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October 14, 2024
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The report "Attention-Deficit/Hyperactivity Disorder Diagnosis, Treatment, and Telehealth Use in Adults" published in the CDC's Morbidity and Mortality Weekly Report provides a detailed examination of the prevalence and treatment of ADHD among U.S. adults based on data collected by the National Center for Health Statistics Rapid Surveys System during October–November 2023. This data is crucial as it offers updated estimates on the prevalence of ADHD in adults, a condition often regarded as primarily affecting children, and highlights the ongoing challenges in accessing ADHD-related treatments, including telehealth services and medication availability.
The methods used in this study involved the National Center for Health Statistics (NCHS) Rapid Surveys System (RSS), which gathers data to approximate the national representation of U.S. adults through two commercial survey panels: the AmeriSpeak Panel from NORC at the University of Chicago and Ipsos’s KnowledgePanel. The data were collected via online and telephone interviews from 7,046 adults. The responses were weighted to reflect the total U.S. adult population, ensuring that the results approximate national estimates. In identifying adults with current ADHD, respondents were asked if they had ever been diagnosed with ADHD and, if so, whether they currently had the condition. The study also collected data on treatment types (including stimulant and nonstimulant medications), telehealth use, and demographic variables such as age, education, race, and household income.
The results showed that approximately 6.0% of U.S. adults, or an estimated 15.5 million people, had a current ADHD diagnosis. Notably, more than half of the adults with ADHD reported receiving their diagnosis during adulthood (age ≥18 years), indicating that diagnosis can occur well beyond childhood. Analysis of demographics showed significant differences between adults with ADHD and those without; adults with ADHD were more likely to be younger, with 84.5% under the age of 50. Adults with ADHD were also less likely to have completed a bachelor's degree and more likely to have a household income below the federal poverty level compared to those without ADHD. Regarding treatment, the report found that approximately one-third of adults with ADHD were untreated, and around one-third received both medication and behavioral treatment. Among those receiving pharmacological treatment, 33.4% used stimulant medications, and 71.5% of these individuals reported difficulties in getting their prescriptions filled due to medication unavailability, reflecting recent stimulant shortages in the United States. Additionally, nearly half of adults with ADHD had used telehealth services for ADHD-related care, including obtaining prescriptions and receiving counseling or therapy.
The discussion emphasizes the public health implications of these findings. ADHD is often diagnosed late, with many individuals not receiving a diagnosis until adulthood, which underscores the need for improved awareness and early identification of ADHD symptoms across the life course. Moreover, the high prevalence of untreated ADHD and the barriers to accessing stimulant medications reveal significant gaps in the healthcare system's ability to support adults with ADHD. These gaps can contribute to poorer outcomes, such as increased risk of injury, substance use, and social impairment. The report also highlights the role of telehealth, which became more prominent during the COVID-19 pandemic. Telehealth appears to provide a viable solution for expanding access to ADHD diagnosis and treatment, though challenges remain regarding the quality of care and potential for misuse. The authors suggest that improved clinical care guidelines for adults with ADHD could help reduce delays in diagnosis and treatment access, thus improving long-term outcomes for affected individuals.
In conclusion, the study provides a comprehensive view of the prevalence, treatment, and telehealth use for ADHD among adults in the U.S. These data are crucial for guiding clinical care and shaping policies related to medication access and telehealth services. The findings underscore the importance of ensuring an adequate supply of stimulant medications and reducing barriers to ADHD care, ultimately enhancing the quality of life for adults with this condition. The good news is that many adults with ADHD are being diagnosed and treated. It is, however, concerning that many are not treated and that many of those treated with stimulants were impacted by the stimulant shortage.
For more details, see: https://www.cdc.gov/mmwr/volumes/73/wr/mm7340a1.htm
Staley BS, Robinson LR, Claussen AH, et al. Attention-Deficit/Hyperactivity Disorder Diagnosis, Treatment, and Telehealth Use in Adults — National Center for Health Statistics Rapid Surveys System, United States, October–November 2023. MMWR Morb Mortal Wkly Rep 2024;73:890–895. DOI: http://dx.doi.org/10.15585/mmwr.mm7340a1
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition that is typically diagnosed in childhood but can persist into adulthood. Its symptoms include inattention, hyperactivity, and impulsivity, and it can significantly affect daily life, academic achievement, and professional success. As scientific understanding of the condition continues to evolve, new research is revealing more insights into the prevalence, comorbidity, treatment, and physiological aspects of ADHD in adults. Here's a roundup of some recent findings:
A recent study assessing the prevalence of treatment for ADHD among US college students found that the location of mental health care significantly affects treatment outcomes. Specifically, students receiving mental healthcare on campus were less likely to receive any medication or therapy for ADHD, suggesting the need to evaluate the quality of mental health services available on college campuses and their effectiveness in treating ADHD.
