OBJECTIVE:

To compare school readiness in preschoolers with and without attention-deficit/hyperactivity disorder (ADHD) symptoms using a comprehensive framework. We hypothesized that preschoolers with ADHD symptoms have higher odds of school readiness impairment.

METHODS:

Children ages 4 to 5 years (n = 93) were divided into 2 groups on the basis of presence of ADHD symptoms (ADHD group, n = 45; comparison group, n = 48). School readiness was assessed through 10 component measures, including direct assessments and standardized questionnaires, regarding 5 school readiness domains: physical well-being and motor development, social and emotional development, approaches to learning, language, and cognition and general knowledge. Analysis of covariance compared group mean scores on component measures. Domain impairment was defined as score ≥1 SD from the test population mean in the unfavorable direction on ≥1 measure in the domain. School readiness impairment was defined as impairment in ≥2 of 5 domains. Logistic regression predicted impairment within domains and overall readiness.

RESULTS:

The ADHD group demonstrated significantly worse mean scores on 8 of 10 component measures and greater odds of impairment in all domains except for cognition and general knowledge. Overall, 79% of the ADHD group and 13% of the comparison group had school readiness impairment (odds ratio 21, 95% confidence interval 5.67–77.77, P < .001).

CONCLUSIONS:

Preschoolers with ADHD symptoms are likely to have impaired school readiness. We recommend early identification of school readiness impairment by using a comprehensive 5-domain framework in children with ADHD symptoms paired with targeted intervention to improve outcomes.

What’s Known on This Subject:

Children with attention-deficit/hyperactivity disorder (ADHD) are at high risk for poor educational outcomes. School readiness impairment in preschoolers predicts poor academic achievement in late elementary school. Data are limited regarding the association between ADHD and school readiness.

What This Study Adds:

Using a comprehensive, 5-domain school readiness assessment, preschoolers with ADHD symptoms demonstrated significantly higher odds of school readiness impairment compared with the comparison group. We recommend early identification and intervention to improve the adverse educational outcomes associated with ADHD.

Adverse educational outcomes are serious long-term sequelae of attention-deficit/hyperactivity disorder (ADHD). Children with ADHD have lower scores on academic achievement tests, increased grade retention, and increased school dropouts compared with peers without ADHD.1 They are 4 to 5 times more likely to receive special education services,2,3 and academic underachievement persists into adulthood.4 Despite the high prevalence of ADHD and associated educational difficulties, limited evidence addresses how to effectively identify those at highest risk for poor academic achievement.

School readiness has been proposed as a framework to assess a child’s risk for poor academic achievement.5,6 The American Academy of Pediatrics adopted the United States National Education Goals Panel school readiness framework as an approach to evaluate multiple, broad factors necessary for success as the child transitions to kindergarten: the child’s skills and behaviors and family, community, and school supports.7,8 Five critical child “readiness to learn” domains are described in this comprehensive framework: physical well-being and motor development, social and emotional development, approaches to learning, language development, and cognition and general knowledge. In studies in which all 5 child school readiness domains were examined, school readiness impairment in preschool years strongly predicted adverse educational outcomes,5,9 demonstrating the use of this framework in early identification of children who may need additional supports. Additionally, a longitudinal, randomized clinical trial of a preschool home visiting program aimed at improving school readiness revealed that intervention led to sustained positive effects on academic performance, improved social-emotional adjustment, and decreased home problems; collectively, these benefits led to reduced need for educational and mental health services in elementary school.10 These results suggest that if we are able to identify preschool-aged children with impairment in school readiness, we have the opportunity to substantially improve academic, social, and behavioral outcomes and reduce the need for services.

