Psychopharm Paper

ORIGINAL ARTICLE

Clinical Profiles of Children with Disruptive Behaviors Based on the Severity of Their Conduct Problems, Callous–Unemotional Traits and Emotional Difficulties

Brendan F. Andrade • Geoff B. Sorge •

Jennifer Jiwon Na • Erika Wharton-Shukster

Published online: 26 September 2014

� Springer Science+Business Media New York 2014

Abstract This study identified clinical profiles of referred

children based on the severity of callous–unemotional

(CU) traits, emotional difficulties, and conduct problems.

Parents of 166 children (132 males) aged 6–12 years

referred to a hospital clinic because of disruptive behavior

completed measures to assess these key indicators, and

person-centered analysis was used to identify profiles. Four

distinct profiles were identified that include: (1) Children

low in severity on the three domains, (2) Children high in

severity on the three domains, (3) Children high in severity

in conduct problems and CU traits with minimal emotional

difficulties, and (4) Children high in severity in conduct

problems and emotional difficulties with minimal CU

traits. Profiles differed in degree of aggression and

behavioral impairment. Findings show that clinic-referred

children with disruptive behaviors can be grouped based on

these important indicators into profiles that have important

implications for assessment and treatment selection.

Keywords Child psychiatry � Disruptive behavior disorders � Conduct problems � Emotional difficulties � Callous–unemotional traits

Introduction

Children with conduct problems are one of the most fre-

quently referred groups to mental health clinics and a tre-

mendous financial burden to the social system [1, 2]. This

is consistent with the negative short- and long-term impacts

of conduct problems in childhood [3–5]. Children with

conduct problems are prone to co-occurring mental health

concerns, including anxiety, mood difficulties, and adjust-

ment difficulties [6, 7]. Moreover, social impairments

including aggression and peer problems are more prevalent

in children with conduct problems compared to typically

developing peers [8, 9]. Approaches to best characterize

the specific clinical and therapeutic needs of these children

are necessary to inform implementation of best practice

treatments.

Children referred to mental health clinics because of

conduct problems demonstrate numerous and pervasive

social and emotional problems [10, 11]. As a result, char-

acterization of these children’s deficits, in order to select a

course of treatment to meet their therapeutic needs, is a

major clinical concern. Of recent interest is the identifica-

tion of specific childhood characteristics that influence the

severity of behavior and conduct problems [7, 12, 13].

Frick and colleagues described two childhood pathways for

the emergence of conduct problems [4, 14]. The first

pathway of ‘‘callous-disruptive’’ children includes those

with low levels of anxiety, apparent guilt, remorse, and

empathy. The second pathway of ‘‘emotionally-disruptive’’

children show elevated emotional difficulties, behavioral

B. F. Andrade (&) � E. Wharton-Shukster Centre for Addiction and Mental Health, Toronto, ON, Canada

e-mail: [email protected]

B. F. Andrade

Department of Psychiatry, University of Toronto, Toronto, ON,

Canada

G. B. Sorge

Department of Psychology, York University, Toronto, ON,

Canada

J. J. Na

Department of Psychology, University of British Columbia,

Vancouver, BC, Canada

123

Child Psychiatry Hum Dev (2015) 46:567–576

DOI 10.1007/s10578-014-0497-8

reactivity and temper outbursts. These two pathways have

important implications for the social and behavioral out-

comes of these children [15, 16] and have potential utility

for characterization of the dimensions of psychopathology

to target in treatment.

Children who show ‘‘callous-disruptive’’ behaviors

demonstrate a constellation of characteristics classified as

callous and unemotional [17, 18]. Callous–unemotional

(CU) traits are common in clinic-referred children with

conduct problems, with prevalence estimates of 30–50 %

[19]. Research over the past two decades has documented

negative outcomes associated with callous-disruptive chil-

dren [14, 20–22]. Children with high levels of CU traits

appear to lack empathy or remorse in social contexts and

appear to react in a seemingly less caring manner to peers’

distress compared to children without CU traits [14, 23,

24]. Importantly, these traits are associated with elevated

severities of conduct problems and aggression [14, 25, 26].

