Utility of the Limited Prosocial Emotions Specifier in Preschoolers With Conduct Problems

Abstract

This study examined the clinical utility of the “Limited Prosocial Emotions” (LPE) specifier (i.e., prevalence rates, group differences, and predictive utility) in a high-risk preschool sample (N = 109, M age = 4.77) presenting with conduct problems (CPs; n = 59). First, LPE prevalence rates ranged from 7.7% to 89.8%. Next, few group differences were observed between with CP-only and CP+LPE; youth with CP+LPE differed from youth with CP-only on callous-unemotional (CU) traits and verbal ability, but not on externalizing or internalizing psychopathology, nor on parenting experiences. In the full sample, youth with LPE differed from youth without LPE on externalizing and internalizing psychopathology, parenting, and verbal ability. Finally, LPE predicted greater baseline CP but did not predict trajectories of CP. Findings highlight the clinical utility of the LPE specifier during early childhood and call for a refinement of the LPE specifier to improve its clinical value.

Keywords

limited prosocial emotions conduct problems early childhood clinical utility callous-unemotional

Callous-unemotional (CU) traits are characterized by four distinct dimensions: (1) lack of remorse or guilt, (2) callousness or lack of empathy for others’ well-being, (3) unconcerned about personal performance, and (4) shallow or deficient affect (Frick et al., 2014b). CU traits are an important factor in understanding the heterogeneity of youth conduct problems (CPs), which encompass the diagnostic categories of oppositional defiant disorder (ODD) and conduct disorder (Frick & Morris, 2004). CU traits have been found to be associated with more stable and aggressive forms of antisocial behavior (Frick et al., 2014a, 2014b). Based on these findings, CU traits were incorporated as a specifier for conduct disorder known as “with Limited Prosocial Emotions” (LPE) in the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association [APA], 2013). The LPE specifier is applied to a conduct disorder diagnosis if at least two of the aforementioned four criteria are met in at least two settings over a 12-month period (APA, 2013). Despite its addition to the DSM-5, few studies have examined the clinical utility of the LPE specifier (e.g., prevalence rates, group differences, and predictive ability). Of these few studies, most have used adolescent samples (e.g., Colins, Van Damme, et al., 2020). There are a few reasons why further work is needed to examine the LPE specifier during preschool. First, the inclusion of the LPE specifier in the DSM-5 implies that this construct is a useful tool in the assessment, diagnosis, and treatment of aggressive behaviors in youth. Furthermore, the onset of both CP and LPE during early childhood is associated with a highly stable and aggressive pattern of antisocial behavior (Kimonis et al., 2016; Moffitt, 1993). This study sought to explore the clinical usefulness of the LPE specifier in a high-risk preschool sample measured at three timepoints over a 1-year period.

There has been an influx of research on CU traits over the past two decades (Waller et al., 2020). Some of this work, using various samples and methods, has shown that CU traits are not globally related to theoretically relevant outcomes, such as aggression or delinquency (e.g., Edens & Cahill, 2007; Lotze et al., 2010; Manti et al., 2009). However, a larger portion of this research contends that CU traits appear to play an important role in contributing to the heterogeneity of CP (e.g., Frick et al., 2014b). For instance, across various early childhood samples, dimensional models of CU traits (e.g., lower vs. higher total CU scores) have found that youth with CP and elevated levels of CU traits can be differentiated from youth with CP and low levels of CU traits across many factors, including: (a) higher rates of disruptive, aggressive, and externalizing psychopathology symptoms (Ezpeleta et al., 2012, 2015; Kimonis et al., 2016; Longman et al., 2016); (b) lower rates of internalizing psychopathology (Dadds et al., 2005; Hawes & Dadds, 2007; Kimonis et al., 2016); (c) lower treatment efficacy (Hawes & Dadds, 2005, 2007; Hawes et al., 2014); and (d) higher rates of punishment insensitivity (Briggs-Gowan et al., 2014). Furthermore, higher intelligence quotient (IQ) and verbal ability are also characteristic of youth with CP and elevated rates of CU traits (Loney et al., 1998; Salekin et al., 2010), although less work has examined this during early childhood. Overall, the existing research suggests that CU traits are an important factor in understanding the heterogeneity of CP.

In contrast, findings regarding the impact of the categorical LPE specifier (i.e., LPE-Absent vs. LPE-Present) on CP are equivocal. In a community sample of elementary-aged youth (8–10 years), one study found that youth with both CP and LPE (CP+LPE) demonstrated significantly higher rates of disruptive behavior disorder symptoms (e.g., attention-deficit/hyperactivity disorder [ADHD], ODD, and conduct disorder) than youth with CP without LPE (CP-only). LPE was also predictive of CP at a 3-year follow-up (Colins et al., 2021). Overall, youth with CP+LPE showed higher and more stable rates of externalizing psychopathology. In contrast, a systematic review of eight studies showed mixed outcomes for the LPE specifier (Colins, Van Damme, et al., 2020). This review found a wide prevalence range of LPE in youth with conduct disorder, with upper estimates close to 85% (Colins, Van Damme, et al., 2020; Sakai et al., 2016). Most notable, however, was that few differences were seen between aggressive youth with and without LPE. While youth with LPE showed higher levels of aggression and past antisocial behavior, no differences were observed on other vital outcomes (e.g., anxiety, depression, psychopathy, and treatment response). This review concluded that the LPE specifier had limited utility (Colins, Van Damme, et al., 2020); however, there are several methodological differences across these eight studies that must be considered before making conclusions about the clinical value of the specifier. For instance, participants were recruited across various settings (e.g., clinical, incarcerated, and community) and demonstrated large age ranges (e.g., 5–18). Furthermore, the informant (self vs. teacher vs. parent) and coding method (extreme vs. split) that were used varied across the studies. These factors may have contributed to the mixed findings regarding the clinical utility of the LPE specifier; thus, caution is warranted in concluding that the specifier has limited value.

