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 DOI: 10.1177/0022219412464351

2013 46: 43 originally published online 9 November 2012J Learn DisabilGeorge J. DuPaul, Matthew J. Gormley and Seth D. Laracy

Comorbidity of LD and ADHD: Implications of DSM-5 for Assessment and Treatment  

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Children and adolescents with attention-deficit/hyperactiv-ity disorder (ADHD) frequently exhibit difficulties with academic performance and achievement. Specifically, stu-dents with this disorder typically obtain lower than average achievement test scores and report card grades (DuPaul & Stoner, 2003), are more likely to be retained in grade or drop out of school (Barkley, Murphy, & Fischer, 2008), and are less likely to attend and complete college (Mannuzza, Gittelman-Klein, Bessler, Malloy, & LaPadula, 1993). For example, in a comprehensive literature review and meta-analysis, Frazier, Youngstrom, Glutting, and Watkins (2007) found that students with ADHD obtained academic achieve-ment test scores that were, on average, 0.71 standard devia-tion units less than those of their non-ADHD classmates. The association of academic underachievement and ADHD is not surprising given that the current and proposed DSM criteria for this disorder mandate that symptoms must lead to academic, social, or occupational impairment (American Psychiatric Association, 2000). What is less clear is the degree to which academic difficulties represent perfor-mance problems directly related to ADHD symptoms or whether students with ADHD are also at increased risk for academic skills deficits (i.e., learning disabilities [LD]).

The purpose of this article is to review available evi-dence regarding comorbidity of LD and ADHD in the

context of upcoming changes to the DSM criteria for both disorders. First, prior reviews of LD-ADHD comorbidity are briefly described. Next, a review of studies conducted over the past 10 years (2001–2011) is presented with spe-cific attention to factors (e.g., inclusion criteria) that may affect comorbidity between the two disorders. Finally, the potential impact of proposed changes to DSM criteria on comorbidity rates between LD and ADHD is discussed with particular attention to implications for assessment and treatment.

Comorbidity of LD and ADHD in Historical ContextSeveral prior reviews have examined the degree to which LD and ADHD are associated (e.g., DuPaul & Stoner, 2003; Semrud-Clikeman et al., 1992). For example, DuPaul and Stoner (2003) reviewed 17 studies conducted between 1978 and 1993 that reported the percentage of students with

464351 LDXXXX10.1177/0022219412464351Journal of Learning DisabilitiesDuPaul et al.

1Lehigh University, Bethlehem, PA, USA

Corresponding Author:George J. DuPaul, Department of Education and Human Services, Lehigh University, 111 Research Drive, Bethlehem, PA 18015, USA Email: gjd3@lehigh.edu

Comorbidity of LD and ADHD: Implications of DSM-5 for Assessment and Treatment

George J. DuPaul, PhD,1 Matthew J. Gormley, MEd,1 and Seth D. Laracy, BA1

Abstract

Attention-deficit/hyperactivity disorder (ADHD) and learning disability (LD) can co-occur for a significant minority of children with each disorder. A total of 17 studies (2001–2011) examining ADHD-LD comorbidity were reviewed, revealing a higher mean comorbidity rate (45.1%) than has been obtained previously. Higher comorbidity may be the result of including students with writing disorders, not just reading and/or math disabilities. Proposed DSM-5 criteria for both disorders will likely affect comorbidity rates; however, it is unclear whether such rates will increase or decrease. Regardless of the specific impact of DSM revisions, academic skill and/or performance deficits should be assessed for students with ADHD as part of screening, comprehensive evaluation, and treatment monitoring. Comprehensive intervention services for students with comorbid ADHD and LD will require empirically supported treatment strategies that address both disorders and that are implemented across school and home settings.

Keywords

attention-deficit/hyperactivity disorder, learning disability, assessment, diagnosis, treatment

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44 Journal of Learning Disabilities 46(1)

ADHD who also were identified with LD in one or more subject areas. There was wide variability in LD prevalence, ranging from 7% to 92%, in large part resulting from differ-ing methods of identifying LD. The median LD prevalence rate across the 17 ADHD samples was 31.1%, indicating that, on average, one out of every three children with ADHD also have an LD. In contrast, rates of LD among students in the control group of these studies ranged from 0% to 22%, with a median prevalence of 8.9%. Thus, children with ADHD appear to be about 3 times more likely to have an LD than are their non-ADHD classmates.

