Trajectories of Emotional and Conduct Problems and Their Association With Early High School Achievement and Engagement for Adolescents With ADHD

Abstract

Objective: This study aimed to (a) identify longitudinal trajectories of emotional and conduct problems from middle childhood to early adolescence in a sample of 130 adolescents diagnosed with ADHD and (b) examine trajectories in relation to early adolescent academic achievement and engagement. Method: Group-based trajectory models (GBTMs) were constructed from the parent-rated strengths and difficulties questionnaire (SDQ) subscales of emotional and conduct problems at three time points, mean ages 10.7 years, 11.6 years, and 13.7 years. Associations between trajectories and adolescent academic achievement and engagement were examined using multivariate regression. Results: Four distinct trajectories were identified within each domain. High-persistent problem trajectories in each domain were associated with poorer achievement and lower school engagement compared with adolescents following persistent-low problem trajectories. Conclusion: Findings highlight the importance of early assessment and management of comorbid emotional and conduct problems for children with ADHD as they predict school difficulties during early high school. (J. of Att. Dis. XXXX; XX(X) XX-XX)

Keywords

ADHD academic achievement engagement emotional conduct

ADHD is a neurodevelopmental disorder, with core symptoms of inattention and hyperactivity-impulsivity that affect daily functioning. Prevalence worldwide is estimated to be 5.4% (Polanczyk, Salum, Sugaya, Caye, & Rohde, 2015). Young people with ADHD are at significant risk of academic underachievement, lower rates of high school completion, and are less likely to complete tertiary education (Loe & Feldman, 2007). In addition, conduct and emotional problems are also more prevalent in children and adolescents with ADHD in comparison with peers (MTA [multimodal treatment of ADHD] Cooperative, 1999), with at least half of children with ADHD having one or more behavioral problems. Emotional and conduct problems are likely to contribute to the academic and school difficulties experienced by adolescents with ADHD (Loe & Feldman, 2007), however, it remains unclear what their impact is over and above ADHD. Furthermore, there is limited research investigating how emotional and conduct problems develop from childhood to adolescence, and how different emotion and conduct problem trajectories are associated with early high school academic and school functioning (Zendarski, Sciberras, Mensah, & Hiscock, 2016).

Early adolescence is a period of risk as is coincides with a range of physical and psychological changes associated with pubertal development (Eccles, 1999) during a time when adolescents face new demands and challenges with the transition to high school. Given these developmental complexities, it is a time of heightened risk for mental health problems (e.g., depression, suicide, conduct disorder [CD]) and adverse consequences including substance use, delinquency, and school failure (Cohen & Smerdon, 2009). Early adolescence is also an important foundation period for future educational success. Students who fail to adjust well risk becoming disengaged with school and have a higher likelihood of dropping out early (Barry & Reschly, 2012). The early high school period is likely to present additional challenges for adolescents with ADHD and in particular, those presenting with additional conduct and emotional problems (Bailey & Baines, 2012). However, it is unclear the extent to which elevated behavioral problems across middle childhood impact on high school adjustment for students with ADHD.

Behavioral problems are generally classified across two domains, which include externalizing difficulties such as oppositional defiant disorder (ODD) and CD (referred to as conduct problems in this article) and/or internalizing difficulties such as anxiety and depression (Elia, Ambrosini, & Berrettini, 2008; referred to as emotional problems). Comorbid behavioral problems affect daily functioning and school life by exacerbating social and academic problems (see Barkley, 2014). Longitudinal population studies have shown that children who exhibit elevated emotional and behavioral problems have poorer academic outcomes (Sayal, Washbrook, & Propper, 2015) and generally poorer psychosocial adjustment (Wertz et al., 2018) in adolescence in comparison with children without these difficulties. Children who are emotionally and behaviorally well-adjusted do better.

Conduct problems including increased aggression, defiance, and disruptive behaviors are common in children and adolescents with ADHD (Larson, Russ, Kahn, & Halfon, 2011). These pervasive problems can interfere with a child’s learning in the classroom, as well as relationships with their peers, teachers, and family members (Connor, Steeber, & McBurnett, 2010). An estimated 40% to 70% of children with ADHD have CD or ODD (MTA Cooperative, 1999), and rates remain high during adolescence (25%; Yoshimasu et al., 2012). Conduct problems in childhood and adolescence for both youth with and without ADHD have been linked to a range of negative outcomes including poorer academic achievement (Fergusson, Horwood, & Ridder, 2005; Timmermans, van Lier, & Koot, 2009), early school leaving, poor education attainment (Masten et al., 2005), fewer qualifications (Fergusson et al., 2005), and with later substance use (Molina & Pelham, 2003). Longitudinal studies have linked childhood conduct problems with poorer adolescent academic achievement on standardized academic tests (Sayal et al., 2015) and directly assessed math and reading achievement (Breslau et al., 2009), as well as poor school attendance (Classi, Milton, Ward, Sarsour, & Johnston, 2012), and poor classroom behavior in elementary school children (Abikoff et al., 2002).