Children with CU traits show higher rates of proactive (i.e.,

planned) and reactive (i.e., retaliatory) aggression [21, 27,

28], which are associated with behavioral impairment and

social problems [29, 30]. Moreover, CU traits emerge early

in childhood and remain relatively stable with development

[4, 18]. As such, CU traits have a pervasive negative

impact on children’s social and emotional development [7,

25, 31].

The last two decades have seen tremendous growth in

research on CU traits. However, recent comprehensive

reviews attest to the need to investigate the behavioral and

emotional processes associated with CU traits that influ-

ence the development of conduct problems in children [17,

32]. Doing so may provide avenues for novel assessment

and treatment for children with these difficulties. Although

much research has used variable-centered methodology to

compare the functional and behavioral impairments among

groups of children and adolescents with and without con-

duct problems and CU traits, few studies have used analytic

approaches that account for the co-occurrence of CU traits

with other dimensions of psychopathology in youth

with conduct problems [28, 32]. These person-centered

approaches are extremely important because unlike vari-

able-centered approaches they allow categorization based

on the constellation of risk factors at the level of the per-

son. The person-centered approach is especially relevant

for application with children with disruptive behaviors who

have varying levels of conduct problems, CU traits, and

emotional and behavioral difficulties. The approach is

‘‘bottom-up’’ and, as opposed to variable-centered approa-

ches, does not assume that the same processes apply to all

individuals. As such, person-centered methodology is rel-

evant to applied clinical research.

Recent research using person-centered methodology

with a community sample of adolescents showed that the

severities of CU traits and conduct problems interact to

produce five risk profiles [28]. These include low risk

(48.7 %), average risk (33.8 %), high conduct problems

and CU traits (5.4 %), high conduct problems and low CU

traits (5.2 %), and low conduct problems and high CU

traits (6.9 %). Findings showed that adolescents clustered

into profiles with high CU traits showed more persistent,

severe, and aggressive patterns of antisocial behavior [28].

Furthermore, Kahn and colleagues used person-centered

methodology to identify profiles based on CU traits, anxi-

ety, and a history of trauma in a sample of clinic-referred

adolescents. This analysis yielded three profiles; a high CU

traits and low anxiety/trauma group, a high CU with high

anxiety/trauma group, and a low CU traits with high anx-

iety/trauma group. These groups differed in their levels of

impulsivity, aggression, and externalizing behavior, with

the high CU traits and high anxiety group showing the most

severe problems [33]. These studies highlight the utility of

the person-centered analytic approach; however, many

important questions remain unanswered.

First, it is not clear whether the association among CU

traits and social and behavioral functioning varies with age.

The influence of CU traits on the severity of social and

behavioral problems may be different in children than in

adolescents. The present study used a childhood sample in

order to uncover potential differences.

Second, determining the interaction among CU traits

and emotional and behavioral difficulties in children with

disruptive behaviors using person-centered methodology

may help to clarify a greater spectrum of psychopathology

associated with children’s presenting problems. This

would build upon previous findings such as those of Kahn

and colleagues (2013). From an intervention perspective

this work is extremely important given that children with

conduct problems with non-normative CU traits show

diminished response to typical treatments compared to

children with conduct problems without CU traits [19, 22,

34]. However, studies suggest that children with CU traits

may benefit most from specialized combinations of

intensive behavioral and pharmacological treatments or

treatments that are targeted to their emotional and

behavioral profiles [19, 34–36]. Evidently children’s lev-

els of CU traits and emotional difficulties may alter their

profile of psychosocial needs that contribute to their

conduct problems, emphasizing the importance of con-

sidering CU traits along with emotional and behavioral

indicators when assessing children’s clinical and thera-

peutic needs.

