Search Results (5)

This study aimed to assess some early symptoms of autism spectrum disorders (ASD) measured by a screening tool developed for an early detection of ASD. We investigated if the early symptoms were associated with toddlers’ age, gender or ASD familial risk status. We used the Polish version of the Quantitative Checklist for Autism in Toddlers (Q-CHAT) to assess 1024 children aged 16 to 36 months. The sample included four groups of participants: typically developing toddlers, toddlers with parent-reported ASD-specific concerns, toddlers at risk for autism due to having an older sibling with ASD, and toddlers with a developmental delay. We found that mean Q-CHAT scores were significantly higher in boys than in girls. We did not find any associations between Q-CHAT scores and age. We observed that toddlers with a familial risk for ASD and those with a developmental delay scored significantly higher than controls. We collated these results with previous studies that used the Q-CHAT and other instruments. Full article

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings. Full article

Pediatric surveillance through screening procedures is needed to detect warning signs of risk for Autism Spectrum Disorder under 24 months of age and to promote early diagnosis and treatment. The main purpose of this study is to extend the literature regarding the psychometric properties of two screening tools, the First Year Inventory (FYI) and the Quantitative-CHecklist for Autism in Toddler (Q-CHAT), testing their criterion validity. They were administered during a three-wave approach involving the general population. At T1, 657 children were tested with the FYI and 36 of them were found to be at risk. At T2, 545 were tested with the Q-CHAT and 29 of them were found to be at risk. At T3, 12 out of the 36 children with a high score on the FYI and 11 out of the 29 children with a high score on the Q-CHAT were compared to 15 typically developing children. The criterion validity was tested considering the severity of the autistic symptoms, emotional/behavioral problems, and limited global functioning as criteria. Accuracy parameters were also calculated. Furthermore, we investigated which dimension of each questionnaire better predicted the aforementioned criterion. The results corroborated the hypotheses and confirmed the criterion validity of FYI and Q-CHAT. Full article

Standardized screening programs ensure that children are monitored for early signs of autism spectrum disorder (ASD) in order to promote earlier diagnosis and intervention. The aim of this study is to identify early signs of atypical development consistent with ASD or other developmental disorders in a population of 224 low-risk toddlers through a two-stage screening approach applied at 12 and 18 months of age. We adopted two screening tools combined: 1. the Communication and Symbolic Behavior Scales Developmental Profile (CSBS DP) Infant–Toddler Checklist (I-TC) and 2. The Quantitative Checklist for Autism in Toddlers (Q-CHAT). We assessed their sensitivity and specificity related to the diagnostic outcome at 36 months. The results showed that autistic signs can be detected as early as the first year even through a few questions extrapolated from both screeners and that our model could be used as a screening procedure in the Italian public health system. Full article

The present study provides a systematic review of level 1 and level 2 screening tools for the early detection of autism under 24 months of age and an evaluation of the psychometric and measurement properties of their studies. Methods: Seven databases (e.g., Scopus, EBSCOhost Research Database) were screened and experts in the autism spectrum disorders (ASD) field were questioned; Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines and Consensus-based Standard for the selection of health Measurement INstruments (COSMIN) checklist were applied. Results: the study included 52 papers and 16 measures; most of them were questionnaires, and the Modified-CHecklist for Autism in Toddler (M-CHAT) was the most extensively tested. The measures’ strengths (analytical evaluation of methodological quality according to COSMIN) and limitations (in term of Negative Predictive Value, Positive Predictive Value, sensitivity, and specificity) were described; the quality of the studies, assessed with the application of the COSMIN checklist, highlighted the necessity of further validation studies for all the measures. According to COSMIN results, the M-CHAT, First Years Inventory (FYI), and Quantitative-CHecklist for Autism in Toddler (Q-CHAT) seem to be promising measures that may be applied systematically by health professionals in the future. Full article