Behavioral interventions that incorporate naturalistic, developmental strategies have been shown to improve outcomes for young children who receive an autism spectrum disorder (ASD) diagnosis. Although there is broad consensus that children on the spectrum should begin supports as soon as possible, the empirical evidence for this is relatively limited and little is known about the optimal age to start autism-specific interventions. Our team conducted a randomized controlled trial (RCT) to test the effects of starting intervention at different ages, using the Early Social Interaction (ESI) model, a parent-implemented intervention for toddlers on the spectrum. Participants included 82 autistic toddlers and their caregiver(s) who received 9 months of Individual-ESI 9 months of Group-ESI, with the timing/order of these two treatment conditions randomized. Thus, families received the more intensive and individualized Individual-ESI at either 18 or 27 months of age. Results revealed that children who received Individual-ESI earlier showed greater treatment gains than those who received this intervention later. Gains were demonstrated in several areas, which included the use and understanding of language, social use of communication skills, and self-help skills. Importantly, these findings were specific to the intensive and individualized parent coaching model compared to group-based treatment, allowing us to rule out the possibility that these timing effects were due to children getting older rather than the treatment itself. Our results suggest that even a narrow window of 18 versus 27 months may have an impact on outcomes and underscore the importance of screening and evaluation as young as possible.

Historically, children from non-Hispanic Black and Hispanic backgrounds, those from lower-income families, and girls are less likely to be diagnosed with autism spectrum disorder. Under-identification among these historically and contemporaneously marginalized groups can limit their access to early, autism spectrum disorder-specific interventions, which can have long-term negative impacts. Recent data suggest that some of these trends may be narrowing, or even reversing. Using electronic health record data, we calculated autism spectrum disorder prevalence rates and age of first documented diagnosis across socio-demographic groups. Our cohort included children seen at young ages (when eligible for screening in early childhood) and again at least after 4 years of age in a large primary care network. We found that autism spectrum disorder prevalence was unexpectedly higher among Asian children, non-Hispanic Black children, children with higher Social Vulnerability Index scores (a measure of socio-economic risk at the neighborhood level), and children who received care in urban primary care sites. We did not find differences in the age at which autism spectrum disorder diagnoses were documented in children's records across these groups. Receiving primary care at an urban site (regardless of location of specialty care) appeared to account for most other socio-demographic differences in autism spectrum disorder prevalence rates, except among Asian children, who remained more likely to be diagnosed with autism spectrum disorder after controlling for other factors. We must continue to better understand the process by which children with autism spectrum disorder from traditionally under-identified and under-served backgrounds come to be recognized, to continue to improve the equity of care.

OBJECTIVE: To assess the impact of the COVID-19 pandemic on screening for autism spectrum disorder (ASD) and screening equity among eligible children presenting for well-child care in a large primary care pediatric network, we compared rates of ASD screening completion and positivity during the pandemic to the year prior, stratified by sociodemographic factors.

METHODS: Patients who presented for in-person well-child care at 16 to 26 months between March 1, 2020 and February 28, 2021 (COVID-19 cohort, n = 24,549) were compared to those who presented between March 1, 2019 and February 29, 2020 (pre-COVID-19 cohort, n = 26,779). Demographics and rates of completion and positivity of the Modified Checklist for Autism in Toddlers with Follow-up (M-CHAT/F) were calculated from the electronic health record and compared by cohort using logistic regression models.

RESULTS: Total eligible visits decreased by 8.3% between cohorts, with a greater decline in Black and publicly insured children. In the pre-COVID-19 cohort, 89.0% of eligible children were screened at least once, compared to 86.4% during the pandemic (P < 0.001). Significant declines in screening completion were observed across all sociodemographic groups except among Asian children, with the sharpest declines among non-Hispanic White children. Sociodemographic differences were not observed in screen-positive rates by cohort.

