Cookie Preferences
By clicking, you agree to store cookies on your device to enhance navigation, analyze usage, and support marketing. More Info
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
May 23, 2025
The United Kingdom has a National Health Service (NHS) that encompasses virtually its entire population, with free access. The NHS records facilitate conducting nationwide studies.
The Study
Using electronic health records from 794 primary care practices (roughly one in ten UK practices), largely representative of the UK population, a research team used mortality data to explore the life expectancy of adults diagnosed with ADHD compared with adults not diagnosed with ADHD.
For each adult diagnosed with ADHD, the team sampled ten controls matched by age, sex, and primary care practice. They identified 30,039 individuals with an ADHD diagnosis in their electronic health records and matched them with 300,390 without an ADHD diagnosis.
The team also gathered data on socioeconomic deprivation, diabetes, elevated cholesterol, hardening of the coronary arteries, high blood pressure, chronic respiratory disease, epilepsy, anxiety, depression, severe mental illness, self-harm/suicide, autism, intellectual disability, personality disorder, current smoking, and potentially harmful alcohol use. All these conditions examined at baseline were more common among participants with ADHD than comparison participants.
Both men and women with ADHD were about twice as likely to die during follow-up as Those without ADHD. A diagnosis of ADHD was associated with a 6.8-year reduction of life expectancy in males and an 8.6-year reduction of life expectancy in females.
Conclusion
The authors wrote, “We believe that this is unlikely to be because of ADHD itself and likely caused by modifiable factors such as smoking, unmet mental and physical health support, and unmet treatment needs. The findings illustrate an important inequity that demands urgent attention.”
They also noted, “…we did not adjust for socioeconomic status (SES), as we believe that SES is best understood as part of the causal pathway between ADHD and premature mortality (i.e. SES is a mediator).” These results confirm other studies which also document that those with ADHD have a decreased life expectancy, primarily due to accidents and suicide.
Elizabeth O’Nions, Céline El Baou, Amber John, Dan Lewer, Will Mandy, Douglas G.J. McKechnie, Irene Petersen, and Josh Stott, “Life expectancy and years of life lost for adults with diagnosed ADHD in the UK: matched cohort study,” The British Journal of Psychiatry (2025), https://doi.org/10.1192/bjp.2024.199.
Researchers from the Swedish Department of Global Public Health, the Swedish Transport Agency, and the Swedish National Road and Transport Research Institute collaborated in a nationwide population study of motor vehicle crashes among the elderly, defined as 65 and older.
They availed themselves of the country's all-encompassing national registers to identify the anonymized records of all such drivers from 2011 through 2016. That enabled them to compare crash records of those with known driving-impairing conditions with matched drivers who had no record of such conditions.
They looked only at road traffic crashes that resulted in injury to the driver or a passenger. For anyone with multiple crash records, they only looked at the first.
This was a case-control study, with two controls matched to each case wherever possible. For every case of a 65 or older driver involved in an injurious crash, the team randomly matched two individual controls by sex, birth year, municipality of residence, and other medical conditions. Place of residence was used to distinguish residents of large cities, who would tend to drive less frequently and in denser traffic, from those in small towns and rural areas. To minimize controls that never drive, only those with a driver's license and car were considered.
Of the thirteen medical conditions examined, elderly drivers with "ADHD, autism spectrum disorder, and similar conditions" had by far the highest odds of being in crashes that resulted in injury "at almost three times the rate of those without those conditions."
But note carefully the serious limitations in the data:
Taiwan has a single-payer healthcare system that covers virtually every inhabitant (99.5%). That makes it relatively easy to track healthcare issues using its comprehensive National Health Insurance Research Database.
This database maintains a subset, the Longitudinal Health Insurance Database (LHID), consisting of a million persons, with no significant differences in sex, age, or healthcare use from the parent database.
A Taiwanese research team used the LHID to identify 114,486 individuals diagnosed with ADHD from 1997 to 2013. It then compared their motor vehicle (including motorcycles, which are extremely common in Taiwan) crash patterns with 338,261 normally developing controls from the same database.
Adjusting for sex, age, and psychiatric comorbidities, persons with ADHD were about a fifth (19%)more likely to be in traffic crashes. Breaking it down further by sex, women with ADHD were no more likely to be in crashes, but men with ADHD were about a quarter (24%) more likely than their healthy counterparts.
