ADHD is underdiagnosed, to varying degrees, among adults of different ethnicities, ages, and education levels in the U.S.
A cohort study looked at over five million adults and over 850,000 children between the ages of five and eleven who received care at Kaiser Permanente Northern California during the ten-year period from the beginning of 2007 through the end of 2016. At any given time, KPNC serves roughly four million persons. It is representative of the population of the region, except for the highest and lowest income strata.
ADHD Diagnosis Rates:
Adults: Diagnosis rates rose from 0.43% in 2007 to 0.96% in 2016.
Children: Diagnosis rates went up from 2.96% to 3.74%, nearly four times higher than in adults.
Diagnosis Rates by Ethnicity:
Non-Hispanic whites had the highest adult diagnosis rates, increasing from 0.67% to 1.42%.
American Indian/Alaska Native (AIAN): Rates grew from 0.56% to 1.14%.
Black and Hispanic adults had similar rates: Black adults increased from 0.22% to 0.69%, and Hispanic adults rose from 0.25% to 0.65%.
Asian adults had the lowest rates (0.11% to 0.35%), followed by Native Hawaiian/Pacific Islanders (0.11% to 0.39%).
ADHD Diagnosis and Age:
The likelihood of being diagnosed with ADHD dropped sharply with age.
(When compared to 18-24-year-olds):
25-34-year-olds were 16% less likely.
35-44-year-olds were 33% less likely.
45-54-year-olds were less than half as likely.
55-64-year-olds were less than a quarter as likely.
Adults over 65 were about 5% as likely.
This matches findings from other studies showing that ADHD diagnoses become less common with age.
Other Factors:
Adults with higher education levels were twice as likely to be diagnosed as those with less education.
Household income had little effect on diagnosis rates.
Women were slightly less likely to be diagnosed than men.
ADHD and Comorbidity:
Adults with ADHD were more likely to have other mental health conditions:
Eating disorders: 5 times more likely.
Bipolar disorder or depression: Over 4 times more likely.
Anxiety: More than twice as likely.
Substance abuse: Slightly more likely.
Key Findings:
Rising ADHD Diagnosis Rates: The increase in diagnoses may be due to better recognition of ADHD by doctors and greater public awareness during the study period.
Differences by Ethnicity: The differences in diagnosis rates by ethnicity could be related to access to healthcare, cultural attitudes toward mental health, or even attempts to obtain ADHD medications for non-medical reasons, which may be more common among white patients.
Conclusion:
The authors speculate that rising rates of diagnosis “could reflect increasing recognition of ADHD in adults by physicians and other clinicians as well as growing public awareness of ADHD during the decade under study.” Turning to the notable differences by ethnicity, they note, “Racial/ethnic differences could also reflect differential rates of treatment-seeking or access to care. … Racial/ethnic background is known to play an important role in opinions on mental health services, health care utilization, and physician preferences. In addition, rates of diagnosis- seeking to obtain stimulant medication for non-medical use may be more common among white vs nonwhite patients.” They conclude, “greater consideration must be placed on cultural influences on health care seeking and delivery, along with an increased understanding of the various social, psychological, and biological differences among races/ethnicities as well as culturally sensitive approaches to identify and treat ADHD in the total population.”
The study highlights that many cases of adult ADHD go undiagnosed. Research shows about 3% of adults worldwide have ADHD, but this study found that less than 1% are diagnosed by doctors. This points to the need for better training for clinicians to recognize, diagnose, and treat ADHD in adults. It also emphasizes the importance of understanding cultural factors that affect how people seek and receive care.
Winston Chung, MD, MS; Sheng-Fang Jiang, MS; Diana Paksarian, MPH, PhD; Aki Nikolaidis, PhD; F. Xavier Castellanos, MD; Kathleen R. Merikangas, PhD; Michael P. Milham, MD, PhD, “Trends in the Prevalence and Incidence of Attention-Deficit/Hyperactivity Disorder Among Adults and Children of Different Racial and Ethnic Groups,” JAMA Network Open (2019) 2(11): e1914344. DOI:10.1521/adhd.2019.27.4.8.
A cohort study looked at over five million adults, and over 850,000 children between the ages of five and eleven, who received care at Kaiser Permanente Northern California during the ten-year period from the beginning of 2007 through the end of 2016. At any given time, KPNC serves roughly four million persons. It is representative of the population of the region, except for the highest and lowest income strata.