DuPaul and Stoner (2003) also reviewed seven studies conducted between 1982 and 1993 that examined the preva-lence of ADHD in samples of students identified with LD. The prevalence rate of ADHD in these samples ranged from 18% to approximately 60%, with a median prevalence of 38.2% across studies. The prevalence of ADHD among students with LD in these samples is roughly 7 times higher than the prevalence of ADHD in the general population, which is approximately 5% (Polanczyk, Silva de Lima, Lessa Horta, Biederman, & Rohde, 2007). Once again, the wide range of ADHD prevalence estimates across LD samples is the result, at least in part, of the varying methods for defining ADHD, with some studies basing this on a single behavior rating whereas others used more comprehensive identifica-tion methods including diagnostic interviews and multiple behavior ratings.

Based on these findings, it is clear that students with ADHD are at higher than average risk for having LD and vice versa. Definitive conclusions regarding comorbidity rates are limited by several factors, including the wide vari-ety of methods used to define ADHD and LD across stud-ies, a frequent reliance on a single measure to identify ADHD and LD, and the use of clinic-referred samples (DuPaul & Stoner, 2003). Comorbidity rates may be inflated in clinic-referred samples because children with multiple problems are more likely to be referred for clinic-based ser-vices (Semrud-Clikeman et al., 1992). Thus, comorbidity rates from these older studies (1978 to 1993) should be viewed as higher bound estimates relative to what may be found in school-based samples.

Although the literature clearly supports an association between ADHD and LD, it allows for several different accounts of the cause for this overlap. The association between ADHD and LD is often viewed as driven primarily by inattentive symptoms, as correlation coefficients between inattentive symptoms and measures of academic achieve-ment are typically stronger than for hyperactive-impulsive symptoms and achievement (e.g., Massetti et al., 2008). However, twin studies have also found a strong genetic link between phenotypic hyperactivity and academic difficulties (Saudino & Plomin, 2007).

Whatever the nature of the overlap, several theories have been suggested to explain the comorbidity of ADHD and

LD. One theory, the phenocopy hypothesis, suggests that for children with comorbid ADHD and reading problems, the expression of inattentive or hyperactive symptoms is a mani-festation of reading difficulties rather than an actual, discrete ADHD diagnosis (Pennington, Groisser, & Welsh, 1993). However, this hypothesis has been largely unsupported within the literature (Willcutt, Pennington, Olson, Chhabildas, & Hulslander, 2005). Alternatively, the cognitive subtype hypothesis suggests that comorbid ADHD and reading disor-der is a distinct disorder with an etiological trajectory that is separate from that of either disorder alone and is associated with greater impairment than would be expected solely from the additive impairments of either disorder. Unfortunately, support for this hypothesis is limited (Willcutt et al., 2005). One promising hypothesis is that ADHD and reading dis-abilities share a common, biological etiology that is based in a genetic predisposition to both disorders (Willcutt et al., 2005). Recent studies have identified specific alleles that may be associated with increased risk for both ADHD and reading disability (RD; Willcutt et al., 2002), and a genetic link between symptoms of ADHD and academic achieve-ment has also been supported by twin studies (Saudino & Plomin, 2007). Based on this hypothesis, individuals with ADHD only, RD only, and ADHD plus RD would share some neuropsychological functioning deficits, a hypothesis supported by the results of Willcutt and colleagues (2005). It is possible that deficits may be related to working memory and processing speed, as such difficulties are shared across ADHD and LD (DuPaul & Volpe, 2009).

