Autism Secondary Data Analysis Program - Addressing the intersection of neurodevelopmental disorders, home environments, and obesity is essential for mitigating the growing public health crisis of childhood obesity. Children with autism and developmental disorders face higher risks for obesity-related complications, including metabolic disorders, cardiovascular issues, and reduced quality of life. Approximately 8.6% of US children have autism or a developmental disorder with the prevalence of autism increasing over the past several decades. While there is strong evidence for increased risk of obesity among children with autism, little is known about how specific types or intensity of symptoms relate to obesity risk. Utilizing the Geisinger Autism and Developmental Medicine Institute (ADMI) Making Advances Possible (MAP) database and the Geisinger Child and Adolescent Trends (CAT) database we propose a secondary data analysis that consists of two principal objectives: 1) to assess the association between autism spectrum disorder (ASD) and other developmental disorders (DDs) with obesity and obesogenic risk behaviors (e.g., physical inactivity, dietary patterns, screen time) among children and adolescent pediatric patients; and 2) investigate social and structural determinants of health as moderators and mediators on the relationships between ASD/DDs and body weight /obesogenic risk behaviors. This proposal aligns with several Healthy People 2030 objectives, Interagency Autism Coordinating Committee’s Strategic Plan Objectives, the Blueprint for Change, and finally the MCHB mission to improve the health and well-being of children, and goal of achieving health equity for MCH populations, specifically children with special health care needs. The results of this project will provide a greater understanding of individual, family, and community factors that contribute to excess risk for obesity and chronic among individuals with autism/DD. By identifying key risk factors, we can improve preventative services by tailoring interventions to target to the most important risk factors or by delivering them to the most at-risk subpopulations, ensuring the most efficient use of limited time and resources in clinical care.
