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Meghna Rajaprakash , Mary L. Leppert; Attention-Deficit/Hyperactivity Disorder. Pediatr Rev March 2022; 43 (3): 135–147. https://doi.org/10.1542/pir.2020-000612

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Attention-deficit/hyperactivity disorder (ADHD) is the most prevalent neurobehavioral disorder in childhood. The major components of this disorder are developmentally inappropriate levels of inattention and hyperactivity/impulsivity, which result in functional impairment in 1 or more areas of academic, social, and emotional function. In addition to the propensity for children to have some compromise of academic and emotional function, children with ADHD also have a higher frequency of co-occurring learning, cognitive, language, motor, and mental health disorders. Similarly, children with developmental disorders have a higher risk of co-occurring ADHD. The diagnosis of ADHD can be ascertained by a review of the risks for the condition, consideration of masquerading conditions, a careful history and physical examination, and the recognition of co-occurring disorders. The signs and symptoms of co-occurring disorders and the management of ADHD differ across early childhood, middle childhood, and adolescence. Management is largely limited to behavioral and pharmacologic interventions, and it favors behavioral strategies in early childhood, pharmacologic and behavioral strategies in middle childhood, and pharmacologic interventions in adolescence. This article offers an approach to the evaluation, presentation, and management of ADHD with a focus on guiding primary care pediatricians.

Given the prevalence of attention-deficit/hyperactivity disorder (ADHD) in primary care and the rapidly evolving guidelines, pediatricians need to know the current best practices for 1) evaluating ADHD, 2) managing ADHD with behavioral and pharmacologic treatment strategies, and 3) using a preventive care approach to optimize outcomes in children with ADHD.

Describe the epidemiology of attention-deficit/hyperactivity disorder (ADHD) in the United States.

Recognize the risk factors for ADHD, including family history, birth circumstances, and established neurodevelopmental disorders.

Understand the evaluation process and diagnostic criteria for ADHD.

Name and recognize the differences between ADHD and comorbid and masquerading conditions.

Describe the age-appropriate behavioral and pharmacologic management strategies for ADHD.

Recognize the educational strategies to support children with ADHD.

The constellation of behavioral characteristics defined by hyperactivity and impulsivity was first attributed to Dr George Still in an address to the Royal College of Physicians in London in 1901. ( 1 ) Similar constellations of acquired signs and symptoms were described in children who had survived the encephalitis epidemic of 1917–1918. ( 2 ) By the 1960s, minimal brain dysfunction (MBD) and minimal cerebral dysfunction were terms used to describe a collection of physical and behavioral disorders that resulted from disordered brain function. Manifestations of MBD included motor incoordination, inattention, and impulse control deficits, as well as difficulties with interpersonal relations and emotionality. ( 3 ) The term MBD was used to distinguish children from those with the major brain dysfunctions such as intellectual disability (ID) and cerebral palsy. The terms MBD and minimal cerebral dysfunction fell out of favor because pathological evidence of brain dysfunction could not be identified.

The Diagnostic and Statistical Manual of Mental Disorders (DSM), Second Edition described childhood hyperactivity and impulsivity as the hyperkinetic impulse disorder of childhood. ( 4 ) Later studies identified inattention and distractibility as the primary dysfunctions and hyperactivity/impulsivity as a secondary phenomenon. Attention-deficit disorder was entered into the DSM, Third Edition ( 5 ) and was defined as occurring with or without hyperactivity. The current conceptualization of developmentally inappropriate levels of inattention and hyperactivity/impulsivity was introduced into the 1987 revision of the DSM . ( 6 ) Subsequent editions of the DSM have included minor revisions to the 1987 publication.

ADHD is one of the most prevalent of the neurodevelopmental disorders, occurring in 8.9% of US children aged 3 to 17 years (2019–2020 National Survey of Children’s Health). ( 7 ) Boys are more than twice as likely as girls to be diagnosed as having ADHD. The prevalence rate of childhood ADHD increases with advancing age such that ADHD is reported in 2.1% of children 2 to 5 years old, 8.9% of 6- to 11-year-olds, and 11.9% of 12- to 17-year-olds. ( 8 ) Note, however, that across age groups, children who are of racial/ethnic minorities are less likely than white children to receive a diagnosis of ADHD and subsequent pharmacologic treatment. ( 9 )

The core features of ADHD are hyperactivity, impulsivity, and inattention, but the expression of the phenotype varies among children. There seems to be a strong heritable, likely polygenic component to the etiology of ADHD, although environmental, prenatal, and perinatal influences complicate the understanding of etiology. Although the heritability of ADHD is estimated to be 76% in twin studies, ( 10 ) little clarity has been offered regarding genetic etiologies for ADHD. Neurotransmitter dysregulation of the frontosubcortical network, particularly of dopaminergic and serotonin receptors and proteins, is implicated in the pathophysiology of ADHD. ( 10 )( 11 ) Although several candidate genes influencing dopamine and serotonin transmission have been suggested, there is insufficient evidence to support an underlying candidate gene or copy number variant to the etiology of ADHD.

Exposure to a variety of substances during intrauterine and early childhood environments is associated with ADHD signs and symptoms in childhood. Intrauterine exposures known to be associated with ADHD include alcohol, ( 12 )( 13 ) tobacco, ( 14 )( 15 ) and opioid use during pregnancy, ( 16 ) although the contribution of maternal therapeutic medications is less clear.

Birth circumstances and early childhood conditions and exposures also may be associated with childhood ADHD. Premature birth and low birthweight are clearly associated with ADHD, particularly inattentive type. Birth complications that cause prolonged stress on the infant, such as intrapartum hemorrhage, toxemia, eclampsia, and prolonged delivery, are also associated with ADHD, although no causal mechanisms have been clearly evidenced. Early childhood exposure to lead, organophosphates, and polychlorinated biphenyls is also linked to higher rates of ADHD signs and symptoms. ( 13 ) More recent studies have shown that adverse childhood experiences and psychosocial adversity are associated with ADHD, and the evidence supports a greater risk of ADHD with an increased number of adversities. ( 17 )

The index of suspicion for ADHD is greater in children with genetic, neurologic, and neurodevelopmental disorders. The prevalence of ADHD is as high as 30% to 65% in children with fragile X syndrome, neurofibromatosis 1, velocardiofacial syndrome, tuberous sclerosis complex, and Williams syndrome. ( 18 ) Similarly, established neurodevelopmental disorders, including epilepsy, ID, autism spectrum disorder (ASD), cerebral palsy, Tourette syndrome, language impairments, and learning disability, confer a significant risk of co-occurring ADHD. ( 8 ) ADHD occurs in approximately 12% to 17% of children with epilepsy, ( 19 ) 30% of children with ID, ( 20 ) 26% of children with ASD, ( 21 ) 18% to 37% of children with speech and language impairments, ( 22 ) and 15% to 48% of children with learning disabilities. ( 23 )

The 2019 updated American Academy of Pediatrics guidelines ( 24 ) state that an ADHD evaluation should be conducted in children or adolescents aged 4 to 18 years who “demonstrate academic or behavioral problems and symptoms of inattention, hyperactivity, or impulsivity.” The initial screening process should include an examination of the child’s academic functioning, behavior, and interpersonal relationships. The diagnosis of ADHD is behaviorally defined and derived from history. Risk factors, screening tools, and physical examination may support the diagnosis. However, there are no diagnostic tests that confirm a diagnosis of ADHD. The history should begin with details relevant to the presenting complaint, including specific behaviors, the settings in which troublesome behaviors occur, and safety concerns surrounding behavior. Academic performance, study skills, and social aptitude are also essential components of the history of the presenting concerns, as is an estimate of the degree to which ADHD is impairing function. The history should include assessment of risk factors, including prenatal, perinatal, and neonatal complications, and medical, psychosocial, and family histories. Family history should include inquiries about academic underachievement, genetic disorders, mental health conditions, and neurodevelopmental disabilities. Family and personal history of cardiac disease is important to elicit because of the very low risk of sudden cardiac events in pediatric patients taking stimulant drugs. The association of stimulant use and risk of sudden death in otherwise healthy children was a transient concern, but recent evidence suggests that there is no increased risk of serious cardiovascular events for healthy children treated with stimulants. ( 25 ) It is recommended, however, that a cardiac evaluation is performed before initiating medication for children with ADHD who exhibit cardiac signs and symptoms or are known to have cardiac defects; syndromes with cardiac disorders, such as Marfan syndrome, Wolff-Parkinson-White syndrome, hypertrophic cardiomyopathy, and long QT syndrome; or a family member with sudden cardiac death. ( 26 ) The developmental history is of particular interest as delays in early childhood language, motor, social, or play skills are often seen. A review of systems is helpful in ruling out conditions that may masquerade as or confound the diagnosis of ADHD, such as sleep apnea.

The physical examination has 2 purposes: to evaluate growth parameters and dysmorphisms that may suggest an underlying genetic condition and to ensure that there are no health conditions that preclude or mitigate medication management. Throughout the history and physical examination, observation of the child’s activity, social interaction, language use, mood, and affect and clinical assessment of developmental level may further inform the diagnostic impression, including associated or comorbid conditions.

The diagnosis of ADHD is based on the DSM, Fifth Edition (DSM-5) . ( 27 ) The ADHD criteria indicate that the child presents with signs and symptoms of developmentally inappropriate levels of inattention or hyperactivity/impulsivity. Six or more signs and symptoms of moderate to severe intensity in inattention without hyperactive/impulsive support a diagnosis of ADHD predominantly inattentive presentation. Similarly, 6 or more moderate to severe signs and symptoms of hyperactive/impulsive behavior support the diagnosis of ADHD predominantly hyperactive-impulsive presentation. Six or more moderate to severe signs and symptoms occurring in both domains meets the diagnostic criteria for ADHD combined presentation. Individuals 17 years and older require only 5 signs and symptoms in either domain to meet the criteria for any type of ADHD. When children exhibit functionally impairing ADHD signs and symptoms but do not meet the criteria for diagnosis, the term other specified ADHD is used, whereas unspecified ADHD is reserved for those who have functionally impairing signs and symptoms characteristic of ADHD but do not meet the full criteria.

In addition, the following criteria must be met: the signs and symptoms must be present for more than 6 months; the signs and symptoms must be present in more than 1 setting (ie, social, academic, occupational); several of the initial signs and symptoms must manifest before age 12 years; and the signs and symptoms must impair function in more than 1 domain of academic, social, or adaptive performance.

The DSM-5 diagnosis of ADHD in preschoolers is more difficult to make for 4 reasons: 1) demonstrating clinically significant signs and symptoms in more than 1 setting is limited if the child is not in child care or preschool; 2) the signs and symptoms must be developmentally inappropriate, which requires good parent and clinician understanding of both typical expectations for age and the child’s developmental level; 3) the measure of functional impairment in preschoolers varies with the expectations of the child; and 4) few symptom rating scales are validated for use in preschool-age children to support clinical assessment.

Teacher- and parent-reported behavioral rating scales are often used in clinical practice to support the diagnosis of ADHD ( Table 1 ). Although these scales are useful in supporting the diagnosis of ADHD and monitoring progress on treatment, they are not diagnostic and must be used in conjunction with the clinical evaluation. Note that there are many limitations to these scales, including reporter bias, poor quantification of signs and symptoms, suboptimal assessment of comorbid conditions, and presentation at younger ages. Vanderbilt Assessment Scales are the most commonly used scales in the school-age population. The scales are freely accessible and include versions for teacher and parent informants and can be used in conjunction with the clinical assessment to diagnose and monitor for efficacy of intervention. To meet DSM-5 criteria for ADHD, the patient must have a minimum of 6 responses with scores of 2 or 3 (often or very often). If scales show fewer than 6 responses with scores of 2 or 3, the patient should still undergo a full clinical assessment to corroborate the findings. In addition, the scales provide information on behavioral issues such as oppositional defiant disorder as well as academic performance. ( 28 ) Primary care providers should opt to refer to a specialist if they are not confident about a diagnosis of ADHD due to the child’s age or confounding masquerading conditions. The primary care provider should remain involved in the care of the patient within the medical home.

Common ADHD Behavior Rating Scales

ADHD=attention-deficit/hyperactivity disorder.

Available in the public domain and does not require special permission or charge a fee.

In early childhood, the diagnosis of ADHD requires signs and symptoms that are significantly greater than the activity level, aggression, or dysregulation expected for age and developmental level. An ADHD diagnosis made in preschoolers with moderate to severe signs and symptoms persists through 6-year follow-up in most children. ( 34 ) Persistence of signs and symptoms beyond the preschool stage is associated with lower socioeconomic status, poorer family support, and lower maternal education. ( 35 ) Inattention, however, is less apparent in preschool years for several reasons; demands for sustained attention are infrequent, and regarding learning, repetition is typical in preschool instruction, allowing multiple opportunities to attend to content. As young children with ADHD advance through the early academic years, and the practice of repeated exposure to learning content diminishes, children have fewer opportunities to attend and are, therefore, more likely to miss content.

School provides more structure, and there is less tolerance for hyperactivity and impulsivity in the classroom. Hyperactivity/impulsivity, when present, is often seen in the less structured activities, such as in the cafeteria and the gym. In contrast, difficulties with inattention, distractibility, listening, and task completion become more evident as academic demands increase. Academic underachievement, poor task completion, or increasing disruptive behavior may be a consequence of ADHD or may be due to an underlying co-occurring specific learning disability or language disorder. Careful evaluation of coexisting specific learning disabilities must be undertaken in any child with academic difficulties and, if present, addressed by the Individualized Education Program (IEP) team with appropriate services. Some school-age children with ADHD are likely to display impulsivity leading to high-risk behaviors leading to injuries and safety concerns.

In adolescence, hyperactivity often wanes but impulsivity and inattentiveness may persist. ( 34 ) The challenge of managing multiple educational, extracurricular, and personal priorities may overwhelm adolescents as their independence is often accompanied by reduced parental and school oversight of daily tasks. Difficulties emerge for adolescents as demands of time management, organization, increased workloads, abstract thinking, and social demands converge and often overwhelm. Moreover, during this period, coexisting mood, substance use, and sleep disorders may interact to exacerbate the difficulties and predispose adolescents to persistence of ADHD signs and symptoms and resulting impairments.

ADHD is not only one of the most common disorders in childhood but it also often co-occurs with a variety of medical, developmental, and mental health disorders. The age of the child may focus the assessment of co-occurring conditions. Preschoolers who present with symptoms of ADHD may have underlying or associated developmental delay, language disorders, ID, ASD, hearing loss, and motor delays. Similarly, learning disabilities, speech and language impairments, and conduct, oppositional defiant, and anxiety disorders are recognized as conditions that may underlie ADHD symptoms or co-occur with ADHD in school-age children. In addition to common comorbid and masquerading conditions ( Tables 2 and 3 ), adolescents often have co-occurring sleep deprivation, substance use, learning disabilities, and new-onset mood and anxiety disorders, which confound the management of ADHD in this population.