Review of Current Comorbidity LiteratureTo provide a current estimate of the comorbidity of ADHD and LD, publications from the past decade (i.e., 2001–2011) were reviewed. In October and November 2011, PsycINFO was searched for any articles published between 2001 and 2011 containing ADHD and learning disability. In addition, the literature between 2001 and 2011 was searched for arti-cles containing the keyword ADHD and one of the follow-ing: reading disorder, reading disability, dyslexia, math disability, math disorder, dyscalculia, writing disorder, writ-ten expression, dysgraphia, nonverbal learning disorder, nonverbal learning disability, NVLD, mathematical learn-ing disorder, mathematical learning disability, math learn-ing disability, reading learning disability, and reading learning disorder. These searches yielded 370 results. Of these, 337 articles did not directly relate to the comorbidity between ADHD and LD or were duplicate articles from other searches and were therefore excluded. An additional 4 articles were excluded because the sample consisted of adults, 10 because they did not explicitly include comorbid-ity rates, and 4 because authors reported “learning prob-lems” but did not specify a diagnosis of LD. Therefore, this

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DuPaul et al. 45

literature search resulted in 15 studies that are included in the current review. An ancestral search of the identified articles yielded an additional 2 articles, resulting in a total of 17 studies included for the present review.

In general, estimates of LD in children with ADHD appear to be higher over the past decade relative to previous estimates. However, the review of the 17 studies revealed considerable variability across estimates. Rates of LD in stu-dents with ADHD ranged from 8% to 76% of students (Mdn = 47%, M = 45.1%; see Table 1). Much of this variability is accounted for by the type of LD diagnosis. For example, several of the studies that reported high overall rates of LD included writing disorder (Mayes & Calhoun, 2004, 2006, 2007b). These studies reported rates of writing disorder ranging from 59% to 65%. When studies found lower rates of writing disorder (Del’Homme, Kim, Loo, Yang, & Smalley, 2007) or excluded writing disorder and consid-ered only reading and math (Capano, Minden, Chen, Schachar, & Ickowicz, 2008; Faraone, Biederman, Monuteaux, Doyle, & Seidman, 2001; Langberg, Vaughn, Brinkman, Froehlich, & Epstein, 2010), overall estimates usually fell within the range of 24% to 38%, with one outlier reporting a higher rate of 50% in a Spanish sample (Miranda, Soriano, Fernández, & Meliá, 2008). When just reading disorder was considered, comorbidity rates fell in the range of 11% to 52% (Capano et al., 2008; Del’Homme et al., 2007; Langberg et al., 2010; Mayes & Calhoun, 2006, 2007a, 2007b; Miranda et al., 2008; Wisniewska, Baranowska, & Wendorff, 2007), with only one of eight studies reporting a comorbidity rate higher than 34% (Mayes & Calhoun, 2007b). It should be noted that the latter study found a rate of 30% in 678 students tested with the third edition of the Wechsler Intelligence Scale for Children (WISC) and a rate of 52% in 92 students tested with the fourth edition of WISC. When just math dis-order was considered, comorbidity rates ranged from 5% to 30% (Capano et al., 2008; Del’Homme et al., 2007; Langberg et al., 2010; Mayes & Calhoun, 2006, 2007b; Miranda et al., 2008). In addition, one study found that 13% of participants with ADHD also met criteria for nonverbal LD (NVLD; Semrud-Clikeman, Walkowiak, Wilkinson, & Minne, 2010).

The studies included in the present review also utilized a wide array of criteria for identifying students as having an LD. DSM-IV-TR criteria for specific LD require a sig-nificant discrepancy between intellectual ability and aca-demic achievement (American Psychiatric Association, 2000). One model that fits with this definition is the simple discrepancy model that was utilized by two of the investi-gations in the current review. Del’Homme et al. (2007) used a 1.5 standard deviation discrepancy model and found a comorbidity rate of 31%. Faraone et al. (2001) utilized a regression-based discrepancy model, resulting in a 29% rate of comorbidity. A second method consistent with this DSM-IV-TR definition, the predicted achievement method, defines an LD as a subject subtest score significantly lower

(p < .05) than would be predicted by the student’s full-scale IQ. Mayes and Calhoun (2004, 2006, 2007a, 2007b) employed this method in four studies and found comorbid-ity rates ranging from 34% to 77%. Although overall rates of LD in these studies were often greater than 70%, the heightened rates appear to be driven by high rates of writ-ing disability (as described previously) rather than by method of diagnosis. Two additional studies did not men-tion specific criteria for the diagnosis of LD but indicated that they followed the DSM-IV-TR guidelines, resulting in comorbidity rates of 35% (Marks, Nichols, Blasey, Kato, & Huffman, 2002) and 50% (Decker, McIntosh, Kelly, Nicholls, & Dean, 2001).

