Objective: To propose a tailored social ecological model for Autism Spectrum Disorders and explore relationships between variables in a large nationally-representative dataset. Methods: A tailored social-ecological model was developed and examined across variables in the 2016/2017 National Survey of Children’s Health. A series of iterative multivariable logistic regressions were performed including individual, family, and community/neighborhood variables. A multivariable logistic regression using state-level fixed effects was performed to understand dynamics related to macro-level policies. Results: In the full model, gender, disability severity, certain types of insurance coverage and household income were significantly related to ASD diagnosis. Females had lower odds of a diagnosis compared to males (aOR: 0.27; CI:0.18–0.41). Children with at least one other moderate/severe disability had odds 7.61 higher (CI:5.36–10.82) of a diagnosis than children without moderate/severe disabilities. Children with public insurance only (aOR:1.66; CI:1.14–2.41) or both private and public insurance coverage (aOR: 2.62; CI:1.6–4.16) had higher odds of a diagnosis compared to children with private insurance only. For those who reported it was “somewhat” or “very often” hard to cover basics with their income, odds of a diagnosis were higher compared to those who reported it was “never” or “hardly ever” hard to cover basics (aOR: 1.676; CI:0.21–2.56). Conclusions for Practice: Patterns of ASD diagnosis are related to individual and family characteristics. There is some evidence that a child’s environment has some relationship to reported ASD diagnosis. Professionals should be aware of an individual’s environmental factors or context when assessing for ASD.
