Development of a brief version of the Autism Detection in Early Childhood

Screening for Autism: A Review of the Current State, Ongoing Challenges, and Novel Approaches on the Horizon

Screening for autism is recommended in pediatric primary care. However, the median age of autism spectrum disorder (ASD) diagnosis is substantially higher than the age at which autism can reliably be identified, suggesting room for improvements in autism recognition at young ages, especially for children from minoritized racial and ethnic groups, low-income families, and families who prefer a language other than English. Novel approaches are being developed to utilize new technologies in aiding in autism recognition. However, attention to equity is needed to minimize bias. Additional research on the benefits and potential harms of universal autism screening is needed. The authors provide suggestions for pediatricians who are considering implementing autism-screening programs.

Tools for early screening of autism spectrum disorders in primary health care – a scoping review

Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that manifests itself in early childhood. Early diagnosis of these disorders allows for the initiation of early therapy, which is crucial for the child's further functioning in society.

Objectives

This review aims to gather and present the existing ASD screening tools that can be used in primary care and adapted to different countries conditions linguistically and culturally.

Eligibility criteria

We searched for English-language publications on ASD screening tools for children aged 0–3 years suitable for use in primary care (i.e. free, requiring no additional training or qualifications).

Sources of evidence

Four databases were explored to find English studies on ASD screening tools intended for the rapid assessment of children aged 0–3.

Charting methods

The information sought (specific features of the questionnaires relevant to primary health care workers, psychometric and diagnostic values of a given cultural adaptation of screening tools, and the linguistic and cultural changes made) were extracted and collected to create profiles of these tools.

Results

We found 81 studies which met inclusion criteria and underwent full data extraction. Three additional data sources were included. These allowed to create 75 profiles of adaptations for 26 different screening tools and collect data on their psychometric values and characteristic features.

Conclusions

The results of our study indicate the availability of several diagnostic tools for early ASD screening in primary care setting concordant culturally and linguistically with a given population. They could be an effective method of accelerating the diagnostic process and starting personalized therapy faster. However, most tools have significant limitations – some are only available for research purposes, while others do not have scientific evidence to prove their effectiveness.

Separate scoring algorithms for specific identification priorities optimize the screening properties of the Screening Tool for Autism in Toddlers (STAT)

The Screening Tool for Autism in Toddlers (STAT) is a validated stage-2 autism spectrum disorder (ASD) screening measure that takes 20 minutes to administer and comprises 12 play-based items that are scored according to specific criteria. This study examines an expanded version (STAT-E) that includes the examiner's subjective ratings of children's social engagement (SE) and atypical behaviors (AB) in the scoring algorithm. The sample comprised 238 children who were 24-35 months old. The STAT-E assessors had limited ASD experience to mimic its use by community-based non-specialists, and were trained using a scalable web-based platform. A diagnostic evaluation was completed by clinical experts who were blind to the STAT-E results. Logistic regression, ROC curves, and classification matrices and metrics were used to determine the screening properties of STAT-E when scored using the original STAT scoring algorithm versus a new algorithm that included the SE and AB ratings. Inclusion of the SE and AB ratings improved positive risk classification appreciably, while the specificity declined. These results suggest that the STAT-E using the original STAT scoring algorithm optimizes specificity, while the STAT-E scoring algorithm with the two new ratings optimizes the positive risk classification. Using multiple scoring algorithms on the STAT may provide improved screening accuracy for diverse contexts, and a 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.

Preliminary Validation and Feasibility of the Autism Detection in Early Childhood-Virtual (ADEC-V) for Autism Telehealth Evaluations in a Hospital Setting

This study provided preliminary validation of the Autism Detection in Early Childhood-Virtual (ADEC-V) for telehealth assessment of possible autism. Participants were 121 children (24.79% female) aged 18–47 months who completed telehealth evaluations at a large pediatric hospital in the Midwestern United States between October 2020 and February 2021. The ADEC-V showed good sensitivity (0.82) and specificity (0.78) and was significantly correlated with other ASD symptom measures (i.e., CARS-2, ADI-R). Internal consistency was acceptable (α = 0.77). These results need replication in a larger and broader sample including more children without ASD. This preliminary validation study identifies the ADEC-V as a promising measure for telehealth ASD assessments in young children.

Pitfalls When Using Area Under the Curve to Evaluate Item Content for Early Screening Tests for Autism

Evaluations of early screening tests for autism commonly rely on receiver operating characteristic (ROC) analysis and comparisons of area under the curve (AUC). Whether AUC differs significantly from chance or between test items is not always assessed. Two recent and independent evaluations of the Brief Autism Detection in Early Childhood (BADEC) constructed a short-form by selecting the five items with the highest AUC values, leading to inconsistencies regarding appropriate item content (Nah et al., 2018; Nevill et al., 2019). Using significance testing to compare AUC values for each test item from each dataset, we demonstrate which items justify inclusion in the BADEC, which items can be ruled out, and highlight key factors influencing AUC significance testing outcomes.