Another study found a correlation between ADHD and the l-Arginine/Nitric oxide (Arg/NO) pathway, a physiological process linked to dopamine release and cardiovascular functioning. The study found that adults with ADHD who were not treated with methylphenidate (a common ADHD medication) showed variations in the Arg/NO pathway. This could have implications for monitoring potential cardiovascular side effects of ADHD medications, as well as for understanding the biochemical changes that occur in ADHD.
ADHD and chronic pain appear to be related, according to a comparative study of clinical and general population samples. Particularly in females with ADHD, the prevalence of chronic and multisite pain was found to be high. This calls for longitudinal studies to understand the complex sex differences of comorbid chronic pain and ADHD in adolescents and the potential impacts of stimulant use on pain.
Finally, a study investigated the comorbidity of ADHD and bipolar disorder (BD) and its potential link to violent behavior. The research revealed a positive effect of ADHD symptoms on violence tendency and aggression scores. Moreover, male gender and young age were also found to have significant positive effects on violence and aggression scores, suggesting an association between these disorders and violent behavior.
There is a growing interest (and controversy) in 'adult-onset ADHD. No current diagnostic system allows for the diagnosis of ADHD in adulthood, yet clinicians sometimes face adults who meet all criteria for ADHD, except for age at onset. Although many of these clinically referred adult-onset cases may reflect poor recall, several recent longitudinal population studies have claimed to detect cases of adult-onset ADHD that showed no signs of ADHD as a youth (Agnew-Blais, Polanczyk et al. 2016, Caye, Rocha, et al. 2016). They conclude, not only that ADHD can onset in adulthood, but that childhood-onset and adult-onset ADHD may be distinct syndromes(Moffitt, Houts, et al. 2015)
In each study, the prevalence of adult-onset ADHD was much larger than the prevalence of childhood-onset adult ADHD). These estimates should be viewed with caution. The adults in two of the studies were 18-19 years old. That is too small a slice of adulthood to draw firm conclusions. As discussed elsewhere (Faraone and Biederman 2016), the claims for adult-onset ADHD are all based on population as opposed to clinical studies.
Population studies are plagued by the "false positive paradox", which states that, even when false positive rates are low, many or even most diagnoses in a population study can be false.
Another problem is that the false positive rate is sensitive to the method of diagnosis. The child diagnoses in the studies claiming the existence of adult-onset ADHDused reports from parents and/or teachers but the adult diagnoses were based on self-report. Self-reports of ADHD in adults are less reliable than informant reports, which raises concerns about measurement error. Another longitudinal study found that current symptoms of ADHD were under-reported by adults who had had ADHD in childhood and over-reported by adults who did not have ADHD in childhood(Sibley, Pelham, et al. 2012). These issues strongly suggest that the studies claiming the existence of adult-onset ADHD underestimated the prevalence of persistent ADHD and overestimated the prevalence of adult-onset ADHD. Thus, we cannot yet accept the conclusion that most adults referred to clinicians with ADHD symptoms will not have a history of ADHD in youth.
The new papers conclude that child and adult ADHD are "distinct syndromes", "that adult ADHD is more complex than a straightforward continuation of the childhood disorder" and that adult ADHD is "not a neurodevelopmental disorder". These conclusions are provocative, suggesting a paradigm shift in how we view adulthood and childhood ADHD. Yet they seem premature. In these studies, people were categorized as adult-onset ADHD if full-threshold add had not been diagnosed in childhood. Yet, in all of these population studies, there was substantial evidence that the adult-onset cases were not neurotypical in adulthood (Faraone and Biederman 2016). Notably, in a study of referred cases, one-third of late adolescent and adult-onset cases had childhood histories of ODD, CD, and school failure(Chandra, Biederman, et al. 2016). Thus, many of the "adult onsets" of ADHD appear to have had neurodevelopmental roots.
Looking through a more parsimonious lens, Faraone and Biederman(2016)proposed that the putative cases of adult-onset ADHD reflect the existence of subthreshold childhood ADHD that emerges with full threshold diagnostic criteria in adulthood. Other work shows that subthreshold ADHD in childhood predicts onsets of full-threshold ADHD in adolescence(Lecendreux, Konofal, et al. 2015). Why is onset delayed in subthreshold cases? One possibility is that intellectual and social supports help subthreshold ADHD youth compensate in early life, with decompensation occurring when supports are removed in adulthood or the challenges of life increase. A related possibility is that the subthreshold cases are at the lower end of a dimensional liability spectrum that indexes risk for onset of ADHD symptoms and impairments. This is consistent with the idea that ADHD is an extreme form of a dimensional trait, which is supported by twin and molecular genetic studies(Larsson, Anckarsater, et al. 2012, Lee, Ripke, et al. 2013). These data suggest that disorders emerge when risk factors accumulate over time to exceed a threshold. Those with lower levels of risk at birth will take longer to accumulate sufficient risk factors and longer to onset.
