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March 7, 2025
The National Health Interview Survey (NHIS) is conducted annually by the National Center for Health Statistics at the Centers for Disease Control and Prevention. The NHIS is done primarily through face-to-face computer-assisted interviews in the homes of respondents. But telephone interviews are substituted on request, or where travel distances make in-home visits impractical.
For each interviewed family, only one sample child is randomly selected by a computer program.
The total number of households with a child or adolescent aged 3-17 for the years 2018 through 2021 was 26,422.
Based on responses from family members, 9.5% of the children and adolescents randomly surveyed throughout the United States had ADHD.
This proportion varied significantly based on age, rising from 1.5% for ages 3-5 to 9.6% for ages 6-11 and to 13.4% for ages 12-17.
There was an almost two-to-one gap between the 12.4% prevalence among males and the 6.6% prevalence among females.
There was significant variation by race/ethnicity. While rates among non-Hispanic whites (11.1%) and non-Hispanic blacks (10.5%) did not differ significantly, these two groups differed significantly from Hispanics (7.2%) and Others (6.6%).
There were no significant variations in ADHD prevalence based on highest education level of family members.
But family income had a significant relationship with ADHD prevalence, especially at lower incomes. For family incomes under the poverty line, the prevalence was 12.7%. That dropped to 10.3% for family incomes above the poverty level but less than twice that level. For all others it dropped further to about 8.5%. Although that might seem like poverty causes ADHD, we cannot draw that conclusion. Other data indicate that adults with ADHD have lower incomes. That would lead to more ADHD in kids from lower income families.
There was also significant geographic variation in reported prevalence rates. It was highest in the South, at 11.3%, then the Midwest at 10%, the Northeast at 9.1%, with a jump down to 6.9% in the West.
Overall ADHD prevalence did not vary significantly by year over the four years covered by this study.
This study highlights a consistently high prevalence of developmental disabilities among U.S. children and adolescents, with notable increases in other developmental delays and co-occurring learning and intellectual disabilities from 2018 to 2021. While the overall prevalence remained stable, these findings emphasize the need for continued research into potential risk factors and targeted interventions to address developmental challenges in youth.
It is also important to note that this study assessed the prevalence of ADHD being diagnosed by healthcare professionals. Due to variations in healthcare accessibility across the country, the true prevalence of ADHD may differ still.
...
Qian Li, Yanmei Li, Juan Zheng, Xiaofang Yan, Jitian Huang, Yingxia Xu, Xia Zeng, Tianran Shen, Xiaohui Xing, Qingsong Chen, and Wenhan Yang, “Prevalence and trends of developmental disabilities among US children and adolescents aged 3 to 17 years, 2018–2021,” Scientific Reports (2023) 13: 17254, https://doi.org/10.1038/s41598-023-44472-1.
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.
Using Statistics New Zealand’s Integrated Data Infrastructure (IDI), a large database of linked de-identified administrative and survey data about people and households, a local study team examined a three-year birth cohort (mid-1992 through mid-1995) totaling 149,076 persons.
The team assessed the presence of ADHD within this cohort through diagnosis codes and inference from medication dispensing, where there was at least one code relating to an ADHD diagnosis in the medication datasets. This subgroup consisted of 3,975 persons.
Next, they related this information to criminal justice system interactions of increasing severity, starting with police proceedings, and continuing with court charges, court convictions, and incarcerations. These interactions were tracked during an eight-year period from participants’ 17th birthday through their 25th birthday.
In this same period the team also tracked types of offenses: against people; against property; against organizations, government, and community; and violent offenses.
In all cases, the study team adjusted for gender, ethnicity, deprivation, and area of residence as potential confounders.
With these adjustments, young adults with ADHD were over twice as likely as their typically developing peers to be proceeded against by police, to be charged with an offense, and to be convicted. They were almost five times as likely to be incarcerated.
With the same adjustments, young adults with ADHD were over twice as likely as their typically developing peers to be convicted of offenses against organizations, government, and community. They were almost three times as likely to be convicted of crimes against persons, and over three and a half times more likely to be convicted of either violent offenses or offenses against property.
The authors noted, “The greater effect size for incarceration observed in our study may be due to the lack of control for comorbid conditions such as CD [conduct disorder], which are known criminogenic risk factors.”