Finally, in addition to CU traits, ‘‘emotionally disrup-

tive’’ children with conduct problems show elevated

emotional dysregulation and a different pattern of social

and behavioral risk compared to those children with con-

duct problems and CU traits [17, 37]. As such, a number of

568 Child Psychiatry Hum Dev (2015) 46:567–576

123

community-based studies have identified emotional pro-

cesses associated with elevated levels of conduct problems

[4, 20, 38]. Emotional dysregulation is related to disruptive

behaviors, aggression and clinical diagnoses of opposi-

tional defiant (OD) disorder and conduct disorder [20, 38,

39]. Children who have difficulty managing their emotions

demonstrate behavioral problems in situations that are

uncomfortable, unpredictable or lack adequate structure

[15, 40]. Although behaviors are manifested as problems

with conduct, these may partly result from dysregulated

emotional processes [41–43]. As such, knowledge of

emotional-functioning in children with disruptive behavior

(with or without CU traits) points to possible therapeutic

components to include in clinical assessment batteries and

treatment programs.

Given the aforementioned evidence that highlights the

importance of CU traits and emotional functioning for the

emergence of conduct problems, it follows that identifying

profiles that include CU traits and emotional functioning

indicators may capture children’s specific clinical needs.

This approach is consistent with recent expert calls by the

National Institutes of Mental Health (NIMH) that empha-

size characterizing the underlying processes that contribute

to mental disorders using a dimensional system [44].

Knowledge of clinical profiles that range in severity of CU

traits, emotional difficulties, and conduct problems may

inform tailored approaches to assessment and treatment.

Additionally, identification of these profiles may inform

clinical-trials research and development of treatments to

best match the needs of these exceptionally challenged

children. To date, no study has determined whether profiles

emerge based on CU traits, emotional difficulties and

conduct problems in children referred to a mental health

clinic because of disruptive behavior. Furthermore, no

study has tested associations between identified profiles

and children’s behavioral and social impairment.

This study used a person-centered analytic approach to

identify clusters (i.e., profiles) of children referred to a

mental health clinic because of disruptive behaviors based

on the severity of CU traits, emotional difficulties (i.e.,

internalizing difficulties such as worrying, low mood, etc.),

and conduct problems. Guided by Kahn et al. 2013 and

Fanti et al. 2013 in which three and five profiles were

identified with adolescents with disruptive behaviors, it

was hypothesized that up to five clusters would be identi-

fied in the present study with a sample of children [6, 28,

45, 46]. Moreover, consistent with Kahn et al. [33] it was

expected that children whose profiles included the most

elevated CU traits would show the most severe problems,

whereas children whose profiles included elevated emo-

tional difficulties without elevated CU traits would show

less severe problems.

Method

Participants and Procedure

Participants were 166 children and their parents referred

because of disruptive behavior to a children’s mental health

program in an urban mental health hospital in Canada. The

specialized service for children with behavioral and related

difficulties receives referrals from parents, schools, and

physicians from a large metropolitan area. The program

offers comprehensive multi-disciplinary assessment and

treatment services. Parents of children aged 6–12 years

were invited to participate in the study at assessment.

Twenty-nine parents did not consent to the study and were

provided clinical services as usual (assent rate of 85 %). If

consent was received, parents were invited to complete

study related measures. All procedures were approved by

the institutional research ethics board.

Children ranged in age from 6.01 to 12.8 years

(M = 8.64, SD = 1.72), with 132 males and 34 females

(see Table 1). As is shown in Table 1, and typical of a

clinical sample, children showed high levels of Opposi-

tional/Defiant (O/D) and Inattentive/Overactive behavior.

Parents identified primarily as Caucasian origins and

reported level of education as follows: ‘‘did not complete

high school’’ (.9 %), ‘‘completed high school’’ (10.3 %),

Table 1 Participant characteristics

Category Child (n = 166)

Age 8.64 (1.72)

Gender (% male) 79.5

Parent Education (%, n = 105)

Some high school .9

Completed high school 10.3

Some college/university 16.8

Completed college/university 72.0

Ethnicity (%, n = 106)

Caucasian 61.3

African origin 7.5

East/South/Southeast 3.8

Asian

Aboriginal .9

Latin American 2.8

Other .9

Multiethnic 22.6

Disruptive behaviour symptoms a

Oppositional/Defiant 9.23 (3.58)

Inattention/Overactivity 8.85 (3.61)

a Mean Oppositional/Defiant and Inattention/Overactivity scores

from the IOWA Conners rating scale (maximum subscale score =15)

Child Psychiatry Hum Dev (2015) 46:567–576 569

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‘‘some college/university’’ (16.8 %), and ‘‘completed uni-

versity’’ (72.0 %).

