Trajectory Moderators of Functional Outcomes and ADHD Symptoms in Children with ADHD
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| Title: | Trajectory Moderators of Functional Outcomes and ADHD Symptoms in Children with ADHD |
|---|---|
| Language: | English |
| Authors: | Margaret Fletcher (ORCID |
| Source: | Journal of Attention Disorders. 2026 30(4):460-475. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 16 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Attention Deficit Hyperactivity Disorder, Symptoms (Individual Disorders), Family Income, Racial Differences, Ethnicity, Barriers, Intervention, Preadolescents, Adolescents, Family Relationship, Prosocial Behavior, Student Experience |
| Assessment and Survey Identifiers: | Strengths and Difficulties Questionnaire |
| DOI: | 10.1177/10870547251367284 |
| ISSN: | 1087-0547 1557-1246 |
| Abstract: | Objective: ADHD can impair children's functioning. Socioeconomic and sociodemographic factors present barriers to treatment access and lead to disparate outcomes in children with ADHD. The purpose of this study was to describe trajectories of functional outcomes and ADHD symptom counts across 3 years and explore the moderating effects of income and race/ethnicity on these trajectories among U.S. children with ADHD. Method: This longitudinal study of children currently and/or previously meeting diagnostic criteria for ADHD (N = 1,587, age = 9-10 years at baseline) used data from the Adolescent Brain Cognitive Development (ABCD) Study®. Outcomes were child-reported functional outcome measures (family conflict, prosocial behavior, and school experiences) and parent-reported inattentive and hyperactive symptom counts across 3 years. Multi-level, mixed-effects models for longitudinal data were used to characterize each outcome trajectory and examine the moderating effects of baseline household income and race/ethnicity. Results: The sample was 68% male and 54% White, with 53% meeting diagnostic criteria for past-only ADHD, 12% current-only ADHD, and 35% both past and current ADHD. Significant changes in family conflict, school experiences, inattentive symptom counts, and hyperactive symptom counts were demonstrated across 3 years (trajectories, p < 0.05). Income significantly moderated prosocial behavior trajectories, while race/ethnicity significantly moderated family conflict and prosocial behavior trajectories (time interaction, p < 0.05). Conclusions: The findings suggest that factors related to income and race/ethnicity influence trajectories of change in family conflict and prosocial behavior outcomes in children with a history of ADHD. Future studies should explore these disparities and identify targets for intervention, such as increased access to diagnosis and treatment for individuals at risk of poorer functional outcomes. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1499916 |
| Database: | ERIC |
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| FullText | Links: – Type: pdflink Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwHTtCBq2D0MOBap6dszLuRLAAAA4TCB3gYJKoZIhvcNAQcGoIHQMIHNAgEAMIHHBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDHzscaQrTzMgqfp3-wIBEICBmRDtAX5bpK4XTqLubQdynLYzW6YEEa8rhl_CcU78_PFH3bljwLhIR9f2-aPe7lIy7es2BwdOsBqX2moLE4Lr-TtFblc5kpRmqGvA8V0THngNINrLgDfLSUb8bVmEFecM7NUgOoDngYLD906SlU3KLEBtRvBWpioO2A3cZKLQASxxs5IhsU02YwUYu72iax5i2U5yWTwuzmN3Uw== Text: Availability: 1 Value: <anid>AN0192008565;gs001apr.26;2026Mar05.05:44;v2.2.500</anid> <title id="AN0192008565-1">Trajectory Moderators of Functional Outcomes and ADHD Symptoms in Children With ADHD </title> <p>Objective: ADHD can impair children's functioning. Socioeconomic and sociodemographic factors present barriers to treatment access and lead to disparate outcomes in children with ADHD. The purpose of this study was to describe trajectories of functional outcomes and ADHD symptom counts across 3 years and explore the moderating effects of income and race/ethnicity on these trajectories among U.S. children with ADHD. Method: This longitudinal study of children currently and/or previously meeting diagnostic criteria for ADHD (N = 1,587, age = 9–10 years at baseline) used data from the Adolescent Brain Cognitive Development (ABCD) Study&lt;sup&gt;®&lt;/sup&gt;. Outcomes were child-reported functional outcome measures (family conflict, prosocial behavior, and school experiences) and parent-reported inattentive and hyperactive symptom counts across 3 years. Multi-level, mixed-effects models for longitudinal data were used to characterize each outcome trajectory and examine the moderating effects of baseline household income and race/ethnicity. Results: The sample was 68% male and 54% White, with 53% meeting diagnostic criteria for past-only ADHD, 12% current-only ADHD, and 35% both past and current ADHD. Significant changes in family conflict, school experiences, inattentive symptom counts, and hyperactive symptom counts were demonstrated across 3 years (trajectories, p &lt;.05). Income significantly moderated prosocial behavior trajectories, while race/ethnicity significantly moderated family conflict and prosocial behavior trajectories (time interaction, p &lt;.05). Conclusions: The findings suggest that factors related to income and race/ethnicity influence trajectories of change in family conflict and prosocial behavior outcomes in children with a history of ADHD. Future studies should explore these disparities and identify targets for intervention, such as increased access to diagnosis and treatment for individuals at risk of poorer functional outcomes.</p> <p>Keywords: ADHD-associated problems; adolescent ADHD; ADHD; functional outcomes; symptoms</p> <hd id="AN0192008565-2">Introduction</hd> <p>ADHD is among the most prevalent neurodevelopmental disorders in children. According to parent report, approximately 11.3% of children in the United States (U.S.) have ever had a diagnosis of ADHD ([<reflink idref="bib20" id="ref1">20</reflink>]; [<reflink idref="bib59" id="ref2">59</reflink>]), although rates vary related to diagnostic procedures and meta-analyses have estimated true prevalence to be 3.4% to 7.1% ([<reflink idref="bib56" id="ref3">56</reflink>], [<reflink idref="bib55" id="ref4">55</reflink>]; [<reflink idref="bib76" id="ref5">76</reflink>]). ADHD is characterized by symptoms of inattention, hyperactivity, and impulsivity which can lead to caregiver strain, family conflict, impaired peer relationships, and problematic social behaviors ([<reflink idref="bib46" id="ref6">46</reflink>]; [<reflink idref="bib62" id="ref7">62</reflink>]). Consequently, children with ADHD demonstrate impaired functioning across multiple settings, resulting in long-term mental health, social, and occupational challenges ([<reflink idref="bib3" id="ref8">3</reflink>]; [<reflink idref="bib4" id="ref9">4</reflink>]; [<reflink idref="bib25" id="ref10">25</reflink>]; [<reflink idref="bib29" id="ref11">29</reflink>]; [<reflink idref="bib37" id="ref12">37</reflink>]; [<reflink idref="bib38" id="ref13">38</reflink>]; [<reflink idref="bib43" id="ref14">43</reflink>]; [<reflink idref="bib45" id="ref15">45</reflink>]; [<reflink idref="bib46" id="ref16">46</reflink>]; [<reflink idref="bib62" id="ref17">62</reflink>]).</p> <p>A diagnosis of ADHD is given when a child displays at least six out of nine inattentive and/or hyperactive/impulsive symptoms and if there is clear evidence that the symptoms interfere with, or reduce the quality of, functioning in at least two settings (e.g., family, school, or social; [<reflink idref="bib2" id="ref18">2</reflink>]). Previous research on functional outcomes of children with ADHD has identified specific areas of difficulty, including family conflict ([<reflink idref="bib44" id="ref19">44</reflink>]; [<reflink idref="bib46" id="ref20">46</reflink>]; [<reflink idref="bib62" id="ref21">62</reflink>]), stigmatization at school ([<reflink idref="bib62" id="ref22">62</reflink>]), impaired academic achievement ([<reflink idref="bib29" id="ref23">29</reflink>]; [<reflink idref="bib43" id="ref24">43</reflink>]; [<reflink idref="bib44" id="ref25">44</reflink>]), and impaired prosocial behavior ([<reflink idref="bib3" id="ref26">3</reflink>]; [<reflink idref="bib4" id="ref27">4</reflink>]; [<reflink idref="bib52" id="ref28">52</reflink>]). Impaired prosocial behavior is associated with peer relationship problems and decreased quality of life in children with ADHD ([<reflink idref="bib72" id="ref29">72</reflink>]). In addition, ADHD is associated with a high prevalence of comorbid internalizing and externalizing disorders, including mood disorders (e.g., depression and bipolar), anxiety disorders (e.g., phobias and panic disorder), and disruptive behavior disorders (e.g., conduct disorder and oppositional defiant disorder), which also influence functional outcomes ([<reflink idref="bib25" id="ref30">25</reflink>], [<reflink idref="bib26" id="ref31">26</reflink>]; [<reflink idref="bib38" id="ref32">38</reflink>]; [<reflink idref="bib77" id="ref33">77</reflink>]).