Differential Diagnosis of ADHD

Comorbid Conditions

Sources: Danielson et al, ( 8 ) Mohammed-Reza et al, ( 36 ) Belanger et al, ( 37 ) and Redmond. ( 38 )

Awareness of the propensity of children with ADHD to have coexisting conditions has led the American Academy of Pediatrics to recommend that clinicians include a process for screening comorbid conditions in their diagnostic evaluation. Coexisting mental health conditions may be treated in the medical home if experience and confidence allows, or alternatively, the patient may be referred to mental health subspecialists, including psychiatrists, psychologists, social workers, and neurodevelopmental or developmental and behavioral pediatricians. ( 24 ) When cognitive, language, or academic difficulties are suspected coexisting conditions, assessment may be performed through neurodevelopmental, developmental, and behavioral consultations or through the IEP team at the child's school. More recently, the Society for Developmental and Behavioral Pediatrics released guidelines for the care of complex ADHD. ( 39 ) These guidelines recommend a comprehensive interprofessional assessment and multimodal treatment plan for children with complex ADHD, defined as ADHD in children who present for initial consideration at ages younger than 4 years or older than 12 years, and those with co-occurring conditions, moderate to severe impairment, diagnostic uncertainty, or inadequate response to treatment. ( 39 )

Early, accurate diagnosis of ADHD and consistent appropriate management ( Fig 1 ) have long-standing effects on the functional outcomes of affected children. Long-term follow-up suggests that children whose symptoms are adequately managed have improved academic outcomes and lower rates of mood disorder, substance abuse, criminal behavior, motor vehicle accidents, injuries, and traumatic brain injury. A recent meta-analysis indicates that there are reductions in traumatic brain injury, suicidal, criminal, and substance use disorders, but these reductions are not statistically significant. However, in children and youth with ADHD who are medicated, there is a statistically significant reduction in the development of mood disorders, in accidents and injuries, and in poor academic outcomes compared with children and youth with ADHD who are not medicated. ( 40 ) Long-term, patients who are not treated for ADHD have lower self-esteem and social functioning compared with non-ADHD controls, and treatment improved outcomes. ( 41 )( 42 ) Moreover, treatment for ADHD improves achievement test outcomes and academic performance outcomes. ( 43 )

Algorithm for managing attention-deficit/hyperactivity disorder (ADHD).

The initial step in management is to address optimization of circumstances that may exacerbate symptoms of ADHD, including poor sleep hygiene, preventive mental health strategies, and discomfort or pain from medical conditions.

Management of ADHD may also include general, behavioral, educational, and pharmacologic approaches that are evidence-based and culturally sensitive and that use developmentally appropriate shared decision making with the child and the family and parent training. Shared decision making would involve an evaluation of the child’s safety and the family’s expectations and a discussion about behavior therapy and medication management, including the target symptoms each intervention is likely to address. If a family chooses to undergo a trial of behavioral therapy before considering medication, clear agreement about the length of the behavioral trial should be included in the discussion, such as setting up a 3-month intervention with behavioral therapy and review of target symptoms. This collaborative approach is particularly effective in families who are initially hesitant about medications and prefer a trial of behavioral therapy as a first step. If starting with behavioral therapy, clinicians should schedule follow-up after an agreed-on duration of a behavioral therapy trial. At follow-up, generally approximately 3 to 6 months, review of the efficacy of therapy and the impact of therapy on the targeted symptoms should be assessed. If the child is attending therapy regularly but the therapy is ineffective, or showing diminishing returns, then a discussion about augmenting behavior therapy with medication management or discontinuing therapy should be undertaken. At this stage, shared decision making would involve identifying the family’s preferences regarding medication formulations and schedule. For children treated primarily for inattention in the school setting, or for those without substantial behavioral difficulties, families and clinicians together can decide whether medication is required on weekends or school breaks.

Behavioral therapy is the first intervention recommended for preschool children with ADHD or for children with unspecified or other specified ADHD. ( 24 ) The evidence for behavior management supports its use in children with ADHD in early and middle childhood. In these age groups, the behavioral management strategies that show evidence of efficacy include parent training, classroom management, and peer intervention training. ( 44 ) Behavioral management is aimed primarily at parents or teachers of children rather than at the child directly. Behavioral interventions use contingent behavior to promote positive responses from the child and reduce negative responses. Parent-directed behavioral management is the recommended first-line intervention for preschoolers with ADHD when sources for such training are available. When preschoolers’ behavior is not sufficiently improved with behavioral management or when behavioral management therapy is not available and ADHD is sufficiently impairing, pharmacologic interventions may be considered. ( 24 )

Behavioral management in school-age children often accompanies pharmacologic management. Although less effective on core symptoms of ADHD, behavioral management is thought to provide some lasting strategies for mitigating behavior. Central to the success of parent and teacher interventions is education on typical developmental expectations, understanding of which behaviors may or may not be related to ADHD, principles of behavior modification, and clear expectations. The effectiveness of behavioral therapy is contingent on the presence and type of co-occurring conditions, the severity and nature of the signs and symptoms, and the consistency of compliance with the suggested behavioral interventions. In adolescents, pharmacologic intervention is the primary treatment mode because there is little evidence of the benefit of behavioral management or cognitive interventions for ADHD. Other therapies, including mindfulness and electroencephalography biofeedback, have little evidence to support their use in children with ADHD. ( 24 )

The National Institute of Mental Health Collaborative’s multisite Multimodal Treatment Study of Children with ADHD showed that pharmacologic therapy was superior to behavioral intervention, but the combination of the 2 approaches offered the most benefit in cases of comorbid conditions. ( 45 )

The mainstay of the pharmacologic treatment of ADHD is stimulant medications, which include methylphenidate and amphetamine preparations. No intervention has been shown to be more effective in treating the core symptoms. Presently, 5.4% of those surveyed in the 2017-2018 National Survey of Children’s Health were taking a prescribed medication for ADHD, and 3.9% were managed by behavioral intervention alone. ( 7 ) Approximately 70% of children respond to a single stimulant, and if treatment with a different stimulant is required, the response rate approximates 90%. Recent studies, however, have pointed to overtreatment in the younger group in a cohort evidenced by an observation that the youngest children in a classroom are more likely to receive a diagnosis of ADHD and treatment compared with their older counterparts. ( 46 )

Methylphenidate and amphetamine preparations both increase the norepinephrine and dopamine transmission of the prefrontal cortex circuits involved in attention and inhibition and are quite effective in treating core features of ADHD. Both methylphenidate and amphetamine are available in numerous short- and long-acting preparations and have l - and d -isomer preparations. The ADHD Medication Guide ( 47 ) is a very useful clinical reference that summarizes stimulant medications by class, preparation, and dose. The type of medication chosen is usually based on discussion with the family and child to determine which preparation is most suitable. Considerations in medication choices include the type of preparation (liquid, chewable, patch, tablet, capsule), the duration of effect of the medication, and the age of the patient. Table 4 provides a summary of commonly used ADHD medications and dosing.

Commonly Used ADHD Medications

ADHD=attention-deficit/hyperactivity disorder, C=chewable, CA=capsule, L=liquid, P=patch, qhs=every night, S=sprinkle, T=tablet.

Not approved in children younger than 6 years but frequently used.

Most commonly used.

Treatment with stimulants should be initiated at the starting dose and then gradually titrated weekly while monitoring for adverse effects and ensuring optimal effect. Practical questions that can be asked by the clinician to help with medication titration include the following: 1) Is the medication dose effective? 2) Are there any adverse effects? 3) When does the medication wear off? It is important to make parents aware that when the medication wears off, there may be a short period during which a child may exhibit an exacerbation of underlying ADHD or symptoms or increased dysphoria. Parents of school-age children who are not with their children during the school day should not judge the effectiveness of the medication during the time the medication is wearing off. As a practical measure, it is prudent for parents to administer new medications or medication doses on the weekend to allow for observation during the most efficacious period of the dose. Once a child has reached a dose that provides efficacy without significant adverse effects, the medication can be continued at that dose and monitored in follow-up for effectiveness and adverse effects. In transitioning off medications, stimulants do not require weaning, whereas alpha agonists must be gradually reduced to prevent rebound hypertension.

Preschool children who continue to demonstrate at least a moderate degree of functional impairment after behavioral management interventions or who do not have access to behavioral management may be prescribed a trial of a methylphenidate preparation. ( 48 ) The evidence for medication in preschool populations is limited, and medication initiation should begin with low doses and small titrations, as preschoolers may exhibit higher rates of dysphoria. ( 49 ) Although the FDA (Food and Drug Administration) has approved only amphetamine preparations for use in children younger than 6 years, there is more safety and efficacy data available on methylphenidates in young children. ( 50 )

School-age children and adolescents have many options for stimulants regarding both preparation and duration of action, but they may fare better on a long-acting medication (8–12 hours) to cover school and homework. For older children and adolescents who require added duration of action, often a lower dose, short-acting preparation is given late afternoon as the effects of the long-acting preparation wane. Medication management also requires consideration of their academic demands during and outside of the school day, as well as consideration of their extracurricular activities.

In adolescence, academic demands often extend far beyond classroom attendance and require a thoughtful balance between evening medication efficacy and sleep requirements. Furthermore, adolescents become very accurate reporters of the efficacy of their medication regimens and the impact medication has on their performance. As they mature, adolescents should be counseled on the indications for their medications, the importance of compliance with medications, and the dangers of substance use while taking their prescribed medications. Compliance with ADHD medication is particularly important in the novice driver as medication use reduces the frequency of motor vehicle crashes in individuals with ADHD. ( 51 ) Youth with substance use disorders and ADHD are best managed by clinicians who specialize in dual diagnosis. As adolescents transition from high school to college, further counseling on adherence to medication schedules and secure storage of controlled substances is prudent.

Stimulant medication initiation for children naive to stimulants should begin at the recommended starting dose for age and assessed at increments of at least 1 week for efficacy and adverse effects. Dose titration should proceed in a stepwise fashion, with reassessment at every incremental increase until there seems to be maximal efficacy and minimal adverse effect. If a child or adolescent fails a trial of a stimulant because of unacceptable adverse effects, it is prudent to try another formulation in the same medication group (methylphenidate or amphetamine) before switching to a different medication group.

The most common adverse effects of stimulant medications include appetite suppression, insomnia, headache, abdominal pain, and tics. Growth effects, if present, are mild. ( 52 ) Preschoolers are more likely to also exhibit irritability or dysphoria while taking stimulants than their older counterparts. Stimulant medications have the potential to raise blood pressure and heart rate, but this is seen uncommonly.

Although adverse effects of insomnia and appetite suppression are common, particularly in the early titration of stimulant medications, they do not infer a need to change or discontinue stimulants unless they are functionally impairing or persistent. Sleep onset is often delayed in children with ADHD, regardless of medication use. If sleep latency is worsened after the initiation of stimulants, consider the timing of the medication administration and its duration of action and encourage good sleep hygiene, including regular bedtime schedules and routines and the discontinuation of electronic device use for a period before bedtime. Children with good sleep hygiene and persistent difficulty with sleep onset may benefit from a brief trial of melatonin. Similarly, for children who exhibit significant or persistent appetite suppression on stimulants, one should ensure that the morning dose is given after a healthy breakfast, encourage meals of higher caloric content while the medication is working, and allow for “catch-up” calories after the medication wanes in the evening. Summer medication “holidays” also provide an opportunity for catch-up growth in children who do not require year-round dosing. If weight loss is problematic on stimulants despite calorie boosting and medication holidays, however, a formulary change may be considered.

The alpha agonists clonidine and guanfacine are nonstimulant preparations for ADHD and are available in short- and long-acting forms. Alpha agonists may be used to treat ADHD in children for whom stimulants have proved ineffective, or if adverse effects are intolerable. Alpha agonists may also be used in conjunction with stimulants because they may act as a stimulant extender and a stimulant enhancer, thus extending the length of stimulant efficacy or improving the efficacy of lower doses of stimulants. An added benefit of alpha agonists is that they also improve sleep onset, which is both a common finding in children with ADHD and a frequent complaint of children taking long-acting stimulants. Adverse effects of alpha agonists include decreased heart rate and blood pressure, which must be monitored closely by the clinician as the medication is titrated. Other adverse effects, including headache, fatigue, and somnolence, tend to subside over time. Caution is advised to ensure that alpha agonists, particularly at higher doses, are not discontinued abruptly because of the potential for rebound hypertension, tachycardia, and agitation.

Atomoxetine, another nonstimulant option for ADHD management, is a selective norepinephrine reuptake inhibitor that increases norepinephrine and dopamine in the prefrontal cortex. Atomoxetine may be used as a single agent or may be used in combination with a stimulant. In clinical trials, however, atomoxetine as a monotherapy has been shown to have a smaller effect size than stimulants for treatment of ADHD in children and adolescents. ( 53 ) Atomoxetine is typically prescribed once daily but can be given twice for extended effect and should be titrated every 3 days. The beneficial effects of atomoxetine may not be appreciated until several weeks after initiation. Common adverse effects include headache, abdominal pain and gastrointestinal upset, and diminished appetite. Rare complications include increased suicidality and hepatitis. ( 54 )

Although single medication prescription for ADHD is preferred and the most observed practice, the use of 2 or more ADHD medications has increased threefold over a 10-year period (2006-2016), with the greatest rate in school-age children. ( 55 ) Methylphenidate and alpha agonists are the most frequent combinations, followed by amphetamines and alpha agonists. Alpha agonists combined with stimulants may extend the duration of the stimulant effect or may enhance the stimulant effect, thus allowing for lower stimulant dosing. Furthermore, alpha agonists are useful as singular prescriptions or combined with stimulants in children with ADHD and mild anxiety, tics, or sleep disruption.

To ensure the most appropriate educational interventions and optimize academic achievement, children with ADHD and academic underachievement or language concerns should be evaluated for co-occurring cognitive, learning, or language disorders that may impact educational performance. Children with ADHD may qualify for special accommodations via Section 504 of the Rehabilitation Act of 1973 or for IEP services under the Individuals with Disabilities Education Improvement Act. ( 56 )

Preschoolers presenting with ADHD may benefit from a Behavioral Intervention Plan implemented by teachers in the classroom setting. ( 57 ) This may involve a functional behavioral assessment to determine the problem behaviors and identify factors that support or extinguish behaviors. Regular communication between the school and the parents is also needed to ensure adherence to behavioral strategies.

