Attention-Deficit/Hyperactivity Disorder Symptoms Are ......children who have an autism specrum disorder (ASD) diagnosis. Method: This case-control study recruited 347 children from

NEW RESEARCH

Attention-Deficit/Hyperactivity Disorder SymptomsAre Associated With Lower Adaptive Behavior Skillsin Children With AutismBenjamin E. Yerys, PhD, Jennifer R. Bertollo, BA, Juhi Pandey, PhD,Lisa Guy, PhD, Robert T. Schultz, PhD

Objective: To investigate the predictive power of comorbid attention-deficit/hyperactivity disorder (ADHD) symptoms on adaptive behavior skills inchildren who have an autism specrum disorder (ASD) diagnosis.

Method: This case-control study recruited 347 children from specialty clinics, primary care, and the community. Linear regression was used to testwhether ADHD Rating Scale, Fourth Edition, scores of autistic children associated with poorer adaptive behavior scores, after controlling for the effectsof age, intelligence, sex, and ASD symptom severity. Adaptive behaviors were measured with the Vineland Adaptive Behavior Scales, Second Edition.Subsequent analyses tested this relation in a subset of the ASD sample with subclinical ADHD symptoms (n ¼ 179) and another with teacher ratings(n ¼ 153). Prior relations between age with adaptive behaviors and ADHD symptoms were replicated and age was explored as a moderator.

Results: ADHD symptoms predicted poor adaptive behavior scores in the full ASD sample (caregiver ratings, DR2 ¼ 0.033–0.119; teacher ratings,DR2 ¼ 0.113–0.163) and in the subset with subclinical ADHD symptoms (caregiver ratings, DR2¼ 0.023–0.030; teacher ratings, DR2 ¼ 0.097–0.159)after controlling for confounds. Age correlated negatively with ADHD symptoms (r ¼ �0.21) and adaptive behaviors (�0.17 < r < �0.39) in the fullASD sample. Age did not moderate the effect of ADHD symptoms on adaptive behaviors.

Conclusion: ADHD symptoms predict poorer adaptive behavior for autistic children across settings, even for children with subclinical co-occurringADHD symptoms. Findings support a Research Domain Criteria framework that behavioral impairments and functional outcome measures exist alonga continuum.

Key words: autism, ADHD, comorbidity, adaptive behavior, outcomes

J Am Acad Child Adolesc Psychiatry 2019;58(5):525–533.

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Journal of tVolume 58

daptive behavior is a key measure of a person’sability to function independently at home, atschool, and in the community.1 This includes

the ability to communicate effectively with others, maintainpersonal hygiene of one’s self and living space, engage in avariety of social relationships, develop hobbies, and partic-ipate in social activities. This set of behaviors is oftenimpaired in individuals who have an autism spectrum dis-order (ASD) diagnosis and is considered a critical measureof outcome for autistic people because of its association withlong-term outcomes in postsecondary education, driving,and employment.2-4 Furthermore, the gap in adaptivebehavior between ASD and neuro-typical populationswidens across adolescence into early adulthood, even forthose without intellectual disability.2,5-7 This suggests thatfactors other than intelligence play a significant role inadaptive behavior impairments. Thus, there is a critical need

he American Academy of Child & Adolescent Psychiatry/ Number 5 / May 2019

to identify factors that increase the risk for adaptive behaviorimpairments in autistic children.

Preschoolers and school-age children with co-occurringASD and attention-deficit/hyperactivity disorder (ADHD)diagnoses are reported by caregivers to have greater adaptivebehavior impairments across communication, daily living, andsocialization skills compared with children who have ASDdiagnoses alone.8-12 However, this DSM-oriented categoricalapproach (ASD versus ASDþADHD)misses an opportunityto determine whether ADHD symptoms explain uniquevariance for adaptive behavior impairments for those autisticchildren who have subclinical ADHD symptoms. To addressthis point, we must test whether ADHD symptoms relate tolower adaptive behavior across a group of autistic childrenwith varying levels of ADHD symptoms. Given that up to25% of autistic children present with subclinical ADHDsymptoms,13 demonstrating that ADHD symptoms have a

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YERYS et al.

dimensional effect on adaptive behavior would highlight atreatment target even for those children who do not meettraditional diagnostic criteria for an ADHD diagnosis. Inaddition, ADHD symptoms tend to decrease with age in thegeneral ADHDpopulation14,15 and in autistic children.16 It isnot known whether the influence of ADHD symptoms onadaptive behavior also changes with age for autistic children.Moreover, prior studies have been limited to caregiver ratings,and it is not known whether the association between ADHDsymptoms and poor adaptive behavior is observed by teachers.

The present case-control investigation sought todemonstrate that a dimensional measure of ADHD symp-toms would predict a dimensional measure of adaptivebehavior across caregiver and teacher ratings. That is, there is alinear relation between increasing ADHD symptoms andweaker adaptive behavior. In line with a “Research DomainCriteria” approach, which posits that psychiatric symptomsexist along a dimension,17 we hypothesized that ADHDsymptomswould contribute significant independent varianceto adaptive behavior scores over and above what is explainedby the effects of age, IQ, sex, and ASD symptom severity.Furthermore, given that ADHDsymptoms decrease with age,we examined whether age moderates this relation betweenADHD symptoms and adaptive behavior. We hypothesizedthat the relation of ADHD symptoms to adaptive behaviorswould not decrease with age, demonstrating that ADHDsymptoms are a relevant factor for adaptive behavior acrosschildhood and adolescence. In addition to these novel ana-lyses, we replicated findings that age would have negativecorrelations with ADHD symptoms and adaptive behaviorsin the full ASD sample.