In conclusion, it is premature to accept the idea that there exists an adult-onset form of ADHD that does not have its roots in neurodevelopment and is not expressed in childhood. It is, however, the right time to carefully study apparent cases of adult-onset ADHD to test the idea that they are late manifestations of a subthreshold childhood condition.
Swedish researchers examined outcomes for adult women who sought treatment at the Stockholm Center for Eating Disorders over two years and nine months. Out of 1,517 women who came to the clinic, 1,143remained eligible for the study, after excluding women whose symptoms did not fulfill the DSM-IV criteria for eating disorders or had incomplete records.
Of these, seven hundred patients could not be reached or declined to participate, leaving 443 for follow-up. To guard against the possibility that the follow-up group might not be representative of the overall treatment group, researchers compared to age, body mass index, and scores on tests for depression, anxiety, compulsively, inattention, and hyperactivity. The only statistically significant differences were small ones. The median age of the group lost to follow-up was one year younger, they were less likely to be living alone, and on average scored a single point higher on the depression test. Otherwise, they were broadly similar.
The one-year follow-up on the study group found a substantial difference in the rate of recovery from eating disorders between those with and without comorbid ADHD. Almost three out of four patients (72%) who scored lower (between 0-17) on the World Health Organization adult ADHD self-report scale had recovered from their eating disorder. Among those scoring18 and higher, on the other hand, it was less than half (47%). This difference was extraordinarily unlikely (one chance in one thousand) to be due to chance(p=.001).
Another way of expressing this is through odds ratios. Those scoring 18 and up on the ADHD self-report scale were about two and a half times less likely to recover from their eating disorders following treatment. More specifically, thy were about three times less likely to recover from the loss of control and binging, and almost three and a half times less likely to recover from purging.
To improve outcomes, the researchers suggest "identifying concomitant ADHD symptoms and customizing treatment interventions based on this." They specifically propose controlled clinical trials to explore the effect of combining stimulant medications with standard treatment for eating disorders
ADHD is commonly treated with medication, but these treatments frequently cause side effects such as reduced appetite and disrupted sleep. Psychological and behavioral therapies exist as alternatives, but they tend to be expensive, hard to scale, and generally do little to address the motor difficulties that many children with ADHD experience — things like clumsy movement, poor handwriting, or difficulty with coordination.
Physical exercise has attracted attention as a more accessible option. But research findings have been mixed, partly because studies vary so widely in how exercise is delivered and what outcomes they measure. This meta-analysis, drawing on 21 studies involving 850 children and adolescents aged 5–20 with a clinical ADHD diagnosis, tries to cut through that noise.
Two types of motor skills
The researchers separated motor skills into two broad categories:
The Data:
Gross motor skills (16 studies, 613 participants)
Overall, exercise produced medium-to-large improvements in gross motor skills. The strongest gains were in:
No significant gains were found in balance or flexibility.
Fine motor skills (13 studies, 553 participants):
Exercise also produced medium-to-large improvements in fine motor skills, specifically:
The Results: What Kind of Exercise Works Best?
Two factors stood out consistently across both gross and fine motor skills: session length and frequency.
The type of exercise mattered; structured programs with clear motor-skill components (rather than unstructured physical activity) yielded stronger results.
These results are not without caveats, however. The authors urge caution in interpreting these findings. A few key limitations include:
The Bottom Line
This meta-analysis provides tentative moderate evidence that structured physical exercise can meaningfully support motor skill development in children and adolescents with ADHD — particularly when sessions run longer than 45 minutes and occur at least three times a week. The benefits appear most robust for object control, locomotion, handwriting, and manual dexterity.
That said, the evidence base still has real gaps. The authors call for better-designed, fully randomized controlled trials with consistent methods, standardized ways of measuring exercise intensity, and greater inclusion of children and adolescents who are not on medication — all of which would help clarify when, how, and for whom exercise works best.
Treatment guidelines for childhood ADHD recommend medications as the first-line treatment for most youth with ADHD. Still, concerns about side effects and long-term outcomes have increased interest in non-pharmacological approaches. Researchers at Saudi Arabian Armed Forces hospitals recently conducted a network meta-analysis comparing several interventions, including mindfulness-based therapy, cognitive behavioral therapy, behavioral parent training, neurofeedback, yoga, virtual reality programs, and digital working memory training.