School-age children with ADHD may struggle academically as they advance through the early academic years, often due to reduced repetition of academic content, the emergence of co-occurring learning difficulties, or difficulties with organization and executive function, or a combination of these difficulties. Appropriate identification of learning differences, optimization of medication management, and provision of predictable expectations and structure to classroom and homework may reduce some of the burden to students with ADHD and improve school performance.

Organizational and executive function demands may further challenge children with ADHD as they face greater demands for academics and independence in middle and high school. Typical accommodations such as extended time on tests and assignments, preferential seating, memory and visual aids, technologies for recording and typing notes, and regular meetings and communication with families may be of value to these students. ( 58 ) However, limited research exists on the effectiveness of these educational accommodations in the overall management of children with ADHD. ( 59 )

Parental supervision of academics and homework is critical to the success of children with ADHD, particularly in the elementary school period. The discipline of study habits is set at home rather than at school. Parents should be encouraged to make homework a structured, time-limited, and predictable routine with minimal distractions. Parents should oversee that homework is completed, returned to appropriate folders, and placed in the book bag. As children advance through elementary school, parents can help with higher organizational skills, such as keeping an agenda or calendar to track academic assignments and extracurricular activities. Setting gradually higher expectations of a child’s academic responsibilities is a gift to children that can carry them through their education.

Based on strong research evidence, attention-deficit/hyperactivity disorder (ADHD) represents one of the most common neurodevelopmental disorders that are referred to primary care.

Based on guidelines and evidence, the primary care physician must initiate an evaluation for children who show inattention, hyperactivity, or impulsivity affecting academics or behavior and assess based on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria.

Based on strong evidence, the presentation of ADHD can be affected by age and comorbidities, and the primary care physician should screen for comorbid and masquerading conditions, including psychiatric diagnoses, neurodevelopmental disorders, and medical conditions.

Based on strong evidence, ADHD should be managed as a chronic condition with a management plan that is age-appropriate and includes ongoing monitoring and combining behavioral, pharmacologic, and educational approaches.

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AUTHOR DISCLOSURE

Drs Rajaprakash and Leppert have disclosed no financial relationships, relevant to this article. This commentary does not contain a discussion of an unapproved or investigative use of a commercial product/device.

attention-deficit/hyperactivity disorder

autism spectrum disorder

Diagnostic and Statistical Manual of Mental Disorders

intellectual disability

Individualized Education Program

minimal brain dysfunction

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The diagnosis of attention deficit hyperactivity disorder (ADHD) has been surrounded by controversy over the last century. Over the past 30 years, however, a consensus has been developed regarding both the existence of attention deficit hyperactivity disorder and the symptoms and signs that define it. Additionally, research has increased knowledge of the neurochemical and physiologic causes of attention deficit hyperactivity disorder. This has led to the development of techniques for effective management of the condition. This activity reviews the evaluation and management of attention deficit hyperactivity disorder and highlights the role of the interprofessional team in collaborating to provide well-coordinated care and enhance outcomes for affected patients.

• Outline the pathophysiology of attention deficit hyperactivity disorder.
• Explain how to evaluate for attention deficit hyperactivity disorder.
• Review treatment considerations for patients with attention deficit hyperactivity disorder.
• Summarize the importance of enhancing coordination amongst the interprofessional team, caregiver, and patient to provide optimal care to patients with attention deficit hyperactivity disorder.
• Introduction

Attention Deficit-Hyperactivity Disorder (ADHD) is a psychiatric condition that has long been recognized as affecting children's ability to function. Individuals suffering from this disorder show patterns of developmentally inappropriate levels of inattentiveness, hyperactivity, or impulsivity. Although there used to be two different diagnoses of Attention Deficit Disorder vs. Attention Deficit Hyperactivity Disorder, the DSM IV combined this into one disorder with three subtypes: predominantly inattentive, predominantly hyperactive, or combined type. 

The symptoms begin at a young age and usually include lack of attention, lack of concentration, disorganization, difficulty completing tasks, being forgetful, and losing things. These symptoms should be present before the age of 12, have lasted six months, and interfere with daily life activities in order to be labeled as 'ADHD.' This must be present in more than one setting (i.e., at home and school, or school and after-school activities). It can have large consequences, including social interactions, increased risky behaviors, loss of jobs, and difficulty achieving in school. 

ADHD must be considered within the context of what is developmentally and culturally appropriate for a person. It is considered a dysfunction of executive functioning, predominantly a frontal lobe activity. Therefore, patients with ADHD show disability not only in attention and focus but also in decision making and emotional regulation. Children with ADHD can have difficulty with social interactions, can be easily frustrated, and can be impulsive. They are often labeled as "trouble makers." 

ADHD is not a new condition and has been called different names throughout history. It was labeled as 'minimal brain dysfunction' in the 1930s and has ever since changed names to ADD and ADHD, respectively. [1]  Its prevalence has increased over time, with a seeming spike in the 1950s as school became more standardized for children. 

It is important to diagnose and treat the disorder at a young age so that the symptoms do not persist into adulthood and cause other comorbid conditions. The treatment for the disorder is mostly related to stimulants and psychotherapy. [2]  This review would further shed light upon the causal factors, pathophysiology, and management of ADHD.

The etiology of ADHD is related to a variety of factors that include both a genetic and an environmental component. It is one of the most heritable conditions in terms of psychiatric disorders. There is a much greater concordance in monozygotic twins than dizygotic. Siblings have twice the risk of having ADHD than the general population. Similarly, viral infections, smoking during pregnancy, nutritional deficiency, and alcohol exposure in the fetus have also been explored as possible causes of the disorder. There are no consistent findings on brain imaging of patients with ADHD. The number of dopaminergic receptors has also been implicated in the development of the disorder whereby research has shown that the receptors are decreased in the frontal lobes in individuals with ADHD. [3] [1]  There is also evidence for the role of noradrenergic receptor involvement in ADHD. 

• Epidemiology

The subtypes of attention deficit disorders are found to have a different rate of prevalence in a group of individuals suffering from the disorders. It is found that the inattentive subtype is prevalent in about 18.3% of the total patients while hyperactive/impulsive and combined represent 8.3% and 70%, respectively. It is also found that the inattentive subtype is more common amongst the female population. The disorders (collectively) are found in a 2:1 male to female ratio as per different researches. [4]  It is prevalent in around 3%-6% of the adult population. [5]  It is one of the most prevalent disorders found in childhood. There is some evidence that ADHD is more prevalent in the United States than in other developed countries. 

• Pathophysiology

ADHD is associated with cognitive and functional deficits that relate to diffuse abnormalities in the brain. The anterior cingulate gyrus and dorsolateral prefrontal cortex (DLFPC) are found to be small in individuals who are suffering from ADHD. It is thought that these changes account for the deficits in goal-directed behavior. Moreover, activity in the frontostriatal region is also reduced in these individuals as measured by fMRI. It is important to understand these pathophysiological mechanisms so that the pharmacotherapy is directed onto them. [6]  It is important to remember that ADHD is a clinical diagnosis. There are no standard laboratory or imaging results among patients with ADHD. 

• History and Physical

In order to diagnose ADHD, it is very important to take a relevant history of the concerned individual. ADHD is diagnosed in children based upon their history, where the children face difficulty in at least 6 of the 9 symptoms as mentioned in DSM 5. Inattentive symptoms include: not paying close attention to tasks, missing small details, rushing through tasks, not seeming to listen when spoken to, difficulty organizing things, not finishing work, dislikes or avoids tasks that take sustained mental effort, losing thins, or being forgetful. Hyperactive symptoms include: fidgeting, feeling like an "internal motor" is always going, leaving their seat, climbing on things, being loud, blurting out answers, talking excessively or out of turn, having trouble waiting their turn, interrupts, or intrudes on others. These symptoms must be present in multiple settings.

In adults, however, these core symptoms may be missing, and they may manifest as other problems such as procrastination, mood instability, and low self-esteem. They will likely be more impulsive in nature or inattentive, as the hyperactivity symptoms can be better controlled. The symptoms of inattention or hyperactivity will likely be elicited when doing a proper history of childhood but may have been missed. 

ADHD interferes with functioning and development. This can be included in adults who do not work and is often dismissed in this population. For example, a stay-at-home mom may have difficulty getting her children to school on time, organizing her home, paying attention while driving, etc., which affects her functioning and daily life even though she is not at work or school. It is important to take this into consideration when making a diagnosis. 

Different scales are used to measure the problems that patients with ADHD are suffering from. One such example is the Brown Attention Deficit Disorder Scale which includes common areas that these individuals face difficulty in and can be used in adults to identify the disorder. For children, the Vanderbilt ADHD scale is often used as it has both a teacher and parent component. A physical examination, on the other hand, is not as useful in the diagnosis of ADHD, but it can still be used to exclude medical causes such as thyroid problems. It could also help to identify any medical issue that could thereby direct the treatment options. For example, individuals with hypertension may not opt for stimulants as a treatment option. [7] [8] [9]

ADHD is a disorder that is diagnosed clinically and does not have any specific laboratory or radiologic tests. The neuropsychological tests are not as sensitive for diagnosing the disorder, and hence the disorder should be diagnosed based upon the history of the patient. [7]  The evaluation of the patient with ADHD is usually done with different rating scales and multiple informants who may include the teachers and parents. It is necessary for a clinician to look for other disorders as they may be a cause for the symptoms that a child is exhibiting. It should not be diagnosed in the context of symptoms from another disorder, for example, a psychotic episode or manic episode.

DSM 5: Types of ADHD

• Predominantly inattentive
• Predominantly impulsive or hyperactive
• Combination of the above
• The onset is usually before age 12
• Symptoms present at school, work, or home
• The disturbance causes significant impairment in social, occupational, and academic functioning.
• The disorder is not accounted for by any other behavior disorder.
• Treatment / Management

Pharmacological therapy remains the mainstay of treatment for patients who have ADHD. It is divided into two major categories, which fall into stimulants or non-stimulants. Stimulants are further broken into amphetamines and methylphenidates. Both types of stimulants block the reuptake of dopamine at the presynaptic membranes and postsynaptic membranes. Amphetamines also directly release dopamine. Stimulants are the mainstay of treatment for ADHD. They are effective in about 70% of patients. There is a number needed to treat of 2. There are multiple formulations of each subtype of stimulants, including immediate-release and extended-release, long-acting, or sustained release. Side effects of stimulants include changes in blood pressure, decreasing appetite and sleep, and risk of dependency. However, there is an increased risk of substance use in patients with ADHD and studies show treating with a stimulant decreases their overall lifetime risk of substance abuse. Because stimulants are controlled substances, providers often are hesitant to use them. However, repeated evidence has shown how imperative it is to try stimulants in ADHD. 

There have been concerns regarding stimulant use in patients with seizures. However, recent studies showed that stimulant use for ADHD is safe in epilepsy. [10] [11]

There can be an increase in the frequency of tics in patients with ADHD and Tic disorders. Adding alpha agonists may help to reduce tics. [12]

Of the non-stimulant option, there are also two types: antidepressants and alpha agonists. Within the antidepressant category, atomoxetine is is the best known and works as a selective norepinephrine reuptake inhibitor. It is known to be effective in many trials as a treatment option for ADHD, though not nearly as effective as stimulants. It also has minimal antidepressant effects. It is often used in children who don't tolerate stimulants or have anxiety. Other antidepressants include bupropion, which targets dopamine and serotonin, and TCAs, which are the last choice options. These work by targeting norepinephrine.

Lastly, alpha agonists such as clonidine and guanfacine can be used as an effective treatment for ADHD. However, these are associated with multiple cardiovascular effects like lowering blood pressure, sedation (clonidine more than guanfacine), weight gain, dizziness, etc. They are found to be more effective in younger children than adults. [6]  

Psychosocial treatment is the other form of treatment that is used for individuals suffering from the disorder. This form of treatment includes psycho-education for the family and patient and cognitive-behavioral training programs designed specifically for the patient to achieve short and long-term goals. Research has found that these training programs prove to be very effective when used along with pharmacotherapy. However, unlike other psychiatric disorders, there is strong evidence for medication management without therapy as being the most efficacious. [13] [14] [15]

The FDA has just approved the trigeminal nerve stimulation system for children not on medications. The device generates a low-level electrical pulse which suppresses hyperactivity.

There is no diet that has been found to improve ADHD

• Differential Diagnosis

It is important to differentiate ADHD from other clinical disorders as it can have symptoms that may overlap with them. Mood disorders such as depression and anxiety can be misdiagnosed in a patient with ADHD as these symptoms (inattention and poor focus, memory loss, distractibility, etc.) generally persist in individuals with the disorder. Substance abuse disorders should also be carefully examined as children with ADHD are prone to substance abuse. It is important to rule out hearing disorders, learning disorders, and developmental disorders from ADHD. [6]

The prognosis of ADHD is variable depending upon the age of the individual who is experiencing the symptoms. It is seen that the symptoms of ADHD persist into the teenage years and may involve the social and academic domains of life. Two-fifths of the patients continue experiencing the symptoms in the teenage years, whereas a quarter of them are also diagnosed with a concurrent antisocial disorder. However, an important trend in the long term was also noted whereby the symptoms of the patients with ADD decreased in adulthood by about 50%. The general rule of thumb is that 50% of patients "grow out of" ADHD, especially with treatment, and another 25% do not need treatment into adulthood. This is theorized twofold; first, that stimulants help improve the development of the frontal lobe over time, and second that adults often choose careers that don't require sustained attention. In adulthood, these patients are able to achieve their educational and vocational goals. [16]

Treatment of ADHD has also been shown to improve symptoms of oppositional defiant disorder and conduct disorder. It has shown a decreased risk of substance use. 

However, untreated ADHD can cause persisting dysfunction and devastating consequences included but are not limited to long-term inability to work, increased car accidents, and increased substance use.  [17] [18]

• Deterrence and Patient Education

Patients with ADHD must be followed up regularly to check upon their symptoms and comorbidities. In order to achieve treatment goals, the role of patient education cannot be emphasized enough. For children who have ADHD, the parents should be formally educated about the disorder so that they understand the concept behind the diagnosis. Medication treatment can only be optimized if there is an ongoing interaction between the primary caregiver and the family. [19]

• Pearls and Other Issues

ADHD is often a very easily treated disorder that is highly stigmatized in society. Proper diagnosis and treatment can change the lives of patients who suffer from these.

Providers should not be hesitant to try stimulant medications. They are highly effective and can be very safe when properly prescribed.

ADHD has multiple comorbidities, including anxiety, depression, and conduct disorder. Treatment of ADHD can improve the symptoms of these other disorders.