Despite the known academic difficulties in children with ADHD, research on school readiness in this population has been limited. To our knowledge, no researchers have applied the comprehensive framework as defined by the National Education Goals Panel to assess school readiness in preschool-aged children with symptoms of ADHD. Therefore, the primary aim of this study was to compare preschool-aged children with and without symptoms of ADHD on components of school readiness and on impairment across the 5 domains constituting a comprehensive view of the child aspect of school readiness. Recognizing that many children later diagnosed with ADHD are often not evaluated by a subspecialist in the preschool years, we included both children with ADHD diagnoses who were referred from subspecialty clinics and children with high levels of parent-reported ADHD symptoms. On the basis of literature showing deficits in children with ADHD in isolated school readiness domains (eg, cognition,11,12 executive function [EF] [operationalized as approaches to learning],13 social development,12 and language development,14) we hypothesized that preschool-aged children with symptoms of ADHD would have worse scores on the component measures within the comprehensive school readiness assessment and higher likelihood of impairment in each domain and overall school readiness.

Children ages 4 years 0 months to 5 years 11 months (n = 93) were recruited from the community by using flyers, advertisements, letters to parents, and referrals from developmental-behavioral pediatrics and child psychiatry clinics. Children were excluded if they were born prematurely (<37 weeks’ gestation), had major neurosensory impairments (eg, blind or deaf), had known genetic syndromes or autism spectrum disorder, or were non-English speaking. Participants were administered the Early Childhood Inventory-4 (ECI-4) parent rating form, a Diagnostic and Statistical Manual of Mental Disorders-based symptom checklist normed for 3- to 5-year-old children.15 In the ECI-4 normative sample, ∼80% of children diagnosed with ADHD received symptom severity scores in the moderate (T-scores of 60–69) or high (T-scores ≥70) severity range based on parent ratings. Therefore, a cutoff T-score of 60 was used to identify children at high risk for ADHD and to account for the difficulty in making ADHD diagnoses at preschool age. Children with T-score ≥60 on the inattentive, hyperactive-impulsive, or combined type categories of the ECI-4 were assigned to the ADHD group (n = 45), and those with a T-score <60 were assigned to the comparison group (n = 48). The Stanford University Institutional Review Board approved the cross-sectional and longitudinal intervention studies from which data were drawn; participants’ baseline data from the intervention study were included. A parent or guardian provided informed consent and families were compensated for participation.

Testing occurred over 2 sessions (1–1.5 hours/session) by trained research assistants. Demographics and information regarding preschool or kindergarten attendance and receipt of medication and/or therapeutic intervention services were obtained from a parent questionnaire. Socioeconomic status (SES) was measured by using a modified Hollingshead 4 Factor Index of SES (A.B. Hollingshead, unpublished manuscript, 1975).

School readiness skills were assessed through performance-based measures and standardized parent questionnaires in the 5 domains of school readiness. These measures did not include the ECI-4, which was used to divide participants into groups. All measures had good psychometric properties (Table 1).

TABLE 1

Description of School Readiness Measures

School Readiness MeasuresNo. ItemsMean (SD)Cutoff Score for ImpairmentTest-Retest Stability or Reliability
Physical well-being and motor development     
ABAS-II motor 27 10 (3) ≤7 0.73–0.84 
 Presence of chronic medical condition n/a n/a n/aa n/a 
Social and emotional development     
 CBCL total problems 99 50 (10) ≥60 0.90 
 ABAS-II social 46 100 (15) ≤85 0.91–0.94 
Approaches to learning     
 BRIEF-P GEC 63 50 (10) ≥ 60 0.90 
 EF z score 5 tasks n/a n/ab n/a 
Language development     
 TOPEL phonological awareness 27 100 (15) ≤85 0.83 
 ABAS-II communication 25 10 (3) ≤7 0.85–0.87 
Cognition and general knowledge     
 Bracken SRC c 100 (15) ≤85 0.86 
 Stanford-Binet ABIQ c 100 (15) ≤85 0.91–0.92 
School Readiness MeasuresNo. ItemsMean (SD)Cutoff Score for ImpairmentTest-Retest Stability or Reliability
Physical well-being and motor development     
ABAS-II motor 27 10 (3) ≤7 0.73–0.84 
 Presence of chronic medical condition n/a n/a n/aa n/a 
Social and emotional development     
 CBCL total problems 99 50 (10) ≥60 0.90 
 ABAS-II social 46 100 (15) ≤85 0.91–0.94 
Approaches to learning     
 BRIEF-P GEC 63 50 (10) ≥ 60 0.90 
 EF z score 5 tasks n/a n/ab n/a 
Language development     
 TOPEL phonological awareness 27 100 (15) ≤85 0.83 
 ABAS-II communication 25 10 (3) ≤7 0.85–0.87 
Cognition and general knowledge     
 Bracken SRC c 100 (15) ≤85 0.86 
 Stanford-Binet ABIQ c 100 (15) ≤85 0.91–0.92 

