the
study (n = 163; mean age:13.4 ± 0.3; range:7–17). After controlling for inter-rater agreement, data were
extracted from medical charts, using previously validated chart-review instruments.
Results: Twenty-two percent of youths were diagnosed with DMDD (compared to 36% for MDD and 25% for
PDD), with substantial overlap between the three disorders. Youths with DMDD were more likely to have a
comorbid non-depressive psychiatric disorder – particularly attention deficit hyperactivity disorder, odds ratio
(OR=3.9), disruptive, impulse-control and conduct disorder (OR=3.0) or trauma- and stressor-related disorder
(OR=2.5). Youths with DMDD did not differ with regard to the level of global functioning, but reported more
school and peer-relationship difficulties compared to MDD and/or PDD. The vulnerability factors associated with
mood disorders (i.e., history of parental depression and adverse life events) were found at a comparable fre-
quency across the three groups. Limitations: The retrospective design and the selection bias for mood disordered
patients restricted the generalizability of the results. Conclusions: Youths with DMDD share several clinical
features with youths with MDD and PDD. Further studies are required to determine the developmental trajec-
tories and the benefits of expanding pharmacotherapy for DD to DMDD.

1. Introduction

Over the last two decades, the diagnosis and treatment of children
presenting with severe and chronic irritability has become a challenge
within the context of the pediatric bipolar controversy (Masi et al.,
2015; Roy et al., 2014; Consoli and Cohen, 2013; Tourian et al., 2015).
On the basis of studies of youths with severe mood dysregulation
(SMD), a clinical presentation characterized by persisting irritability

and recurrent temper outbursts, the disruptive mood dysregulation
disorder (DMDD) was included as a new diagnostic entity in the de-
pressive disorders (DD) section of the Diagnostic and Statistical Manual
of Mental Disorders, Fifth Edition (DSM-5) (American Psychiatric
Association 2013). The DMDD is characterized by persistent irritable
mood and, severe (i.e. out of proportion in intensity or duration) and
frequent (i.e. three or more times per week) temper outbursts.

The discriminant validity of DMDD and its inclusion among DD

https://doi.org/10.1016/j.jad.2020.01.020
Received 12 March 2019; Received in revised form 27 November 2019; Accepted 5 January 2020

⁎ Corresponding author at: Child and Adolescent Psychiatry Services, Amiens University Hospital, Amiens, France, CHU Amiens-Picardie, Site Sud, F-80054
Amiens, France.

E-mail address: guile.jean-marc@chu-amiens.fr (J.-M. Guilé).

Journal of Affective Disorders 265 (2020) 207–

215

Available online 08 January 2020
0165-0327/ © 2020 Published by Elsevier B.V.

http://www.sciencedirect.com/science/journal/01650327

https://www.elsevier.com/locate/jad

https://doi.org/10.1016/j.jad.2020.01.020

mailto:guile.jean-marc@chu-amiens.fr

https://doi.org/10.1016/j.jad.2020.01.020

http://crossmark.crossref.org/dialog/?doi=10.1016/j.jad.2020.01.020&domain=pdf

have been, and still are, controversial issues (Roy et al., 2014;
Stringaris et al., 2017). The inclusion of DMDD in the DD section of the
DSM-5 was supported by longitudinal studies showing that chronic ir-
ritability in childhood led to internalizing disorders in adolescence and
early adulthood, in particular anxiety and depressive disorders
(Brotman et al., 2006; Stringaris et al., 2009; Stringaris et al., 2010;
Leibenluft E. Severe Mood Dysregulation 2011; Vidal-Ribas et al.,
2016). The relationship between childhood irritability and depression
in adolescence was also supported by genetic and family studies (Cross-
Disorder Group of the Psychiatric Genomics Consortium 2013;
Propper et al., 2017; Krieger et al., 2013; Wiggins et al., 2018;
Brotman et al., 2007; Stringaris et al., 2012; Savage et al., 2015).

The delimitation of DMDD from other psychiatric disorders, and, in
particular mood disorders, is an essential step towards the establish-
ment of its diagnostic validity (Robins and Guze, 1970). To date, the
literature has mainly focused on the distinction between DMDD (and
the research clinical entity of SMD) and bipolar disorder (BD)
(Stringaris et al., 2010). In contrast, the validity of DMDD has not been
questioned against DD, i.e., major depressive disorder (MDD) and
persistent depressive disorder (PDD). PDD differs from MDD with re-
spect to the severity and duration of depressive symptoms. PDD is a
chronic DD whereas MDD is regarded as mostly, but not exclusively,
episodic. Cumulative findings showed that the chronic versus episodic
course of mood symptoms is a key clinical feature with distinctive
clinical correlates (Masi et al., 2006) and outcome in adulthood
(Leibenluft et al., 2006). DMDD differs from DD, both PDD and MDD,
with respect to the presence of temper outbursts and the nature of mood
symptoms. Findings from the prospective population-based Great
Smoky Mountains Study (Stringaris et al., 2013) showed that depressed
mood was the most common cardinal mood symptom in youth meeting
criteria for DD. On the contrary, irritable mood alone was rare (5.7%).
Given that no study has questioned DMDD against MDD or PDD, the
differences between MDD, PDD and DMDD (in terms of clinical corre-
lates, natural course, and vulnerability factors) have yet to be char-
acterized. The present study aimed to address these issues.

The first objective of the present study was to determine the fre-
quency of DMDD (compared to MDD and PDD) in a clinical outpatient
sample. The frequency of DMDD is expected to be similar to the values
reported in outpatient samples (22–31%) (Margulies et al., 2012;
Axelson et al., 2012; Freeman et al., 2016; Tufan et al., 2016). The
extent of overlap between DMDD, MDD and PDD would help to docu-
ment the validity of the diagnosis of DMDD. Indeed, if DMDD is a
distinct, valid clinical entity, its overlap with MDD and/or PDD should
be moderate – or at least no larger than for the two other disorders.

The study’s second objective was to compare the clinical char-
acteristics of youths with DMDD, MDD and PDD. We hypothesized that
the comorbidity rate would be higher for DMDD than for the two other
disorders. This would be in line with previous studies showing a high
rate of comorbidity in children and adolescents with DMDD compared
to other psychiatric disorders (Margulies et al., 2012; Axelson et al.,
2012; Freeman et al., 2016; Tufan et al., 2016; Dougherty et al., 2014;
Axelson, 2013; Stringaris and Taylor, 2015; Copeland et al., 2013). This
would also be consistent with the assumption that irritability, the core
symptom in DMDD, is located at the interface between internalizing
disorders and externalizing disorders (Stringaris and Taylor, 2015).

The study’s third objective was to compare the impairments re-
spectively associated with DMDD, MDD and PDD. We expected that
youths with DMDD would present a range of peer-relationship diffi-
culties that went beyond aggressive behavior. Several dysfunctions in
social information processing have been reported in youths with
chronic irritability; this exposes them to a greater risk of experiencing
repeated interpersonal difficulties (Leibenluft E. Severe Mood
Dysregulation 2011; Vidal-Ribas et al., 2016; Vidal-Ribas et al., 2018).
Based on preliminary findings, we would also expect school functioning
to be more impaired in youths with DMDD than in youths with MDD or
PDD (Dougherty et al., 2014; Copeland et al., 2013).