</p> <p>While prior research has explored trajectories of functional outcomes and ADHD symptom severity ([<reflink idref="bib41" id="ref34">41</reflink>]; [<reflink idref="bib49" id="ref35">49</reflink>]), ratings of functional outcomes (academic, social, recreational, and overall) have been provided by parents without taking into account the child's perspective ([<reflink idref="bib41" id="ref36">41</reflink>]; [<reflink idref="bib67" id="ref37">67</reflink>]). This is a reasonable approach related to the understanding that children and adolescents with ADHD have an inflated perception of their own competence, or "positive illusory bias" (PIB) and as such are unreliable reporters of their functional status ([<reflink idref="bib12" id="ref38">12</reflink>]; [<reflink idref="bib66" id="ref39">66</reflink>], [<reflink idref="bib65" id="ref40">65</reflink>]). However, reports from parents are limited regarding the child's functioning outside the home, and several studies indicate that there is value in children's own appraisal of their functional status. [<reflink idref="bib12" id="ref41">12</reflink>] observed that PIB occurred in less than half of children aged 10 to 14 years with ADHD, with 18% experiencing "global" PIB across social, scholastic, and behavioral domains. [<reflink idref="bib18" id="ref42">18</reflink>] compared child and parent report of health-related quality of life (HRQoL) measures for three groups of children aged 6 to 16 years: children with ADHD, children with type 1 diabetes, and a control group. The Pediatric Quality of Life Inventory (PedsQL) was used to assess domains of physical, emotional, social, and school functioning. While correlations between child and parent report were small-to-moderate, the ADHD group reported significantly lower ratings of functional outcomes compared to both the type 1 diabetes and control groups ([<reflink idref="bib18" id="ref43">18</reflink>]). A meta-analysis of HRQoL in children with ADHD also evaluated results from six studies using child-reported PedsQL and found large-to-very large differences in children with ADHD compared to healthy controls ([<reflink idref="bib75" id="ref44">75</reflink>]). The findings of these studies provide evidence that children with ADHD have some perception of their functional challenges. While this study is not evaluating HRQoL, the functional outcomes measured represent similar constructs to those measured using the PedsQL scale (e.g., social functioning and school functioning). Thus, although child and adolescent self-reports of ADHD-related functioning may lack the accuracy of parent or teacher reports, early adolescence is a developmental stage when youth increasingly desire involvement in their own care decisions ([<reflink idref="bib11" id="ref45">11</reflink>]; [<reflink idref="bib68" id="ref46">68</reflink>]). Including their perspectives in the literature offers valuable insight into their lived experiences with ADHD ([<reflink idref="bib37" id="ref47">37</reflink>]; [<reflink idref="bib44" id="ref48">44</reflink>]).</p> <p>Socioeconomic and sociodemographic factors have been found to influence the prevalence of ADHD and associated outcomes. Low-income children and children of parents with low levels of educational attainment have been found to be at an increased risk of ADHD ([<reflink idref="bib61" id="ref49">61</reflink>]; [<reflink idref="bib69" id="ref50">69</reflink>]), and childhood ADHD is associated with reduced earnings and employment as an adult ([<reflink idref="bib28" id="ref51">28</reflink>]). Additionally, neighborhood poverty is associated with decreased rates of medication use and increased symptom severity in children with ADHD ([<reflink idref="bib51" id="ref52">51</reflink>]). Racial and ethnic disparities in ADHD diagnosis and treatment are apparent as well. Between 2020 and 2022, parent-reported estimates of ADHD prevalence in the U.S. indicate that 13.4% of White children have been diagnosed with ADHD, while Black and Hispanic children have a lower reported prevalence (10.8% and 8.9%, respectively; [<reflink idref="bib59" id="ref53">59</reflink>]), and Black children are less likely than White children to receive a diagnosis of ADHD prior to Kindergarten ([<reflink idref="bib48" id="ref54">48</reflink>]); however these differences likely result from lack of access to diagnosis rather than truly lower prevalence rates ([<reflink idref="bib63" id="ref55">63</reflink>]). A recent systematic review and meta-analysis conversely found <emph>higher</emph> rates of ADHD among Black individuals, calling attention to the need for improved detection of ADHD in Black children ([<reflink idref="bib16" id="ref56">16</reflink>]). It was also noted that higher socioeconomic status (SES) is not protective for Black families as it is for White families ([<reflink idref="bib6" id="ref57">6</reflink>]; [<reflink idref="bib16" id="ref58">16</reflink>]). Asian children are also less likely to receive a diagnosis of ADHD ([<reflink idref="bib22" id="ref59">22</reflink>]; [<reflink idref="bib59" id="ref60">59</reflink>]; [<reflink idref="bib63" id="ref61">63</reflink>]), and language barriers limit access to care for some Hispanic families ([<reflink idref="bib36" id="ref62">36</reflink>]). It is important to note that disparities in ADHD diagnosis, treatment, and associated outcomes are not the direct result of a child's race, ethnicity, or income level but rather structural factors leading to inequitable and unjust treatment of certain individuals. The goal of exploring these disparities is to identify targets for interventions which will ameliorate the disproportionate effects of ADHD in children from minoritized or low-income groups.</p> <p>As such, the aims of this study were to (<reflink idref="bib1" id="ref63">1</reflink>) characterize trajectories of child-reported functional outcomes (family conflict, prosocial behavior, and school experiences) and parent-reported inattentive and hyperactive ADHD symptom counts; and (<reflink idref="bib2" id="ref64">2</reflink>) explore the moderating effects of household income and race/ethnicity on these outcome trajectories in this clinical population. This study represents initial exploratory work seeking to understand child-reported outcome trajectories and identify areas for future health disparities research.</p> <hd id="AN0192008565-3">Methods</hd> <p></p> <hd id="AN0192008565-4">Design</hd> <p>This observational, longitudinal study used data from the Adolescent Brain Cognitive Development (ABCD) Study<sups>®</sups> to describe trajectories of functional outcomes and ADHD symptoms across a 3-year period in U.S. children currently and/or previously meeting diagnostic criteria for ADHD during early adolescence (aged 9–10 years at baseline). Additionally, this study explored the moderating roles of household income and race/ethnicity of the child on these trajectories. Functional outcomes included child-reported measures of family conflict, prosocial behavior, and school experiences. Inattentive and hyperactive symptom counts were defined by number of parent-reported symptoms. Each outcome was assessed at baseline (T0) and annually thereafter for 3 years (T1, T2, and T3). Since ADHD is associated with high prevalence of mental health comorbidities ([<reflink idref="bib25" id="ref65">25</reflink>]; [<reflink idref="bib38" id="ref66">38</reflink>]; [<reflink idref="bib77" id="ref67">77</reflink>]), each outcome analysis was adjusted for baseline internalizing and externalizing disorders. Participants with internalizing disorders included those meeting diagnostic criteria for any past or current anxiety or depressive disorder (including panic disorder, agoraphobia, separation anxiety disorder, social anxiety disorder, specific phobia, generalized anxiety disorder, other specified anxiety disorder, persistent depressive disorder (dysthymia), major depressive disorder, or unspecified depressive disorder; [<reflink idref="bib2" id="ref68">2</reflink>]). Participants with externalizing disorders included those meeting diagnostic criteria for any past or current oppositional defiant disorder or conduct disorder ([<reflink idref="bib2" id="ref69">2</reflink>]). The Duke University Health System Institutional Review Board (IRB) deemed this analysis exempt from IRB approval. Children and parents provided informed consent upon enrollment in the ABCD Study, and a data use certificate was obtained to access ABCD Study data ([<reflink idref="bib34" id="ref70">34</reflink>].).</p> <hd id="AN0192008565-5">Data Source</hd> <p>The ABCD Study is a large, longitudinal cohort study of 11,868 U.S. children and uses an open science model of data sharing for survey, neuroimaging, and genetic/genomic data ([<reflink idref="bib73" id="ref71">73</reflink>]). Recruitment occurred in 2016 through 2018 and participants were 9 to 10 years of age at study enrollment, were fluent in English, attended school, and could undergo a magnetic resonance imaging (MRI) procedure (i.e., no exclusionary metals and no claustrophobia; [<reflink idref="bib30" id="ref72">30</reflink>]). Yearly follow-up visits are conducted. At the time of these analyses, complete cohort data were available through the year 3 follow-up visit.</p> <p>Given the large number of publications resulting from the ABCD Study, a search was conducted of the ABCD Study publications archive on March 14, 2025, revealing 1,384 articles published using ABCD Study data (<emph>Publications Archive</emph>, [<reflink idref="bib57" id="ref73">57</reflink>]). Eighty-one published articles using ABCD Study data address ADHD, with topics including the genetic/genomic basis of ADHD, neuroimaging findings associated with ADHD, medication use prevalence and effects, comorbid symptoms such as sleep disturbances or suicidality, and sex and gender issues related to ADHD. After careful review, no publications using ABCD Study data that have longitudinally examined functional outcomes in ADHD were found, and one other article was found that characterized trajectories of ADHD symptoms ([<reflink idref="bib53" id="ref74">53</reflink>]). In the study by Pang et al., trajectories of ADHD symptoms were measured using the child behavior checklist (CBCL) attention and ADHD subscales (2025). Change in inhibitory control over time predicted the trajectory of ADHD symptoms, as did polygenic risk scores related to both ADHD and inhibitory control. While the CBCL is a widely used, valid measure of ADHD symptoms ([<reflink idref="bib1" id="ref75">1</reflink>]), it does not differentiate between inattentive and hyperactive symptoms ([<reflink idref="bib53" id="ref76">53</reflink>]). Our study therefore addresses important questions not yet explored in this database by modeling trajectories of both inattentive and hyperactive symptoms, examining functional outcomes over time, and evaluating disparities by income and race/ethnicity.