GEC, Global Executive Composite; n/a, not applicable.

a

Children were classified as impaired if they had a chronic medical condition other than ADHD for which they were managed by a specialist or took daily medications (not including medication for allergic rhinitis due to lower presumed likelihood that this condition would significantly impact a child’s educational success).

b

Children were classified as impaired if they had an EF composite average z score ≤1 SD from the mean of the comparison group.

c

Number of items depends on basal and ceiling of the individual participant.

Physical well-being and motor development was evaluated by using the parent-rated Adaptive Behavior Assessment System, Second Edition (ABAS-II),16 Motor skill area (gross and fine motor) and the child’s medical history. The ABAS-II motor skill area evaluates both gross and fine motor skills. The child’s medical history assessed the presence of a chronic medical condition, hospitalizations, treatment by medical subspecialists, and medication use.

Social and emotional development was evaluated by using the parent-rated Child Behavior Checklist (CBCL)17 for ages 1.5 to 5 years and the parent-rated ABAS-II Social Composite.16 The CBCL assesses behavioral problems. The ABAS-II Social Composite includes the 24-item social skill area and the 22-item leisure skill area.

Approaches to learning was evaluated by using the parent-rated Behavior Rating Inventory of Executive Function, Preschool (BRIEF-P) version18 and a battery of EF performance tests. The BRIEF-P consists of 5 subscales that measure several aspects of EF. The EF performance tests included verbal fluency, day-night, bird-dragon, advanced card sort, and backward digit span (Supplemental Table 5).19,20 Scores from the 5 EF performance tasks were converted to z scores, derived from the mean and SD of the comparison group divided into younger and older age by median split.

Language development was evaluated by using the Test of Preschool Early Literacy (TOPEL)21 Phonological Awareness subtest and the parent-rated ABAS-II communication skill area.16 The TOPEL phonological awareness subtest measures skills in elision and blending. The ABAS-II communication skill area evaluates verbal and nonverbal expressive and receptive communication.

Cognition and general knowledge was evaluated by using the Bracken School Readiness Composite (SRC)22 and the Stanford-Binet Abbreviated Battery IQ (ABIQ).23 The Bracken SRC measures skills on 5 subtests: colors, letters, numbers and/or counting, sizes and/or comparisons, and shapes. The Stanford-Binet Intelligence Scales, fifth edition, is an assessment of intelligence and cognitive abilities in individuals ages 2 to 85+ years. In the ABIQ, full-scale IQ is estimated by using 2 subtests to assess fluid reasoning (object series and/or matrices) and verbal knowledge (vocabulary).

Study data were collected and managed by using REDCap electronic data capture tools hosted at Stanford University School of Medicine.24 Data were analyzed by using IBM SPSS Statistics v25. Demographics were compared by group (ADHD versus comparison) by using t test for normally distributed variables, and χ2 or Fisher’s exact for categorical variables. Calculated P values <.05 were considered statistically significant. Any baseline demographics that significantly differed by group were included as covariates in the adjusted models.

Recognizing the heterogeneity of the ADHD group, additional baseline analyses were conducted to describe ADHD symptom severity and receipt of intervention services in children with ADHD symptoms who were referred from clinic with an official and/or provisional diagnosis of ADHD (clinic referred) and children with ADHD symptoms who were recruited from the community due to parental concerns (community recruited).

