Early Identification of Autism Spectrum Disorder (ASD): Strategies for Use in Local Communities

Early diagnosis of autism spectrum disorder (ASD) is essential for improved outcomes. There is a paucity of data on the prevalence of ASD in low- and middle-income countries (LMIC), but early identification may be further delayed in those communities. In this paper, recent studies on strategies for the early detection of ASD, and the prevalence of ASD in LMIC are reviewed. The limitations that can arise in the early identification of ASD in LMIC communities are discussed, and screening tools and strategies that can be helpful are identified. The goal is to recommend models that are culturally appropriate and scientifically valid, easily integrated within community settings while strengthening community systems and reducing disparities in the early identification of ASD. Starting locally by simplifying and demystifying the ASD identification process and building community connections will inform global researchers and policymakers while making a difference in the lives of the children and families affected by ASD.

The RITA-T (Rapid Interactive Screening Test for Autism in Toddlers) Community Model to Improve Access and Early Identification of Autism in Young Children

Objective: To evaluate improved identification and the generalization of the RITA-T (Rapid interactive Screening Test for Autism in Toddlers) model through partnerships with Primary Care (PC), Early Intervention (EI), and Autism Diagnosticians. Methods: Over 3 years (2018-2021), 15 EI and 9 PC (MD and NP) centers participated in this project. We trained providers on the RITA-T and established screening models. We reviewed charts of all toddlers referred through this model and compared wait times, and diagnoses, to those evaluated through regular referral in a tertiary-based autism clinic. We also examined the RITA-T psychometrics. Results: 377 toddlers met our inclusion criteria. Wait time for diagnosis was an average of 2.8 months and led to further collaboration between community providers. RITA-T cut-off scores stayed consistent. Providers reported improved confidence and easy integration of this model. Conclusions: This model is generalizable and improves the Early Identification of ASD.