</p> <hd id="AN0192008565-6">Analysis Sample</hd> <p>Additional eligibility criteria were applied to the ABCD Study sample to extract our analysis sample, which included those participants currently and/or previously meeting diagnostic criteria at study enrollment (Figure 1). Participants were excluded for Bipolar I, intellectual disability (estimated IQ &lt;70), schizophrenia/psychosis, brain injury, cerebral palsy, epilepsy, lead poisoning, and alcohol use disorder. The exclusion criteria align with a previous study, which aimed to determine the prevalence of ADHD in this sample and define phenotypes ([<reflink idref="bib19" id="ref77">19</reflink>]). Unlike previous work, our study included children meeting diagnostic criteria for past-only ADHD. Including past-only ADHD allowed for inclusion of children with ADHD for which symptoms may have been successfully treated or managed, which we felt added ecological validity. Cordova et al. also used teacher report to identify cases of ADHD, which aligns with the DSM-V diagnostic standards (2022; [<reflink idref="bib2" id="ref78">2</reflink>]), however 65% of the baseline teacher report data were missing. Even with multiple imputation, this amount of missingness could introduce bias, so diagnostic criteria were evaluated based on parent report only. The final analysis sample consisted of 1,587 children who currently or previously met criteria for ADHD, with 1,334 participants retained at the year 3 follow-up visit (16% attrition). Due to item-level nonresponse, outcome-specific sample sizes varied, ranging from 1,254 (79%) for symptom counts to 1,333 (84%) for functional outcomes.</p> <p>Graph: Figure 1. Final analysis sample determination.</p> <hd id="AN0192008565-7">Measures</hd> <p>Sample characteristics, ADHD diagnosis, internalizing disorders, and externalizing disorders were assessed at baseline only. Functional outcomes and ADHD symptom outcomes were assessed at baseline (T0) and each subsequent annual assessment across 3 years (T1, T2, and T3).</p> <hd id="AN0192008565-8">Sample Characteristics</hd> <p>Sociodemographic characteristics at baseline were obtained from the ABCD Demographics Survey ([<reflink idref="bib31" id="ref79">31</reflink>]; [<reflink idref="bib79" id="ref80">79</reflink>]), including the child's age, sex, parent-reported total combined household annual income, and race/ethnicity.</p> <hd id="AN0192008565-9">ADHD Diagnosis and Symptom Counts</hd> <p>Past and current ADHD diagnostic criteria at baseline as well as the two symptom outcomes (inattentive and hyperactive) at all timepoints were assessed using the Kiddie Schedule of Affective Disorders and Schizophrenia (KSADS-5) Parent Diagnostic Interview ([<reflink idref="bib70" id="ref81">70</reflink>]). This computerized version of the instrument has high agreement with the clinician-administered KSADS-5, which is widely-used in clinical and research settings. ADHD diagnosis was determined using diagnostic criteria for past or current ADHD per the Diagnostic and Statistical Manual of Mental Disorder 5 (DSM-5), which requires presence of at least six of nine inattentive and/or hyperactive symptoms, and functional impairment in at least two settings prior to the age of 12 years ([<reflink idref="bib2" id="ref82">2</reflink>]). Each parent-reported symptom was coded absent (0) or present (<reflink idref="bib1" id="ref83">1</reflink>). Symptom counts at each timepoint were calculated by summing the number of inattentive and hyperactive symptoms (each range = 0–9), with higher scores reflecting greater symptom counts.</p> <hd id="AN0192008565-10">Functional Outcomes</hd> <p>Domains of functional outcomes were measured at all timepoints via child-report questionnaires and included perceived levels of family conflict, prosocial behavior, and school experiences ([<reflink idref="bib71" id="ref84">71</reflink>]). Family conflict was measured by the ABCD Youth Family Environment Scale – Family Conflict Subscale ([<reflink idref="bib31" id="ref85">31</reflink>]; [<reflink idref="bib47" id="ref86">47</reflink>]; [<reflink idref="bib79" id="ref87">79</reflink>]). This scale includes nine items pertaining to the child's perception of interactions among family members, including handling of disagreements and feelings of support and togetherness. Items were coded as "yes" (<reflink idref="bib1" id="ref88">1</reflink>) or "no" (0), with some items reverse-scored. A family conflict total score was derived at each timepoint by summing the nine items (range = 0–9), with higher scores indicating a greater degree of family conflict (α =.65–.72).</p> <p>Prosocial behavior was measured using three items from the prosocial behavior subscale of the Strengths and Difficulties Questionnaire ([<reflink idref="bib31" id="ref89">31</reflink>]; [<reflink idref="bib32" id="ref90">32</reflink>]; [<reflink idref="bib79" id="ref91">79</reflink>]). Items were scored as "not true" (0), "sometimes true" (<reflink idref="bib1" id="ref92">1</reflink>), and "certainly true" (<reflink idref="bib2" id="ref93">2</reflink>), and the overall score was the mean of the item scores (range: 0–2). Higher scores represented a greater level of prosocial behavior (e.g., behaviors of helping and caring for others; α =.55–.70).</p> <p>School experiences were assessed using three subscales from the School Risk and Protective Factors Scale ([<reflink idref="bib5" id="ref94">5</reflink>]). The subscales included "school environment" (e.g., safety, class rules, and relationship with teachers; six items; α =.64–.71), "school involvement" (e.g., participating in classroom activities; four items; α =.68–.73), and "school disengagement" (e.g., boredom and disinterest in grades; two items; α =.22–.31). Each item was coded as "NO!" (<reflink idref="bib1" id="ref95">1</reflink>), "no" (<reflink idref="bib2" id="ref96">2</reflink>), "yes" (<reflink idref="bib3" id="ref97">3</reflink>), or "YES!" (<reflink idref="bib4" id="ref98">4</reflink>). The three subscale scores were summed, with disengagement subscale items reverse-coded to reflect the same directionality as the other subscales. The 12-item composite score ranged from 12 to 48, with internal consistency ranging from α =.63 to.68, and higher scores reflecting more positive school experiences. This composite scale demonstrated acceptable reliability and captured a broad perspective on school-related functioning. Psychometric properties were calculated based on the selected analysis sample for this study, and reflect similar values to those previously published for the first three timepoints in the full ABCD cohort ([<reflink idref="bib31" id="ref99">31</reflink>]; Supplemental Table S1).</p> <p>Teachers are invaluable sources of information for both ADHD diagnosis and assessments of functioning. However, in the ABCD dataset, teacher-reported data were missing for 50% to 85% of participants across timepoints, limiting their utility for longitudinal modeling. Given this high level of missingness, we excluded teacher report as an outcome measure in this study. Our primary aim was to explore children's self-reported experiences and the moderating effects of race/ethnicity on symptom and outcome trajectories. While we recognize the importance of multi-informant perspectives, this study's design and the available data supported a focus on child and caregiver perspectives.</p> <hd id="AN0192008565-11">Mental Health Comorbidities</hd> <p>The KSADS-5 was also used to assess comorbidities at baseline, including internalizing (anxiety or depressive) or externalizing (oppositional defiant or conduct) disorders. Internalizing disorder and externalizing disorder were covariates in the outcome analyses, with each coded as absent (0) or present (<reflink idref="bib1" id="ref100">1</reflink>).</p> <hd id="AN0192008565-12">Analysis</hd> <p>Descriptive statistics were used to detail baseline characteristics and outcomes at the four timepoints. Non-directional statistical tests were performed with significance set at.05 for all tests. The analyses were not adjusted for multiple outcomes, as this is exploratory work prioritizing pattern identification and hypothesis generation to inform future research efforts. All analyses were conducted using SAS 9.4 analytic software (Cary, NC).</p> <hd id="AN0192008565-13">Trajectory Analyses</hd> <p>Random coefficients regression models (RRM) for longitudinal data were used to characterize trajectories of three functional outcomes (family conflict, prosocial behavior, and school experiences) and two symptom outcomes (inattentive and hyperactive symptom counts) across 3 years, covarying for internalizing and externalizing disorders. An RRM is a multi-level, mixed-effect model for repeated measurement that allows the intercept (baseline outcome value) and outcome trajectory for each participant to vary at random ([<reflink idref="bib33" id="ref101">33</reflink>]). The fixed effects were time and the two baseline covariates (internalizing and externalizing disorders). Random effects were participant and participant-by-time (linear trajectory). When time effects were non-linear, polynomial terms were added, allowing the model to fit the data for the temporal effect observed (e.g., quadratic trajectory: participant-by-time<sups>2</sups>). Outcome data were determined to be missing at random, an assumption of trajectory analysis ([<reflink idref="bib33" id="ref102">33</reflink>]). The best fitting trajectory model for each outcome was determined using model fit indices Akaike information criterion and Bayesian information criterion. Unconditional means models were used to determine within- and between-individual variation, and intraclass correlation coefficients (ICCs) for each outcome were <emph>p</emph> &gt;.05, verifying the suitability of multi-level modeling for these analyses ([<reflink idref="bib35" id="ref103">35</reflink>]). Adjusted means, the mean estimated outcome value after adjusting for fixed and random effects, were derived from the trajectory model for each outcome at each timepoint.</p> <hd id="AN0192008565-14">Moderator Analyses</hd> <p>Income and race/ethnicity (White, Black, Hispanic, and Another Identity) were examined separately as candidate moderators of each outcome. The candidate moderator and its interaction with time (moderator-by-time) were added as explanatory variables for linear trajectory models, while candidate moderator and its interaction with time (moderator-by-time) and time<sups>2</sups> (moderator-by-time<sups>2</sups>) were added for quadratic models. A significant main effect of a candidate moderator indicated that it was a non-specific predictor of the outcome regardless of time. In contrast, a significant interaction of the candidate moderator with time or time<sups>2</sups> indicated that the baseline variable moderated the effects of time on the outcome (trajectory moderator). When a non-specific predictor or moderator was detected, <emph>a posteriori</emph> pairwise contrasts were conducted to further delineate between-groups differences in the outcome trajectory.