Patterns and Predictors of Service Utilization Among Youth With ADHD-Predominantly Inattentive Presentation

Abstract

Objective: This study examined rates and predictors of educational and mental health service utilization among youth with ADHD-predominantly inattentive presentation (ADHD-I). Method: Participants were 199 children with ADHD-I in Grades 2 to 5. Parents reported past-year child service utilization. Parents and teachers rated child ADHD and oppositional defiant disorder (ODD) symptom severity and functional impairment. Children completed an academic achievement test. Results: All children had impairment at school and home. Most received some sort of school service (79%), but only 23% received community-based services. ADHD symptom severity was unrelated to service utilization. However, higher parent-rated functional impairment predicted community service utilization. Academic underachievement and higher teacher-rated functional impairment predicted school service utilization. Conclusion: Many youth with ADHD-I experience impairment across domains without receiving adequate services for these problems. Functional impairment appears to be a stronger predictor of service utilization than ADHD symptom severity, demonstrating the importance of impairment in understanding service needs for ADHD-I.

Keywords

service use ADHD subtypes functional impairment ADHD-Inattentive Presentation

ADHD is a childhood-onset neurobehavioral disorder marked by symptoms of inattention, hyperactivity, and impulsivity associated with functional impairment (American Psychiatric Association [APA], 2013). Empirical research suggests that symptoms co-occur in two clusters: inattention and hyperactivity/impulsivity (Lahey, 2006). Children with ADHD may present with clear symptoms of both clusters, or may show elevations in one symptom cluster only. Historically, these different presentations have been considered as distinguishable-but-related diagnostic entities, defined as three presentations (formerly, “subtypes”): predominantly inattentive (ADHD-I), predominantly hyperactive/impulsive, and combined presentation (ADHD-C). The combined and predominantly inattentive presentations are the most common in school-age youth (Merikangas et al., 2010; Willcutt, 2012).

Different ADHD presentations confer risk for distinct patterns of comorbidity and functional impairment. Broadly, youth with ADHD show elevated rates of disruptive behavior disorders such as oppositional defiant disorder (ODD) and conduct disorder, internalizing disorders such as anxiety and depression, and substantial rates of learning disorders (Jensen et al., 2001; Jensen, Martin, & Cantwell, 1997; Larson, Russ, Kahn, & Halfon, 2011; Visser et al., 2014; Willcutt et al., 2012). Relative to their counterparts with ADHD-C, children with ADHD-I have lower rates of comorbid disruptive behavior disorders but roughly comparable rates of internalizing disorders (Elia, Arcos-Burgos, Bolton, & Muenke, 2009; Willcutt et al., 2012; Yuce, Zoroglu, Ceylan, Kandemir, & Karabekiroglu, 2013). Youth with ADHD-I often struggle socially due to a lack of assertion and other social skill deficits. Children with ADHD-I also have significant learning challenges, with more pronounced and persistent academic impairment than those typical of youth with ADHD-C (Massetti et al., 2008). In addition, youth with ADHD-I are at increased risk for speech and language problems, relative to both children with ADHD-C and youth without ADHD (Weiss, Worling, & Wasdell, 2003; Willcutt et al., 2012). Longitudinal data show poor psychosocial adult outcomes for youth with any ADHD subtype (Hinshaw, 2002; Hinshaw et al., 2012). In short, youth with different ADHD subtypes present not only with partially distinct behavioral phenotypes but also with different sets of impairments and clinical problems.

Despite this heterogeneity, most studies examining service utilization in youth with ADHD have not specified participants’ diagnostic presentation/subtype. In general, findings suggest that large proportions of youth who meet criteria for ADHD are not receiving services. Within nonreferred samples, 26% to 65% of youth with ADHD are reported to be taking medication (Bussing et al., 2005; Centers for Disease Control and Prevention [CDC], 2005; Froehlich et al., 2007; Larson et al., 2011; Leslie et al., 2005; Visser et al., 2014; Wolraich, Hannah, Baumgaertel, & Fuerer, 1998) and 26% to 34% are reported to be receiving counseling or psychosocial interventions in the community (Bauermeister et al., 2003; Jensen et al., 1999; Leslie, Lambros, Aarons, Haine, & Hough, 2008). As a result, approximately 50% of youth who meet criteria for ADHD do not receive any mental health services to manage their condition (Merikangas et al., 2011). Many youth with ADHD utilize services at school, though estimates of school service utilization rates vary across studies (i.e., 24%-65%; Leslie et al., 2005; Leslie et al., 2008; Leslie & Wolraich, 2008). Furthermore, “school services” for ADHD may refer to any one or combination of various services (e.g., classroom accommodations, modified instruction/assignments, academic interventions, school counseling, and classroom behavioral interventions). Limited available evidence suggests that the kinds of school services received by students with ADHD are unlikely to be evidence based (Harrison, Bunford, Evans, & Owens, 2013; Schnoes, Reid, Wagner, & Marder, 2006).

Differences in Service Utilization Across ADHD Presentations

Although a fair amount of research has aimed to characterize service utilization rates among youth with ADHD, only a few studies have compared service utilization patterns by diagnostic presentation/subtype. Several studies have compared pharmacological treatment utilization across ADHD subtypes. Barbaresi and colleagues (2006) found that treatment-seeking youth in the United States with ADHD-C began taking ADHD medication at an earlier age and had a longer duration of medication use than those with ADHD-I. Similarly, Weiss et al. (2003) found that, in a Canadian ADHD clinic, youth with ADHD-C were more likely than those with ADHD-I to be on a stimulant medication (78% vs. 66%) and had a younger mean age at the time of treatment (11.0 vs. 12.4 years). In an epidemiological survey of Australian youth, recent medication use was higher in youth with ADHD-C than in those with ADHD-I (35% vs. 11%; Sawyer et al., 2004). An epidemiological study of U.S. youth found a similar difference in past-year medication use between ADHD-C and ADHD-I (51% vs. 39%), though this difference did not reach statistical significance (Froehlich et al., 2007). In a single-site study of U.S. elementary school students, 26% of youth meeting criteria for ADHD-C took stimulant medication, compared with 11% for ADHD-I (Wolraich et al., 1998).