Another noteworthy limitation of existing work is that many studies have applied the LPE specifier to youth meeting criteria for conduct disorder in adolescent-aged samples. This greatly limits our understanding of how the LPE specifier impacts aggressive behavior during early childhood. While the LPE specifier is applied to diagnoses of both ODD and conduct disorder in the 11th edition of the International Classification of Disease (World Health Organization, 2018), it is currently only applicable to conduct disorder in the U.S.-based DSM-5 (APA, 2013) which highlights cross-cultural differences in how LPE is associated with disruptive behavior disorders. Indeed, there has been a call to move away from DSM-centric approaches and to apply the LPE specifier to youths who display severe CP but may not meet diagnostic criteria for conduct disorder (Frick et al., 2014a, 2014b, 2014c; Hawes, 2014; Van Damme et al., 2016). Application of the LPE specifier to CP is particularly important for childhood samples, given that the onset of CP, especially conduct disorder, in youth as young as 3 years old is associated with lifespan-persistent forms of antisocial behavior (Moffitt, 1993; Moffitt et al., 1996). Yet, only one study has examined the LPE specifier during childhood (Déry et al., 2019). Therefore, additional research is needed to expand our knowledge of how CP develops, and the role of LPE during one of the earliest times that CP and LPE can be assessed (Keenan et al., 2007; Kimonis et al., 2016; Willoughby et al., 2015). To this end, investigation of key components of the specifier during early childhood is critical, including its ability to (1) identify prevalence rates of LPE in youth with CP; (2) differentiate between youth with CP-only and CP+LPE; and (3) predict more aggressive and stable forms of CP across time.

Prevalence Rates

First, the LPE specifier should be able to identify a subgroup of youth with CP (Frick, 2009). Yet the review by Colins, Van Damme, et al. (2020) found that youth meeting criteria for LPE ranged from 6% to 85%. Such high estimates (>50%) contrast prior work showing that between 20% and 50% of youth with CP will also show clinically significant rates of LPE. Importantly, this prevalence range was identified using a variety of samples (e.g., community, clinical, and forensic), age ranges, and reporting methods (e.g., parent, self, and clinician; Frick et al., 2014b). Thus, it may be that these wide prevalence ranges are due to methodological differences in prior work rather than the LPE construct itself having “limited” value. Additional work is needed to examine LPE prevalence rates using the Inventory of Callous-Unemotional Traits (ICU; Frick, 2004). The ICU is one of the most widely used measures of LPE but has been used infrequently in empirical studies. Yet, it may be the most optimal method to determine LPE prevalence rates (and overall clinical utility). The ICU has a version appropriate for preschool youth (Frick, 2004) and offers four distinct coding methods to determine LPE status (Kimonis et al., 2015): 4-item versus 9-item sets, and split coding (item ratings of very true [2] or definitely true [3]) versus extreme method (item ratings of only definitely true [3]). In an adolescent sample, Sakai and colleagues (2016) observed a wide LPE prevalence range using these algorithms but found the 4-item split, 4-item extreme, and 9-item extreme methods to produce LPE prevalence rates consistent with typical estimates (Frick et al., 2014b). These algorithms have only been used in adolescent samples (Kimonis et al., 2015; Sakai et al., 2016), warranting additional work to determine prevalence rates of LPE during early childhood.

Group Comparisons

Another key function of the LPE specifier is to differentiate between youth with CP-only versus CP+LPE. Past work has had limited success in distinguishing between these groups (Colins, Van Damme, et al., 2020). Furthermore, many external correlates have been overlooked in prior work, particularly in studies conducted during early childhood. In fact, only one study during childhood examined CP-only versus CP+LPE group differences on measures of psychological functioning, including measures of internalizing symptoms and ultimately found no differences (Kolko & Pardini, 2010). However, relative to youth with CP-only, youth with CP+LPE are theorized to demonstrate higher rates of aggressive and externalizing symptoms (Frick et al., 2014b; Frick & Morris, 2004) and lower rates of internalizing symptoms and emotion dysregulation (Blair, 1999; Frick & Morris, 2004). Two other key variables that have been overlooked are parenting practices and verbal ability, both of which have been implicated in youth with CP, with and without LPE (Salekin, 2006; Waller et al., 2013). First, evidence suggests that youth with CP+LPE experience greater rates of harsh and ineffective parenting relative to youth with CP-only, although these findings are based on older childhood and adolescent samples (for review, see Waller et al., 2013). Furthermore, LPE is associated with increased verbal ability; in fact, the interaction between verbal ability and LPE has predicted greater rates of aggressive behaviors (Salekin et al., 2010). Additional research is necessary to determine if the LPE specifier can identify group differences on several key variables including psychological functioning, parenting, and verbal ability during preschool.

Longitudinal Trajectories

Finally, there are few studies testing the LPE specifier’s ability to predict trajectories of aggressive behavior and CP over time, particularly during the understudied early childhood period when these traits typically develop and begin to cause impairment (Keenan et al., 2007; Kimonis et al., 2016). In fact, there appears to be only one study that has investigated the predictive ability of the LPE specifier using a childhood sample (Déry et al., 2019). Ultimately, CP trajectories did not differ as a function of LPE status, but youth with CP+LPE showed significantly higher baseline levels of CP. However, after controlling for ADHD and ODD, this difference was reduced to nonsignificant. Although controlling for ADHD is theoretically important, given its association with later CP (Pardini & Fite, 2010), controlling for ODD may have inadvertently partialed out necessary variance, given that CP is comprised of both ODD and conduct disorder symptoms (Frick & Morris, 2004). Therefore, additional work is needed to determine how the LPE specifier predicts trajectories of CP during childhood.