CONCLUSIONS: Well-child visits and ASD screenings declined across groups, but with different patterns by race and ethnicity during the COVID-19 pandemic. Findings regarding screen-completion rates should not be interpreted as a decline in screening disparities, given differences in who presented for care. Strategies for catch-up screening for all children should be considered.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restricted, repetitive behavior, and impaired social communication and interactions. However, significant challenges remain in diagnosing and subtyping ASD due in part to the lack of a validated, standardized vocabulary to characterize clinical phenotypic presentation of ASD. Although the human phenotype ontology (HPO) plays an important role in delineating nuanced phenotypes for rare genetic diseases, it is inadequate to capture characteristic of behavioral and psychiatric phenotypes for individuals with ASD. There is a clear need, therefore, for a well-established phenotype terminology set that can assist in characterization of ASD phenotypes from patients' clinical narratives.

METHODS: To address this challenge, we used natural language processing (NLP) techniques to identify and curate ASD phenotypic terms from high-quality unstructured clinical notes in the electronic health record (EHR) on 8499 individuals with ASD, 8177 individuals with non-ASD psychiatric disorders, and 8482 individuals without a documented psychiatric disorder. We further performed dimensional reduction clustering analysis to subgroup individuals with ASD, using nonnegative matrix factorization method.

RESULTS: Through a note-processing pipeline that includes several steps of state-of-the-art NLP approaches, we identified 3336 ASD terms linking to 1943 unique medical concepts, which represents among the largest ASD terminology set to date. The extracted ASD terms were further organized in a formal ontology structure similar to the HPO. Clustering analysis showed that these terms could be used in a diagnostic pipeline to differentiate individuals with ASD from individuals with other psychiatric disorders.

CONCLUSION: Our ASD phenotype ontology can assist clinicians and researchers in characterizing individuals with ASD, facilitating automated diagnosis, and subtyping individuals with ASD to facilitate personalized therapeutic decision-making.

Vocabulary checklists completed by caregivers are a common way of measuring children's vocabulary knowledge. We provide evidence from checklist data from 31 children with and without autism spectrum disorder. When asked to report twice about whether or not their child produces a particular word, caregivers are largely consistent in their responses, but where they are inconsistent, these inconsistencies affect verbs more than nouns. This difference holds both for caregivers of children with autism spectrum disorder and caregivers of typically-developing children. We suggest that caregivers may be less sure of their child's knowledge about verbs than nouns. This data converges with prior evidence comparing language samples of words children produce in a recorded interaction with checklist data, and it has implications for how researchers use checklist data in cases where the reliability of estimates of verb knowledge is critical.

To describe healthcare utilization patterns among children with autism (n = 1821), and compare these patterns to children with other developmental delays (DD; n = 12,336) and a population comparison (PC; n = 18,210) cohort. Retrospective study of administrative billing data. Children with autism had roughly six-times more annual outpatient visits as PC children and twice as many as children with DD. Children with autism were more likely than PC children to use nearly all services, but comparisons between the autism and DD cohorts were mixed. Children with autism were more likely to have psychiatry/psychology visits, 'other' specialty care visits and psychotropic prescriptions, but less likely to have pediatric specialty care visits, immunizations and some prescriptions. Findings reveal opportunities to streamline, coordinate or improve care for young children with autism, particularly for outpatient services, and to give caregivers appropriate anticipatory guidance about what to expect after an autism diagnosis.

<p><strong>OBJECTIVE: </strong>To assess the impact of the COVID-19 pandemic on screening for autism spectrum disorder (ASD) and screening equity among eligible children presenting for well-child care in a large primary care pediatric network, we compared rates of ASD screening completion and positivity during the pandemic to the year prior, stratified by socio-demographic factors.</p>

<p><strong>METHODS: </strong>Patients who presented for in-person well-child care at 16-26 months between 3/1/2020 and 2/28/2021 (COVID-19 cohort, n=24,549) were compared to those who presented between 3/1/2019 and 2/29/2020 (pre-COVID-19 cohort, n= 26,779). Demographics and rates of completion and positivity of the Modified Checklist for Autism in Toddlers with Follow-up (M-CHAT/F) were calculated from the electronic health record (EHR) and compared by cohort using logistic regression models.</p>

