Concordance between a U.S. Educational Autism Classification and the Autism Diagnostic Observation Schedule

An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG

Autism is a neurodevelopmental disorder that cannot be completely cured, but early intervention during childhood can improve outcomes. Identifying autism spectrum disorder (ASD) has relied on subjective detection methods that involve questionnaires, medical professionals, and therapists and are subject to observer variability. The need for early diagnosis and the limitations of subjective detection methods has led researchers to explore machine learning-based approaches, such as Random Forests, K-Nearest Neighbors, Naive Bayes, and Support Vector Machines, to predict ASD meltdowns. In recent years, deep learning techniques have gained traction for early ASD detection. This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.

State Variation Regarding Other Health Impairment Eligibility Criteria for Attention Deficit Hyperactivity Disorder

The special education eligibility category that has come to be most commonly associated with Attention-Deficit Hyperactivity Disorder (ADHD) in recent years is Other Health Impairment (OHI). However, the eligibility criteria for the OHI disability category have been criticized for being especially vague, given that the disability category incorporates a wide range of health impairments without providing any additional specificity. Because states have the latitude to utilize more specific eligibility criteria than what is provided at the federal level, the purpose of the current study was to review state-level special education eligibility criteria for OHI, with particular interest in identifying the degree to which eligibility guidance exists specific to students with ADHD and the extent to which this guidance varies across states. Results suggested that wide state variation exists regarding eligibility guidance, with 22% of states utilizing the federal definition and only 14% of states providing elaboration regarding all three components of the federal definition. Whereas it was most common for states to provide additional guidance surrounding what is needed to establish that a student has a health impairment, less than half of states provided specific guidance surrounding the other two components of the federal definition. Implications for policy and practice are discussed.

Emotion Regulation and Executive Function: Associations with Depression and Anxiety in Autism

Background

Adolescents and young adults with autism spectrum disorder (ASD) are prone to experience co-occurring mental health conditions such as mood or anxiety disorders, as well as impairments in emotion regulation and executive functioning. However, little research has examined inter-relationships among these constructs, despite evidence of additional stressors and increased risk of internalizing disorders at this age, relative to non-autistic individuals. If either emotion regulation or executive functioning are shown to have patterns of association with mental health, this can inform mechanism-based intervention.

Method

Fifty-seven autistic adolescents and adults (16-25 years) with ASD in a transition intervention completed questionnaires and clinician-administered measures at baseline. Analyses assessed whether executive functioning impairment, above and beyond emotion regulation impairment, were associated with depression and anxiety symptoms.

Results

ASD characteristics, emotion regulation, anxiety, and depression were significantly correlated. ASD characteristics was a significant contributor to depression and emotion regulation impairments were significant contributors to anxiety and depression. Findings indicated that inhibition difficulties did not uniquely contribute to depression or anxiety above emotion regulation impairment. Difficulties in cognitive flexibility were associated with depression above and beyond ASD characteristics, IQ, and emotion regulation, but not associated with anxiety.

Conclusions

Although preliminary, findings suggest that inflexibility and regulatory impairment should be considered in depression remediation approaches. Improving ER, on the other hand, may have broader transdiagnostic impact across both mood and anxiety symptoms in ASD.

Racial and Ethnic Disparities in Geographic Access to Autism Resources Across the US

Importance

While research has identified racial and ethnic disparities in access to autism services, the size, extent, and specific locations of these access gaps have not yet been characterized on a national scale. Mapping comprehensive national listings of autism health care services together with the prevalence of autistic children of various races and ethnicities and evaluating geographic regions defined by localized commuting patterns may help to identify areas within the US where families who belong to minoritized racial and ethnic groups have disproportionally lower access to services.

Objective

To evaluate differences in access to autism health care services among autistic children of various races and ethnicities within precisely defined geographic regions encompassing all serviceable areas within the US.

Design, Setting, and Participants

This population-based cross-sectional study was conducted from October 5, 2021, to June 3, 2022, and involved 530 965 autistic children in kindergarten through grade 12. Core-based statistical areas (CBSAs; defined as areas containing a city and its surrounding commuter region), the Civil Rights Data Collection (CRDC) data set, and 51 071 autism resources (collected from October 1, 2015, to December 18, 2022) geographically distributed into 912 CBSAs were combined and analyzed to understand variation in access to autism health care services among autistic children of different races and ethnicities. Six racial and ethnic categories (American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or other Pacific Islander, and White) assigned by the US Department of Education were included in the analysis.

Main Outcomes and Measures

A regularized least-squares regression analysis was used to measure differences in nationwide resource allocation between racial and ethnic groups. The number of autism resources allocated per autistic child was estimated based on the child's racial and ethnic group. To evaluate how the CBSA population size may have altered the results, the least-squares regression analysis was run on CBSAs divided into metropolitan (>50 000 inhabitants) and micropolitan (10 000-50 000 inhabitants) groups. A Mann-Whitney U test was used to compare the model estimated ratio of autism resources to autistic children among specific racial and ethnic groups comprising the proportions of autistic children in each CBSA.