Few data are available comparing nonpharmacological services received by youth with different ADHD presentations. Sawyer and colleagues (2004) found that Australian children with ADHD-C were more likely to receive counseling than those with ADHD-I (28% vs. 15%) but found no differences for parent-inclusive services (e.g., parent counseling/training, family counseling/therapy). This study found that subtype did not influence overall rates of school and community services. In addition, a single-county study of U.S. youth served by public mental/behavioral services found no link between subtype and use of school-based services (Leslie et al., 2008). Among treatment-seeking Canadian youth, Weiss et al. (2003) reported comparable rates of learning assistance in youth with ADHD-C and ADHD-I (69% vs. 64%) but greater rates of speech/language service use in ADHD-I (27% vs. 15%).

Factors Influencing Service Utilization

Several factors may drive the documented differences in service utilization patterns across ADHD presentations. One possibility is that ADHD-I is a less impairing condition than ADHD-C and, therefore, warrants less treatment, especially pharmacological treatment. Alternatively, inattentive symptoms, which tend to have later onset than hyperactive/impulsive symptoms, may begin to cause impairment at a later age than evaluated in prior studies (McBurnett, Pfiffner, & Ottolini, 2000; Milich, Balentine, & Lynam, 2001). A third possibility is that youth with ADHD-I experience impairments that are “overlooked,” due to a less disruptive behavioral presentation than youth with ADHD-C; that is, because youth with ADHD-I often do not display salient disruptive behavior problems, they may not be as easily recognized as experiencing difficulties. This possibility is indirectly supported by the fact that ADHD-I is more prevalent than ADHD-C in community samples, but ADHD-C is more commonly seen among treatment-seeking samples of youth with ADHD (Froehlich et al., 2007; Willcutt, 2012).

Aside from describing overall rates of service utilization among youth with ADHD-I, very little is known about the conditions under which these individuals do receive services. Common models of health care service utilization point to perceived need (i.e., “need-related factors” or “problem recognition”) as important determinants of child mental health service utilization in general, and among youth with ADHD in particular (Aday & Andersen, 1974; Brinkman et al., 2009; Eiraldi, Mazzuca, Clarke, & Power, 2006; Leslie, Plemmons, Monn, & Palinkas, 2007). Parents’ and teachers’ perceptions of a child’s psychosocial needs may be driven in part by ADHD symptom severity but are also likely influenced by other factors (e.g., degree of associated impairment, presence of co-occurring behavioral/emotional problems, academic underachievement). In line with this, prior research has suggested that school service utilization among youth with ADHD is associated with increased recognition of child impairment in the school environment (e.g., as perceived by teachers; Angold, Costello, Farmer, Burns, & Erkanli, 1999; Eiraldi et al., 2006; Leslie et al., 2008; Simon, Pastor, Reuben, Huang, & Goldstrom, 2015). In parallel, epidemiological research (e.g., Merikangas et al., 2011) suggests that, among youth meeting criteria for ADHD, children who experience greater associated impairment and distress are more likely to receive treatment services.

Present Investigation and Study Hypotheses

The goal of this study was to address gaps in our understanding of utilization of treatment services specifically for ADHD-I and factors that may influence service utilization in affected youth. This age group was selected for study because this is the age when children are typically first assessed for ADHD and referred for treatment (APA, 2013). We utilized data from a large, carefully diagnosed sample of youth with ADHD-I who enrolled in a trial of psychosocial treatments for ADHD-I. Based on previous research and the above considerations, we made the following a priori hypotheses. We predicted that the proportion of participants with past-year service use would be smaller than the proportion with psychosocial impairment (Hypothesis 1). We evaluated the link between ADHD symptom severity and service utilization, and, based on previous research showing a link between ADHD diagnostic status and service use, we predicted that parent-rated ADHD symptom severity would be related to past-year use of community-based mental health services (Hypothesis 2A). We also predicted that parent-rated overall child functional impairment would be related to past-year use of community-based mental health services (Hypothesis 2B), to a greater degree than ADHD symptom severity (Hypothesis 2C). We made parallel predictions about the relations between school service utilization and teacher-rated ADHD symptom severity and functional impairment (Hypothesis 3). Again, we predicted that past-year receipt of school services would be associated with teacher-rated ADHD symptom severity (Hypothesis 3A) and teacher-rated overall functional impairment (Hypothesis 3B). We also predicted that teacher-rated overall functional impairment would predict past-year school service receipt more strongly than ADHD symptom severity (Hypothesis 3C).

Based on previous research, we sought to evaluate the contributions of two common correlates of ADHD, oppositional behavior and academic underachievement, to service utilization. Because ODD has been linked to high rates of service utilization (Merikangas et al., 2011), and oppositional behavior can be experienced as particularly noxious by caregivers (Patterson & Yoerger, 2002), we hypothesized that parent-rated ODD symptom severity would be positively related to community-based service utilization (Hypothesis 4). We evaluated, but did not make a directional prediction about, the link between teacher-reported ODD severity and school service use, due to a paucity of previous research on this relation. Because academic skill deficits have been widely described among youth with ADHD-I (e.g., Massetti et al., 2008; Weiss et al., 2003) and are common targets of school services, we expected that academic underachievement (defined as low performance on a normed academic achievement test) would predict school service utilization (Hypothesis 5). We were particularly interested in whether considering these additional variables (i.e., ODD symptom severity and academic achievement) in addition to ADHD symptom severity and functional impairment was incrementally useful in predicting past-year service utilization.

Method

Procedure and Participants

These data were collected within the pretreatment assessment stage of a two-site, clinic-based randomized controlled trial comparing psychosocial treatments for youth with ADHD-I and their families with usual care. Pfiffner et al. (2014) provide a detailed account of participant characteristics and study methods. Demographic and clinical background information is included in Table 1. Participating children were recruited through schools, pediatricians, community mental health professionals, online parent networks, patient organizations, and word of mouth; 65% were referred by their school, 18% by community health professionals, 11% through media or parent groups, and 6% through word of mouth. After completing a phone screen, all families completed an in-person baseline assessment which provided data for the present study.