Analysis of covariance compared group mean scores on school readiness component measures while adjusting for differences in demographic factors. We defined domain impairment as standardized score ≥1 SD from the test population mean in the unfavorable direction on ≥1 measure within the domain. The EF z score was derived from mean scores of the comparison group due to lack of standardized norms. One SD was used as the cutoff in line with previous school readiness studies,5,25 including one which revealed significantly increased odds of later educational delays with impairment in ≥2 domains.5 On each measure, 0 to 3 participants in each group had missing data. These participants were included in the domains for which sufficient data were available to determine impairment.

School readiness impairment was defined as impairment in ≥2 domains.5 Logistic regression models predicted impairment in each of the 5 domains and overall school readiness as a function of ADHD group while controlling for differences in demographic factors.

To further characterize our sample, we compared overall school readiness between the clinic-referred and community-recruited subgroups using Fisher’s exact test. We also examined whether receipt of intervention services (behavior therapy or ADHD medication) was associated with a significant difference in school readiness impairment (Supplemental Table 6).

The groups did not differ significantly in sex, race, or preschool or kindergarten attendance (Table 2). Participants in the ADHD group had higher age, lower SES, and a lower percentage of non-Hispanic/non-Latino ethnicity than the comparison group. Among children in the ADHD group, 52% were referred from subspecialty clinics with either a formal or provisional and/or suspected diagnosis of ADHD. Clinic-referred children had significantly higher T-scores on the ECI-4 inattentive scale but not the hyperactive-impulsive scale. Of the clinic-referred subgroup, 23% had ever received behavior therapy, 23% were taking ADHD medications at the time of assessment, and 36% had received behavior therapy and/or were taking ADHD medications. Of the community-recruited subgroup, 10% had received behavior therapy and no children were taking ADHD medications.

TABLE 2

Demographic Characteristics of the Sample

ADHD (N = 45)Comparison (N = 48)t or X2Cohen’s d or Phi (φ)P
Child characteristics      
 Male sex, % 62 58 0.15a −0.04b .70 
 Age at initial assessment (mo), mean (SD) 61 (6.6) 58 (6.2) −2.14c 0.44d .04 
 Non-Hispanic or Non-Latino ethnicity, % 61 91 11.36a 0.36b .001 
 White, % 68 57 1.30a 0.12b .25 
 SES, mean (SD)e 52 (12.4) 60 (6.0) 3.96c 0.85d <.001 
 Preschool attendance (current or past), % 91 91 f −0.01b .92 
 Kindergarten attendance, % 30 15 2.88a 0.18b .09 
ADHD subgroups Clinic referred (N = 23)g Community recruited (N = 21)    
 ECI-4 inattentive T-score, mean (SD) 77 (11.4) 63 (11.9) −3.82c 1.20d <.001 
 ECI-4 hyperactive T-score, mean (SD) 72 (11.6) 70 (8.2) −0.93c 0.20d .36 
 Taking ADHD medication, % 23 f 0.35b .049 
 Current or past behavioral therapy, % 23 10 f 0.17b .41 
 ADHD medication or behavior therapy, % 36 10 f 0.31b .07 
ADHD (N = 45)Comparison (N = 48)t or X2Cohen’s d or Phi (φ)P
Child characteristics      
 Male sex, % 62 58 0.15a −0.04b .70 
 Age at initial assessment (mo), mean (SD) 61 (6.6) 58 (6.2) −2.14c 0.44d .04 
 Non-Hispanic or Non-Latino ethnicity, % 61 91 11.36a 0.36b .001 
 White, % 68 57 1.30a 0.12b .25 
 SES, mean (SD)e 52 (12.4) 60 (6.0) 3.96c 0.85d <.001 
 Preschool attendance (current or past), % 91 91 f −0.01b .92 
 Kindergarten attendance, % 30 15 2.88a 0.18b .09 
ADHD subgroups Clinic referred (N = 23)g Community recruited (N = 21)    
 ECI-4 inattentive T-score, mean (SD) 77 (11.4) 63 (11.9) −3.82c 1.20d <.001 
 ECI-4 hyperactive T-score, mean (SD) 72 (11.6) 70 (8.2) −0.93c 0.20d .36 
 Taking ADHD medication, % 23 f 0.35b .049 
 Current or past behavioral therapy, % 23 10 f 0.17b .41 
 ADHD medication or behavior therapy, % 36 10 f 0.31b .07 
a

X2 statistic.

b

Effect size measured by using Phi (φ).

c

t statistic.

d

Effect size measured by using Cohen’s d.

e

Assessed by using modified Hollingshead 4 Factor Index of Socioeconomic Status.

f

Fisher’s exact test used because of cell size <5.

g

Recruitment information (clinic versus community) missing for 1 child in the ADHD group.