METHODParticipantsA total of 347 individuals with an ASD who were between6- and 17-years-old completed the present study. Of these347 children, 179 were identified as having ASD withsubclinical ADHD symptoms based only on caregiverreport of 1 to 5 symptoms in the 2 domains of the ADHDRating Scale, Fourth Edition (ADHD-RS-IV; ie, Inatten-tion and Hyperactivity/Impulsivity). Teacher report alsowas available for 153 of the 347 children. Of these 153, 108were identified as having an ASD diagnosis with subclinicalADHD symptoms based only on teacher report of 1 to 5symptoms in the 2 domains of the ADHD-RS-IV. Werecruited children from specialty clinics, our hospital’s pri-mary care network, and the community through localevents for ASD and other special needs. ADHD medica-tions were prescribed for 113 children (32.6%). An addi-tional 110 children were excluded for not meeting the

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inclusion criteria listed below (n ¼ 97) and missing mea-sures (n ¼ 13). Thus, we had an overall participation rate of75.9%. Participants were enrolled and tested at a hospital-based research center from 2008 to 2015 as part of atleast 1 case-control study. The sample was reported previ-ously in an article examining the validity of the ADHD-RS-IV in ASD.16 Participant characteristics by caregiver-rateddiagnostic group and by teacher-rated diagnostic group arepresented in Table 1.

Exclusion criteria included IQ lower than 70 (measuredas the General Conceptual Ability score on the DifferentialAbilities Scale, Second Edition)18 and caregiver report of acurrent severe mood or psychotic disorder that would inter-fere with participation, any known genetic or neurologicdisorder, other significant medical condition affecting theirfunctioning, and extreme premature birth (gestational age <32 weeks). The sample was limited to an IQ above 70,because the ADHD-RS has not been properly validated inpopulations with an IQ below 70 with or without ASD.16

Expert clinical judgment confirmed that all childrenenrolled in the ASD group met DSM-IV-TR19 criteria forautism, Asperger syndrome, or pervasive developmental dis-order not otherwise specified. Clinical judgment wasinformed by the Autism Diagnostic Observation Schedule(ADOS)20 and Autism Diagnostic Interview—Revised.21

Co-occurring behavioral health concerns were screenedwith the Child and Adolescent Screening Inventory, FourthEdition, and T scores are reported in Table S1, availableonline. Children were not excluded if caregivers reportedcommon behavioral health disorders other than ADHD inASD (eg, anxiety, oppositional defiant disorder), because wewanted to examine the relation of ADHD symptoms in aprototypical sample of children with ASD. Children takingstimulant medications might have enrolled in a secondarystudy that required them to withhold medication for the dayof the study visit for cognitive and diagnostic assessment.Data collection occurred from 2008 to 2015, and theseclinical instruments and use of the revised algorithm scoringof the original ADOS were state of the art at that time.

MeasuresAdaptive behavior was measured using reports from theParent/Caregiver and Teacher Forms on the VinelandAdaptive Behavior Scales, Second Edition (Vineland-II),22

which yields standard scores (mean 100, standard devia-tion 15) in 4 domains: Communication, Daily Living Skills,Socialization, and Motor. Because the Motor subdomaindoes not include age-specific norms for children older than6 years, it was not included in our analysis. A lower standardscore indicates poorer adaptive skills; a score no higher than70 indicates a “low” skill level, score of 71 to 85 is

Journal of the American Academy of Child & Adolescent PsychiatryVolume 58 / Number 5 / May 2019


TABLE 1 Participant Characteristics by Diagnostic Group

Using Caregiver ADHD-RS Ratings Using Teacher ADHD-RS Ratings

All ASD (n ¼ 347)ASD þ Subclinical ADHD

(n ¼ 179) All ASD (n ¼ 153)ASD þ Subclinical ADHD

(n ¼ 108)Age (mo) 124.9 (35.2) 128.4 (38.8) 119.7 (36.0) 127.0 (38.0)Boys/girls 303/44 153/26 139/14 95/13GCA 100.5 (18.0) 100.8 (18.0) 99.6 (17.4) 101.0 (17.6)ADOS CSS 6.9 (2.2)a 6.7 (2.3)b 7.12 (2.0) 7.1 (2.0)ADHD-RS-IV-P Total (raw) 26.5 (12.2) 17.5 (7.5) — —

ADHD-RS-IV-T Total (raw) — — 26.4 (12.5) 13.3 (7.5)VABS-II Communication 84.1 (13.1)c 86.7 (14.0)d 79.6 (11.0)e 90.3 (10.1)f

