SDQ Dysregulation Profile and Its Relation to the Severity of Psychopathology and Psychosocial Functioning in a Sample of Children and Adolescents With ADHD

Abstract

Objective: A substantial number of children and adolescents with ADHD show severe affective and behavioral dysregulation. A dysregulation profile (DP) distinguishes those participants at a higher risk of comorbidity, dysfunction, and a poorer response to treatment. It was identified as high scoring in certain subscales of the Strengths and Difficulties Questionnaire (SDQ-DP). We developed a cross-sectional study to assess the clinical utility of the SDQ-DP in ADHD. Method: Two hundred fifty clinic-referred children and adolescents with ADHD were assessed. Univariate and linear regression analyses were performed to compare those participants with high levels of SDQ-DP with those with low levels, as well as to examine the association between the SDQ-DP and clinical variables. Results: In all, 28% of ADHD participants had high levels of SDQ-DP. These participants showed higher functional impairment and clinical severity. Conclusion: The use of the SDQ-DP will enable identification of those ADHD patients at greater risk.

Keywords

dysregulation profile ADHD SDQ adolescent children

Introduction

ADHD is one of the most frequent disorders in childhood, with an estimated prevalence of 5.29% (Hervás et al., 2016). Patients with ADHD exhibit a profile of impulsivity, inattention, and poor executive function that interfere in social, emotional, and cognitive functioning (Diagnostic and Statistical Manual of Mental Disorders [4th ed., text rev.; DSM-IV-TR]; American Psychiatric Association [APA], 2000).

Despite the great amount of evidence regarding ADHD, it is still a controversial concept, especially due to comorbidity and secondary effects of pharmacological treatments (Naguy, 2016; Partridge, Lucke, & Hall, 2014; Thapar, Cooper, Eyre, & Langley, 2013).

However, a great amount of children and adolescents assessed in clinical settings are diagnosed with ADHD. Besides, the clinical presentation of children and adolescents diagnosed with ADHD is usually complicated by severe affective and behavioral dysregulation, beyond the diagnosis of ADHD. This dysregulation includes irritability, restlessness, “affective storms,” mood instability, and aggression (Althoff, 2010) that do not correspond to any of the diagnoses proposed by existing classification systems (Dougherty et al., 2014).

Due to the difficulties in the clinical handling of patients with co-occurring affective and behavioral dysregulation, clinicians began regarding the concept of dysregulation profile (DP). To test for this DP, an assessment based on high scoring on the Aggressive Behavior, Anxious/Depressed, and Attention Problems subscales of the Child Behavior Checklist (CBCL) was designed (Biederman, 1995). At first, it was believed that it was an indicator of Juvenile Bipolar Disorder (Biederman, 1995), but later, evidence has ruled out this hypothesis and it was described as “Dysregulation Profile” (CBCL-DP; Ayer et al., 2009) as a marker of severe psychopathology. This implies severer symptoms and greater functional impairment (Holtmann, Becker, Banaschewski, Rothenberger, & Roessner, 2011).

In addition to the CBCL-DP, a DP based on certain subscales of the Strengths and Difficulties Questionnaire (SDQ-DP) has recently been described (Holtmann, Becker, et al., 2011). The SDQ-DP seems to be the equivalent of the CBCL-DP in its clinical implications (Holtmann, Buchmann, et al., 2011) and correlates (Carballo et al., 2014).

The DP is defined by a “disengaged” temperamental pattern, characterized by high harm avoidance, certain levels of novelty seeking, low reward dependence, and low persistence (Althoff et al., 2012). So, it has been considered as an index of self-regulatory problems. The DP seems to be an indicator of overall psychopathology, symptom severity, and functional impairment (Holtmann, Becker, et al., 2011) related to increased levels of psychopathology.