Emotional problems, including increased anxiety and depressive symptoms, are also common in children with ADHD (Larson et al., 2011). In the general population, emotional problems are theorized to negatively influence individual motivation and cognitive processes such as attention and working memory, adaptive functioning, and social relationships (Andrés et al., 2017). Conversely, individuals who have good emotional regulation are more likely to have higher academic achievement (Masten et al., 2005). Studies examining the effects of childhood emotional problems on school functioning in adolescents with ADHD have linked emotional problems with lower academic achievement (Breslau et al., 2009), higher school absenteeism (Classi et al., 2012), and poorer attitudes toward school (Wehmeier, Schacht, & Barkley, 2010). However, findings have been mixed. For example, one study showed middle childhood emotional problems did not predict academic functioning in a study of adolescent girls with ADHD (Lee & Hinshaw, 2006) and Breslau et al. (2009) found this relationship attenuated when simultaneously examining emotional problems with attention and conduct problems. Patterns of childhood emotional problems and their effect on early high school functioning have not been extensively studied in youth with ADHD (Steinhausen et al., 2006).

In the general student population, a recent meta-analysis of 13 studies examining conduct problem trajectories found that students following high conduct problem trajectories have poorer educational outcomes in comparison with students following low problem trajectories (Bevilacqua, Hale, Barker, & Viner, 2018). However, there has been no examination of the school outcomes associated with different childhood conduct problems trajectories for adolescents with ADHD. It also remains unclear whether conduct or emotional problems have unique effects on different aspects of school functioning (i.e., academic performance vs. student engagement). Findings from the from the MTA study found that children with comorbid anxiety performed worse on standardized tests of math and literacy in comparison to children with comorbid conduct problems (Jensen et al., 2001), however, other studies have not found this (Booster, DuPaul, Eiraldi, & Power, 2012).

There is a great deal of heterogeneity in educational outcomes of individuals with ADHD. Although some of this variability has been attributed to ADHD symptoms, as well as general intelligence, other factors including children’s behavioral problems have been shown to affect learning and educational outcomes (Loe & Feldman, 2007). These studies have tended to examine the impact of early childhood behavioral problems at a single time point on later school outcomes. Looking at behavior over multiple time points is likely to provide a more accurate view of the child’s developmental course. Several studies in children with ADHD have used a group-based modeling approach (Muthén & Muthén, 1998-2012; Nagin & Tremblay, 2005) to examine individual developmental trajectories across the life course. For example, DuPaul, Morgan, Farkas, Hillemeier, and Maczuga (2018) modeled trajectories of social and academic impairments in children with ADHD from kindergarten to fifth grade. Molina et al. (2009) examined how ADHD symptom trajectories through 3 years (7 years-11 years) predicted adolescent functioning, finding children following more severe trajectories were likely to respond less well to initial treatment and have poorer outcomes during adolescence, across most domains, including school and academic functioning. To date, studies have not examined the developmental course of children’s behavioral problems for children with ADHD on early adolescent school outcomes.

To address these gaps, this study aimed to identify distinct emotional and conduct problem trajectories in middle childhood for adolescents with ADHD and to investigate whether these trajectories predict academic achievement and student engagement in early adolescence. We hypothesized that (a) there will be distinct trajectories of emotional and conduct problems from middle childhood to adolescence and (b) high-persistent trajectories of emotional and conduct problems will predict poorer academic functioning and lower school engagement (i.e., higher absenteeism and lower academic motivation and connectedness).

Method

Participants

Participants were adolescents with ADHD recruited as part of the attention to sleep (ATS) study (Lycett, Sciberras, Mensah, Gulenc, & Hiscock, 2014). Participants were recruited from 21 pediatric practices across Victoria, Australia. Only participants with an existing clinical diagnosis of ADHD were invited to take part. Participants were recruited in middle childhood (M_age = 10.7, SD = 1.1). They were subsequently followed up 12 months later (M_age = 11.6, SD = 1.2) and in early adolescence (M_age = 13.7, SD = 1.1). The present study includes data for the subgroup of 130 participants followed across this period. Outcomes are measured in early adolescence, post transition to high school. Ethics approval was granted by the Human Research Ethics Committees of the Royal Children’s Hospital (33206), the Department of Education and Training (DET; 2013_002202), and the Catholic Education Office (1958), Victoria, Australia.

Eligibility and Recruitment

Children eligible to take part in the study were aged 5 years to 13 years at recruitment and had an ADHD diagnosis from their pediatrician. To prevent false-positive cases of ADHD, children were also required to meet Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994) criteria for ADHD on study entry. Trained postgraduate-level researchers, via semistructured telephone interviews with parents, assessed if children met DSM-IV criteria for ADHD (APA, 1994) using the validated 18-item ADHD Rating Scale-IV to assess symptoms (Dupaul, Power, Anastopoulos, & Reid, 1998; rated off-medication). Researchers also assessed cross-sectional impairment, age of onset, and symptom duration criteria.

Children were excluded from participating in the ATS study if they had an intellectual disability (IQ < 70), a serious medical condition (e.g., severe cerebral palsy), or their parents had insufficient English to complete assessments. Children with no/mild sleep problems and moderate/severe sleep problems were recruited, with the moderate/severe sleep problems group also participating in a randomized controlled trial (RCT) of a brief behavioral sleep intervention (Sciberras et al., 2010). Families were followed up 12 months later.

A third follow-up was conducted (2014-2015) for participants in the ATS study who had reached high school (n = 200), of which 130 (65%) agreed to take part. Participating families did not differ to non-participant families on baseline measures of age, sex, medication use, ADHD symptoms, and socioeconomic status (SES). Furthermore, baseline emotional and conduct problems as measured using the strengths and difficulties questionnaire (SDQ) subscale did not differ. Data were collected when eligible participants were in either their first or third year of high school, to align with the national assessment program (Australian Curriculum, Assessment and Reporting Authority [ACARA], 2015). Participants attended high schools across government, Catholic and independent sectors, which were representative of the broader Victorian student population. A graphical overview or recruitment is shown (Figure 1).

