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March 20, 2026
A new study in the respected journal PLOS One analyzes data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to examine trends in the incidence, prevalence, and disability-adjusted life-years associated with ADHD among adolescents and young adults aged 10 to 24 years between 1990 and 2021.
The GBD 2021, released by the Institute for Health Metrics and Evaluation (U.S.), is a comprehensive global analysis of 371 diseases, injuries, and risk factors – such as ADHD – across 204 countries from 1990 to 2021. Its open-source data are publicly available.
First, a distinction. Incidence measures the number of new cases of a disease that develop in a specific population each year. Prevalence measures the total number of existing cases – both new and pre-existing – in a population each year.
The estimated global incidence of ADHD declined marginally from 12.61 per 100,000 population in 1990 to 11.89 per 100,000 population in 2021, representing an average annual decrease of 0.6% in age-standardized incidence. The rates observed were comparable between males and females.
Regional trends varied: Western Europe had the highest rise in ADHD incidence (0.5% annually), while North Africa and the Middle East saw the largest drop (0.7% annually). Overall, a higher Socio-Demographic Index (SDI) is linked to a greater incidence, although it is far from a perfect fit. Nationally, showed the highest increase in ADHD incidence (1.15% annually), while Qatar showed the largest decrease with an annualized reduction of 1.77%.
The estimated global prevalence of ADHD declined marginally from 2.38% in 1990 to 2.17% in 2021. Again, the decline was similar for males and females, and across all age groups (10-14, 15-19, 20-24). Higher SDI was associated with higher prevalence, but inconsistently.
Disability-adjusted life-years (DALYs) combine years lost from early death and years lived with disability to measure disease burden. Globally, the age-standardized DALYs rate for ADHD decreased slightly from 30.3 per 100,000 population to 26.6 per 100,000 population, for an average annual decline of 0.6%. The decline occurred across age groups and was similar between males and females.
The authors concluded that ADHD rates and related health burdens have generally declined over the past quarter century, though recent patterns are less consistent due to factors like socioeconomic changes and evolving diagnostic standards. Continued research is needed to improve the accuracy and accessibility of ADHD diagnosis and treatment to further reduce its global impact.
Take-Away:
The broader takeaway is one of cautious reassurance. Despite rising public awareness and diagnosis rates in many Western countries, the global picture over 25 years shows a gentle decline in ADHD burden among young people as opposed to a crisis of escalating proportions as social media may make one think. That said, the variation between regions suggests that access to diagnosis, cultural factors, and reporting standards are shaping the numbers as much as underlying biology. Progress is real but uneven, and the work of improving equitable access to diagnosis and care is far from finished.
Bo Yuan, Bo Ao, Sajid Ali, Qiong Cai, Yanan Chen, and Youliang Huang, “Global, regional, and national burdens of attention deficit hyperactivity disorder in adolescents and young adults aged 10–24 years from 1990 to 2021: A trend analysis,” PLOS One (2026), 21(2) e0341076, published online, https://doi.org/10.1371/journal.pone.0341076.
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 …”
The diagnosis of ADHD should only be done by a licensed clinician, and that clinician should have one goal in mind: to plan a safe and effective course of evidence-based treatment. The infographic gives a summary of this diagnostic approach which my colleagues and I prepared for our "Primer" about ADHD,http://rdcu.be/gYyV. A key point that parents of ADHD youth and adults with ADHD should keep in mind is that there is only one way to diagnose ADHD.An expert clinician must document the criteria for the disorder as specified by either the Diagnostic and Statistical Manual of the American Psychiatric Association, which is now in its fifth edition (DSM-5), or the World Health Organization's International Classification of Diseases (ICD-10). The two sets of criteria are nearly identical. These criteria are most commonly applied by a clinician asking questions of the parent (for children) and/or patient (for adolescents and adults).For children, information from the teacher can be useful. Some clinicians get this information by having the parent ask the teacher to fill out a rating scale. This information can be very useful if it is available. When diagnosing adults, it is also useful to collect information from a significant other, which can be a parent for young adults or a spouse for older adults. But when such informants are not available, diagnosing ADHD based on the patient's self-report is valid. As the infographic indicates, any diagnosis of ADHD should also assess for comorbid psychiatric disorders, as these have implications for which ADHD medications will be safe and effective. And because a prior history of cardiovascular disease or seizures frequently contraindicate stimulants. These must also be assessed.