</p> <p>This approach is advantageous insofar as the multi-level mixed effect model for longitudinal data allows for inclusion of explanatory variables as categorical variables with multiple levels, resulting in direct comparison between different racial/ethnic groups. This avoids the need to collapse race/ethnicity into sets of binary "dummy" or indicator variables (e.g., "White" vs. "not White") forcing comparison against an arbitrary reference group and leading to inflated variance in the "not" category. The additional use of pairwise contrasts preserves the distinctiveness of each group, leading to more precise and meaningful analyses. This aligns with emerging best practices in health disparities research, and with recommendations specific to large, openly available data sets for which the responsible use of racial and ethnic data is of particular importance ([<reflink idref="bib14" id="ref104">14</reflink>]).</p> <hd id="AN0192008565-15">Statistical Power</hd> <p>The sample size of 1,587 provided at least 80% statistical power to detect small effect sizes for change in outcome trajectories (ICC &lt;.30) with and without the main and interaction terms in the model with two-tailed significance set at.05. All categories of income and race/ethnicity had sample sizes of over 200, providing at least 80% power to explore whether these explanatory variables were non-specific predictors of an outcome or trajectory modifiers, assuming small effects when testing for between-group trajectory slope differences or between-group cross-sectional simple effects at each timepoint (Cohen's <emph>d</emph> =.30). Statistical power was estimated using software for random coefficients regression models for longitudinal data and confirmed using G*Power ([<reflink idref="bib27" id="ref105">27</reflink>]).</p> <hd id="AN0192008565-16">Results</hd> <p></p> <hd id="AN0192008565-17">Sample Characteristics</hd> <p>Baseline characteristics for the analysis sample are detailed in Table 1. Notably, 53% were determined to meet criteria for past-only ADHD, 12% current-only, and 35% both past and current ADHD. A single categorical race/ethnicity variable was provided in the ABCD Study, which consisted of "White," "Black," "Hispanic," "Asian," and "Other." A separate set of binary variables provided more detailed racial and ethnic identity data. Of note, the original "Asian" category consisted of only 12 participants who endorsed a race of "Other Asian," while all other Asian participants were included in the "Other" category. Thus, the "Asian" and "Other" categories were combined and will be referred to as Another Identity, defined as including participants who self-reported Asian, American Indian, Alaska Native, Pacific Islander, or Multiracial identities. Although the statistical methods used in this study necessitated combining racial and ethnic groups, it is important to acknowledge the diversity within these categories. White participants self-reported only White, non-Hispanic identity, and Black participants self-reported only Black, non-Hispanic identity. Hispanic participants reported several Hispanic ethnicities as well as other racial identities, and Another Identity participants represented a range of racially and ethnically diverse groups. Table 1 includes frequency counts for each of the broader racial/ethnic groups used in the analyses as well as the more specific identities reported by participants.</p> <p>Table 1. Baseline Sample Characteristics (N = 1,587).</p> <p>Graph</p> <p> <ephtml> &lt;table&gt;&lt;colgroup&gt;&lt;col align="left" /&gt;&lt;col align="char" char="." /&gt;&lt;/colgroup&gt;&lt;thead&gt;&lt;tr&gt;&lt;th align="center"&gt;Characteristic&lt;/th&gt;&lt;th align="center"&gt;&lt;italic&gt;n&lt;/italic&gt; (%)&lt;/th&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td colspan="2"&gt;Age&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; 9 years&lt;/td&gt;&lt;td&gt;827 (52.1)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; 10 years&lt;/td&gt;&lt;td&gt;760 (47.9)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td colspan="2"&gt;Sex&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Male&lt;/td&gt;&lt;td&gt;1,070 (67.5)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Female&lt;/td&gt;&lt;td&gt;516 (32.5)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td colspan="2"&gt;Total combined annual household income&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Less than $25,000&lt;/td&gt;&lt;td&gt;244 (16.7)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; $25,000-$49,999&lt;/td&gt;&lt;td&gt;248 (17.0)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; $50,000-$74,999&lt;/td&gt;&lt;td&gt;212 (14.5)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; $75,000-$99,999&lt;/td&gt;&lt;td&gt;212 (14.5)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; $100,000 or greater&lt;/td&gt;&lt;td&gt;546 (37.4)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td colspan="2"&gt;Race/Ethnicity&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; White&lt;xref ref-type="table-fn" rid="tfn1"&gt;a&lt;/xref&gt;&lt;/td&gt;&lt;td&gt;860 (54.2)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Black&lt;xref ref-type="table-fn" rid="tfn1"&gt;a&lt;/xref&gt;&lt;/td&gt;&lt;td&gt;234 (14.8)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Hispanic&lt;xref ref-type="table-fn" rid="tfn2"&gt;b&lt;/xref&gt;&lt;/td&gt;&lt;td&gt;281 (17.7)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Another identity&lt;xref ref-type="table-fn" rid="tfn3"&gt;c&lt;/xref&gt;&lt;/td&gt;&lt;td&gt;211 (13.3)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td colspan="2"&gt;Diagnostic criteria met&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Past only&lt;/td&gt;&lt;td&gt;849 (53.3)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Current only&lt;/td&gt;&lt;td&gt;189 (11.9)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Past and current&lt;/td&gt;&lt;td&gt;549 (34.6)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td colspan="2"&gt;Current presentation&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Inattentive&lt;/td&gt;&lt;td&gt;295 (18.6)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Hyperactive&lt;/td&gt;&lt;td&gt;93 (5.9)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Combined&lt;/td&gt;&lt;td&gt;378 (23.8)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Symptoms remitted&lt;/td&gt;&lt;td&gt;821 (51.7)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td colspan="2"&gt;Past presentation&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Inattentive&lt;/td&gt;&lt;td&gt;460 (29.0)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Hyperactive&lt;/td&gt;&lt;td&gt;148 (9.3)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Combined&lt;/td&gt;&lt;td&gt;865 (54.5)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; No past diagnosis or unknown&lt;/td&gt;&lt;td&gt;114 (7.2)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td colspan="2"&gt;Comorbidities&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; No internalizing or externalizing disorders&lt;/td&gt;&lt;td&gt;359 (22.7)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Internalizing only&lt;xref ref-type="table-fn" rid="tfn4"&gt;d&lt;/xref&gt;&lt;/td&gt;&lt;td&gt;444 (28.0)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Externalizing only&lt;xref ref-type="table-fn" rid="tfn5"&gt;e&lt;/xref&gt;&lt;/td&gt;&lt;td&gt;223 (14.1)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Both&lt;xref ref-type="table-fn" rid="tfn6"&gt;f&lt;/xref&gt;&lt;/td&gt;&lt;td&gt;558 (35.2)&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt; </ephtml> </p> <p>1 Non-Hispanic.</p> <ulist> <item>2 Hispanic identity: Puerto Rican (<emph>n</emph> = 46), Dominican (<emph>n</emph> = 3), Mexican (<emph>n</emph> = 45), Mexican American (<emph>n</emph> = 76), Chicano (<emph>n</emph> = 5), Cuban (<emph>n</emph> = 22), Cuban American (<emph>n</emph> = 6), Central or South American (<emph>n</emph> = 49), Another Latin American (<emph>n</emph> = 10), Another Hispanic (<emph>n</emph> = 14); Other racial/ethnic identities: Alaska Native (<emph>n</emph> = 1), American Indian/Native American (<emph>n</emph> = 10), Black (<emph>n</emph> = 23), Chinese (<emph>n</emph> = 1), Filipino (<emph>n</emph> = 2), Pacific Islander (<emph>n</emph> = 1), Vietnamese (<emph>n</emph> = 1), White (<emph>n</emph> = 158), multiracial (<emph>n</emph> = 14), refuse or don't know (<emph>n</emph> = 13), another race (<emph>n</emph> = 57).</item> <item>3 Racial/ethnic identities: Alaska Native (<emph>n</emph> = 1), American Indian/Native American (<emph>n</emph> = 30), Samoan (<emph>n</emph> = 1), Chinese (<emph>n</emph> = 19), Filipino (<emph>n</emph> = 9), Japanese (<emph>n</emph> = 7), Korean (<emph>n</emph> = 8), Pacific Islander (<emph>n</emph> = 6), Multiracial (<emph>n</emph> = 41), another race (<emph>n</emph> = 18), refuse or don't know (n=1).</item> <item>4 Anxiety disorder (AD) only (<emph>n</emph> = 363), Depressive disorder (DD) only (<emph>n</emph> = <emph>n</emph> = 20), AD + DD (<emph>n</emph> = 61).</item> <item>5 Oppositional defiant disorder (ODD) only (<emph>n</emph> = 163), Conduct disorder only (CD) (<emph>n</emph> = 15); ODD + CD (<emph>n</emph> = 45).</item> <item>6 AD + ODD (<emph>n</emph> = 324), AD + CD (<emph>n</emph> = 18), DD + ODD (<emph>n</emph> = 25), DD + CD (<emph>n</emph> = 1), AD + DD + ODD (<emph>n</emph> = 82), AD + DD + CD (<emph>n</emph> = 7), AD + ODD + CD (<emph>n</emph> = 68), DD + ODD + CD (<emph>n</emph> = 8), AD + DD + ODD + CD (<emph>n</emph> = 25).</item> </ulist> <hd id="AN0192008565-18">Baseline Functional Outcomes and Symptom Counts</hd> <p>Table 2 provides the unadjusted and adjusted means and standard deviations (<emph>SD</emph>) at each timepoint for all outcomes. The baseline means for unadjusted scores were 2.4 (family conflict), 1.6 (prosocial behavior), 37.7 (school experience), 3.7 (inattentive symptom count), and 3.1 (hyperactive symptom count). On average, a low level of family conflict, high level of prosocial behavior, and positive overall school experience was reported. Children averaged three-to-four out of nine possible symptoms in each category.</p> <p>Table 2. Descriptive Statistics.