• Enhancing Healthcare Team Outcomes

ADHD is a condition that can be managed, but the protocols for managing it must be followed effectively in order to achieve a fruitful result. The management involves an interprofessional team that includes the specialist psychiatrist, pediatrician, pharmacist, and other health care professionals, including nurse practitioners who help in diagnosing the disorder. The collaboration on the part of the family and the health care team becomes important so as to know the exact history of the events that the patient has gone through.

The team should then make up a management plan that may include a pharmacologic treatment, a psychosocial intervention, or both. The comorbid disorders of ADHD would have to be analyzed by the team as depression, and anxiety disorders are much more common in this set of population.  In order to make a diagnosis of ADHD, regular follow-ups with the primary caregiver of the child should be scheduled along with the child. The clinician can then ascertain and evaluate the child himself and clinically co-relate it to the findings as provided by the caregiver. This can further be taken to a specialist psychiatrist, whereby a confirmatory diagnosis can be made. This would then involve a set of other healthcare professionals such as a psychologist or a trained psychotherapist along with the psychiatrist. The treatment plan is then formulated by the team, and the caregiver himself is given an important role along with the healthcare team. The caregiver has to observe the patient and help in noticing the changes that the child may exhibit. Hence, it can be concluded that an integrated healthcare plan should be followed for the diagnosis and treatment of ADHD so that the long-term goals of the treatment can be achieved. [20]

Open communication between the interprofessional team is the key to improve outcomes. The team should have a conference as that everyone knows what message is to be sent to the caregiver, who often gets upset with mixed messages.

Despite decades of research, the outcomes for patients with ADHD are guarded. Noncompliance with medications is common, and follow-up is difficult as many patients seek alternative treatments. Many parents do not trust the drugs and often seek alternative care. There is no question that currently available treatments do help some patients improve functionally. Still, without treatment, the individuals continue to deteriorate and eventually end up in financial, legal, and social difficulties. [4] [21] [22]

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Disclosure: Warren Magnus declares no relevant financial relationships with ineligible companies.

Disclosure: Saad Nazir declares no relevant financial relationships with ineligible companies.

Disclosure: Arayamparambil Anilkumar declares no relevant financial relationships with ineligible companies.

Disclosure: Kamleh Shaban declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Magnus W, Nazir S, Anilkumar AC, et al. Attention Deficit Hyperactivity Disorder. [Updated 2023 Aug 8]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Understanding Factors that Influence Caregiver Recognition of ADHD Symptomatology in Children- A Scoping Literature Review

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Systematic review and meta-analysis: relative age in attention-deficit/ hyperactivity disorder and autism spectrum disorder

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Youngest students in their class, with birthdates just before the school entry cut-off date, are overrepresented among children receiving an Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis or medication for this. This is known as the relative age effect. This systematic review and meta-analysis summarises the evidence on the influence of relative age on ADHD symptoms, diagnosis and medication prescribing. As no review to date has investigated the association with autism spectrum disorder (ASD) diagnosis, this is also examined. Following prospective registration with PROSPERO, we conducted a systematic review according to the PRISMA guidelines. We searched seven databases: Medline, Embase, PsycInfo, Web of Science Core Collection, ERIC, Psychology and Behavioural Sciences Collection, and Cochrane Library. Additional references were identified from manual search of retrieved reviews. We performed a meta-analysis of quantitative data. Thirty-two studies were included, thirty-one investigated ADHD and two ASD. Younger relative age was associated with ADHD diagnosis and medication, with relative risks of 1.38 (1.36–1.52 95% CI) and 1.28 (1.21–1.36 95% CI) respectively. However, risk estimates exhibited high heterogeneity. A relative age effect was observed for teacher ratings of ADHD symptoms but not for parent ratings. With regard to ASD, the youngest children in their school year were more likely to be diagnosed with ASD. This review confirms a relative age effect for ADHD diagnosis and prescribed ADHD medication and suggests that differences in teacher and parent ratings might contribute to this. Further research is needed on the possible association with ASD.

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Introduction

Relative age refers to the age difference between children grouped together in the same school year due to the school entry cut-off date. The youngest students in the school year have birthdates just before the cut-off date while their older peers have birthdates up to 12 months earlier [ 1 , 2 ]. Children of younger relative age are expected by adults to match the educational and behavioural expectations of their relatively older classmates. Although there are interindividual differences in maturation besides relative age, younger students are likely to be less developmentally mature than their older classmates [ 3 ]. Previous systematic reviews and meta-analyses summarising data from different countries, have shown that youngest students in their school year are overrepresented among children with an Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis or receiving ADHD medication [ 3 , 4 , 5 , 6 ], a phenomenon referred to as the relative age effect (RAE). ADHD is a neurodevelopmental condition, usually diagnosed in childhood, characterised by developmentally inappropriate levels of motor hyperkinesis, impulsivity and difficulties in attention and organisation [ 7 , 8 ]. Although the prevalence of ADHD varies globally, with administrative clinical diagnosis rates ranging from around 2% to 7% [ 9 ], the RAE has been observed in both countries with lower and higher prescribing rates of ADHD medication [ 3 , 4 ]. Given that most countries have different cut-off dates for school entry with differing policies, international comparisons can be helpful in understanding the underlying reasons for diagnostic and prescribing variations observed with relative age [ 3 , 10 , 11 ].

ADHD assessment and diagnosis is a multi-step process, requiring an evaluation by a clinician, observational reports from school professionals on behavioural or academic problems and input from parents [ 12 ]. In addition to studies using administrative record data from population-wide databases, teacher and parent ratings of ADHD symptoms have been increasingly reported in literature [ 13 ]. Accounts from teachers and parents on ADHD-related symptoms are highly relevant due to their essential contribution to the diagnostic process, providing insight into the child’s symptoms and functional difficulties in multiple settings [ 14 , 15 ]. Understanding the extent of the RAE on teacher and parent reporting of children’s symptoms is important in the assessment process for children with possible ADHD [ 13 ] and so this review aims to expand the scope to explore the role of different informants in the relative age effect phenomenon, an area not extensively investigated in previous reviews.

The RAE in ADHD has been widely studied; however, the effect on autism spectrum disorder (ASD) diagnosis much less extensively investigated [ 16 ]. Children who are relatively younger in the school year may show variation in language skills and be less likely to meet the social demands of their classroom [ 17 ], both of which could resemble common features of ASD [ 18 ].

The topic of the relative age effect remains a rapidly progressing area of international research, with a continuously expanding body of evidence. This systematic review summarises the evidence on how being relatively younger in the school year could affect three domains: (1) ratings of ADHD symptoms by teachers or parents, (2) receiving a diagnosis of ADHD and (3) receiving ADHD medications; aiming to quantify this effect wherever possible using meta-analysis. We hypothesised that the relative age effect may have become attenuated over recent years as clinicians diagnosing and prescribing medications for ADHD may have been more aware of the relative age phenomenon, therefore taking a child’s relative age into account as part of their assessment. Additionally, this review broadens the scope by including ASD as a comparator neurodevelopmental condition to provide a more comprehensive understanding of the relative age effect across different diagnostic categories. We hypothesised that the effect would be observable within the context of ASD.

Registration

This systematic review was prospectively registered with PROSPERO (ID: CRD42022373175) and was conducted in accordance with the PRISMA reporting guidelines [ 19 ]. (see online resource 1).

Two separate searches for primary studies investigating the effect of relative age within a school year on symptoms, diagnosis or prescription of medication or ADHD or diagnosis of ASD were conducted. They were searched on the 23rd August 2022 in the following databases: MEDLINE® ALL, Embase, PsycInfo (via Ovid), the Cochrane Central Register of Controlled Trials (CENTRAL) and the Cochrane Database of Systematic Reviews (through the Cochrane Library), Web of Science Core Collection, ERIC, and Psychology and Behavioral Sciences Collection (via EBSCOhost). The search strategies were undertaken by an information specialist using free text terms (searching the title and abstract) as well as advanced search syntax (truncation, Boolean logic AND/OR, and proximity searching) to ensure all relevant studies were identified. Relevant controlled vocabulary headings for each database were searched and relevant terms were identified. The search terms included the following themes, with synonyms to describe each: relative age and ADHD (see online resource 2); relative age and autism spectrum disorder (see online resource 3). Additionally, a manual search of the references of systematic reviews and meta-analyses retrieved in the database search was conducted for any possible studies missed by the database search.

Study selection

Rayyan software was used to assist with study selection. Duplicates were removed and two independent reviewers (EF, JH) screened the titles and abstracts of the search results; 100% consensus was reached between reviewers (initial 2% discrepancy resolved with discussion). Full text assessments were completed independently by EF and JH, who agreed on final paper inclusion (7% discrepancy resolved with discussion to reach 100% consensus).

Inclusion criteria

There were no date restrictions, and all observational studies were eligible for inclusion if they reported a measure of ADHD symptoms, diagnosis or prescription of medication, or ASD symptoms or diagnosis, for participants up to 18 years of age, in relation to their age within a school year, including month (either recorded grouped months or month) of birth. For studies measuring prescribed medications for ADHD, this was used as a proxy for confirmed clinical diagnosis.

Exclusion criteria

Publications were excluded if they: (1) were review papers or meta-analyses, (2) were case or conference abstracts with no corresponding full text-paper for retrieval, (3) were not available in the English language, (4) included adult populations only, (5) did not report month (or grouped months) of birth of participants in relation to school entry, (6) commented only on effect of relative age on symptoms or behaviours not directly relevant to ADHD or ASD. Grey literature was not searched.

Data extraction

For the included studies, the following data were extracted on Microsoft excel: authors, year of publication, country of study, total sample size, years studied, the cut-off date for school entrance in the source population, age range of studied population, data source, reported socio-demographic characteristics, the time period/calendar month used as exposure measure in each study between younger and older children, the number of children with symptoms, the symptom measure used, who rated the symptoms, and/or clinical diagnosis and/or being prescribed medication for ADHD or ASD (absolute number or risk ratios as available). If a study met inclusion criteria but did not provide sufficient data for analysis, the authors were contacted once to provide additional information.

Risk of bias

The risk of bias was assessed by EF for each study included using the Newcastle–Ottawa Quality Assessment tool [ 20 ]. The Newcastle–Ottawa Scale (NOS) measures the selection, comparability and outcome measures of the included studies, and is rated out of 9 in total. Not all domains were applicable to all studies and so some studies were rated out of lower total scores. Studies scoring between 7 and 9 were considered of high methodological quality, between 5 and 7 of moderate and below 5 of low quality. A full breakdown of NOS scores for individual studies is available in online resource 4. The authors have no conflicts of interest.

Data analysis and synthesis

We conducted a narrative synthesis of the available evidence on the relative age effect on ADHD symptoms, diagnosis or prescribed medication, and ASD, and as far as possible quantitatively describe these relationships using meta-analysis. Meta-analysis was performed using Review Manager version 5.3, with a random effects model due to the expected heterogeneity of data based on previous meta-analyses [ 3 ]. Missing data were excluded from the data synthesis and all materials used in the review are available upon request. Outcome measures are presented as relative risk (RR) with 95% confidence intervals.

A total of 2120 papers were retrieved in the database search, leaving 1012 papers for screening after duplicates were removed (Fig.  1 ). Following initial title and abstract screening, 60 full-text papers were assessed for eligibility, of which 31 were excluded. Five further papers were identified from manual citation search, three of which were included. In total, 32 papers were included in the review. Study characteristics are presented in Table  1 . Most included studies examined the role of relative age and ADHD (n = 31) with only two investigating ASD. Due to the low number of studies on ASD, no meta-analysis was possible, although a narrative review is presented.

Prisma flow diagram

ADHD symptom ratings

Nine studies measured ratings of ADHD symptoms, three of which provided ratings from teachers, two from parents and four from both. Different types of questionnaires were utilised in different studies, with the Strengths and Difficulties Questionnaire (SDQ) (hyperactivity/inattention scale) being the most common (n = 3), followed by the Swanson, Nolan, and Pelham (SNAP- IV) Questionnaire (n = 2). Other measures included the Autism, Tics, ADHD and other Comorbidities Inventory (A-TAC), Conner’s Teacher Rating Scale (CTRS), Perceived Competence Scale (PCS), Children’s Behavioural Scale (CBS) and two unspecified scales. The risk of bias was assessed using the NOS with three scoring as high methodological quality and six as moderate. A RAE was observed for teacher ratings of ADHD symptoms in two of the three studies investigating teacher ratings only, with positive findings showing up to a 3.6-fold increase in the youngest quartile [ 14 , 21 ]. Three studies showed a RAE on teachers’ ratings, with up to threefold increase in reported ADHD symptoms by teachers, but not parents’ [ 13 , 22 , 23 ], and one study reported a association with both, by estimating a 3.5% and 1.1% difference in teacher and parental ratings respectively [ 23 ]. There was no association between relative age and parent ratings for ADHD using the A-TAC inventory [ 24 ], while one study using the hyperactivity/inattention section of SDQ reported an association between younger relative age and parental ratings [ 25 ]. One study found an association between younger relative age and higher scores for ADHD symptoms for both teacher and parent ratings (75% and 54% higher respectively)[ 26 ].

ADHD clinical diagnosis

Seventeen papers used clinical diagnosis of ADHD as an outcome measure, with twelve using database records and five parent-reported clinician diagnoses [ 13 , 23 , 26 , 27 , 28 ]. Ten studies were of high methodological quality and seven of moderate. Fifteen studies found a relative age effect on the likelihood of ADHD diagnosis [ 1 , 2 , 10 , 13 , 23 , 24 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], and two found no association [ 26 , 36 ].

ADHD medications

Seventeen papers used prescription of ADHD medications, almost all derived from administrative databases, apart from two papers relying on parent-reported ADHD medications [ 13 , 23 ]. Ten studies were of high methodological quality on NOS, six moderate and one low quality. Fourteen of the studies demonstrated a relative age effect for ADHD medication prescriptions, [ 2 , 23 , 27 , 30 , 31 , 32 , 34 , 37 , 38 , 39 , 40 , 41 , 42 ] with three studies finding no association [ 13 , 43 , 44 ]. Two studies investigated prescribed medication in countries with high rates of held-back children, accounting for children with delayed school entry, by collecting data from educational databases [ 11 , 42 ]. Both studies showed that children who were the eldest within the school year, because they had been held-back, were more likely to be prescribed ADHD medications than their younger classmates who had entered school when first eligible [ 11 , 42 ].