ADHD is a neurodevelopmental condition rooted in delayed or atypical maturation of the prefrontal cortex (the brain region that governs self-regulation). This maturational lag underlies the hallmark difficulties with attention, hyperactivity, and impulsivity, and also impairs what researchers call executive function: the cognitive toolkit we rely on for working memory, impulse control, mental flexibility, emotional regulation, and the ability to tolerate delays in reward.
The Background:
Standard treatments work through two main routes. Stimulant and non-stimulant medications are considered very safe and effective treatments, but are not without risk of side effects and are not appropriate for every ADHD patient. Behavioral and psychosocial interventions can improve self-regulation and social functioning, but they require sustained effort and produce variable results. These limitations have kept the search for better alternatives active.
One candidate that has drawn growing attention is transcranial direct current stimulation (tDCS). The technique is appealingly simple: a weak electrical current is applied to the scalp through small electrodes, modulating the excitability of neurons in the underlying cortex without requiring surgery, anesthesia, or significant discomfort. Its safety profile and ease of use have made it attractive to researchers.
The Study:
A newly published meta-analysis set out to give the technique its most rigorous test yet, pooling results from randomized controlled trials, including crossover designs, that compared active tDCS against sham stimulation in people with ADHD across all age groups.
The Results:
The findings were consistently null. Across seven trials enrolling 303 participants, tDCS produced no significant reduction in overall ADHD symptom severity compared with sham. Breaking symptoms into their components made no difference: neither hyperactivity/impulsivity nor inattention improved. Turning to executive function, 18 studies with 872 participants found no meaningful gain in inhibitory control, and 12 studies with 506 participants found the same for working memory. Smaller bodies of evidence, including three studies on cognitive flexibility (122 participants) and two on hot executive function, the motivational and emotional dimension of self-regulation (86 participants), similarly came up empty. Variation in outcomes across studies was small to moderate, and there was no evidence of publication bias skewing the picture.
The authors’ conclusion was succinct: tDCS was well tolerated but “did not demonstrate significant overall efficacy for core ADHD symptoms or executive functions.”
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Disordered eating (a broad category of persistent, harmful patterns in eating or weight control) affects between 5% and 22% of children and adolescents worldwide, with similar rates seen in the United States. The consequences are far-reaching: these conditions are linked to bone fractures, anemia, malnutrition, dental erosion, obesity, diabetes, hypertension, and elevated cholesterol and triglycerides. They also carry one of the highest mortality rates of any psychiatric illness.
Eating disorders rarely occur in isolation. They frequently arise alongside other psychiatric and neurological conditions. Yet, until now, no large-scale study had examined these co-occurrences in a nationally representative U.S. sample. A new study addresses that gap, focusing on children and adolescents aged 6–17 and the conditions most commonly associated with disordered eating, including ADHD.
The Study:
Researchers drew on data from the 2022–2023 National Survey of Children's Health (NSCH), a nationally representative, cross-sectional survey covering all 50 states and Washington, D.C. Households were selected using stratified, address-based sampling, and parents or guardians completed surveys about one randomly selected child per household. The final sample included 68,000 children and adolescents.
Results:
After accounting for factors including sex, age, race and ethnicity, household income, educational attainment, insurance status, and household language, children and adolescents with ADHD were 2.6 times more likely to have some form of disordered eating compared to their typically developing peers.
The elevated risk appeared across a range of specific behaviors:
A greater tendency toward using diet pills, laxatives, or diuretics was also observed in the ADHD group, though this finding did not reach statistical significance.
The Take-Away:
These findings underscore a need to improve both prevention and treatment strategies for disordered eating, particularly in children and adolescents who have ADHD. Clinicians working with this population are advised to screen for a wide spectrum of disordered eating behaviors.
For centuries, we’ve called the eyes the "windows to the soul," but for modern neurologists, they are quite literally a window into the brain. The retina and the central nervous system share the same embryonic origins, developing from the same neural tissue in the womb. Because of this deep biological connection, the back of your eye acts as a non-invasive map of your brain's health, displaying a complex web of nerves and blood vessels that can (theoretically!) mirror certain neurodevelopmental conditions.