Current Study

This study sought to examine the clinical usefulness of the LPE specifier in a high-risk preschool sample, the majority of whom meet criteria for CP. The first aim of the study was to establish LPE prevalence rates using the four algorithms. It was hypothesized that (1) LPE prevalence rates would fall between 20% and 50%; no hypotheses were generated about which algorithm would produce the most accurate rates, given the lack of research during early childhood. The second aim of the study was to examine group differences on key external correlates. It was hypothesized that (2) relative to youth without LPE, youth with LPE would show (a) higher aggression and externalizing symptoms, (b) lower internalizing symptoms and emotion dysregulation scores, (c) greater harsh and inconsistent parenting, and (d) greater verbal ability. The final aim of the study was to determine the predictive ability of the LPE specifier, and it was hypothesized that (3) the presence of LPE would predict more stable trajectories of CP relative to the absence of LPE.

Method

Participants

The total sample consisted of 109 children aged 3 to 6 years old (M = 4.77, SD = 1.11). Participant caregivers included (1) biological mothers only (n = 75, 69%); (2) both biological mother and father (n = 20, 18.3%); (3) biological fathers only (n = 6, 5.5%); and (4) other (stepparent, adoptive parent; n = 5, 4.6%); these caregivers completed all parent-reported measures. The sample of children was comprised of 64 (59%) males, and 36 youth (33%) represented a racial or ethnic minority (e.g., African American, American Indian, and Alaskan Native). The average yearly family income fell between $40k and $60k. Of the total sample, 20 youth (18%) were diagnosed with either ODD and/or conduct disorder without comorbid ADHD, while an additional 43 (39%) were diagnosed with either ODD and/or conduct disorder with comorbid ADHD. These rates exceed the typical 7% to 10% lifetime prevalence rates for ODD and conduct disorder (Ghandour et al., 2019; Nock et al., 2006, 2007), as this sample was over-recruited for disruptive and aggressive behaviors. Seventeen (16%) youth met diagnostic criteria for ADHD without any CP, while 29 (27%) youth did not meet diagnostic criteria for any psychological disorder and were included in the sample to provide a dimensional measure of disruptive behavior problems.

Procedures

This study’s hypotheses and analytic plan were preregistered at osf.io/erxgu. Consistent with the university’s Institutional Review Board, the National Institute of Mental Health, and APA guidelines, written and verbal consent were obtained from all families involved within the study. Participants were recruited from urban, suburban, and rural areas surrounding a medium-sized metropolitan city in the Southeastern area of the United States. Recruitment was done primarily through the use of fliers, which were posted in various community areas (e.g., physician offices, daycares, and online) and also directly mailed to families. One set of fliers was aimed toward recruiting children, aged 3 to 6, with disruptive behavior and/or attention problems, while a second set of fliers targeted same-aged typically developing controls (i.e., children without disruptive behavior and/or attention problems). Exclusion criteria included: (a) use of psychotropic medication; (b) neurological impairments (e.g., seizure issues, head injury with loss of consciousness); and (c) more severe forms of psychopathology (e.g., psychosis and autism spectrum disorders).

After an initial phone screen to rule out ineligible participants, eligible families were mailed caregiver and teacher questionnaires 1 week prior to their scheduled laboratory visit. These forms were used to gather information regarding behavior and attention problems. Diagnostic information was obtained through the parent-report on the Kiddie Disruptive Behavior Disorders Schedule (K-DBDS; Keenan et al., 2001) and through parent and teacher-report on the Disruptive Behavior Rating Scale (DBRS; Barkley & Murphy, 1998). Final diagnoses were determined by a licensed clinical psychologist using best practice procedures for disruptive behavior disorders (using multiple sources of information from multiple methods; McMahon & Frick, 2010; Pelham et al., 2005). Following this baseline laboratory visit, follow-up phone calls were conducted on two separate occasions, one call occurring 6 months after the baseline visit and another call that occurred 1 year after the baseline visit. The K-DBDS was re-administered during these follow-up calls. Approximately 50% of the families completed the 6-month call, and roughly 70% of the families completed the 1-year phone call. A priori power analyses determined that the sample of 109 youth was adequate (.80) to detect medium to large statistical effects (Martel et al., 2012).

Measures

Grouping Variables/Predictors

CP

The K-DBDS interview obtained diagnostic information regarding ADHD and disruptive behavior disorders (i.e., ODD and conduct disorder). The K-DBDS was designed to discriminate between appropriate and problematic behaviors during preschool (Keenan et al., 2001) and is a reliable and valid measure of disruptive behavior symptoms (Bunte et al., 2013; Keenan et al., 2001). Using Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) criteria, the K-DBDS assessed all 18 symptoms of ADHD, all eight symptoms of ODD, and 11 of the 15 symptoms of conduct disorder. The remaining four conduct disorder symptoms (i.e., breaking into a house, car, or building; running away from home overnight; often stays out late; and truancy) were not assessed because of a lack of face validity for preschool children (Bunte et al., 2013). Youth were considered as having CP if they met diagnostic criteria for ODD and/or conduct disorder.