It is important to note that ability–achievement discrep-ancy methods, as well as related methods that examine vari-ability among cognitive subscales, for identifying students with LD have been criticized from conceptual and statisti-cal perspectives. In particular, these methods may have lim-ited reliability and validity (Fletcher, Francis, Morris, & Lyon, 2005) as well as unacceptable levels of sensitivity and positive predictive values (Steubing, Fletcher, Branum-Martin, & Francis, 2012). Alternatively, response to inter-vention (RTI) identification models may have more promise; however, more empirical research is necessary to fully document the technical adequacy of RTI models in identification of students with LD (e.g., Barth et al., 2008). Fortunately, proposed DSM-5 criteria for LD now include RTI considerations.

The remaining studies in this review used methods of diag-nosis that are not consistent with DSM-IV-TR. Three investi-gations relied on parent report and found comorbidity rates of 24% and 51% with any LD and 18% with dyslexia among children with ADHD (Barry, Lyman, & Klinger, 2002; Smith & Adams, 2006; Wisniewska et al., 2007). Six studies diag-nosed children as having an LD if they scored below a strict cutoff point (e.g., 1.5 SD below the mean) on a measure of academic achievement. Four of these studies used achieve-ment tests normed on an American sample. Two of these stud-ies employed a cutoff of two standard deviations below the mean and reported comorbidity rates of 38% (Langberg et al., 2010) and 41% (Dietz & Montague, 2006). One study required two subtest scores to be one standard deviation below the mean or one subtest score at least 1.5 standard deviations below the mean and found that 34% of children with ADHD also had an LD (Capano et al., 2008). Del’Homme et al. (2007) used a cutoff of 1.5 standard deviations below the mean and reported a comorbidity rate of 24%. Two interna-tional studies also employed a cutoff method. A cutoff of 1.5 standard deviations below the mean led to a 54% comorbidity rate in Israeli children (Perets-Dubrovsky, Kaveh, Deutsh-Castel, Cohen, & Tirosh, 2010). Miranda et al. (2008) used a cutoff of two standard deviations below the mean for reading and 25th percentile for math and found a 50% comorbidity rate among students from Spain.

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46 Journal of Learning Disabilities 46(1)

If writing disorder is not included, comorbidity rates reported by the studies included in the current article are generally consistent with the rates reported in previous reviews. Studies that used a predicted achievement model reported substantially higher rates of overall LD, but this was the case only when writing disorder was considered. Of interest, rates of writing disorder diagnosis among students with ADHD were not elevated when a simple discrepancy model was used. Thus, it is possible that that the predicted achievement model may lead to higher rates of diagnosis for writing disorder among children with ADHD, but not for other learning disorders, although the available data are not extensive enough to strongly support this conclusion. A

cutoff method of diagnosis also produced higher rates in international samples, although it did not appear to raise rates above the expected 30% level when a cutoff of 1.5 standard deviations was used in an American sample (Capano et al., 2008; Del’Homme et al., 2007). However, a cutoff of one standard deviation produced comorbidity rates about 10 percentage points higher than previous estimates. This cutoff method, although not consistent with DSM-IV-TR criteria, would be acceptable under the new DSM-5 criteria. Given these findings, it appears that rates of diag-nosis of LD, and thus of comorbidity of ADHD and LD, will be affected more by the cutoff point that is chosen than the cutoff method itself.

