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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.
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:
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-analysis Study?
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:
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:
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.
ADHD is one of the most common neurodevelopmental disorders in children, yet anyone familiar with this disorder, from clinicians and researchers to parents and patients, knows how differently it can manifest from one individual to the next. One person diagnosed with ADHD may primarily struggle with focus and staying on-task; another may find it nearly impossible to regulate their impulses or even start tasks; a third may frequently find themselves frozen with overwhelm and subject to emotional reactivity…
These are not just variations in severity; they may reflect genuinely different patterns of brain organization.
Our current diagnostic system groups all of these presentations under a single label (ADHD), with three behavioral subtypes (Hyperactive, Inattentive, and Combined) defined by symptom checklists. This framework has real clinical value of course, but it was built from behavioral observation rather than neurobiology, and may leave room for substantial heterogeneity to remain unexplained. In a new study, published in JAMA Psychiatry, researchers asked whether it’s possible to identify distinct neurobiologically subgroups within ADHD by analyzing patterns of brain structure, and whether those subgroups would map onto meaningful clinical differences.
How the Brain Was Analyzed
Researchers analyzed structural MRI scans from 446 children with ADHD and 708 typically-developing children across multiple research sites. From each scan, they constructed a morphometric similarity network; that is, a map of how different brain regions resemble one another in their structural properties. These networks reflect underlying biological organization, including shared patterns of cellular architecture and gene expression across brain regions.
From each individual's network, the research team calculated three properties that capture how each brain region functions within the broader network: how many connections it has, how efficiently it communicates with other regions, and how well it bridges different functional communities in the brain. Regions that score highly on these measures are sometimes called "hubs" and they play particularly influential roles in how information is integrated across the brain.
Rather than comparing the ADHD group to controls as a whole and looking for average differences, they used a normative modeling approach. This works similarly to a growth chart in pediatric medicine: instead of asking whether a child is above or below the group average, it asks how much a given child deviates from the expected range for their age and sex. This allows for individual variation across the ADHD group rather than flattening it into a single average profile.
The team then applied a data-driven clustering algorithm to these individual deviation profiles, allowing the data to reveal whether subgroups of children with ADHD shared similar patterns of brain network atypicality, without using any clinical symptom information to guide the clustering.
The Results:
Three stable, reproducible subtypes emerged from this analysis.
The first subtype was characterized by the most widespread differences from the normative range, particularly in regions connecting the medial prefrontal cortex to the pallidum (a deep brain structure involved in motivation and emotional regulation). Children in this group had the highest levels of both inattention and hyperactivity/impulsivity, and over a four-year follow-up period showed more persistent difficulties with emotional self-regulation than the other groups. They also had a higher rate of mood disorder comorbidity during follow-up, though this difference did not reach statistical significance given the sample size. The brain deviation patterns of this subtype showed correspondence with the spatial distributions of several neurotransmitter systems, including serotonin, dopamine, and acetylcholine, all of which have been previously implicated in ADHD pathophysiology.
The second subtype showed alterations concentrated in the anterior cingulate cortex and pallidum, a circuit involved in action control and response selection. This subtype had a predominantly hyperactive/impulsive profile, and its brain deviation patterns were associated with glutamate and cannabinoid receptor distributions.
The third subtype showed more focal differences in the superior frontal gyrus, a region involved in sustained attention. This subtype had a predominantly inattentive profile, with brain patterns linked to a specific serotonin receptor subtype.
A particularly important observation was that these brain-derived groupings aligned with clinically meaningful symptom differences, even though no symptom information was used in the clustering process. The fact that an analysis of brain structure alone arrived at groupings that correspond to recognizable clinical patterns is meaningful evidence that these subtypes reflect genuine neurobiological differences rather than statistical noise.
Replication in an Independent Sample
Scientific findings are only as trustworthy as their ability to replicate. The research team tested this clustering model in an entirely independent cohort of 554 children with ADHD from the Healthy Brain Network, a large, publicly available dataset collected under different conditions. The three subtypes were successfully identified in this new sample, with strong correlations between the brain deviation patterns observed in the original and validation cohorts. Differences in hyperactivity/impulsivity across subtypes were consistent with the discovery cohort, providing meaningful external validation of the approach.