CU Traits

The parent-report preschool version of the ICU was completed by the participants’ caregivers. The ICU was used to assess CU traits in children using 24 items rated on a 0 to 3 Likert-type scale (Frick, 2004). This version of the ICU has been used in prior studies to measure CU traits during preschool (Bansal, Goh, et al., 2020; Ezpeleta et al., 2012; Willoughby et al., 2015). The 12 positively worded items were reverse scored prior to analysis and summed with the remaining items to yield a total score. Based on prior work (Kimonis et al., 2015), LPE status was determined using two item sets: a 4-item set (items 3, 5, 6, and 8) and a 9-item set (items 5, 13, 16 [lack of remorse/guilt]; 8, 17, 24 [callous/lack of empathy]; 3, 15 [unconcerned about personal performance]; and 1 [shallow/deficit affect]). The 4-item and 9-item sets were subjected to the two coding methods: split-method (i.e., items rated as either very true [2] or definitely true [3]) or extreme method (i.e., items rated only as definitely true [3]). Youth who met criteria for only CP but not LPE were considered CP-only, while youth who met criteria for both CP and LPE were considered part of the CP+LPE group.

ADHD

Caregivers completed the DBRS for preschool children (Barkley & Murphy, 1998). The DBRS consists of 26 items assessing DSM-IV symptoms of ADHD and ODD. This measure has been used in prior studies using preschool samples (Martel et al., 2016; Smith et al., 2017). Parents report on the frequency of symptoms over the past 6 months on a 0 to 3 Likert-type scale (Barkley & Murphy, 1998). The 18 ADHD items (α = .96) were summed, and the total score was included as a covariate in the trajectory analyses due to the relationship between ADHD and later CP (Pardini & Fite, 2010).

Outcomes

Disruptive Behavior Symptoms

ADHD (α = .96) and ODD (α = .92) continuous counts were measured using the DBRS while conduct disorder (α = .59) and CP (α = .82) continuous scores were assessed via the K-DBDS. This was done to reduce the number of constructs which were used as grouping variables and as outcomes. Baseline and longitudinal CP scores were calculated by summing the ODD and conduct disorder scores on the K-DBDS.

CU Traits

Continuous measures of CU traits were assessed using the ICU. The 12 positively worded items were reversed scored and then summed with the remaining 12 items to create a total CU score (α = .89). In addition, a callous score (α = .82; items 4, 6, 9, 11, 12, 18, 21) and an uncaring score (α = .82; items 5, 8, 16, 17, 24) were also computed. A score for the unemotional scale was not calculated as this scale does not have support in early childhood samples (Bansal, Babinski, et al., 2020; Bansal, Goh, et al., 2020; Willoughby et al., 2015).

Psychological Functioning

The preschool version of the Child Behavior Checklist (CBCL; Achenbach & Rescorla, 2000) was completed by caregivers. The CBCL contains 99 items rated on a 0 to 2 Likert-type scale. Items were scored using available software and computed into diagnostic and syndrome scales. This study used the following syndrome scales: emotionally reactive, anxiety/depression (e.g., comorbid anxiety and depression), aggression, anxiety (e.g., anxiety-oriented), and withdrawn/depressed (e.g., depression-oriented).

Parenting Practices

Caregivers completed the Alabama Parenting Questionnaire (APQ; Frick, 1991) which consists of 42 items rated on a 1 to 5 Likert-type scale. Following prior research (Hinshaw et al., 2000), three subscale scores were computed: positive parental involvement (α = .84; you have a friendly talk with your child), negative/ineffective discipline (α = .72; you let your child out of punishment early), and deficient monitoring (α = .52; your child is at home without supervision).

Verbal Ability

Verbal ability was assessed using the Peabody Picture Vocabulary Test—Third Edition (PPVT; Dunn & Dunn, 1997) and the Expressive Vocabulary Test (EVT; Williams, 1997). These instruments have been developed for early childhood use (Restrepo et al., 2006). Both measures were used for a few reasons. First, it appears that the PPVT and EVT assess unique aspects of verbal ability, the former measuring general verbal intelligence, whereas the latter assesses a child’s one-vocab ability. Next, some research has suggested biases in only using the PPVT (Restrepo et al., 2006), reinforcing the need to use multiple measure of verbal ability. Both the PPVT and the EVT produce their own standard t-scores and percentile scores, both of which were used in this study.

Data Analytic Plan

LPE status was first determined using four distinct algorithms: (a) 4-item split coding, (b) 4-item extreme coding, (c) 9-item split coding, and (d) 9-item extreme coding. Based on a lack of established guidelines for early childhood, the algorithm(s) which yielded an LPE prevalence rate consistent with past research (Frick et al., 2014b) was used as the grouping variable for all analyses. The study aims were to examine (1) the prevalence rates of LPE, (2) group differences based on LPE, and (3) whether LPE predicted more stable trajectories of CP. Study aims 1 and 2 were examined in two distinct fashions. First, analyses focused on the subsample of youth who met diagnostic criteria for ODD and/or conduct disorder (i.e., CP), to examine differences between youth with CP, with and without LPE (i.e., CP-only vs. CP+LPE). Next, analyses used the entire sample to determine whether the presence of LPE differentiated youth who met criteria for LPE, regardless of CP status (i.e., LPE-Absent vs. LPE-Present). The final aim of the study was examined only in the full sample, rather than restricting the range to only the CP subgroup. This was done due to recent arguments to examine LPE beyond DSM-centric approaches (Frick et al., 2014a, 2014b, 2014c; Hawes, 2014; Van Damme et al., 2016) and to increase sample size to provide more statistical power for all analyses.

Prevalence rates were first computed among the 59 youth who met criteria for CP and had complete ICU data (i.e., CP-only vs. CP+LPE) and then again among the entire sample of 104 youth with complete diagnostic and ICU data (i.e., LPE-Absent vs. LPE-Present). Next, independent samples t-tests were conducted in SPSS v26 and compared group differences on the study outcomes for the CP subsample and then again on the entire sample. Due to the small samples, effect sizes were calculated using Hedge’s g (Fritz et al., 2012; Hedges & Olkin, 2014) which is interpreted similarly to Cohen’s d (Cohen, 1988).