Measures

Strengths and Difficulties Questionnaire (SDQ)

Conduct problems and emotional difficulties were assessed

using independent scales on the Strengths and Difficulties

Questionnaire (SDQ; [47, 48]. The SDQ is a brief parent or

teacher completed screener which enquires about 25 attri-

butes that are evenly divided among five behavioral

dimensions; prosocial behaviors, emotional difficulties,

conduct problems, hyperactivity-inattention, and peer

problems. Subscales do not overlap, and each produces a

total score. The conduct problems scale includes questions

such as ‘‘often lies or cheats’’ and ‘‘steals from home, school

or elsewhere.’’ The emotional difficulties scale includes

items such as ‘‘many worries, often seems worried’’ and

‘‘often unhappy, downhearted or tearful.’’ Each item is rated

on a 3-point Likert scale ranging from 0 (not true), 1

(somewhat true) to 2 (certainly true). The SDQ shows

strong psychometric properties [49]. Internal consistency of

the conduct problems and emotional difficulties subscales

in the present study were .63 and .75, respectively.

Callous–Unemotional Traits: Brief Measure

The CU scale used is a three item parent-report measure

derived from previous research [50, 51]. Items include (1)

appears to lack remorse, (2) seems to enjoy being mean,

and (3) is cold or uncaring. These items were embedded

within a broader measure to assess behavioral dysfunction.

Items are rated on a 4-point Likert scale: 0 (not at all), 1

(just a little), 2 (pretty much), 3 (very much). Items show

strong internal consistency in this sample (a = .80).

Aggression Rating Scale (ARS)

A six-item aggression rating scale was used to measure

reactive and proactive aggression (PA) [52]. The scale was

included within a broader measure to assess behavioral

dysfunction. Items were rated on a 4-point Likert scale: 0

(not at all), 1(just a little), 2 (pretty much), 3 (very much).

Reactive aggression (RA) items include (1) when teased,

strikes back, (2) blames others in fights, (3) overreacts

angrily to accidents. Items that measure PA include (1)

uses physical force to dominate, (2) gets others to gang up

on peers, (3) threatens and bullies others. Items show high

internal consistency in this study (overall a = .81, reactive a = .77, proactive a = .67) and past research with these items show similar values (proactive a = .87, reactive a = .86) [52, 53].

Impairment Rating Scale (IRS)

The IRS measures the child’s current functioning and need

for treatment in several developmentally important areas

[54]. Parent’s respond to visual-analogue scales that are

scored using a 0 (no problems/no need for treatment) to 6

(severe problems/definitely needs treatment) metric.

Alphas are not reported for the IRS because each item is

scored separately, but the reliability and validity of the IRS

have been supported in several samples. For example,

Fabiano et al. (2006) reported criterion validity correlations

ranging from .44 to .80, 1-year test–retest reliability cor-

relations from .40 to .67 and inter-rater (parent and teacher)

reliability correlations ranging from .47 to .64. The overall

impairment rating was used in the present study.

IOWA Conners Rating Scale (IOWA)

The IOWA is a brief measure of child behavior. The scale

examines inattentive-impulsive-overactive (IO) and OD

domains. Independent and non-overlapping scales derived

from responses include the five-item IOWA Inattention/

Overactivity (I/O) subscale and five-item O/D subscale.

Items that make up the I/O scale include ‘‘Fidgeting’’,

‘‘Hums and makes other odd noises’’, ‘‘Excited, impulsive’’,

‘‘Inattentive, easily distracted’’, and ‘‘Fails to finish things he

or she starts (short attention span)’’. The O/D scale includes,

‘‘Quarrelsome’’, ‘‘Acts ‘smart’’’, ‘‘Temper outburst, behav-

ior explosive and unpredictable’’, ‘‘Defiant’’, and ‘‘Unco-

operative’’. Items are scored on a four-point Likert scale: 0

(Not at All), 1 (Just a Little), 2 (Pretty Much), 3 (Very Much).

