Association Between Age-Specific Sleep Sufficiency and Autism Spectrum Disorder in U.S. Children

Comprehensive Summary

This study, conducted by Ahmmad et al., investigates the association between age-specific sleep sufficiency (ASSS) and Autism Spectrum Disorder (ASD) diagnosis among children aged 6-17. Data utilized for this study was obtained from the 2022 - 2023 National Survey of Children’s Health from the U.S. Census Bureau (n = 63,866). After adjustment for variables such as sex, age, race/ethnicity, and BMI, the dataset was analyzed using descriptive statistics, and a machine learning model utilizing a conditional inference tree algorithm was trained with the dataset and tested. From the results, it was found that 67.49% of the children analyzed had met recommended amounts of sleep daily, relative to age group, and 4.41% of the children were reported to have ASD. Through 95% confidence interval testing, it was found that children who did not meet ASSS experienced a higher prevalence of ASD reporting than children who did meet ASSS. Additionally, it was found that among other variables, sex was the most influential predictor of ASD, with male children making up 78.22% of ASD reporting. From the machine learning model, it was found that the area-under-curve value for the receiver operating characteristic curve was 0.72, demonstrating a moderate ability of the model to accurately diagnose ASD with ASSS and demographic information.

Outcomes and Implications

ASD is a neurological condition that is characterized by a broad set of symptoms, such as deficits in communication and repetitive behaviors. Early diagnosis and treatment among children have been shown by previous studies to greatly benefit children with ASD, and along with the rising prevalence of ASD among children, it is crucial to find indicators and methods that can be utilized to streamline the diagnosis process. Ahmmad et al. aims to address this through their examination of sleep and ASD and the ability of machine learning to identify ASD.