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Mandell, D. S., Wiggins, L. D., Carpenter, L. A., Daniels, J., DiGuiseppi, C., Durkin, M. S., Giarelli, E., Morrier, M. J., Nicholas, J. S., Pinto-Martin, J. A., Shattuck, P. T., Thomas, K. C., Yeargin-Allsopp, M., & Kirby, R. S. (2009). Racial/Ethnic Disparities in the Identification of Children with Autism Spectrum Disorders. American Journal of Public Health, 99, 493-498. https://doi.org/10.2105/AJPH.2007.131243

has been cited by the following article:

• TITLE: Predicting Autism Spectrum Disorder in Young Children: The Predictive Utility of the Ghuman-Folstein Screen for Social Interaction and Sociodemographic Factors

  AUTHORS: Dana K. Princiotta, Sarah Hamill Skoch, Randi Phelps, Richard J. Morris, Nicholas Breitborde, Irena Bukelis, Marco Grados, Jaswinder K. Ghuman

  KEYWORDS: Autism Spectrum Disorder, Ghuman-Folstein Screen for Social Interaction, Screening Tool, Early Identification/Detection

  JOURNAL NAME: Psychology, Vol.11 No.7, July 30, 2020

  ABSTRACT: Background: Autism Spectrum Disorder (ASD) is characterized by having deficits in social interactions. Screening measures for ASD are often used for children over five years of age, ultimately leading to diagnosis later in development. Identification of ASD in young children is critical for early intervention. Aims: The purpose of the present study was to examine whether prediction of ASD could be improved in young children by combining social interaction scores, as measured by the Ghuman-Folstein Screen for Social Interaction (GF-SSI), with the presence of selected demographic variables (sex, age, ethnicity, mother’s educational level, and socioeconomic status). Methods and Procedures: One-hundred and seventy-one clinically referred children with previously diagnosed ASD or non-ASD developmental disorders and their caregivers were included in the study. Caregivers completed a sociodemographic survey and the GF-SSI. Outcomes and Results: Results demonstrated that the final model correctly identified 74% of the cases, and the GF-SSI was found to be the greatest predictor of children having ASD. The selected demographic variables were not found to be significant predictors of the diagnosis of ASD. Conclusions and Implications: These results are discussed in relation to the literature on predicting ASD in young children. Future directions for research are also discussed.