Figure 1.

Recruitment flow chart depicting participation and attrition.

At each time point, ADHD symptoms were assessed by parent and teacher report on the ADHD rating scale-IV. A high proportion (70%) of participants in this study reported persistent ADHD medication use from middle childhood to adolescence. There was a small decline in medication used from 84% at baseline to 73% in early adolescence. Study procedures are summarized below, but detailed study protocols are available for ATS (Lycett et al., 2014) and the subsequent adolescent follow-up (Zendarski et al., 2016).

Procedure

At baseline and at each follow-up, families were mailed a letter describing the study and inviting them to hear more about the study. Those who did not opt out after 10 working days were contacted by the research team to discuss participation. Informed written consent was obtained by the parent/guardian of participants in the study at all time points. At the high school follow-up, adolescents also provided informed written consent. Once enrolled, parents completed a baseline questionnaire about their child’s daily functioning, the child’s school and their family at baseline, 12 months and 36 months (adolescent follow-up). At the adolescent follow-up, the adolescents completed a direct academic assessment (30 min) and a questionnaire during a home visit. Postgraduate-level psychology students conducted all aspects of the home visits under the supervision of a qualified clinical psychologist. With consent, participant’s homeroom teachers also completed an online questionnaire about the adolescent’s behavior at school and academic competence.

Measures

Emotional and conduct problems were measured using the two correlating subscales of the parent-report version of the SDQ (Goodman, 1997), a well-validated and reliable instrument to measure behavioral problems in children and adolescents in both community and clinical samples (Goodman, 2001). In this study, scores on the emotional (five items, for example, nervous or clingy in new situations; α = .83) and conduct problems subscales (five items, for example, often fights with other children; α = .82), collected at the three time points were used to derive symptom trajectories. The parent-reported conduct problems subscale has good sensitivity (0.75) and high specificity (0.91) for the detection of CDs (Warnick, Bracken, & Kasl, 2008) and the emotional problems have adequate sensitivity (0.54) and high specificity (0.89) for the detection of depression or anxiety disorders in children and adolescents. Rates are similar to other common behavioral assessment tools such as the child behavior checklist (CBCL; Achenbach & Rescorla, 2004).

Outcomes

Standardized literacy and numeracy test scores were obtained from the National Assessment Program—Literacy and Numeracy (NAPLAN). This national testing program is conducted annually in May for Australian high school students in Years 7 and 9 (ACARA, 2015). Scaled scores between 0 and 100 are provided for each domain of reading, writing, numeracy, spelling and grammar, and punctuation. Tests are conducted at the same time and under similar test conditions for all students nationally.

Word reading and math computation skills were directly assessed by trained researchers on the Wide Range Achievement Test 4 (WRAT4), which is designed to measure core academic domains (Wilkinson & Robertson, 2006). The WRAT4 is a normed-referenced and validated psychometric test for use with children and adolescents. In this study, the word reading subscale was used to measure word reading and decoding ability, and the math computation scale measured the ability to perform basic mathematical computations. Raw scores on both subscales were converted to age-based standardized scores (M = 100, SD = 15). Internal consistency for word reading (α = .90) and math computation (α = .84) were high. Scores obtained were correlated with NAPLAN test scores (reading: r = .63 and math: r = .69).

Academic competence was rated on the academic competence scale (ACS), a subscale of the social skills improvement system (SSIS). The ACS is a seven-item teacher-rated measure of a student’s overall academic performance, motivation, reading, and math ability compared with classmates. It is well validated for use in high schools, is normed-referenced and has been used previously to measure academic performance in school children with ADHD (Efron, Furley, Gulenc, & Sciberras, 2018). Raw scores were summed (α = .94) and the total score was converted to standard score, which was moderately correlated with NAPLAN reading (r = .57) and math (r = .37).

School engagement scales used in this study are adapted from the Victorian Department of Education and Training Attitude to School Survey (Department of Education and Training, 2015). Scales included (a) school connectedness (five items) measuring the extent to which students feel they belong and enjoy attending school, (b) student motivation (four items) measuring the extent to which students are motivated to achieve and learn, and (c) connectedness to peers (four items) measuring the extent to which students feel socially connected with their peers. Students rated items on a 5-point Likert-type scale (1 = strongly disagree to 5 = strongly agree) with higher scores indicating greater engagement. Internal consistency ranged from α = .85 to .89. Subscale totals were summed to form a composite attitude score.

A single dichotomous measure of absenteeism (high/low) was formed based on parent-reported days absent in the previous 3 months. Students who were absent for 4 days or more were dichotomized to regularly absent (high; 25%) and students who had been absent for <4 days were dichotomized to low absence (low; 74%; Daraganova, 2012).

ADHD symptoms were assessed using the ADHD Rating Scale-IV (Dupaul et al., 1998) at all assessments. The scale has 18 items, assessing inattention and hyperactivity/impulsivity symptoms, as reflected in the DSM-IV. Behaviors exhibited by the child over the previous 6 months are rated on a 4-point scale from 0 = never or rarely to 3 = very often. A total symptom severity score was summed from parent-rated scores (Cronbach’s α = .90), with higher scores representing greater symptom severity.

Other sample characteristics included child age and gender, ADHD medication use, initial sleep difficulties (none/mild vs. moderate/severe; Lycett et al., 2014), and parent mental health (depression anxiety stress scales 21-item short form; DASS-21; Lovibond & Lovibond, 1995). Family SES was measured by the Socio-Economic Indexes for Areas (SEIFA) scales (Australian Bureau of Statistics, 2011), and higher scores indicate greater advantage.