Finally, linear mixed models of CP trajectories on the entire sample were conducted using PROC MIXED in SAS v9.4. Two unconditional models were run, Model A (means model) and Model B (growth model), followed by two conditional models. The first conditional model, Model C, used LPE status as a dummy-variable (no vs. yes), with main effects representing differences between the LPE-Absent group and LPE-Present group on initial status. The time variable represented CP symptoms across the three timepoints, and LPE * Time interactions represented the effects of LPE-Present (relative to LPE-Absent) on changes in CP symptoms across the three timepoints. The second conditional model, Model D, used the same predictors as Model C and also controlled for ADHD symptom counts (mean-centered continuous score). To account for missing data, models were first analyzed using full-information maximum likelihood (FIML) estimates and then reanalyzed using restricted maximum likelihood (REML) estimates, given the relatively modest sample size (e.g., McNeish & Stapleton, 2016).

Results

Missing Data

There was minimal data loss (i.e., <10%) for the study variables at baseline: K-DBDS (0%), ICU (4.8%), CBCL (3.7%), APQ (6.4%), PPVT (1.8%), and EVT (2.8%). Substantially more data loss was observed for the CP outcome variable at the 6-month (50%) and 1-year (26.6%) follow-up points. Dichotomous variables were created where youth with missing data = 0 and youth with complete data = 1 for the 6-month and 1-year timepoints to determine if there were any patterns in the missing data. Independent samples t-tests were conducted across various demographics (e.g., age, sex, and income) and all outcomes. There were no significant differences between youth who were and were not missing data at the 6-month timepoint on any outcomes (all p values > .05). At the 1-year timepoint, youth missing data showed lower verbal ability on the PPVT and EVT relative to youth not missing data (all p values < .05). By definition, this pattern of missing data appears to meet criteria for “missing at random” (Bennett, 2001).

Aim 1: Prevalence Rates

First, ICU-determined prevalence rates of LPE were examined within the CP subgroup (n = 59). Prevalence rates of youth who met LPE criteria were as follows: 4-item split = 66.1% (n = 39); 4-item extreme = 13.6% (n = 8); 9-item split = 89.8% (n = 53); and 9-item extreme = 25.4% (n = 15). Next, prevalence rates were examined within the total sample of 104 youth with complete K-DBDS and ICU data. LPE prevalence rates were as follows: 4-item split = 45.2% (n = 47); 4-item extreme = 7.7% (n = 8); 9-item split = 73.1% (n = 76); 9-item extreme = 14.4% (n = 15). Based on past work (Colins & Andershed, 2015; Déry et al., 2019; Sakai et al., 2016; Van Damme et al., 2016), the 4-item split method produced one acceptable rate (45.2%) but one larger rate (66.1%). Both prevalence rates produced by the 4-item extreme method were low (13.6%, 7.7%), whereas both rates by the 9-item split method were high (89.8%, 73.1%). The 9-item extreme coding method produced the most acceptable prevalence rates (25.4%, 14.4%) and was used as the grouping variable for subsequent analyses.

Aim 2: Group Comparisons

Results of the between-group comparisons for the CP subgroup and full sample can be found in Table 1. First, comparisons were conducted on the CP subgroup (i.e., CP-only vs. CP+LPE). Youth with CP+LPE scored significantly higher than youth with CP-only on measures of (a) psychological functioning (emotional reactivity, aggression, and withdrawal; all p values < .05), (b) all facets of CU traits (all p values ≤ .01), and (c) parenting practices (negative/ineffective discipline; p < .01). Youth with CP+LPE scored significantly lower than youth with CP-only on verbal ability (PPVT and EVT percentile scores, both p values < .01). As seen in Table 1, Hedge’s g effect sizes indicated that the mean differences ranged from medium to large across all significant comparisons (|g| range = .55–1.24). In contrast, marginal but ultimately nonsignificant differences were found between groups on ADHD (p = .055), ODD (p = .07), conduct disorder (p = .09), and CP (p = .08) symptoms, nor on other measures of psychological functioning (anxiety/depression p = .07, anxiety p = .31), or parenting (positive involvement p = .99, deficient monitoring p = .24).

Table 1.

Between-Group Comparisons for CP Subgroups (CP-Only vs. CP+LPE) and Full-Sample (LPE-Absent vs. LPE-Present).