The psychometric properties of the IOWA have been dem-

onstrated [55] and in this study showed high internal con-

sistency (IO a = .78 and OD a = .84). The IO and OD scales were used in this study to provide additional

description of the level of severity of behavioral difficulties

of this clinically-referred sample of children.

Analysis Plan

Hierarchical cluster analysis was performed using SPSS v18.

This form of cluster analysis involves successive steps to

identify clusters and was chosen based on previous studies

with similar designs and suitability for small to moderate

sample sizes [56]. Variables entered were mean CU traits

score, mean Emotional Difficulties subscale score, and mean

Conduct Problems subscale score. Correlations were com-

puted among all cluster variables. The assumptions of hier-

archical cluster analysis were investigated (e.g., normality of

variables). Due to differing numbers of items per variable,

mean scores were used for all continuous variables.

Following recommended guidelines for hierarchical clus-

teranalysis,the SquaredEuclidianDistancewasappliedasthe

570 Child Psychiatry Hum Dev (2015) 46:567–576

123

distance metric [57], while Ward’s Method was the algorithm

used to combine cases. This approach has been shown to be

robust [58]. The cluster analytic approach comprised two

phases. In the first phase a hierarchical cluster analysis was

performed and the statistical output visually appraised (i.e.,

dendogram, scree plot) to determine the number of clusters to

be retained. Second, Hoeve and colleague’s methodology

(2008) was followed for cluster validation. A k-means cluster

analysis was computed to derive cluster solutions. The num-

ber of clusters specified for the k-means cluster analyses was

based on the initial hierarchical cluster analysis. Kappa values

were calculated to assess cluster membership agreement

among the k-means and hierarchical cluster solutions. The

final cluster solution was selected based on kappa values and

theoretical interpretation.

The stability of the selected cluster solution was exam-

ined in the second phase in three ways. First, the sample was

randomly split into half and hierarchical analyses run on

each sample. The resulting cluster means from each half

were compared using ANOVA in order to investigate the

consistency of the cluster solutions across halves [59]. The

process of splitting the sample in half was repeated five

times. Second, the stability of clusters was further estab-

lished by inputting centroids from the hierarchical solution

into a k-means cluster procedure [60]. The extent to which

the final cluster solution from this k-means cluster analyses is

consistent with that generated in phase one is evidence of the

robustness of the retained cluster solution [60]. Last, the

external validity of clusters was examined. ANOVAs were

computed to compare clusters on outcome variables (RA,

PA and behavioral impairment).

Results

Descriptive Statistics

Correlations

Mean CU traits, Emotional Difficulties and Conduct

Problems were correlated (see Table 2). As is shown, low

to moderate correlations were found among cluster vari-

ables. Furthermore, with the exception of PA and emo-

tional difficulties, all cluster variables were significantly

correlated with outcome variables [proactive and RA and

overall behavioral impairment (OBI)].

Cluster Analysis

Normality of variables was established by examining

skewness and kurtosis. As expected given the relatively

low frequency of severe CU traits, mean CU traits was

positively skewed; skewness = 1.17(.19). As such, a

square root transformation was applied to reduce skewness.

The square root transformed mean CU traits score was used

in further analyses.

Appraising the hierarchical cluster analysis output visu-

ally (i.e., dendogram, scree plot) revealed three-, four-, and

five-cluster solutions that fit the data. Computation of a

k-means cluster analysis showed that the three-, four-, and

five-cluster solutions each obtained substantial agreement

with the originally derived hierarchical solution (j = .793, j = .648, and j = .715, respectively). Upon further inspection, the five-cluster solution was imbalanced with

respect to number of participants in each group (ranging

from 8 to 55). Imbalanced groups is not consistent with the

use of Ward’s Method for combining cases [60] or ANOVAs

for comparing groups on outcome variables (see below). As

such, this solution was determined unsatisfactory. Both the

three- and four-cluster solutions were equally balanced;

however, the four-cluster solution was selected because it

was most theoretically and clinically meaningful, as well as

consistent with foundational research in the area [4].