Lastly, the study’s fourth objective was to compare the vulnerability
factors profile for mood disorders in youths with DMDD, MDD and PDD.
The focus was placed on vulnerability factors consistently associated
with DD in children and adolescents, i.e., a first-degree family history of
depression, a history of adverse life events and an impaired develop-
mental history (Thapar et al., 2012).

2. Methods

2.1. Participants

Data were retrospectively reviewed after their extraction from the
medical records of youths referred to the two mood disorder outpatient
clinics in Montreal (Canada) between November 2006 and December
2010. The recruitment sites for French-speaking and English-speaking
youths were respectively the Rivière des Prairies Hospital (RPH) and
the Douglas Mental Health University Institute (DMHU). The main in-
clusion criteria were age between 7 and 17, and admission to one of the
two outpatient clinics following a standardized diagnostic evaluation
by a multidisciplinary team (including a child psychiatrist). A total of
163 consecutive participants were assessed (114 at RPH and 49 at
DMHU) during the study period. The study population comprised 65
males (40%) and 98 females (60%), and the mean± standard deviation
(range) age was 13.4 ± 0.3 (7‒17).

2.2. Setting and study design

Clinical and sociodemographic data were gathered using a chart
review instrument that had previously been validated in a retrospective
study of adolescent outpatients with DD (Breton et al., 2012;
Guile et al., 2016; LeBoeuf et al., 2017). The instrument recorded all the
clinical data noted by healthcare professionals (clinicians, social
workers and nurses) in the patient’s medical file. All information per-
taining to a participant’s identity was removed. The inter-rater agree-
ment (κ = 0.80) had previously been measured at each site, using a
sample of ten charts. Data on sociodemographic characteristics, family
and personal medical histories, DSM diagnoses, symptoms, and treat-
ment were extracted using the instrument. In the medical records, the
psychiatric diagnoses had been defined according to the DSM-IV-TR
criteria and categories. In the current analysis, the psychiatric diag-
noses were presented with respect to the DSM-5 criteria and categories.
At both recruitment sites, the routine diagnostic work-up encompassed
several standardized evaluations including the Schedule for Affective
Disorders and Schizophrenia for School-Age Children (K-SADS-PL)
(Kaufman et al., 1997) and the Children-Global Assessment Scale (C-
GAS) (Shaffer et al., 1983). In accordance with the ethics regulatory
framework enforced in the province of Québec (Canada), access to
medical files was authorized by the Director of Professional Services at
each of the two investigating centers.

2.3. Measurements

2.3.1. Psychiatric diagnoses
The K-SADS-PL was completed as part of each patient’s routine

clinical assessment at DMHU and RPH. The parents were the informants
in the present study. The K-SADS-PL’ internal validity (inter-rater re-
liability: 93–100%; test-retest reliability: 0.74–0.90) and external va-
lidity are excellent. Clinical data relative to psychiatric diagnoses were
extracted using the chart review instrument. Apart from the removal of
the bereavement exclusion criteria, the diagnostic criteria for MDD in
DSM-5 (notably the presence of symptoms for at least two weeks) are
the same as those in DSM-IV-TR. The diagnosis of PDD was introduced
in the DSM-5 as a consolidation of the previously defined DSM-IV-TR
category of dysthymia and the chronic subtype of MDD. All participants
meeting criteria for dysthymia have been identified as PDD. None of the
study participants met the criteria for chronic subtype of MDD.

X. Benarous, et al. Journal of Affective Disorders 265 (2020) 207–215

208

With respect to the DMDD diagnosis, symptoms reported in the
patient’s medical file were compared with the DSM-5 criteria for
DMDD. Data were abstracted using an additional chart review instru-
ment based on the criteria for temper dysregulation disorder with
dysphoria – a research entity developed by the DSM-5 Task Force prior
to the publication of the final criteria for DMDD (Leibenluft E. Severe
Mood Dysregulation 2011; American Psychiatric Association Taskforce
DV 2010) . Each criterion was scored as present, absent or unknown. The
diagnostic algorithm is provided in the Supplementary Material, and
follows the international guidelines (Table S1) (American Psychiatric
Association Taskforce DV 2010). In particular, DMDD was endorsed
only if the patient met the criteria for duration, cross-domain impair-
ment, and age of onset, with the exclusion criteria rule for bipolar
disorder. The psychometric properties of this diagnostic instrument for
DMDD have previously been explored in another sample of 12- to 15-
year-old outpatients (n= 192; Cronbach’s α for internal validity: 0.90;
κ for test-retest reliability: 0.87) (Boudjerida et al., 2018).

2.3.2. Suicidal behavior and substance use
Suicidal behavior was documented by rating a set of four items:

“prior suicidal ideation”, “a single prior suicide attempt”, “multiple prior
suicide attempts”, and “prior self-aggressive behavior”. The Columbia-
Suicide Severity Rating Scale (C-SSRS) (Posner et al., 2007) was com-
pleted for the study participants at the DMHU clinic. The C-SSRS as-
sesses various forms of suicidal behaviors (active suicidal ideation, in-
terrupted suicide attempts, and aborted suicide attempts), and non-
suicidal self-injuries.

Substance use was documented using a set of five items: “prior use of
a substance”, “regular alcohol use”, “regular alcohol use before the age of
12″, “regular tobacco use”, and “regular tobacco use before the age of 12″.
In the study sample from DMHU, the DEP-ADO questionnaire
(Germain et al., 2007) was additionally used to document substance use
in the previous 12 months. The screening question was “During the last
twelve months, how often have you [has X] used one of the following sub-
stances: alcohol, cannabis, cocaine, inhalant/solvent, stimulant, halluci-
nogen, or heroin”; examples and trivial names were provided for each
substance.

2.3.3. Functional impairment
The level of global functioning was evaluated using the Children-

Global Assessment Scale (C-GAS) (Shaffer et al., 1983) on the basis of
the multidisciplinary evaluation at the first medical visit. Peer-re-
lationship problems were assessed by the clinician using an eight-item
checklist used in chart reviews for the assessment of social functioning
in youths with DD (Breton et al., 2012; Guile et al., 2016; LeBoeuf et al.,
2017). The instrument whose reliability has been previously assessed
(Breton et al., 2012), regroups the following items: physical and ag-
gressive behaviors, stealing goods from other youths, passive and active
social withdrawal, feeling of being rejected by other youths, victim of
physical or verbal aggression. School impairments were assessed using
the school reports annexed to the medical file with regard to the fol-
lowing domains: prior grade repetition, reported learning difficulties,
repeated unjustified school absence.

2.3.4. Vulnerability factors
A modified version of the Family History Screen (Weissman et al.,

2000) was used to retrospectively collect information on the psychiatric
history of the participants’ first- and second-degree relatives. This in-
cluded the history of paternal and maternal depression, which is the
most well-recognized risk factor for children and adolescent depression
(Thapar et al., 2012). Adverse childhood experiences (ACE) were as-
sessed using all available data, with respect to a set of ten items from
the Adverse Childhood Experience Questionnaire (Anda et al., 2010) A
major ACE was defined as an episode of physical and/or sexual and/or
emotional abuse and/or a severe form of emotional and/or physical
neglect (Anda et al., 2010). Data from administrative sources (e.g. a

history of placement in foster care) were also collected following the
method used in previous chart-review studies for pediatric mood dis-
orders (Garno et al., 2005; Benarous et al., 2017a). The developmental
history was explored regarding four domains: a history of complicated
pregnancy, a history of delayed psychomotor development, the pre-
sence of an associated neurological disorder, and a history of head
trauma.