</p> <p>Graph</p> <p> <ephtml> &lt;table&gt;&lt;colgroup&gt;&lt;col align="left" /&gt;&lt;col align="char" char="." /&gt;&lt;col align="char" char="." /&gt;&lt;col align="char" char="." /&gt;&lt;col align="char" char="." /&gt;&lt;/colgroup&gt;&lt;thead&gt;&lt;tr&gt;&lt;th align="left" rowspan="2"&gt;Outcome&lt;/th&gt;&lt;th align="center"&gt;Baseline (T0)&lt;/th&gt;&lt;th align="center"&gt;Year 1 (T1)&lt;/th&gt;&lt;th align="center"&gt;Year 2 (T2)&lt;/th&gt;&lt;th align="center"&gt;Year 3 (T3)&lt;/th&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th align="center"&gt;Mean &amp;#177; &lt;italic&gt;SD&lt;/italic&gt;&lt;/th&gt;&lt;th align="center"&gt;Mean &amp;#177; &lt;italic&gt;SD&lt;/italic&gt;&lt;/th&gt;&lt;th align="center"&gt;Mean &amp;#177; &lt;italic&gt;SD&lt;/italic&gt;&lt;/th&gt;&lt;th align="center"&gt;Mean &amp;#177; &lt;italic&gt;SD&lt;/italic&gt;&lt;/th&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td colspan="5"&gt;Family conflict&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Unadjusted &lt;italic&gt;N&lt;/italic&gt;&lt;/td&gt;&lt;td&gt;1,585&lt;/td&gt;&lt;td&gt;1,491&lt;/td&gt;&lt;td&gt;1,433&lt;/td&gt;&lt;td&gt;1,333&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Unadjusted scores&lt;/td&gt;&lt;td&gt;2.4 &amp;#177; 2.1&lt;/td&gt;&lt;td&gt;2.3 &amp;#177; 2.0&lt;/td&gt;&lt;td&gt;2.4 &amp;#177; 2.0&lt;/td&gt;&lt;td&gt;2.5 &amp;#177; 2.1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Adjusted scores&lt;/td&gt;&lt;td&gt;2.4 &amp;#177; 1.1&lt;/td&gt;&lt;td&gt;2.3 &amp;#177; 1.1&lt;/td&gt;&lt;td&gt;2.4 &amp;#177; 1.1&lt;/td&gt;&lt;td&gt;2.5 &amp;#177; 1.1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td colspan="5"&gt;Prosocial behavior&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Unadjusted &lt;italic&gt;N&lt;/italic&gt;&lt;/td&gt;&lt;td&gt;1,583&lt;/td&gt;&lt;td&gt;1,488&lt;/td&gt;&lt;td&gt;1,434&lt;/td&gt;&lt;td&gt;1,333&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Unadjusted scores&lt;/td&gt;&lt;td&gt;1.6 &amp;#177;.4&lt;/td&gt;&lt;td&gt;1.6 &amp;#177;.4&lt;/td&gt;&lt;td&gt;1.6 &amp;#177;.4&lt;/td&gt;&lt;td&gt;1.6 &amp;#177;.4&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Adjusted scores&lt;/td&gt;&lt;td&gt;1.6 &amp;#177;.2&lt;/td&gt;&lt;td&gt;1.6 &amp;#177;.2&lt;/td&gt;&lt;td&gt;1.6 &amp;#177;.2&lt;/td&gt;&lt;td&gt;1.6 &amp;#177;.2&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td colspan="5"&gt;School experiences&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Unadjusted &lt;italic&gt;N&lt;/italic&gt;&lt;/td&gt;&lt;td&gt;1,586&lt;/td&gt;&lt;td&gt;1,491&lt;/td&gt;&lt;td&gt;1,433&lt;/td&gt;&lt;td&gt;1,332&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Unadjusted scores&lt;/td&gt;&lt;td&gt;37.7 &amp;#177; 6.3&lt;/td&gt;&lt;td&gt;38.0 &amp;#177; 5.8&lt;/td&gt;&lt;td&gt;37.0 &amp;#177; 5.8&lt;/td&gt;&lt;td&gt;36.5 &amp;#177; 5.4&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Adjusted scores&lt;/td&gt;&lt;td&gt;37.8 &amp;#177; 3.3&lt;/td&gt;&lt;td&gt;37.8 &amp;#177; 3.3&lt;/td&gt;&lt;td&gt;37.3 &amp;#177; 3.3&lt;/td&gt;&lt;td&gt;36.4 &amp;#177; 3.3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td colspan="5"&gt;Inattentive symptoms&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Unadjusted &lt;italic&gt;N&lt;/italic&gt;&lt;/td&gt;&lt;td&gt;1,584&lt;/td&gt;&lt;td&gt;1,471&lt;/td&gt;&lt;td&gt;1,403&lt;/td&gt;&lt;td&gt;1,254&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Unadjusted scores&lt;/td&gt;&lt;td&gt;3.7 &amp;#177; 3.7&lt;/td&gt;&lt;td&gt;3.1 &amp;#177; 3.7&lt;/td&gt;&lt;td&gt;2.6 &amp;#177; 3.5&lt;/td&gt;&lt;td&gt;3.6 &amp;#177; 3.3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Adjusted scores&lt;/td&gt;&lt;td&gt;3.8 &amp;#177; 2.0&lt;/td&gt;&lt;td&gt;2.9 &amp;#177; 2.0&lt;/td&gt;&lt;td&gt;2.8 &amp;#177; 2.0&lt;/td&gt;&lt;td&gt;3.5 &amp;#177; 2.0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td colspan="5"&gt;Hyperactive symptoms&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Unadjusted &lt;italic&gt;N&lt;/italic&gt;&lt;/td&gt;&lt;td&gt;1,584&lt;/td&gt;&lt;td&gt;1,471&lt;/td&gt;&lt;td&gt;1,403&lt;/td&gt;&lt;td&gt;1,254&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Unadjusted scores&lt;/td&gt;&lt;td&gt;3.1 &amp;#177; 3.4&lt;/td&gt;&lt;td&gt;2.5 &amp;#177; 3.3&lt;/td&gt;&lt;td&gt;1.7 &amp;#177; 2.7&lt;/td&gt;&lt;td&gt;2.3 &amp;#177; 2.7&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Adjusted scores&lt;/td&gt;&lt;td&gt;3.1 &amp;#177; 1.7&lt;/td&gt;&lt;td&gt;2.2 &amp;#177; 1.7&lt;/td&gt;&lt;td&gt;1.9 &amp;#177; 1.7&lt;/td&gt;&lt;td&gt;2.2 &amp;#177; 1.7&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt; </ephtml> </p> <p>7 <emph>Note</emph>: Unadjusted <emph>N</emph> = data available (raw scores); Adjusted scores for <emph>N</emph> = 1,587; <emph>SD</emph> = standard deviation.</p> <hd id="AN0192008565-19">Trajectories of Functional Outcomes and Symptom Counts</hd> <p>Linear trajectory models provided the best fit for prosocial behavior and school experiences, while quadratic models provided the best fit for family conflict as well as both inattentive and hyperactive symptom counts (quadratic term: time-by-time). Results indicated a significant trajectory of change in family conflict (quadratic pattern: <emph>p</emph> =.0224), school experience (linear pattern: <emph>p</emph> &lt;.0001), inattentive symptom counts (quadratic pattern: <emph>p</emph> &lt;.0001), and hyperactive symptom counts (quadratic pattern: <emph>p</emph> &lt;.0001). There was no significant change in prosocial behavior over time (<emph>p</emph> =.2971).[<reflink idref="bib5" id="ref106">5</reflink>] The pattern of change of outcomes across time are depicted in Figure 2.</p> <p>Graph: Figure 2. Trajectories of functional outcomes and symptom counts.</p> <p>Externalizing disorders at baseline were a significant covariate for all outcomes (all <emph>p</emph> &lt;.03), and were associated with greater family conflict, lower prosocial behavior, less positive school experiences, greater inattentive symptom counts, and greater hyperactive symptom counts across time. The presence of an internalizing disorder was significantly associated with greater inattentive and hyperactive symptom counts across time (both <emph>p</emph> &lt;.04).</p> <hd id="AN0192008565-20">Moderating Effects of Income and Race/Ethnicity</hd> <p>Each candidate moderator and its interactions with time (for linear and quadratic models) and time<sups>2</sups> (for quadratic models) were incorporated as explanatory variables in the above trajectory models for each outcome. Table 3 presents the results for all models with main and interaction effects of candidate moderators included.</p> <p>Table 3. Trajectory Analysis Results (N = 1,587).</p> <p>Graph</p> <p> <ephtml> &lt;table&gt;&lt;colgroup&gt;&lt;col align="left" /&gt;&lt;col align="char" char="." /&gt;&lt;col align="char" char="." /&gt;&lt;col align="char" char="." /&gt;&lt;/colgroup&gt;&lt;thead&gt;&lt;tr&gt;&lt;th align="center"&gt;Outcome&lt;/th&gt;&lt;th align="center"&gt;Explanatory variables&lt;/th&gt;&lt;th align="center"&gt;&lt;italic&gt;F&lt;/italic&gt; (&lt;italic&gt;df,df&lt;/italic&gt;)&lt;/th&gt;&lt;th align="center"&gt;&lt;italic&gt;p&lt;/italic&gt;&lt;/th&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td rowspan="14"&gt;Family conflict&lt;/td&gt;&lt;td&gt;Time (linear)&lt;/td&gt;&lt;td&gt;1.03 (1, 4,339)&lt;/td&gt;&lt;td&gt;.3111&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Time-by-time (time2, quadratic)&lt;/td&gt;&lt;td&gt;1.74 (1, 4,333)&lt;/td&gt;&lt;td&gt;.1870&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Race/ethnicity&lt;/td&gt;&lt;td&gt;1.74 (3, 3,770)&lt;/td&gt;&lt;td&gt;.2270&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Race/ethnicity-by-time&lt;/td&gt;&lt;td&gt;2.66 (3, 4,337)&lt;/td&gt;&lt;td&gt;.0465&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Race/ethnicity-by-time2&lt;/td&gt;&lt;td&gt;2.07 (3, 4,330)&lt;/td&gt;&lt;td&gt;.1018&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline internalizing disorder(s)&lt;/td&gt;&lt;td&gt;1.77 (1, 1,459)&lt;/td&gt;&lt;td&gt;.1831&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline externalizing disorder(s)&lt;/td&gt;&lt;td&gt;12.90 (1, 1,571)&lt;/td&gt;&lt;td&gt;.0003&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Time (linear)&lt;/td&gt;&lt;td&gt;2.24 (1, 4,018)&lt;/td&gt;&lt;td&gt;.1349&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Time-by-time (time2, quadratic)&lt;/td&gt;&lt;td&gt;2.58 (1, 4,012)&lt;/td&gt;&lt;td&gt;.1084&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Income&lt;/td&gt;&lt;td&gt;7.47 (4, 3,585)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Income-by-time&lt;/td&gt;&lt;td&gt;.85 (4, 4,016)&lt;/td&gt;&lt;td&gt;.4947&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Income-by-time2&lt;/td&gt;&lt;td&gt;1.11 (4, 4,010)&lt;/td&gt;&lt;td&gt;.3479&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline internalizing disorder(s)&lt;/td&gt;&lt;td&gt;3.03 (1, 1,443)&lt;/td&gt;&lt;td&gt;.0820&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline externalizing disorder(s)&lt;/td&gt;&lt;td&gt;14.80 (1, 1,445)&lt;/td&gt;&lt;td&gt;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td rowspan="10"&gt;Prosocial behavior&lt;/td&gt;&lt;td&gt;Time (linear)&lt;/td&gt;&lt;td&gt;8.78 (1, 4,420)&lt;/td&gt;&lt;td&gt;.0031&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Race/ethnicity&lt;/td&gt;&lt;td&gt;2.93 (3, 3,200)&lt;/td&gt;&lt;td&gt;.0323&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Race/ethnicity-by-time&lt;/td&gt;&lt;td&gt;5.98 (3, 4,414)&lt;/td&gt;&lt;td&gt;.0005&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline internalizing disorder(s)&lt;/td&gt;&lt;td&gt;.83 (1, 1,569)&lt;/td&gt;&lt;td&gt;.3611&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline externalizing disorder(s)&lt;/td&gt;&lt;td&gt;10.45 (1, 1,571)&lt;/td&gt;&lt;td&gt;.0012&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Time (linear)&lt;/td&gt;&lt;td&gt;2.44 (1, 4,076)&lt;/td&gt;&lt;td&gt;.1185&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Income&lt;/td&gt;&lt;td&gt;.67 (4, 2,930)&lt;/td&gt;&lt;td&gt;.6166&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Income-by-time&lt;/td&gt;&lt;td&gt;4.33 (4, 4,072)&lt;/td&gt;&lt;td&gt;.0017&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline internalizing disorder(s)&lt;/td&gt;&lt;td&gt;.45 (1, 1,440)&lt;/td&gt;&lt;td&gt;.5045&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline