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.
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.
A commonly reported risk associated with ADHD medication is reduced growth in height. But studies to date have generally not adequately described or measured possible confounders, such as genetic factors, prenatal factors, or socioeconomic factors. What if ADHD were associated with reduced height even in the absence of medications?
An international study team explored this question by performing a nationwide population study comparing data from before (1968-1991) and after (1992-2020) the adoption of stimulant therapy for ADHD in Sweden.
The country’s single-payer health insurance system that connects patient records with all other national registers through unique personal identification numbers makes such analysis possible. Sweden also has military service conscription, which records the heights of 18-year-old males.
The participants were all 14,268 Swedish males with a diagnosis of ADHD who were drafted into military service at any time from 1968 through 2020.
Up to five non-ADHD controls were identified for each ADHD case, matched by sex (they had to be male), birth year, and county. The total number of controls was 71,339.
Among 34,586 participants in the period before adoption of stimulant medications (1968-1991), those diagnosed with ADHD had roughly 30% greater odds of being shorter than normal (166-172 vs. 173-185 cm) than typically developing controls. That dropped to 20% greater odds among the 34,714 participants in the cohort following adoption of stimulant medications.
The odds of those diagnosed with ADHD being much shorter than normal (150-165 vs. 173-185 cm) remained identical (about 55% greater) among the almost 30,000 participants in both cohorts.
In other words, there was no increase in the odds of ADHD individuals being shorter than normal after adoption of stimulant therapy in Sweden compared with before such adoption.
Furthermore, after adjusting for known confounders, including birth weight, inflammatory bowel disease, celiac disease, hypothyroidism, anxiety disorders, depression, substance use disorder, and highest parental education, the odds of those diagnosed with ADHD being shorter than normal or much shorter than normal in the 1992-2020 cohort dropped to roughly 10% and 30% greater, respectively.
Could it be the disorder itself rather than stimulant treatment that is associated with reduced height in individuals diagnosed with ADHD?
To address effects of environmental and familial/genetic confounding, the team then compared the entire cohort of males diagnosed with ADHD from 1968 through 2020 with typically developing male relatives, ranging from first cousins to full siblings.
Among full siblings, the odds of those with ADHD diagnoses being shorter (over 90,000 participants) or much shorter (over 77,000 participants) were a statistically significant 14% and 18%, respectively.
The authors concluded, “Our findings suggest that ADHD is associated with shorter height. On a population level, this association was present both before and after ADHD-medications were available in Sweden. The association between ADHD and height was partly explained by prenatal factors, psychiatric comorbidity, low SES [socioeconomic status] and a shared familial liability for ADHD.”
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