Building a theoretical framework for autism spectrum disorders screening instruments in Europe

Abstract

Background: This study addresses the need for a theoretical base to develop more effective early autism spectrum disorders (ASD) detection tools. The structure that underlies early ASD detection is explored by evaluating the opinions of experts on ASD screening tools currently used in Europe. Method: A process of face and content validity was performed. First, the best constructs were selected from the relevant tests: Checklist for Early Signs of Developmental Disorders (CESDD), Checklist for Autism in Toddlers (CHAT), Early Screening of Autistic Traits Questionnaire (ESAT), Modified Checklist for Autism in Toddlers (M-CHAT), Social Communication Questionnaire (SCQ) and Communication and Symbolic Behaviour Scales Developmental Profile (CSBS-DP). The diagnostic content validity model by Fehring (1986Fehring ( , 1994 was adapted to make the selection. Afterwards, the items, taken from these tests, were selected to fit into each construct, using the same methodology. Results: Twelve of the 18 constructs were selected by the experts and 11 items were chosen from a total of 130, reduced to eight after eliminating tautologies. Conclusions: Mapping these constructs and items on to the DSM-5 diagnostic criteria for ASD indicated good face and content validity. Results of this research will contribute to efforts to improve early ASD screening instruments and identify the key behaviours that experts in ASD see as the most relevant for early detection. Key Practitioner Message• The study outlines the main screening instruments used across Europe and the need to improve early detection of autism spectrum disorders (ASD).• A novel approach, based on content validity, was used to identify the key behaviours that experts in ASD see as the most relevant for early detection.• Results of this study can help practitioners improve their understanding of early ASD and be more confident in their screening results, while also serving as a theoretical framework to develop more effective screening tests.

Cross-cultural similarities and differences in reporting autistic symptoms in toddlers: A study synthesizing M-CHAT(-R) data from ten countries

“…Although using a different approach from studying the rate of endorsement, Kamio et al (2015) in the Japanese version of the M-CHAT(-R) also identified item points to get help (i.e., imperative pointing) and understands what is said to be best discriminating for ASD, while Darquah (2021) identified only item understands what is said. The item proto-declarative pointing (i.e., points to show) was recognized as one of the most important items within a theoretical framework for ASD screening (Magán-Maganto et al, 2018).…”

Use of Machine Learning to Investigate The Quantitative Checklist For Autism in Toddlers (Q-CHAT) Towards Early Autism Screening

“…Early screening and developmental surveillance have represented a primary goal in the past two decades and many screening tools have been developed and tested. However, performance, classi cation accuracy and reliability of those screening instruments vary depending on different settings, samples and screening designs, thus posing critical issues for clinical application [2][3][4][5][6]. Among the most popular and replicated screening tools, the M-CHAT and the subsequent revised M-CHAT/RF [7] have been applied in large mixed samples including both high and low likelihood groups, demonstrating low to moderate accuracy in detecting autism [8].…”