Table 1.

Demographic and Clinical Background for Participants.

Variable	M (SD)/%
Gender (male)	58
Age at time of study (years)	8.6 (1.1)
Age of ADHD onset (years)	5.1 (1.4)
Race/ethnicity
White/Caucasian, non-Latino/Hispanic	54
Latino/Hispanic	17
Asian	8
African American	5
Mixed race/ethnicity	17
Family income (in US$)
<41,000	8
41,000-60,000	9
61,000-80,000	13
81,000-100,000	12
≥101,000	58
Comorbid K-SADS diagnoses
ODD	6
Anxiety disorder (any)	8
Mood disorder (any)	2
CSI symptom severity
ADHD	25.3 (6.9)
ODD	6.7 (4.4)
Overall child functional impairment (IRS)
Parent rated	3.56 (1.10)
Teacher rated	3.59 (1.12)
Academic achievement (WJ-III)
Reading composite	100.0 (10.4)
Mathematics composite	96.1 (11.0)

Note. K-SADS = Kiddie Schedule for Affective Disorders and Schizophrenia; ODD = oppositional defiant disorder; CSI = Child Symptom Inventory; IRS = Impairment Rating Scale; WJ-III = Woodcock–Johnson Tests of Achievement–III.

Participants had a primary diagnosis of ADHD-I according to Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; APA, 2000; ascertained by the Kiddie Schedule for Affective Disorders and Schizophrenia [K-SADS; Kaufman et al., 1997]) criteria, were 7 to 11 years of age, and were in school grades 2 to 5 (inclusive) in regular education classrooms. Other inclusion criteria were as follows: (a) ≥6 Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) ADHD inattention symptoms rated as “often” or “very often” on the Child Symptom Inventory (CSI) by parents or teachers, with at least two symptoms endorsed by each informant; (b) ≤5 ADHD hyperactivity/impulsivity symptoms rated as “often” or “very often” by parents or teachers on the CSI; and (c) psychosocial impairment as evidenced by ratings of ≥3 in one or more Impairment Rating Scale (IRS) domains by both the child’s parent and teacher; and (d) living with at least one parent for 1 year prior to study entry. Children currently taking a psychostimulant medication were eligible to participate following a 1-week washout if no other changes in regimen were planned during the study period. Exclusion criteria were as follows: (a) attending a school more than 45 min away from a study site, (b) teacher refusal to participate, (c) taking a nonstimulant psychotropic medication, (d) IQ < 80 (as assessed by the Wechsler Intelligence Scale for Children, Version 4 [WISC-IV]; Wechsler, 2003), or (e) positive diagnosis of a significant developmental disorder (e.g., pervasive developmental disorder) or neurological illnesses.

Measures

Psychiatric diagnoses

ADHD and other psychiatric diagnoses were ascertained via the K-SADS (Kaufman et al., 1997), a validated, clinician-administered parent diagnostic interview. An independent clinician rated 20% of randomly selected audiorecorded interviews and obtained 100% diagnostic agreement for ADHD-I (k = 1.0).

ADHD and ODD symptom severity

ADHD and ODD symptom severities were assessed using parent and teacher versions of the CSI. ADHD and ODD symptoms are rated on a 4-point Likert-type scale (0 = never, 3 = very often). The ADHD Combined Symptom Severity Score (sum of all ADHD symptom item scores) was used as the measure of ADHD symptom severity in the present study. The ODD Symptom Severity Score (sum of all ODD symptom item scores) was used to measure ODD symptom severity in the present study. Both of these scales have demonstrated strong temporal stability, convergent validity, and discriminant validity (Gadow & Sprafkin, 2002).

Child functional impairment

Child functional impairment was assessed using parent- and teacher-report versions of the IRS (Fabiano et al., 2006), a psychometrically sound and widely used measure of functional impairment. The IRS has demonstrated good convergent validity with both global and domain-specific measures of impairment (Fabiano et al., 2006). Children were categorized as “impaired” in a specific domain when an informant (parent or teacher) rated them as ≥3 (range = 0-6) on that domain (Fabiano et al., 2006). To measure overall child functional impairment, we used the IRS Average Impairment score, calculated as the mean score across five domains of impairment (peers/social, family, parent–child, academic, and global; Fabiano et al., 2006). Separate overall impairment scores were computed for parent and teacher ratings.

Service utilization

Service utilization was assessed using the Services for Children and Adolescents–Parent Interview (SCAPI), which was developed for and validated with parents of youth with ADHD (Jensen et al., 2004). The SCAPI is a structured parent interview that queries about services children have received for behavioral, emotional, and academic problems in community, school, and afterschool program settings. For services received within the past year, detailed questions are asked to ascertain the frequency and duration of the service, the kind of provider who performed the service, and other relevant details (e.g., for psychopharmacological treatment, the name and dosage of medication).

To evaluate service utilization, we created two composite categories to characterize whether or not a child had received any community-based mental health service or any school-based service in the past year. Community-based mental health services included four designated SCAPI categories: psychiatric medication, individual therapy/counseling (exclusive of school counseling), family therapy, and parent training/groups/classes/support groups. School services were defined as receiving any school service in designated categories (i.e., school counseling, academic help in a regular classroom [e.g., classroom accommodations, push-in instruction], resource/special education classes, in-school tutoring, occupational/physical therapy, speech/language therapy, and/or having an active Individualized Education Plan (IEP) or 504 Plan).

Academic achievement

Academic achievement was assessed using four subtests from the Woodcock–Johnson Tests of Achievement–III (3rd ed.; WJ-III; Woodcock, McGrew, & Mather, 2007): Reading Fluency, Passage Comprehension, Calculation, and Math Fluency. These subtests have good psychometric properties, including evidence of temporal stability and internal consistency (αs > .85; Woodcock et al., 2007). For WJ-III academic achievement, we created Reading and Math Composite scores by averaging scores on the two subtests in each domain (for reading, Reading Comprehension and Reading Fluency; for math, Calculation and Math Fluency). We operationalized “low academic achievement” as obtaining a composite score ≥1 SD below the normative mean in either Reading or Math (i.e., Composite Score <85). Forty-two children (21%) met this criterion.

