Validation of the Quantitative Checklist for Autism in Toddlers in an Italian Clinical Sample of Young Children With Autism and Other Developmental Disorders

Abstract

Background: The Quantitative Checklist for Autism in Toddlers (Q-CHAT) is parent-report screening questionnaire for detecting threshold and sub-threshold autistic features in toddlers. The Q-CHAT is a dimensional measure normally distributed in the general population sample and is able to differentiate between a group of children with a diagnosis of autism and unselected toddlers. Objectives: We aim to investigate the psychometric properties, score distribution, and external validity of the Q-CHAT in an Italian clinical sample of young children with autism versus children with developmental delay and typically developing children. Method: N = 126 typically developing children (TD), n = 139 children with autism, and n = 50 children presenting developmental delay (DD) were administered the Q-CHAT. Standardized measures of cognitive functions, language, and behaviors were also obtained. Results: The Q-CHAT scores were normally distributed and demonstrated adequate internal consistency and good item to total score correlations. The mean Q-CHAT score in the autism group was significantly higher than those found in the DD sample and TD children. No difference on the mean Q-CHAT score between DD and TD children was found. The accuracy of the Q-CHAT to discriminate between autism and TD was very good. Two different cut-points (27 and 31, respectively) maximized sensitivity and specificity for autism versus TD and DD, respectively. Finally, higher Q-CHAT scores were correlated with lower language and social communication skills. Conclusions: In clinical settings, the Q-CHAT demonstrated good psychometric properties and external validity to discriminate autism children not just from children with typical development but also from children with developmental delay.

A Two-Stage Screening Approach with I-TC and Q-CHAT to Identify Toddlers at Risk for Autism Spectrum Disorder within the Italian Public Health System

“…Moreover, at 18 months, these last two clusters of the I-TC, combined with item 20 of the Q-CHAT, constitute a model of three predictors with very high sensitivity and specificity. This confirms that the I-TC is a broadband screener which covers multiple developmental areas while the Q-CHAT seems more specific for autism and better discriminates among autism children, typical development and also from other developmental conditions, as suggested by Ruta et al [25].…”

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

“…The top-performing SVM model reached an overall accuracy of 95% with a sensitivity and speci city of 90% and 100% respectively, compared to RF (sensitivity=85% and speci city=95%) and NB (sensitivity=82% and speci city=100%). If we compare these results with those obtained applying the standard ROC analysis to the same participant sample [13], we found that ML algorithms were able to improve the classi cation accuracy of the Q-CHAT. In the previous study [13], the ROC curve showed an accuracy of 89.5% (vs. 95%), sensitivity = 83% (vs. 90%) and speci city = 78% (vs. 100%).…”

“…If we compare these results with those obtained applying the standard ROC analysis to the same participant sample [13], we found that ML algorithms were able to improve the classi cation accuracy of the Q-CHAT. In the previous study [13], the ROC curve showed an accuracy of 89.5% (vs. 95%), sensitivity = 83% (vs. 90%) and speci city = 78% (vs. 100%). Furthermore, by running the SVM-RF algorithm, we were able to select a sub-group of 14 items which maintained a very high accuracy, sensitivity and speci city (93%, 87% and 96% respectively for SVM).…”

“…Other screening tools, such as the Social Communication Questionnaire (SCQ), reported poor balance between sensitivity and speci city in high-likelihood toddlers [9], while measures like the Screening Tool for Autism in Two-years-old (STAT) [10] and the Baby and Infant Screen for Children with aUtIsm Traits (BISCUIT) [11] have been tested only in case-control studies, requiring further prospective population studies. With the shift from a categorical to a dimensional approach to autism diagnosis, a quantitative measure of autistic traits, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) has been tested in different sample populations and cultures, in both case-control studies and primary care settings, displaying fair to good psychometric properties and predictive validity as well as good cultural stability [12][13][14][15][16][17][18]. A short Q-CHAT version, the Q-CHAT-10, including the best 10 predictive items was also developed [19], aiming to create a quick "red ags" tool suitable for the time constraints of pediatric check-ups and to help further reduce the delay for potential referrals.…”

Early Screening of the Autism Spectrum Disorders: Validity Properties and Cross-Cultural Generalizability of the First Year Inventory in Italy

“…For these reasons, the EFA estimating the two-factor structure was preferred and reached a moderate fit of the data, χ 2 (1651) = 2940.15, p < 0.001, CFI = 0.79, RMSEA = 0.034 (LO90% = 0.032, HI90% = 0.036). Nineteen items (Items 4,5,6,16,(27)(28)(29)31,32,39,41,(49)(50)(51)(52)(53)(54)(55)(56) were excluded from the subsequent analysis because the factor loadings loaded for two or more factors. After exclusion of those items, the subsequent fourth EFA reached moderate fit of the data, χ 2 (739) = 1185.47, p < 0.001, CFI = 0.92, RMSEA = 0.03 (LO90% = 0.027, HI90% = 0.033).…”