2.4. Statistical analyses

Considering the small sample size and the non-Gaussian data dis-
tribution, non-parametric Kruskal-Wallis tests were used to compare
three groups: (i) youths with DMDD (and, in some cases, a concomitant
DD), (ii) youths with MDD only (i.e., without DMDD and/or PDD), and
(iii) youths with PDD only (i.e., without DMDD and/or MDD). First, we
aimed at increasing the homogeneity of the control groups and thus
raise the study’s statistical power. Second, in order to increase external
validity, we wanted to form the largest possible and most representative
clinical sample of youths with DMDD. In order to facilitate the inter-
pretation of our findings with respect to the previous studies
(Margulies et al., 2012; Axelson et al., 2012; Freeman et al., 2016;
Tufan et al., 2016), we compared the DMDD group with the rest of the
sample (“non-DMDD youths”) (studies detailed in Table S2). The groups
were compared using Pearson’s chi-squared test or Fisher’s exact test for
categorical variables (e.g., DSM-5 diagnoses), and Student’s test or a
Mann-Whitney test for continuous variables (e.g., age). Similar analyses
were performed for the study’s third and fourth objectives. The results
were not corrected for multiple comparisons; we considered that in the
context of an exploratory analysis, the type II error outweighed the type
I error. All statistical analyses were performed with STATASE software
(version 12). The threshold for statistical significance was set to p < .05. All analyses were replicated with (i) a more homogeneous DMDD group (i.e., after the exclusion of youths with associated MDD and/or PDD), and (ii) a DMDD group without associated Attention Deficit with/out Hyperactivity Disorder (ADHD) (Table S3).

3. Results

3.1. Objective 1. Frequency of DMDD and overlap with MDD and/or PDD

Thirty-six youths (22%) met the criteria for DMDD (Table 1). The
frequencies of MDD and PDD were respectively 36% and 25%. The two
sites did not differ significantly (p= .191) with regard to the frequency
of DMDD. Thirty-six percent of the youths with DMDD were also di-
agnosed with a DD (MDD n = 7, PDD n = 6), but none met the di-
agnostic criteria for all three disorders (Fig. 1).

3.2. Objective 2. Clinical characteristics of youths with DMDD, MDD and
PDD

Youths with DMDD were an average of 3.3 years younger than those
with MDD and 2.5 years younger than those with PDD. The proportion
of boys was significantly higher in the DMDD group than in the other
two groups.

The comorbidity profiles of youths with DMDD and youths with
other DD are summarized in Table 2. All the youths with DMDD had at
least one other comorbid DSM-5 diagnosis. The number of comorbid-
ities was significantly higher among youths with DMDD than among
youths with MDD or PDD. Compared with youths with PDD or MDD,
youths with DMDD were more likely to have concurrent trauma- and
stressor-related disorders (odds ratio (OR)=2.5, p=.004), ADHD
(OR=3.9, p<.001) or disruptive, impulse-control and conduct dis- orders (DICCD) (OR=3.0, p=.006). The frequency of anxiety disorders was highest in the PDD group and lowest in the MDD group. The DMDD, MDD or PDD groups did not differ significantly with regard to the frequency of substance use (see Fig. 3), learning disorders or other

X. Benarous, et al. Journal of Affective Disorders 265 (2020) 207–215

209

psychiatric disorders.
The proportion of youths with suicidal ideation was significantly

lower for the youths with DMDD than for youths with MDD or PDD

(Fig. 2). However, the frequencies of suicide attempts, multiple suicide
attempts, and non-suicidal self-injuries were similar in the three groups.
After examining the C-SSRS data for the 14 youths with DMDD from the
DMHU site, we found that 8 participants had made a suicide attempt
(hanging in one case, wrist-cutting in three cases, and another method
in four cases). Six of the 8 suicide attempts were impulsive. Only one
participant had made multiple suicide attempts.

3.3. Objective 3. Impairments associated with DMDD, MDD and/or PDD

The C-GAS score, as measured during the first medical visit, did not
statistically differ between the three groups (Table 3). However, youths
with DMDD reported more learning difficulties and a more frequent
history of grade repetition, relative to youths with MDD or PDD. Peer
relationships were significantly more impaired in youths with DMDD
compared to youths with MDD or PDD. Youths with DMDD were 3–4
times more likely to display physically or verbally aggressive behavior;
they were also more likely to have been victims of verbal and physical
aggression.

3.4. Objective 4. Vulnerability factors associated with DMDD, MDD and/or
PDD

Exposure to parental mental illness was more frequent in youths
with MDD than in youths with DMDD or PDD (Table 4). However, no
statistically significant difference was found between the three groups
with regard to the family history of psychiatric diagnoses, including
depression and substance use.

The proportion of major ACE and the frequency of foster care pla-
cement did not differ between the DMDD, MDD and PDD groups.

Youths with DMDD were significantly more likely to have a history
of complicated pregnancy than youths with MDD or PDD, but the as-
sociation was no longer statistically significant after youths with DMDD

Table 1
Frequency of psychiatric disorders among recruitment sites.

RPH
(n = 114)

DMHU
(n = 49)

Total
(N = 163)

Internalizing disorders
Mood disorders
MDD 40 (35%) 19 (39%) 59 (36%)
PDD 30 (26%) 10 (20%) 40 (25%)
DMDD 22 (19%) 14 (29%) 36 (22%)
BD-I/II 12 (11%) 1 (2%) 13 (8%)

Anxiety disorders 18 (16%) 21 (43%) 39 (24%)
Trauma- and stressor-
related disorders a

13 (11%) 14 (29%) 27 (17%)

Externalizing disorders
ADHD 28 (25%) 16 (33%) 44 (30%)
DICCD 34 (30%) 9 (18%) 43 (26%)
Substance use disorder 7 (6%) 4 (8%) 11 (7%)

Psychotic and developmental disorder
Schizophrenic disorder and
other psychotic disorder

1 (1%) 0 1 (1%)

Learning disorder 10 (9%) 11 (22%) 21 (13%)
Other psychiatric disorders b 8 (7%) 7 (14%) 15 (9%)

Note. RPH= Mood Disorders Clinic at the Rivière-des-Prairies Hospital;
DMHU= Program of DD Pediatric section of the Douglas Mental Health
University Institute; MDD= major depressive disorder; PDD= persistent de-
pressive disorder; DMDD= disruptive mood dysregulation disorder; ADHD=
attention deficit hyperactivity disorder; BD= bipolar disorder type 1 and type
2; DICCD= disruptive, impulse-control and conduct disorders.

a The category “Trauma- and Stressor-Related Disorders” encompasses ad-
justment disorder, acute stress disorder and post-traumatic stress disorder.

b The category “Other psychiatric disorders” encompasses sleep disorder, tics
and Tourette syndrome, obsessive compulsive disorder, eating disorder.

Fig. 1. Overlap between disruptive mood dysregulation disorder, major depressive disorder and persistent depressive disorder.

X. Benarous, et al. Journal of Affective Disorders 265 (2020) 207–215

210

and ADHD were excluded from the analysis (Table S3). No difference
was observed regarding the psychomotor development and the asso-
ciation with a neurological disorder.