Data Analytic Plan

All analyses were run using SPSS Statistics (version 23) in accordance with a protocol approved by the University of California San Francisco Committee on Human Research. Descriptive statistics were computed to characterize service utilization patterns. Fisher’s exact tests were used to test Hypothesis 1, assessing whether the proportion of children receiving services was smaller than the proportion experiencing impairment in various domains. Relations between background demographic variables (i.e., child gender, child race/ethnicity, and family income) and past-year service utilization were evaluated to determine whether any of these variables should be included in models testing predictors of service use. We used binary logistic regressions to evaluate child gender, child race/ethnicity, and family income as predictors of service use. For Hypotheses 2 and 3, multiple logistic regression was used to analyze potential predictors of service use. To test Hypotheses 4 and 5, we probed zero-order relations between hypothesized predictors and service utilization, and, if a variable was significantly related to service utilization, we added the predictor variables to the multiple logistic regressions used for Hypotheses 2 and 3.

To test Hypothesis 2, we fit three different models to predict community-based mental health service utilization. In Model 1, ADHD symptom severity was entered as the sole predictor (testing Hypothesis 2A). In Model 2, both ADHD symptom severity and parent-rated child functional impairment simultaneously predicted community-based mental health service utilization (Hypotheses 2B and 2C). In Model 3, parent-rated ODD symptom severity was added to these predictors to test Hypothesis 4.

We used a similar approach to test Hypothesis 3, related to predictors of school service utilization. In Model 1, we entered teacher-rated ADHD symptom severity as the lone predictor of school service utilization (testing Hypothesis 3A). In Model 2, we added teacher-rated child functional impairment as a predictor (Hypotheses 3B and 3C). In Model 3, academic underachievement was added as a predictor to test Hypothesis 5.

Results

Very little missing data were noted overall (0.4% of all variables), so missing data were excluded on a casewise basis rather than being imputed.

Service Utilization Rates and Functional Impairment

Table 2 shows past-year service utilization rates for specific services among study participants. A minority of children (23%) had received at least one community-based mental health service in the community within the past year. Few children (8.2%) received an ADHD medication in the past year, with only 4.5% currently on medication. Few youth received community-based psychosocial inventions (i.e., 4.2%-12.9%, across specific psychosocial services). A majority of youth (78.9%) received some form of school-based service, but only 13.5% had an IEP in place. Relatively, common school services included special help in regular classroom (including classroom accommodations, help from a paraprofessional, push-in instruction, and other in-class supports), pull-out special education or resource classes, and academic tutoring. Few children received speech and language or occupational/physical therapy services at school.

Table 2.

Past-Year Service Utilization Rates.

Service	% receiving service (N = 199)
Community-based mental health services (any)	23
Counseling (outside of school)	12
Medication	8
Parent training/support	8
Family therapy	4
School services (any)	79
Accommodations in regular classroom	36
Special education or resource classes	30
Tutoring	16
IEP	14
School counseling	10
Speech/language	5
OT/PT	2

Note. Counseling includes group or individual psychotherapy or psychological counseling where the child was the identified client/patient. IEP = Individualized Education Plan; OT = occupational therapy; PT = physical therapy.

To test Hypothesis 1, we first examined rates of child functional impairment in various domains. Academic impairment was most commonly reported by parents (93%, n = 185), followed by negative impact on family functioning (73%; n = 145) and social impairment (41%; n = 82). Teachers rated 91% (n = 180) of students as academically impaired and 49% (n = 98) as socially impaired. Table 3 shows rates of service utilization as a function of different parent- and teacher-reported child impairments. The majority of children (85%) with teacher-identified academic and/or social impairments were receiving some special school-based service. In contrast, minorities of children with parent-reported impairments were receiving community-based mental health services (35% of children with social impairment, 28% of children with family impairment, and 24% of children with academic impairment). Past-year medication use was low (8%-13%) among all impairment-based subgroups. Fisher’s exact tests revealed that, for each domain of impairment category, fewer children were receiving medication, any community-based mental health service, and any school service than had each impairment (p < .01 for all comparisons). Thus, Hypothesis 1, that more children would experience impairment than have received services in the past-year, was well supported. The gap between impairment and service utilization rates was particularly large for use of community services (including medication).

Table 3.

Past-Year Service Utilization as a Function of Domain-Specific Child Impairment.

Impairment domain	Medication	Any community clinical service	Any school service
Parent rated
Academic	9	24	81
Social	11	35	76
Family	9	28	82
Teacher rated
Academic	8	24	85
Social	13	25	85

Note. For each domain of impairment, significantly fewer participants received each service than were impaired (ps < .01).

Predictors of Service Utilization

Background variables

Past-year use of community and school services was unrelated to family income, child gender, and child race/ethnicity (ps > .27), so these demographic variables were not included in models predicting service use described below.

Clinical predictors of community service use

Table 4 shows results of analyses related to Hypothesis 2A, 2B, and 2C. Binary logistic regression indicated that parent ratings of ADHD symptom severity were not associated with past-year community-based mental health service use, contrary to Hypothesis 2A. However, parent ratings of overall child impairment were significantly related to service utilization, controlling for ADHD symptom severity, consistent with Hypothesis 2B. Results from Model 2 (Table 4) are also consistent with Hypothesis 2C, that parent-rated overall child impairment would be a stronger predictor of community clinical service utilization than ADHD symptom severity.

Table 4.

Predictors (Parent Rated) of Community Clinical Service Use.

	Predictor	Wald χ²	OR (95% CI [LL, UL])	p
Model 1	ADHD symptom severity	2.45	1.04 [0.99, 1.09]	.12
Model 2	ADHD symptom severity	0.49	1.02 [0.97, 1.07]	.48
Model 2	Overall functional impairment	11.2	1.86 [1.29, 2.67]	<.01
Model 3	ADHD symptom severity	0.02	1.00 [0.95, 1.06]	.89
	Overall functional impairment	7.77	1.70 [1.17, 2.46]	<.01
	ODD symptom severity	3.30	1.08 [0.99, 1.18]	.069

Note. OR = odds ratio; CI = confidence interval; LL = lower limit; UL = upper limit; ODD = oppositional defiant disorder.