Results

Among 530 965 autistic children aged 5 to 18 years, 83.9% were male and 16.1% were female; 0.7% of children were American Indian or Alaska Native, 5.9% were Asian, 14.3% were Black or African American, 22.9% were Hispanic or Latino, 0.2% were Native Hawaiian or other Pacific Islander, 51.7% were White, and 4.2% were of 2 or more races and/or ethnicities. At a national scale, American Indian or Alaska Native autistic children (β = 0; 95% CI, 0-0; P = .01) and Hispanic autistic children (β = 0.02; 95% CI, 0-0.06; P = .02) had significant disparities in access to autism resources in comparison with White autistic children. When evaluating the proportion of autistic children in each racial and ethnic group, areas in which Black autistic children (>50% of the population: β = 0.05; <50% of the population: β = 0.07; P = .002) or Hispanic autistic children (>50% of the population: β = 0.04; <50% of the population: β = 0.07; P < .001) comprised greater than 50% of the total population of autistic children had significantly fewer resources than areas in which Black or Hispanic autistic children comprised less than 50% of the total population. Comparing metropolitan vs micropolitan CBSAs revealed that in micropolitan CBSAs, Black autistic children (β = 0; 95% CI, 0-0; P < .001) and Hispanic autistic children (β = 0; 95% CI, 0-0.02; P < .001) had the greatest disparities in access to autism resources compared with White autistic children. In metropolitan CBSAs, American Indian or Alaska Native autistic children (β = 0; 95% CI, 0-0; P = .005) and Hispanic autistic children (β = 0.01; 95% CI, 0-0.06; P = .02) had the greatest disparities compared with White autistic children.

Conclusions and Relevance

In this study, autistic children from several minoritized racial and ethnic groups, including Black and Hispanic autistic children, had access to significantly fewer autism resources than White autistic children in the US. This study pinpointed the specific geographic regions with the greatest disparities, where increases in the number and types of treatment options are warranted. These findings suggest that a prioritized response strategy to address these racial and ethnic disparities is needed.

Efficacy and moderating factors of the Early Start Denver Model in Chinese toddlers with autism spectrum disorder: a longitudinal study

BACKGROUND

Several studies have shown the effectiveness of the Early Start Denver Model (ESDM), but few studies have explored the long-term efficacy of ESDM. This study aimed to explore the efficacy and moderating factors of ESDM in Chinese toddlers with autism spectrum disorder (ASD) in a longitudinal way.

METHODS

A total of 60 toddlers with ASD were recruited and randomly divided into two groups: ESDM group all received 24 weeks intervention; Control group were waiting for intervention. Baseline assessment (T0) was conducted before intervention, including Gesell Developmental Scale (GDS) and Psycho-educational Profile-3rd Edition (PEP-3). All toddlers with ASD were examined in the first assessment (T1) at 6 months and in the second assessment (T2) at 12 months.

RESULTS

In T1 assessment, the increments in speech and personal communication development quotient in GDS were significantly larger in the ESDM group than in the control group (P = 0.010, 0.047). In T2 assessment, the ESDM group had higher elevation in cognitive verbal/preverbal (CVP), social reciprocity and characteristic verbal behaviors assessed by PEP-3 (P = 0.021, 0.046, 0.014). In addition, the severity of stereotyped behavior was negatively associated with improvement in CVP. Family income was positively associated with improvement in speech and CVP (all P < 0.05).

CONCLUSIONS

ESDM can effectively improve speech and communication in toddlers with ASD after 24-week intervention. More importantly, ESDM can promote cognition and social interaction and can reduce stereotyped verbal behavior in toddlers with ASD in longitudinal observation. The severity of stereotyped behavior and family ecological factors may be considered as affecting the efficacy of ESDM.

Patterns of Special Education Eligibility and Age of First Autism Spectrum Disorder (ASD) Identification Among US Children with ASD

The study examined timing of autism spectrum disorder (ASD) identification in education versus health settings for 8-year-old children with ASD identified through records-based surveillance. The study also examined type of ASD symptoms noted within special education evaluations. Results indicated that children with records from only education sources had a median time to identification of ASD over a year later than children with records from health sources. Black children were more likely than White children to have records from only education sources. Restricted and repetitive behaviors were less frequently documented in educational evaluations resulting in developmental delay eligibility compared to specific ASD eligibility among children with ASD. Future research could explore strategies reduce age of identification in educational settings and increase equitable access to health evaluations.

An Informatics Analysis to Identify Sex Disparities and Healthcare Needs for Autism across the United States

Autism is among the most common neurodevelopmental conditions. Timely diagnosis and access to therapeutic resources are essential for positive prognoses, yet long queues and unevenly dispersed resources leave many untreated. Without granular estimates of autism prevalence by geographic area, it is difficult to identify unmet needs and mechanisms to address them. Mining a dataset of 53M children using meaningful geographic regions, we computed autism prevalence across the country. We then performed comparative analysis against 50,000 resources to identify the type and extent of gaps in access to autism services. We find a steady increase in autism diagnoses from K-5, supporting delayed diagnosis of autism, and consistent under-diagnosis of females. We find a significant inverse relationship between prevalence and availability of resources (p < 0.001). While more work is needed to characterize additional trends including racial and ethnicity-based disparities, the identification of resource gaps can direct and prioritize new innovations.

Code-related literacy profiles of kindergarten students with autism

LAY ABSTRACT

Many children and young students with autism have difficulties learning how to read. This study investigated early literacy development in children with autism spectrum disorder during their first year of formal schooling. The study found that children with autism spectrum disorder differ greatly on their early literacy skills, with some showing strengths in their understanding of the alphabet, spelling, and reading words. Other students in the sample had difficulties with these early reading skills. The findings of this study are important to better understand the most effective way to teach early literacy skills to children with autism spectrum disorder.

School-Based Autism Rates by State: An Analysis of Demographics, Political Leanings, and Differential Identification

We reviewed federal special education data to determine school-identified prevalence of Autism Spectrum Disorder (ASD) and other disability categories by U.S. state. We also examined whether state-level policies, demographic factors, and rates of other eligibility categories are predictive of these state ASD rates. Results indicate that overall, 1 of 81 school-aged children are served under an ASD special education eligibility. State-level demographic factors, such as socioeconomic status and political leanings were highly predictive of rates of ASD. States with higher rates of ASD had lower rates of intellectual and learning disabilities, but higher rates of Other Health Impairment (OHI).