Tracey Chau, Jeggan Tiego, Louise E. Brown, Olivia J. Mellahn, Beth P. Johnson, Mark A. Bellgrove
{"title":"The distribution of parent-reported autistic and subclinical ADHD traits in children with and without an autism diagnosis","authors":"Tracey Chau, Jeggan Tiego, Louise E. Brown, Olivia J. Mellahn, Beth P. Johnson, Mark A. Bellgrove","doi":"10.1002/jcv2.12259","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Attention-deficit/hyperactivity disorder (ADHD) traits often co-occur in autistic children. The presence of subclinical ADHD traits can significantly impact upon different aspects of daily living. As such, understanding the distribution of these traits in autistic children may have important implications for the validity of diagnostic tools and subsequent intervention choices. This study builds on previous latent models of parent-reported autistic and ADHD traits to propose a preliminary model of their distribution in two independent samples of autistic and neurotypical children.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Factor mixture modelling was applied to caregiver responses to the Social Responsiveness Scale - 2<sup>nd</sup> edition and the Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour Scale (SWAN) of participants aged 4–18 years who participated in one of two studies in Australia or in the United States.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>A 2-factor, 3-class factor mixture model demonstrated the best fit to the data across both independent samples. The factors represented the latent constructs of ‘autism’ and ‘ADHD’. The latent classes represented subtypes of children with different levels of autistic traits, with higher levels of ADHD traits as autistic trait endorsement increased. Some sample-specific differences were observed for each model's item thresholds and factor covariance matrices.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Our findings suggest that the endorsement of subclinical ADHD traits tends to increase alongside autistic trait endorsement across neurotypical and autistic presentations. There may be clinical utility in routinely screening for ADHD traits in children with clinically elevated levels of autistic traits.</p>\n </section>\n </div>","PeriodicalId":73542,"journal":{"name":"JCPP advances","volume":"5 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcv2.12259","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JCPP advances","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jcv2.12259","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The distribution of parent-reported autistic and subclinical ADHD traits in children with and without an autism diagnosis
Background
Attention-deficit/hyperactivity disorder (ADHD) traits often co-occur in autistic children. The presence of subclinical ADHD traits can significantly impact upon different aspects of daily living. As such, understanding the distribution of these traits in autistic children may have important implications for the validity of diagnostic tools and subsequent intervention choices. This study builds on previous latent models of parent-reported autistic and ADHD traits to propose a preliminary model of their distribution in two independent samples of autistic and neurotypical children.
Methods
Factor mixture modelling was applied to caregiver responses to the Social Responsiveness Scale - 2nd edition and the Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour Scale (SWAN) of participants aged 4–18 years who participated in one of two studies in Australia or in the United States.
Results
A 2-factor, 3-class factor mixture model demonstrated the best fit to the data across both independent samples. The factors represented the latent constructs of ‘autism’ and ‘ADHD’. The latent classes represented subtypes of children with different levels of autistic traits, with higher levels of ADHD traits as autistic trait endorsement increased. Some sample-specific differences were observed for each model's item thresholds and factor covariance matrices.
Conclusions
Our findings suggest that the endorsement of subclinical ADHD traits tends to increase alongside autistic trait endorsement across neurotypical and autistic presentations. There may be clinical utility in routinely screening for ADHD traits in children with clinically elevated levels of autistic traits.