Meta-analysis

For ADHD diagnosis and medication, twenty-three studies were included in the meta-analysis. The rest could not be added due to: case control study design (n = 1) [ 24 ] or presenting insufficient data (n = 3) [ 26 , 33 , 34 ]. Corresponding authors were contacted to provide supplementary data to allow for inclusion in the meta-analysis (n = 6), and sufficient data were available and provided by one. For two papers, although authors did not provide supplementary data, this was retrieved from a previous meta-analysis in 2018 [ 3 ]. Two separate meta-analyses were conducted for studies investigating clinical diagnosis and medication prescription in relation to children’s relative age within school year. However, both showed high levels of heterogeneity (I 2  = 99%, I 2  = 94% respectively) (Figs.  2 , 3 ).

Number of children receiving a diagnosis of ADHD (events), comparing the and younger and older children within the school year

Number of children receiving ADHD medications (events), comparing the younger and older children within the school year

Younger relative age was associated with ADHD diagnosis and medication, with RR of 1.38 (95% CI 1.36, 1.52) and 1.28 (95% CI 1.21, 1.36) respectively. This means that children who are younger relative to their peers within the same school year are 38% more likely to receive an ADHD diagnosis and 28% more likely to be prescribed ADHD medications. Risk ratio plots (Figs.  2 , 3 ) showed the risk ratio estimates from individual studies. All included studies investigating ADHD diagnosis showed higher risk ratios for younger children in their school year compared with their older classmates, apart from one in Denmark, which found an opposite effect with RR 0.91 (95%CI 0.86,0.96) [ 36 ]. (Fig.  2 ) For prescribed medication, all studies showed higher risk of ADHD medication prescriptions for younger relative age, except for one study in Scotland [ 11 ]. (Fig.  3 ).

For studies investigating ADHD symptoms, a meta-analysis was not possible due to the variability in rating scales used, differences in the informant, and methodological differences (Table 2 ).

Autism spectrum disorder

The two studies investigating the effect of relative age on diagnosis of ASD were both from Taiwan [ 5 , 34 ]. In both studies, children who were the youngest in their school year were more likely to be diagnosed with ASD than those who were the eldest. Both studies were found to be of high methodological quality, scoring seven on the NOS. One considered the relative age effect on multiple neurodevelopmental disorders and reported a highly pronounced drop in diagnosis rate between the birth months of August and September, i.e. the month immediately before and after the school entry cut-off, for both ADHD and ASD with the relationship being most pronounced for ADHD [ 5 ].

This systematic review summarises the available evidence on the influence of relative age on the rating of ADHD symptoms by teachers or parents, revealing a discrepancy between the impact of relative age on teacher and parent ratings. These findings contribute to our understanding of this topic by showing that teacher ratings of ADHD symptoms were influenced by relative age; in contrast, parent ratings showed no or weak association with relative age [ 23 , 24 ]. Our review also confirms findings from the previous literature [ 3 , 4 ] by incorporating data from more recent studies, showing that the effect of relative age on clinical ADHD diagnosis and prescribed medication has persisted. Furthermore, we extend previous systematic reviews by investigating the relative age effect in ASD, a different neurodevelopmental disorder usually diagnosed in childhood [ 45 ].

The presence of ADHD symptoms in children as reported by teachers and parents, does not automatically translate to a formal diagnosis. Distinguishing between adult-reported symptoms and the diagnostic process allowed us to explore how relative age influences symptom interpretation, independent of diagnosis. Improved knowledge about how teachers and parents perceive and report ADHD symptoms is important as both are essential informants about a child’s ADHD-type behaviours in different settings [ 13 , 46 ]. Overall, our results show that teacher ratings for ADHD-related symptoms are more influenced by relative age, in contrast to parent ratings. This difference between parent and teacher reporting of ADHD symptoms could be influenced by several factors. The higher demands and limited flexibility of the school environment, the presence of large numbers of peers to compare the child with, and the less close and shorter duration of the relationship of teacher to child compared with parent to child could all cause this observational bias in teacher ratings [ 3 , 14 , 26 ]. The limited classroom-specific support strategies for teachers to help relatively younger children with ADHD symptoms meet classroom expectations may also influence their assessments of ADHD symptoms. Parents may also be subject to social desirability bias towards their child, potentially overlooking certain behaviours. Teacher perceptions and susceptibility to relative age bias may impact a child’s referral and diagnostic process. Teachers are more likely to identify ADHD symptoms in younger children in the school year and give higher scores on symptom scales, which are then taken into account by clinicians when doing a diagnostic assessment. Conversely, teachers might also miss ADHD symptoms in relative older children in the class as they are being referenced against their younger and slightly less mature classmates [ 23 ].

In terms of diagnosis and prescriptions, our results overall confirm an association between younger relative age and a clinical diagnosis of ADHD or prescribed medication. The strength of this association showed high heterogeneity for both outcomes. This could be explained by methodological factors such as different ways of measuring exposure and outcomes, variability in sample characteristics, sample sizes and cut-offs for ‘oldest’ and ‘youngest’ in the year; educational differences such as different curriculums, systems and policies including but not limited to rates of delayed school entry, rates of repeating school year due to failure to progress, absolute age at starting school, school classroom size; and cultural differences such as societal attitudes towards neurodevelopmental disorders or expectations around conformity and educational achievements. International variations in diagnostic and prescription practices, including access to services and who can make a clinical diagnosis, likely contribute to the observed heterogeneity, reflecting discrepancies in ADHD identification and medication use rates across countries. Such cross-cultural differences in ADHD diagnostic and treatment guidelines should be considered when interpreting the findings of international studies [ 3 , 27 , 44 ]. Most studies did not consistently report sociodemographic characteristics of their total sample; although six studies adjusted their analysis for some sociodemographic characteristics as potential confounders. Due to many studies relying on nationwide prescription or health record databases, data collected by primary authors were often representative of the clinically relevant populations of the area.

A persistent relative age effect was found for studies published since 2018 (when the previous systematic review was conducted) for both diagnosis and medication [ 1 , 5 , 13 , 29 ]. This phenomenon has been documented in the literature over the past decade, so one might expect that clinicians to factor relative age into assessments, however, there is little evidence that this has occurred [ 37 ]. One reason could be the lack of guidance on how to best account for relative age in the diagnostic process, as international guidelines such as NICE and American Academy of Pediatrics do not incorporate relative age considerations [ 46 , 47 ]. Other factors could be diagnostic uncertainty, clinicians’ time pressure, reliance on subjective evaluations or over-reliance on standardised questionnaires, limiting the ability perform age-matched comparisons for younger children.

The relative age effect on ADHD shows a pronounced impact in younger children attending primary school, with peak age varying in individual studies, but overall gradually diminishing through adolescence. This observation suggests that actual age and developmental expectations significantly influence ADHD identification, with early schooling years witnessing the greatest disparities [ 1 , 2 , 32 , 33 ]. Existing literature has discounted the presence of a seasonal effect on ADHD, as variations do not align with specific seasons but shift according to local school entrance policies [ 38 ]. Previous meta-analyses have speculated that more flexible school entry could reduce the relative age effect [ 3 , 4 ]. However, two recent studies found that children who were held back a year, entering school relatively later than their classmates, were more likely to be prescribed ADHD medication [ 11 , 42 ]. The authors explained this may be due to systematic differences in children with delayed school entry, such as having more complex special developmental needs [ 42 ] or parents who worry more about perceived relative immaturity and neurodevelopmental diagnoses [ 11 ]. However, these findings suggest that changes to school entry policy may not have the desired effect of reducing the relative age effect. Importantly, families of higher socioeconomic status are more likely to afford deferring their child’s school entry while less affluent families are more likely to have children in the youngest school year cohort [ 11 ]. This introduces a social inequity aspect to the disparity in diagnostic rates in areas with flexible school entry policies.

While the relative age effect is well documented in ADHD, data for other neurodevelopmental disorders are still emerging. Only two studies, both from the same country using the same data source, were identified investigating the relative age effect in ASD diagnosis and children who were the youngest in their school year were more likely to be diagnosed with ASD compared with their older classmates. The reasons behind this are not clear but it is possible that immature speech or social skills of relatively younger children may be interpreted as traits of autism by referrers [ 5 , 48 ]. Although the timing of identification of characteristics for many autistic children takes place before they reach school age [ 49 , 50 ], no information on the age of autism diagnosis was available in the two studies to comment on the differences between autistic children diagnosed before school age and those after.

Strengths and limitations

A strength of this review was the systematic search of the available literature across seven databases by two independent reviewers and quality of studies was assessed as high or moderate for all except one study. Studies from eighteen countries were included, collecting data from diverse settings. However, there is a risk of potential overlap in populations across studies from the same countries and using similar databases, especially among the Taiwanese studies, which should be taken into account when interpreting the weight of our findings. For studies investigating ADHD-related symptoms, there was wide variability in chosen assessment tools, leading to challenges in comparing the results between studies. Even though our meta-analysis aimed at combining quantitative data from studies that offered sufficient detail, the high heterogeneity across studies, (despite our attempts to explain the reasons behind it), made it challenging to generalize our findings reliably. Additionally, not all retrieved studies could be included in the meta-analysis due to the way data were analysed and presented, meaning some studies with large datasets were excluded from the quantitative synthesis. Although searches were comprehensive there is a small risk of publication bias, as smaller studies or ones with negative results may have not been published and so might have been missed from our database search, potentially causing an over-estimation of the studied effect. Pervasive developmental disorder not otherwise specified (PDD-NOS) was not included in our search criteria, which could be seen as a limitation. For studies investigating teacher ratings, there was no information on the training and experience of individual teachers in identifying ADHD symptoms or on how long they had known the child.

Clinical and research implications

Teacher ratings form an important part of ADHD assessments, and so it is important to understand the effect of relative age on their perception of what are normative or immature behaviours. Clinicians would benefit from collaborative involvement of parents and teachers in their assessments, whilst taking into account the possible differences in the relative age effect bias of these two informants. Despite the relative age effect being previously described in the literature, this has not translated into a change in clinical practice for diagnosis or medication prescribing, although a conclusion around the magnitude of the relative age effect is difficult to draw given the level of heterogeneity observed. It will be useful to incorporate this phenomenon in the clinical guidelines and training of healthcare professionals specialising in neurodevelopmental disorders as well as teachers to help them think critically about children’s symptoms during their assessments. Importantly, diagnosis of ADHD in relatively younger children is no more likely to decrease in persistence than diagnoses in relatively older children and so the relative age effect should not necessarily deter clinicians from diagnosing relatively younger children with ADHD [ 51 ]. Referrers and clinicians should consider the possibility that the relative age effect may be leading to a decreased likelihood of older children in the class being identified with ADHD symptoms [ 51 ]. Systematically considering contributing factors like relative immaturity as part of a child’s presentation is important to improve accuracy of ADHD diagnosis and subsequent appropriate treatment with medications.

Given the prevalence of ADHD [ 52 ], addressing the diagnostic challenges and accounting for biases becomes increasingly relevant. From a research perspective, a more standardised methodology (e.g. choice of measures) across future studies would allow for a more reliable quantitative analysis due to more comparable results, which was not possible in this work.

In terms of educational implications, studies investigating the effect of delaying school entry on children’s likelihood of being prescribed ADHD medication have found that held-back children were more likely to be treated for ADHD. As this work is crucial to informing educational policies, further research on the impact of flexible school entry would be valuable, as current evidence is limited.

Further research is necessary to replicate and extend the current limited findings on ASD, investigating if this effect is also present in other countries and healthcare settings.

The relative age effect in ADHD, despite being well documented within research for over a decade, is still present in diagnostic and prescribing practice across the world. This review extends previous findings by showing consistent evidence across studies that compared to parent ratings, teacher ratings of ADHD-related symptoms are more influenced by relative age. Emerging findings also suggest it may be a factor in the diagnosis of other neurodevelopmental disorders such as ASD.

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Dr Eleni Frisira is a National Institute for Health and Care Research (NIHR) Academic Clinical Fellow and Professor Kapil Sayal is an NIHR Senior Investigator. The authors have no financial relationships relevant to this article to disclose.

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Frisira, E., Holland, J. & Sayal, K. Systematic review and meta-analysis: relative age in attention-deficit/ hyperactivity disorder and autism spectrum disorder. Eur Child Adolesc Psychiatry (2024). https://doi.org/10.1007/s00787-024-02459-x

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Article Contents

Introduction, materials and methods, conflict of interest, appendix a. risk-of-bias assessment, appendix b. narrative summary.

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Cognitive Profile in Autism and ADHD: A Meta-Analysis of Performance on the WAIS-IV and WISC-V

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Alexander C Wilson, Cognitive Profile in Autism and ADHD: A Meta-Analysis of Performance on the WAIS-IV and WISC-V, Archives of Clinical Neuropsychology , Volume 39, Issue 4, June 2024, Pages 498–515, https://doi.org/10.1093/arclin/acad073

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Previous research has suggested that neurodevelopmental conditions may be associated with distinctive cognitive profiles on the Wechsler intelligence tests (of which the most recent editions are the WAIS-IV and WISC-V). However, the extent to which a cognitive profile can be reliably identified for individuals meeting criteria for autism or ADHD remains unclear. The present review investigated this issue.

A search was conducted in PsycInfo, Embase, and Medline in October 2022 for papers reporting the performance of children or adults diagnosed with autism or ADHD on the WAIS-IV or the WISC-V. Test scores were aggregated using meta-analysis.

Scores were analyzed from over 1,800 neurodivergent people reported across 18 data sources. Autistic children and adults performed in the typical range for verbal and nonverbal reasoning, but scored ~1 SD below the mean for processing speed and had slightly reduced scores on working memory. This provides evidence for a “spiky” cognitive profile in autism. Performance of children and adults with ADHD was mostly at age-expected levels, with slightly reduced scores for working memory.

Although the pattern of performance on the Wechsler tests is not sufficiently sensitive or specific to use for diagnostic purposes, autism appears to be associated with a cognitive profile of relative strengths in verbal and nonverbal reasoning and a weakness in processing speed. Attention deficit hyperactivity disorder appears less associated with a particular cognitive profile. Autistic individuals may especially benefit from a cognitive assessment to identify and support with their strengths and difficulties.