Among adults rates of ADHD diagnosis rose from 0.43% to 0.96%. Among children the diagnosis rates rose from 2.96% to 3.74%, ending up almost four times as high as for adults.
Non-Hispanic whites had the highest adult rates throughout, increasing from 0.67% in 2007 to 1.42% in 2016. American Indian or Alaska Native (AIAN) had the second highest rates, rising from 0.56% to 1.14%. Blacks and Hispanics had roughly comparable rates of diagnosis, the former rising from 0.22% to 0.69%, the latter from 0.25% to 0.65%. The lowest rates were among Asians (rising from 0.11% to 0.35%) and Native Hawaiian or other Pacific Islanders (increasing from 0.11% to 0.39%).
Odds of diagnosis dropped steeply with age among adults. Relative to 18-24-year-olds, 25-34-year-olds were 1/6th less likely to be diagnosed with ADHD, 35-44-year-olds 1/3rd less likely, 45-54-year-olds less than half as likely, 55-64-year-olds less than a quarter as likely, and those over 65 about a twentieth as likely. This is consistent with other studies reporting and age dependent decline in the diagnosis.
Adults with the highest levels of education were twice as likely to be diagnosed as those with the lowest levels. But variations in median household income had almost no effect. Women were marginally less likely to be diagnosed than men.
ADHD is associated with some other psychiatric disorders. Compared with normally developing adults, and adjusted for confounders, those with ADHD were five times as likely to have an eating disorder, over four times as likely to be diagnosed with bipolar disorder or depression, more than twice as likely to suffer from anxiety, but only slightly more likely to abuse drugs or alcohol.
The authors speculate that rising rates of diagnosis could reflect increasing recognition of ADHD in adults by physicians and other clinicians as well as growing public awareness of ADHD during the decade under study. Turning to the strong differences among ethnicities, they note, Racial/ethnic differences could also reflect differential rates of treatment seeking or access to care. Racial/ethnic background is known to play an important role in opinions on mental health services, health care utilization, and physician preferences. In addition, rates of diagnosis- seeking to obtain stimulant medication for nonmedical use may be more common among white vs nonwhite patients. They conclude, greater consideration must be placed on cultural influences on health care seeking and delivery, along with an increased understanding of the various social, psychological, and biological differences among races/ethnicities as well as culturally sensitive approaches to identify and treat ADHD in the total population.
But the main take home message of this work is that most cases of ADHD in adults are not being diagnosed by clinicians. We know from population studies, worldwide, that about three percent of adults suffer from the disorder. This study found that less than 1 percent are diagnosed by their doctors. Clearly, more education is needed to teach clinicians how to identify, diagnose and treat ADHD in adults.
A transcontinental study team (California, Texas, Florida) used a nationally representative sample – the 2018 National Survey of Children’s Health – to query 26,205 caregivers of youth aged 3 to 17 years old to explore inequities in ADHD diagnosis.
With increasing accessibility of the internet in the U.S., more than 80% of adults now search for health information online. Recognizing that search engine data could help clarify patterns of inequity, the team also consulted Google Trends.
The team noted at the outset that “[d]ocumenting the true prevalence of ADHD remains challenging in light of problems of overdiagnosis (e.g., following quick screening rather than full evaluation incorporating multi-informant and multi-method data given limited resources) and underdiagnosis (e.g., reflecting inequities in healthcare and education systems).” Underdiagnosis is known to be influenced by lack or inadequacy of health insurance, inadequate public health funding, stigma, sociocultural expectations in some ethnic groups, and structural racism, among other factors.
After controlling for poverty status, highest education in household, child’s sex, and child’s age, the team reported that Black youth were a quarter (22%) less likely to receive ADHD diagnoses than their white peers. Latino/Hispanic youth were a third (32%) less likely and Asian youth three-quarters (73%) less likely to receive ADHD diagnoses than their white peers.
The team also found that state-level online search interest in ADHD was positively associated with ADHD diagnoses, after controlling for race/ethnicity, poverty status, highest education in household, child’s sex, and child’s age. However, the odds ratio was low (1.01), “suggesting the need for additional evaluation.” Furthermore, “There was no interaction between individual-level racial/ethnic background and state-level information-seeking patterns. … the state-level online information-seeking variation did not affect the odds that youth of color would have a current ADHD diagnosis over and above other included characteristics.”