Although the authors aimed to “provide a rigorous methodological approach to combine evidence from multiple treatment comparisons,” the study illustrates several pitfalls that arise when network meta-analysis is applied to a thin and heterogeneous evidence base.
What Network Meta-analysis Can and Cannot Do:
Network meta-analysis extends conventional meta-analysis by combining:
When the evidence network is large and well-connected, this approach can provide useful estimates of comparative effectiveness among many treatments.
This method is not always best, however, as many networks are sparse. This is especially true in areas such as complementary or behavioral therapies. In sparse networks, estimates rely heavily on indirect comparisons, and single studies can exert disproportionate influence over the results.
Conventional meta-analysis focuses on heterogeneity, meaning differences in results across studies within the same comparison.
Network meta-analysis must additionally evaluate consistency, whether the direct and indirect evidence agree.
However, when comparisons are supported by only one or two studies and the network is weakly connected, statistical tests for heterogeneity and consistency have very little power. In practice, this means the analysis often cannot detect problems even if they are present.
Sparse networks also make publication bias difficult to evaluate. This concern is particularly relevant in fields dominated by small trials and emerging therapies.
Why Such Treatment Rankings Are Appealing, but Potentially Problematic:
Many network meta-analyses summarize results using SUCRA, which estimates the probability that each treatment ranks best.
SUCRA, or Surface Under the Cumulative Ranking, is a key statistical metric in network meta-analyses. It is used to rank treatments by efficacy or safety. This is achieved by summarizing the probabilities of a treatment's rank into a single percentage, where a higher SUCRA value indicates a superior treatment. Ultimately, SUCRA helps pinpoint the most effective intervention among the ones compared.
Again, in well-supported networks, SUCRA can provide a useful summary of comparative effectiveness. But in sparse networks, rankings can create an illusion of precision, because treatments supported by a single small study may appear highly ranked simply due to random variation.
What Did this New Network Meta-analysis Study?
The study includes 16 trials with a total of 806 participants. But the structure of the evidence network is far weaker than this headline number suggests.
Based on the underlying studies:
This produces a very thin network, in which several interventions rely entirely on single studies.
Another challenge is that the included trials measure different outcomes. Some evaluate ADHD symptom severity, while others measure parental stress.
When studies use different outcome scales, meta-analysis typically relies on standardized measures such as the standardized mean difference to allow comparisons across studies. However, the analysis reports only mean-average differences, making it difficult to interpret the relative effect sizes.
Study Issues (including Limited Evidence and Risk of Bias):
The intervention supported by the largest number of studies (family mindfulness-based therapy) was one of the two approaches reported as producing statistically significant results. The other was BrainFit, which is supported by only a single previous trial.
Despite this limited evidence base, the study ranks interventions using SUCRA:
Notably, none of the runner-up interventions demonstrated statistically significant efficacy.
The authors acknowledge methodological limitations in the included studies:
“Blinding of participants and personnel (performance bias) exhibited notable concerns, as blinding for active treatment was not applicable in most studies.”
Such limitations are common in behavioral intervention trials, but they further increase uncertainty in already small evidence networks.
Conclusions:
The study ultimately concludes:
“This network meta-analysis supports MBT and BPT as effective non-pharmacological treatments for ADHD.”
However, the evidence underlying these claims is limited. Some analyses rely on very small numbers of studies and participants, and the network structure depends heavily on indirect comparisons.
Network meta-analysis can be a powerful tool when applied to a large, consistent, and well-connected body of evidence. When the evidence base is sparse, however, the resulting rankings and comparisons may appear statistically sophisticated while resting on a fragile evidentiary foundation.
ADHD is one of the most common neurodevelopmental disorders in children, yet anyone familiar with this disorder, from clinicians and researchers to parents and patients, knows how differently it can manifest from one individual to the next. One person diagnosed with ADHD may primarily struggle with focus and staying on-task; another may find it nearly impossible to regulate their impulses or even start tasks; a third may frequently find themselves frozen with overwhelm and subject to emotional reactivity…
These are not just variations in severity; they may reflect genuinely different patterns of brain organization.
Our current diagnostic system groups all of these presentations under a single label (ADHD), with three behavioral subtypes (Hyperactive, Inattentive, and Combined) defined by symptom checklists. This framework has real clinical value of course, but it was built from behavioral observation rather than neurobiology, and may leave room for substantial heterogeneity to remain unexplained. In a new study, published in JAMA Psychiatry, researchers asked whether it’s possible to identify distinct neurobiologically subgroups within ADHD by analyzing patterns of brain structure, and whether those subgroups would map onto meaningful clinical differences.