Recently, a buzz rippled through the mental health community when a study published in partnership with Seoul National University Bundang Hospital claimed a massive breakthrough. Researchers developed an Artificial Intelligence (AI) model that could screen children for Attention-Deficit/Hyperactivity Disorder (ADHD) using nothing more than a simple retinal photograph. The study, which prospectively recruited children from Severance Hospital and Eunpyeong St. Mary’s Hospital, produced results that were staggering: the AI reportedly achieved an accuracy rate of 96.9%!
In the world of medical testing, scientists use a metric called AUROC (Area Under the Receiver Operating Characteristic) to measure how well a test works.
An AUROC of 96.9% is a near-perfect score, suggesting a tool is ready for immediate, real-world deployment. While headlines promised a revolution in mental health screening, a deeper look into this research and the study’s design has exposed that this 96.9% AUROC was more likely evidence of a flawed methodology rather than a biological reality.
To build their screening tool, researchers analyzed over 1,100 retinal images using a digital pipeline called AutoMorph and a machine-learning model known as XGBoost. The AI was trained to hunt for physical signals of the "Dopamine Connection." Dopamine is the primary neurotransmitter involved in ADHD, but it is also essential to the eye. It regulates synaptic formation, retinal blood flow, and vascular endothelial regulation. Because dopamine dysregulation influences how blood vessels grow and remodel, the study hypothesized that an ADHD brain would leave a unique "fingerprint" on the retinal vasculature, resulting in denser, thicker vessel structures.
On paper, the logic was sound: use AI to spot the subtle vascular remodeling caused by dopaminergic shifts. But a closer look at the investigation revealed that the AI wasn't just spotting ADHD; it was over-indexing on technical noise.
The most significant "smoking gun" flagged by critics is a massive temporal mismatch. In other words, there was a severe disparity in the timeframes and conditions under which the retinal images for the two comparison groups were collected. For an AI to learn a biological condition, it must compare groups under identical technical conditions. Instead, this study created a time-traveling dataset:
A scientific study is only as reliable as its control group. The control in any experiment acts as a baseline against which the study group is compared. In this case, the control group should be composed of children without any neurodevelopmental disorders, or of “typically developing” children.
In this study, the control group wasn't composed of healthy children from the community. Instead, they were patients visiting a tertiary ophthalmology clinic. Children visiting a specialist eye hospital are rarely "typical." They are there because they have symptomatic eye issues. This introduced a massive selection bias involving three major confounders:
When training AI, you must never allow the "test questions" to leak into the "study material." The researchers, however, committed a fundamental violation of machine learning hygiene known as Eye-to-Eye Data Leakage. The study split the data by the eye rather than by the participant.
Human eyes are highly correlated; the left eye is a near-mirror of the right. If a child's left eye was used for training and their right eye was used for testing, the AI was effectively "cheating." Instead of learning the general traits of ADHD, the model was potentially memorizing individuals. This error artificially balloons accuracy metrics.
The true test of medical AI is diagnostic specificity, or differential diagnosis. This refers to the ability to tell one condition apart from another. While the model claimed 96.9% accuracy against a flawed control group, its performance collapsed when faced with real-world complexity.
When the researchers asked the AI to differentiate between ADHD and Autism Spectrum Disorder (ASD), the accuracy plummeted to a poor 63% AUROC. In real-world clinical settings, an accuracy of 63% is dangerously close to a 50% coin flip. Since ADHD frequently co-occurs with ASD, anxiety, or intellectual disabilities, an AI that cannot handle these "clinical differentials" is functionally useless in a doctor's office. The failure at this stage proves the model was likely detecting technical quirks of the dataset rather than a unique biological marker for ADHD.
To move from the lab to the clinic, we must establish a foundation built on rigor rather than high-speed data scraping. Moving forward, we must demand these 3 Pillars of Trusted Medical AI :
The dream of a quick eye scan to diagnose ADHD is not dead, but it must be rescued from "fast science" shortcuts and buzzy headlines.
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