Of increasing scientific interest is the study of DP in population with ADHD (Bellani, Negri, & Brambilla, 2012). Authors suggest a greater likelihood of displaying a DP among ADHD samples compared with other childhood psychiatric disorders (Doerfler, Connor, & Toscano, 2011; Halperin, Rucklidge, Powers, Miller, & Newcorn, 2011; Holtmann, Buchmann, et al., 2011). In addition, children and adolescents with ADHD who show high levels of DP have higher rates of anxiety and disruptive behavior disorders (Spencer et al., 2011) when compared with those with low levels of DP (Biederman, 1995). In longitudinal studies, adolescents diagnosed with ADHD who show high levels of DP present more impaired psychosocial functioning and a higher risk of psychiatric hospitalization 7 years later, compared with those adolescents diagnosed with ADHD who show low levels of DP. Emotional dysregulation has also been associated with social impairment and risky behaviors, and its absence or presence is associated with a better or worse response to treatment, respectively (Bunford, Evans, & Wymbs, 2015). Studies on physiological (Musser, Galloway-Long, Frick, & Nigg, 2013), cognitive (McGough et al., 2013), and brain function models (Herrmann, Biehl, Jacob, & Deckert, 2010) are helping to elucidate biological and cognitive correlates of this clinical presentation.

Taking into account available evidence, it seems clear that making a correct and early diagnosis, not only of ADHD symptoms but also of DP, is crucial. As the SDQ seems to be of easier application than CBCL, and is also widely used in clinical settings (Carballo et al., 2014; Essau et al., 2012; Goodman & Scott, 1999; Mason, Chmelka, & Thompson, 2012; Rodríguez-Hernández et al., 2012), its use to identify ADHD patients at higher clinical risk would be of great interest. However, most of the studies regarding DP and ADHD are based on the CBCL-DP. To determine the SDQ-DP’s clinical utility in the evaluation of patients diagnosed with ADHD, we will examine its prevalence and its relation with the severity of psychopathology in a clinical sample of children and adolescents. We will also explore the potential influence of sociodemographic and perinatal risk factors presenting with DP.

Method

Participants

Participants were recruited from the Child and Adolescent Outpatient Psychiatric Services of the Jiménez Díaz Foundation in Madrid, Spain. The study included patients referred to this Unit from March 22, 2010, to May 18, 2013. After describing the study, a written informed consent was obtained from Spanish-speaking parents, or legally authorized representatives, and patients who agreed to participate, as appropriate.

Exclusion criteria included patients’ aged above 18 years old and patients’ and parents’ inability to understand written or spoken Spanish. The study was approved by the Ethics Committee of the Jiménez Díaz Foundation (Madrid, Spain).

All children and adolescents were assessed on a clinical basis by experienced psychiatrists and completed the study questionnaires. Those who met diagnosis criteria for ADHD (DSM-IV-TR, APA, 2000) were included in this study (N = 250). All interviewers were blind to the purpose of this study.

To identify ADHD children and adolescents who met the SDQ-DP criteria for high levels of DP, parents or legal authorized representatives filled out the Spanish version of the parent-rated SDQ (described below). Comorbidity symptoms and global functionality were also assessed using well-established scales. Demographic data, developmental features, medical and psychiatric history, family history, and past treatment were obtained by a semistructured interview.

Instruments

SDQ-DP: The SDQ-DP is calculated from the Parent-Rated SDQ (Holtmann, Becker, et al., 2011). It is comprised of two items from the SDQ Emotional Symptoms subscale, two from the Conduct Problems subscale, and one from the Hyperactivity subscale. Overall, the SDQ-DP score is composed of the unweighted sum of these five items.

ADHD Rating Scale-IV: Home Version (ADHD RS-IV): The ADHD RS-IV consists of 18 attributes that assess DSM-IV-TR (APA, 2000) criteria for inattention, hyperactivity, and impulsivity. Each item is scored on a 0 to 3 scale: 0 = none (never or rarely), 1 = mild (sometimes), 2 = moderate (often), 3 = severe (very often). Good validity, test–retest reliability, and internal consistency have been demonstrated for this scale (DuPaul, Power, Anastopoulos, & Reid, 1998).

Obstetric Complications Scale: Obstetric complications were retrospectively assessed on parent interview using the items of the Lewis–Murray Scale (Lewis & Murray, 1987). The Lewis–Murray scale is derived from a consensus of six previous scales and consists of 15 items which assess complications during the prenatal and postnatal period.