They also noted, “The sharp increase in the risk of incarceration observed may also signal differences in the NZ justice system’s approach to ADHD, which may be less responsive to the condition than other nations, particularly the steps in the justice system between conviction and sentence. This would suggest that the UNCRPD [United Nations Convention on the Rights of Persons with Disabilities] obligations of equal recognition before the law and the elimination of discrimination on the basis of disability are not being met for individuals with ADHD in NZ.”
They concluded, “Our findings revealed that not only were individuals with ADHD overrepresented at all stages of the CJS [criminal justice system] and offense types examined, there was also a pattern of increasing risk for CJS interactions as these individuals moved through the system. These results highlight the importance of early identification and responsivity to ADHD within the CJS and suggest that the NZ justice system may require changes to both of these areas to ensure that young individuals with ADHD receive equitable access to, and treatment within, the CJS.”
An international team of researchers conducted a comprehensive search of the peer-reviewed literature to perform a meta-analysis, with three aims:
1) assess the global prevalence of adult ADHD
2) explore possible associated factors
3) estimate the 2020 global population of persons with adult ADHD.
In doing so, they distinguished between studies requiring childhood-onset of ADHD to validate adult ADHD (persistent adult ADHD) and studies that make no such requirement and examine ADHD symptoms in adults regardless of previous childhood diagnosis (symptomatic adult ADHD).
The search yielded forty articles covering thirty countries. Twenty reported prevalence data on symptomatic adult ADHD, 19 on persistent adult ADHD, and one on both. Thirty-five studies were published in the last decade (2010-2019). Thirty-one included both urban and rural populations. Thirty-five had a quality score of six or above (out of ten). Twenty-five had sample sizes greater than a thousand.
Because the prevalence of ADHD is age-dependent, and different countries vary widely in the age structure of their populations, the authors adjusted country results for their structures. This allowed for meaningful global estimates of the prevalence of adult ADHD.
Twenty studies covering a total of 107,282 participants reported the prevalence of persistent adult ADHD. The pooled prevalence was 4.6%. After adjustment for the global population structure, the pooled prevalence was 2.6%, equivalent to roughly 140 million cases globally.
Twenty-one studies covering 50,098 participants reported on the prevalence of symptomatic adult ADHD. The pooled prevalence was 8.8%. After adjustment for the global population structure, the pooled prevalence was 6.7%, equivalent to roughly 366 million cases globally.
For persistent adult ADHD, adjusted prevalence declined steeply from 5% among 18- to 24-year-olds to 0.8% among those 60 and older.
For symptomatic adult ADHD, adjusted prevalence declined less steeply from 9% among 18- to 24-year-olds to 4.5% among that 60 and older.
In each case, subgroup analyses found no significant differences based on sex, urban or rural setting, diagnostic tool, DSM version, or investigation period, although pooled prevalence estimates of persistent adult ADHD from 2010 onward were almost twice the previous pooled prevalence estimates. For symptomatic adult ADHD, however, differences between WHO (World Health Organization) regions were highly significant, although the outliers(Southeast Asia at 25% and Eastern Mediterranean at 16%) were based on small samples(304 and 748 respectively).
In both cases, between-study heterogeneity was very high (over 97%). The authors noted, "the age of interviewed participants in the included studies was not unified, ranging from young adults to the elderly. Given the fact that the prevalence of adult ADHD decreases with advancing age, as revealed in previous investigations and our meta-regression, it is not surprising to observe such a diversity in the reported prevalence, and the considerable heterogeneity across included studies could not be fully ruled out by a priori selected variables, including diagnostic tool, DSM version, sex, setting, investigation period, WHO region, and WB [World Bank] region. The effects of other potential correlates of adult ADHD, such as ethnicity, were not able to be addressed due to the lack of sufficient information."
In both cases, there was also evidence of publication bias. The authors stated, "we did not try to eliminate publication bias in our analyses, because we deemed that an observed prevalence of adult ADHD that substantially differed from previous estimates was likely to have been published."
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.
Background:
One of the more persistent concerns among parents of children with ADHD is whether stimulant medications will stunt their child's growth. A large Israeli cohort study now offers some of the most rigorous reassurance to date, and its methodology sets it apart from earlier research.