VABS-II Daily Living Skills 84.0 (13.6)a 86.4 (13.2)b 88.3 (10.6)g 91.7 (12.4)h

VABS-II Socialization 74.7 (13.1)i 77.4 (13.6)d 89.3 (12.9)g 81.9 (10.7)h

Note: ADHD ¼ attention-deficit/hyperactivity disorder; ADHD-RS-IV Total ¼ ADHD Rating Scale, Fourth Edition, total raw score; ADHD-RS-IV-PTotal ¼ ADHD Rating Scale, Fourth Edition, caregiver report, total raw score; ADHD-RS-IV-T Total ¼ ADHD Rating Scale, Fourth Edition, teacherreport, total raw score; ADOS CSS ¼ Autism Diagnostic Observation Schedule Calibrated Severity Score; ASD ¼ autism spectrum disorder; GCA ¼general conceptual ability; VABS-II ¼ Vineland Adaptive Behavior Assessment System, Second Edition.an ¼ 343.bn ¼ 177.cn ¼ 340.dn ¼ 175.en ¼ 152.fn ¼ 107.gn ¼ 139.hn ¼ 97.in ¼ 341.

ADHD AND ADAPTIVE BEHAVIOR IN AUTISM

considered “moderately low” compared with same-age in-dividuals, and a score of 86 or higher is indicates “adequate”skill level. Caregiver raters were self-selected as those whoknew the child best. If the child had more than 1 teacher,then families were prompted to ask the teacher who “knewthem best” to complete the form.

Symptoms of inattention, hyperactivity, and impul-sivity were measured using the ADHD-RS-IV.14 Caregiversand teachers were asked to rate the frequency of the 18ADHD symptoms at home, from 0 (“never/rarely”) to 3(“often”). Caregivers and teachers were instructed to ratetheir child’s behavior on an average day, and they were notgiven explicit instructions to account for being “on” or “off”ADHD medications. Selection of caregiver and teacherraters for the ADHD-RS-IV was identical to that of theVineland-II. The total score is a sum of these raw itemvalues (range 0–54), with a higher score indicating moreand/or greater frequency of ADHD symptoms. A score of 2or 3 is considered a significant symptom. For our replicationanalysis that split the ASD group into those with andwithout a co-occurring ADHD diagnosis, we placed allchildren receiving caregiver ratings of at least 6 symptoms inthe Inattention or Hyperactivity/Impulsivity domains in theASD plus ADHD group. For all regression analyses, thetotal raw score was used as the independent variable.


The ADOS is a semistructured interview that assessesSocial Affect and Restricted and Repetitive Behavior do-mains. The total score is a sum of these 2 domains and isconverted into a Calibrated Comparison Score, taking ageinto account. The Calibrated Comparison Score, used as acovariate in the present study, ranges from 1 to 10, with ahigher score indicating greater severity of autism symptoms.To improve diagnostic validity, all ADOS administrationswere rescored according to the revised algorithm.23 There-fore, the Calibrated Comparison Scores provided corre-spond to ADOS-2 Calibrated Comparison Scores.

IQ was assessed with the Differential Abilities Scale,Second Edition,18 which results in standard scores in verbal,nonverbal reasoning, and spatial abilities, which togetheryield an overall General Conceptual Ability standard score(mean 100, standard deviation 15).

ProceduresParticipants who were recruited for this study came to theCenter for Autism Research at the Children’s Hospital ofPhiladelphia, and all procedures were approved by theinstitutional review board at the Children’s Hospital ofPhiladelphia. Informed consent was obtained from parentsor legal guardians, and assent was obtained from all chil-dren, before enrollment in this study. Participants who met

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eligibility criteria completed a series of diagnostic, cognitive,and neuropsychological assessments, and their caregiverscompleted a battery of questionnaires. If participantscompleted a prior study at our center within 1 year, diag-nostic and cognitive assessments were not readministered.

Analysis PlanTo address the primary question of whether ADHDsymptoms are associated with lower levels of adaptivebehavior in the domains of Communication, Daily LivingSkills, and Socialization above and beyond age, intelligence,sex, and autism symptoms, we conducted a series of linearregression models. With each caregiver-reported adaptivebehavior domain as the dependent variable, we conducted alinear regression model with covariates of no interest first(age, intelligence, sex, and ADOS Calibrated SeverityScores) and then a second model with caregiver-reportedADHD-RS-IV total score as an additional independentvariable. We repeated this process for teacher-reportedmeasures. We elected to use the ADHD-RS-IV (referredto hereafter as ADHD symptoms) as the singular measurefor ADHD symptoms, because the measure was validated inautistic children16 and it was the only measure collectedfrom caregivers and teachers. Because ADHD symptomsabate with age,14-16 a 2-step moderation analysis24 wasconducted to examine whether the relation between ADHDscores and adaptive behavior also decreased with age. Thefirst step tests the interaction term (age-by-ADHD symp-toms); this was nonsignificant, so the second step was notconducted. All analyses were conducted in R25 using thenlme26 and lmsupport27 packages. Because of our a priorihypothesis of ADHD symptoms predicting adaptivebehavior domains, we set the a value equal to .05. Wereport the full model’s statistic and the F change and R2

change of the model when comparing the model of allcovariates with the model of covariates plus ADHDsymptoms. For the moderation analyses, we conducted athird model of covariates plus ADHD symptoms and theinteraction term of age-by-ADHD symptoms andcompared this with the prior model.