Analysis Plan

Trajectories were identified using continuous scores of emotional and conduct problems to develop group-based trajectory models (GBTM) using the Stata “traj” plugin (Jones & Nagin, 2013; Nagin & Odgers, 2010). The program models the probability of group membership and the posterior probabilities of group membership, which are used to classify study participants. GBTM’s were derived using parent ratings of emotional and conduct problems on the SDQ subscale at each time point (T1: M_age = 10.7 years; T2: M_age = 11.6 years; T3: M_age = 13.7 years). The time variable used was the child’s age in years. A model fit process was undertaken based on GBTM guidelines for health research, which involved a combination of formal selection criteria, sector knowledge, and model usability. The Bayesian information criterion (BIC) was used for selection of the optimal number of trajectory groups, in conjunction with ensuring that the trajectories had an adequate proportion and sample in each group for clinical interpretation (Nagin & Odgers, 2010). The probability of group membership for each individual participant was estimated and group memberships assigned based on the highest probability. Group membership is described in Supplementary Table S1. Missing data were handled by GBTM by fitting data using the maximum likelihood estimation. We explored the effects of missing data on the models and found that including missing data resulted in similar trajectories to the complete-case analyses. Therefore, we chose to include all participants with data at two or more time points (Jones & Nagin, 2013; Nagin & Odgers, 2010). Although including cases with missing data enabled the inclusion of the more representative cohort of children, we noted that the confidence intervals (CIs) did widen as a result.

Multivariable regression models were used to examine the effects of longitudinal behavioral trajectories on academic achievement and student engagement in early adolescence. Descriptive statistics as a function of estimated trajectory group is described in Supplementary Table 2. The lowest problem group (persistent-low) in each domain served as the reference group on all outcomes of interest. Continuous outcome variables were standardized to z scores and examined using linear regression to estimate effect sizes (ES); 95% CIs and p values. Categorical outcome variables were examined using logistic regression to estimate odds ratios (ORs). Analyses were adjusted for baseline ADHD symptoms and any residual effects of the sleep intervention only to ensure adequate power. A sensitivity analysis was conducted: Regression analyses were repeated for the group of participants (23%) who followed high problem trajectories on both domains. All analyses were conducted using STATA Version 14.0 (Stata Corp, College Station, TX, USA).

Results

Sample Characteristics

Sample characteristics are shown in Table 1. The mean age of the participants at the third follow-up was 13.7 years (SD = 1.1, range = 12.1 years-15.9 years), 89% were male, and the majority (73%) were taking ADHD medication. Most (66%) were in their first year of high school. The mean levels of emotion and conduct problems (SDQ-parent) were within the at-risk/clinically significant range, and academic achievement was below the national average.

Table 1.

Sample Characteristics (n = 130).

Characteristics	n ^a	M, SD, Range or n (%)
Adolescent
Male	130	118 (89%)
Age in years, M	130	13.7 (1.1, 12.1-15.9)
First year high school (Year 7)	130	86 (66%)
ADHD medication use	130	95 (73%)
ADHD total symptoms—Parent	130	30.1 (9.8, 3-50)
School functioning
NAPLAN assessments^b
Numeracy (NAPLAN) Year 7	69	524.3 (79.2, 384-890)
Reading (NAPLAN) Year 7	70	507.5 (64.7, 416-678)
Numeracy (NAPLAN) Year 9	32	566.6 (77.6, 386-763)
Reading (NAPLAN) Year 9	33	575.4 (77.4, 422-781)
Math computation (WRAT4)	111	89.0 (13.4, 60-125)
Word reading (WRAT4)	111	102.2 (19.1, 56-145)
Academic competence (SSIS)—Teacher^c	84	84.8 (15.5, 55-124)
Attitudes toward school—Self-rated	120	4.0 (0.65, 1-5)
Frequently absent (>4 days current term)	122	28 (23%)
Primary caregiver and family baseline
Mothers; n, (%)	130	124 (95%)
Parent mental health—DASS total	124	30.0 (24.2, 0-112)
Family SES (SEIFA) baseline	128	1014.0 (66.0, 816-1114)

Note. NAPLAN = National Assessment Program—Literacy and Numeracy; WRAT4 = Wide Range Achievement Test 4; SSIS = social skills improvement system; DASS = depression anxiety stress scales; SES = socioeconomic status; SEIFA = socioeconomic indexes for areas.

n varies due to subgroups or missing data.

NAPLAN state means numeracy: Year 7 = 548 and 9 = 597, reading: Year 7 = 551 and 9 = 586 (ACARA, 2015).

All other academic measures population: M = 100, SD = 15.

Trajectories

Adolescents followed one of the four emotional problem trajectories from middle childhood to adolescence, which are as follows: (a) persistent-low 28.5 % (n = 37), (b) moderate resolving 10.0% (n = 13), (c) moderate increasing 28.5% (n = 37), or (d) high persistent 33.0% (n = 43) as illustrated in Figure 2. For the small group or participants following the resolving trajectory, their moderate emotional problems declined to levels commensurate with the persistent-low trajectory group by adolescence. In contrast, a group of adolescents with similar moderate problems in middle childhood experienced an increase in emotional problems in early adolescence.

Figure 2.

Emotional problem trajectory groups (n = 130).