Table 1. Studies Including Data on the Comorbidity of ADHD and LD from 2001 to 2011

Study LD Dx ADHD Dx N % LD % ADHD

Barry et al. (2002) Parent report Previous Dx, current Tx, and parent ratings

66 24 N/A

Capano et al. (2008) 1 SD/1.5 SD cutoff Semistructured interview, parent/teacher ratings

476 66 N/A

Decker et al. (2001) DSM-IV, ICD-9 DSM-IV, ICD-9 577 50 N/ADel’Homme et al. (2007) > 7th percentile cutoff/1.5 SD

DiscrepancySemistructured interview, DSM-IV

507 24–31 N/A

Dietz & Montague (2006) Multiple methods T-score > 70 on BASC TRS 12 8–75 N/AFaraone et al. (2001) Discrepancy score > 1.65 DSM-III-R, K-SADS-E 133 29 N/ALangberg et al. (2010) 85 or lower on any WIAT-II subtest DISC and VARS 128 38 N/AMarks et al. (2002) DSM-III-R or DSM-IV T-score > 67 on CBCL, DSM

III-R or DSM-IV40 35 N/A

Mayes & Calhoun (2004) Predicted achievement sig. lower than FSIQ (p < .05)

Parent/child semistructured interview and rating scales

630 76 N/A

Mayes & Calhoun (2006) Predicted achievement sig. lower than FSIQ (p < .05)

Parent/child semistructured interview and rating scales

949 66–71 N/A

Mayes & Calhoun (2007a) Predicted achievement sig. lower than FSIQ (p < .05)

Parent/child semistructured interview and rating scales

724 29–65 N/A

Mayes & Calhoun (2007b) Predicted achievement sig. lower than FSIQ (p < .05)

DSM-IV, independent Dx parent and teacher report

678 76–77 N/A

Miranda et al. (2008) Teacher information and two cutoff scores (< 25th percentile/2 SD below M)

DSM-IV-TR, CPRS-RL 72 50 N/A

Perets-Dubrovsky (2010) 1.5 SD cutoff DSM-IV-TR, CPRS, CTRS 126 55 67Semrud-Clikeman et al. (2010)

NVLD defined as (a) 1 SD below average on SSRS; (b) WJ-Ach III math calculation 16 points below FSIQ; (c) WASI Verbal IQ ≥ 85; (d) 1 SD below average on VMI; and (e) left hand below average relative to right hand on Purdue Pegboard

SIDAC, BASC-2 PRS 342 13 83

Smith & Adams (2006) Parent report Parent report 9,583 51 44Wisniewska et al. (2007) Parent report Parent report 28 18 N/A

Note: ADHD = attention-deficit/hyperactivity disorder; BASC = Behavior Assessment System for Children; CBCL = Child Behavior Checklist; CPRS = Conners’ Parent Rating Scale; CTRS = Conners’ Teacher Rating Scale; DISC = Diagnostic Interview Schedule for Children; DSM = Diagnostic and Statistical Manual of Mental Disorders; Dx = diagnosis; FSIQ = full-scale IQ; ICD = International Classification of Diseases; K-SADS-E = Schedule for Affective Disorders and Schizophrenia for School-Age Children: Epidemiologic; LD = learning disability; NVLD = nonverbal learning disability; PRS = Parent Rating Scale; RL = Revised Long; SIDAC = Structured Interview for Diagnostic Assessment of Children; SSRS = Social Skills Rating System; TRS = Teacher Rating Scale; VARS = Vanderbilt ADHD Rating Scale; VMI = Test of Visual-Motor Integration; WASI = Wechsler Abbreviated Scale of Intelligence; WIAT = Wechsler Individual Achievement Test; WJ-Ach III = Woodcock–Johnson–III Tests of Achievement.

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The majority of studies examining the comorbidity of ADHD and LD consider the prevalence of LD in children with ADHD. A large-scale study relying on parental report found that 44% of students with LD also have a diagnosis of ADHD (Smith & Adams, 2006). In a small sample of Israeli children, 67% of those with LD also met criteria for ADHD (Perets-Dubrovsky et al., 2010). Among children with NVLD, 83% meet criteria for ADHD (Semrud-Clikeman et al., 2010). It should be noted that NVLD does not appear as a separate diagnosis in either DSM-IV-TR or revisions suggested for DSM-5.

It should be noted that this summary is not intended to be an exhaustive analysis. In fact, the available data do not per-mit drawing firmer conclusions given the variability in methods for diagnosing LD and of specific criteria within each broader method of diagnosis. For example, 6 of the 17 studies utilized a strict cutoff score to diagnose LD; how-ever, there was no consensus on what the specific cutoff point should be. Although this variability impedes consen-sus on the rate of comorbidity, it does facilitate a compari-son of the rates yielded by different methods of diagnosis.