As a measure of robustness, to clarify that the relationbetween ADHD symptoms and adaptive behaviors waslinear and not driven by those with co-occurring ADHD,we repeated these regression analyses using an ASD andsubclinical ADHD symptoms group (caregiver ratings,n ¼ 179; teacher ratings, n ¼ 91). The subclinicalADHD group for caregiver ratings was identified basedonly on caregiver ratings (179 of 347 children), and thesubclinical ADHD group for teacher ratings was identi-fied based only on teacher ratings (108 of 153 children).As an additional measure of robustness, we integrated

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caregiver and teacher ratings (n ¼ 153) using the “AND”ADHD diagnostic rule for identifying children with ASDand subclinical ADHD symptoms. The AND ADHDdiagnostic rule required the 2 raters to identify 6 symp-toms in the same domain. Then, we repeated theregression analyses for the 119 children who did not meetcriteria for an ADHD diagnosis using the AND rule(participant characteristics are presented in Table S2,available online).

RESULTSCaregiver RatingsThe initial regression models in the full ASD sample withour covariates of no interest that included age, IQ, sex, andthe ADOS Calibrated Severity Score were significant pre-dictors for Communication, Daily Living Skills and So-cialization (overall models: F > 10, p < .001, R2 > 0.10 forall comparisons). When ADHD symptoms were enteredinto the models, there was a significant change in R2 (DR2)in all 3 adaptive behavior domains: Communication(F1,330 ¼ 54.13, p < .001, DR2 ¼ 0.096), Daily LivingSkills (F1,333 ¼ 54.13, p < .001, DR2 ¼ 0.033), and So-cialization (F1,331 ¼ 54.47, p < .001, DR2 ¼ 0.119;regression details are presented in Table 2 and scatterplotsare presented in Figure S1, available online).

The robustness analysis on the subset with an ASDdiagnosis and subclinical ADHD symptoms showed thatentering ADHD symptoms into the model was still asso-ciated with a significant increase in R2 in 2 domains:Communication (F1,167 ¼ 8.05, p ¼ .005, DR2 ¼ 0.030)and Socialization (F1,167 ¼ 4.91, p < .05, DR2 ¼ 0.023),but not Daily Living Skills (F1,169 ¼ 0.00, p ¼ .948, DR2 ¼0.000; Table 3). The age-by-ADHD symptom interactiondid not explain significant variance in any model, doc-umenting that age did not moderate the relation betweenADHD symptoms and adaptive behavior domains acrossthe entire ASD sample or in the subset with subclinicalADHD symptoms.

Teacher RatingsResults using teacher ratings for ADHD symptoms andadaptive behaviors were consistent with caregiver-reportedfindings. Entering ADHD symptoms into the regressionmodel was associated with a significant increase in R2 in all 3domains: Communication (F1,133¼ 22.01, p< .001,DR2¼0.113), Daily Living Skills (F1,133¼ 29.40, p< .001,DR2¼0.163), and Socialization (F1,146 ¼ 26.33, p < .001, DR2 ¼0.147; regression details are presented in Table 4).

Using teacher report, the robustness analysis on thesubset with an ASD diagnosis and subclinical ADHD



TABLE 2 Linear Regression: Full Sample Based on Caregiver Ratings

Communication (n ¼ 336) Daily Living Skills (n ¼ 339) Socialization (n ¼ 337)

B SE B t B SE B t B SE B tModel 1 (covariates) R2 [ 0.32 R2 [ 0.11 R2 [ 0.16Age L0.15 0.02 L8.74*** L0.07 0.02 L3.33*** L0.13 0.02 L6.78***

IQ 0.30 0.03 8.95*** 0.16 0.04 4.08*** 0.04 0.04 1.00Male sex L0.15 1.77 0.41 1.78 2.13 0.84 2.05 1.98 1.04ADOS CSS L0.05 0.27 L0.20 L0.87 0.33 L2.67** L0.87 0.30 L2.87**

Model 2 R2 [ 0.41 R2 [ 0.14 R2 [ 0.28CovariatesADHD total L0.34 0.05 L7.36*** L0.21 0.06 L3.59*** L0.38 0.05 L7.38***

Model 3 R2 [ 0.42 R2 [ 0.14 R2 [ 0.28CovariatesADHD totalAge 3 ADHD total 0.00 0.00 L0.82 0.00 0.00 0.78 0.00 0.00 L0.27

Note: The change in R2 is significant for model 2 versus model 1 but not model 3 versus model 2 in each adaptive behavior domain. ADHD ¼attention-deficit/hyperactivity disorder; ADOS CSS ¼ Autism Diagnostic Observation Schedule Calibrated Severity Score; SE ¼ standard error.**p < .01; ***p < .001.


symptoms showed that entering ADHD symptoms intothe model was still associated with a significant increasein R2 in all 3 domains: Communication (F1,91 ¼ 11.26,p ¼ .001, DR2 ¼ 0.097), Daily Living Skills (F1,91 ¼17.16, p < .001, DR2 ¼ 0.159), andSocialization (F1,91 ¼ 10.71, p ¼ .001, DR2 ¼ 0.096;Table 5).