Adolescents typically followed one of the four trajectories of conduct problems from middle childhood to adolescence: (a) persistent-low 27.0% (n = 35), (b) moderate 47.0% (n = 61), (c) high-persistent 19.0% (n = 25), and (d) high-declining 7.0% (n = 9) illustrated in Figure 3. Patterns show conduct problems largely remained stable from middle childhood to adolescence. Except for participants in the high-declining trajectory group who experienced a small decline in conduct problems over this period.

Figure 3.

Conduct problem trajectory groups (n = 130).

Association Between Emotional Problem Trajectories and Early High School Outcomes

Adolescents following a high-persistent trajectory of emotional problems had lower NAPLAN scores in reading, spelling, grammar, and numeracy (effect sizes between −0.53 and −0.70 compared with those in the persistent-low group in unadjusted analyses, see Table 2). In addition, the high-persistent trajectory was negatively associated with directly assessed math computation scores, ES = −0.81; 95% CI = [−1.26, −0.37], p < .001, and word reading (ES = −0.53; 95% CI = [−0.99, −0.07], p = .03), as well as with student attitudes toward school (ES = −0.57; 95% CI = [−1.02, −0.11], p = .01). After adjusting for baseline ADHD symptom severity, conduct problems and sleep status’ most significant relationships were held (see Table 2, adjusted), with the exception of attitudes toward school, which attenuated to nonsignificant. The resolving trajectory group had improved reading achievement on NAPLAN (ES = 0.65, 95% CI = [0.02, 1.28], p = .04) compared with those in the persistent-low group, which attenuated to nonsignificant in the adjusted results. Otherwise, there were no significant differences between the groups.

Table 2.

Association Between Emotional Problem Trajectories and Early High School Outcomes.

Reference trajectory: persistent-low (n = 37)	Emotional problem trajectories, unadjusted
Reference trajectory: persistent-low (n = 37)	Resolving (n = 13)			Moderate-increasing (n = 37)			High-persistent (n = 43)
Transition outcomes	ES	95% CI	p ^a	ES	95% CI	p ^a	ES	95% CI	p ^a
NAPLAN tests
Numeracy	0.20	[−0.45, 0.85]	.55	−0.17	[−0.67, 0.33]	.50	−0.56	[−1.04, −0.07]	.03
Reading	0.65	[0.02, 1.28]	.04	−0.15	[−0.64, 0.34]	.54	−0.53	[−1.00, −0.07]	.03
Writing	0.57	[−0.09, 1.22]	.09	0.22	[−0.29, 0.73]	.40	−0.21	[−0.70, 0.29]	.41
Spelling	−0.31	[−0.95, 0.34]	.35	0.00	[−0.50, 0.50]	1.00	−0.70	[−1.18, −0.22]	<.001
Grammar	0.30	[−0.34, 0.94]	.35	−0.24	[−0.73, 0.26]	.35	−0.65	[−1.12, −0.17]	<.001
Math (WRAT4)	0.19	[−0.48, 0.86]	.58	−0.33	[−0.80, 0.13]	.16	−0.81	[−1.26, −0.37]	.00
Reading (WRAT4)	0.11	[−0.60, 0.81]	.77	−0.25	[−0.74, 0.24]	.32	−0.53	[−0.99, −0.07]	.03
Academic competence	0.37	[−0.38, 1.13]	.32	−0.25	[−0.78, 0.28]	.35	−0.48	[−1.03, 0.06]	.08
Attitudes to school	−0.09	[−0.76, 0.59]	.80	−0.22	[−0.69, 0.25]	.35	−0.57	[−1.02, −0.11]	.01
Frequently absent (OR)	1.84	[0.44, 7.76]	.41	1.80	[0.59, 5.47]	.30	2.49	[0.87, 7.06]	.09
Emotional problem trajectories, adjusted^b
NAPLAN tests
Numeracy	0.12	[−0.54, 0.79]	.71	−0.19	[−0.72, 0.35]	.49	−0.60	[−1.15, −0.04]	.04
Reading	0.48	[−0.16, 1.12]	.14	−0.23	[−0.74, 0.29]	.38	−0.61	[−1.13, −0.08]	.02
Writing	0.47	[−0.21, 1.15]	.56	0.16	[−0.39, 0.71]	.18	−0.25	[−0.82, 0.31]	.38
Spelling	−0.43	[−1.09, 0.23]	.20	−0.04	[−0.58, 0.49]	.87	−0.73	[−1.28, −0.19	.01
Grammar	0.19	[−0.47, 0.84]	.58	−0.29	[−0.82, 0.24]	.27	−0.74	[−1.28, −0.19]	.01
Math (WRAT4)	0.15	[−0.51, 0.81]	.65	−0.24	[−0.74, 0.25]	.34	−0.91	[−1.40, −0.42]	<.001
Reading (WRAT4)	0.11	[−0.59, 0.82]	.75	−0.11	[−0.65, 0.42]	.68	−0.52	[−1.04, 0.01]	.06
Academic competence	0.37	[−0.42, 1.16]	.36	−0.25	[−0.85, 0.34]	.40	−0.46	[−1.10, 0.18]	.16
Attitudes to school	−0.08	[−0.77, 0.62]	.82	−0.19	[−0.71, 0.34]	.34	−0.44	[−0.95, 0.08]	.10
Frequently absent (OR)	1.65	[0.37, 7.27]	.51	1.34	[0.41, 4.38]	.63	2.09	[0.65, 6.74]	.22

Note. ES = effect sizes; NAPLAN = National Assessment Plan Literacy and Numeracy; WRAT4 = Wider Range Achievement Test v4; OR = odds ratio.