The Autism Detection in Early Childhood Tool: Level 2 autism spectrum disorder screening in a NICU Follow-up program

OBJECTIVE

Children born preterm are at increased risk for autism spectrum disorder (ASD). However, early diagnosis of ASD is challenging because conventional screening Level 1 tools are less reliable in this population. We sought to determine whether the Autism Detection in Early Childhood (ADEC) and Child Behavior Checklist (CBCL) could accurately identify children at risk for ASD in a NICU Follow-up setting and thus facilitate referral for formal ASD evaluation.

METHOD

Children aged 18-36 months were recruited from a NICU Follow-up program. All children received presumptive diagnoses based on DSM-5 criteria and were screened for ASD risk with the ADEC and CBCL. Children scoring in the "at risk" range on either tool were referred for a full diagnostic ASD evaluation.

RESULTS

Sixty-nine patients (median birth weight 1140 g; median gestational age 28 weeks) were included with 18 designated "at risk" for ASD. Nine (13 %) scored "at risk" on the ADEC and 12 (17 %) on the CBCL. Thirteen children underwent diagnostic ASD evaluation with 9 receiving a formal diagnosis of ASD. The ADEC demonstrated the best performance (sensitivity 89 %, specificity 98 %). The CBCL was less sensitive (sensitivity 50 %, specificity 90 %). Requiring elevated scores on both the CBCL and ADEC was specific but not sensitive (sensitivity 33 %, specificity 100 %).

CONCLUSION

The ADEC performed well in identifying children at risk for ASD within this high-risk NICU cohort, adding benefit as an autism-specific screening tool over the CBCL alone.

Autism Screening in Early Childhood: Discriminating Autism From Other Developmental Concerns

Early identification of autism, followed by appropriate intervention, has the potential to improve outcomes for autistic individuals. Numerous screening instruments have been developed for children under 3 years of age. Level 1 screeners are used in large-scale screening to detect at-risk children in the general population; Level 2 screeners are concerned with distinguishing children with signs of autism from those with other developmental problems. The focus here is evaluation of Level 2 screeners. However, given the contributions of Level 1 screeners and the necessity to understand how they might interface with Level 2 screeners, we briefly review Level 1 screeners and consider instrument characteristics and system variables that may constrain their effectiveness. The examination of Level 2 screeners focuses on five instruments associated with published evaluations in peer-reviewed journals. Key criteria encompass the traditional indices of test integrity such as test reliability (inter-rater, test-retest) and construct validity, including concurrent and predictive validity, sensitivity (SE), and specificity (SP). These evaluations reveal limitations, including inadequate sample sizes, reliability issues, and limited involvement of independent researchers. Also lacking are comparative test evaluations under standardized conditions, hindering interpretation of differences in discriminative performance across instruments. Practical considerations constraining the use of such instruments—such as the requirements for training in test administration and test administration time—are canvassed. Published Level 2 screener short forms are reviewed and, as a consequence of that evaluation, future directions for assessing the discriminative capacity of items and measures are suggested. Suggested priorities for future research include targeting large and diverse samples to permit robust appraisals of Level 2 items and scales across the 12–36 month age range, a greater focus on precise operationalization of items and response coding to enhance reliability, ongoing exploration of potentially discriminating items at the younger end of the targeted age range, and trying to unravel the complexities of developmental trajectories in autistic infants. Finally, we emphasize the importance of understanding how screening efficacy is dependent on clinicians' and researchers' ability not only to develop screening tests but also to negotiate the complex organizational systems within which screening procedures must be implemented.

Brief Report: Replication and Validation of the Brief Autism Detection in Early Childhood (BADEC) in a Clinical Sample

We investigated the clinical validity of the BADEC, an abbreviated, five-item version of the Autism Detection in Early Childhood, level-2 screening tool for autism. Initially developed by Nah et al. (2019) using a research sample, the present study replicated Nah et al. (2019) procedures in a clinical population. Using a cutoff score of five, five items were identified as most effective in discriminating children who later received an ASD diagnosis by an interdisciplinary team. This algorithm had improved validity compared to the original research algorithm. Results supported the efficacy of a very brief, easy to administer ASD screening tool in identifying children under three who are likely to have ASD.

Piloting the Use of a Short Observation List for ASD-Symptoms in Day-Care: Challenges and Further Possibilities

Early symptoms of autism spectrum disorder (ASD) develop through the second year of life, making a stable ASD diagnosis possible around 24 months of age. However, in general, children with ASD are diagnosed later. In this study we explored the use of a short observation list to detect symptoms associated with ASD in children 12-24 months of age attending typical day-care centers. The results indicate that a short observation list used by day-care teachers does not reveal sufficient properties to be independently used in young children in day-care centers. Further studies should explore multiple and repeated measures for early detection of symptoms associated with ASD in typical day-care centers.