Separate scoring algorithms for specific identification priorities optimize the screening properties of the Screening Tool for Autism in Toddlers (STAT).

Abstract

The Screening Tool for Autism in Toddlers (STAT) is a validated stage-2 autism spectrum disorder (ASD) screening measure that takes 20 minutes to administer and comprises 12 play-based items that are scored according to specific criteria. This study examines an expanded version (STAT-E) that includes the examiner's subjective ratings of children's social engagement (SE) and atypical behaviors (AB) in the scoring algorithm. The sample comprised 238 children who were 24-35 months old. The STAT-E assessors had limited ASD experience to mimic its use by community-based non-specialists, and were trained using a scalable web-based platform. A diagnostic evaluation was completed by clinical experts who were blind to the STAT-E results. Logistic regression, ROC curves, and classification matrices and metrics were used to determine the screening properties of STAT-E when scored using the original STAT scoring algorithm versus a new algorithm that included the SE and AB ratings. Inclusion of the SE and AB ratings improved positive risk classification appreciably, while the specificity declined. These resultssuggest that the STAT-E using the original STAT scoring algorithm optimizes specificity, while the STAT-E scoring algorithm with the two new ratings optimizes the positive risk classification. Using multiple scoring algorithms on the STAT may provide improved screening accuracy for diverse contexts, and a scalable web-based tutorial may be a pathway for increasing the number of community providers who can administer the STAT and contribute toward increased rates of autism screening.