Overall, children in the ADHD group had significantly worse mean scores on 7 of 9 school readiness measures (Table 3) and more chronic medical conditions after controlling for group differences in age and SES. ABAS-II motor and ABIQ scores did not differ between groups. There were no main effects of Hispanic versus non-Hispanic ethnicity. Age was significant for the Bracken SRC. SES was significant for the CBCL, EF composite, TOPEL, ABAS-II communication, and Bracken SRC. Group by ethnicity interaction was significant only for the Bracken SRC.

TABLE 3

Group Differences in Mean Scores or Proportions on School Readiness Measures, Adjusted

School Readiness Measures Mean ScoresADHD (N = 45)Comparison (N = 48)FP
Physical well-being and motor development     
 ABAS-II motor, mean (SD) 9.0 (3.2) 11.4 (2.7) 3.54 .06 
 Presence of chronic medical condition, No. (%) 7 (16.3) 0 (0) n/aa .005* 
Social and emotional development     
 CBCL total problems, mean (SD) 59.0 (11.1) 42.7 (9.3) 11.70 .001* 
 ABAS-II social, mean (SD) 79.0 (27.6) 105.1 (25.8) 5.42 .02* 
Approaches to learning     
 BRIEF-P GEC, mean (SD) 76.1 (12.0) 45.5 (8.7) 69.68 <.001* 
 EF z score, mean (SD) −1.0 (1.5) −0.001 (0.6) 9.96 .002* 
Language development     
 TOPEL phonological awareness, mean (SD) 102.1 (14.8) 114.3 (9.1) 8.05 .006* 
 ABAS-II communication, mean (SD) 7.7 (2.7) 10.9 (2.4) 10.08 .002 
Cognition and general knowledge     
 Bracken SRC, mean (SD) 102.6 (16.4) 115.2 (10.0) 9.39 .003* 
 Stanford-binet ABIQ, mean (SD) 98.6 (16.4) 109.7 (13.8) 3.41 .07 
School Readiness Measures Mean ScoresADHD (N = 45)Comparison (N = 48)FP
Physical well-being and motor development     
 ABAS-II motor, mean (SD) 9.0 (3.2) 11.4 (2.7) 3.54 .06 
 Presence of chronic medical condition, No. (%) 7 (16.3) 0 (0) n/aa .005* 
Social and emotional development     
 CBCL total problems, mean (SD) 59.0 (11.1) 42.7 (9.3) 11.70 .001* 
 ABAS-II social, mean (SD) 79.0 (27.6) 105.1 (25.8) 5.42 .02* 
Approaches to learning     
 BRIEF-P GEC, mean (SD) 76.1 (12.0) 45.5 (8.7) 69.68 <.001* 
 EF z score, mean (SD) −1.0 (1.5) −0.001 (0.6) 9.96 .002* 
Language development     
 TOPEL phonological awareness, mean (SD) 102.1 (14.8) 114.3 (9.1) 8.05 .006* 
 ABAS-II communication, mean (SD) 7.7 (2.7) 10.9 (2.4) 10.08 .002 
Cognition and general knowledge     
 Bracken SRC, mean (SD) 102.6 (16.4) 115.2 (10.0) 9.39 .003* 
 Stanford-binet ABIQ, mean (SD) 98.6 (16.4) 109.7 (13.8) 3.41 .07 

Mean scores are unadjusted. F statistic and P value are noted for group status, adjusting the model for age and SES. GEC, Global Executive Composite.

a

Fisher’s exact test used because of cell size <5.