In addition, patients answered the Spanish adaptation of the State-Trait Anxiety Inventory (STAI) or alternatively the State-Trait Anxiety Inventory for Children (STAIC; Seisdedos, 1990) where applicable by age, the State-Trait Anger Expression Inventory for child and adolescent populations (STAXI-NA; Del Barrio, Aluja, & Spielberger, 2004), and the Spanish adaptation of the Children’s Depression Inventory (CDI; Del Barrio, Moreno, & López, 2000) to assess comorbid depressive state, anxiety and anger state, and anger expression and control.

Clinical diagnoses were assigned by experienced clinicians who also completed the Clinical Global Impression Scale (CGI; Guy, 1976) which measure the severity of a given patient’s symptomatology impairment.

Statistical Analyses

For the purpose of analysis, participants were divided into two groups: the group of patients diagnosed with ADHD who show high levels of SDQ-DP (called SDQ_DP to simplify naming) and the group of patients diagnosed with ADHD who show low levels of SDQ-DP (called SDQ_NO_DP). The cutoff point recommended to define high levels of SDQ-DP is ≥5 points in the SDQ-DP profile (sensitivity = 94.6%; specificity = 80%; Cronbach’s α = .52; Holtmann, Becker, et al., 2011). It also has been used a population distribution criteria in which the cutoff point was defined by the percentile 80 (SDQ Information for researchers and professionals about the Strengths & Difficulties Questionnaires, 2016). We used the criteria defined by Holtmann, Becker, et al. (2011) as in previous studies (Carballo et al., 2014) to discriminate between adolescents with and without severe affective and behavioral dysregulation. It is remarkable that the percentile 80 in our sample happened to be also 5.

Univariate analyses were performed to compare demographic and clinical variables between groups. Correlation analysis (chi-square t test and student t test, as appropriate) was used to determine the association of the SDQ-DP group with sociodemographic (gender, age, ethnicity, academic performance, adoption, parental educational level, parent’s past psychiatric history, and obstetric complications) and clinical (family functioning, overall psychological functioning, clinical severity, clinical diagnoses, and functional impact) variables.

Logistic and linear regression models were performed to examine the association between the SDQ-DP and the clinical variables (ADHD RS, STAI, STAXI, CDI, and CGI). A two-step model was developed, introducing parents’ educational level and obstetric complications as covariates to estimate the effect of the SDQ-DP in clinical presentation of ADHD independently from these potential confounders.

Results

Sample Features

Two hundred fifty participants (74.8% males, confidence interval [CI] = [69, 79]; 25.2% females, CI = [20, 30]) aged between 4 and 17 years old (M = 10.67, SD = 3.47) took part in the present study. Most participants were Caucasian (84.4%, CI = [79.39, 88.37]) and lived with their family of origin (90.8%, CI = [86.57, 93.79]). Only 3.2% of participants were adopted children (CI = [1.63, 6.19], n = 8), 37.2% of participants had a mother with psychiatric history (CI = [31.00, 42.94], n = 92), 20.3% of participants had a father with psychiatric history (CI = [15.5, 25.4], n = 50), 27.6% of participants had repeated at least one academic year (CI = [22.4, 33.45], n = 69), and 88.8% had suffered obstetrical and newborn complications (CI = [84.29, 92.14], n = 222). Sociodemographic results for both groups are reported in Table 1.

Table 1.

Sociodemographic Characteristics of the Sample, Categorized by SDQ-DP Status.