	Variable	CP subgroup (N = 59)				Total sample (N = 104)
Measure	Variable	CP-only (n = 44)M (SD)	CP+LPE (n = 15)M (SD)	p value	Hedge’s g	LPE-absent (n = 89)M (SD)	LPE-present (n = 15)M (SD)	p value	Hedge’s g
DBRS	ADHD symptoms	28.77	37.27	.055	0.59	21.98	37.27	<.001	1.05
DBRS	ODD symptoms	9.84	13.60	.07	0.55	7.01	13.60	.01	1.06
K-DBDS	CD symptoms	2.16 (1.40)	3.40 (2.56)	.09	0.72	1.30 (1.39)	3.40 (2.56)	<.01	1.32
K-DBDS	CP symptoms	7.23 (2.14)	9.13 (3.76)	.08	0.73	4.69 (3.14)	9.13 (3.76)	<.001	1.39
CBCL	Emotional reactivity	4.36 (3.34)	6.40 (2.92)	.04	0.64	3.28 (3.03)	6.40 (2.92)	<.001	1.04
	Anxiety/depression	4.23 (2.63)	5.67 (2.64)	.07	0.55	3.31 (2.77)	5.67 (2.64)	<.01	0.86
	Aggression	16.66 (8.03)	24.53 (6.71)	<.01	1.03	11.98 (8.37)	24.53 (6.71)	<.001	1.55
	Anxiety	5.32 (3.04)	6.27 (3.37)	.31	0.31	4.36 (3.05)	6.27 (3.37)	.03	0.62
	Withdrawn/depressed	3.14 (2.41)	5.73 (2.96)	<.01	1.03	2.51 (2.31)	5.73 (2.96)	<.001	1.35
ICU	Total	23.77 (8.77)	35.27 (11.14)	<.001	1.24	18.63 (9.37)	35.27 (11.14)	<.001	1.74
	Callous	8.93 (5.31)	13.47 (6.84)	.01	0.80	6.63 (4.89)	13.147 (6.84)	<.01	1.32
	Uncaring	11.50 (3.84)	16.20 (4.18)	<.001	0.121	9.21 (4.61)	16.20 (4.18)	<.001	1.55
APQ	Positive involvement	64.23 (10.15)	64.21 (10.03)	.99	−0.002	65.74 (9.77)	64.21 (10.03)	.59	−0.16
	Negative/ineffective discipline	21.64 (5.16)	25.86 (4.83)	<.01	0.84	20.38 (5.43)	25.86 (4.83)	.001	1.03
	Deficient Monitoring	8.59 (1.98)	7.79 (2.83)	.24	−0.37	8.20 (2.00)	7.79 (2.83)	.49	−0.19
PPVT	Standard Score	95.47 (30.23)	84.29 (25.79)	.22	−0.39	95.05 (31.30)	84.29 (25.79)	.26	−0.35
PPVT	Percentile	51.14 (31.67)	27.57 (17.39)	.001	−0.83	51.44 (31.04)	27.57 (17.39)	<.001	−0.81
EVT	Standard Score	95.95 (26.01)	86.47 (25.13)	.23	−0.37	95.14 (29.29)	86.47 (25.13)	.28	−0.30
	Percentile	46.22 (30.29)	31.33 (17.05)	.02	−0.55	49.47 (29.91)	31.33 (17.05)	<.01	−0.64

Note. Bolded values indicate significant differences, p < .05; italicized values indicate marginally significant differences, p < .10. CD = conduct disorder; CP = conduct problems; LPE = limited prosocial emotions; DBRS = Disruptive Behavior Rating Scale; ADHD = attention-deficit/hyperactivity disorder; ODD = oppositional defiant disorder; CBCL = Child Behavior Checklist; ICU = Inventory of Callous-Unemotional Traits; APQ = Alabama Parenting Questionnaire; PPVT = Peabody Picture Vocabulary Test; EVT = Expressive Vocabulary Test.

Next, independent samples t-tests were conducted on the full sample (i.e., LPE-Absent vs. LPE-Present). Several differences were detected as youth with LPE-Present scored significantly higher than youth with LPE-Absent on all aspects of (a) ADHD, ODD, conduct disorder, and CP symptoms (all p values ≤ .05), (b) psychological functioning (all p values < .05), and (c) CU traits (all p values ≤ .01). Youth with LPE-Present also scored significantly higher than youth with LPE-Absent on certain subscales of parenting practices (negative/ineffective discipline p < .01). Relative to youth with LPE-Absent, youth with LPE-Present scored significantly lower on verbal ability (PPVT and EVT percentile scores, both p values ≤ .01). Hedge’s g effect sizes in Table 1 ranged from medium to large across the significant comparisons (|g| range = .64–1.74). No significant differences were observed on the parenting aspects of positive involvement (p = .59) nor deficient monitoring (p = .49).

Aim 3: Longitudinal Trajectories

Table 2 presents the effects of the linear-mixed models using FIML. Models using REML can be found in online supplemental material.¹ First, baseline CP scores for LPE-Absent youth were significantly greater than 0 in Model A (t-value = 15.31, p < .001) and in Model B (t-value = 12.53, p < .001). However, there were no significant effects of Time in either unconditional model (both p values > .05), suggesting little change in CP symptoms over time.

Table 2.

Linear Mixed Models Examining LPE Status as a Predictor of CP Using FIML Estimator.

Model	Effect	Est.	SE	t-value
A	Intercept	5.07	0.33	15.31***
B	Intercept	5.37	0.43	12.53***
B	Time	−0.17	0.22	−.77
C	Intercept	4.75	0.45	10.46***
	LPE	4.21	1.23	3.41**
	Time	−0.10	0.24	−.41
	LPE * Time	−0.11	0.70	−.16
D	Intercept	4.96	0.42	11.75***
	LPE	2.44	1.21	2.01*
	Time	−0.08	0.24	−.32
	LPE * Time	−0.22	0.73	−.31
	ADHD	0.11	0.02	4.28***
	ADHD * Time	0.01	0.02	.36

Note. LPE = limited prosocial emotions; CP = conduct problems; FIML = full-information maximum likelihood; Model A = unconditional means model; Model B = unconditional growth model; Model C = conditional growth model; Model D = conditional growth model + ADHD covariate; ADHD = attention-deficit/hyperactivity disorder.

p < .05. **p < .01. ***p < .001.

Next, conditional Model C predicted CP trajectories based on LPE status (Table 2). This model found significant effects at the intercept, with LPE-Absent youth demonstrating CP scores greater than 0 (p < .001). Furthermore, significant effects of LPE status on baseline CP were also observed. More specifically, youth with LPE-Present demonstrated significantly greater rates of CP at baseline than youth with LPE-Absent status (t-value = 3.41, p < .01). However, no significant effects of Time were observed (p > .05), nor was there a significant LPE * Time interaction (p > .05), suggesting that there was no effect of LPE status on the trajectory of CP.