Bolded estimates are significance at p < .05.

Adjusted for baseline ADHD symptom total on the ADHD-rating Scale IV, conduct problems subscale SDQ, and sleep group (moderate/severe or mild/none).

Association Between Conduct Problem Trajectories and Early High School Outcomes

Adolescents following a high-persistent trajectory of conduct problem had lower NAPLAN scores in reading, spelling, grammar, and numeracy (effect sizes between −0.68 and −0.85 compared to those in the persistent-low group in unadjusted analyses, see Table 3). In addition, the high-persistent trajectory was negatively associated with directly assessed math computation scores (ES = −0.70; 95% CI = [−1.26, −0.14], p < .01), teacher-rated academic competence (ES = −0.69; 95% CI = [−1.33, −0.50], p < .03), and higher odds of frequent absenteeism (OR = 4.62 95% CI = [1.31, 16.2], p < .02). Results held after adjustment (see Table 3 adjusted results) with the exception of academic competence, which attenuated to nonsignificant. The moderate-declining group had lower NAPLAN tests scores in numeracy, reading, writing, and grammar (ES ranging from −0.50 to −0.73, p < .05) and lower teacher-rated academic competence (ES = −0.65, 95% CI = [−1.13, −0.17], p = .01) compared with those in the persistent-low group. Adolescents in the moderate-declining group were also 3 times more likely to be regularly absent. Results held after adjustment (see Table 3, adjusted), with the exception of writing and academic competence, which attenuated to nonsignificant.

Table 3.

Association Between Conduct Problem Trajectories and Early High School Outcomes.

Reference trajectory: persistent-low (n = 35)	Conduct problem trajectories, unadjusted
Reference trajectory: persistent-low (n = 35)	Moderate-declining (n = 61)			High-declining (n = 9)			High-persistent (n = 25)
Transition outcomes	ES	95% CI	p ^a	ES	95% CI	p ^a	ES	95% CI	p ^a
NAPLAN tests
Numeracy	−0.52	[−0.96, −0.08]	.02	−0.99	[−1.91, −0.07]	.04	−0.73	[−1.30, −0.17]	.01
Reading	−0.70	[−1.11, −0.21]	.01	−1.06	[−1.96, −0.16]	.02	−0.85	[−1.41, −0.28]	<.001
Writing	−0.50	[−0.95, −0.04]	.03	0.06	[−0.97, 1.09]	.91	−0.68	[−1.27, −0.09]	.02
Spelling	−0.37	[−0.82, 0.09]	.11	−0.72	[−1.77, 0.33]	.18	−0.31	[−0.90, 0.29]	.31
Grammar	−0.73	[−1.16, −0.29]	<.001	−0.61	[−1.61, 0.38]	.23	−0.78	[−1.34, −0.21]	.01
Maths (WRAT4)	−0.34	[−0.77, 0.09]	.12	−0.65	[−1.51, 0.21]	.14	−0.70	[−1.26, −0.14]	.01
Reading (WRAT4)	0.00	[−0.44, 0.43]	.99	−0.79	[−1.66, 0.08]	.08	−0.30	[−0.86, 0.26]	.29
Academic competence (SSIS)	−0.65	[−1.13,−0.17]	.01	−0.28	[−1.15, 0.60]	.53	−0.69	[−1.33, −0.50]	.03
Attitudes to school	−0.35	[−0.78, −0.77]	.11	−0.61	[−1.43, 0.21]	.14	−0.43	[−0.97, 0.11]	.12
Frequently absent (OR)	2.92	[0.97, 8.7]	.05	3.00	[4.42, 20.9]	.27	4.62	[1.31, 16.2]	.02
Conduct problem trajectories, adjusted^b
NAPLAN tests
Numeracy	−0.58	[−1.05, −0.11]	.02	−0.76	[−1.84, 0.32]	.17	−0.82	[−1.45, −0.20]	.01
Reading	−0.64	[−1.10, −0.18]	.01	−0.54	[−1.59, 0.51]	.31	−0.83	[−1.44, −0.22]	.01
Writing	−0.45	[−0.94, 0.03]	.07	0.28	[−0.83, 1.39]	.62	−0.67	[−1.32,−0.02]	.05
Spelling	−0.22	[−0.71, 0.26]	.37	−0.32	[−1.43, 0.79]	.57	−0.10	[−0.74, 0.55]	.77
Grammar	−0.70	[−1.17, −0.23]	<.001	−0.42	[−1.49, 0.65]	.44	−0.76	[−1.39, −0.13]	.02
Maths (WRAT4)	−0.25	[−0.68, 0.18]	.25	−0.54	[−1.50, 0.41]	.26	−0.60	[−1.19, −0.02]	.04
Reading (WRAT4)	0.00	[−0.45, 0.45]	.99	−0.85	[−1.85, 0.15]	.10	−0.30	[−0.90, 0.31]	.34
Academic competence (SSIS)	−0.67	[−1.19, −0.15]	.01	−0.28	[−1.25, 0.69]	.56	−0.69	[−1.40, 0.03]	.06
Attitudes to school	−0.34	[−0.79, 0.12]	.15	−0.46	[−1.41, 0.49]	.34	−0.35	[−0.95, 0.24]	.24
Frequently absent (OR)	2.85	[0.91, 8.92]	.07	4.33	[0.49, 38.40]	.19	4.42	[1.14, 17.22]	.03

Note. ES = effect sizes; NAPLAN = National Assessment Plan Literacy and Numeracy; WRAT4 = Wider Range Achievement Test v4; SSIS = social skills improvement system; OR = odds ratio; SDQ = strengths and difficulties questionnaire.