	SDQ_DPn (%, 95% CI)	Article I. SDQ_NO_DPArticle II. n (%, 95% CI)	Total
Sample	70 (28, [22, 33])	180 (72, [66, 77])	N = 250 (100%)
	M (SD)	M (SD)	M (SD)	t	df	Student t testp value
Age (range: 4-17 years)	10.50 (3.47)	10.73 (3.48)	10.67 (3.47)	.476	248	.634
	n (%, 95% CI)	n (%, 95% CI)	n (%, 95% CI)	χ²	df	χ² p value
Sex				0.734	1	.422
Male	55 (78.6, [67, 86])	132 (73.3, [66, 79])	187 (74.8, [69, 79.78])
Female	15 (21.4, [13, 32])	48 (26.7, [20, 33])	63 (25.2, [20, 30])
Parents’ psychiatric history
Mother	30 (43.5, [31, 54])	62 (34.8, [27.89, 41.65])	92 (37.2, [31, 42.94])	1.591	1	.207
Father	15 (21.7, [13, 32])	35 (19.8, [14.33, 25.84])	50 (20.3, [15.5, 25.4])	0.118	1	.731
Obstetrical and newborn complications
Prenatal	46 (70.8, [54, 75.75])	88 (48.8, [41.69, 56.14])	134 (53, [47.41, 59.68])	6.930	1	.008
Peri-postnatal	27 (38.6, [28, 50])	61 (33.9, [27.38, 41])	88 (35.2, [29.54, 41.3])	0.484	1	.486
Ethnicity				5.833	4	.212
Caucasian	55 (78.6, [67.61, 86.56])	156 (86.7, [80.9, 90.8])	211 (84.4, [79.39, 88.37])
Romani	0 (0, [0, 5])	2 (1.1, [0.3, 3.9])	2 (0.8, [0.2, 2.87])
African American	0 (0, [0, 5])	2 (1.1, [0.3, 3.9])	2 (0.8, [0.2, 2.87])
Other	5 (7.1, [3.95, 24.34])	6 (3.3, [1.5, 7])	11 (4.4, [2.47, 7.71])
Unknown	10 (14.3 [7.95-24.34])	14 (7.8, [4.69, 12.63])	24 (9.6, [6.54, 13.89])
Academic performance				5.833	4	.247
Repeated grades (yes)	25 (35.7, [25.5, 47.4])	44 (24.4, [18.74, 31.21])	69 (27.6, [22.4, 33.45])
Adopted				0.37	1	.848
Yes	2 (2.9, [0.79, 9.83])	6 (3.3, [1.54, 7])	8 (3.2, [1.63, 6.19])
Live with family of origin				0.074	1	.785
Yes	63 (90, [80.77, 95.07])	164 (91.1, [86.05, 94.45])	227 (90.8, [86.57, 93.79])
Educational level (mother)				2.722	4	.65
No education	3 (4.3, [1.47, 11.86])	4 (2.2, [0.8, 5.57])	7 (2.8, [1.36, 5.67])
Primary	13 (18.6, [11, 29.23])	28 (15.6, [10.9, 21.56])	41 (16.4, [12.3, 21.49])
Secondary	27 (38.6, [28, 50.2])	63 (35, [28.4, 42.2])	90 (36, [30.3, 42.12])
University	25 (35.7, [25.5, 47.4])	82 (45.6, [38.4, 52.8])	107 (42.8, [36.8, 49])
Unknown	2 (2.9, [0.79, 9.83])	3 (1.7, [0.5, 4.78])	5 (2, [0.86, 4.6])
Educational level (father)				13.10	4	.011
No education	1 (1.4, [0.25, 7.66])	8 (4.4, [2.2, 8.5])	9 (3.6, [1.9, 6.7])
Primary	19 (30, [18.12, 38.54])	23 (30.6, [8.67, 18.4])	42 (16.8, [12.68, 21.9])
Secondary	21 (27.1, [20.5, 41.5])	55 (12.8, [24.29, 37.6])	76 (30.4, [25, 36.36])
University	18 (25.7, [16.9, 37])	77 (42.8, [35.78, 50])	95 (38, [21.2, 44.16])
Unknown	11 (15.7, [9, 25.99])	17 (9.4, [5.98, 14.6])	28 (11.2, [7.86, 15.7])

Note. SDQ-DP = Strengths and Difficulties Questionnaire–Dysregulation Profile; SDQ_DP = the group of patients diagnosed with ADHD who show high levels of SDQ-DP; SDQ_NO_DP = the group of patients diagnosed with ADHD who show low levels of SDQ-DP; CI = confidence interval.