*

P < .05

To examine the impact of group status on impairment in the 5 domains and overall school readiness (impairment in ≥2 domains), we conducted 6 separate logistic regression models controlling for age, SES, and ethnicity. Table 4 shows the odds ratios for each of the 6 models. Each column corresponds to 1 adjusted logistic regression model. In every model except cognition and general knowledge (P = .27), children in the ADHD group had significantly greater odds of impairment than children without ADHD symptoms.

TABLE 4

Logistic Regression Predicting Impairment in School Readiness and Each Domain

Odds Ratio (95% CI)
School ReadinessPhysical Well-Being and Motor DevelopmentSocial and Emotional DevelopmentApproaches to LearningLanguageCognition and General Knowledge
Age (mo) 1.11* (1.01–1.22) 1.03 (0.95–1.11) 1.04 (0.96–1.14) 1.15* (1.02–1.31) 1.05 (0.96–1.14) 1.07 (0.94–1.22) 
Non-Hispanic or non-Latino ethnicity 0.63 (0.13–3.10) 1.39 (0.38–5.04) 0.71 (0.18–2.85) 3.21 (0.34–29.90) 0.99 (0.25–3.82) 4.12 (0.76–22.50) 
SESa 0.98 (0.92–1.05) 1.01 (0.96–1.07) 0.95 (0.89–1.01) 0.94 (0.85–1.04) 0.95 (0.90–1.01) 0.95 (0.89–1.01) 
ADHD group 21.00*** (5.67–77.77) 3.27* (1.01–10.57) 7.55*** (2.43–23.49) 73.32*** (13.55–396.90) 6.14** (1.72–21.92) 3.68 (0.36–37.31) 
Odds Ratio (95% CI)
School ReadinessPhysical Well-Being and Motor DevelopmentSocial and Emotional DevelopmentApproaches to LearningLanguageCognition and General Knowledge
Age (mo) 1.11* (1.01–1.22) 1.03 (0.95–1.11) 1.04 (0.96–1.14) 1.15* (1.02–1.31) 1.05 (0.96–1.14) 1.07 (0.94–1.22) 
Non-Hispanic or non-Latino ethnicity 0.63 (0.13–3.10) 1.39 (0.38–5.04) 0.71 (0.18–2.85) 3.21 (0.34–29.90) 0.99 (0.25–3.82) 4.12 (0.76–22.50) 
SESa 0.98 (0.92–1.05) 1.01 (0.96–1.07) 0.95 (0.89–1.01) 0.94 (0.85–1.04) 0.95 (0.90–1.01) 0.95 (0.89–1.01) 
ADHD group 21.00*** (5.67–77.77) 3.27* (1.01–10.57) 7.55*** (2.43–23.49) 73.32*** (13.55–396.90) 6.14** (1.72–21.92) 3.68 (0.36–37.31) 
a

SES assessed by using modified Hollingshead 4 Factor Index of Socioeconomic Status.

*

P < .05; ** P < .01; *** P < .001.

In overall school readiness, 79% of children in the ADHD group were classified as impaired compared with 13% of the comparison group (Fig 1). The number of impaired domains by group is shown in Fig 2. The percentage of children with impairments in 0 or 1 domain (classified as “school ready”) was higher in the comparison group than the ADHD group. The percentage of children with impairments in 2, 3, 4, or 5 domains (classified as “school readiness impaired”) was higher in the ADHD group than the comparison group.

FIGURE 1

Percent of ADHD and comparison groups impaired in each domain and overall school readiness. School readiness impairment was defined as impairment in ≥2 domains.

FIGURE 1

Percent of ADHD and comparison groups impaired in each domain and overall school readiness. School readiness impairment was defined as impairment in ≥2 domains.

Close modal
FIGURE 2

Percent of ADHD and comparison groups by number of domains impaired. Children were classified as school ready if they were impaired in 0 to 1 domains and school readiness impaired if they were impaired in 2 to 5 domains. *Number of subjects based on those with sufficient data to calculate number of impaired domains; ADHD n = 41, comparison n = 44.