We conducted parallel analyses to test Hypothesis 3, regarding relations between teacher ratings of child characteristics and past-year school service utilization (Table 5). Teacher ratings of child ADHD severity were not related to past-year receipt of school services, contrary to Hypothesis 3A. However, consistent with Hypothesis 3B, teacher ratings of child impairment were significantly associated with school service utilization, accounting for ADHD symptom severity (Model 2). Results from Model 2 are also consistent with Hypothesis 3C, that school service use would be more strongly related to teacher-rated overall functional impairment than to ADHD symptom severity.

Table 5.

Predictors of School Service Use.

	Predictor	Wald χ²	OR (95% CI [LL, UL])	p
Model 1	ADHD symptom severity	0.27	1.01 [0.96, 1.07]	.27
Model 2	ADHD symptom severity	1.18	0.97 [0.91, 1.03]	.28
Model 2	Overall functional impairment	9.63	1.81 [1.24, 2.63]	<.01
Model 3	ADHD symptom severity	1.32	0.97 [0.91, 1.03]	.25
	Overall functional impairment	8.49	1.77 [1.21, 2.59]	<.01
	Academic underachievement	4.00	4.60 [1.03, 20.5]	.045

Note. ADHD and ODD symptom severity scores were based on teacher ratings. Academic underachievement was based on Reading and Math subtests from the Woodcock–Johnson Test of Academic Achievement. OR = odds ratio; CI = confidence interval; LL = lower limit; UL = upper limit; ODD = oppositional defiant disorder.

We also explored the relation between parent-rated ODD symptom severity and community clinical service utilization. Binary logistic regression showed that parent-rated ODD symptom severity was related to past-year community clinical service use (Wald χ² = 9.32, p < .01, odds ratio [OR] = 1.13, 95% confidence interval [CI] = [1.04, 1.21]). Table 4 (Model 3) shows findings from a multiple logistic regression with parent ratings of overall child functional impairment and ODD severity predicting past-year community clinical service utilization. Results indicated that parent-rated ODD symptom severity was a marginally significant predictor of past-year community clinical service use when accounting for overall child functional impairment and ADHD symptom severity, in the direction consistent with Hypothesis 4. In contrast, teacher-rated ODD symptom severity was not related to past-year school service utilization (Wald χ² = 1.37, p = .24, OR = 0.96, 95% CI = [0.89, 1.03]).

Finally, we evaluated the relation between academic underachievement and school service utilization. Binary logistic regression revealed that academic underachievement was associated with past-year school service use (Wald χ² = 3.93, p = .047, OR = 3.50, 95% CI = [1.02, 12.04]). Academic underachievement remained significantly related to past-year service utilization, when accounting for teacher ratings of overall child impairment and ADHD symptom severity (Table 5, Model 3); this finding is consistent with Hypothesis 5.

Discussion

This is the first study to systematically examine rates of educational and mental health service utilization and associated factors in a large, clinically diagnosed sample of youth with ADHD-I. Results indicated high rates of academic, social, and family impairment, accompanied by relatively low rates of mental health service utilization. At school, a large majority of children were receiving some special services, though the nature of these services varied widely. These findings run counter to the idea that youth with ADHD-I forgo service utilization due to low levels of need. On the contrary, the present findings suggest that children with ADHD-I indeed exhibit significant clinical needs, often without receiving appropriate services.

Perhaps the most striking finding of this study is the high percentage of children (77%) who were experiencing impairment but not receiving community-based mental health services. Furthermore, just over half of children receiving these services were receiving individual counseling, which is not an empirically supported intervention for school-age children with ADHD (Evans, Owens, & Bunford, 2014). Strikingly, only 8% of children were taking medication for ADHD. This figure is lower than in some other studies of medication use in children with ADHD-I (e.g., Froehlich et al., 2007; Weiss et al., 2003) but aligns closely with Wolraich and colleagues’ (1998) finding that only 11% of elementary school youth with ADHD-I were taking medication. Future research is needed to explore parents’ decision-making processes around whether to seek community treatment generally (and medication specifically) for their children with ADHD-I.

Although global rates of community clinical service utilization were lower than may have been expected, the present findings do speak to the conditions under which youth with ADHD-I do receive these treatments. Parent-reported functional impairment, but not ADHD symptom severity, was associated with past-year utilization of community-based mental health services. The lack of association between ADHD symptom severity and service utilization ran contrary to our hypotheses; however, this may be explained by considering the nature of this sample, compared with others in which higher ADHD symptom severity has been linked to service use. In prior studies (e.g., Bussing et al., 2003; Leslie et al., 2005; Merikangas et al., 2011; Wolraich et al., 1998), ADHD symptom severity was associated with service use inasmuch as participants who had sufficient symptoms to qualify for an ADHD diagnosis were more likely to receive services. In contrast, this study considered ADHD symptom severity as a continuous variable within a sample of children who met diagnostic criteria for the disorder. Thus, it may be that within the general population, higher ADHD symptom severity is linked to service use, but that, among youth with ADHD, differences in symptom severity are not substantively related to service use.

In contrast, higher levels of functional impairment were consistently and strongly related to service use in this sample. This may be seen as consistent with other data suggesting that functional impairment is a more valid index of disorder severity than “core” ADHD symptom severity per se (Gordon et al., 2006; Pelham, Fabiano, & Massetti, 2005). This is also consistent with the view that the degree of ADHD-associated functional impairment/distress, rather than the severity of inattention and hyperactivity/impulsivity alone, is most strongly associated with service utilization (Angold et al., 1999; Merikangas et al., 2011).