Two methods were applied to establish the stability of

the four-cluster solution. First, the sample was randomly

split into halves and a four-cluster hierarchical analysis run

on each sample. The majority of differences among cluster

means across halves were not significant. A few differences

emerged during these split-halve analyses of the four-

cluster solution that were attributed to uneven sample sizes

among comparison groups (i.e., group with eight partici-

pants). Regardless, consistency among cluster means in the

two halves demonstrates stability of the cluster solution

[60]. Second, a k-means cluster procedure was computed

using centroids calculated from the hierarchical solution

[59]. Applying this procedure revealed minimal changes

among the initial and final cluster centers, providing further

evidence of cluster stability [59]. The cluster mean of each

variable for the final solution is reported in Table 3.

Defining the Clusters

Inspection of cluster centers revealed distinct groups. First,

as can be seen in Table 3, cluster one was characterized by

Table 2 Correlations between cluster variables

Variable 1 2 3 4 5 6

1. Callous/unemotional –

2. Emotional difficulties .29* –

3. Conduct problems .37* .26* –

4. Proactive aggression .38* .10 .51* –

5. Reactive aggression .44* .24* .59* .61* –

6. Overall behavioral

impairment

.23* .40* .46* .23* .33* –

* p \ .01

Child Psychiatry Hum Dev (2015) 46:567–576 571

123

CU traits, Emotional Difficulties, and Conduct Problems,

each below their respective means. This group of children

was classified as the Low cluster (n = 36). Clusters two

and three were characterized by symptom severity above

the mean in two domains and below the mean in the third.

Thus, these were classified as High Emotional/Conduct

cluster (i.e., High Emotional Difficulties and High Conduct

Problems; n = 34) and High CU/Conduct cluster (i.e.,

High CU traits and High Conduct Problems; n = 57),

respectively. The fourth cluster was defined by symptom

severity above the mean in all domains and was classified

as the High cluster (n = 39). Post-hoc ANOVA’s were

computed to further describe how CU traits, Emotional

Difficulties and Conduct Problem severity differed between

clusters (see Table 3).

Differences Between Clusters on Outcome Variables

First, clusters were compared on demographic variables—

age, gender, ethnicity, medication status, and socioeco-

nomic status (as indexed by parental education level),

yielding no significant differences on these variables

between clusters. To establish external validity, clusters

were compared on RA, PA and OBI (see Table 4). All

comparisons reached significance, suggesting important

differences between clusters. Post-hoc analyses revealed

that the High CU/Conduct and High clusters showed the

most severe levels of PA (see Table 4). The High CU/

Conduct cluster showed significantly more PA compared to

the High Emotional/Conduct and Low clusters. Addition-

ally, the High cluster showed more severe RA compared to

the High Emotional/Conduct and Low clusters. The High

and High CU/Conduct clusters did not statistically differ on

RA severity. The Low cluster showed the least severe

reactive and PA.

Post-hoc comparisons of OBI ratings showed that the

Low cluster had significantly less impairment compared to

other clusters. Impairment ratings for the High CU/Con-

duct, High Emotional/Conduct, and High clusters did not

differ significantly.

Discussion

This study tested whether clinic-referred children with

disruptive behavior cluster based on severity of CU traits,

emotional difficulties, and conduct problems. Results show

four reliable and clinically useful profiles that include (1)

children below the mean in severity on all domains; Low

cluster, (2) children below the mean in severity on CU

traits but above on emotional difficulties and conduct

problems; High Emotional/Conduct cluster, (3) children

Table 3 Means and standard deviations for each cluster group

Variable Overall sample Cluster group ANOVA g2 Post hoc

1. Low 2. emotional/conduct 3. CU/conduct 4. High F(3, 162)

(n = 36) (n = 34) (n = 57) (n = 39)