The recent literature highlights the importance of assess-ing writing ability, in addition to reading and math, for chil-dren with ADHD. Regardless of assessment method, it appears likely that rates of writing disorder are at least as high as rates of reading or math disorder among students with ADHD. Though the link between ADHD and writ-ing disorder is not as well established as the links between ADHD and reading and math disorders, the high reported rate of writing disorder makes sense conceptually; success-ful writing requires skills that are problematic for many chil-dren with ADHD, such as organization, perseverance to task, and attention to detail. Despite the potential comorbid-ity of ADHD and writing disorder, much of the literature on ADHD and LD neglects to consider writing disorder. If a similar omission occurs among practitioners, it is quite pos-sible that many students with ADHD have unaddressed chal-lenges with writing performance. This review highlights the need to assess students with ADHD for writing problems and to provide effective interventions when necessary. The efficacy of writing interventions with students with ADHD has received relatively little attention in the literature (e.g., Lienemann & Reid, 2008). Thus, it may be important to carefully monitor the efficacy of such treatments and to make adjustments to help account for attentional and orga-nizational deficits among children with ADHD. Although interventions specifically targeting writing disorder in chil-dren with ADHD are sparse, some evidence suggests that methylphenidate may be effective in improving the writing performance of students with ADHD (Evans et al., 2001). Because other academic skills deficits often show little or no improvement with medication treatment (Loe & Feldman, 2007), the impact of medication on writing performance suggests that writing deficits in children with ADHD may, in

some cases, be related more to symptoms of ADHD rather than specific LD.

Implications of Proposed DSM-5 Criteria on ComorbidityProposed changes to DSM criteria for LD and ADHD are likely to affect the comorbidity of the two disorders. The most important proposed provisions for learning disorders (along with dyslexia and dyscalculia) include the require-ment for multiple assessment measures, at least one of which is individually administered and culturally appropriate, as well as consideration of impairment in achievement in the absence of accommodations. It is likely that the requirement for multiple measures including those that are individually administered and culturally appropriate will make LD iden-tification more conservative and therefore lessen the proba-bility for comorbid LD among students with ADHD. As mentioned previously, prior studies finding higher comorbid-ity rates were likely to be those that used only a single mea-sure for LD identification. Alternatively, it is unclear how the requirement for evaluating achievement difficulties in the absence of accommodations will affect LD-ADHD comor-bidity as most prior studies do not address the issue of accommodations or whether the latter were even considered in the identification process.

One proposed change for the DSM-5 may lead to more liberal identification of LD and, therefore, could increase LD-ADHD comorbidity. Specifically, changes in the lan-guage of the DSM-5 criteria regarding LD (viz., current skills in one or more of these academic skills are well-below the average range for the individual’s age or intelligence, cultural group or language group, OR level of education vs. the DSM-IV-TR criteria “expected given the person’s chronologi-cal age, measured intelligence, AND age-appropriate educa-tion”; emphasis added) allows for a less stringent level of impairment to meet diagnostic criteria. Specifically, the word or in proposed DSM-5 criteria implies that the student’s per-formance must only be inconsistent with one of the three cri-teria. Conversely, the word and in DSM-IV-TR criteria implies that performance must be inconsistent with all three criteria for a student to be eligible for a diagnosis of LD.

Several proposed changes for the DSM-5 criteria for ADHD may lead to an increase in identification of students with this disorder and, therefore, increase LD-ADHD comorbidity. First, DSM-5 will now require the presence of some symptoms of inattention, hyperactivity, or impulsivity that cause impairment prior to age 12, instead of age 7 as required by DSM-IV-TR. Thus, any child for whom impair-ing symptoms first appear between the ages of 8 and 12 will now be eligible for diagnosis. Second, several of the criteria now include language to adapt symptoms for adults (e.g., “often blurts out an answer before a question has been com-pleted” has been updated to include “older adolescents or

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adults may compete people’s sentences and ‘jump the gun’ in conversations”). These changes may allow for increased identification among older children, adolescents, and adults who do not manifest symptoms that are primarily exhibited by younger children with the disorder. In addition, at the time of this writing (June 2012), the ADHD and Disruptive Behavior Disorders Workgroup is considering a modifica-tion that would require only four symptoms of either inat-tention or hyperactivity for individuals 17 or older to be diagnosed with ADHD. If adopted, the lower diagnostic threshold would likely lead to higher levels of diagnosis.