4. Discussion

4.1. Interpretation

Regarding the study’s first objective, the frequency of DMDD ob-
served (22%) was slightly lower than that reported for two other out-
patient studies using retrospective diagnoses (see Table S2)
(Axelson et al., 2012; Freeman et al., 2016). Twenty-six to 31% of the
706 children aged 6–12 years in the Longitudinal Assessment of Manic
Symptoms study met the criteria for DMDD (Axelson et al., 2012).
Among the 597 children and adolescents (ages 6–18) treated at a
community mental health center in the US, 31% were diagnosed with

DMDD (Freeman et al., 2016). The frequencies observed in child psy-
chiatry facilities clearly contrast with the relatively low prevalence
estimate of 1% in the general population (Stringaris et al., 2018). The
disparity in the reported frequencies may result from disparities in
countries’ prevalence rates, sampling bias, and more likely, differences
in the diagnostic procedures, our study being uniquely based on the
DMDD algorithm of the DSM-5.

The degree of overlap between DMDD and DD was substantial
(36%), but comparable to the overlap observed with each other group:
30% with PDD and 22% with MDD.

Regarding our second objective, youths with DMDD were younger
and more likely to be male than youths with either PDD or MDD. This
higher proportion of males among youths with DMDD has been con-
sistently reported in previous studies (Margulies et al., 2012;
Axelson et al., 2012; Freeman et al., 2016; Tufan et al., 2016;
Dougherty et al., 2014; Axelson, 2013; Stringaris and Taylor, 2015;

Table 2
Clinical characteristics of children and adolescents with DMDD, MDD, and PDD.

DMDD (n = 36) MDD only (n = 46) PDD only(n = 28) Comparisons between the
three groupsd

Non DMDD
(n = 127)

Comparisons DMDD vs.
non-DMDDe

Socio-demographic features
Gender, male, n (%) 22 (61%) 13 (28%) 11 (39%) p = .01 43 (34%) p < .01 Age (years) (mean±SD) 11.5 ± 3.4 14.8 ± 2.53 14.0 ± 2.93 p < .01 13.9 ± 2.91 p < .01 Socio economic difficulties 4 (11%) 2 (4%) 12 (43%) p < .01 24 (19%) p = .274

Number of psychiatric disorders 2.5 ± 0.6 1.9 ± 0.8 2.2 ± 0.7 p < .01 2.1 ± 0.8 p < .01 DSM-5 associated psychiatric disorders Internalizing disorders Anxiety disorders 9 (25%) 5 (11%) 11 (39%) p = .016 30 (24%) p = .829 Trauma- and stressor- related disorders a

10 (28%) 0 1 (4%) p < .01 17 (13%) p = .040

Externalizing disorders
ADHD 18 (50%) 5 (11%) 4 (14%) p < .01 26 (21%) p < .01 DICCD 16 (44%) 11 (24%) 3 (11%) p = .009 27 (21%) p = .005 Substance use disorder 3 (8%) 2 (4%) 1 (4%) p = .651 8 (6%) p = .668

Developmental and other disorders b

Learning disorder 7 (19%) 5 (11%) 3 (11%) p = .472 14 (11%) p = .257
Other psychiatric disorders
c

4 (11%) 4 (9%) 3 (11%) p = .927 11 (9%) p = .654

Note. Statistically significant results are presented in bold. ADHD= attention deficit hyperactivity disorder; DICCD= disruptive, impulse-control and conduct
disorders.

a The category “Trauma- and Stressor-Related Disorders” encompasses adjustment disorder, acute stress disorder and post-traumatic stress disorder.
b Nobody presented a schizophrenic disorder or other psychotic disorder among youths with DMDD, MDD or PDD.
c The category “Other psychiatric disorder” encompasses sleep disorder, tics and Tourette syndrome, obsessive compulsive disorder, eating disorder.
d Kruskal-Wallis test.
e Fisher’s Exact test, except for “number of psychiatric diagnosed” where Mann-Whitney U-test was used.

Fig. 2. Suicidal behaviors among youth with DMDD, MDD without DMDD/PDD, and PDD without DMDD/MDD.

X. Benarous, et al. Journal of Affective Disorders 265 (2020) 207–215

211

Copeland et al., 2013; Copeland et al., 2014). The higher comorbidity,
observed in our sample, with externalizing disorders associated with a
predominantly male sex ratio (Breton et al., 1999), could have con-
tribute to this finding. Of note, the sex ratio observed with DMDD
differs from the more balanced sex ratio reported in depressed pre-
pubertal children (Angold and Rutter, 1992). The high comorbidity rate
among youths with DMDD reported in the present study is in line with
all the prior studies, in which other psychiatric disorders were observed
in between 60% and 92% of outpatients with DMDD (Margulies et al.,
2012; Axelson et al., 2012; Freeman et al., 2016; Tufan et al., 2016;
Dougherty et al., 2014; Axelson, 2013; Stringaris and Taylor, 2015;
Copeland et al., 2013). In our sample, ADHD and DICCD were the most
frequently associated diagnoses in youths with DMDD. Most interest-
ingly, youths with DMDD were 2.5 times more likely to have an asso-
ciated trauma-and stressor-related disorder, compared to youths

without DMDD. To our knowledge this is the first study reporting a
positive association between DMDD and trauma-related disorders
(Table S2). Albeit preliminary, this finding brings additional support to
the postulated relationship between the exposure to chronic stress/re-
peated trauma and chronic mood dysregulation (Dvir et al., 2014).

In the present study, youths with DMDD presented with a lower
frequency of suicidal ideation than the other two groups. In keeping
with the relationship between suicidal behavior and the DMDD core
symptom of irritability, and as previously discussed (Benarous et al.,
2018), suicide attempts might result from the intermixture of covert
suicidal ideation and a temporary increase in irritability caused by in-
tercurrent triggering stressors. This model described in literature
(Benarous et al., 2018; Stringaris and Vidal-Ribas, 2019; Orri et al.,
2018) is further supported by our data as youths with DMDD mostly
reported suicidal attempts as unplanned and impulsive.

Fig. 3. Substance use among youth with DMDD, MDD without DMDD/PDD, and PDD without DMDD/MDD.

Table 3
Functional impairment associated with DMDD, MDD, and PDD.