ED .75 (.53) .29 (.28) 1.09 (.27) .41 (.29) 1.37 (.33) 126.26* .7 1, 3 \ 2 \ 4 CP .98 (.40) .67 (.32) .95 (.39) 1.03 (.36) 1.21 (.38) 14.46* .21 1 \ 2, 3; 2 \ 4 CU .66 (.73) .04 (.11) .06 (.13) .90 (.49) 1.40 (.78) 73.18* .58 1, 2 \ 3 \ 4

CU is reported without transformation

ED Emotional difficulties, CP conduct problems, CU callous/unemotional

* p \ .001

Table 4 Difference between cluster groups on outcome variables and standardized effect sizes

Variable 1. Low

(n = 36)

2. Emotional/Conduct

(n = 34)

3. CU/Conduct

(n = 57)

4. High

(n = 39)

ANOVA F(3,

161)

g2 Post hoc

RA 2.97 (1.84) 4.15 (2.49) 5.23 (2.43) 5.87 (2.22) 12.02* .18 4 [ 1, 2 3 [ 1

PA .57 (.64) .81 (.81) 1.30 (.71) 1.14 (.84) 8.00* .13 3 [ 1, 2 4 [ 1

OBI 3.94 (1.69) 4.79 (1.02) 4.65 (1.23) 5.26 (.69) 7.42* .12 2, 3, 4 [ 1

Square root transformation reported in brackets

ED/CP Emotional difficulties/conduct problems, CU/CP callous–unemotional/conduct problems, RA reactive aggression, PA proactive

aggression, OBI overall behavioral impairment

* p \ .001

572 Child Psychiatry Hum Dev (2015) 46:567–576

123

below the mean in severity on emotional difficulties but

above on CU traits and conduct problems; High CU/Con-

duct cluster, and (4) children above the mean in severity on

all domains; High cluster. Importantly, clusters showed

differences on reactive and PA and OBI.

A first novel finding of the present study is the appli-

cation of person-centered methodology to demonstrate that

children referred to a mental health clinic because of dis-

ruptive behaviors can be reliably grouped into clinically-

relevant profiles. Two profiles of children with conduct

problems were identified that comprised children with

either high levels of emotional difficulties or CU traits.

This finding in a clinical sample of children adds to pre-

vious research that describes developmental pathways of

conduct problems distinguished by emotionally reactive or

CU processes [20]. Further, in the present study the CU/

Conduct profile showed higher levels of PA and behavioral

difficulties compared to the Emotional/Conduct profile.

Consistent with past research, this finding highlights the

importance of assessing emotional processes and CU traits

to best determine the dimensions of psychopathology that

are associated with children’s conduct problems.

Overall, identified clusters differentiated children based

on key aspects of social behavior (i.e., reactive and proac-

tive aggression) and functional difficulties (i.e., OBI). The

finding that children in the High and High CU/Conduct

clusters showed the most elevated levels of aggression adds

to a growing body of knowledge [26, 27, 61–63]. Further-

more, consistent with past research, the High CU/Conduct

cluster showed greater PA compared to the High Emotional/

Conduct and Low clusters [64]. This finding is conceptually

important given that PA and CU traits share similarities in

that actions perceived by others to be ‘‘cold or uncaring’’

(i.e., CU) may also display power or achieve social goals

through aggression (i.e., PA). This finding has important

implications for the social and behavioral development of

this cluster of children and for interventions to reduce

children’s conduct problems and aggressive behaviors.

Also of clinical relevance, clusters differed in slightly

different ways on severity of RA. Although the High

cluster showed the most severe RA, this was not signifi-

cantly different than the High CU/Conduct cluster. This

similarity may be attributed to high levels of CU traits

demonstrated by children in both clusters. Of note, the

High cluster showed significantly more severe levels of RA

compared to clusters of children with elevated emotional

difficulties and conduct problems without elevated CU

traits (i.e., High Emotional/Conduct cluster) and children

below the mean in all domains (i.e., Low cluster).