externalizing disorder(s)&lt;/td&gt;&lt;td&gt;8.85 (1, 1,442)&lt;/td&gt;&lt;td&gt;.0030&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td rowspan="10"&gt;School experiences&lt;/td&gt;&lt;td&gt;Time (linear)&lt;/td&gt;&lt;td&gt;51.12 (1, 4,389)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Race/ethnicity&lt;/td&gt;&lt;td&gt;1.07 (3, 2,961)&lt;/td&gt;&lt;td&gt;.3608&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Race/ethnicity-by-time&lt;/td&gt;&lt;td&gt;.52 (3, 4,383)&lt;/td&gt;&lt;td&gt;.6658&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline internalizing disorder(s)&lt;/td&gt;&lt;td&gt;.14 (1, 1,552)&lt;/td&gt;&lt;td&gt;.7040&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline externalizing disorder(s)&lt;/td&gt;&lt;td&gt;5.87 (1, 1,554)&lt;/td&gt;&lt;td&gt;.0155&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Time (linear)&lt;/td&gt;&lt;td&gt;56.95 (1, 4,051)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Income&lt;/td&gt;&lt;td&gt;1.85 (4, 2,702)&lt;/td&gt;&lt;td&gt;.1171&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Income-by-time&lt;/td&gt;&lt;td&gt;2.36 (4, 4,047)&lt;/td&gt;&lt;td&gt;.0512&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline internalizing disorder(s)&lt;/td&gt;&lt;td&gt;.34 (1, 1,427)&lt;/td&gt;&lt;td&gt;.5622&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline externalizing disorder(s)&lt;/td&gt;&lt;td&gt;7.50 (1, 1,428)&lt;/td&gt;&lt;td&gt;.0062&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td rowspan="14"&gt;Inattentive symptoms&lt;/td&gt;&lt;td&gt;Time (linear)&lt;/td&gt;&lt;td&gt;116.44 (1, 4,223)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Time-by-time (time2, quadratic)&lt;/td&gt;&lt;td&gt;91.58 (1, 4,223)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Race/ethnicity&lt;/td&gt;&lt;td&gt;.80 (3, 3,747)&lt;/td&gt;&lt;td&gt;.4914&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Race/ethnicity-by-time&lt;/td&gt;&lt;td&gt;1.98 (3, 4,220)&lt;/td&gt;&lt;td&gt;.1150&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Race/ethnicity-by-time2&lt;/td&gt;&lt;td&gt;1.11 (3, 4,220)&lt;/td&gt;&lt;td&gt;.3455&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline internalizing disorder(s)&lt;/td&gt;&lt;td&gt;6.21 (1, 1,562)&lt;/td&gt;&lt;td&gt;.0128&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline externalizing disorder(s)&lt;/td&gt;&lt;td&gt;22.35 (1, 1,565)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Time (linear)&lt;/td&gt;&lt;td&gt;94.96 (1, 3,900)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Time-by-time (time2, quadratic)&lt;/td&gt;&lt;td&gt;79.50 (1, 3,900)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Income&lt;/td&gt;&lt;td&gt;1.36 (4, 3,425)&lt;/td&gt;&lt;td&gt;.2439&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Income-by-time&lt;/td&gt;&lt;td&gt;.55 (4, 3,898)&lt;/td&gt;&lt;td&gt;.7010&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Income-by-time2&lt;/td&gt;&lt;td&gt;1.59 (4, 3,898)&lt;/td&gt;&lt;td&gt;.1753&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline internalizing disorder(s)&lt;/td&gt;&lt;td&gt;5.87 (1, 1,436)&lt;/td&gt;&lt;td&gt;.0155&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline externalizing disorder(s)&lt;/td&gt;&lt;td&gt;23.35 (1, 1,438)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td rowspan="14"&gt;Hyperactive symptoms&lt;/td&gt;&lt;td&gt;Time (linear)&lt;/td&gt;&lt;td&gt;129.57 (1, 4,213)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Time-by-time (time2, quadratic)&lt;/td&gt;&lt;td&gt;66.98 (1, 4,213)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Race/ethnicity&lt;/td&gt;&lt;td&gt;1.89 (3, 3,741)&lt;/td&gt;&lt;td&gt;.1288&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Race/ethnicity-by-time&lt;/td&gt;&lt;td&gt;1.23 (3, 4,210)&lt;/td&gt;&lt;td&gt;.2986&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Race/ethnicity-by-time2&lt;/td&gt;&lt;td&gt;.60 (3, 4,209)&lt;/td&gt;&lt;td&gt;.6123&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline internalizing disorder(s)&lt;/td&gt;&lt;td&gt;4.86 (1, 1,552)&lt;/td&gt;&lt;td&gt;.0276&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline externalizing disorder(s)&lt;/td&gt;&lt;td&gt;58.24 (1, 1,555)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Time (linear)&lt;/td&gt;&lt;td&gt;120.50 (1, 3,891)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Time-by-time (time2, quadratic)&lt;/td&gt;&lt;td&gt;64.04 (1, 3,890)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Income&lt;/td&gt;&lt;td&gt;.45 (4, 3,364)&lt;/td&gt;&lt;td&gt;.7747&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Income-by-time&lt;/td&gt;&lt;td&gt;1.42 (4, 3,889)&lt;/td&gt;&lt;td&gt;.2230&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Income-by-time2&lt;/td&gt;&lt;td&gt;2.28 (4, 3,887)&lt;/td&gt;&lt;td&gt;.0582&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline internalizing disorder(s)&lt;/td&gt;&lt;td&gt;4.62 (1, 1,429)&lt;/td&gt;&lt;td&gt;.0318&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Baseline externalizing disorder(s)&lt;/td&gt;&lt;td&gt;56.48 (1, 1,431)&lt;/td&gt;&lt;td&gt;&amp;#60;.0001&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt; </ephtml> </p> <p>Figure 3(A) shows the non-specific effects of income on family conflict (income main effect only: <emph>p</emph> &lt;.0001), with the highest income group ($100K or greater) reporting less family conflict on average during the 3-years compared to other income groups (income: <emph>a posteriori</emph> pairwise comparisons: all <emph>p</emph> &lt;.008). Race/ethnicity significantly moderated family conflict across time (race/ethnicity-by-time: <emph>p</emph> =.0465). The trajectories for family conflict were similar for White, Black, and Hispanic children (U-shaped), but the trajectory pattern for the Another Identity group was different (inverted U-shaped, Figure 3(B)). <emph>A posteriori</emph> pairwise comparisons of the trajectory slopes indicated that the Another Identity group had a significantly different pattern of change over time compared to other three race/ethnic groups (all <emph>p</emph> &lt;.03), which did not differ across time.</p> <p>Graph: Figure 3. Family conflict and prosocial behavior by income and race/ethnicity: (a) family conflict and income, (b) family conflict and race/ethnicity, (c) prosocial behavior and income, and (d) prosocial behavior and race/ethnicity.</p> <p>Income was significant moderator of prosocial behavior across time (income-by-time interaction: <emph>p</emph> =.0017, Figure 3(C)). Decreasing prosocial behavior across time was observed in the two lowest income groups compared to an increase in prosocial behavior in the three higher income groups (slope comparison, <emph>p</emph> &lt;.04). The slope for the trajectories of the two lowest income groups (&lt;$25,000 and $25,000–$49,999) did not significantly differ (both: <emph>p</emph> =.6802). while the trajectory slopes for the three highest income groups did not differ (all <emph>p</emph> &gt;.05). Race/ethnicity also significantly moderated prosocial behavior across time (race/ethnicity-by-time interaction: <emph>p</emph> =.0005, Figure 3(D)). White participants had a significant linear increase in prosocial behavior compared to the other racial/ethnic groups (all <emph>p</emph> &lt;.04), while all other racial/ethnic groups showed a decrease in prosocial behavior and did not differ from each other.</p> <hd id="AN0192008565-21">Discussion</hd> <p>This study examined trajectories of child-reported functional outcomes and parent-reported ADHD symptom counts in a large sample of U.S. children with ADHD. Consistent with previous literature, family conflict and school experiences worsened on average during the years of early adolescence ([<reflink idref="bib21" id="ref107">21</reflink>]; [<reflink idref="bib74" id="ref108">74</reflink>]), while prosocial behavior remained stable ([<reflink idref="bib50" id="ref109">50</reflink>]). Both inattentive and hyperactive symptom counts initially decreased, but subsequently increased, with a greater increase seen for inattentive symptom counts. This aligns with previous literature, which has found that inattentive symptoms are more persistent than hyperactive symptoms in adolescence ([<reflink idref="bib23" id="ref110">23</reflink>]; [<reflink idref="bib49" id="ref111">49</reflink>]). Baseline externalizing disorders were a significant covariate for all outcomes of interest. This echoes previous research demonstrating that externalizing disorders are a risk factor for greater functional impairment and particularly lower levels of prosocial behavior ([<reflink idref="bib60" id="ref112">60</reflink>]; [<reflink idref="bib72" id="ref113">72</reflink>]).</p> <p>The present study also identified disparities in two functional outcomes: family conflict and prosocial behavior. The highest income group had the lowest levels of family conflict overall, regardless of time. This finding is unsurprising, as economic hardship has been shown to be associated with higher levels of family conflict ([<reflink idref="bib24" id="ref114">24</reflink>]). There was also a difference in the trajectory of family conflict among Another Identity participants, which warrants further exploration and analyses of differences among the many racial and ethnic identities represented in this group. Prosocial behavior increased over time in higher income participants but decreased in lower-income participants, and also increased in White participants and decreased over time in all other racial/ethnic groups. Disparities in prosocial behavior related to socioeconomic status and race/ethnicity have been previously described ([<reflink idref="bib10" id="ref115">10</reflink>]; [<reflink idref="bib39" id="ref116">39</reflink>]; [<reflink idref="bib54" id="ref117">54</reflink>]), however the moderating role of income and race/ethnicity in child-reported prosocial behavior demonstrates disparate trajectories, indicating that early adolescence may be a time that development of prosocial behavior is especially vulnerable to the effects of children's social environment. No significant relationships were detected between income or race/ethnicity and school experiences. While previous studies have found that poverty was associated with poorer educational outcomes in adolescents with ADHD ([<reflink idref="bib13" id="ref118">13</reflink>]), the child-reported school experiences measure in our study captures the child's perspective on their school environment, involvement, and disengagement rather than grades or test scores. Further examination should explore protective factors that could explain this finding and examine the relationship between these perceived experiences and other educational outcomes. Neither income nor race/ethnicity moderated the trajectories of the two symptom outcomes. This is in contrast to a previous study, in which socioeconomic and racial disparities in parent-reported symptoms were reported ([<reflink idref="bib40" id="ref119">40</reflink>]). However, the previous study represented a wider range of ages which could explain the differing results.