Advancing Success & Developmental Outcomes in Autism Spectrum Disorder through Analysis of Secondary Data (ASD3 Outcomes Project)

Project Abstract Autistic children experience some of the lowest health care quality and highest unmet needs of any pediatric chronic condition. Additionally, disparities persist in service use and life course outcomes among autistic people. These problems exist because (1) we have not adequately assessed which outcomes are most essential for autistic children and their caregivers, and (2) few large-scale studies have assessed which individual and family factors, service use factors, and local/state environmental features are associated with optimal and suboptimal health outcomes. The proposed project, Advancing Success and Developmental Outcomes in Autism Spectrum Disorder through the Analysis of Secondary Data (ASD3 Outcomes), will fill these evidence gaps by linking multiple large, population-based data sets providing rich information on various factors driving health outcomes for autistic children. By the end of this project, we will have generated actionable evidence to guide national and state-level strategies for improving health outcomes for autistic children ages 1–17 years. Leveraging cutting-edge data science methodologies including multi-source data harmonization and deep learning, this initiative will identify the most salient predictors of optimal and suboptimal outcomes among children and youth, examine geographic and demographic disparities in these outcomes, inform system-level interventions, and advance evidence-based policy change to improve health for autistic individuals. First, we will assemble a community advisory panel of autistic youth and adults, parents and caregivers, and health and educational providers, and a technical advisory panel of autism and data science researchers. We will use the panelists’ expertise to identify key health and educational outcomes that can be measured in Medicaid claims data in all 50 states, the National Survey of Children’s Health (NSCH) in all 50 states, and/or Early Intervention state data (HI, IN, MN, OR, VT). Next, we will use the NSCH to create state-level, age-specific Autism Quality Indices that measure factors driving health outcomes for autistic children at different developmental stages. We will apply these indices, along with child-level demographic markers, neighborhood measures (using the Child Opportunity Index 3.0), and other state health and education systems variables, to model key outcomes in Medicaid Claims and State Early Intervention data, through interpretable machine and deep learning models. Finally, we will translate evidence into action by harnessing the collective expertise of our community and technical advisory panels to develop recommendations based on the community-engaged and data- driven findings generated. These efforts will produce actionable insights to guide policy, programs, and practice that optimize health outcomes for autistic children and their families nationwide. We will disseminate this work broadly.