Bolded estimates are significance at p < .05.

Adjusted for baseline ADHD symptom total on the ADHD-rating Scale IV, emotional problems subscale SDQ, and sleep group (moderate/severe or mild/none).

The small number of adolescents grouped in the high-declining trajectory (n = 9) scored significantly worse on NAPLAN numeracy and reading tests (effect sizes between −0.99 and −1.06, p < .05, respectively), compared with the persistent-low group. These findings attenuated in adjusted analyses, which is not surprising given the small sample size.

Analyses comparing achievement and engagement for adolescents with cooccurring high-persistent and high declining emotional problems and high-persistent conduct problems (n = 30, 23%) to adolescents with few problems on both domains (n = 18,14%) showed significant negative effects of dual problems on numeracy, grammar, and reading tests (ES from −1.2 to −1.4, 95% CI = [−1.88, −0.38], p < .01) and 10-fold higher odds (OR = 9.8, 95% CI [1.23, 77.8], p = .03) of frequent absenteeism after adjustment for covariates.

Discussion

This research examined continuity and change in behavioral problems for youth with ADHD across middle childhood and early adolescence. Longitudinal trajectories of emotional and conduct problems were established and examined in relation to early high school adjustment. We found that four problem trajectories best summarized participant emotional and conduct problem profiles. The majority of adolescents on each behavioral domain followed elevated problem trajectories from moderate to high severity. Trajectory profiles indicated problems remain relatively stable, however, for a small group of adolescents problems fluctuated (declined or increased). Consistent with our hypothesis high-persistent trajectories of emotional and conduct problems predicted poorer academic functioning and lower student engagement.

This study highlights that children with ADHD who continue to experience high and unremitting emotional and conduct problems over the middle school years are more likely to experience academic difficulties in comparison with children with ADHD following low problem trajectories. We found medium to large independent negative effects (ES = −0.54 to −0.85), after adjusting for potential confounding variables across multiple academic domains. These gaps are clinically substantial, given that an effect size of one has been shown to correspond to a difference of more than one academic school year for high school students (Coe, 2002). Young people who fall behind in these important foundation years are unlikely to bridge the academic gap without additional support and services (Cohen & Smerdon, 2009).

The trajectories show a clear dose–response relationship between problem severity and degree of school difficulties. Higher problem severity was associated with lower functioning across more indicators and was evident in both problem domains. There were some trajectories that indicated a change in problem severity over this period. For a small number of children (10%), early emotional symptoms had remitted by early high school, while there was a slight increase in problem severity for children with moderate emotional symptoms in middle childhood (28.5 %). This is consistent with findings in the general population that have examined emotional trajectories (Reef, Diamantopoulou, van Meurs, Verhulst, & van der Ende, 2011). A small group of children (7%) with chronic conduct problems in middle childhood experienced a small decline in problem severity in adolescence. Studies in the general population have also shown that as some children grow and mature develop better emotional and behavioral control (Eisenberg, Spinrad, & Eggum, 2010). Interestingly, children with moderate emotional problems were no more likely to experience academic or school engagement problems than those children in the low group and children with resolving emotional problems were doing significantly better in some academic areas (e.g., reading). This provides some evidence that children with moderate or resolving problems over time are likely to be less impaired in the early high school setting than those with persistent difficulties. Contrary to this, even moderately elevated conduct problems over this period are associated with poorer achievement and lower engagement.

Our findings also indicate that conduct problems in particular may impact school attendance. For example, high-persistent conduct problems were associated with a fivefold higher odds of regular absence from school. High absenteeism has previously been linked with conduct problems, anxiety, and depressive disorders (Egger, Costello, & Angold, 2003). Overall, there was a trend toward higher absenteeism for all children following elevated behavioral problem trajectories. One possible explanation is that children with ADHD and behavioral problems may feel unsupported in the classroom and have more difficulties with peers at school. Studies have shown high rates of school suspension (Zendarski, Sciberras, Mensah, & Hiscock, 2017) and high rates of peer victimization (Efron, Wijaya, Hazell, & Sciberras, 2018) for children with ADHD and behavioral problems. Therefore, school is likely to be particularly stressful and anxiety provoking for these children. As a consequence they may choose to avoid school more regularly. In the general population, studies have also shown higher rates of school refusal for children with conduct problems (Wood et al., 2012).

The complex interconnectedness between healthy social and emotional development and school success is just starting to be understood. For example, a recent study examining the reverse association found evidence of a relationship between group membership on trajectories of academic and social functioning and externalizing problems in younger children (kindergarten to Grade 5) with ADHD (DuPaul, Morgan, Farkas, Hillemeier, & Maczuga, 2016). It may be useful in future to also employ a dual trajectory analyses as outlined by Nagin and Tremblay (2005) to further understand the combination of group membership across domains, as our sensitivity analyses showed there may be additional negative impacts on school outcomes for individuals with both emotional and conduct problems.

This study has several strengths. Notably, it is the first study to comprehensively examine emotional and conduct problems in middle childhood using a trajectory-based approach for children with ADHD and to examine how these different behavioral patterns are associated with early high school functioning. The sample is well phenotyped, with child ADHD diagnosis confirmed at initial recruitment by trained researchers against DSM-IV criteria. The sample was originally recruited from 21 pediatric practices across rural and metropolitan areas in the state of Victoria and, as such, is likely a good reflection of children being treated for ADHD by pediatricians in the Victorian community. The sample includes families from a range of SES backgrounds and adolescents with differing cognitive abilities.