<p><strong>RESULTS: </strong>Total eligible visits decreased by 8.3% between cohorts, with a greater decline in Black and publicly insured children. In the pre-COVID-19 cohort, 89.0% of eligible children were screened at least once, compared to 86.4% during the pandemic (p&lt;0.001). Significant declines in screening completion were observed across all socio-demographic groups except among Asian children, with the sharpest declines among non-Hispanic White children. Socio-demographic differences were not observed in screen-positive rates by cohort.</p>

<p><strong>CONCLUSIONS: </strong>Well-child visits and ASD screenings declined across groups, but with different patterns by race and ethnicity during the COVID-19 pandemic. Findings regarding screen-completion rates should not be interpreted as a decline in screening disparities, given differences in who presented for care. Strategies for catch-up screening for all children should be considered.</p>

<p><strong>LAY ABSTRACT: </strong>There is a critical need for accurate screening tools for autism spectrum disorder in very young children so families can access tailored intervention services as early as possible. However, there are few screeners designed for children 18-24 months. Developing screeners that pick up on the signs of autism spectrum disorder in very young children has proved even more challenging. In this study, we examined a new autism-specific parent-report screening tool, the Early Screening for Autism and Communication Disorders for children between 12 and 36 months of age. Field-testing was done in five sites with 471 children screened for communication delays in primary care or referred for familial risk or concern for autism spectrum disorder. The Early Screening for Autism and Communication Disorders was tested in three age groups: 12-17, 18-23, and 24-36 months. A best-estimate diagnosis of autism spectrum disorder, developmental delay, or typical development was made. Analyses examined all 46 items and identified 30 items that best discriminated autism spectrum disorder from the non-spectrum groups. Cutoffs were established for each age group with good sensitivity and specificity. Results provide preliminary support for the accuracy of the Early Screening for Autism and Communication Disorders as an autism-specific screener in children 12-36 months with elevated risk of communication delay or autism spectrum disorder.</p>

<p><strong>BACKGROUND: </strong>Natural language processing (NLP) tools can facilitate the extraction of biomedical concepts from unstructured free texts, such as research articles or clinical notes. The NLP software tools CLAMP, cTAKES, and MetaMap are among the most widely used tools to extract biomedical concept entities. However, their performance in extracting disease-specific terminology from literature has not been compared extensively, especially for complex neuropsychiatric disorders with a diverse set of phenotypic and clinical manifestations.</p>

<p><strong>METHODS: </strong>We comparatively evaluated these NLP tools using autism spectrum disorder (ASD) as a case study. We collected 827 ASD-related terms based on previous literature as the benchmark list for performance evaluation. Then, we applied&nbsp;CLAMP, cTAKES, and MetaMap on 544 full-text articles and 20,408 abstracts from PubMed to extract&nbsp;ASD-related terms. We evaluated the predictive performance using precision, recall, and F1 score.</p>

<p><strong>RESULTS: </strong>We found that CLAMP has the best performance in terms of F1 score followed by cTAKES and then MetaMap. Our results show that CLAMP has much higher precision than cTAKES and MetaMap, while cTAKES and MetaMap have higher recall than CLAMP.</p>

<p><strong>CONCLUSION: </strong>The analysis protocols used in this study can be applied to other neuropsychiatric or neurodevelopmental disorders that lack well-defined terminology sets to describe their phenotypic presentations.</p>

<p>Although norm-referenced scores are essential to the identification of disability, they possess several features which affect their sensitivity to change. Norm-referenced scores often decrease over time among people with neurodevelopmental disorders who exhibit slower-than-average increases in ability. Further, the reliability of norm-referenced scores is lower at the tails of the distribution, resulting in floor effects and increased measurement error for people with neurodevelopmental disorders. In contrast, the person ability scores generated during the process of constructing a standardized test with item response theory are designed to assess change. We illustrate these limitations of norm-referenced scores, and relative advantages of ability scores, using data from studies of autism spectrum disorder and creatine transporter deficiency.</p>