In addition, we found that parent ratings of ODD symptom severity were marginally associated with community clinical service utilization, independent of child functional impairment and ADHD symptom severity. Notably, these factors may account for unique variance in service utilization. This is consistent with other research suggesting child externalizing behavioral problems (e.g., oppositionality) are common motivators of pediatric mental health service utilization (e.g., Merikangas et al., 2011). It is possible that coercive parent–child interaction cycles common in ODD (Patterson & Yoerger, 2002) contribute to additional parental distress, distinct from that caused by child impairment per se, that yields additional motivation for families to seek and use mental health services.

The present findings also yield novel information on school service utilization among youth with ADHD-I. We found that a clear majority of youth were receiving some form of special service or support at school. The nature of these services was variable, and it is important to note that many of the school-based services captured in this study (e.g., classroom accommodations) have limited empirical support their use (Harrison et al., 2013). Considered together with the high rates of academic impairment noted at time of study entry, this suggests the possibility that school services received by participants in this study were not sufficient to address and ameliorate their academic impairments. Future research is needed to develop, test, and disseminate evidence-based classroom accommodation approaches for youth with ADHD-I.

We also found that academic underachievement accounted for unique variance in school service receipt, when considered alongside functional impairment. This finding provides yet another line of support for the notion that functional impairments (here, academic impairment) are the primary drivers of service utilization (here, school services) among youth with ADHD. Based on this, it appears that youth with ADHD-I are particularly likely to be recognized as needing school services when they fall behind in academic skill development. This is consistent with the possibility that these youths’ inattentive behaviors (e.g., daydreaming, difficulty paying attention) may be “overlooked” initially, until they begin to have deleterious effects in other areas (e.g., academic achievement). If future research supported this notion, this would underscore the importance of addressing classroom inattention early on, before it begins to hinder development of academic skills and other areas of functioning.

The present study was also subject to several limitations. Assessment of school service utilization relied exclusively on parental report. Also, study data came from a single time point, and therefore, it was not possible to determine whether ADHD/ODD symptom severity, child impairment, and/or academic achievement predict service utilization over time. The generalizability of these findings is also limited by the nature of our sample. These were families seeking participating in a clinical trial of a novel psychosocial treatment for attention problems, primarily referred by their schools and residing in the San Francisco Bay Area. Thus, these findings may not be representative of all youth with ADHD-I. Furthermore, use of ADHD medication is known to vary according to geographic region, with higher medication rates in the Southern and Midwestern United States than in the area from which this sample was drawn (Cox, Motheral, Henderson, & Mager, 2003; Visser et al., 2014). Finally, this study did not include children with other ADHD presentations, such that it was not possible to compare findings across these subgroups. Future research should include geographically diverse samples of both referred and nonreferred youth with different ADHD presentations to address these issues.

Conclusion

The present study makes several important contributions to the extant literature on ADHD-I. It provides the most detailed account of service utilization among youth with ADHD-I to date. This is also the first study to link functional impairment with service utilization among youth with ADHD-I. In all, results of this study indicate a noteworthy possibility that ADHD-I may cause significant functional impairments which are often not met with adequate treatment services. This stands in contrast to clinical lore that ADHD-I is a less impairing and problematic condition than other ADHD presentations. ADHD is an impactful disorder that, left untreated, carries high personal, familial, and fiscal costs (Pelham, Foster, & Robb, 2007). Taken together with the present findings, this indicates a need for further study of impairment and service utilization in community samples of youth with ADHD-I.

The present findings also speak to the importance of considering extra-symptom variables in conducting clinical research with youth with ADHD. In this study, functional impairment and academic underachievement were significantly linked to service utilization, but ADHD symptom severity was not. This pattern aligns with a large body of existing research demonstrating the necessity of considering functional impairment, in addition to symptom severity, to understand the psychosocial and clinical picture of youth with ADHD (Pelham et al., 2005). Findings from this study extend this theme to the study of factors that may drive service utilization among youth with ADHD. Given the findings herein, it appears that comprehensive study of functional impairment, beyond “core” ADHD symptoms, will be needed to move toward an evidence-based understanding of service utilization among youth with ADHD-I.

Footnotes

Authors’ Note

Matthew R. Capriotti is now at the Department of Psychology, San Jose State University.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a grant from the National Institute of Mental Health, MH077671, PIs: Linda Pfiffner (contact PI) and Stephen Hinshaw.

Author Biographies

Matthew R. Capriotti, PhD, is an assistant professor in the Psychology Department at San José State University and a Research Associate in the Department of Medicine at the University of California San Francisco. His research interests include emprically-supported assessment and treatment of childhood neurobehavioral disorders (tic disorders, obsessive-compulsive spectrum disorders, and ADHD) and LGBTQ mental health.

Linda J. Pfiffner, PhD, is a professor in the Department of Psychiatry at the University of California, San Francisco where she also directs an ADHD specialty program. Her current research interests include psychosocial treatments for ADHD and disruptive behavior problems, training models for school-based interventions, family/social factors and developmental psychopathology.

References

Aday

L. A.

Andersen

(1974). A framework for the study of access to medical care. Health Services Research, 9, 208-220.

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.

American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author.

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing.

Angold

Costello

E. J.

Farmer

E. M.

Burns

B. J.

Erkanli

(1999). Impaired but undiagnosed. Journal of the American Academy of Child & Adolescent Psychiatry, 38, 129-137.

Barbaresi

W. J.

Katusic

S. K.

Colligan

R. C.

Weaver

A. L.

Leibson

C. L.

Jacobsen

S. J.

(2006). Long-term stimulant medication treatment of attention-deficit/hyperactivity disorder: Results from a population-based study. Developmental and Behavioral Pediatrics, 27, 1-10.

Bauermeister

J. J.

Canino

Bravo

Ramirez

Jensen

P. S.

Chavez

. . . Garcia

(2003). Stimulant and psychosocial treatment of ADHD in Latino/Hispanic children. Journal of the American Academy of Child & Adolescent Psychiatry, 42, 851-855.

Brinkman

W. B.

Sherman

S. N.

Zmitrovich

A. R.

Visscher

M. O.

Crosby

L. E.

Phelan

K. J.

Donovan

E. F.

(2009). Parental angst making and revisiting decisions about treatment of attention-deficit/hyperactivity disorder. Pediatrics, 124, 580-589.