How the Brain Was Analyzed
Researchers analyzed structural MRI scans from 446 children with ADHD and 708 typically-developing children across multiple research sites. From each scan, they constructed a morphometric similarity network; that is, a map of how different brain regions resemble one another in their structural properties. These networks reflect underlying biological organization, including shared patterns of cellular architecture and gene expression across brain regions.
From each individual's network, the research team calculated three properties that capture how each brain region functions within the broader network: how many connections it has, how efficiently it communicates with other regions, and how well it bridges different functional communities in the brain. Regions that score highly on these measures are sometimes called "hubs" and they play particularly influential roles in how information is integrated across the brain.
Rather than comparing the ADHD group to controls as a whole and looking for average differences, they used a normative modeling approach. This works similarly to a growth chart in pediatric medicine: instead of asking whether a child is above or below the group average, it asks how much a given child deviates from the expected range for their age and sex. This allows for individual variation across the ADHD group rather than flattening it into a single average profile.
The team then applied a data-driven clustering algorithm to these individual deviation profiles, allowing the data to reveal whether subgroups of children with ADHD shared similar patterns of brain network atypicality, without using any clinical symptom information to guide the clustering.
The Results:
Three stable, reproducible subtypes emerged from this analysis.
The first subtype was characterized by the most widespread differences from the normative range, particularly in regions connecting the medial prefrontal cortex to the pallidum (a deep brain structure involved in motivation and emotional regulation). Children in this group had the highest levels of both inattention and hyperactivity/impulsivity, and over a four-year follow-up period showed more persistent difficulties with emotional self-regulation than the other groups. They also had a higher rate of mood disorder comorbidity during follow-up, though this difference did not reach statistical significance given the sample size. The brain deviation patterns of this subtype showed correspondence with the spatial distributions of several neurotransmitter systems, including serotonin, dopamine, and acetylcholine, all of which have been previously implicated in ADHD pathophysiology.
The second subtype showed alterations concentrated in the anterior cingulate cortex and pallidum, a circuit involved in action control and response selection. This subtype had a predominantly hyperactive/impulsive profile, and its brain deviation patterns were associated with glutamate and cannabinoid receptor distributions.
The third subtype showed more focal differences in the superior frontal gyrus, a region involved in sustained attention. This subtype had a predominantly inattentive profile, with brain patterns linked to a specific serotonin receptor subtype.
A particularly important observation was that these brain-derived groupings aligned with clinically meaningful symptom differences, even though no symptom information was used in the clustering process. The fact that an analysis of brain structure alone arrived at groupings that correspond to recognizable clinical patterns is meaningful evidence that these subtypes reflect genuine neurobiological differences rather than statistical noise.
Replication in an Independent Sample
Scientific findings are only as trustworthy as their ability to replicate. The research team tested this clustering model in an entirely independent cohort of 554 children with ADHD from the Healthy Brain Network, a large, publicly available dataset collected under different conditions. The three subtypes were successfully identified in this new sample, with strong correlations between the brain deviation patterns observed in the original and validation cohorts. Differences in hyperactivity/impulsivity across subtypes were consistent with the discovery cohort, providing meaningful external validation of the approach.
What This Does and Doesn't Mean
It is important to be clear about what these findings do and do not imply. This study does not establish that these three subtypes are categorically distinct biological entities with sharp boundaries. They probably represent distinguishable regions along an underlying continuum of neurobiological variation. The neurochemical associations reported are exploratory and spatial in nature; they describe correspondences between brain deviation maps and neurotransmitter receptor density maps derived from separate imaging studies, and do not directly establish that any particular neurotransmitter system is altered in each subtype, nor do they currently inform treatment decisions.
The samples were not entirely medication-naive, and the strict comorbidity exclusion criteria may limit how well these findings generalize to typical clinical populations where comorbidities are the rule rather than the exception. All data came from research sites in the United States and China, and broader generalizability remains to be established.
What the study does demonstrate is that structured neurobiological heterogeneity exists within the ADHD diagnosis, that it can be reliably detected using brain imaging and data-driven methods, and that it aligns with meaningful clinical differences. The subtype defined by the most extensive brain network differences and the most severe, persistent clinical profile may be of particular importance, representing a group that could benefit most from early identification and targeted support.
The longer-term goal of this line of research is to move toward a more biologically grounded understanding of ADHD that complements existing diagnostic approaches and that may ultimately help guide more individualized treatment decisions. That goal, for now, remains a research ambition rather than a clinical reality, but this study takes a meaningful step in that direction.
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