September 14, 2023
Taiwan's single-payer National Health Insurance system encompasses its entire population, and it's National Health Insurance Research Database tracks all medical claims in the system. That makes it easy to conduct nationwide population studies.
Two Taiwanese research teams availed themselves of that database to explore in-depth a surprising relationship between the birth month of children and rates of ADHD diagnosis.
In principle, the two should be unrelated. The likelihood of diagnosis should be the same regardless of the month a child is born. But the data are clear that this is not so. Children born late in summer are the most likely to be diagnosed with ADHD, and those in autumn are the least likely.
Using a nationwide database of over 29 million persons, one of the teams (Hsu et al.) found that children born in April were 6% more likely to be diagnosed with ADHD than the year-round mean, those in May 12% more likely, those in June 20% more likely, and those in July and August well over 25% more likely.
Conversely, children born in September were 19% less likely to be diagnosed with ADHD than the year-round mean, followed by a gradual increase in likelihood with each succeeding month until the following September.
The second team (Chen et al.) analyzed some 9.5 million children and adolescents in the same reserch database, and found that those born in August were 67% more likely to be diagnosed with ADHD than those born in September, after adjusting for age, sex, residence, and income. August births were also almost twice as likely (80% more likely) as September births to be on long-term treatment with ADHD medications.
The first team also performed a meta-analysis of eleven studies with a combined total of over 580,000 participants in North America (the U.S. and Canada), Europe (U.K., Germany, Norway, Sweden, Denmark), Asia (China, Taiwan, South Korea), and Oceania (Australia). Children born in the summer (June through August) were 13% more likely to be diagnosed with ADHD than the year-round mean, whereas those born in autumn were 13% less likely to be diagnosed with ADHD. This confirms that this pattern is not confined to Taiwan. It is worldwide.
Note carefully that the sharp discontinuity between August and September corresponds with the break-of point that decides which children get assigned to which school class. Anyone who turns a certain age by the start of the school year in September is included in the class associated with that age, whereas those turning the same age later are held back in the following class. That means that in any given class, those born in September are the oldest children and those born in August the youngest.
As signaled earlier, the likelihood of an ADHD diagnosis should be independent of something as obviously arbitrary as a birth month. That suggests there may be an unconscious bias trending against younger students when it comes to diagnosis.
Chen et al. concluded, "The effect of relative age on diagnoses and prescriptions was determined to last from childhood to adolescence but attenuated with age. Relative age is an indicator of brain maturity in cognition, behavior, and emotion and may thus play a critical role in the likelihood of being diagnosed as having childhood mental disorders and subsequently being prescribed psychotropic medication. Therefore, clinicians should consider the relative age effect in the childhood mental health care context."
Mu-Hong Chen, Kai-Lin Huang, Ju-Wei Hsu, Shih-Jen Tsai, Tung-Ping Su, Tzeng-Ji Chen, Ya-Mei Bai, "Effect of relative age on childhood mental health: A cohort of 9,548,393 children and adolescents," Acta PsychiatricaScandinavica (2021), online ahead of print, https://doi.org/10.1111/acps.13327.
Chih-Wei Hsu, Ping-Tao Tseng, Yu-Kang Tu, Pao-Yen Lin, Chi-Fa Hung, Chih-Sung Liang, Yun-Yu Hsieh, Yao-Hsu Yang, Liang-Jen Wang, Hung-YuKao, "Month of birth and mental disorders: A population-based study and validation using global meta-analysis," Acta Psychiatrica Scandinavica (2021), online ahead of print, https://doi.org/10.1111/acps.13313.
This study investigates how certain genetic disorders, called RASopathies, affect the structure of the brain in children. RASopathies are conditions caused by mutations in a specific signaling pathway in the body. Two common RASopathies are Noonan syndrome (NS) and neurofibromatosis type 1 (NF1), both of which are linked to a higher risk of autism spectrum disorder (ASD) and attention deficit and hyperactivity disorder (ADHD).
