Predictors of age at diagnosis in autism spectrum disorders: the use of multiple regression analyses and a classification tree on a clinical sample

Michal Hrdlicka, Tomas Urbanek, Adela Rotreklova, Aneta Kultova, Ondrej Valek, Iva Dudova. European Child & Adolescent Psychiatry. March 2023. IF: 6,4. doi

prof. MUDr. Michal Hrdlička, CSc.

Prof. MUDr. Michal Hrdlička, CSc., Department of Paediatric Psychiatry, Second Faculty of Medicine and Motol University Hospital


The increasing prevalence of autism spectrum disorders (ASD) has led to worldwide interest in factors influencing the age of ASD diagnosis. Parents or caregivers of 237 ASD children (193 boys, 44 girls) diagnosed using the Autism Diagnostic Observation Schedule (ADOS) completed a simple descriptive questionnaire. The data were analyzed using the variable-centered multiple regression analysis and the person-centered classification tree method. We believed that the concurrent use of these two methods could produce robust results. The mean age at diagnosis was 5.8 ± 2.2 years (median 5.3 years). Younger ages for ASD diagnosis were predicted (using multiple regression analysis) by higher scores in the ADOS social domain, higher scores in ADOS restrictive and repetitive behaviors and interest domain, higher maternal education, and the shared household of parents. Using the classification tree method, the subgroup with the lowest mean age at diagnosis were children, in whom the summation of ADOS communication and social domain scores was  17, and paternal age at the delivery was  29 years. In contrast, the subgroup with the oldest mean age at diagnosis included children with summed ADOS communication and social domain scores < 17 and maternal education at the elementary school level. The severity of autism and maternal education played a significant role in both types of data analysis focused on age at diagnosis.

Vytvořeno: 15. 1. 2024 / Upraveno: 16. 2. 2024 / PhDr. Mgr. Kateřina Křenová