Early Autism Screening: A Comprehensive Review

Fadi Thabtah and David Peebles
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Fadi Thabtah: Department of Psychology, School of Human and Health Sciences, University of Huddersfield, Huddersfield HD1 3DH, UK
David Peebles: Department of Psychology, School of Human and Health Sciences, University of Huddersfield, Huddersfield HD1 3DH, UK

IJERPH, 2019, vol. 16, issue 18, 1-28

Abstract: Autistic spectrum disorder (ASD) refers to a neurodevelopmental condition associated with verbal and nonverbal communication, social interactions, and behavioural complications that is becoming increasingly common in many parts of the globe. Identifying individuals on the spectrum has remained a lengthy process for the past few decades due to the fact that some individuals diagnosed with ASD exhibit exceptional skills in areas such as mathematics, arts, and music among others. To improve the accuracy and reliability of autism diagnoses, many scholars have developed pre-diagnosis screening methods to help identify autistic behaviours at an early stage, speed up the clinical diagnosis referral process, and improve the understanding of ASD for the different stakeholders involved, such as parents, caregivers, teachers, and family members. However, the functionality and reliability of those screening tools vary according to different research studies and some have remained questionable. This study evaluates and critically analyses 37 different ASD screening tools in order to identify possible areas that need to be addressed through further development and innovation. More importantly, different criteria associated with existing screening tools, such as accessibility, the fulfilment of Diagnostic and Statistical Manual of Mental Disorders ( DSM-5 ) specifications, comprehensibility among the target audience, performance (specifically sensitivity, specificity, and accuracy), web and mobile availability, and popularity have been investigated.

Keywords: autism spectrum disorder; behaviour science; DSM-5; screening methods; clinical diagnosis; child psychology; machine learning; public health (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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