The researchers analyzed brain scans of children with RASopathies (91 participants) and compared them to typically developing children (74 participants). They focused on three aspects of brain structure: surface area (SA), cortical thickness (CT), and subcortical volumes.
The results showed that children with RASopathies had both similarities and differences in their brain structure compared to typically developing children. They had increased SA in certain areas of the brain, like the precentral gyrus, but decreased SA in other regions, such as the occipital regions. Additionally, they had thinner CT in the precentral gyrus. However, the effects on subcortical volumes varied between the two RASopathies: children with NS had decreased volumes in certain structures like the striatum and thalamus, while children with NF1 had increased volumes in areas like the hippocampus, amygdala, and thalamus.
Overall, this study highlights how RASopathies can impact the development of the brain in children. The shared effects on SA and CT suggest a common influence of RASopathies on brain development, which could be important for developing targeted treatments in the future.
In summary, understanding how these genetic disorders affect the brain's structure can help researchers and healthcare professionals develop better treatments for affected children.
In a recent study, researchers delved into the complex interplay of cognitive processes and eye movements in children with dyslexia and Attention-Deficit/Hyperactivity Disorder. Their findings shed light on predictive models for reading outcomes in these children compared to typical readers.
The study involved 59 children: 19 typical readers, 21 with ADHD, and 19 with developmental dyslexia (DD), all in the 4th grade and around 9 years old on average. Each group underwent thorough neuropsychological and linguistic assessments to understand their psycholinguistic profiles.
During the study, participants engaged in a silent reading task where the text underwent lexical manipulation. Researchers then analyzed eye movement data alongside cognitive factors like memory, attention, and visual processes.
Using multinomial logistic regression, the researchers evaluated predictive models based on three key measures: a linguistic model focusing on phonological awareness, rapid naming, and reading fluency; a cognitive neuropsychological model incorporating memory, attention, and visual processes; and an additive model combining lexical word properties with eye-tracking data, specifically examining word frequency and length effects.
By integrating eye movement data with cognitive factors, the researchers enhanced their ability to predict the development of dyslexia or ADHD, in comparison to typically developing readers. This approach significantly improved the accuracy of predicting reading outcomes in children with learning disabilities.
These findings have profound implications for understanding and addressing reading challenges in children. By considering both cognitive processes and eye movement patterns, educators and clinicians can develop more effective interventions tailored to the specific needs of children with dyslexia and ADHD.
The Study Setup
The researchers recruited children aged 6-12 years diagnosed with ASD and/or ADHD, along with their non-ASD/ADHD siblings and the unrelated non-ASD/ADHD volunteers. The diagnoses were confirmed using standardized assessments like the Autism Diagnostic Observation Schedule-2 (ADOS-2). The study looked at gut microbial diversity using advanced DNA extraction and sequencing techniques, comparing alpha-diversity indices (which reflect the variety and evenness of microbial species within each gut sample) across different groups. They also assessed dietary diversity through standardized questionnaires.
Key Findings
The study included 98 subjects, comprising children with ASD, ADHD, both ASD and ADHD, their non-ASD/ADHD siblings, and the unrelated controls. Here's what they discovered:
What Does This Mean?
The study highlights intriguing connections between gut microbiota and neurodevelopmental disorders like ASD and ADHD. The lower gut microbial diversity observed in children with ASD points towards potential links between gut health and the pathophysiology of ASD. Understanding these connections is crucial for developing targeted therapeutic interventions.
Implications and Future Directions
This research underscores the importance of considering gut microbiota in the context of neurodevelopmental disorders. Moving forward, future studies should account for factors like co-occurrence of ASD and ADHD, as well as carefully control for dietary influences. This will help unravel the complex interplay between gut microbiota, diet, and neurodevelopmental disorders, paving the way for innovative treatments and interventions.
In summary, studies like this shed light on the intricate relationship between our gut health, diet, and brain function. By unraveling these connections, researchers are opening new avenues for understanding and potentially treating conditions like ASD and ADHD.
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