DMDD (n = 36)

MDD only
(n = 46)

PDD only(n = 28) Comparisons between the
three groupsb

Non DMDD
(n = 127)

Comparisons DMDD vs.
non-DMDDc

C-GAS: Mild or severe functional
impairment a

10 (28%) 23 (50%) 9 (32%) p = .092 58 (45%) p = .064

School achievement
Prior grade repetition 14 (39%) 6 (15%) 5 (18%) p = .030 23 (20%) p = .044
Reported learning difficulties 9 (25%) 2 (4%) 4 (14%) p = .025 15 (12%) p = .049
Repeated school absence 7 (19%) 9 (20%) 9 (32%) p = .394 26 (21%) p = .892

Peer relationship problems
1. Physical aggressive behaviors 14 (39%) 5 (11%) 2 (7%) p < .01 14 (11%) p < .01 2. Verbal aggressive behaviors 17 (47%) 5 (11%) 4 (14%) p < .01 19 (15%) p < .01 3. Stealing goods from other youths

5 (14%) 5 (11%) 2 (7%) p = .698 10 (8%) p = .326

4. Passive social withdrawal 20 (56%) 26 (56%) 15 (54%) p = .971 69 (54%) p = .956
5. Active social avoidance 3 (8%) 2 (4%) 1 (4%) p = .651 5 (4%) p = .376
6. Rejected by other youths 13 (36%) 9 (20%) 8 (29%) p = .249 46 (35%) p = .960
7. Victim of physical aggression 5(14%) 3 (7%) 1 (4%) p = .205 5 (4%) p = .026
8. Victim of verbal aggression 14 (39%) 10 (22%) 5 (18%) p = .109 28 (22%) p = .037
Mean total score (0 to 8) 2.4 ± 1.6 1.3 ± 1.2 1.3 ± 1.2 p < .001 1.5 ± 1.2 p < .01

Note. Statistically significant results are presented in bold.
a Mild or severe functional impairment is defined as a C-GAS score strictly above 60.
b Kruskal-Wallis test.
c Fisher’s Exact test.

X. Benarous, et al. Journal of Affective Disorders 265 (2020) 207–215

212

With respect to the study’s third objective, unlike prior studies
(Dougherty et al., 2014; Copeland et al., 2013), no significant difference
was observed between the three groups relative to the measure of
global functioning (C-GAS score). However, several other findings mi-
tigated this overall absence of association. First, youths with DMDD
presented more school difficulties (leaning difficulties and a more fre-
quent history of grade repetition) and peer-relationship problems
compared to youths with MDD or PDD. The poor levels of school
achievement of youths with DMDD was in line with previous commu-
nity studies (Dougherty et al., 2014; Copeland et al., 2013). Copeland
et al. (2013) reported that the frequency of recent school suspension
was higher among youths with DMDD than among psychiatric case-
controls (Copeland et al., 2013). Dougherty et al. (2014)
(Dougherty et al., 2014) in turn, noted that youths with DMDD were
more likely than their counterparts to require remedial education.
Further studies are needed to determine whether the association be-
tween DMDD and learning difficulties can be explained by the presence
of perinatal risk factors and other developmental difficulties. Our data
support this hypothesis because the relationship was no longer statis-
tically significant after youths with ADHD had been excluded from the
analysis (Table S3).

Second, the positive association between DMDD and aggressive
behavior might be seen as somewhat inevitable, since aggressive reac-
tions can be seen as a consequence of irritability. However, youths with
DMDD were more likely to be victims of aggressive behavior by peers
than youths without DMDD; this finding is in line with earlier reports
on youths displaying reactive aggression (Geoffroy et al., 2018). Only
longitudinal studies might be able to determine the interplay between
victimization by peers, reactive aggression, and mood symptoms in
chronically irritable youths. Among youths with chronic irritability, it
has been noted neurocognitive impairments (e.g. emotional recognition
difficulties), which were involved in the daily expression of social skills
(Vidal-Ribas et al., 2018; Stoddard et al., 2015) . They would be worth
studying to better understand peer-relationship difficulties in youths
with DMDD, in comparison to MDD and PDD.

With respect to the last objective relative to vulnerability factors, no
statistically significant difference was found between the three groups
with regard to the family history of psychiatric diagnoses. This meant
that the well-documented vulnerability factor for childhood depression,
i.e., the history of parental depression, was observed at a similar rate
among the three groups. The association between a history of compli-
cated pregnancy and DMDD is probably partly mediated by the pre-
sence of developmental cognitive impairments, since the association

was no longer significant after the exclusion of youths with DMDD and
ADHD (Table S3).

On should remain cautious with regard to these preliminary find-
ings. First, reporting no differences does not mean that the role of the
vulnerability factor is comparable across the three groups. Second, in-
formation on the vulnerability factors resulted from a cross-sectional
study design rather than a prospective longitudinal study. We can only
conclude that on a small sample of help-seeking outpatients, no sig-
nificant difference was observed with respect to the youth’s profile of
environmental vulnerability factors associated with mood disorders
between DMDD, MDD, and PDD.

4.2. Strength and limitations

The results of this study should be interpreted in the context of its
limitations. Firstly, the study suffered from retrospective bias and a
small number of patients in the study groups – despite the four-year
recruitment period. The study’s statistical power was reduced by the
substantial number of variables, and the small sample size also pre-
cluded the use of multivariate analysis. It will be important to confirm
that the observed differences between the DMDD, MDD and PDD
groups do not reflect sociodemographic features. Secondly, the eva-
luation of psychosocial risk factors (such as ACE) may be prone to recall
bias. To limit the potential bias, we measured the data’s validity against
official records (i.e. administrative data from child protection agencies).
Thirdly, the lack of a control group without psychiatric disorders makes
it more difficult to interpret intergroup differences between groups. To
facilitate comparisons with previous studies, we compared DMDD and
non-DMDD subsets. Fourthly, the study sample comprised outpatients
consulting at specialist mood disorders clinics in a large urban area.
This might account for the high observed prevalence of mood disorders
and the relative paucity of other conditions for which there are specific
care pathways (e.g. severe developmental disorders). Our results could
only be generalized to other clinics with similar patient profiles.

The study also had a number of strengths: (i) the use of a well-
validated, DSM-5-based instrument for the clinical diagnosis of DMDD,
(ii) our comparison of mood disorder groups addresses important issues
in how DMDD can be discriminated from pre-existing DD diagnoses in
clinical practice, and (iii) some environmental risk factors (such as the
developmental history and ACE) have never been investigated in
DMDD.

Table 4
Vulnerability factors associated with DMDD, MDD, and PDD.

DMDD
(n = 36)

MDD only
(n = 46)

PDD only(n = 28) Comparisons between the
three groupsa

Non DMDD
(n = 127)

Comparisons DMDD vs. non-
DMDDb

Family psychiatric history
Maternal depression 11 (31%) 21 (46%) 8 (29%) p = .231 54 (43%) p = .198
Paternal depression 6 (17%) 13 (28%) 4 (14%) p = .273 24 (19%) p = .762

Stressful life events
History of major ACEs 14 (39%) 15 (33%) 16 (57%) p = .111 56 (44%) p = .703
Exposure to parental substance
abuse

11 (31%) 8 (17%) 2 (7%) p = .057 23 (18%) p = .110

Exposure to parental mental
illness

19 (53%) 33 (72%) 12 (43%) p = .036 83 (64%) p = .249

Foster care placement 5 (14%) 4 (9%) 1 (3%) p = .367 7 (19%) p = .326
Developmental history
Complicated pregnancy 8 (22%) 2 (4%) 2 (7%) p = .027 7 (6%) p < .01 Delay in psychomotor development

2 (6%) 0 2 (7%) p = .217 4 (3%) p = .614

Neurological disorder
associated

5 (14%) 4 (9%) 4 (14%) p = .696 12 (10%) p = .536

Note. Statistically significant results are presented in bold.
a Kruskal-Wallis test.
b Fisher’s Exact test.