When assessing general cognitive ability, psychologists often use the Wechsler series of intelligence tests, which include the Wechsler Adult Intelligence Scale (WAIS; Wechsler, 2008 ) and the Wechsler Intelligence Scale for Children (WISC; Wechsler, 2014 ). Surveys of clinicians indicate that these tests are the most commonly used neuropsychological assessments ( Rabin et al., 2016 ), and have a number of routine uses, including to characterize cognitive functioning in people with neurodevelopmental conditions like autism and attention deficit hyperactivity disorder (ADHD; Aiello et al., 2017 ). In the United Kingdom, the National Institute of Health and Care Excellence ( NICE, 2011 ) recommends that a diagnostic assessment for autism must construct a profile of the person’s strengths and difficulties, potentially including an individual’s intellectual ability and academic skills. Likewise, several policy documents suggest use of cognitive assessment within neurodevelopmental diagnostic services ( Hayes et al., 2018 ). The purpose of this cognitive assessment may be to identify support needs for intervention—however, to assess cognition as part of a neurodevelopmental diagnostic assessment may imply that cognitive assessment is helpful for diagnosis itself. There is some uncertainty about this. On the one hand, research has suggested specific cognitive profiles on the Wechsler scales in autism and ADHD ( Ehlers et al., 1997 ; Kanai et al., 2017 ; Mayes & Calhoun, 2004 ). However, research has not always supported the idea of specific profiles on the Wechsler tests ( Charman et al., 2011 ). Due to this uncertainty, the present review will assess the state of evidence surrounding this question, asking whether autism and ADHD are associated with a distinctive cognitive profile on the Wechsler scales. This is likely to have clinical relevance, as these are the most commonly used cognitive tests ( Aiello et al., 2017 ; Rabin et al., 2016 ).

Autism and ADHD are both neurodevelopmental conditions diagnosed on the basis of behavioral features that emerge in childhood ( American Psychiatric Association, 2013 ). In the case of autism, these include social/communication difficulties and restricted and repetitive behaviors and interests. In the case of ADHD, individuals show a pattern of inattention and/or hyperactivity and impulsivity. These behavioral features are thought to result from differences in genetically mediated brain development, although evidence suggests subtle differences only discernible at the group level. One of the most replicated findings in autism is of accelerated brain growth early in life ( Donovan & Basson, 2017 ), which appears to result in changes in connectivity with reduced long-range connectivity and, potentially, enhanced local connectivity ( O'Reilly et al., 2017 ). In the case of ADHD, there also appear to be alterations in connectivity, specifically affecting cortical attention and inhibitory control networks, alongside subtle structural differences including reduced cortical surface area ( Faraone et al., 2021 ).

In this review, “cognitive profile” is taken to mean a person’s pattern of performance across the different cognitive domains covered in the assessment, including their strengths and weaknesses. There are several versions of the WAIS and WISC, each incorporating a range of subtests targeting different aspects of intellectual functioning. The WAIS-IV is the latest version for adults and consists of 10 core subtests grouped under four cognitive domains ( Wechsler, 2008 ): verbal comprehension, perceptual reasoning, working memory, and processing speed. The latest version for children (WISC-V; Wechsler, 2014 ) measures five cognitive domains: the same as the WAIS-IV, except that visual spatial cognition and fluid reasoning replace perceptual reasoning. Standardized scores can be determined for a person’s performance on each subtest, each domain, and overall intellectual functioning (full-scale IQ, or FSIQ). By reviewing a person’s performance across subtests and indices reflecting different domains, it is possible to identify their profile of strengths and difficulties, relative to both general population norms and the person’s overall ability level. Profiles are therefore inter-individual (comparing across people) as well as intra-individual (comparing skills within a person).

There is a longstanding topic of interest that different neurodevelopmental conditions may be characterized by a distinctive “profile” of performance on the Wechsler scales. In autism, this has been described as an uneven profile with a peak on the Block Design subtest and a particular weakness in the Comprehension subtest (see Happé, 1994 for a review of early research). Happé (1994) linked this pattern to theories of autistic cognition: (1) the strength in Block Design may relate to a cognitive preference for processing local detail and (2) the weakness in Comprehension may relate to differences in a person’s social reasoning. Zayat and colleagues (2011) came to a similar conclusion in their study comparing the verbal profile on the WISC in autism to ADHD; they highlighted that the pattern of performance observed in autistic individuals across verbal subtests (strongest on Similarities, then Vocabulary, then Comprehension) may have been due to autistic difficulties with social reasoning and formulating verbal discourse. Researchers have also been interested in characterizing different profiles within autistic subgroups, in particular differentiating the presentation known as Asperger’s syndrome in DSM-IV from other autistic presentations. A somewhat stronger performance on verbal than nonverbal subtests has been identified as characteristic of Asperger’s, whereas no discrepancy exists for “high-functioning” autism (see Chiang et al., 2014 for a meta-analysis). On the other hand, ADHD may possibly be characterized by lower scores on the working memory and/or processing speed subtests. In over 700 children with ADHD, 90% of the sample received their lowest scores in these domains ( Mayes & Calhoun, 2006 ). The literature also mentions the so-called ACID profile (lower scores on Arithmetic, Coding, Information and Digit Span subtests) and SCAD profile (lower scores on Symbol Search, Coding, Arithmetic and Digit Span subtests), which appear sensitive to ADHD ( Snow & Sapp, 2000 ). All these subtests (except Information) belong to the working memory and processing speed domains, which reinforces the idea that these domains are the most likely areas of difficulty for individuals with ADHD.

There has been some attempt to review evidence of a distinctive cognitive profile in autism, although this research is limited. Takayanagi and coworkers (2022) found that studies tended to identify a strength in Block Design and a weakness in Coding among autistic children. However, this narrative review has several limitations, as it only focuses on certain subtests (neglecting many papers looking at the domain-level index scores), excludes adults, and lacks overall quantitative analysis or comparison with other neurodevelopmental conditions. Chiang and associates (2014) have also reviewed research into intellectual profiles in autism, specifically whether discrepancies between verbal IQ (VIQ) and nonverbal, or performance, IQ (PIQ) distinguish Asperger’s syndrome from other forms of autism. Therefore, Chiang and associates (2014) focus on rather a specific question (which is now less relevant as Asperger’s syndrome is no longer diagnosed under DSM-5), and it would be helpful to understand wider aspects of performance on the Wechsler scales while also investigating whether results are valid for current versions of the Wechsler scales (as this earlier review looked at earlier versions). As for ADHD, there is no existing review of Wechsler profiles in this population.

This meta-analytic review seeks to address the evidence gap, asking whether there are distinctive profiles in autism and ADHD on the latest versions of the Wechsler scales, the WAIS-IV (for adults) and the WISC-V (for children). Restricting attention to the latest versions is likely to be most relevant to current clinical practice, as guidance would be for services to use the latest assessment tools with up-to-date norms and improved psychometric properties. In addition, focusing on more recent research allows us to study neurodevelopmental populations likely to be seen now, which may differ compared to the past due to different diagnostic criteria and much increased diagnostic rates (e.g., Russell et al., 2022 ). This review asks two questions via meta-analysis:

(a) Are autism and ADHD associated with a cognitive profile on the Wechsler scales?

(b) Can we differentiate between autism and ADHD based on Wechsler profile?

This review included peer-reviewed papers published between 2008 (the publication year of the WAIS-IV) and 2022 where they present standardized scores on the WAIS-IV or WISC-V by a sample of children or adults diagnosed with autism or ADHD who underwent a cognitive assessment. Scores needed to be presented for all indexes of the WAIS-IV/WISC-V. A search was conducted using Ovid of databases PsycINFO, Embase and Medline on October 20, 2022 using the following search terms: (attention deficit OR attention-deficit OR hyperactivity OR ADHD* OR autis* OR ASD OR pervasive development* or Asperger*) (in the title) AND (WAIS-IV or WISC-V) (in any element of the source). Reference lists of included papers and the Wechsler manuals were also screened for additional sources.

Papers were screened for inclusion in two steps. In the first step, titles and/or abstracts were screened to ensure the paper reported on original research in a sample of people diagnosed with autism and/or ADHD. In the second step, abstracts and full papers were screened to ensure the paper included index and/or subtest scores on the WAIS-IV/WISC-V. Papers were included if there was a sample of people with a clinical diagnosis of autism or ADHD with standardized scores on all indexes of a fully administered WAIS-IV/WISC-V. Note that some studies administered the WAIS-IV or WISC-V to some/all participants, but only used FSIQ scores to check eligibility for the study, characterize the sample, match clinical participants with control participants, or use as a covariate in analysis. As index scores were not available for these papers, the papers were not included in analysis. Where papers were not published in English, the plan was to translate sections of relevant peer-reviewed papers. This was only required for one paper published in German ( Pauls et al., 2018 ), with support of a German-speaking psychologist.

The following background information was recorded from papers deemed eligible for inclusion: sample size, demographic information about the sample (including age, sex, and race), clinical details about the sample (diagnostic information, age at diagnosis, co-occurring diagnoses, and use of psychostimulants), country of study, purpose of study, method of recruitment/data collection, timing of cognitive assessment in relation to diagnosis, and eligibility criteria for participation. The key study data used in the meta-analysis included means and standard deviations for standardized scores on each index and optionally on each subtest too if subtest-level data were presented by the paper. Index scores were recorded as standard scores (mean = 100; SD  = 15) and subtest scores as scaled scores (mean = 10; SD  = 3). In addition, any information about WAIS-IV/WISC-V scores and (1) their sensitivity/specificity to neurodevelopmental conditions and (2) their relationships with clinical variables/outcomes was also noted. This data coding process was completed with support of a second rater who coded a random 25% of the data set, with full agreement with the original coding. The risk-of-bias of studies included in the review was also considered, drawing on the Observational Study Quality Evaluation (OSQE; Drukker et al., 2021 ). Using this tool, factors were identified a priori that are most likely to introduce bias: eligibility criteria reducing representativeness, selection bias in recruitment, unsatisfactory neurodevelopmental assessment, and biases in administration/scoring of cognitive assessments. Papers were reviewed in relation to these factors. See   Appendix A for a summary of this risk-of-bias assessment.

All papers provided index scores, and these were analyzed through multilevel meta-analyses using R package metafor ( Viechtbauer, 2010 ) in the R computing environment ( R Core Team, 2022 ). For each index (VCI, PRI, VSI, FRI, WMI, PSI, and FSIQ), means were aggregated across four neurodivergent subgroups (autistic children, children with ADHD, autistic adults, and adults with ADHD). As some papers contributed more than one sample, multilevel models were specified with random effects of the sample embedded in paper. Restricted maximum likelihood estimation was used to generate estimates and error terms. These meta-analyses gave an overall mean score for each neurodivergent subgroup for each index. Scores were labeled according to the qualitative descriptors set out by the American Academy of Clinical Neuropsychology ( Guilmette et al., 2020 ); for example, standard scores of 90–110 are “Average.”

Having calculated mean index scores, differences between and within the subgroups were tested in three ways. In each case, the size of effects was quantified according to the guideline by Cohen (1988) , and alpha levels were adjusted for multiple comparisons. The first analysis asked whether mean index scores significantly differed from the normative mean of 100 in the four neurodivergent subgroups (children/adults diagnosed with autism/ADHD). For this analysis, the multilevel meta-analyses were rerun with index scores of all samples centered at zero (by subtracting 100), and the significance of the intercept was tested. As this analysis was carried out for each index in the four subgroups, a correction for multiple comparisons was used for each subgroup. For adult samples, the alpha level was set to .01 (.05 divided by 5, as the WAIS-IV has five indexes), and .008 in child samples (.05 divided by 6, as the WISC-V has six indexes). The second analysis explored whether index scores differed between diagnostic groups. For each index, multilevel meta-analyses were rerun for children and adults across diagnostic groups, including Diagnosis (autism vs. ADHD) as a fixed effect. The significance of this fixed effect allowed testing for group differences between people diagnosed with autism and ADHD. The alpha level was set to the same thresholds as earlier to allow for multiple comparisons. The third analysis shifted from inter-group comparisons to intra-group comparisons. This allows us to assess whether index scores significantly differ from each other within the four neurodivergent subgroups (i.e., to reflect a pattern of relative strengths and difficulties specific to the neurodevelopmental condition). For this analysis, meta-analyses were run within each of the four neurodivergent subgroups, with index as a fixed effect and with a multi-level structure (i.e., random effects clustered indexes within samples). Nonverbal ability was used as a reference level of the fixed effect (PRI or FRI, depending on age), which was compared to each other’s index one at a time. It should be noted that error terms will not be independent in this analysis (as each sample contributed an error term for more than one index). However, the covariance for these error terms is unknown (as papers have no reason to report correlations between indexes in their samples), so cluster robust estimation was used, as suggested by Hedges and colleagues (2010) . For these final meta-analyses, alpha levels were left uncorrected at .05, as these analyses were likely underpowered and more exploratory.

When carrying out the first analysis described previously (quantifying overall means for each index in each neurodivergent subgroup), heterogeneity was assessed across samples contributing to these means. The I 2 value (percent of variance attributable to variance in true effects of samples) was calculated for each multilevel meta-analysis. It was hypothesized that general cognitive ability would influence index scores across samples, so a measure of nonverbal ability (PRI or FRI, depending on age) was included as a moderator in follow-up multilevel meta-analyses, and the I 2 value was recalculated. Analyses were also tested for publication bias by including sample size as a moderator in other follow-up meta-analyses.

In addition to these detailed quantitative analyses of index scores, data were synthesized at the subtest level in the few studies that presented this information. Overall means across studies were computed using random effects meta-analysis, and these summary means (with 95% CIs) are presented in charts showing performance across each subtest. Some papers offered further investigation into cognitive profiles in autism and ADHD, including psychometric properties, ability to discriminate between groups, and correlations with other phenotypic variables. This information is presented as a narrative synthesis in   Appendix B .

Participants

A literature search generated 306 sources, of which 16 were eligible for inclusion. Two papers passing initial screening stages were excluded as they included data overlapping with other sources ( Holdnack et al., 2011 ; Theiling et al., 2013 ). One additional paper was identified through review of reference lists of included papers ( Becker et al., 2021 ), and standardization data were included from the WAIS-IV and WISC-V (however, as Dale et al., 2022 used standardization data in their analysis, the WISC-V had effectively already been counted as a data source). This gave a total of 18 sources of cross-sectional data included in the meta-analysis. Figure 1 shows a flow chart presenting this screening process.

Flow chart showing movement of sources through the screening process of the literature search.

Across the 18 sources of data, there were 24 separate samples of individuals, comprising 1,842 individuals in total. Of these, 968 were adults assessed with the WAIS-IV: 437 came from samples focused on autism (mean age = 30 years; 23% female), and 527 came from samples focused on ADHD (mean age = 29 years; 35% female). In addition, there were 856 children aged between 6 and 16 assessed with the WISC-V: 630 came from samples focused on autism (mean age = 10 years; 18% female), and 226 came from samples focused on ADHD (mean age = 11 years; 34% female). Information on race/ethnicity was not consistently available across studies, although most participants were White where this information was given. Tables 1 and 2 present full information on the papers included in this review.

