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May 31, 2021
To what extent are ADHD medications insufficiently used to address properly diagnosed ADHD? To what extent are they misused by persons who are either undiagnosed or improperly diagnosed? In search of answers, an international team of researchers from Brazil, the United Kingdom, and the United States conducted a systematic review of the peer-reviewed literature and a meta-analysis of studies from four continents - South America, North America, Europe, and Australia.
The benchmarks set for proper ADHD diagnosis were any of the following:
· Criteria established in the Diagnostic and Statistical Manual of Mental Disorders (DSM)or the International Statistical Classification of Diseases and Related Health Problems (ICD), confirmed by validated diagnostic instruments or clinical interviews.
· Use of validated ADHD symptom scales with pre-specified thresholds.
· Participants or caregivers affirming ADHD diagnosis by a physician.
Medications reviewed were those recommended by the majority of the international guidelines-both stimulant(methylphenidate, dexmethylphenidate, amphetamines), and non-stimulant (atomoxetine).
The team excluded studies relying on the insurance health system and third-party reimbursement datasets because the focus was on rates of ADHD medication use in the entire population rather than among individuals searching for treatment.
A meta-analysis of 18 studies with a total of 3,311 children and adolescents properly diagnosed with ADHD in seven countries on four continents (Canada, United States, Australia, Brazil, Netherlands, England, Venezuela) found an overall pharmacological treatment rate of only 19%. There was considerable variation, with the highest treatment rates in the United States (frequently over 40%) and the lowest treatment rates in Brazil, Venezuela, and Canada (under 10%). There was no sign of publication bias.
A second meta-analysis pooled 14 studies with a total of 29,559 children and adolescents without a proper diagnosis of ADHD in five countries on four continents (United States, Canada, Venezuela, Australia, Netherlands). Roughly 1% were using ADHD medications. Again, there was considerable variation, with the highest rates of medication misuse being reported in the United States and Venezuela (3-7%). Again, there was no sign of publication bias.
The authors cautioned, "it is important to note that even though the data collected constitute the most comprehensive evidence available in the literature and response/completion rates observed are acceptable, it does not constitute a world representative sample." Also, the predominance of samples from prosperous countries "most certainly inflates the treatment rates due to the exclusion of a large proportion of the world population with significant financial, cultural, and health access barriers to ADHD treatment."
They concluded, "Despite these limitations, our meta-analysis provides evidence for substantial under-treatment of children and adolescents affected by ADHD in different countries. This is a relevant public health issue worldwide since ADHD under treatment is associated with known negative outcomes in education, healthcare, and productivity systems. At the same time, we found evidence of overtreatment/misuse in individuals without a formal ADHD diagnosis. This practice might expose individuals to undesirable side effects of medications, increased risk of medication misuse, and unmeasured costs for the health care system."
RafaelMassuti, Carlos RenatoMoreira-Maia, FaustoCampani, MárcioSônego, JuliaAmaro, GláuciaChiyokoAkutagava-Martins, LucaTessari, Guilherme V.Polanczyk, SamueleCortese, Luis Augusto Rohde, “Assessing undertreatment and overtreatment/misuse of ADHD medications in children and adolescents across continents: A systematic review and meta-analysis,” Neuroscience &Biobehavioral Reviews(2021), Vol. 128, 64-73, published online ahead of print, https://doi.org/10.1016/j.neubiorev.2021.06.001.
A Chinese research team performed two types of meta-analyses to compare the risk of suicide for ADHD patients taking ADHD medication as opposed to those not taking medication.
The first type of meta-analysis combined six large population studies with a total of over 4.7 million participants. These were located on three continents - Europe, Asia, and North America - and more specifically Sweden, England, Taiwan, and the United States.
The risk of suicide among those taking medication was found to be about a quarter less than for unmediated individuals, though the results were barely significant at the 95 percent confidence level (p = 0.49, just a sliver below the p = 0.5 cutoff point). There were no significant differences between males and females, except that looking only at males or females reduced sample size and made results non-significant.
Differentiating between patients receiving stimulant and non-stimulant medications produced divergent outcomes. A meta-analysis of four population studies covering almost 900,000 individuals found stimulant medications to be associated with a 28 percent reduced risk of suicide. On the other hand, a meta-analysis of three studies with over 62,000 individuals found no significant difference in suicide risk for non-stimulant medications. The benefit, therefore, seems limited to stimulant medication.
The second type of meta-analysis combined three within-individual studies with over 3.9 million persons in the United States, China, and Sweden. The risk of suicide among those taking medication was found to be almost a third less than for unmediated individuals, though the results were again barely significant at the 95 percent confidence level (p =0.49, just a sliver below the p = 0.5 cutoff point). Once again, there were no significant differences between males and females, except that looking only at males or females reduced the sample size and made results non-significant.