X. Benarous, et al. Journal of Affective Disorders 265 (2020) 207–215

213

4.3. Clinical and research implications

In this study, we aimed at documenting the common and distinctive
features of DMDD, MDD, and PDD. As mentioned in the introduction,
there is still an ongoing debate about the best diagnostic approach for
youths with chronic irritability, i.e. whether adding a new specifier for
youths with oppositional defiant disorder (ODD) or subtyping DD with
a new diagnostic entity as proposed with the DSM-5 (Roy et al., 2014;
Stringaris et al., 2017). Therefore, following Occam’s razor principle, it
is mandatory to compare DMDD with preexisting DD diagnostic cate-
gories, i.e. MDD and PDD, to ensure that the new clinical entity is not a
more fashionable label for youths with chronic depression, i.e. PDD. In
keeping with the aforementioned debate, this study has several im-
plications.

First, DMDD could be discriminated from MDD and PDD. Of the 36
youths with DMDD, only six met the criteria for PDD. This is in line
with the assumption that youths with chronic irritability do not fit prior
DD diagnostic categories, and therefore deserve a more specific DD
diagnostic category. In contrast, the high comorbidity rate consistently
reported for DMDD (Margulies et al., 2012; Axelson et al., 2012;
Freeman et al., 2016; Tufan et al., 2016)., may dismiss the claim for
specificity. However, in this study and all the prior studies, the DSM-5
non-dual diagnosis criterium for ODD was not applied, resulting in an
overestimation of the comorbidity rate of DMDD with externalizing
disorders. In addition, youths with DMDD presented a more mixed
clinical presentation with mood and developmental disturbances com-
ponents (i.e., younger age, predominantly male sex ratio, history of
complicated pregnancy, persisting peer-relationship and school diffi-
culties) compared to youths with MDD and/or PDD. This clinical pre-
sentation with an admixture of mood and developmental disturbances
was only partially mediated by the association with ADHD (Table S3).
Therefore, youths presenting with DMDD should be carefully assessed
for neurodevelopmental disorders.

Second, from a research standpoint, the aforementioned neurocog-
nitive characteristics could be seen as promising mediators between
childhood irritability and later depression (Vidal-Ribas et al., 2018).
The relation between chronic irritability in childhood and, depression
in adolescence and adulthood has been well demonstrated, in particular
on the basis of longitudinal studies (Brotman et al., 2006;
Stringaris et al., 2009; Stringaris et al., 2010; Leibenluft E. Severe Mood
Dysregulation 2011; Vidal-Ribas et al., 2016; Whelan et al., 2013). In a
meta-analysis, Vidal-Ribas et al. (2016) reported a positive and in-
dependent relation between chronic irritability in childhood on the one
hand and on the other hand, depression in adolescence and adulthood
(OR=1.80) (Vidal-Ribas et al., 2016). Consequently, search for
common pathophysiological underpinnings is under way. Families
studies unveiled an association between childhood irritability and a
history of parental depression (Propper et al., 2017; Krieger et al., 2013;
Wiggins et al., 2018; Brotman et al., 2007), in coherence with our
findings. Moreover, two twin-studies suggested that the association
between irritability and depression might be, in part, genetically
mediated (Stringaris et al., 2012; Savage et al., 2015). Another study
showed that irritability partly mediated the link between maternal
depression and depression in adolescents (Whelan et al., 2015). Future
research would help better disentangling the interplay between risk
factors for depression, irritability, and depressive disorders in adoles-
cence and adulthood, and clarifying transition pathways from one
disorder to the other.

Third, our study has several evaluation and therapeutics implica-
tions. With respect to our findings, clinicians caring for youths with
DMDD should better recognize clinical dimensions generally associated
with internalized symptoms, in particular suicidal behaviors, the pre-
sence of adverse life events, and the presence of trauma-related symp-
toms. Second, some therapeutic interventions for DMDD have been
justified by the association with depression (Benarous et al., 2017b). As
depression and irritability might share common pathophysiological

mechanisms (Stringaris et al., 2012; Savage et al., 2015; Vidal-
Ribas et al., 2018), effective treatment for the former could be useful for
the later. So far, only one randomized controlled trial showed that a
positive effect of 8 weeks of citalopram, a selective serotonin reuptake
inhibitor (SSRI), in youths with SMD + ADHD previously treated by
methylphenidate (Towbin et al., 2019).

In conclusion, we identified DMDD as a distinct entity from MDD
and/or PDD in this clinical sample of outpatient youths. While we
found some clinical features more specifically associated with DMDD
(association with the externalized disorders and trauma-related dis-
order, the severity of peer-relationship and school difficulties), the
vulnerability factors studied were broadly comparable across disorders.
Further studies are needed to confirm this findings and determine on
which extend DMDD youths differ from those with other types of mood
disturbances.

Funding

The study was funded by the Quebec Network on Suicide, Mood
Disorders and Related Disorders RQSHA (grant: ASClin #2).

Disclosure

Dr. Benarous, Pr. Labelle, Pr. Guilé, Pr. Cohen, Dr. Renaud, and Dr.
Breton report no biomedical financial interests or potential conflicts of
interest.

Declaration of Competing Interest

None

Acknowledgments

The authors would like to thank Christophe Huynh Ph.D. for his
help with data collection, Cosmin Lancu (psychiatry resident) for his
assistance in studying the reliability of the chart review instrument’s
DMDD module, and Hughes Pellerin MSc for assistance with statistical
analysis. Professor Guilé pursue his contribution during his time as
invited researcher at the Center for Research and Intervention on
Suicide, Ethical Issues and End-of-life practices (CRISE), Montreal,
Canada. Therefore, the corresponding author would like to thank Pr
Choukroun, Dean of Faculty of Medicine (Amiens) and Pr Mishara,
Director of CRISE (Montreal).

Supplementary materials

Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.jad.2020.01.020.

References

American Psychiatric Association Taskforce DV, 2010. Justification for Temper
Dysregulation Disorder with Dysphoria. Association AP, Washington, DC.

American Psychiatric Association, 2013. Diagnostic and Statistical Manual of Mental
Disorders, 5th Edition. American Psychiatric Association.

Anda, R.F., Butchart, A., Felitti, V.J., Brown, D.W., 2010. Building a framework for global
surveillance of the public health implications of adverse childhood experiences. Am.
J. Prev. Med. 39 (1), 93–98.

Angold, A., Rutter, M., 1992. Effects of age and pubertal status on depression in a large
clinical sample. Dev. Psychopathol. 4 (1), 5–28.

Axelson, D., 2013. Taking disruptive mood dysregulation disorder out for a test drive.
Am. J. Psychiatry 170 (2), 136–139.

Axelson, D., Findling, R.L., Fristad, M.A., et al., 2012. Examining the proposed disruptive
mood dysregulation disorder diagnosis in children in the longitudinal assessment of
manic symptoms study. J. Clin. Psychiatry 73 (10), 1342–1350.

Benarous, X., Consoli, A., Cohen, D., Renaud, J., Lahaye, H., Guile, J.M., 2018. Suicidal
behaviors and irritability in children and adolescents: a systematic review of the
nature and mechanisms of the association. Eur. Child. Adolesc. Psychiatry 28 (5),
667–683.

Benarous, X., Consoli, A., Guile, J.M., Garny de La Riviere, S., Cohen, D., Olliac, B.,

X. Benarous, et al. Journal of Affective Disorders 265 (2020) 207–215

214

https://doi.org/10.1016/j.jad.2020.01.020

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0001

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0002

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0003

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0004

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0005

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0006

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0007

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0008

2017b. Evidence-based treatments for youths with severely dysregulated mood: a
qualitative systematic review of trials for SMD and DMDD. Eur. Child. Adolesc.
Psychiatry 26 (1), 5–23.