Correlation AnalysesFor correlations with age in the full ASD sample, wefound that age was significantly and negatively correlated

TABLE 3 Linear Regression: Autism Spectrum Disorder Sample W(ADHD) Symptoms Based on Caregiver Ratings

Communication (n ¼ 173) Dai

B SE B t BModel 1 (covariates) R2 [ 0.34Age L0.14 0.02 L6.21*** L0.07IQ 0.31 0.05 6.36*** 0.19Male sex 3.09 2.46 1.25 2.40ADOS CSS L0.13 0.18 L0.73 L0.33

Model 2 R2 [ 0.37CovariatesADHD total 0.35 0.12 2.88** L0.01

Model 3 R2 [ 0.37CovariatesADHD totalAge 3 ADHD total 0.00 0.00 0.14 0.00

Note: The change in R2 is significant for model 2 versus model 1 but not for mAutism Diagnostic Observation Schedule Calibrated Severity Score; SE ¼ st*p < .05; **p < .01; ***p < .001; §p � .1.


with each adaptive behavior domain (Communication,r338 ¼ �0.38, p < .001; Daily Living Skills,r341 ¼ �0.17, p < .005; Socialization, r339 ¼ �0.36, p <.001). As expected given prior literature, we also foundthat age was negatively correlated with total scores on theADHD-RS (r345 ¼ �0.21, p < .001; scatterplots arepresented in Figure S2, available online).

AND Rule AnalysesAs shown in detail in the supplementary material(Tables S2–S4, available online), we replicated the same

ith Subclinical Attention-Deficit/Hyperactivity Disorder

ly Living Skills (n ¼ 175) Socialization (n ¼ 173)

SE B t B SE B tR2 [ 0.15 R2 [ 0.19

0.02 L2.94** L0.13 0.02 L5.23***

0.05 3.56*** 0.01 0.05 0.172.66 0.90 4.81 2.66 1.81§

0.20 L1.69§ L0.50 0.20 L2.54*

R2 [ 0.15 R2 [ 0.21

0.13 L0.10 L0.29 0.13 L2.27*

R2 [ 0.15 R2 [ 0.21

0.00 0.58 0.00 0.00 L0.27

odel 3 versus model 2 in each adaptive behavior domain. ADOS CSS ¼andard error.

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TABLE 4 Linear Regression: Full Sample Based on Teacher Ratings

Communication Daily Living Skills Socialization

B SE B t B SE B t B SE B tModel 1 (covariates) R2 [ 0.21 R2 [ 0.10 R2 [ 0.04Age 0.01 0.02 0.50*** 0.03 0.03 1.14 0.02 0.02 0.65IQ 0.26 0.05 5.37 0.23 0.06 3.68*** 0.04 0.05 0.71Male sex L3.42 2.87 L1.20*** 2.05 3.71 0.55 L3.04 3.11 L0.98ADOS CSS L0.13 0.41 L0.32 L0.05 0.52 L0.88 L0.78 0.44 L1.76§


Model 3 R2 [ 0.32 R2 [ 0.26 R2 [ 0.19CovariatesADHD totalAge 3 ADHD total 0.00 0.00 0.83 0.00 0.00 0.34 0.00 0.00 0.94

Note: The change in R2 is significant for model 2 versus model 1 but not for model 3 versus model 2 in each adaptive behavior domain. ADHD ¼attention-deficit/hyperactivity disorder; ADOS CSS ¼ Autism Diagnostic Observation Schedule Calibrated Severity Score; SE ¼ standard error.***p < .001; §p � .1.

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relation between ADHD symptoms and adaptive be-haviors observed with parent-only or teacher-only defi-nitions of subclinical ADHD in a subclinical ADHDgroup defined by the AND rule. That is, caregiver ratingsshowed that entering ADHD symptoms into the modelwas associated with a significant increase in DR2 forCommunication and Socialization domains. Teacherratings showed that entering ADHD symptoms into themodel was associated with a significant increase in DR2 alldomains.

TABLE 5 Linear Regression: Autism Spectrum Disorder Sample w(ADHD) Symptoms Based on Teacher Ratings

Communication

B SE B t BModel 1 (covariates) R2 [ 0.12Age L0.01 0.03 L0.53 0.01IQ 0.18 0.06 2.95** 0.18Male sex L3.27 3.08 L1.06 2.73ADOS CSS L0.23 0.51 L0.44 L0.21

Model 2 R2 [ 0.22CovariatesADHD total L0.44 0.13 L3.36 L0.67

Model 3 R2 [ 0.23CovariatesADHD totalAge 3 ADHD total 0.00 0.00 1.20 0.00

Note: The change in R2 is significant for model 2 versus model 1 but not for mAutism Diagnostic Observation Schedule Calibrated Severity Score; SE ¼ st*p < .05; **p < .01; ***p < .001.

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DISCUSSIONThe present study provides the first dimensional assessmentof the relation between ADHD symptoms and adaptivebehavior in autistic children across home and school settings.In a large pediatric ASD sample who had absent to severeADHD symptoms, results indicate that ADHD symptomsassociated with poorer adaptive behavior—a finding that issustained across settings and even after controlling for keyconfounds of age, intelligence, sex, and ASD symptomseverity. This dimensional approach shows that the relation of

ith Subclinical Attention-Deficit/Hyperactivity Disorder

Daily Living Skills Socialization

SE B t B SE B tR2 [ 0.06 R2 [ 0.01

0.03 0.28 0.01 0.03 0.350.08 2.35* L0.02 0.06 L0.313.88 0.71 L0.78 3.26 L0.240.65 L0.32 L0.55 0.55 L1.00

R2 [ 0.21 R2 [ 0.11

0.16 L4.14*** L0.46 0.14 L3.27**

R2 [ 0.22 R2 [ 0.18

0.00 1.16 0.01 0.00 2.97**

odel 3 versus model 2 in each adaptive behavior domain. ADOS CSS ¼andard error.