What This Does and Doesn't Mean
It is important to be clear about what these findings do and do not imply. This study does not establish that these three subtypes are categorically distinct biological entities with sharp boundaries. They probably represent distinguishable regions along an underlying continuum of neurobiological variation. The neurochemical associations reported are exploratory and spatial in nature; they describe correspondences between brain deviation maps and neurotransmitter receptor density maps derived from separate imaging studies, and do not directly establish that any particular neurotransmitter system is altered in each subtype, nor do they currently inform treatment decisions.
The samples were not entirely medication-naive, and the strict comorbidity exclusion criteria may limit how well these findings generalize to typical clinical populations where comorbidities are the rule rather than the exception. All data came from research sites in the United States and China, and broader generalizability remains to be established.
What the study does demonstrate is that structured neurobiological heterogeneity exists within the ADHD diagnosis, that it can be reliably detected using brain imaging and data-driven methods, and that it aligns with meaningful clinical differences. The subtype defined by the most extensive brain network differences and the most severe, persistent clinical profile may be of particular importance, representing a group that could benefit most from early identification and targeted support.
The longer-term goal of this line of research is to move toward a more biologically grounded understanding of ADHD that complements existing diagnostic approaches and that may ultimately help guide more individualized treatment decisions. That goal, for now, remains a research ambition rather than a clinical reality, but this study takes a meaningful step in that direction.
Managing high blood pressure requires more than just getting a prescription; it means taking medication consistently, day after day, often for years. For people with ADHD, that kind of routine can be genuinely difficult. In our new study, published in BMC Medicine, we set out to understand just how much ADHD affects whether people stick with their blood pressure medication, and whether ADHD treatment itself might make a difference.
Why This Question Matters
Hypertension affects nearly a third of adults worldwide and is one of the leading drivers of heart disease and stroke. At the same time, ADHD, long thought of as a childhood disorder, affects around 2.5% of adults and is increasingly recognized as a risk factor for cardiovascular problems, including high blood pressure. Yet no large-scale study had ever examined whether having ADHD affects how well people follow through with their blood pressure treatment. We wanted to fill that gap.
What We Did
We analyzed health records from over 12 million adults across seven countries, Australia, Denmark, the Netherlands, Norway, Sweden, the UK, and the US, who had started antihypertensive (blood pressure-lowering) medication between 2010 and 2020. About 320,000 of them had ADHD. We tracked two things: whether they stopped their blood pressure medication entirely within five years, and whether they were taking it consistently enough (covering at least 80% of days) over one, two, and five years of follow-up.
What We Found
Across nearly all countries, adults with ADHD were more likely to stop their blood pressure medication and less likely to take it consistently. Overall, those with ADHD had about a 14% higher rate of discontinuing treatment within five years, and were 45% more likely to have poor adherence in the first year, a gap that widened to 64% by the five-year mark. These patterns were most pronounced in middle-aged and older adults.
Interestingly, young adults with ADHD were actually slightly less likely to discontinue treatment than their peers without ADHD, a finding we think may reflect the fact that younger people with ADHD are often more actively engaged with healthcare systems, especially given the cardiovascular monitoring that comes with ADHD medication use.
Perhaps the most encouraging finding was this: among people with ADHD who were also taking ADHD medication, adherence to blood pressure treatment was substantially better. Those on ADHD medication were about 38% less likely to have poor adherence at one year, and nearly 50% less likely at five years. While we can't establish causation from this type of study, one plausible explanation is that treating ADHD, reducing inattention and impulsivity, makes it easier to maintain the routines that consistent medication use requires. It's also possible that people on ADHD medication simply have more regular contact with healthcare providers, which keeps other health problems better monitored and managed.
What This Means in Practice
The core ADHD symptoms of inattention and poor organization are precisely the traits that make long-term medication adherence difficult. Add in the complexity of managing multiple disorders and medications, and it's easy to see why people with ADHD face extra challenges. Our findings suggest that clinicians treating adults with ADHD for cardiovascular disorders should be aware of these challenges and consider tailored support strategies, things like regular follow-up appointments, patient education, and tools that help with routine and organization.
There's also a broader message here about the potential ripple effects of treating ADHD well. Supporting someone in managing their ADHD may not just improve their attention and daily functioning; it may also help them take better care of their physical health, including disorders as serious as hypertension.
Future research should explore which specific support strategies are most effective, and whether these findings hold in lower- and middle-income countries where the data don't yet exist.
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