Benarous, X., Raffin, M., Bodeau, N., Dhossche, D., Cohen, D., Consoli, A., 2017a. Adverse
childhood experiences among inpatient youths with severe and early-onset psychia-
tric disorders: prevalence and clinical correlates. Child. Psychiatry Hum. Dev. 48 (2),
248–259.

Boudjerida, A., Labelle, R., Bergeron, L., Berthiaume, C., Breton, J.J, 2018. Disruptive
Mood Dysregulation Disorder Scale in Adolescence. International Association for
Child & Adolescent Psychiatry and Allied Professions, Prague 2018.

Breton, J.J., Bergeron, L., Valla, J.P., et al., 1999. Quebec child mental health survey:
prevalence of DSM-III-R mental health disorders. J. Child. Psychol. Psychiatry 40 (3),
375–384.

Breton, J.J., Labelle, R., Huynh, C., Berthiaume, C., St-Georges, M., Guile, J.M., 2012.
Clinical characteristics of depressed youths in child psychiatry. J. Can. Acad. Child.
Adolesc. Psychiatry 21 (1), 16–29.

Brotman, M.A., Kassem, L., Reising, M.M., et al., 2007. Parental diagnoses in youth with
narrow phenotype bipolar disorder or severe mood dysregulation. Am. J. Psychiatry
164 (8), 1238–1241.

Brotman, M.A., Schmajuk, M., Rich, B.A., et al., 2006. Prevalence, clinical correlates, and
longitudinal course of severe mood dysregulation in children. Biol. Psychiatry 60 (9),
991–997.

Consoli, A., Cohen, D., 2013. Manic-like symptoms in youths: diagnosis issues and con-
troversies. Neuropsychiatrie de l’Enfance et de l’Adolescence 61, 154–159.

Copeland, W.E., Angold, A., Costello, E.J., Egger, H., 2013. Prevalence, comorbidity, and
correlates of DSM-5 proposed disruptive mood dysregulation disorder. Am. J.
Psychiatry 170 (2), 173–179.

Copeland, W.E., Shanahan, L., Egger, H., Angold, A., Costello, E.J., 2014. Adult diagnostic
and functional outcomes of DSM-5 disruptive mood dysregulation disorder. Am. J.
Psychiatry 171 (6), 668–674.

Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013. Identification of
risk loci with shared effects on five major psychiatric disorders: a genome-wide
analysis. Lancet 381 (9875), 1371–1379.

Dougherty, L.R., Smith, V.C., Bufferd, S.J., et al., 2014. DSM-5 disruptive mood dysre-
gulation disorder: correlates and predictors in young children. Psychol. Med. 44 (11),
2339–2350.

Dvir, Y., Ford, J.D., Hill, M., Frazier, J.A., 2014. Childhood maltreatment, emotional
dysregulation, and psychiatric comorbidities. Harv. Rev. Psychiatry 22 (3), 149–161.

Freeman, A.J., Youngstrom, E.A., Youngstrom, J.K., Findling, R.L., 2016. Disruptive mood
dysregulation disorder in a community mental health clinic: prevalence, comorbidity
and correlates. J. Child. Adolesc. Psychopharmacol. 26 (2), 123–130.

Garno, J.L., Goldberg, J.F., Ramirez, P.M., Ritzler, B.A., 2005. Impact of childhood abuse
on the clinical course of bipolar disorder. Br. J. Psychiatry 186, 121–125.

Geoffroy, M.C., Boivin, M., Arseneault, L., et al., 2018. Childhood trajectories of peer
victimization and prediction of mental health outcomes in midadolescence: a long-
itudinal population-based study. CMAJ 190 (2), E37–e43.

Germain M., Guyon L., Landry M., Tremblay J., Brunelle N., Bergeron J. DEP-ADO grille de
dépistage de consommation problématique d’alcool et de drogues chez les adolescents et les
adolescentes. version 3.2, septembre 2007. Recherche et intervention sur les substances
psychoactives – Québec (RISQ); 2007, 26p.

Guile, J.M., Huynh, C., Breton, J.J., et al., 2016. Familial and clinical correlates in de-
pressed adolescents with borderline personality disorder traits. Front. Pediatr. 4, 87.

Kaufman, J., Birmaher, B., Brent, D., et al., 1997. Schedule for affective disorders and
schizophrenia for school-age children-present and lifetime version (K-SADS-PL): in-
itial reliability and validity data. J. Am. Acad. Child. Adolesc. Psychiatry 36 (7),
980–988.

Krieger, F.V., Polanczyk, V.G., Goodman, R., et al., 2013. Dimensions of oppositionality in
a Brazilian community sample: testing the DSM-5 proposal and etiological links. J.
Am. Acad. Child. Adolesc. Psychiatry 52 (4), 389–400.

LeBoeuf, A., Breton, J.J., Berthiaume, C., et al., 2017. Exploratory study of the clinical
characteristics of adolescent girls with a history of physical or sexual abuse consulting
in a mood disorder clinic. J. Can. Acad. Child. Adolesc. Psychiatry 26 (2), 114–118.

Leibenluft E. Severe Mood Dysregulation, 2011. Irritability, and the diagnostic bound-
aries of bipolar disorder in youths. Am. J. Psychiatry 168, 129–142.

Leibenluft, E., Cohen, P., Gorrindo, T., Brook, J.S., Pine, D.S., 2006. Chronic versus epi-
sodic irritability in youth: a community-based, longitudinal study of clinical and
diagnostic associations. J. Child. Adolesc. Psychopharmacol. 16 (4), 456–466.

Margulies, D.M., Weintraub, S., Basile, J., Grover, P.J., Carlson, G.A., 2012. Will dis-
ruptive mood dysregulation disorder reduce false diagnosis of bipolar disorder in
children? Bipolar Disord. 14 (5), 488–496.

Masi, G., Perugi, G., Toni, C., et al., 2006. The clinical phenotypes of juvenile bipolar
disorder:toward a validation of the episodic-chronic-distinction. Biol. Psychiatry 59,
603–610.

Masi, G., Pisano, S., Milone, A., Muratori, P., 2015. Child behavior checklist dysregulation
profile in children with disruptive behavior disorders: a longitudinal study. J. Affect.
Disord. 186, 249–253.

Orri, M., Galera, C., Turecki, G., et al., 2018. Association of childhood irritability and
depressive/anxious mood profiles with adolescent suicidal ideation and

attemptsassociation of childhood irritability and depressive/anxious mood profiles
with adolescent suicidalityassociation of childhood irritability and depressive/an-
xious mood profiles with adolescent suicidality. JAMA Psychiatry 75 (5), 465–473.

Posner, K., Oquendo, M.A., Gould, M., Stanley, B., Davies, M., 2007. Columbia
Classification Algorithm of Suicide Assessment (C-CASA): classification of suicidal
events in the FDA’s pediatric suicidal risk analysis of antidepressants. Am. J.
Psychiatry 164 (7), 1035–1043.

Propper, L., Cumby, J., Patterson, V.C., et al., 2017. Disruptive mood dysregulation dis-
order in offspring of parents with depression and bipolar disorder. Br. J. Psychiatry
210 (6), 408–412.