Since the database also tracks pharmaceutical prescriptions, the team also looked into the effect of methylphenidate (MPH), the medication that is the first-line treatment for ADHD under Taiwanese guidelines, and the only approved stimulant. Atomoxetine, a non-stimulant, is used where MPH is either ineffective or not indicated for any other reason and is only used in 4% of all cases.
Of the 114,486 persons diagnosed with ADHD, 89,826 used MPH, and 24,660 did not.
Compared with persons with ADHD who were not on methylphenidate, those with ADHD who were on MPH for 180 days (roughly half a year) or less had 77% fewer accidents, and those on MPH for over 180 days had 93% fewer accidents. This strong dose-response relationship is suggestive of a causal relationship, with MPH perhaps reducing impulsive behavior, particularly among young men with ADHD.
The team also conducted within-person analyses, comparing times when persons with ADHD were taking MPH with periods when they were not. These showed no effect within 30 days of use, rising to a 65%reduction in crashes within 60 to 90 days of use, which was barely outside the 95% confidence interval (p = .07), very likely because of "the extremely low incidence of transport accidence (i.e. 0.6%)enlarged the confidence interval."
The authors concluded, "All registration medical claim data came from the nationally-representative sample of NHI, minimizing the selection and recall bias. By excluding transport accidents before ADHD diagnosis, we have precluded the reverse association between ADHD and road traffic accidents as much as possible. The advantage of the between-subjects comparison was that we were able to examine the MPH effect in different dose groups. However, confounding by indication cannot be eliminated. For example, those with a severe degree of ADHD symptoms, an exhibition of risky behaviors, or comorbid with other psychiatric illnesses were more likely to be prescribed medication. Hence, we also performed within-subject comparisons to adjust for time-invariant factors."
Transport safety thus offers another compelling reason to treat ADHD symptoms. Methylphenidate in particular seems to be especially effective in reducing traffic fatalities and injuries.
Suicide is one of the most feared outcomes of any psychiatric condition. Although its association with depression is well known, a small but growing research literature shows that ADHD is also a risk factor for suicidality. Suicide is difficult to study. Because it is relatively rare, large samples of patients are needed to make definitive statements.
Studies of suicide and ADHD must also consider the possibility that medications might elevate that risk. For example, the FDA placed a black box warning on atomoxetine because that ADHD medication had been shown to increase suicidal risk in youth. A recent study of 37,936 patients with ADHD now provides much insight into these issues (Chen, Q., Sjolander, A., Runeson, B., D'Onofrio, B. M., Lichtenstein, P. & Larsson, H. (2014). Drug treatment for attention-deficit/hyperactivity disorder and suicidal behavior: a register-based study. BMJ 348, g3769.). In Sweden, such large studies are possible because researchers have computerized medical registers that describe the disorders and treatments of all people in Sweden. Among 37,936 patients with ADHD, 7019 suicide attempts or completed suicides occurred during 150,721 person-years of follow-up. This indicates that, in any given year, the risk for a suicidal event is about 5%. For ADHD patients, the risk for a suicide event is about 30% greater than for non-ADHD patients. Among the ADHD patients who attempted or completed suicide, the risk was increased for those who had also been diagnosed with a mood disorder, conduct disorder, substance abuse, or borderline personality. This is not surprising; the most serious and complicated cases of ADHD are those that have the greatest risk for suicidal events. The effects of the medication were less clear. The risk for suicide events was greater for ADHD patients who had been treated with non-stimulant medication compared with those who had not been treated with non-stimulant medication. A similar comparison showed no effect of stimulant medications. This first analysis suffers from the fact that the probability of receiving medication increases with the severity of the disorder. To address this problem, the researchers limited the analyses to ADHD patients who had some medication treatment and then compared suicidal risk between periods of medication treatment and periods of no medication treatment. This analysis found no increased risk for suicide from non-stimulant medications and, more importantly, found that for patients treated with stimulants, the risk for suicide was lower when they were taking stimulant medications. This protective effect of stimulant medication provides further evidence of the long-term effects of stimulant medications, which have also been shown to lower the risks for traffic accidents, criminality, smoking, and other substance use disorders.
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.
_logo%201.svg)