Details of papers included in the review: demographic and clinical variables

Details of studies included in the review: methodological variables

Neurodevelopmental diagnoses were based on DSM-IV, DSM-5, or ICD-10. Among autistic adults, most had a DSM-5 diagnosis ( n  = 234); others had a DSM-IV diagnosis of Asperger’s syndrome ( n  = 72), autistic disorder ( n  = 30), or atypical autism ( n  = 16). For some autistic adults, the criteria for diagnosis were not specified in the paper ( n  = 85). Autistic adults were largely diagnosed as adults (322 vs. 8 diagnosed as children), although for 107, age of diagnosis was not specified. Most papers excluded autistic adults with co-occurring diagnoses. In the few studies with more relaxed eligibility criteria, papers reported on co-occurring diagnoses (ADHD had been diagnosed in 10 autistic adults). In the samples of adults with ADHD, 103 people were diagnosed with the inattentive subtype, 349 with the combined subtype, and 8 with hyperactive subtype; for 61, information was not available about the subtype. The majority of adults with ADHD were diagnosed as adults (445 vs. 28 diagnosed as children and 44 with age of diagnosis unspecified).

Moving on to the child samples, 183 of the autistic children had a DSM-5 diagnosis, but for many, diagnostic criteria were not specified ( n  = 447). For a large number of children, there were no exclusionary criteria regarding co-occurring diagnoses and information was not reported about co-occurring diagnoses. In 183 autistic children for whom this information was available, 21 had co-occurring ADHD. Among children in the ADHD samples, information was not given on subtype, although 178 were diagnosed using ICD-10 criteria and 48 with DSM-5 criteria. Looking across all samples of people with autism or ADHD, the majority of papers excluded individuals with intellectual disability and/or FSIQ below a certain threshold (varying between 60 and 80).

Risk-of-Bias Assessment

See   Appendix A for a table showing ratings for risk-of-bias for each paper. A summary of issues is provided here, as all papers showed the same limitations that could introduce systematic bias:

There was no detailed assessment for co-occurring conditions in any paper. Some papers screened for co-occurring conditions, but this was limited to asking if diagnoses had been made, which will not pick up undiagnosed conditions or subthreshold co-occurring traits. This was the case in studies excluding individuals with co-occurring conditions, as well as studies without these exclusion criteria. Across all samples, only 1% of individuals had autism and ADHD diagnoses, which may underestimate true co-occurring traits. Rates of co-occurrence vary widely in the literature (depending on the sample characteristics), but, for the purpose of illustration, a meta-analysis found 40% of autistic people met criteria for ADHD ( Rong et al., 2021 ). However, co-diagnosis of autism and ADHD is a complex task, as both conditions present with traits that may look like the other condition. For instance, attentional differences appear to be an early precursor to core processes affected in autism (including joint attention, arousal regulation, and perceptual processing; Keehn et al., 2013 ). Similar traits may lead to an overestimation of co-occurring autism and ADHD within the wider literature; however, a co-occurrence rate of just 1% in the studies reviewed here seems low. If papers have not fully assessed for co-occurring conditions, it may be difficult to conclude whether a cognitive profile is explained by the condition of interest or other unassessed co-occurring features.

FSIQ thresholds were often used for inclusion, possibly biasing the cognitive profiles present in the samples, especially as some cognitive domains contribute more strongly to FSIQ than other domains. Only one study ( Cicinelli et al., 2022 ) included a significant proportion of individuals meeting criteria for intellectual disability.

Researchers were not blind to the diagnostic status of participants. Where cognitive assessments occurred following diagnosis, this may have biased administration/scoring of assessments if assessors had preconceived ideas about the possible performance of participants.

Quantitative Analyses

Tables 3 and 4 show the key variables included in the meta-analyses that follow. These include the mean scores on the indices of the WAIS-IV or WISC-V for each sample included in the review.

Key study variables: index scores across the WAIS-IV in neurodivergent adults

Note: VCI = Verbal Comprehension Index; PRI = Perceptual Reasoning Index; WMI = Working Memory Index; PSI = Processing Speed Index; FSIQ = Full-Scale Intelligence Quotient.

Key study variables: index scores across the WISC-V in neurodivergent children

Note: VCI = Verbal Comprehension Index; VSI = Visual Spatial Index; FRI = Fluid Reasoning Index; WMI = Working Memory Index; PSI = Processing Speed Index; FSIQ = Full-Scale Intelligence Quotient.

Meta-Analysis 1. Comparing index scores to the normative mean

Table 5 shows mean WAIS-IV index scores generated through meta-analysis across the samples of neurodivergent adults. All mean index scores were in the Average range, except for PSI in autistic adults which was Low Average. Most mean index scores did not differ significantly from the normative mean of 100, indicating that we cannot reliably distinguish adult neurodivergent groups from the general population for most indexes. Among autistic adults, only PSI was significantly lower than the normative mean, p  < .001; this was by 13.3 points. Given that 15 points is 1 SD , this represents a large effect size ( Cohen, 1988 ). Among adults with ADHD, WMI was slightly lower than the normative mean, p  < .001, although the effect size was small (4.8 points below the normative mean).

Summary scores for neurodivergent adults across the index scores of the WAIS-IV. Scores are shown as standard scores (mean = 100; SD  = 15)

There was significant heterogeneity in WAIS-IV index scores in neurodivergent adults. I 2 values varied between 96.2% and 98.3% across the five indexes in autistic adults and between 75.3% and 96.1% in adults with ADHD. It was hypothesized that heterogeneity was likely influenced by general ability in the samples (i.e., samples of higher ability likely performed more highly on all the indexes). Nonverbal ability (PRI) was specified as a moderator, and all meta-analyses (except for the ones modeling PRI and FSIQ) were rerun with PRI as a moderator. This time, I 2 values varied between 53.7% and 88.8% in autistic adults and between 7.5% and 70.4% in adults with ADHD (in adults with ADHD, WMI that had a much lower heterogeneity when controlling for FRI, whereas other I 2 values remained high). Overall, general ability explained some of the heterogeneity among neurodivergent adults, but there was still considerable variability across the samples included in the meta-analyses, suggesting variable patterns of performance across the WAIS-IV in the different samples. The possibility of publication bias was also assessed for these meta-analyses. Sample size was not associated with index scores in neurodivergent adults, all p s > 0.200, indicating no evidence for publication bias.

Table 6 shows mean WISC-V index scores generated in meta-analysis for neurodivergent children. Most mean index scores were in the Average range, except for WMI and PSI in autistic children, which were Low Average. Controlling for multiple comparisons, autistic children scored significantly lower than the normative mean on WMI (by 10.9 points, a medium effect), p  < .001, and PSI (by 13.4 points, a large effect), p  < .001. This was also the case in children with ADHD, who scored lower than the normative mean on WMI (by 6.3 points, a small effect), p  = .006, and PSI (by 7.7 points, a medium effect), p  < .001.

Summary scores for neurodivergent children across the index scores of the WISC-IV. Scores are shown as standard scores (mean = 100; SD  = 15)

There was significant heterogeneity in WISC-V index scores in neurodivergent children. I 2 values varied between 92.1% and 95.6% for indexes in autistic children and between 0% and 88.7% in children with ADHD. As with the adult samples, it was hypothesized that heterogeneity was likely influenced by general ability in the samples. Nonverbal ability (FRI) was specified as a moderator and all meta-analyses were rerun (except for the ones modeling FRI and FSIQ) with FRI as a moderator. This time, I 2 values were 0% across all child samples for every index. This indicates that, when controlling for general cognitive ability, patterns of index scores were similar across the samples included in this meta-analysis. The possibility of publication bias was also assessed. Sample size was not associated with index scores in autistic children, all p s > 0.600, but was in children with ADHD for all indexes except PSI, p  > 0.001. Larger samples were associated with lower scores in children diagnosed with ADHD, so the overall scores may slightly overestimate ability in children with ADHD. This is likely due to a methodological artifact rather than publication bias per se, as one of the two samples contributed by Becker and coworkers (2021) was very small and only consisted of children with no evidence of specific learning difficulties, whereas eligibility criteria in other samples were more inclusive.

Meta-Analysis 2. Comparing index scores between autism and ADHD

There were no significant differences in index scores when comparing people diagnosed with autism to those with ADHD, although we should note that this analysis was not well powered to detect differences due to the relatively small groups and large variances. Among adults, PSI tended to be lower in autistic adults compared to adults with ADHD, 9.6 points [−.4, 19.5]; however, this was not statistically significant, p  = .059. Differences in other index scores were more convincingly nonsignificant among adults, all p s > 0.450. Neurodivergent children showed a similar pattern to adults, with similar scores for most indexes, all p s > 0.180, except PSI, which showed a trend for lower scores in autistic children compared to children with ADHD, 5.9 points [−.3, 12.1], p  = .062.

Meta-Analysis 3. Comparing index scores within groups

Lastly, index scores were compared within groups to explore whether there was a pattern of relative strengths and difficulties for each group. Among autistic groups, there were difficulties in working memory and processing speed in the context of relative strengths in verbal and nonverbal reasoning. Compared to their PRI, autistic adults had significantly lower WMI, 5.84 points [1.9, 9.8], p  = .010, and lower PSI, 9.2 points [6.1, 12.4], p  < .001. PRI and VCI did not differ in autistic adults, p  = .692. Compared to their FRI, autistic children showed lower WMI, 7.6 points [6.2, 9.0], p  < .001, and lower PSI, 10.4 points [6.7, 14.0], p  = .003. VCI and VSI did not differ from FRI, p s > 0.250.

ADHD samples showed less marked patterns than autistic groups. Compared to their PRI, adults with ADHD had lower WMI by 4.2 points [1.5, 6.8], p  = .015. There was a trend for PSI to be lower by 3.6 points [−1.3, 8.5], but this was nonsignificant, p  = .103. PRI and VCI did not differ among adults with ADHD, p  = .780. Among children with ADHD, there were no statistical differences between FRI and any other index, all p s > 0.180.

Subtest performance

See Fig. 2a–d for subtest scores. These plots report data from 95 autistic adults, 532 autistic children, 160 adults with ADHD, and 151 children with ADHD.

(a) Plot showing subtest scaled scores of autistic adults on the WAIS-IV. (b) Plot showing subtest scaled scores of adults with ADHD on the WAIS-IV. (c) Plot showing subtest scaled scores of autistic children on the WISC-V. (d) Plot showing subtest scaled scores of children with ADHD on the WISC-V. (a-d) In all plots, the thick black line represents summary means calculated through meta-analysis. The thinner coloured lines represent means from the individual studies. Note : SI = Similarities; VC = Vocabulary; IN = Information; BD = Block Design; VP = Visual Puzzles; MR = Matrix Reasoning; DS = Digit Span; AR = Arithmetic; CD = Coding; SS = Symbol Search; FW = Figure Weights; PS = Picture Span.

There was evidence of a particular cognitive profile on the Wechsler intelligence tests in autistic groups, both in children and adults. This was characterized by verbal and nonverbal reasoning in the Average range, with slightly lower working memory and more significantly reduced processing speed. For both autistic children and adults, mean processing speed was Low Average (i.e., below the 25th percentile). Among individuals with ADHD, there was less robust evidence of a profile. Means of all index scores were in the Average range for people with ADHD, with a pattern of slightly lower working memory in adults and children. Processing speed was possibly slightly reduced in children with ADHD compared to the norm, but not necessarily in relation to their own ability. It remains to be seen whether the differences in groups with ADHD are clinically meaningful. Subtest-level analyses gave a similar pattern of results. There were flat profiles in the typical range for the ADHD samples, and a more uneven profile in the autistic samples, with lowest scores in subtests requiring time-pressured efficient processing of information. However, these results need to be held in mind with several factors. First, the quantitative analyses showed significant variability in performance across samples, as shown by the heterogeneity and large confidence intervals around values. In addition, several papers attempted to use cognitive profile on the Wechsler tests to distinguish individuals with an autism diagnosis from those without a neurodevelopmental diagnosis, but with limited success (e.g., Dale et al., 2021 ; see   Appendix B for a narrative review of papers relevant to this issue). Importantly, as highlighted by Stephenson and associates (2021) , there may also be psychometric issues with conducting fine-grained analysis of profiles, as domain-level indexes show poor reliability after accounting for variance associated with FSIQ. This is a key issue that needs to be further researched, as fine-grained analysis of an individual’s performance on a cognitive assessment is commonplace in clinical practice.

Despite these factors, reduced processing speed among autistic people does stand out, given the extent of this reduction compared to the norm. Across samples of autistic children and adults, mean processing speed was ~1 SD lower than the normative mean, and lower than participants’ mean verbal and nonverbal reasoning by ~10 points. Studies reporting subtest-level data also support this picture, as autistic children and adults tended to receive their lowest scores on processing speed tests (especially Coding). This is consistent with previous research, as autistic people have been reported to score low on the Coding subtest in older versions of the Wechsler tests (see Takayanagi et al., 2022 for a review) as well as on other measures of speed including simple reaction time ( Zapparrata et al., 2022 ). Lower processing speed is also consistent with biological accounts of the autistic brain, which indicates reduced long-range connectivity in the brain (in particular, between frontal and posterior regions of cortex; Just et al., 2012 ). However, low processing speed would not be appropriate as a diagnostic marker for autism, as this is found across different neurological and psychiatric conditions ( Rommelse et al., 2020 ). Nonetheless, low processing speed is likely to be common enough in autistic people that it may be useful to screen for these difficulties, as they have real-world impacts. For instance, lower processing speed has been found to correlate with adaptive communication skills ( Oliveras-Rentas et al., 2012 ) and predicts academic underachievement ( Mayes & Calhoun, 2007 ) in autistic children. However, the extent to which low processing speed predicts real-world impacts, independently of other Wechsler indices, is unclear from the literature and would be worth further investigation.

It is worth bearing in mind what “low processing speed” might mean, as it may or may not reflect slower mental processing. The motor demand in tasks may partly explain the differences between autistic and nonautistic groups in PSI, as increasing the motor requirement increases group differences on speeded tasks ( Kenworthy et al., 2013 ) and pure inspection time (without a motor requirement) may represent a relative strength for autistic people ( Barbeau et al., 2013 ). Thus, we should be cautious about necessarily equating lower PSI with lower mental speed. As noted by Rommelse and colleagues (2020) , Wechsler processing speed tests are not pure measures of speed, but tap multiple processes including visual scanning, fine motor skills, short-term memory, interference control, and choice of strategies to optimize speed-accuracy trade-offs—in essence, these tests are measures of complex processing capacity. Another issue is that “processing speed” may or may not be the same construct when measured in autistic compared to neurotypical people using the Wechsler tests, as these tests were not devised with the autistic brain in mind. It is notable, for instance, that the WISC was found to underestimate autistic intelligence in relation to another intelligence test (Raven’s Progressive Matrices) whereas this was not the case in neurotypical controls ( Dawson et al., 2007 ).It would be helpful, therefore, to test the equivalence of the “processing speed” construct between neurodivergent and neurotypical groups to ensure that differences are not due to a measurement bias.