A number of study limitations must be taken into account. Our small sample size reduces the level of certainty that these trajectory groups would apply more broadly within clinical samples of children with ADHD. In addition, this clinical sample has significant levels of impairment, which may not be representative of adolescents with ADHD in the community. In this study, we used a brief measure of emotional and conduct problems reported by parents (typically mothers) on the SDQ at each time point. Parent report was used to maintain consistency, as youth-reported data were not collected at baseline. Participating children were not formally assessed for clinical diagnosis of a comorbid mental health disorder. We did not assess for specific learning disorders, which may have moderated academic outcomes (Loe & Feldman, 2007). We were unable to adjust for baseline levels of academic or school difficulties or IQ, and it is likely that there is a reciprocal relationship between school problems and behavioral problems over time. The majority of our sample were males, therefore, future research could further explore potential gender differences within trajectory groups and aim to replicate this study in a larger sample. There is some evidence to suggest trajectory models have limitations as they converge to have similar patterns despite differing cohorts and age ranges (Sher, Jackson, & Steinley, 2011). Therefore, it is important to consider that within the cohort there may be individual participants that do not fit neatly into the trajectory groups that we have identified.

These findings have a number of important implications. The results show that elevated emotional or conduct problems that persist over middle childhood are associated with poorer outcomes in early high school. Helping children with elevated emotional and conduct symptoms, in addition to treating the core symptoms of ADHD, during the middle school years may be an effective way to improve educational outcomes. For example, findings from a trial of a cognitive behavioral therapy to treat anxiety in primary school age children with ADHD have shown positive results (Sciberras et al., 2018). Furthermore, the middle and early adolescent years may represent an opportune time to address these problems as children begin to develop more sophisticated thinking and higher order cognitive abilities that make them better equipped to learn new skills and strategies to moderate their own behaviors (Eccles, 1999). Evaluating treatment approaches to address these problems over the middle school years is an important next step.

Conclusion

Our findings suggest that different developmental trajectories of emotional and conduct problems experienced by adolescents with ADHD across middle childhood have the propensity to influence early high school outcomes. Findings reinforce the importance of positive social and emotional development to school success. Addressing conduct and emotional problems in the middle years of childhood may alter problem trajectories and this, in turn, may lead to improved educational outcomes for young people with ADHD.

Supplemental Material

JADSupplementary_Table_(2) – Supplemental material for Trajectories of Emotional and Conduct Problems and Their Association With Early High School Achievement and Engagement for Adolescents With ADHD

Supplemental material, JADSupplementary_Table_(2) for Trajectories of Emotional and Conduct Problems and Their Association With Early High School Achievement and Engagement for Adolescents With ADHD by Nardia Zendarski, Fiona Mensah, Harriet Hiscock and Emma Sciberras in Journal of Attention Disorders

Supplemental Material

JADSupplementary_Table_2_(1) – Supplemental material for Trajectories of Emotional and Conduct Problems and Their Association With Early High School Achievement and Engagement for Adolescents With ADHD

Supplemental material, JADSupplementary_Table_2_(1) for Trajectories of Emotional and Conduct Problems and Their Association With Early High School Achievement and Engagement for Adolescents With ADHD by Nardia Zendarski, Fiona Mensah, Harriet Hiscock and Emma Sciberras in Journal of Attention Disorders

Footnotes

Acknowledgements

The authors thank the families, teachers, and schools for their participation in this study and the initial baseline data collection conducted by the Sleeping Sound with ADHD and ATS research teams. MCRI is supported by the Victorian Government’s Operational Infrastructure Support Program.

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: Dr. Zendarski was funded by a postdoctoral award from the Cripps Foundation. Dr. Mensah’s position was funded by an NHMRC ECF (1037449) and an NHMRC CDF in population health (1111160). Prof. Harriet Hiscock’s position was funded by an NHMRC Career Development Award (607351). Dr. Sciberras is funded by an NHMRC Early Career Fellowship (ECF) in Population Health 1037159 (2012–2015) and an NHMRC Career Development Fellowship (CDF) 1110688 (2016-19).

Supplemental Material

Supplemental material for this article is available online.

ORCID iD

Nardia Zendarski

Author Biographies

Nardia Zendarski is a postdoctoral researcher at Murdoch Children’s Research Institute and provisional clinical psychologist. She is involved in research investigating ways to improve outcomes for children with ADHD and development of school-based interventions.

Fiona Mensah is a research fellow at the Murdoch Children’s Research Institute, Royal Children’s Hospital based within the Clinical Epidemiology and Biostatistics Unit (CEBU). She is also an honorary research fellow at the Department of Pediatrics, the University of Melbourne. She has an interest in population health, policy equity, and translation.

Harriet Hiscock is a consultant pediatrician and director of the Health Services Research Unit at the Royal Children’s Hospital and Co-Group Leader of the Community Health Services Research group at the Murdoch Children’s Research Institute. Her research interests include developing and testing interventions for common, high impact health problems and optimizing access to quality health services for children.

Emma Sciberras is a senior lecturer and clinical psychologist at Deakin University. She is also an honorary research fellow at the Murdoch Children’s Research Institute and the Department of Pediatrics, the University of Melbourne. She leads a research program focused on understanding developmental trajectories for children with ADHD and developing interventions that can improve outcomes for children with ADHD.

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