Autism screening at 18 months of age: a comparison of the Q-CHAT-10 and M-CHAT screeners.

Funder: Peterborough NHS Foundation TrustFunder: Collaboration for Leadership in Applied Health Research and Care - Greater ManchesterFunder: NIHR Cambridge Biomedical Research CentreBACKGROUND: Autism screening is recommended at 18- and 24-month pediatric well visits. The Modified Checklist for Autism in Toddlers-Revised (M-CHAT-R) authors recommend a follow-up interview (M-CHAT-R/F) when positive. M-CHAT-R/F may be less accurate for 18-month-olds than 24-month-olds and accuracy for identification prior to two years is not known in samples that include children screening negative. Since autism symptoms may emerge gradually, ordinally scoring items based on the full range of response options, such as in the 10-item version of the Quantitative Checklist for Autism in Toddlers (Q-CHAT-10), might better capture autism signs than the dichotomous (i.e., yes/no) items in M-CHAT-R or the pass/fail scoring of Q-CHAT-10 items. The aims of this study were to determine and compare the accuracy of the M-CHAT-R/F and the Q-CHAT-10 and to describe the accuracy of the ordinally scored Q-CHAT-10 (Q-CHAT-10-O) for predicting autism in a sample of children who were screened at 18 months. METHODS: This is a community pediatrics validation study with screen positive (n = 167) and age- and practice-matched screen negative children (n = 241) recruited for diagnostic evaluations completed prior to 2 years old. Clinical diagnosis of autism was based on results of in-person diagnostic autism evaluations by research reliable testers blind to screening results and using the Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) Toddler Module and Mullen Scales of Early Learning (MSEL) per standard guidelines. RESULTS: While the M-CHAT-R/F had higher specificity and PPV compared to M-CHAT-R, Q-CHAT-10-O showed higher sensitivity than M-CHAT-R/F and Q-CHAT-10. LIMITATIONS: Many parents declined participation and the sample is over-represented by higher educated parents. Results cannot be extended to older ages. CONCLUSIONS: Limitations of the currently recommended two-stage M-CHAT-R/F at the 18-month visit include low sensitivity with minimal balancing benefit of improved PPV from the follow-up interview. Ordinal, rather than dichotomous, scoring of autism screening items appears to be beneficial at this age. The Q-CHAT-10-O with ordinal scoring shows advantages to M-CHAT-R/F with half the number of items, no requirement for a follow-up interview, and improved sensitivity. Yet, Q-CHAT-10-O sensitivity is less than M-CHAT-R (without follow-up) and specificity is less than the two-stage procedure. Such limitations are consistent with recognition that screening needs to recur beyond this age

Separate Scoring Algorithms Optimize the Screening Properties of the Screening Tool for Autism in Toddlers for Different Screening Priorities

Thesis (Master's)--University of Washington, 2021Detecting autism in young children allows for timely access to specialized early intervention services. The Screening Tool for Autism in Toddlers (STAT) is a validated stage-2 Autism Spectrum Disorders (ASD) screening measure that takes 20 minutes to administer and comprises 12 play-based items that are scored according to specific criteria. An expanded version (STAT-E) includes the examiner’s subjective ratings of children’s social engagement and atypical behaviors. This study examines the screening properties of STAT-E using the original STAT scoring algorithm and the extent to which an algorithm that includes the subjective ratings of social engagement and atypical behaviors improves the screening properties of the STAT-E relative to the original STAT scoring algorithm. Two-hundred and thirty-eight (238) families of children between 24 and 35 months old participated. The STAT-E was administered by assessors with limited experience who were trained using a scalable web-based platform and children received a comprehensive evaluation from a separate team of ASD research or clinical experts who were blind to the STAT-E results. Logistic regression, ROC curves, and classification matrices and metrics (Youden’s J and F1 score) were used to determine the screening properties of the STAT-E using the original STAT scoring algorithm and the extent to which an algorithm that included the subjective ratings of social engagement and atypical behaviors improved the screening properties of the STAT-E relative to the original STAT scoring algorithm. The concurrent validity of the STAT-E using the original STAT scoring algorithm in this sample was fair (sensitivity = .67, specificity = .66). Inclusion of the examiner ratings of social engagement and atypical behaviors on the STAT-E improved positive risk classification appreciably (F1 score = .80-.85 versus .74), while the specificity declined (specificity = .62). Results suggest that the STAT-E using the original STAT scoring algorithm optimizes specificity, while the STAT-E scoring algorithm with two new ratings optimizes the positive risk classification. Using multiple scoring algorithms on the STAT may provide improved scoring accuracy for diverse contexts and children. A fast and scalable web-based tutorial may be a pathway for increasing the number of community providers who can administer the STAT and contribute toward increased rates of autism screening