ADHDsymptoms and adaptive behaviors extends to childrenwith subclinical symptoms of ADHD. Furthermore, wereplicated the subclinical ADHD findings when using a singlerater to define the group or 2 raters using the AND ADHDdiagnostic rule. Our correlation analyses in the entire ASDgroup showed that adaptive behaviors and ADHD symptomsdecreased with age.

Our findings that ADHD symptoms can predict up toapproximately 12% additional variance in adaptive behaviorscores in children with ASD is consistent with 3 prior studiesexamining adaptive behavior differences in categorical group-ings of school-age children with (ASD versus ASD plusADHD) based on caregiver report.8,10,11 Replication of exist-ing findings is critical given that a large portion of studies donot replicate28 and ASD is such a heterogeneous disorder Thepresent study extends these findings in a number of importantways. First, this study is the first to extend the relation betweenADHD symptoms and adaptive behaviors into a secondsetting with teacher ratings. The DR2 of ADHD symptomspredicting each adaptive behavior domain is as large or largerin the school setting compared with the home setting. Thisimportant finding demonstrates that the relation betweenADHD symptoms and adaptive behaviors is not limited tocaregiver observations restricted to the home setting. As such,our findings demonstrate that targeting ADHD behaviors inthe school setting is critical to improve adaptive functioningand independence. Second, our finding that the associationbetween ADHD symptoms and adaptive behavior extends tothose with an ASD diagnosis and subclinical symptoms ofADHD across home and school settings has scientific andclinical importance. Scientifically, this finding aligns with theResearch Domain Criteria approach,17 that posits psychopa-thology likely exists along a continuum or dimension fromtypical to atypical rather than as discrete categories of disorders.Clinically, this finding is of particular concern because ADHDsymptoms might be deprioritized as a treatment goal in autisticchildren if they do not manifest as a co-occurring ADHDdiagnosis. Thus, providers might want to consider augmentingtreatment plans for subclinical ADHD symptoms if the fewsymptoms are noted by caregivers or teachers as having asignificant effect on adaptive functioning. These children withfew, but impairing, ADHD symptoms would likely meet thediagnostic criteria for other specified (or unspecified) ADHDin current diagnostic classification systems.29,30 Third, thisstudy is the first to demonstrate that ADHD symptomscontinue to associate with adaptive behaviors, even after con-trolling for ASD symptom severity. Only one of the priorstudies in school-age children matched on ASD symptomseverity between ASD-only and ASD plus ADHD groups8;the dimensional approach of the present study allowed us totake the additional step of identifying unique variance added


by ADHD symptoms over and above ASD symptoms.Fourth, this study is the first to demonstrate that althoughADHD symptoms decrease modestly with age in autisticchildren, the relation of ADHD symptoms to adaptivebehavior was not moderated by age in either setting. This is acritical point because adolescence, and the eventual transitionto adulthood, is a period when the gap in adaptive behaviorbetween those on and off the autism spectrum.2,5-7 Thus, thepresent study provides evidence of the extent to which co-occurring ADHD symptoms can contribute to negativeadaptive behavior skills during this critical developmentalperiod.

Primary strengths of the study include the large ASDsample of school-age children with ASD without intellectualdisability, the assessment of ADHD symptoms with ameasure that captures all relevant symptoms, across 2important settings, controlling for ASD symptom severity inall analyses, and the evaluation of the relation of ADHDsymptoms to adaptive behaviors in those with subclinicalADHD symptoms. A primary limitation of the study is thatit relies on informant report over direct observation orinterview measures of adaptive behavior and ADHD.However, to date, this is the state of the science becausedirect measures do not exist for adaptive behavior andADHD is defined behaviorally. Future investigations shouldexamine the relation between empirically supported endo-phenotypes of ADHD (inhibitory control/interferencesuppression, working memory, temporal discounting) andadaptive behavior in autistic children. Future investigationsalso could examine whether behavior problems or simplybeing “off task” results in missed opportunities for childrento engage in and strengthen adaptive behaviors in varioussettings, mediating the relation between ADHD symptomsand adaptive behavior in autistic children. Also, our priorwork on the psychometrics of the ADHD-RS-IV in ASDdemonstrated mild concern regarding factorial validity, inthat not all items associated with their hypothesized factorof Inattention or Hyperactivity/Impulsivity.16 However, the4 items that failed to associate with their appropriate factor(items 3, 5, and 15 for caregivers and item 2 for teachers)align with the few failed factor analyses in the ADHDpopulation,31 suggesting these items might be weak formeasuring ADHD more generally and not a specific prob-lem in the ASD population. In addition, cross-sectionalstudies, such as the present one, are less than ideal forestablishing the persistence of ADHD symptoms on adap-tive behavior across development. Thus, it will be vital forthe field’s foundational evidence base to examine develop-mental trajectories of adaptive behavior in autistic childrenwith co-occurring ADHD symptoms to assess and adjudi-cate between various potential temporal and causal relations.

