Developing a Framework to Advance Cultural Sensitivity and Accommodate Behavioral Variability in Toddler Autism Screening: A Mixed-Methods Cross-Cultural Approach to Improve Autism Screening Accuracy

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PROJECT SUMMARY Autism Spectrum Disorder (ASD) is a prevalent, lifelong condition that manifests early in life and is associated with significant social communication challenges1–3 and economic needs1 for children with ASD and their families. Access to early, ASD-specific services can profoundly improve long-term outcomes for children with ASD4–9 and depends on timely detection of the condition. However, the predictive accuracy of ASD screening is well below recommended standards for screening tools10,15,21 and is even lower for racial and ethnic minority children10,31–38 when compared to White children. Screening tools, which typically rely on parental report, may lack predictive accuracy because they do not sufficiently account for heterogeneity between and within cultural groups. Variation in culture-based parental perception of early ASD expressions has been noted between groups29,46–51, and some social communication behaviors used in ASD screening perform significantly differently between racial and ethnic groups31,32,36–38. Additionally, existing phenomenological heterogeneity has been observed via clinicians in the development of social communication emergence across prodromal individuals44, 53–58 within cultural groups. However, current screeners employ methods that do not sufficiently capture this existing heterogeneity, and this heterogeneity has not been precisely characterized as prospectively observed and reported by caregivers. Therefore, it remains difficult to develop new parent-report screeners designed to account for heterogeneity in developmental trajectories in an informed way. The primary objective of this proposal is to develop and assess a framework for developing culturally-sensitive and highly predictive parental-report screening measures to improve the equity and accuracy of future ASD screeners for use with diverse families. This will be achieved by i) developing culturally-sensitive ASD screening items; ii) using these items in data collection via ecological momentary assessment (EMA); and iii) employing Machine Learning (ML) to develop prototype algorithms that maximize ASD prediction between and within different cultural groups. EMA provides real-time in-context monitoring at time-periods proximal to target dynamics60–62. EMA is therefore an ideal choice to capture the heterogeneity in developmental trajectories of social-communication behaviors over time as observed and reported by caregivers. ML develops prediction algorithms that can differentially weigh and combine ASD indicators based on group affiliation, individual differences, and temporal specificity and is an optimal approach for algorithm development in the presence of developmental heterogeneity76 but is underutilized in ASD screening efforts77. Through the proposed research and training plan, the applicant will develop the skills and expertise needed to make a substantive contribution to ASD research as an independent clinical scientist.