That could be due in part to the gap in high-speed broadband access between Black and Hispanic in contrast to white populations, but that would not explain the even larger gaps in diagnosis for Asian youth, who tend to come from more prosperous backgrounds.
The team concluded, “Persistent racial/ethnic inequities warrant systematic changes in policy and clinical care that can attend to the needs of underserved communities. The digital divide adds complexity to persistent racial/ethnic and socioeconomic inequities in ADHD diagnosis …”
Typically, clinicians rely on both subjective and objective observations, patient interviews and questionnaires, as well as reports from family and (in the case of children) parents and teachers, in order to diagnose ADHD.
A group of researchers are aiming to find a diagnostic test that is purely objective and utilizes recent technological advancements. The method they developed involves analyzing videos of children in outpatient settings, focusing on their movements. The study included 96 children, half of whom had ADHD and half who did not.
How It Works
Video Recording: Children were recorded during their outpatient visits.
Skeleton Detection: Using a tool called OpenPose, the researchers detected and tracked the children's skeletons (essentially a map of their body's movements) in the videos.
Movement Analysis: The researchers analyzed these movements, looking at 11 different movement features. They specifically focused on the angles of different body parts and how much they moved.
Machine Learning: Six different machine learning models were used to see which movement features could best distinguish between children with ADHD and those without.
Key Findings
Movement Differences: Children with ADHD showed significantly more movement in all the features analyzed compared to children without ADHD.
Thigh Angle: The angle of the thigh was the most telling feature. On average, children with ADHD had a thigh angle of about 157.89 degrees, while those without ADHD had an angle of 15.37 degrees.
High Accuracy: Using thigh angle alone, the model could diagnose ADHD with 91.03% accuracy. It was very sensitive (90.25%) and specific (91.86%), meaning it correctly identified most children with ADHD and correctly recognized most children without it.
This new method could potentially provide a more objective way to diagnose ADHD, reducing the reliance on subjective observations and reports. It can help doctors make more accurate diagnoses, ensuring that those who need help get it and that those who don't aren't misdiagnosed.
Stimulant medications, such as methylphenidate (Ritalin) and amphetamines (Adderall), are among the most widely prescribed drugs in the world. In the United States alone, prescription rates have climbed more than 50% over the past decade, driven largely by growing awareness of ADHD in both children and adults. Yet stimulants also have a long history of non-medical use, and concerns about their psychological risks persist among patients, families, and clinicians alike.
Two major studies now offer the clearest picture yet of what that risk actually looks like, and who it may affect.
The Background:
Before turning to the research, it helps to understand the landscape. A notable share of stimulant users misuse their medication: roughly one in four takes it in ways other than prescribed, and about one in eleven meets criteria for Prescription Stimulant Use Disorder (PSUD). Counterintuitively, most people with PSUD aren’t obtaining drugs illicitly — they’re misusing their own prescriptions.
This distinction between therapeutic and non-therapeutic use turns out to be critical when evaluating psychosis risk.
The Study:
A comprehensive meta-analysis by Jangra and colleagues pooled data across more than a dozen studies to compare psychotic outcomes in people using stimulants therapeutically versus non-therapeutically. The contrast was striking.
Among therapeutic users (more than 220,000 individuals taking stimulants at prescribed doses under medical supervision), psychotic episodes occurred in roughly one in five hundred people. When symptoms did appear, they typically emerged after prolonged treatment or in individuals with pre-existing psychiatric vulnerabilities, and they usually resolved when the medication was stopped.
Among non-therapeutic users (over 8,000 participants across twelve studies, many using methamphetamine or high-dose amphetamines), nearly one in three experienced psychotic symptoms. These episodes tended to be more severe, involving persecutory delusions and hallucinations, with faster onset and a greater likelihood of recurrence or persistence.
The biology underlying this difference is well understood. When stimulants are taken orally at guideline-recommended doses, they produce moderate, gradual changes in neurotransmitter activity central to attention and executive functions. The brain tolerates these changes relatively well. Non-therapeutic use, by contrast, often involves much higher doses that are frequently delivered through non-oral routes such as injection or smoking. This produces a rapid, excessive surge in dopamine activity, which is precisely the neurochemical pattern associated with psychotic symptoms.