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This line of work would confirm the present findings thatco-occurring ADHD symptoms contribute to greateradaptive behavior impairments. The present study includesfew girls, particularly in our teacher-rated data. Future in-vestigations should attempt to over-recruit girls to identifypotentially meaningful biological sex differences that wouldmoderate the relation between ADHD symptoms andadaptive behaviors. The present study did not collectdetailed information on teacher raters and classroom envi-ronment. Future investigations might seek to capture thispotentially important source of variability that could explainadditional variance in teacher ratings. Exclusion of autisticchildren and severe, active psychiatric disorders might havedecreased the range of scores on ADHD symptom ratingsand adaptive behavior. However, we contend this decisionto remove this small group of children likely worked againstour hypothesis to detect a relation because of the morelimited range of scores. This same concern could be appliedto our findings that the additional variance explained byADHD symptoms in the subset with an ASD diagnosis andsubclinical ADHD symptoms is less than what is observedin the total sample. Nevertheless, these secondary analysesin this selected subset bolster the argument of the dimen-sional nature of this relation.

Autistic children exhibit significant adaptive behaviorimpairments, and these impairments are worse for chil-dren with co-occurring ADHD symptoms. The associa-tion between ADHD symptoms and adaptive behaviordomains does not interact with age and is independentfrom the relation of sex, IQ, and ASD symptom severity.This relation of ADHD symptoms and adaptive behavioralso is present in children with subclinical ADHDsymptoms. The present study’s results support a ResearchDomain Criteria perspective that ADHD symptoms havea dimensional relation to adaptive behavior in autisticchildren, regardless of whether their ADHD symptomsmanifest as a traditional co-occuring ADHD diagnosis.

NOTE TO READERSThe article by Yerys et al. uses identity-first language(“autistic children”) or neutral terms (“children on theautism spectrum” or “children who have an ASD diag-nosis”) rather than person-first language (children withASD). In keeping with the AMA Manual of Style, 10thEdition, JAACAP has a standing policy of using person-first language, but the authors have requested andreceived an exemption to use identity-first language. Theauthors’ request stems from a recent article by Kennyet al. (2016)32 that interviewed nearly 3,500 stakeholders

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from various backgrounds (autistic individuals, parents,family/friends, and professionals) asking them to reporton preferred terms. Kenny et al. demonstrated thatautistic individuals, parents, and family memberspreferred identity-first language, whereas professionalspreferred person-first language. We encourage all stake-holders to read Kenny et al. and the current paper andinvite readers to offer their opinions on this importantissue by submitting letters to the editor.

Accepted September 13, 2018.

Drs. Yerys, Pandey, and Schultz and Ms. Bertollo are with the Center for AutismResearch at the Children’s Hospital of Philadelphia, PA. Drs. Yerys, Pandey, andSchultz also are with the Perelman School of Medicine, Philadelphia. At thetime of the study, Dr. Guy was with the Center for Autism Research at theChildren’s Hospital of Philadelphia and is currently with the University of NorthCarolina at Chapel Hill.

The present study was sponsored by grants from the National Institute ofMental Health (NIMH; K23MH086111, principal investigator B.E. Yerys;R21MH092615, principal investigator B.E. Yerys; RC1MH088791, R.T. Schultz), aNew Program Development Award to B.E. Yerys through the Intellectual andDevelopmental Disabilities Research Center funded by the National Institute ofChild and Human Development (NICHD; P30HD026979, principal investigatorM. Yudkoff), a grant from the Philadelphia Foundation, a grant from thePennsylvania Department of Health (SAP 4100042728) to R.T. Schultz, a grantfrom the Pennsylvania Department of Health (SAP 4100047863) to R.T. Schultz,a grant from Pfizer to R.T. Schultz, and a grant from the Robert Wood JohnsonFoundation (6672) to R.T. Schultz. The sponsors had no involvement in thestudy design, collection, analysis and interpretation, the writing of the report,or the decision to submit the article for publication.

The authors thank the families who participated in the present research andWhitney Guthrie, PhD, Center for Autism Research at the Children’s Hospital ofPhiladelphia, for useful discussions regarding the article.

Disclosure: Dr. Yerys has received research funding from the NICHD, theNIMH, Akili Interactive Labs Inc., Aevi Genomic Medicine, and the McMorrisFamily Foundation. He expects to receive additional funding for research orhonoraria from the NIMH, Akili Interactive Labs Inc., Aevi Genomic Medicine,the American Academy of Child and Adolescent Psychiatry, and Roche Phar-maceuticals in the foreseeable future. He has served as a consultant for AeviGenomic Medicine. All funding mentioned here is unrelated to the research inthe present article. Dr. Pandey has received research funding from the NICHD,the National Institute of Environmental Health Sciences (NIEHS), the SimonsFoundation Autism Research Institute, and the Eagles Charitable Foundation.Dr. Schultz has received research funding from the NICHD, the NIMH, theNIEHS, Aevi Genomic Medicine, the Simons Foundation, and the NJ Gover-nor’s Council for Medical Research. He expects to receive funding from theNICHD, the NIMH, and the Eagles Charitable Foundation in the foreseeablefuture. He has served as a consultant for Akili Interactive Labs Inc., AeviGenomic Medicine, CB Partners, and Roche Pharmaceuticals. He has receivedhonoraria from Yale University and the Autism Science Foundation. All fundingmentioned here is unrelated to the research in the present article. He also hasa patent pending application for a “biometric sensor device for quantitativephenotyping,” consisting of a sensor device that collects audio, video, andwearable sensor data of human behavior and human interactions and softwareto analyze these data to make predictions, including diagnostic classificationsand assessment of quantitative traits. This patent pending application is un-related to the research reported in this article. Dr. Guy and Ms. Bertollo reportno biomedical financial interests or potential conflicts of interest.