The question has long been complicated by a more fundamental uncertainty: do growth differences in children with ADHD stem from the condition itself, from stimulant treatment, or from factors present before any medication is ever prescribed? Without a clear answer, clinicians and families have faced a genuine dilemma when weighing the benefits of stimulant therapy against potential long-term physical costs.
Most previous studies compounded this difficulty by comparing group-average heights, which ignores the crucial variable of genetic potential. A child who is short relative to the general population may simply have short parents. Failing to account for this introduces systematic bias and can make medications appear more harmful than they are.
The Study:
The Israeli research team addressed this directly. Using health records from a nationwide provider, they assembled a retrospective cohort of children born between 1995 and 2003, following them through 2023. This amount of time was long enough for all participants to have reached adult stature (defined as 17 or older for females, 19 or older for males). Their sample included 5,671 children with untreated ADHD, 11,846 who received stimulant treatment, and 47,258 non-ADHD controls. Children who took stimulants for only one to two months, or who had chronic medical conditions requiring long-term medication, were excluded to avoid confounding the results.
Crucially, adult height was evaluated not against population norms but against each individual's expected height, calculated from parental heights using the Tanner-Goldstein-Whitehouse method, a standard approach for estimating genetic height potential via mid-parental height.
When the researchers compared adult heights across the three groups using analysis of variance (ANOVA), they did find statistically significant differences. But statistical significance, particularly in studies with tens of thousands of participants, does not automatically translate into clinical significance. The effect sizes were consistently very small, and the absolute differences were under one centimeter, which is a margin considered clinically negligible.
Their conclusion is measured but clear: after accounting for genetic growth potential, neither an ADHD diagnosis nor stimulant treatment was associated with meaningful reductions in adult height. The findings, they argue, support prioritizing behavioral and functional outcomes when making treatment decisions, since the risk of clinically significant height loss appears to be minimal.
The Take-Away:
For families navigating ADHD treatment, the practical implication is significant: concerns about permanent growth suppression, while understandable, should not be the primary driver of whether or how long a child receives stimulant therapy.
A recent meta-analysis examined how well cognitive behavioral therapy (CBT) improves not just symptoms, but everyday functioning and quality of life in adults with ADHD.
The Background:
ADHD in adults affects far more than attention or impulsivity. It often disrupts key areas of life:
These broad impacts highlight a key issue: reducing symptoms does not automatically translate into better day-to-day functioning.
CBT is a structured, skills-based therapy that helps people:
While both medication (especially stimulants) and CBT improve core ADHD symptoms, CBT is particularly aimed at improving real-world functioning.
The Study:
The researchers analyzed studies involving adults diagnosed with ADHD (or showing clinically significant symptoms). They included:
They focused specifically on outcomes beyond symptoms:
The Results:
1. Strongest Effects: Occupational functioning
CBT showed consistently strong improvements in work-related functioning compared to control groups, both immediately after treatment and at follow-up. This was the most robust finding across domains.
2. Moderate Improvement: Global Functional Impairment
CBT led to moderate improvements in overall daily functioning, with some evidence that gains persist over time. In studies tracking individuals over time, improvements were even stronger at follow-up.
3. Modest Gains: Social Relationships
CBT produced small to moderate improvements in social functioning. Benefits were present both after treatment and at follow-up, but were less pronounced than in work-related outcomes.
4. Limited Effects: Academic Functioning
There were moderate short-term gains when CBT was compared to control groups, but these did not persist at follow-up. Within-subject studies showed only small improvements overall.
5. Modest and Inconsistent Effects: Quality of Life
Improvements in quality of life were small when compared to control groups and often did not last. However, studies tracking individuals over time showed moderate improvements, suggesting some benefit that may not always show up clearly in between-group comparisons.
Overall, the findings suggest:
One notable nuance: CBT did not always outperform other active treatments (like medication or other therapies). This suggests that while CBT is effective, its benefits may partly overlap with broader therapeutic or support effects rather than relying on a single, unique mechanism.
The Take-Away:
CBT is a valuable, evidence-based treatment for adults with ADHD, especially for improving work functioning and overall daily life management. However, its impact on relationships, academic outcomes, and quality of life is more limited and less consistent, pointing to the need for more targeted or combined approaches in those areas.
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