Differentiating between patients receiving stimulant and non-stimulant medications once again produced divergent outcomes. Meta-analysis of the same three studies found a 25 percent reduced risk of suicide among those taking stimulant medications. But as in the population studies, a meta-analysis of two studies with over 3.9 million persons found no reduction in risk among those taking non-stimulant medications.
A further meta-analysis of two studies with 3.9 million persons found no reduction in suicide risk among persons taking ADHD medications for 90 days or less, "revealing the importance of duration and adherence to medication in all individuals prescribed stimulants for ADHD."
The authors concluded, "exposure to non-stimulants is not associated with a higher risk of suicide attempts. However, a lower risk of suicide attempts was observed for stimulant drugs. However, the results must be interpreted with caution due to the evidence of heterogeneity ..."
With the growth of the Internet, we are flooded with information about attention deficit hyperactivity disorder from many sources, most of which aim to provide useful and compelling "facts" about the disorder. But, for the cautious reader, separating fact from opinion can be difficult when writers have not spelled out how they have come to decide that the information they present is factual.
My blog has several guidelines to reassure readers that the information they read about ADHD is up-to-date and dependable. They are as follows:
Nearly all the information presented is based on peer-reviewed publications in the scientific literature about ADHD. "Peer-reviewed" means that other scientists read the article and made suggestions for changes and approved that it was of sufficient quality for publication. I say "nearly all" because in some cases I've used books or other information published by colleagues who have a reputation for high-quality science.
When expressing certainty about putative facts, I am guided by the principles of evidence-based medicine, which recognizes that the degree to which we can be certain about the truth of scientific statements depends on several features of the scientific papers used to justify the statements, such as the number of studies available and the quality of the individual studies. For example, compare these two types of studies. One study gives drug X to 10 ADHD patients and reported that 7 improved. Another gave drug Y to 100 patients and a placebo to 100 other patients and used statistics to show that the rate of improvement was significantly greater in the drug-treated group. The second study is much better and much larger, so we should be more confident in its conclusions. The rules of evidence are fairly complex and can be viewed at the Oxford Center for Evidenced Based Medicine (OCEBM;http://www.cebm.net/).
The evidenced-based approach incorporates two types of information: a) the quality of the evidence and b) the magnitude of the treatment effect. The OCEBM levels of evidence quality are defined as follows (higher numbers are better:
Non-randomized, controlled studies. In these studies, the treatment group is compared to a group that receives a placebo treatment, which is a fake treatment not expected to work.
It is possible to have high-quality evidence proving that a treatment works but the treatment might not work very well. So it is important to consider the magnitude of the treatment effect, also called the "effect size" by statisticians. For ADHD, it is easiest to think about ranking treatments on a ten-point scale. The stimulant medications have a quality rating of 5 and also have the strongest magnitude of effect, about 9 or 10.Omega-3 fatty acid supplementation 'works' with a quality rating of 5, but the score for the magnitude of the effect is only 2, so it doesn't work very well. We have to take into account patient or parent preferences, comorbid conditions, prior response to treatment, and other issues when choosing a treatment for a specific patient, but we can only use an evidence-based approach when deciding which treatments are well-supported as helpful for a disorder.
Older adults are at greater risk for cardiovascular disease. Psychostimulants may contribute to that risk through side effects, such as elevation of systolic blood pressure, diastolic blood pressure, and heart rate.
On the other hand, smoking, substance abuse, obesity, and chronic sleep loss - all of which are associated with ADHD - are known to increase cardiovascular risk, and stimulant medications are an effective treatment for ADHD.
So how does this all shake out? A Dutch team of researchers sets out to explore this. Using electronic health records, they compared all 139 patients 55 years and older at PsyQ outpatient clinic, Program Adult ADHD, in The Hague. Because a principal aim of the study was to evaluate the effect of medication on cardiovascular functioning after first medication use, the 26 patients who had previously been prescribed ADHD medication were excluded from the study, leaving a sample size of 113.
The ages of participants ranged from 55 from 79, with a mean of 61. Slightly over half were women. At the outset, 13 percent had elevated systolic and/or diastolic blood pressure, 2 percent had an irregular heart rate, 15 percent had an abnormal electrocardiogram, and 29 percent had some combination of these (a "cardiovascular risk profile"), and 21 percent used antihypertensive medication.
Three out of four participants had at least e comorbid disorder. The most common are sleep disorders, affecting a quarter of participants, and unipolar mood disorders (depressive or more rarely manic episodes, but not both), also affecting a quarter of participants.