The takeaway here is not that therapeutic stimulant use is risk-free, but that risk is strongly modulated by dose, route of administration, and individual psychiatric history. Clinicians are advised to monitor patients with pre-existing mood or psychotic disorders, particularly carefully.
A Nationwide Study Focuses on Methylphenidate Specifically:
Where the meta-analysis cast a wide net, a large-scale population study by Healy and colleagues drilled into a specific and clinically pressing question: does methylphenidate (the most commonly prescribed ADHD medication, also known as Ritalin) increase the risk of developing a psychotic disorder?
To find out, the researchers analyzed Finland's national health insurance database, tracking nearly 700,000 individuals diagnosed with ADHD. Finland's single-payer system made this kind of comprehensive, long-term tracking possible in a way that fragmented healthcare systems rarely allow.
Critically, the team adjusted for a range of confounding factors that have clouded previous research, including sex, parental education, parental history of psychosis, and the number of psychiatric visits and diagnoses prior to the ADHD diagnosis itself (a proxy for illness severity). After these adjustments, they found no significant difference in the risk of schizophrenia or non-affective psychosis between patients treated with methylphenidate and those who remained unmedicated. This held true even among patients with four or more years of continuous methylphenidate use.
The Take-Away:
When considered together, these studies offer meaningful reassurance without encouraging complacency.
For patients and families weighing ADHD treatment, the evidence suggests that methylphenidate used as prescribed does not increase psychosis risk, even over years of use. The rare cases of stimulant-associated psychosis in therapeutic settings are typically linked to high doses, pre-existing vulnerabilities, or both, and tend to resolve with discontinuation.
For clinicians, the findings reinforce the importance of baseline psychiatric assessment before initiating stimulant therapy, ongoing monitoring in patients with mood or psychotic disorder histories, and clear patient education about the risks of dose escalation or non-oral use.
The picture that emerges is one of a meaningful distinction between a medication used carefully within its therapeutic window and a drug misused outside of it. This distinction matters enormously when communicating risk to patients, policymakers, and the public.
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:
Gross motor skills — movements involving large muscle groups, such as running, jumping, throwing, and maintaining balance
Fine motor skills — precise, controlled movements, typically of the hands and fingers, such as handwriting and manual dexterity (the ability to handle objects skillfully)
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:
Object control (e.g., throwing, kicking) — large improvement
Locomotion (e.g., running, swimming), body coordination, and strength — medium improvements
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:
Handwriting: large improvement
Manual dexterity: medium-to-large improvement
Hand-eye coordination: moderate improvement
The Results: What Kind of Exercise Works Best?
Two factors stood out consistently across both gross and fine motor skills: session length and frequency.
Sessions longer than 45 minutes produced roughly twice the benefit of shorter sessions
Three or more sessions per week outperformed less frequent programs for gross motor gains
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:
Potential Publication Bias: Studies showing positive results are more likely to be published, which can inflate apparent benefits. For gross motor skills, adjusting for this bias reduced the effect size from medium-to-large, to medium.
Active vs. Passive Controls: When exercise was compared against doing nothing (a passive control), improvements looked significant. When compared against regular school activities (an active control), the gains were no longer statistically significant. This is a meaningful distinction: it suggests exercise may be beneficial, but not dramatically more so than simply being physically active in a structured school setting.
Medication status: Most participants were taking ADHD medication, so it’s unclear how well these findings apply to unmedicated children who might stand the most to benefit from structured exercise.
Study quality: Many studies lacked proper randomization, weakening confidence in the conclusions.
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:
Direct comparisons (treatment A vs. treatment B tested in clinical trials), and
Indirect comparisons (A vs. B inferred through a common comparator such as placebo or usual care).
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-analysisStudy?
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:
Six interventions are supported by a single trial each (digital cognitive mindfulness training, BrainFit, neurofeedback, online mindfulness-based program, cognitive behavioral therapy, and working-memory training)
Three interventions are supported by two trials each
Only one intervention is supported by three trials (family mindfulness-based therapy)
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:
Family MBT: 92% probability of being best
Behavioral parent training (BPT): 65%
Online mindfulness program: 49%
Cognitive behavioral therapy: 48%
Yoga: 39%
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.