Correspondence to Benjamin E. Yerys, PhD, Center for Autism Research, 2716South Street, 5th floor, Philadelphia, PA 19146; e-mail: [email protected]

0890-8567/$36.00/ª2019 American Academy of Child and AdolescentPsychiatry

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TABLE S2 Participant Characteristics for the AutismSpectrum Disorder Group That Do Not Meet Criteria for aCo-occuring Attention-Deficit/Hyperactivity Disorder(ADHD) Diagnosis After Applying the “AND” Rule

“AND” Rule (n ¼ 119)Age (mo) 122.9 (36.9)Boys/girls 105/14GCA 101.1 (16.9)ADOS CSS 7.1 (2.0)ADHD-RS total (raw) 23.2 (11.4)

Note: ADHD-RS ¼ ADHD Rating Scale; ADOS CSS ¼ Autism DiagnosticObservation Schedule Calibrated Severity Score; GCA ¼ General Con-ceptual Ability.

TABLE S1 T Scores (Mean 50, SD 10) From the Child andAdolescent Symptom Inventory, Fourth Edition for Co-occurring Conditions Other Than Attention-Deficit/Hyperactivity Disorder

Diagnosis Mean (SD)Oppositional defiant disorder 59.61 (9.83)Conduct disorder 52.92 (6.08)Generalized anxiety disorder 64.94 (10.13)Social phobia 60.20 (9.30)Separation anxiety disorder 57.37 (9.23)Schizoid personality disorder 68.67 (8.65)Schizophrenia 66.94 (8.66)Major depressive episode 54.47 (8.73)Dysthymic disorder 57.86 (8.70)

Note: We only report on disorders that provided a T score rather than athreshold of absent/present (eg, enuresis was excluded) and weresampled across all ages in our study (eg, personality disorders wereexcluded).

TABLE S3 Linear Regression: Caregiver Ratings With the “AND” Rule Sample


B SE B t B SE B t B SE B tModel 1 (covariates) R2 [ 0.42 R2 [ 0.17 R2 [ 0.20Age 0.17 0.03 L6.56*** L0.08 0.03 L2.45* L0.13 0.03 L4.64***

IQ 0.34 0.06 6.13*** 0.23 0.07 3.46*** 0.06 0.06 0.93Male sex L0.35 2.98 L0.12 2.23 3.55 0.63 2.56 3.25 0.79ADOS CSS 0.17 0.49 L0.35 L0.78 0.57 L1.37 L0.69 0.52 L1.731

Model 2 R2 [ 0.54 R2 [ 0.18 R2 [ 0.32CovariatesADHD Total L0.42 0.08 L5.47*** L0.11 0.10 L1.10 L0.38 0.09 L4.45***

Note: The change in R2 is significant for model 2 versus model 1 in each adaptive behavior domain. ADHD ¼ attention-deficit/hyperactivity disorder;ADOS CSS ¼ Autism Diagnostic Observation Schedule Calibrated Severity Score; SE ¼ standard error.*p < .05; ***p < .001.

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TABLE S4 Linear Regression: Teacher Ratings With the “AND” Rule Sample


B SE B t B SE B t B SE B tModel 1 (covariates) R2 [ 0.13 R2 [ 0.06 R2 [ 0.02Age 0.00 0.02 0.11 0.03 0.03 0.82 0.02 0.03 0.59IQ 0.19 0.06 3.41*** 0.18 0.07 2.50*** L0.01 0.06 L0.12Male sex L3.12 2.92 L1.07 3.11 3.64 0.86 1.57 3.09 L0.51ADOS CSS L0.10 0.47 L0.20 L0.01 0.59 L0.23 0.61 0.50 L1.21


Note: The change in R2 is significant for model 2 versus model 1 in each adaptive behavior domain. ADOS CSS ¼ Autism Diagnostic ObservationSchedule Calibrated Severity Score; SE ¼ standard error.***p < .001.

FIGURE S1 Scatterplots of Attention-Deficit/Hyperactivity Disorder (ADHD) Symptoms (Total Raw Score) and AdaptiveBehavior Domains (Standard Scores)

Note: Scatterplots show the relation between ADHD Rating Scale total scores and each adaptive behavior domain: (A) Communication; (B) Daily Living Skills; and (C)Socialization.

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FIGURE S2 Scatterplots of Age With Adaptive Behavior Domains (Standard Score) and Attention-Deficit/Hyperactivity DisorderSymptoms (Total Raw Score)

Note: Scatterplots show the relation between age and (A) Communication, (B) Daily Living Skills, and (C) Socialization adaptive behavior domains and (D) attention-deficit/hyperactivity disorder symptoms.

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