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Background: To advance early identification efforts, we must detect and characterize neurodevelopmental sequelae of risk among population-based samples early in development. However, variability across the typical-to-atypical continuum and heterogeneity within and across early emerging psychiatric/neurodevelopmental disorders represent fundamental challenges to overcome. Identifying multidimensionally determined profiles of risk, agnostic to DSM categories, via data-driven computational approaches represents an avenue to improve early identification of risk. Methods: Factor mixture modeling (FMM) was used to identify subgroups and characterize phenotypic risk profiles, derived from multiple parent-report measures of typical and atypical behaviors common to autism spectrum disorder, in a community-based sample of 17- to 25-month-old toddlers (n = 1,570). To examine the utility of risk profile classification, a subsample of toddlers (n = 107) was assessed on a distal, independent outcome examining internalizing, externalizing, and dysregulation at approximately 30 months. Results: FMM results identified five asymmetrically sized subgroups. The putative high- and moderate-risk groups comprised 6% of the sample. Followup analyses corroborated the utility of the risk profile classification; the high-, moderate-, and low-risk groups were differentially stratified (i.e., HR > moderate-risk > LR) on outcome measures and comparison of high- and low-risk groups revealed large effect sizes for internalizing (d = 0.83), externalizing (d = 1.39), and dysregulation (d = 1.19). Conclusions: This data-driven approach yielded five subgroups of toddlers, the utility of which was corroborated by later outcomes. Data-driven approaches, leveraging multiple developmentally appropriate dimensional RDoC constructs, hold promise for future efforts aimed toward early identification of at-risk-phenotypes for a variety of early emerging neurodevelopmental disorders.

Early Intensive Behavioral Intervention (EIBI) can be effective for supporting skills acquisition among children with autism spectrum disorder (ASD). Few studies have followed children with ASD who received EIBI into schools. The purpose of this study was to investigate educational outcomes specific to instructional placement, standardized test proficiency, and special education eligibility under the ASD category for children who received EIBI. Medicaid records were utilized to create a cross-systems data set of 3- to 5-year-old children with ASD (n = 667). Most students were placed in general education, and males and White students were more likely to receive special education services for ASD. Only half of the students participated in standardized testing and met proficiency standards. Implications for future research and advocacy for early intervention are discussed.

Increased prevalence of autism spectrum disorder (ASD) has underscored the need for early intervention services. Early Intensive Behavioral Intervention (EIBI) is among the most common evidence-based approaches, however, stakeholders report significant waitlists. The effects of these delays to intervention are unknown. The purpose of this study was to evaluate the effects of delay to EIBI for preschool aged children with ASD on later educational outcomes. Medicaid records from Minnesota (2008-2010) were used to evaluate a cohort diagnosed with ASD and their later educational outcomes from 2010 to 2014 (n = 667) using generalized estimating equations. Approximately 70% of children experienced a delay to EIBI and children that experienced less delay and started EIBI at a younger age had better educational outcomes.