SDQ-DP Comparison Studies

In all, 28% of the participants (CI = [22, 33], n = 70) met criteria for the DP (SDQ_DP) and 72% of them (CI = [66, 77], n = 180) did not (SDQ_NO_DP).

Regarding sociodemographic characteristics, only the differences in patients’ fathers’ educational level (χ² = 13.10, df = 4, p = .011) were statistically significant. Significant differences in obstetrical prenatal complications (χ² = 6.930, df = 1, p = .008) were also found.

Comparisons of clinical variables between both groups are reported in Table 2. Most of the clinical variables studied, with the exception of STAI/STAIC and some STAXI-NA subscales (Externalization and Internalization of Anger), showed significant differences between groups. Both inattention (t = −4.00, df = 238, p < .001) and hyperactivity (t = −7.942, df = 237, p < .001) measures were significantly more severe in the SDQ_DP group compared with the SDQ_NO_DP group, according to the ADHD RS.

Table 2.

Clinical Characteristics of the Sample, Categorized by SDQ-DP Status.

	SDQ_DP	SDQ_NO_DP	Total
Sample	n = 70 (28%)	n = 180 (72%)	N = 250 (100%)
	M (SD)	M (SD)	M (SD)	t	df	Student t testp value
ADHD RS-IV
Inattention	20.37 (5.85)	16.76 (6.39)	17.75 (6.44)	−4.00	238	<.001
Hyperactivity	18.38 (6.30)	10.86 (6.58)	12.91 (7.31)	−7.942	237	<.001
Total	38.98 (10.46)	27.69 (11.26)	30.74 (12.11)	−6.978	235	<.001
CDI
Dysphoria/negative mood	62.35 (25.6)	53.42 (25.82)	55.93 (26)	−2.657	172	.041
Negative self-esteem	64.14 (26.08)	55.49 (28.74)	58.01 (28.19)	−1.839	170	.041
Total	60.96 (27.07)	51.02 (28.3)	53.90 (28.23)	−2.09	167	.037
STAXI-NA
Externalization of anger	56.81 (31.23)	49.32 (29.48)	51.44 (30.09)	−1.539	185	.125
Internalization of anger	44.7 (32.21)	49.24 (28.71)	47.95 (29.73)	0.941	185	.348
External control	45.51 (36.57)	58.15 (31.9)	54.51 (33.7)	2.33	182	.021
Internal control	43.79 (34.04)	59.66 (32.74)	55.21 (33.78)	2.96	187	.003
Anger/trait (total)	68.14 (26.79)	58.13 (24.97)	60.88 (25.80)	−2.388	184	.018
STAIC
State	37.67 (32.69)	33.12 (33.76)	34.57 (33.37)	−.738	133	.462
Trait	40 (33.19)	39.89 (31.04)	39.92 (31.62)	−.20	139	.984
STAI
State	12 (15.71)	30.32 (28.94)	27.82 (27.98)	1.057	20	.303
Trait	27 (33.04)	46.84 (29.36)	44.14 (29.86)	1.033	20	.296
	n (%)	n (%)	n (%)	χ²	df	χ² p value
CGI				10.504	4	.033
Doubtingly ill	0 (0)	5 (2.8)	5 (2)
Mildly ill	9 (12.9)	51 (28.3)	60 (24)
Moderately ill	59 (84.3)	116 (64.4)	175 (70)
Markedly ill	1 (1.4)	2 (1.1)	3 (1.2)
Unknown	1 (1.4)	6 (3.3)	7 (2.8)

Note. SDQ-DP = Strengths and Difficulties Questionnaire–Dysregulation Profile (N = 250); ADHD RS = ADHD Rating Scale-IV (n = 240); CDI = Children’s Depression Inventory (n = 174); STAXI-NA = State-Trait Anger Expression Inventory for child and adolescent (n = 189); STAIC = State-Trait Anxiety Inventory for Children (n = 141); STAI = State-Trait Anxiety Inventory (n = 22); CGI = Clinical Global Impression Scale (N = 250).