FIGURE 2

Percent of ADHD and comparison groups by number of domains impaired. Children were classified as school ready if they were impaired in 0 to 1 domains and school readiness impaired if they were impaired in 2 to 5 domains. *Number of subjects based on those with sufficient data to calculate number of impaired domains; ADHD n = 41, comparison n = 44.

Close modal

Impairment in school readiness was found in 100% of clinic-referred children versus 53% of children in the ADHD group recruited from the community (P < .001 by Fisher’s exact test). There was no significant difference in school readiness impairment when comparing children who had received behavior therapy and/or ADHD medications to those who had not (Supplemental Table 6).

In this study, we found that preschool-aged children with ADHD symptoms demonstrated significantly worse performance on 8 of 10 school readiness measures and significantly greater odds of impairment in 4 of 5 domains and overall school readiness. In our sample, 79% of children in the ADHD group had overall school readiness impairment compared with 13% of the comparison group. The proportion impaired in our comparison group is similar to comparison groups in other studies in which the 5-domain school readiness framework is used,5,25 suggesting that although component measures are not identical across studies, school readiness can be assessed by using a variety of standardized instruments in the 5 critical domains with similar results. This consistency allows for greater flexibility in choosing instruments for school readiness assessment. There are currently efforts underway to develop a national school readiness measure.26 

Children with symptoms of ADHD had comparable impairment in cognition and general knowledge to those without symptoms of ADHD; however, they had significantly more impairment in the other 4 domains. Only 20% of the ADHD group were impaired in cognition and general knowledge, whereas 79% of the ADHD group showed overall school readiness impairment. This finding highlights the importance of assessing school readiness in children with symptoms of ADHD by using measures beyond IQ and letter and/or number knowledge, which likely identifies only a fraction of children at risk for adverse educational outcomes. Studies in which the comprehensive 5-domain school readiness framework is used in other clinical populations demonstrated that impairment in school readiness predicts later lower academic achievement,5,9 and previous research documents that children with ADHD are at high risk for poor educational outcomes.1,2,27,28 Therefore, a comprehensive school readiness framework could be used to identify preschool-aged children at high risk who need additional educational supports.

Previous studies have determined that low SES and other social risk factors contribute to school readiness impairment.29,30 We also found a contribution of SES to mean scores on several measures. Thus, it is important to consider social risk factors when assessing school readiness, particularly in vulnerable populations. These children should have particularly close attention paid to their school readiness skills in their formative years and research should evaluate interventions that address social risk factors. Even after controlling for differences in demographic factors, including SES; however, the differences in school readiness impairment persisted for overall school readiness and for each domain other than cognition and general knowledge. This indicates that children with ADHD symptoms are at higher risk for school readiness impairment, beyond risk conferred by social risk factors, and they should be assessed and managed appropriately.

Our ADHD group included both clinic-referred children with an ADHD diagnosis and community-recruited children with symptoms of ADHD but without a clinical diagnosis. There was a significant difference in proportion of children with impaired school readiness between clinic-recruited versus community-referred children (100% vs 53%, respectively), and the higher mean ADHD inattention symptoms in the clinic-recruited subgroup (Table 2) reflects greater ADHD symptom severity. Nonetheless, both groups demonstrated high rates of school readiness impairment. Thus, it is important for pediatricians to assess school readiness in children with parent-reported symptoms of ADHD, even in the absence of a formal diagnosis.

We also examined the rate of therapeutic interventions in the ADHD group. In preschoolers with ADHD, evidence-based parent- and/or teacher-administered behavior therapy is the first-line recommended treatment and medication is second line.31 Examination of our ADHD group revealed low rates of behavior therapy and medication use in both the clinic-referred and community-recruited subgroups. We did not find significant differences in school readiness between children who had received behavior therapy and/or medication and those who had not; however, our interpretation is limited given that we did not collect longitudinal data and have no knowledge of the baseline characteristics of these children before beginning therapy and/or medication nor the quality of their preschool or kindergarten programs.