Bussing

Zima

B. T.

Gary

F. A.

Garvan

C. W.

(2003). Barriers to detection, help-seeking, and service use for children with ADHD symptoms. The Journal of Behavioral Health Services and Research, 30, 176-189.

10.

Bussing

Zima

B. T.

Mason

Hou

Garvan

C. W.

Forness

(2005). Use and persistence of pharmacotherapy for elementary school students with attention-deficit/hyperactivity disorder. Journal of Child and Adolescent Psychopharmacology, 15, 78-87.

11.

Centers for Disease Control and Prevention. (2005). Mental health in the United States: Prevalence of diagnosis and medication treatment for attention-deficit/hyperactivity disorder—United States, 2003. Morbidity and Mortality Weekly Reports, 54, 84-847.

12.

Cox

E. R.

Motheral

B. R.

Henderson

R. R.

Mager

(2003). Geographic variation in the prevalence of stimulant medication use among children 5 to 14 years old: Results from a commercially insured US sample. Pediatrics, 111, 237-243.

13.

Eiraldi

R. B.

Mazzuca

L. B.

Clarke

A. T.

Power

T. J.

(2006). Service utilization among ethnic minority children with ADHD: A model of help-seeking behavior. Administration and Policy in Mental Health and Mental Health Services Research, 33, 607-622.

14.

Elia

Arcos-Burgos

Bolton

K. L.

Muenke

(2009). ADHD latent class clusters: DSM-IV subtypes and comorbidity. Psychiatry Research, 170, 192-198.

15.

Evans

S. W.

Owens

J. S.

Bunford

(2014). Evidence-based psychosocial treatments for children and adolescents with attention-deficit/hyperactivity disorder. Journal of Clinical Child & Adolescent Psychology, 43, 527-551.

16.

Fabiano

G. A.

Pelham

W. E.

Jr. Waschbusch

D. A.

Gnagy

E. M.

Lahey

B. B.

Chronis

A. M.

. . . Burrows-Maclean

(2006). A practical measure of impairment: Psychometric properties of the impairment rating scale in samples of children with attention deficit hyperactivity disorder and two school-based samples. Journal of Clinical Child & Adolescent Psychology, 35, 369-385.

17.

Froehlich

T. E.

Lanphear

B. P.

Epstein

J. N.

Barbaresi

W. J.

Katusic

S. K.

Kahn

R. S.

(2007). Prevalence, recognition, and treatment of attention-deficit/hyperactivity disorder in a national sample of US children. Archives of Pediatric and Adolescent Medicine, 161, 857-864.

18.

Gadow

K. D.

Sprafkin

(2002). Child Symptom Inventories manual. Stony Brook, NY: Checkmate Plus.

19.

Gordon

Antshel

Faraone

Barkley

Lewandowski

Hudziak

J. J.

. . . Cunningham

(2006). Symptoms versus impairment: The case for respecting DSM-IV’s Criterion D. Journal of Attention Disorders, 9, 465-475.

20.

Harrison

J. R.

Bunford

Evans

S. E.

Owens

J. S.

(2013). Educational accommodations for students with behavioral challenges: A systematic review of the literature. Review of Educational Research, 83, 551-597.

21.

Hinshaw

S. P.

(2002). Preadolescent girls with attention-hyperactivity disorder: I. Background characteristics, comorbidity, cognitive and social functioning, and parenting practices. Journal of Consulting and Clinical Psychology, 70, 1086-1098.

22.

Hinshaw

S. P.

Owens

E. B.

Zalecki

Huggins

S. P.

Montenegro-Nevado

A. J.

Schrodek

Swanson

E. N.

(2012). Prospective follow-up of girls with attention-deficit/hyperactivity disorder into early adulthood: Continuing impairment includes elevated risk for suicide attempts and self-injury. Journal of Consulting and Clinical Psychology, 80, 1041-1051.

23.

Jensen

P. S.

Hinshaw

S. P.

Kraemer

H. C.

Lenora

Newcorn

J. H.

Abikoff

H. B.

. . . Vitiello

(2001). ADHD comorbidity findings from the MTA study: Comparing comorbid subgroups. Journal of the American Academy of Child & Adolescent Psychiatry, 40, 147-158.

24.

Jensen

P. S.

Hoagwood

K. E.

Roper

Arnold

L. E.

Odbert

Crowe

. . . Wells

(2004). The Services for Children and Adolescents–Parent Interview: Development and performance characteristics. Journal of the American Academy of Child & Adolescent Psychiatry, 43, 1334-1344.

25.

Jensen

P. S.

Kettle

Roper

M. T.

Sloan

M. T.

Dulcan

M. K.

Hoven

. . . Payne

J. D.

(1999). Are stimulants overprescribed? Treatment of ADHD in four US communities. Journal of the American Academy of Child & Adolescent Psychiatry, 38, 797-804.

26.

Jensen

P. S.

Martin

Cantwell

D. P.

(1997). Comorbidity in ADHD: Implication for research practice and DSM-V. Journal of the American Academy of Child & Adolescent Psychiatry, 36, 1065-1079.

27.

Kaufman

Birmaher

Brent

Rao

Flynn

Moreci

. . . Ryan

(1997). Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): Initial reliability and validity data. Journal of the American Academy of Child & Adolescent Psychiatry, 36, 980-988.

28.

Lahey

B. B.

(2006). Should the combined and predominantly inattentive types of ADHD be considered distinct and unrelated disorders? Not now, at least. Clinical Psychology: Science and Practice, 8, 494-497.

29.

Larson

Russ

S. A.

Kahn

R. S.

Halfon

(2011). Patterns of comorbidity, functioning, and service use for children with ADHD, 2007. Pediatrics, 127, 462-470.

30.

Leslie

L. K.

Canino

Landsverk

Wood

P. A.

Chavez

Hough

R. L.

. . . Ramirez

(2005). ADHD treatment patterns of youth served in public sectors in San Diego and Puerto Rico. Journal of Emotional and Behavioral Disorders, 13, 224-236.

31.