Implications for Assessment and InterventionRegardless of proposed changes to DSM criteria, most chil-dren with ADHD will not have LD and most students with LD will not have ADHD. However, the fact that a signifi-cant minority (e.g., 45%) of these students will have comor-bid ADHD and LD should be considered when planning school-based assessment and intervention services. In terms of assessment, practitioners should always (a) screen for academic skills deficits among students with ADHD and for ADHD symptoms among students with LD, (b) assess aca-demic functioning across subject areas (e.g., reading, math, writing) when evaluating students with ADHD, and (c) care-fully evaluate whether interventions for ADHD enhance academic functioning (DuPaul & Stoner, 2003).

Given the relatively high comorbidity rate between ADHD and LD, students who are evaluated for one of these disorders should always be screened for possible symptoms of the other disorder. For example, for students with LD, teachers could complete a brief rating of ADHD symptoms to determine whether further evaluation of ADHD is neces-sary (e.g., if symptom ratings exceed a specific clinical threshold such as the 90th percentile). In similar fashion, classroom performance data (e.g., curriculum-based mea-surement probes) for reading, math, and writing can be used to determine whether further evaluation of skills deficits is necessary for students with ADHD.

Assessment of academic functioning is critically impor-tant when evaluating students for possible ADHD. First, DSM criteria require documentation of impairment (either academic or social) secondary to ADHD symptoms; thus, specific data on academic functioning will aid in this deter-mination. Second, assessment of academic functioning can help identify specific targets for intervention beyond reduc-tion of ADHD symptomatic behaviors. Third, possible changes in academic functioning associated with treatment, including medication and behavioral interventions, should be assessed on a regular basis. Documenting the relative success of different intervention components in enhancing academic achievement is critically important, arguably as important as assessing reduction in ADHD symptoms.

It is equally important to identify successful interven-tions for students with comorbid LD and ADHD, as these students struggle with greater academic impairment and show more stable deficits than children with either disorder alone (Willcutt et al., 2007). Unfortunately, few studies have evaluated treatments specifically for students with both ADHD and LD. Despite the lack of research specifi-cally addressing interventions for this population, the ADHD and LD research literatures offer some principles to guide treatment. First, it is important to differentiate between those academic difficulties that may be secondary to ADHD symptoms (i.e., performance deficits) and those academic difficulties that represent actual skill deficits (i.e., that are LD related). Performance deficits can presumably be addressed by behavioral interventions and/or psychotro-pic medication as alleviating ADHD symptoms may lead to better academic performance (for a review of effective behavioral interventions, see Pelham & Fabiano, 2008). Alternatively, academic skill deficits must be addressed directly through empirically supported instruction in the relevant skill area. In fact, it is probably most efficient to address academic skill deficits before focusing on challeng-ing behaviors because improvements in skills may be asso-ciated with behavior change but not vice versa. This conclusion is based on findings from meta-analyses of school interventions for ADHD that show equivalent behav-ioral effect sizes for psychosocial and academic interven-tions (DuPaul & Eckert, 1997; DuPaul, Eckert, Vilardo, & Koenig, 2010). Specific intervention strategies will vary based both on the specific LD diagnosed and the specific deficits currently presented by the student. However, as a general rule, direct and explicit instruction in specific skills has been shown to be effective for the remediation of many academic difficulties (Shapiro, 2011). There are many available resources that provide evidence-based interven-tions matched to specific skill deficits; interested readers are referred to Rathvon (2008), Shapiro (2011), and Shinn and Walker (2010) for examples.