Robins, E., Guze, S.B., 1970. Establishment of diagnostic validity in psychiatric illness: its
application to schizophrenia. Am. J. Psychiatry 126 (7), 983–987.

Roy, A.K., Lopes, V., Klein, R.G., 2014. Disruptive mood dysregulation disorder: a new
diagnostic approach to chronic irritability in youth. Am. J. Psychiatry 171 (9),
918–924.

Savage, J., Verhulst, B., Copeland, W., Althoff, R.R., Lichtenstein, P., Roberson-Nay, R.,
2015. A genetically informed study of the longitudinal relation between irritability
and anxious/depressed symptoms. J. Am. Acad. Child. Adolesc. Psychiatry 54 (5),
377–384.

Shaffer, D., Gould, M.S., Brasic, J., et al., 1983. A children’s global assessment scale
(CGAS). Arch. Gen. Psychiatry 40 (11), 1228–1231.

Stoddard, J., Hsu, D., Reynolds, R.C., et al., 2015. Aberrant amygdala intrinsic functional
connectivity distinguishes youths with bipolar disorder from those with severe mood
dysregulation. Psychiatry Res. 231 (2), 120–125.

Stringaris, A., Baroni, A., Haimm, C., et al., 2010. Pediatric bipolar disorder versus severe
mood dysregulation: risk for manic episodes on follow-up. J. Am. Acad. Child.
Adolesc. Psychiatry 49 (4), 397–405.

Stringaris, A., Cohen, P., Pine, D.S., Leibenluft, E., 2009. Adult outcomes of youth irrit-
ability: a 20-year prospective community-based study. Am. J. Psychiatry 166 (9),
1048–1054.

Stringaris, A., Maughan, B., Copeland, W.S., Costello, E.J., Angold, A., 2013. Irritable
mood as a symptom of depression in youth: prevalence, developmental, and clinical
correlates in the great smoky mountains study. J. Am. Acad. Child. Adolesc.
Psychiatry 52 (8), 831–840.

Stringaris, A., Taylor, E., 2015. Disruptive Mood: Irritability in Children and Adolescent.
Oxford University Press, New York.

Stringaris, A., Vidal-Ribas, P., 2019. Probing the irritability-suicidality nexus. J. Am.
Acad. Child. Adolesc. Psychiatry 58 (1), 18–19.

Stringaris, A., Vidal-Ribas, P., Brotman, M.A., Leibenluft, E., 2017. Practitioner review:
definition, recognition, and treatment challenges of irritability in young people. J.
Child. Psychol. Psychiatry.

Stringaris, A., Vidal-Ribas, P., Brotman, M.A., Leibenluft, E., 2018. Practitioner review:
definition, recognition, and treatment challenges of irritability in young people. J.
Child. Psychol. Psychiatry 59 (7), 721–739.

Stringaris, A., Zavos, H., Leibenluft, E., Maughan, B., Eley, T.C., 2012. Adolescent irrit-
ability: phenotypic associations and genetic links with depressed mood. Am. J.
Psychiatry 169 (1), 47–54.

Thapar, A., Collishaw, S., Pine, D.S., Thapar, A.K., 2012. Depression in adolescence.
Lancet 379 (9820), 1056–1067.

Tourian, L., LeBoeuf, A., Breton, J.J., et al., 2015. Treatment options for the cardinal
symptoms of disruptive mood dysregulation disorder. J. Can. Acad. Child. Adolesc.
Psychiatry 24 (1), 41–54.

Towbin, K., Vidal-Ribas, P., Brotman, M.A., et al., 2019. A double-blind randomized
placebo-controlled trial of citalopram adjunctive to stimulant medication in youth
with chronic severe irritability. J. Am. Acad. Child Adoles. Psychiatry.

Tufan, E., Topal, Z., Demir, N., et al., 2016. Sociodemographic and clinical features of
disruptive mood dysregulation disorder: a chart review. J. Child. Adolesc.
Psychopharmacol. 26 (2), 94–100.

Vidal-Ribas, P., Brotman, M.A., Salum, G.A., et al., 2018. Deficits in emotion recognition
are associated with depressive symptoms in youth with disruptive mood dysregula-
tion disorder. Depress. Anxiety 35 (12), 1207–1217.

Vidal-Ribas, P., Brotman, M.A., Valdivieso, I., Leibenluft, E., Stringaris, A., 2016. The
status of irritability in psychiatry: a conceptual and quantitative review. J. Am. Acad.
Child. Adolesc. Psychiatry 55 (7), 556–570.

Weissman, M.M., Wickramaratne, P., Adams, P., Wolk, S., Verdeli, H., Olfson, M., 2000.
Brief screening for family psychiatric history: the family history screen. Arch. Gen.
Psychiatry 57 (7), 675–682.

Whelan, Y.M., Leibenluft, E., Stringaris, A., Barker, E.D., 2015. Pathways from maternal
depressive symptoms to adolescent depressive symptoms: the unique contribution of
irritability symptoms. J. Child. Psychol. Psychiatry 56 (10), 1092–1100.

Whelan, Y.M., Stringaris, A., Maughan, B., Barker, E.D., 2013. Developmental continuity
of oppositional defiant disorder subdimensions at ages 8, 10, and 13 years and their
distinct psychiatric outcomes at age 16 years. J. Am. Acad. Child. Adolesc. Psychiatry
52 (9), 961–969.

Wiggins, J.L., Briggs-Gowan, M.J., Estabrook, R., et al., 2018. Identifying clinically sig-
nificant irritability in early childhood. J. Am. Acad. Child. Adolesc. Psychiatry 57 (3),
191–199.

X. Benarous, et al. Journal of Affective Disorders 265 (2020) 207–215

215

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0008

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0009

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0010

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0011

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0012

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0013

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0014

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0015

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0016

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0017

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0018

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0019

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0020

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0021

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0022

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0023

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0024

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0025

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0026

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0027

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0028

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0029

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0030

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0031

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0032

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0033

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0034

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0035

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0036

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0037

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0038

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0039

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0040

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0041

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0042

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0043

http://refhub.elsevier.com/S0165-0327(19)30626-3/othref0002

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0044

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0045

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0046

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0047

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0048

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0049

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0050

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0051

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0052

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0053

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0054

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0055

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0056

http://refhub.elsevier.com/S0165-0327(19)30626-3/sbref0057

Are youths with disruptive mood dysregulation disorder different from youths with major depressive disorder or persistent depressive disorder?

Introduction

Methods

Participants

Setting and study design

Measurements

Psychiatric diagnoses

Suicidal behavior and substance use

Functional impairment

Vulnerability factors

Statistical analyses

Results

Objective 1. Frequency of DMDD and overlap with MDD and/or PDD

Objective 2. Clinical characteristics of youths with DMDD, MDD and PDD

Objective 3. Impairments associated with DMDD, MDD and/or PDD

Objective 4. Vulnerability factors associated with DMDD, MDD and/or PDD

Discussion

Interpretation

Strength and limitations

Clinical and research implications

Funding

Disclosure

mk:H1_22

Acknowledgments

Supplementary materials

References

Turn in your highest-quality paper
Get a qualified writer to help you with

“ discussion 3 ”

Get high-quality paper

Guarantee! All work is written by expert writers!

Still stressed from student homework?

Get quality assistance from academic writers!

Order now