Finally, in conditional Model D, LPE status predicted CP trajectories while controlling for ADHD. Significant effects were observed for baseline CP scores for LPE-Absent youth (p < .001). Significant effects were also seen for LPE status, suggesting that youth with LPE-Present presented with significantly greater baseline CP scores than LPE-Absent youth (t-value = 2.01, p < .05). The effect of LPE survived controlling for ADHD, which also demonstrated a significant effect on baseline CP symptoms (t-value = 4.28, p < .001). No significant LPE * Time nor ADHD * Time interactions were observed. Overall, the presence of LPE had a significant effect on baseline levels of CP, but LPE had no effect on the change in CP symptoms across time.

Discussion

Within a high-risk, clinical sample of preschool youth who met criteria for CP, this study examined (1) prevalence rates of LPE, (2) group differences between youth with and without LPE, and (3) the ability of the LPE specifier to predict trajectories of CP. Results demonstrated LPE prevalence rates ranging from 13.6% to 89.8% within the CP subgroup, and 7.7% to 73.1% in the full sample. Next, consistent group differences were observed between those without LPE versus those with LPE across a number of measures. Those with LPE had higher scores (i.e., worse outcomes) on certain aspects of psychological functioning, CU traits, and negative/ineffective discipline, and lower scores on measures of verbal ability. Finally, the presence of LPE was associated with higher levels of baseline CP, but LPE did not predict more stable trajectories of CP over time.

Prevalence Rates

Similar to prior work (Sakai et al., 2016), prevalence rates of LPE drastically differed as a function of the coding algorithms. The 4-item extreme and 9-item extreme methods yielded prevalence rates of LPE that were roughly in line with prior estimates (Frick et al., 2014b), as well as with our hypothesis, in both the CP subgroup and the full sample (ranging from 7% to 25%). This suggests that the extreme approaches (i.e., using items only endorsed as definitely true [3]) may be more accurate in identifying children who are displaying clinically significant levels of LPE. These rates are reinforced by the notion that only a small group of youth with CP are expected to show elevated rates of LPE (Frick, 2009). Importantly, the decision to conduct analyses with the 9-item extreme method was done so out of a statistical need, given that 15 youth with CP+LPE were identified using the 9-item extreme method, whereas the 4-item extreme method only identified eight youth with CP+LPE. The 4-item split and 9-item split methods yielded high (i.e., >50%) LPE prevalence rates, suggesting that more liberal criteria (i.e., using items rates as very true [2] or definitely true [3]) may identify false-positive cases of youth categorized with LPE.

It is important to consider that the ICU may play an important role in these wide prevalence ranges. More specifically, recent studies have highlighted how certain factors on the ICU may be a product of method variance, where items worded in the same direction load onto a given factor (Bansal, Babinski, et al., 2020; Ray & Frick, 2020). Consequently, it takes a higher score on a positively worded item (where higher scores indicate greater LPE) to discriminate between youth with and without LPE (Ray et al., 2016). Thus, the wide prevalence ranges seen in this study and prior work signal a need for further refinement of how the LPE specifier is measured, particularly if measured using the ICU.

Group Comparisons

Group comparisons were conducted in the CP subgroup sample (CP-only vs. CP+LPE) and the full sample (LPE-Absent vs. LPE-Present). More variability was seen in the outcomes for the CP subgroup. Several findings were consistent with the hypotheses. First, youth with CP+LPE scored significantly higher (i.e., worse) than youth with CP-only on (a) CBCL aggression, (b) all three ICU facets (total, callous, and uncaring), and (c) the negative/ineffective discipline scale of the APQ. These results are consistent with prior work showing that the LPE specifier was able to identify a subgroup of youth who exhibited greater rates of aggression (Colins & Andershed, 2015; Kahn et al., 2012; Van Damme et al., 2016) and CU traits (Sakai et al., 2016). Furthermore, youth with CP+LPE were significantly more likely to experience ineffective disciplining strategies relative to youth with CP-only. This trend had been found in older samples (Waller et al., 2013) but, until now, had not been investigated in a preschool sample.

Several outcomes within the CP subgroup sample ran counter to our hypotheses. First, youth with CP+LPE scored higher (i.e., worse) than youth with CP-only on the CBCL emotional reactivity and withdrawn/depression scales. This is a departure from prior work, which observed inverse associations between LPE and internalizing psychopathology and general emotional reactivity (Dadds et al., 2005; Herpers et al., 2014). A profile consisting of CP+LPE and elevated internalizing problem may be reflective of the secondary variant of psychopathy as seen in the adult literature (Kimonis et al., 2012), which is characterized by elevations in both callousness and emotional distress and may be a function of the high-risk sample used in this study. This may also be explained by a growing body of work highlighting an association between callousness and irritability (e.g., Bansal et al., 2021; Waschbusch et al., 2020). In this sample, it may be that youth who presented with high rates of callousness also exhibited elevated rates of reactive aggression when certain goal-directed activity was blocked.

Next, youth with CP+LPE scored lower than youth with CP-only on measures of verbal ability. While the presence of LPE has been theorized to buffer against cognitive deficits (Salekin, 2006), empirical findings are mixed and suggest that other facets of psychopathy—such as interpersonal glibness—may be the facet driving the association between psychopathy and verbal ability (e.g., to manipulate/deceive people) rather than the affective component (Salekin et al., 2004). Finally, no differences were found between the two groups on ADHD, ODD, conduct disorder, and CP symptoms, nor on the anxiety and anxiety/depression scales. These outcomes are consistent with prior work (Colins & Andershed, 2015; Colins et al., 2021). The lack of difference on the disruptive behavior symptoms is particularly noteworthy, as these are key factors that should differentiate youth with CP+LPE from youth with CP-only. The reason for these findings is unclear but there may be a few explanations. One possibility may be a ceiling effect, where rates of disruptive behavior and anxiety were already relatively high for this sample, leaving LPE little room to add any meaningful variance above and beyond existing psychopathology. Another possibility may be how certain items are worded. Bansal, Babinski, et al. (2020) posited that certain items may be assessing for constructs unrelated to LPE. For instance, “is concerned about schoolwork” may be assessing anxiety more than “uncaring” LPE behavior, whereas “does not let feelings control them” may be interpreted as having better emotion regulation strategies rather than being “unemotional.” Overall, these findings suggest that the LPE specifier was able to detect differences between youth with CP, with and without LPE, but many of the differences occurred in the unanticipated direction.

