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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.”
Francesca Anns, Stephanie D’Souza, Conrad MacCormick, Brigit Mirfin-Veitch, Betony Clasby, Nathan Hughes, Warren Forster, Eden Tuisaula, and Nicholas Bowden, “Risk of Criminal Justice System Interactions in Young Adults with Attention-Deficit/Hyperactivity Disorder: Findings From a National Birth Cohort,” Journal of Attention Disorders (2023), 1-11, https://doi.org/10.1177/10870547231177469.
Children with disabilities are known to be at heightened risk of violence compared to their non-disabled peers. To what extent does this hold true for ADHD?
Denmark has a single-payer health insurance system through which health data about virtually the entire population can be cross-referenced with population, crime, welfare, and other registers through unique individual person numbers.
A Danish study team accessed national registers to examine the relationship between ADHD and criminal victimhood among nine yearly birth cohorts totaling more than 570,000 children and adolescents.
Of these, 557,521, among them 12,040 with ADHD, were not reported as being exposed to violence, and 12,830, among which 1,179 with ADHD, were exposed to violence.
From the raw data, children and adolescents with ADHD were more than four times as likely to be exposed to violence than their typically developing peers.
The team then adjusted for other disabilities, family risk factors, gender, birth year, and ethnic background.
With these confounders out of the way, children and adolescents with ADHD remained more than twice as likely to be exposed to violence than their typically developing peers.
To place this outcome in further perspective:
Certain family risk factors further aggravated the odds:
Perhaps surprisingly, substance abuse by family members had no effect whatsoever after adjusting for confounders.
Child abuse includes any of the following inflicted on a minor under 18 years old: physical or emotional harm, sexual abuse, or neglect.
It is known to be associated with environmental factors such as poverty, parents or neighbors with a history of violence, and gender inequality.
Chronic mental disorders in minors are also associated with child abuse. To what extent, if any, might that be true of ADHD?
Taiwan has a single-payer national health insurance system that covers more than 99.6% of all residents, enabling nationwide population studies.
A local research team used data from almost two million Taiwanese in their country’s National Health Insurance Research Database (NHIRD) spanning 15 years (2000-2015) to carry out a matched-cohort study.
All diagnoses of ADHD were made by board-certified specialists such as psychiatrists, pediatricians, neurologists, or physiatrists with a specialty in child and adolescent development.
3,540 children and adolescents between 6 and 18 years old with a diagnosis of ADHD were matched on a one-to-three basis with 10,620 peers from the NHIRD without an ADHD diagnosis.
The team adjusted for age, gender, location of residence (Northern, Central, Southern, and Eastern Taiwan), urbanization level of residence, level of hospitals as medical centers, and monthly insured premium. They further adjusted for comorbid conditions: intellectual disability, autistic disorder/pervasive developmental disorder, conduct disorder (CD)/oppositional defiant disorder (ODD), other developmental disorders, childhood emotional disorder, Tourette syndrome/tics disorders, and involuntary urination and defecation.
Overall, children and adolescents with an ADHD diagnosis were 1.8 times as likely to be abused as those without an ADHD diagnosis.
Unmedicated children and adolescents with an ADHD diagnosis were three times more likely to be abused. ADHD medication cut that risk in half.
That held true whether the medication used was methylphenidate or atomoxetine. Methylphenidate appeared to be slightly more effective than atomoxetine, and the combination of methylphenidate and atomoxetine slightly more effective yet, but these differences were not statistically significant.
The team concluded, “The results support that pharmacotherapy may attenuate the risk of child abuse in ADHD patients.”
Taiwan's National Health Insurance program is a single-payer system that covers 99.6% of the island's 23 million residents. It includes family relationships.
This enabled a Taiwanese study team to examine the comorbidity of psychiatric disorders among close relatives in the entire population over eleven years, beginning at the start of 2001 and concluding at the end of2011.
For greater certainty of diagnosis, only persons twice diagnosed with the same psychiatric disorder were included as index individuals. There were 431,887 index patients, 152,443 of whom were ADHD index patients.
These index patients were then compared with all of their first-degree relatives (FDRs): parents, children, siblings, and twins. This produced 1,017,430 patient-FDR pairs, of which 401,301 were ADHD patient-FDR pairs.
Next, four controls were matched by age, gender, and type relative to each case, resulting in 4,069,720 control pairs.
After adjusting for age, gender, urbanization, and income level, ADHD patients were seven times more likely than controls to have first-degree relatives with ADHD. They were also seven times more likely to have FDRs with major depressive disorder, four times more likely to have FDRs with autism spectrum disorder, twice as likely to have FDRs with bipolar disorder, and 80%more likely to have FDRs with schizophrenia.
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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