Twenty-four patients did not initiate pharmacological treatment. Of the 89 who received ADHD medication, 58 (65%) reported positive effects, and five experienced no effect. Thirty-eight (43%) discontinued ADHD medication while at the clinic due to lack of effect or to side effects. The most commonly reported positive effects were enhanced concentration, more overview, less restlessness, more stable mood, and having more energy. The principal reasons for discontinuing medication were anxiety/depression, cardiovascular complaints, and lack of effect.
Methylphenidate raised heart rate and lowered weight, but had no significant effect on systolic and diastolic blood pressure. Moreover, there was no significant correlation between methylphenidate dosage and any of these variables, nor between methylphenidate users taking hypertensive medication and those not taking such medication. There was no significant difference in systolic or diastolic blood pressure and heart rate before and after the use of methylphenidate among patients with the cardiovascular risk profiles.
Systolic blood pressure rose in ten out of 64 patients, with two experiencing an increase of at least 20 mmHg. It descended in five patients, with three having a decrease of at least 20 mmHg. Diastolic blood pressure rose by at least 10 mmHg in four patients, while dropping at least 10 mmHg in five others.
The authors concluded "that the use of a low dose of ADHD-medication is well tolerated and does not cause clinically significant cardiovascular changes among older adults with ADHD, even among those with an increased cardiovascular risk profile. Furthermore, our older patients experienced significant and clinically relevant improvement of their ADHD symptoms using stimulants, comparable with what is found among the younger age group," and that "the use of methylphenidate may be a relatively safe and effective treatment for older adults with ADHD, under the condition that all somatic complaints and especially cardiovascular parameters are monitored before and during pharmacological treatment."
Yet they cautioned that "due to the observational nature of the study and the lack of a control group, no firm conclusions can be drawn as to the effectiveness of the stimulants used. ... Important factors that were not systematically reported were the presence of other risk factors, such as smoking, substance (ab)use, aspirin use, and level of physical activity. In addition, the response to medication was not systematically measured"
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.
If you or someone you know has ADHD, you may be familiar with the challenge of staying on medication. Stimulants like methylphenidate (Ritalin) are the most common and effective treatment for ADHD, but a surprisingly large number of people stop taking them within the first year. In our new study, published in Translational Psychiatry, we sought to determine whether a person's genetic makeup plays a role in the development of the disorder.
What We Did
We analyzed data from over 18,000 people with ADHD in Denmark, all of whom had started stimulant medication. We tracked whether they stopped treatment within the first year, defined as going more than six months without filling a prescription. Nearly 4 in 10 (39%) had discontinued by that point. We then looked at their genetic data to see whether DNA differences could help explain who was more likely to stop.
What We Found
The short answer is: genetics does play a role, but it's modest. No single gene had a dramatic effect. Instead, we found that a collection of small genetic influences—distributed across the genome—contributed to the likelihood of stopping treatment early.
One of the most consistent findings was that people with a higher genetic predisposition for psychiatric disorders like schizophrenia, depression, or general mental health difficulties were more likely to discontinue their medication. This was true across all age groups. Interestingly, having a higher genetic risk for ADHD itself was not associated with stopping treatment, suggesting that the genetics of having ADHD and the genetics of staying on medication are quite different things.
We also found that the genetic picture looks different depending on age. In children under 16, body weight genetics (BMI) played a surprising role, children with a genetic tendency toward higher weight were actually less likely to stop, possibly because stimulant-related appetite suppression is less of a problem for them. In older adolescents and adults, higher genetic potential for educational attainment and IQ was linked to staying on treatment, possibly reflecting better access to information and healthcare support.
On the rare variant side, we found a tentative signal that people who stopped treatment had fewer disruptive variants in genes involved in dopamine, the brain chemical that stimulants work on. This might mean that those who continue on medication genuinely have more disruption in their dopamine system and benefit more from stimulant treatment.
What This Means
Our findings suggest that stopping ADHD medication early isn't simply a matter of willpower or forgetting to take a pill. Biology matters. A person's broader genetic vulnerabilities, particularly for other psychiatric disorders, may make it harder to stay on treatment, perhaps because of side effects, poor response, or the complexity of managing multiple mental health challenges at once.
We're still far from being able to use genetics to predict who will stop their medication, the effects we found are real but small, and much of the variation in treatment persistence remains unexplained. But this work is a step toward understanding the biological foundations of treatment challenges in ADHD, and hopefully toward more personalized approaches to care in the future.
Larger studies and research that can distinguish why people stop (side effects versus poor response versus practical barriers), will be the next steps.
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