Depressive symptoms were also significantly higher in the SDQ_DP group (t = −2.09, df = 167, p = .037), including both Dysphoric (t = −2.657, df = 172, p = .041) and Negative Self-Esteem (t = −1.839, df = 170, p = .041) subscales.

The anger trait/state was significantly higher in SDQ_DP group (p < .05) compared with SDQ_NO_DP. Although no significant differences were found in the way anger was expressed (internal or external), both internal and external anger control scoring was significantly higher in the SDQ_NO_DP group (p < .005 and p < .05, respectively).

Global severity was significantly higher in the SDQ_DP group compared with the SDQ_NO_DP group.

Regression Analysis Results

The results of linear regression analysis are summarized in Table 3. The linear regression model examining the relationship between the SDQ-DP and clinical variables which showed significant differences between groups was significant for all variables, with the exception of the Negative Self-Esteem subscale of the CDI.

Table 3.

Linear Regression Between SDQ-DP and Clinical Variables (Father’s Educational Level and Obstetrical Complications as Covariates in the Second Model).

Linear regression	SDQ-DP	SDQ-DP (father’s educational level and obstetrical complications as covariates)
Linear regression	t (p)	t (p)
ADHD RS-IV
Inattention	4 (.000)***	1.848 (.067)
Hyperactivity	7.942 (.000)***	6.783 (.000)**
Total	6.978 (.000)***	4.947 (.000)**
CDI
Dysphoria/negative mood	2.057 (.041)*	1.875 (.064)
Negative self-esteem	1.839 (.068)	1.732 (.087)
Total	2.098 (.037)*	1.931 (.057)
STAXI-NA
External control	−2.330 (.021)*	−1.833 (.070)
Internal control	−2.960 (.003)**	−2.022 (.046)*
Anger/trait (total)	2.388 (.018)*	1.016 (.312)

Note. SDQ-DP = Strengths and Difficulties Questionnaire–Dysregulation Profile; ADHD RS = ADHD Rating Scale-IV; CDI = Children’s Depression Inventory; STAXI-NA = State-Trait Anger Expression Inventory for child and adolescent.

p < 0.05; **p < 0.01.

In the second model, when controlling for obstetrical prenatal complications and the patients’ fathers’ educational level, the correlation between the SDQ_DP and clinical variables was still significant with the exception of the “Anger/Trait Total” STAXI-NA subscale.

Collinearity test was performed sustaining the model. The lower value from Tolerance index was 0.837 (from 0.837 to 0.937); the VIF (Variance Inflation Factor) index higher value was 1.213 (from 1.067 to 1.213); and the Condition Index higher value was 8.096 (from 2.187 to 8.096).

Discussion

A great amount of children and adolescents attending mental health units are diagnosed with ADHD and treated for this disorder. However, clinical experience show there is a sensitive group of these patients who also show severe affective and behavioral dysregulation. This group usually has more difficulties to treatment adherence and recovery than those patients diagnosed with ADHD who do not show dysregulation difficulties. Identifying this group of patients could help in clinical practice. We developed a cross-sectional study to determine the clinical utility of the SDQ-DP in patients diagnosed with ADHD.

Our study supports a high prevalence of DP among children and adolescents with ADHD. This prevalence is similar as that found in a sample of schoolchildren between 6 and 8 years old with ADHD and Disruptive Mood Dysregulation Disorder (DMDD; Mulraney et al., 2016).

When compared with ADHD children and adolescents with low levels of DP, those in our sample with high levels of DP showed severer inattention and hyperactivity symptoms. In addition, ADHD children with DP displayed severer symptoms of dysphoria, an increased tendency to angry reactions and lower internal and external ability to control their response to anger. Interestingly, no differences were found between groups regarding anxiety state and traits. These results suggest that the differences between the DP and no-DP group are not related with diagnosis but with emotional regulation.

Longitudinal studies have shown that higher DP scores in 8-year-old children are related with higher risk of ADHD at age 19 (Holtmann, Buchmann, et al., 2011), indicating that emotional dysregulation may be useful as a risk marker of ADHD, even at 6 months of age (Sullivan et al., 2015). Moreover, it has been suggested that DP may play a casual role regarding ADHD symptomatology (Villemonteix, Purper-Ouakil, & Romo, 2015).