Leslie

L. K.

Lambros

K. M.

Aarons

G. A.

Haine

R. A.

Hough

R. L.

(2008). School-based service use by youth with ADHD in public-sector settings. Journal of Emotional and Behavioral Disorders, 16, 163-177.

32.

Leslie

L. K.

Plemmons

Monn

A. R.

Palinkas

L. A.

(2007). Investigating ADHD treatment trajectories: Listening to families’ stories about medication use. Journal of Developmental and Behavioral Pediatrics, 28, 179-188.

33.

Leslie

L. K.

Wolraich

M. R.

(2008). ADHD service use patterns in youth. Ambulatory Pediatrics, 7(Suppl. 1), 107-120.

34.

Massetti

Lahey

B. B.

Pelham

W. E.

Jr. Loney

Ehrhardt

Lee

S. S.

Kipp

(2008). Academic achievement over eight years among children who met modified criteria for attention-deficit/hyperactivity disorder at 4-6 years of age. Journal of Abnormal Child Psychology, 36, 399-410.

35.

McBurnett

Pfiffner

L. J.

Ottolini

Y. L.

(2000). Types of ADHD in DSM-IV. In Accardo

P. J.

Blondis

T. A.

Whitman

B. A.

Stein

M. A.

(Eds.), Attention deficits and hyperactivity in children and adults: Diagnosis, treatment, management (pp. 229-240) (2nd ed.). New York, NY: Marcel Dekker.

36.

Merikangas

K. R.

Burstein

Swanson

S. A.

Avenevoli

Case

. . . Olfson

(2011). Service utilization for lifetime mental disorders in U.S. adolescents: Results of the National Comorbidity Survey–Adolescent Supplement (NCS-A). Journal of the American Academy of Child & Adolescent Psychiatry, 50, 32-45.

37.

Merikangas

K. R.

Burstein

Swanson

S. A.

Avenevoli

Cui

. . . Swendsen

(2010). Lifetime prevalence of mental disorders in US adolescents: Results from the National Comorbidity Study-Adolescent Supplement (NCS-A). Journal of the American Academy of Child & Adolescent Psychiatry, 49, 980-989.

38.

Milich

Balentine

A. C.

Lynam

D. R.

(2001). ADHD combined type and ADHD predominantly inattentive type are distinct and unrelated disorders. Clinical Psychology: Science and Practice, 8, 463-488.

39.

Patterson

G. R.

Yoerger

(2002). A developmental model for early and late-onset antisocial behavior. In Reid

J. B.

Snyder

Patterson

G. R.

(Eds.), Antisocial behavior in children and adolescents: A developmental analysis and model for intervention (pp. 147-172). Washington, DC: American Psychological Association.

40.

Pelham

W. E.

Jr. Fabiano

G. A.

Massetti

G. M.

(2005). Evidence-based assessment of attention deficit hyperactivity disorder in children and adolescents. Journal of Clinical Child & Adolescent Psychology, 34, 449-476.

41.

Pelham

W. E.

Foster

E. M.

Robb

J. A.

(2007). The economic impact of attention-deficit/hyperactivity disorder in children and adolescents. Journal of Pediatric Psychology, 32, 711-727.

42.

Pfiffner

L.J.

Hinshaw

S.P.

Owens

Zalecki

Kaiser

Villodas

McBurnett

(2014). A two-site randomized clinical trial of integrated psychosocial treatment for ADHD-Inattentive Type. Journal of Consulting and Clinical Psychology, 82, 1115-1127.

43.

Sawyer

M. G.

Rey

J. M.

Arney

F. M.

Whitham

J. N.

Clark

J. J.

Baghurst

P. A.

(2004). Use of health and school-based services in Australia by young people with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 43, 1355-1363.

44.

Schnoes

Reid

Wagner

Marder

(2006). ADHD among students receiving special education services: A national survey. Exceptional Children, 72, 483-496.

45.

Simon

A. E.

Pastor

P. N.

Reuben

C. A.

Huang

L. N.

Goldstrom

I. D.

(2015). Use of mental health services by children ages six to 11 with emotional or behavioral difficulties. Psychiatric Services, 66, 930-937.

46.

Visser

S. N.

Danielson

M. L.

Bitsko

R. H.

Holbrook

J. R.

Kogan

M. D.

Ghandour

R. M.

. . . Blumberg

S. J.

(2014). Trends in the parent-report of health care provider-diagnosed and medicated attention-deficit/hyperactivity disorder: United States, 2003-2011. Journal of the American Academy of Child & Adolescent Psychiatry, 53, 34-46.

47.

Wechsler

(2003). The Wechsler Intelligence Scale for Children–Fourth Edition. London, England: Pearson Assessment.

48.

Weiss

Worling

Wasdell

(2003). A chart review study of the inattentive and combined types of ADHD. Journal of Attention Disorders, 7, 1-9.

49.

Willcutt

E. G.

(2012). The prevalence of DSM-IV attention-deficit/hyperactivity disorder: A meta-analytic review. Neurotherapeutics, 9, 490-499.

50.

Willcutt

E. G.

Nigg

J. T.

Pennington

B. F.

Solanto

M. V.

Rohde

L. A.

Tannock

. . . Lahey

B. B.

(2012). Validity of DSM-IV attention deficit/hyperactivity disorder symptom dimensions and subtypes. Journal of Abnormal Psychology, 121, 991-1010.

51.

Wolraich

M. L.

Hannah

J. N.

Baumgaertel

Feurer

(1998). Examination of DSM-IV criteria for attention deficit/hyperactivity disorder in a county-wide sample. Journal of Developmental and Behavioral Pediatrics, 19, 162-168.

52.

Woodcock

R. W.

McGrew

K. S.

Mather

(2007). Woodcock-Johnson III Normative Update. Rolling Meadows, IL: Riverside Publishing.

53.

Yuce

Zoroglu

S. S.

Ceylan

M. F.

Kandemir

Karabekiroglu

(2013). Psychiatric comorbidity distribution and diversities in children and adolescents with attention deficit/hyperactivity disorder: A study from Turkey. Neuropsychiatric Disease and Treatment, 9, 1791-1799.