As noted previously, there was less robust evidence of a specific profile in ADHD. WMI was the one index where there was a reliable difference from the norm in both children and adults, but this was a modest effect size (about half an SD or lower). This is similar to existing literature, which has documented working memory difficulties in individuals with ADHD of a similar effect size (e.g., see Ramos et al., 2020 for a review). The lack of a reliable difference in PSI is surprising, as there seems to be a view in the literature that significantly reduced processing speed is common in ADHD. For instance, Cook and coworkers (2018) start from the assumption that processing speed is significantly reduced in ADHD in their review of functional correlates of lower processing speed in ADHD. However, this may not be a key feature in ADHD. Where there are slightly lower scores among groups with ADHD, we also need to be mindful of possible co-occurring specific learning difficulties that may account for scores (e.g., Becker et al., 2021 ). Overall, the rather flat cognitive profiles of the ADHD groups echo previous research that has highlighted the questionable utility of neuropsychological assessment in the diagnosis of ADHD ( Lange et al., 2014 ; Pettersson et al., 2018 ). We might conclude, therefore, that the Wechsler tests are not helpful in diagnosing ADHD, although this is not to underplay that the tests will help identify any individual’s personal strengths and weaknesses, which may support their learning and occupational functioning. For instance, the indexes appear to be associated with various measures of adaptive behavior across different papers reviewed in the narrative synthesis. This can likely be understood in the context of the well-established finding that FSIQ predicts a variety of outcomes ( Nisbett et al., 2012 ). Therefore, a cognitive assessment can tell us something about a person’s functioning whether or not they have a neurodevelopmental condition. One thing that remains unclear is whether an assessment actually improves a person’s functioning (e.g., through building insight into their personal cognitive profile). This has only received limited attention in the research literature, and no studies have evaluated the added value of a cognitive assessment for autistic people ( Donders, 2020 ).

Limitations

There are several limitations to hold in mind about this review. The papers included are all subject to various limitations including (i) use of convenience samples, (ii) poor characterization of co-occurring neurodevelopmental traits, which makes it difficult to be sure that an observed profile is truly related to autism or ADHD and not a co-occurring condition (e.g., dyslexia), and (iii) arbitrary exclusion criteria based on cognitive functioning (i.e., individuals with an IQ below a certain threshold were not included in many studies). These factors may bias estimates in this review. For instance, the FSIQ exclusion criteria may have meant individuals with lower scores on certain indexes were more likely to be excluded (as subtests within the verbal and perceptual domains contribute more strongly to FSIQ than those within the working memory and processing speed domains). These criteria may have biased the results—although the large reduction in processing speed in autistic people found in this review was possibly too large to be simply an artifact of this sampling approach. Certainly, the FSIQ exclusion criteria do mean that a large population of autistic individuals has been undersampled in this review—those with co-occurring intellectual disability. In addition, autistic adults diagnosed as children have been undersampled in this review, as most adults were diagnosed as adults, which is important, as age at diagnosis is likely to influence presentation ( Lai & Baron-Cohen, 2015 ). This review has also undersampled females and individuals with diverse gender identities. Although the ratio of males to females in this review (~3:1) is largely representative of neurodevelopmental conditions ( Bölte et al., 2023 ), the small numbers do make it difficult to know how well the results specifically apply beyond males.

This paper has been restricted to the latest versions of the Wechsler tests (WAIS-IV and WISC-V). The purposes of this were to optimize the relevance of the review for clinicians practicing today, as well as to restrict the amount of research to review—the Wechsler tests are so widely used in research that a review covering all versions would be impractical and overly heterogeneous. There was also the related issue that even when looking at just these two versions of the Wechsler tests, use of the tests is very variable across the research literature, especially when the tests are just used to characterize the sample or apply eligibility criteria (in these cases, it was common for only certain participants to be tested or only certain subtests to be given). This meant it was necessary to approach the review in a principled manner, only including papers where the 10 core subtests were administered and the main indexes reported, which had the positive effect of making the review manageable but with the possible side-effect of reducing overall sample size. This means the overall sample size, while reasonably big, is not well powered for some analyses (such as directly comparing the autistic and ADHD samples).

In addition, an inherent weakness of this type of meta-analysis is the lack of analysis at the individual level. Although the studies showed evidence of cognitive profiles at a group level, it is unclear how common these are on the individual level. In addition, it is plausible that there may be cognitive markers on the individual level that get obscured at the group level (i.e., individuals with neurodevelopmental differences may be more likely to show a variable profile, but this may be distinct to the individual rather than the condition). We know that variability is an element in the cognitive profile of neurodevelopmental conditions (e.g., there is greater variability in reaction time across tasks in people diagnosed with autism and ADHD; Karalunas et al., 2014 ), but we don’t know how this may (or not) present on the Wechsler tests. It is not possible to assess for these issues when only dealing with summary statistics rather than raw data. An interesting suggestion that variability may be a part of the cognitive profile was the heterogeneity analyses among adults diagnosed with autism or ADHD. These analyses showed that patterns of performance across the indices varied across samples. This may have been influenced by variability on the individual level.

Clinical Implications

This review suggests there is no clinically significant cognitive profile that can be detected in ADHD using a WISC-V or WAIS-IV assessment. In the case of autism, processing speed is reduced in both children and adults with a large effect size, which might be important to bear in mind when working with this population. It should be noted that processing speed differences are neither sufficiently sensitive nor specific to use for diagnostic purposes, and there would be a danger of biasing diagnostic decisions if reduced processing speed was considered a cognitive marker of autism. Nonetheless, there is good evidence for a pattern of strengths and difficulties on the Wechsler tests among autistic people, defined by relatively good reasoning skills in the presence of reduced processing speed. This is a pattern that can be supported with appropriate adjustments (e.g., more time for tasks), and the recognition of strengths may improve self-confidence and other people’s understanding of the issues, as well as prompt ideas for person-centered compensatory strategies.

In summary, this review investigated evidence for a distinctive cognitive profile in people with neurodevelopmental conditions on the most recent editions of the Wechsler intelligence tests, the WAIS-IV and WISC-V. Test performance was collated from over 1,800 individuals with a diagnosis of autism or ADHD reported in 18 different sources of data. Among autistic children and adults, there was a consistent pattern of verbal and nonverbal reasoning representing relative strengths, with a significant weakness in processing speed and slight weakness in working memory. People diagnosed with ADHD tended to show slightly weaker working memory than their other abilities, but this was a modest difference. Based on the review, it is unlikely to be helpful to conduct a cognitive assessment for diagnostic purposes, but an assessment may be helpful in determining an individual’s profile of strengths and difficulties, especially among autistic people.

None declared.

Studies are rated for high (-), medium (±), and low (+) risk of bias for the following qualities:

Internal Validity of the Sample I. Are we confident the study recruited individuals meeting criteria for the relevant neurodevelopmental diagnosis?

Internal Validity of the Sample II. Are we confident that co-occurring conditions have been adequately assessed? As this study compares autism and ADHD, it is important to know the extent to which these might co-occur at clinical or subclinical levels in the sample.

External Validity of the Sample I. Are we confident the sample is inclusive of individuals of varying intellectual ability? Given the focus of this study on cognitive abilities, we required studies to be inclusive of individuals regardless of IQ to avoid biasing profiles (although we allowed for exclusion of individuals unable to complete a cognitive assessment).

External Validity of the Sample II. Are we confident that the sample is a relatively random community/clinical sample? For this, we disregarded IQ eligibility criteria as that was considered in (3), and considered issues such as other eligibility criteria, missing data, recruitment pathway, and site of recruitment.

Validity of Assessment. Are we confident that assessors were blinded to avoid bias in profiling the abilities of neurodivergent people?

A few studies investigated aspects of cognitive profile beyond index and subtest means, including whether information from more than one index could distinguish neurodivergent from control participants on the individual level. Dale and associates (2021) found that index scores were only effective in distinguishing autistic children with a co-occurring language impairment, suggesting that the WISC-V was sensitive to language/learning difficulties rather than autism. Shepler & Callan (2022) found that WMI was moderately effective in differentiating adults with ADHD from those with a mental health diagnosis; however, the WAIS-IV was administered during the diagnostic process, so there is a significant risk of bias (i.e., participants scoring lower on the WAIS-IV may have been more likely to receive an ADHD diagnosis). Two studies looked at discrepancies between indexes ( Leung et al., 2019 ; Theiling & Petermann, 2016 ), and both studies’ discrepancies were more common in autistic adults (approximately twice as common as the norm). However, both studies had small samples, and it is unclear whether these were planned analyses, so there is a high risk of type I error. Overall, studies present limited and weak evidence for using a specific profile to distinguish neurodivergent individuals from the general population at the individual level.

Several studies found that Wechsler index scores correlated with adaptive behavior in autistic children ( Audras-Torrent et al., 2021 ) and adults ( Leung et al., 2019 ; Nyrenius & Billstedt, 2020 ). In autistic children, this included moderate correlations between all WISC-V indexes and communication skills (.31 <  r  < .50) and small correlations between VSI, FRI, and FSIQ and daily living skills ( r  = .20 or .21), but no correlations with social skills ( Audras-Torrent et al., 2021 ). In autistic adults, there were moderate correlations between FSIQ, WMI, and PSI and adaptive behavior in the general, conceptual, and practical domains (.40 <  r  < .49; Nyrenius & Billstedt, 2020 ). Leung and colleagues (2019) found that parent- and self-rated social skills correlated in some cases with WAIS-IV indexes, but many correlations were computed, and so, there is a risk of false positives. In adults with ADHD, Baggio and coworkers (2020) found that lower FSIQ was associated with lower probability of higher education and currently being in work/education and greater probability of repeating a grade at school ( N  = 66). Overall, there is evidence that cognitive scores correlate with adaptive skills, but these relationships do not seem specific to any one index.

Stephenson and associates (2021) looked at psychometric properties of the WISC-V in autistic children ( N  = 349). They found that the WISC-V did not have the same psychometric structure across autistic and nonautistic groups; specifically, autistic children performed lower on the Digit Span and Coding subtests than would be expected based on their performance across all other subtests. In addition, the researchers found limited reliability of the domain-level indexes after accounting for variance relating to FSIQ. Overall, results from this paper suggest that the WISC-V may detect some autism-specific cognitive differences on particular subtests, but this should be interpreted cautiously on the individual level as such differences may not be psychometrically reliable.

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Differential Outcomes of Placebo Treatment Across 9 Psychiatric Disorders : A Systematic Review and Meta-Analysis

• 1 Department of Psychiatry and Psychotherapy, University Hospital, Technical University of Dresden, Dresden, Germany
• 2 Federal Joint Committee (G-BA), Berlin, Germany
• 3 Social Psychiatric Service, Berlin district of Reinickendorf, Berlin, Germany
• 4 Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
• 5 Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Cologne, Cologne, Germany
• 6 Government Commission for Modern and Needs-Based Hospital Care, Berlin, Germany

Question   Which psychiatric disorder exhibits the strongest improvement associated with placebo treatment in randomized clinical trials (RCTs)?

Findings   This systematic review and meta-analysis of 90 high-quality RCTs with 9985 participants found significant improvement under placebo treatment for all 9 disorders, but the degree of improvement varied significantly among diagnoses. Patients with major depressive disorder experienced the greatest improvement, followed by those with generalized anxiety disorder, panic disorder, attention-deficit/hyperactivity disorder, posttraumatic stress disorder, social phobia, mania, and OCD, while patients with schizophrenia benefited the least.

Meaning   These findings may inform planning of RCTs, interpreting of uncontrolled studies, and advising patients for or against a specific treatment.

Importance   Placebo is the only substance systematically evaluated across common psychiatric diagnoses, but comprehensive cross-diagnostic comparisons are lacking.

Objective   To compare changes in placebo groups in recent high-quality randomized clinical trials (RCTs) across a broad spectrum of psychiatric disorders in adult patients.

Data Sources   MEDLINE and the Cochrane Database of Systematic Reviews were systematically searched in March 2022 for the latest systematic reviews meeting predetermined high-quality criteria for 9 major psychiatric diagnoses.

Study Selection   Using these reviews, the top 10 highest-quality (ie, lowest risk of bias, according to the Cochrane Risk of Bias tool) and most recent placebo-controlled RCTs per diagnosis (totaling 90 RCTs) were selected, adhering to predetermined inclusion and exclusion criteria.

Data Extraction and Synthesis   Following the Cochrane Handbook, 2 authors independently carried out the study search, selection, and data extraction. Cross-diagnosis comparisons were based on standardized pre-post effect sizes (mean change divided by its SD) for each placebo group. This study is reported following the Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guideline.

Main Outcome and Measure   The primary outcome, pooled pre-post placebo effect sizes ( d av ) with 95% CIs per diagnosis, was determined using random-effects meta-analyses. A Q test assessed statistical significance of differences across diagnoses. Heterogeneity and small-study effects were evaluated as appropriate.

Results   A total of 90 RCTs with 9985 placebo-treated participants were included. Symptom severity improved with placebo in all diagnoses. Pooled pre-post placebo effect sizes differed across diagnoses ( Q  = 88.5; df  = 8; P  < .001), with major depressive disorder ( d av  = 1.40; 95% CI, 1.24-1.56) and generalized anxiety disorder ( d av  = 1.23; 95% CI, 1.06-1.41) exhibiting the largest d av . Panic disorder, attention-deficit/hyperactivity disorder, posttraumatic stress disorder, social phobia, and mania showed d av between 0.68 and 0.92, followed by OCD ( d av  = 0.65; 95% CI, 0.51-0.78) and schizophrenia ( d av  = 0.59; 95% CI, 0.41-0.76).

Conclusion and Relevance   This systematic review and meta-analysis found that symptom improvement with placebo treatment was substantial in all conditions but varied across the 9 included diagnoses. These findings may help in assessing the necessity and ethical justification of placebo controls, in evaluating treatment effects in uncontrolled studies, and in guiding patients in treatment decisions. These findings likely encompass the true placebo effect, natural disease course, and nonspecific effects.

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Bschor T , Nagel L , Unger J , Schwarzer G , Baethge C. Differential Outcomes of Placebo Treatment Across 9 Psychiatric Disorders : A Systematic Review and Meta-Analysis . JAMA Psychiatry. Published online May 29, 2024. doi:10.1001/jamapsychiatry.2024.0994

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