The four profiles identified in this study highlight

important differences in clinical needs of children referred

because of disruptive behavior. Findings indicate that overt

disruptive behavior and conduct problems may be best

understood when considered along with associated clinical

characteristics such as CU traits and emotional difficulties;

each of which may qualitatively change the child’s profile

of concerns. For example, children with conduct problems

and emotional difficulties (i.e., High Emotional/Conduct)

showed less severe aggression compared to those with high

CU traits. As such, consideration of conduct problems in

the context of CU traits and emotional difficulties further

describes potential clinical needs and areas for assessment

and treatment.

Importantly, this study used a person-centered approach

for analyses, which grouped children into clusters accord-

ing to research-based clinical indicators. When clinical

indicators are considered as a continuum, this approach

adds to determination of children’s specific clinical needs.

Using a person-centered dimensional approach to identi-

fying factors that are most closely associated with disrup-

tive behavior is consistent with NIMH guidelines and much

emerging research to clarify the negative outcomes asso-

ciated with mental and behavioral health disorders in

childhood [6, 65, 66]. Further uses of person-centered

analytic approaches to study processes that contribute to

disruptive behavior in childhood are needed.

Limitations and Considerations

This study included children referred to a specialized

mental health clinic because of challenging behaviors. As

such, findings from this study are most generalizable to

clinical populations of children. Further, this study used a

previously developed and internally consistent measure of

CU traits. It was necessary to include a brief measure to

maximize clinical feasibility (i.e., the measure was inclu-

ded among other measures to assess children’s function-

ing). Further person-centered research with children that

includes more comprehensive measurement of psychopa-

thology that are also consistent with DSM-5 criteria, may

be beneficial to highlight additional complexities within

clinical profiles [23, 67]. Lastly, this study used one

informant per child. Future studies that incorporate multi-

ple informants may be useful to gather additional per-

spectives on child characteristics and behavior.

Clinical Implications

Imperative within clinical settings is the efficient determi-

nation of therapeutic needs to specify suitable treatment.

This study provides initial support for screening CU traits

and emotional difficulties to guide treatment selection of

children with conduct problems. For example, children with

high levels of emotional difficulties and conduct problems

without pronounced CU traits may be best served by inter-

ventions to manage underlying emotional dysregulation.

Child Psychiatry Hum Dev (2015) 46:567–576 573

123

However, children with elevated CU traits and conduct

problems may require treatments that target cognitive,

behavioral, and emotional processes related to CU traits.

Evidence-based treatments that address children’s cog-

nitive, emotional, and behavioral skills may benefit from

modules that specifically target underlying emotional dif-

ficulties and cognitions and behaviors associated with CU

traits. Although current programs target development of

emotional regulation, problem-solving and behavioral

skills, few explicitly target CU traits [17, 19, 34, 68, 69].

Additionally, clinical trials research to investigate the

impacts of best-practice interventions may be strengthened

by comparing treatments based on profiles that include

children classified based on their severity of CU traits and

emotional difficulties. Some work to test the moderating

influence of these domains of psychopathology has begun;

however, further research is needed [19, 35, 36]. Identifi-

cation of profiles of children with conduct problems based on

severity of CU traits and emotional difficulties is another

step towards considering unique aspects of children’s psy-

chological make-up that influence behavior and develop-

ment. This multi-component perspective may be essential to

tailor intervention approaches to best match the specificity of

children’s clinical concerns.

Summary

This study identified profiles of referred children based on

the severity of CU traits, emotional difficulties, and con-

duct problems that may have application for assessment

and treatment selection procedures. Person-centered ana-

lysis was used to identify four distinct profiles (1) Children

low in severity on the three domains, (2) Children high in

severity on the three domains, (3) Children high in severity

in conduct problems and CU traits with minimal emotional

difficulties, and (4) Children high in severity in conduct

problems and emotional difficulties with minimal CU

traits. Profiles differed in degree of reactive and PA and

behavioral impairment. Despite having similar levels of

conduct problems, profiles that included children with

higher levels of CU traits showed the most PA and

behavioral impairment. Findings show that clinic-referred

children with disruptive behaviors can be grouped based on

these important indicators into profiles that have important

implications for assessment and treatment selection.

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