Although students with LD alone may typically receive intervention only at school, those with comorbid ADHD and LD should receive intervention addressing skill and perfor-mance deficits across settings (DuPaul & Stoner, 2003). Treatment strategies focused on reducing ADHD symptoms while enhancing academic and social impairment are opti-mized when delivered across home and school. For example, the use of conjoint behavior consultation, where parents and teachers serve as collaborative consultees guided by a men-tal health or educational consultant, can be helpful in design-ing and implementing effective cross-setting interventions (Sheridan & Kratochwill, 2008). Furthermore, skill deficits associated with LD may also be addressed at home if parents are provided with strategies to support skill practice and homework completion. This cross-setting collaboration may be particularly important for students with comorbid ADHD

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DuPaul et al. 49

and LD who presumably face greater challenges to educa-tional success.

Although psychotropic medication, primarily stimulants (e.g., methylphenidate), will significantly reduce ADHD symptoms for the majority of students, in cases of comorbid ADHD and LD, one should not assume that pharmacother-apy will ameliorate academic skill deficits. In fact, stimu-lant medication typically is associated with small effects on academic achievement (Van der Oord, Prins, Oosterlaan, & Emmelkamp, 2008). Academic skill deficits will probably require intensive, direct instruction and modification of antecedent events beyond medication and motivational (i.e., consequence-based) behavior modification strategies typically used to treat ADHD (DuPaul & Stoner, 2003).

Several studies have identified interventions that are effective in improving the academic functioning of students with ADHD. Among students with ADHD and difficulties with organization or homework completion, an organiza-tional skills intervention has been shown to improve report card grades and teacher ratings of academic impairment (Langberg, Epstein, Urbanowicz, Simon, & Graham, 2008). Among students with ADHD, computer-assisted instruction led to improvements in reading fluency, math fluency, and teacher ratings of academic performance (Mautone, DuPaul, & Jitendra, 2005; Ota & DuPaul, 2002; Rabiner, Murray, Skinner, & Malone, 2010). Classwide peer tutoring has also been associated with academic improvements among chil-dren with ADHD (DuPaul, Ervin, Hook, & McGoey, 1998; Plummer & Stoner, 2005). Consultation-based academic interventions for children with ADHD have led to improve-ments in the trajectories of math and reading skill growth and teacher ratings of skills enabling academic success (DuPaul et al., 2006). Other studies have found short-term academic gains associated with common classroom inter-ventions for ADHD, such as antecedent or consequent inter-ventions (see Trout, Lienemann, Reid, & Epstein, 2007, for a review). Although most participants in these studies were not diagnosed with LD, these interventions could poten-tially be effective for the population with comorbid diagno-ses. Given the high rate of comorbidity and the negative outcomes associated with comorbid ADHD and LD (Willcutt et al., 2007), further research evaluating interven-tion efficacy with this population is needed.

ConclusionsThe comorbidity of LD and ADHD is relatively high, with approximately 31% to 45% of students with ADHD hav-ing LD and vice versa. Impending changes to DSM crite-ria for both disorders will likely affect comorbidity rates; however, it is unclear whether such rates will increase or decrease. Some recommended changes (e.g., requirement of multiple measures of LD) are likely to lessen the prob-

ability of comorbidity, whereas other changes (e.g., older age of onset criterion for ADHD) may increase comorbidity. It also is unclear how proposed DSM-5 criteria, especially for learning disorders, will be integrated with the RTI model (Jimerson, Burns, & VanDerHeyden, 2007) currently imple-mented in many school districts and how that integration with RTI will affect ADHD-LD comorbidity identification. Regardless of the specific impact of DSM revisions, aca-demic skill and/or performance deficits should be assessed for students with ADHD as part of screening, comprehen-sive evaluation, and treatment monitoring. Furthermore, students with LD should be screened for exhibition of possible ADHD symptoms. Comprehensive intervention services for students with comorbid ADHD and LD will require empirically supported treatment strategies that address both disorders and that are implemented across school and home settings. Ultimately, empirical investiga-tions need to employ consistent, evidence-based diagnostic criteria so that accurate estimates of ADHD-LD comorbidity can be ascertained and students with both disorders can be identified and treated in a reliable and valid fashion.

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) received no financial support for the research, authorship, and/or publication of this article.

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