</p> <p>One potential explanation for the effects of income and race/ethnicity in this study is that after a diagnosis is made, disparities in treatment access and utilization persist. Compared to White children, Black, Hispanic, and Asian children with ADHD are significantly less likely to access mental health treatment (by 16%, 14%, and 27%, respectively) or to fill a prescription for ADHD medication (by 8%, 11%, and 30%, respectively; [<reflink idref="bib78" id="ref120">78</reflink>]). Non-white adolescents are also less likely to be "adherent" to their ADHD medications, as defined by a medication possession ratio (number of days medication available/number of total days in a given time period) of &gt;70% ([<reflink idref="bib58" id="ref121">58</reflink>]). Additionally, parent training for behavioral management is less beneficial for families of lower socioeconomic status ([<reflink idref="bib42" id="ref122">42</reflink>]). Evidence-based interventions to address barriers to ADHD care and enhance treatment engagement, such as telehealth, standardized care pathways, psychoeducation, and integration of ADHD care into primary practice should be evaluated in the context of health disparities ([<reflink idref="bib8" id="ref123">8</reflink>]). Future research should also investigate the effects of both pharmacological and non-pharmacological ADHD treatment trajectories on long-term functional outcomes.</p> <hd id="AN0192008565-22">Limitations</hd> <p>When conducting secondary data analysis, nuances in the data collection process are unknown to the researchers who are conducting secondary analyses, which could influence interpretation of the results ([<reflink idref="bib17" id="ref124">17</reflink>]). In addition, data are not collected to answer the specific research question and thus variables must be selected from what is available in the data, despite the existence of potentially more appropriate measures. This is evidenced by the limited availability of teacher-reported measures, which would have added to the validity of the diagnostic criteria for sample selection. The resulting sample was based on parent-reported measures and consisted of a large percentage of past-only ADHD cases, which exceeds previous estimates of ADHD remission rates ([<reflink idref="bib7" id="ref125">7</reflink>]; [<reflink idref="bib9" id="ref126">9</reflink>]; [<reflink idref="bib15" id="ref127">15</reflink>]; [<reflink idref="bib64" id="ref128">64</reflink>]). Acknowledging that some participants may not represent true cases of ADHD, we nevertheless opted to retain these participants so as not to exclude effectively treated or remitted cases. The secondary nature of these analyses also necessitated the categorization of some variables, which would be better represented if continuous values were available.</p> <p>Other limitations specific to the present study include: (<reflink idref="bib1" id="ref129">1</reflink>) The small number of participants in the "Asian" race/ethnicity category (<emph>n</emph> = 12) required combining this group with participants originally classified as "Other," resulting in a broader "Another Identity" category. While this approach was necessary for statistical modeling, it limited our ability to examine distinct experiences within these diverse racial and ethnic subgroups and may obscure important group-specific differences; (<reflink idref="bib2" id="ref130">2</reflink>) some Year 2 and Year 3 study visits occurred in 2020 to 2021, after the onset of the Coronavirus Disease 2019 (COVID-19) pandemic, which could confound the results; and (<reflink idref="bib3" id="ref131">3</reflink>) due to software limitations, this initial, exploratory study did not incorporate ABCD design variables (sampling weights and domain variables), to adjust for complex survey design. Therefore future analyses should incorporate more detailed racial and ethnic data, address the potential confounding effect of the COVID-19 pandemic, and include sample weights and domain variables to obtain more precise standard error and other parameter estimates. Additionally, while the purpose of this study was to examine functional outcome and symptom trajectories exclusively in children with ADHD, further investigations would benefit from the inclusion of a neurotypical matched comparison group to ascertain whether these trajectories differ between ADHD and non-ADHD individuals. Despite these limitations, this study offers a novel exploration of disparities in ADHD-related outcome trajectories in children, providing a foundation for future work.</p> <hd id="AN0192008565-23">Conclusion</hd> <p>In this study, income moderated the trajectory of prosocial behavior, and race/ethnicity moderated trajectories of family conflict and prosocial behavior in children with ADHD. These findings suggest that functional outcome trajectories from childhood to early adolescence in this clinical population are influenced by socioeconomic and sociodemographic factors reflected in income and race/ethnicity. Income and race/ethnicity, however, were not significantly associated with trajectories of symptoms or school experiences. Future studies should explore these disparities and identify evidence-based interventions to improve access to diagnosis and treatment for individuals at risk of poorer functional outcomes.</p> <hd id="AN0192008565-24">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-jad-10.1177_10870547251367284 for Trajectory Moderators of Functional Outcomes and ADHD Symptoms in Children With ADHD by Margaret Fletcher, Susan Silva, Wei Pan and Karin Reuter-Rice in Journal of Attention Disorders</p> <hd id="AN0192008565-25">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-2-jad-10.1177_10870547251367284 for Trajectory Moderators of Functional Outcomes and ADHD Symptoms in Children With ADHD by Margaret Fletcher, Susan Silva, Wei Pan and Karin Reuter-Rice in Journal of Attention Disorders</p> <p>Data used in the preparation of this manuscript were obtained from the National Institute of Mental Health (NIMH) Data Archive (NDA). NDA is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in mental health. Data from the Adolescent Brain Cognitive Development (ABCD) Study ® (https://abcdstudy.org/) data release 5.0 were obtained from the NDA for use in this study. Dataset identifier(s): 10.15154/8873-zj65. ABCD Study ® data are currently available upon request at https://<ulink href="http://www.nbdc-datahub.org/">www.nbdc-datahub.org/</ulink>. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or of the ABCD Study ®.</p> <ref id="AN0192008565-26"> <title> References </title> <blist> <bibl id="bib1" idref="ref63" type="bt">1</bibl> <bibtext> Achenbach T. M., Dumenci L., Rescorla L. A. (2003). DSM-oriented and empirically based approaches to constructing scales from the same item pools. 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Developmental Cognitive Neuroscience, 32, 107–120. https://doi.org/10.1016/j.dcn.2018.03.004</bibtext> </blist> </ref> <ref id="AN0192008565-27"> <title> Footnotes </title> <blist> <bibtext> Margaret Fletcher</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0002-8739-5978</bibtext> </blist> <blist> <bibtext> The authors received no financial support for the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> <blist> <bibtext> Given the low internal consistency of the prosocial behavior measure at T1 (α =.55), a sensitivity analysis was conducted to determine whether the observed pattern of change reflected true effects rather than measurement error. When T1 was excluded due to its unreliability, the results remained consistent, showing no significant interaction between prosocial behavior and time (<emph>p</emph> =.2963)</bibtext> </blist> </ref> <aug> <p>By Margaret Fletcher; Susan Silva; Wei Pan and Karin Reuter-Rice</p> <p>Reported by Author; Author; Author; Author</p> <p></p> <p>Margaret Fletcher, BSN, RN is a PhD student in the School of Nursing at Duke University. Her research focuses on health disparities in developmental trajectories of ADHD during childhood and adolescence, long-term effects of pharmacological and non-pharmacological treatment, and positive youth development in children with ADHD.</p> <p>Susan Silva, PhD is an Associate Research Professor in the Duke University School of Nursing and School of Medicine, with specialty training in neurobehavioral assessment, cognitive neuropsychology, and biostatistics. Her extensive research career has included serving as Statistical PI for multiple NIMH clinical trials, and as statistical co-investigator for NIDA and NICHD trials. As a researcher and statistician, Dr. Silva has expertise in multi-level mixed-effects trajectory models for longitudinal data, ecological momentary assessment, moderator and mediator analyses, and structural equation modeling.</p> <p>Wei Pan, PhD is a Professor and Director of Health Statistics and Data Science at the Duke University School of Nursing. He also has a secondary appointment with the Department of Population Health Sciences at the Duke University School of Medicine. His research interests are causal inference, advanced statistical modeling, data analytics, meta-analysis, and psychometrics, and their applications in the social, behavioral, and health sciences.</p> <p>Karin Reuter-Rice, PhD, CPNP-AC, FCCM, FAAN is a Professor in the School of Nursing and in the School of Medicine at Duke University. An international expert in pediatric brain injury, her research uses novel methodologies to improve the health and quality of life for children with neurological conditions. Her federally, foundation, and industry-supported work has been presented globally.