Taken together, previous studies and results in our study suggest a circular process. It seems that those children with attention problems are generally more exposed to critics and hostility (Dougherty et al., 2014). This placed them in risk of developing DP, which, in turn, makes them more sensitive to negative responses of the context, which probably will make adaptative responses less probable, making the problem worse and worse. Moreover, as results show, ADHD patients with DP develop severer depressive symptoms as compared with non-DP patients. As pointed out in previous studies, the presence of depressive symptoms may modulate the response to pharmacological treatment (Molina-Carballo et al., 2014). Therefore, identifying those at greater risk will be useful for guiding a treatment plan.

Assessing DP in children and adolescents diagnosed with ADHD may help to distinguish those at a higher risk of comorbidity, dysfunction, and poorer response to standard treatment. The use of the SDQ-DP will help to identify those ADHD patients with severer presentation. In this population, incorporating interventions focused in improving family environment (Waxmonsky et al., 2013), interpersonal abilities (Mikami et al., 2013; Wilkes-Gillan, Bundy, Cordier, & Lincoln, 2014), and emotional regulation (Waxmonsky et al., 2013) may increase the number of responders to treatment and decrease the prevalence or severity of these impairments and behaviors among young people with ADHD (Bunford, Evans, & Wymbs, 2015).

Some limitations should be taken into account for the interpretation of this study. First, the generalization of the results is limited to the clinical sample, although most ADHD care is provided by health systems. Second, the results have not been controlled for comorbid diagnosis or ADHD subtype, and thus, we cannot address whether results are better explained by comorbidity or subtype. Finally, although no differences are found among groups regarding family psychiatric history, we did not specifically screen for a family history of bipolar disorder, which has been found to relate to mood dysregulation in young people (Sparks et al., 2014).

Conclusion

There is a high prevalence of DP in ADHD. This finding is associated with a severer presentation and poorer functioning. The use of the SDQ-DP will help to identify those ADHD patients at greater risk who would benefit from a multilevel intervention, including social skills, family focus intervention, and self-regulation. In addition, monitoring comorbid depressive symptoms would be of interest as they have been related with poorer response to usual pharmacological treatment. Further studies are needed to understand the role of neurobiological and social factors such as obstetrical complications and fathers’ level of education in modulating the appearance of DP in ADHD.

Footnotes

Authors’ Note

Elena Serrano-Drozdowskyj and Irene Caro-Cañizares have contributed equally to the work.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Irene Caro-Cañizares is a clinical psychologist. She graduated in psychology at the Autonomous university of Madrid and specialized in clinical psychology at Fundación Jiménez Diaz. Currently she is developing her doctoral these at the Autonomous University of Madrid. She also works in the adult outpatient unit at Fundación Jiménez Díaz in Madrid. Temporary she is tutorizing other psychologist in their residency training.

Elena Serrano-Drozdowskyj is a Child and Adolescent psychiatrist with specific training in neurodevelopmental disorders and perinatal mental health. She has been collaborating in research on mental disorders, with especial emphasis on early detection and intervention. She obtained his PhD at Universidad Autonoma of Madrid.

Bernadette Pfang is currently completing her residency training in Internal Medicine at Fundación Jiménez Díaz. She has been collaborating in research on child and adolescent mental health.

Enrique Baca-García is the chief of the Department of Psychiatry at Fundación Jiménez Díaz. He is professor at Universidad Autónoma de Madrid and collaborates with the Columbia University in New York and other Universities along the word. He has a vast curricum in research.

Juan J. Carballo is a child and adolescent psychiatrist at Child and Adolescent Psychiatry Department, Hospital General Universitario Gregorio Marañón in Madrid, Spain. He graduated from Medical School at Universidad Complutense of Madrid and obtained his PhD at Universidad Autonoma of Madrid. He was trained as a general psychiatrist at Puerta de Hierro Universitary Hospital in Madrid and then was Alicia Koplowitz Fellow in Child and Adolescent Psychiatry at Columbia University, New York, USA.

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