The findings from our study have significant clinical implications for pediatricians caring for children with symptoms of ADHD. The ability to identify preschool-aged children at high risk for poor educational outcomes allows for earlier interventions. We recommend that pediatricians screen preschool-aged children for ADHD symptoms in the context of assessing school readiness because behavior is an important component of school readiness. Our findings show that children in our sample with symptoms of ADHD are receiving low rates of first-line ADHD treatment, particularly those who are not seeing a subspecialist. Identifying these children earlier and providing early, accessible treatment (such as parent management training), as well as additional community supports and educational opportunities (eg, high-quality preschool programs that focus on “Ready to Learn” skills) might help reduce the disparities in school readiness. Our findings also have significant policy implications as legislators consider programs, such as free universal preschool or transitional kindergarten, which could offer children the opportunity to build “Ready to Learn” skills that will better prepare them for the school learning environment.

The study is composed of a convenience sample, limiting generalizability. However, children were recruited from a variety of settings to mitigate this limitation. ADHD symptom ratings were based only on parent, and not teacher, report on the ECI-4. Still, parent report on the ECI-4 has good sensitivity and specificity for ADHD, particularly when children with pervasive developmental disorders are excluded.15 Different assessment tools relevant to practice could be considered to identify children with symptoms of ADHD. Non–English speaking participants were excluded, limiting generalizability. Spanish versions of many of the tests (or equivalent tests) are available; our study was limited by lack of Spanish-speaking personnel for test administration. Almost all participants attended preschool. Therefore, preschool attendance as a moderator of scores in domains of school readiness or overall rates of school readiness could not be evaluated. The quality of preschool programs was also unknown. We do not have baseline school readiness data before initiation of ADHD treatments, limiting our ability to evaluate the effect of treatment on school readiness. We also do not have follow-up data to assess long-term outcomes. Future longitudinal research paired with intervention should clarify the degree of association between school readiness and educational outcomes in children with ADHD, and the impact of interventions, including duration and quality indicators of preschool experiences, in addition to medication and behavior management therapy on school readiness or academic success. Additional research into school readiness screening in preschoolers could identify best practices for general pediatricians to implement in clinic visits.

Preschool-aged children with parent- or clinician-reported ADHD symptoms are likely to have impaired school readiness. These children require early identification and intervention. Family dynamics and social-emotional functioning should be assessed for each preschool-aged child with ADHD symptoms, and appropriate therapeutic interventions and community supports should be prescribed to enhance school readiness.

Dr Perrin conceptualized and designed the study, conducted the statistical analyses, and drafted the initial manuscript; Ms Heller collected data; Dr Loe conceptualized and designed the study, coordinated and supervised data collection, and supervised all statistical analyses; and all authors reviewed and revised the manuscript and approved the final manuscript as submitted and are responsible for all aspects of the work.

FUNDING: Funding sources include the Maternal and Child Health Bureau, T77MC09796 (principal investigator: H.M. Feldman), for training support of Dr Perrin; the Katharine McCormick Faculty Scholar Award, Stanford Children’s Health and Child Health Research Institute Pilot Early Career Award, and the National Institutes of Health (NIH) Mentored Patient-oriented Research Career Development Award grant K23HD071971 awarded to Dr Loe; and the Stanford Clinical and Translational Science Award to Spectrum (UL1 TR0001085), sponsored by the National Center for Advancing Translational Sciences at the NIH. Funded by the National Institutes of Health (NIH).

     
  • ABAS-II

    Adaptive Behavior Assessment System, Second Edition

  •  
  • ABIQ

    Abbreviated Battery Intelligence Quotient

  •  
  • ADHD

    attention-deficit/hyperactivity disorder

  •  
  • BRIEF-P

    Behavior Rating Inventory of Executive Function, Preschool

  •  
  • CBCL

    Child Behavior Checklist

  •  
  • ECI-4

    Early Childhood Inventory-4

  •  
  • EF

    executive function

  •  
  • SES

    socioeconomic status

  •  
  • SRC

    School Readiness Composite

  •  
  • TOPEL

    Test of Preschool Early Literacy

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Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

Supplementary data