</p> </aug> <nolink nlid="nl1" bibid="bib20" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib59" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib56" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib55" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib76" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib46" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib62" firstref="ref7"></nolink> <nolink nlid="nl8" bibid="bib25" firstref="ref10"></nolink> <nolink nlid="nl9" bibid="bib29" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib37" firstref="ref12"></nolink> <nolink nlid="nl11" bibid="bib38" firstref="ref13"></nolink> <nolink nlid="nl12" bibid="bib43" firstref="ref14"></nolink> <nolink nlid="nl13" bibid="bib45" firstref="ref15"></nolink> <nolink nlid="nl14" bibid="bib44" firstref="ref19"></nolink> <nolink nlid="nl15" bibid="bib52" firstref="ref28"></nolink> <nolink nlid="nl16" bibid="bib72" firstref="ref29"></nolink> <nolink nlid="nl17" bibid="bib26" firstref="ref31"></nolink> <nolink nlid="nl18" bibid="bib77" firstref="ref33"></nolink> <nolink nlid="nl19" bibid="bib41" firstref="ref34"></nolink> <nolink nlid="nl20" bibid="bib49" firstref="ref35"></nolink> <nolink nlid="nl21" bibid="bib67" firstref="ref37"></nolink> <nolink nlid="nl22" bibid="bib12" firstref="ref38"></nolink> <nolink nlid="nl23" bibid="bib66" firstref="ref39"></nolink> <nolink nlid="nl24" bibid="bib65" firstref="ref40"></nolink> <nolink nlid="nl25" bibid="bib18" firstref="ref42"></nolink> <nolink nlid="nl26" bibid="bib75" firstref="ref44"></nolink> <nolink nlid="nl27" bibid="bib11" firstref="ref45"></nolink> <nolink nlid="nl28" bibid="bib68" firstref="ref46"></nolink> <nolink nlid="nl29" bibid="bib61" firstref="ref49"></nolink> <nolink nlid="nl30" bibid="bib69" firstref="ref50"></nolink> <nolink nlid="nl31" bibid="bib28" firstref="ref51"></nolink> <nolink nlid="nl32" bibid="bib51" firstref="ref52"></nolink> <nolink nlid="nl33" bibid="bib48" firstref="ref54"></nolink> <nolink nlid="nl34" bibid="bib63" firstref="ref55"></nolink> <nolink nlid="nl35" bibid="bib16" firstref="ref56"></nolink> <nolink nlid="nl36" bibid="bib22" firstref="ref59"></nolink> <nolink nlid="nl37" bibid="bib36" firstref="ref62"></nolink> <nolink nlid="nl38" bibid="bib34" firstref="ref70"></nolink> <nolink nlid="nl39" bibid="bib73" firstref="ref71"></nolink> <nolink nlid="nl40" bibid="bib30" firstref="ref72"></nolink> <nolink nlid="nl41" bibid="bib57" firstref="ref73"></nolink> <nolink nlid="nl42" bibid="bib53" firstref="ref74"></nolink> <nolink nlid="nl43" bibid="bib19" firstref="ref77"></nolink> <nolink nlid="nl44" bibid="bib31" firstref="ref79"></nolink> <nolink nlid="nl45" bibid="bib79" firstref="ref80"></nolink> <nolink nlid="nl46" bibid="bib70" firstref="ref81"></nolink> <nolink nlid="nl47" bibid="bib71" firstref="ref84"></nolink> <nolink nlid="nl48" bibid="bib47" firstref="ref86"></nolink> <nolink nlid="nl49" bibid="bib32" firstref="ref90"></nolink> <nolink nlid="nl50" bibid="bib33" firstref="ref101"></nolink> <nolink nlid="nl51" bibid="bib35" firstref="ref103"></nolink> <nolink nlid="nl52" bibid="bib14" firstref="ref104"></nolink> <nolink nlid="nl53" bibid="bib27" firstref="ref105"></nolink> <nolink nlid="nl54" bibid="bib21" firstref="ref107"></nolink> <nolink nlid="nl55" bibid="bib74" firstref="ref108"></nolink> <nolink nlid="nl56" bibid="bib50" firstref="ref109"></nolink> <nolink nlid="nl57" bibid="bib23" firstref="ref110"></nolink> <nolink nlid="nl58" bibid="bib60" firstref="ref112"></nolink> <nolink nlid="nl59" bibid="bib24" firstref="ref114"></nolink> <nolink nlid="nl60" bibid="bib10" firstref="ref115"></nolink> <nolink nlid="nl61" bibid="bib39" firstref="ref116"></nolink> <nolink nlid="nl62" bibid="bib54" firstref="ref117"></nolink> <nolink nlid="nl63" bibid="bib13" firstref="ref118"></nolink> <nolink nlid="nl64" bibid="bib40" firstref="ref119"></nolink> <nolink nlid="nl65" bibid="bib78" firstref="ref120"></nolink> <nolink nlid="nl66" bibid="bib58" firstref="ref121"></nolink> <nolink nlid="nl67" bibid="bib42" firstref="ref122"></nolink> <nolink nlid="nl68" bibid="bib17" firstref="ref124"></nolink> <nolink nlid="nl69" bibid="bib15" firstref="ref127"></nolink> <nolink nlid="nl70" bibid="bib64" firstref="ref128"></nolink> |
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| Header | DbId: eric DbLabel: ERIC An: EJ1499916 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Trajectory Moderators of Functional Outcomes and ADHD Symptoms in Children with ADHD – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Margaret+Fletcher%22">Margaret Fletcher</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8739-5978">0000-0002-8739-5978</externalLink>)<br /><searchLink fieldCode="AR" term="%22Susan+Silva%22">Susan Silva</searchLink><br /><searchLink fieldCode="AR" term="%22Wei+Pan%22">Wei Pan</searchLink><br /><searchLink fieldCode="AR" term="%22Karin+Reuter-Rice%22">Karin Reuter-Rice</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Journal+of+Attention+Disorders%22"><i>Journal of Attention Disorders</i></searchLink>. 2026 30(4):460-475. – Name: Avail Label: Availability Group: Avail Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 16 – Name: DatePubCY Label: Publication Date Group: Date Data: 2026 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Research – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Attention+Deficit+Hyperactivity+Disorder%22">Attention Deficit Hyperactivity Disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Symptoms+%28Individual+Disorders%29%22">Symptoms (Individual Disorders)</searchLink><br /><searchLink fieldCode="DE" term="%22Family+Income%22">Family Income</searchLink><br /><searchLink fieldCode="DE" term="%22Racial+Differences%22">Racial Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Ethnicity%22">Ethnicity</searchLink><br /><searchLink fieldCode="DE" term="%22Barriers%22">Barriers</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Preadolescents%22">Preadolescents</searchLink><br /><searchLink fieldCode="DE" term="%22Adolescents%22">Adolescents</searchLink><br /><searchLink fieldCode="DE" term="%22Family+Relationship%22">Family Relationship</searchLink><br /><searchLink fieldCode="DE" term="%22Prosocial+Behavior%22">Prosocial Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Experience%22">Student Experience</searchLink> – Name: SubjectThesaurus Label: Assessment and Survey Identifiers Group: Su Data: <searchLink fieldCode="SU" term="%22Strengths+and+Difficulties+Questionnaire%22">Strengths and Difficulties Questionnaire</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1177/10870547251367284 – Name: ISSN Label: ISSN Group: ISSN Data: 1087-0547<br />1557-1246 – Name: Abstract Label: Abstract Group: Ab Data: Objective: ADHD can impair children's functioning. Socioeconomic and sociodemographic factors present barriers to treatment access and lead to disparate outcomes in children with ADHD. The purpose of this study was to describe trajectories of functional outcomes and ADHD symptom counts across 3 years and explore the moderating effects of income and race/ethnicity on these trajectories among U.S. children with ADHD. Method: This longitudinal study of children currently and/or previously meeting diagnostic criteria for ADHD (N = 1,587, age = 9-10 years at baseline) used data from the Adolescent Brain Cognitive Development (ABCD) Study®. Outcomes were child-reported functional outcome measures (family conflict, prosocial behavior, and school experiences) and parent-reported inattentive and hyperactive symptom counts across 3 years. Multi-level, mixed-effects models for longitudinal data were used to characterize each outcome trajectory and examine the moderating effects of baseline household income and race/ethnicity. Results: The sample was 68% male and 54% White, with 53% meeting diagnostic criteria for past-only ADHD, 12% current-only ADHD, and 35% both past and current ADHD. Significant changes in family conflict, school experiences, inattentive symptom counts, and hyperactive symptom counts were demonstrated across 3 years (trajectories, p < 0.05). Income significantly moderated prosocial behavior trajectories, while race/ethnicity significantly moderated family conflict and prosocial behavior trajectories (time interaction, p < 0.05). Conclusions: The findings suggest that factors related to income and race/ethnicity influence trajectories of change in family conflict and prosocial behavior outcomes in children with a history of ADHD. Future studies should explore these disparities and identify targets for intervention, such as increased access to diagnosis and treatment for individuals at risk of poorer functional outcomes. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2026 – Name: AN Label: Accession Number Group: ID Data: EJ1499916 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/10870547251367284 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 460 Subjects: – SubjectFull: Attention Deficit Hyperactivity Disorder Type: general – SubjectFull: Symptoms (Individual Disorders) Type: general – SubjectFull: Family Income Type: general – SubjectFull: Racial Differences Type: general – SubjectFull: Ethnicity Type: general – SubjectFull: Barriers Type: general – SubjectFull: Intervention Type: general – SubjectFull: Preadolescents Type: general – SubjectFull: Adolescents Type: general – SubjectFull: Family Relationship Type: general – SubjectFull: Prosocial Behavior Type: general – SubjectFull: Student Experience Type: general – SubjectFull: Strengths and Difficulties Questionnaire Type: general Titles: – TitleFull: Trajectory Moderators of Functional Outcomes and ADHD Symptoms in Children with ADHD Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Margaret Fletcher – PersonEntity: Name: NameFull: Susan Silva – PersonEntity: Name: NameFull: Wei Pan – PersonEntity: Name: NameFull: Karin Reuter-Rice IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1087-0547 – Type: issn-electronic Value: 1557-1246 Numbering: – Type: volume Value: 30 – Type: issue Value: 4 Titles: – TitleFull: Journal of Attention Disorders Type: main |
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