Group comparison analyses were re-run within the entire sample. The pattern of results was similar to the CP subgroup sample, providing some replication. In addition, youth with LPE-Present scored significantly higher than youth with LPE-Absent on all measures of disruptive behavior and psychological functioning. One possible reason for the vast number of statistical differences in the full sample may be due the inclusion of youth with less-severe disruptive behaviors, which may have contributed to greater mean differences between youth with LPE-Absent versus LPE-Present. However, the pattern of these findings may be indicative of how the LPE would function in a clinical sample, providing some support for applying the LPE specifier beyond the diagnosis of conduct disorder in a non-DSM centric approach (Hawes et al., 2014).

Longitudinal Trajectories

Finally, LPE was associated with greater baseline CP between youth with LPE-Absent versus LPE-Present. However, LPE was not associated with a significantly greater slope in CP trajectory over time. This finding is consistent with prior work in which the LPE specifier did not predict more stable trajectories of CP during early childhood (Déry et al., 2019). This may be because rates of CP were relatively stable across time, leaving little room for LPE to predict changes in CP. This appears to oppose the general notion that youth with CP and elevated rates of CU traits are at significantly greater risk to exhibit a pattern of highly persistent and aggressive antisocial behaviors (e.g., Frick et al., 2014b). These results suggest that the LPE specifier is able to identify youths who are experiencing current difficulties but may need further refinement to detect youths who will engage in persistent and long-term antisocial behavior.

Limitations and Future Directions

The findings of this study must be interpreted in light of a few limitations. The most salient is that LPE status was established using a single rating scale (ICU), with the majority of the participants being rated by a single informant (~70% biological mothers only). As per the DSM-5, LPE status should be assessed using multiple informants through a variety of methods, including rating scales, clinical interviews, and semistructured interviews (e.g., Clinical Assessment for Prosocial Emotions; Hawes et al., 2020). Next, ODD and conduct disorder were diagnosed using DSM-IV criteria rather than DSM-5 criteria, although the diagnostic criteria are nearly identical across DSMs. Furthermore, the sample size was relatively small, and there was a noticeable amount of missing data at both follow-up timepoints. These factors may have contributed to limited power to detect short-term trajectory effects. However, the sample was adequately powered to detect medium to large effects in a clinical sample (Martel et al., 2012). In addition, this study is one of the few to examine the LPE specifier using a longitudinal design during early childhood and provides the foundation for future research to examine concurrent and longitudinal use of the LPE specifier during early childhood. Future research should continue to investigate the utility of this specifier during childhood and employ robust methods to reduce participant attrition. The use of split and extreme coding methods has not yet been validated for preschool samples, particularly a high-risk sample as used in this study. However, this study is the first to apply these algorithms to a preschool sample and lays the foundation for future research to build upon these methods. Finally, these results only generalize to high-risk preschool samples and may not apply to other samples (e.g., community and forensic). More work is needed in larger, nationally representative samples, including underrepresented groups identifying as minorities, to determine generalizability of results.

Despite these limitations, findings of this study highlight the clinical utility of the LPE specifier. Youth with CP+LPE showed higher levels of aggression, CU traits, and ineffective parenting, suggesting that there is value in assessing for LPE during early childhood to identify youth who are at-risk for experiencing ineffective parenting practices and elevated rates of concurrent aggression. In contrast, the LPE specifier was unable to differentiate between youth with CP on several key variables (i.e., ADHD, ODD, conduct disorder, and CP symptoms) and could not predict more stable trajectories of CP across time. There are a few clinical implications of these findings. First, the current findings suggest the need for continued refinement of the LPE specifier, which could be reconceptualized in a dimensional manner with normative data, cut scores, and corresponding severity ratings (e.g., mild, moderate, and severe). Next, future research may consider assessment of the broader construct of psychopathy in children which includes grandiose-manipulative and daring-impulsive traits, in addition to LPE (Salekin, 2016). It may be that the grandiose-manipulative and daring-impulsive traits explain variance above and beyond LPE, leading to further revision of the specifier in the DSM-5. Overall, findings of this study highlight the need for continued examination of the LPE specifier, particularly using longitudinal designs during childhood. This work is necessary to reliably assess for LPE and to establish it as a factor in clearly identifying youth at risk to exhibit stable patterns of antisocial behavior.

Supplemental Material

sj-docx-1-asm-10.1177_10731911211051070 – Supplemental material for Utility of the Limited Prosocial Emotions Specifier in Preschoolers With Conduct Problems

Supplemental material, sj-docx-1-asm-10.1177_10731911211051070 for Utility of the Limited Prosocial Emotions Specifier in Preschoolers With Conduct Problems by Pevitr S. Bansal, Patrick K. Goh, Ashley G. Eng, Anjeli R. Elkins, Melina Thaxton and Michelle M. Martel in Assessment

Footnotes

Acknowledgements

The authors thank all participants for making this work possible.

Authors’ Note

The data sets generated and analyzed for this study are not publicly available but are available from the corresponding author on reasonable request.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by NIMH National Institute of Mental Health Grant 5R03 HD062599-02 and K12 DA 035150 to Michelle Martel.

ORCID iD

Pevitr S. Bansal

Supplemental Material

Supplemental material for this article is available online.

Notes

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