Developmental Disabilities Monitoring Research Articles

Unpacking the prevalence: A warning against overstating the recently narrowed gap for Black autistic youth.

Recent findings from the Centers for Disease Control and Prevention's (CDC) Autism and Developmental Disabilities Monitoring (ADDM) Network's 2020 prevalence report indicate that disparities in autism diagnoses between Black and White youth have narrowed, reflecting improved screening, awareness, and access to services (Maenner et al., 2023. Morbidity and Mortality Weekly Report. Surveillance Summaries (Washington, D.C.: 2002), 72, 1-14.). Claims of reducing disparities beyond prevalence rates, however, are not fully supported, as indicated by the reality that Black youth whose screenings indicate autistic traits are still not being referred for full evaluation or early intervention services at the same rate as their White peers (Major et al., 2020. Autism, 24, 1629-1638; Smith et al., 2020. Pediatrics, 145, S35-S46.). Black 8-year-olds identified as autistic still experience disparate educational placements (Waitoller et al., 2010. The Journal of Special Education. 44, 29-49.) where services may not be autism-specific or have Individual Education Plan goals only focused on "behavior problems" (Severini et al., 2018. Journal of Autism and Developmental Disorders, 48, 3261-3272.), are served in the most restrictive environments (Skiba et al., 2006. Exceptional Children, 72, 411-424.) and lack consistent augmentative and alternative communication support (Pope et al., 2022. American Journal of Speech-Language Pathology, 31, 2159-2174.). Additionally, ADMM researchers report consistent disparities in the identification of co-occurring intellectual disability where Black autistic children have significantly more co-occurrences than White autistic children. The purpose of this commentary is to first examine the assertion that the narrowed gap indicates, "…improved…access to services among historically underserved groups," (p. 9) (Maenner et al., 2023. Morbidity and Mortality Weekly Report. Surveillance Summaries (Washington, D.C.: 2002), 72, 1-14.). We will then recommend strategies to address the ongoing disparities.

Community testing practices for autism within the autism and developmental disabilities monitoring network.

No data exist at the population level on what tests are used to aid in the diagnosis of autism spectrum disorder in community practice. To describe autism spectrum disorder testing practices to inform autism spectrum disorder identification efforts. Data are from the Autism and Developmental Disabilities Monitoring Network, a multi-site surveillance system reporting prevalence estimates and characteristics of 8-year-old children with autism spectrum disorder. Percentages of children with autism spectrum disorder who received any autism spectrum disorder test or a 'gold standard' test were calculated by site, sex, race, median household income, and intellectual ability status. Risk ratios were calculated to compare group differences. Of 5058 8-year-old children with autism spectrum disorder across 11 sites, 3236 (64.0%) had a record of any autism spectrum disorder test and 2136 (42.2%) had a 'gold standard' ADOS or ADI-R test. Overall, 115 children (2.3%) had both the ADOS and ADI-R in their records. Differences persisted across race, median household income, and intellectual ability status. Asian/Pacific Islander children had the highest percent receiving any ASD test (71.8%; other groups range: 57.4-66.0%) and White children had the highest percent receiving 'gold standard' tests (46.4%; other groups range: 35.6-43.2%). Children in low-income neighbourhoods had a lower percent of any test (62.5%) and 'gold standard' tests (39.4%) compared to medium (70.2% and 47.5%, respectively) and high (69.6% and 46.8%, respectively) income neighbourhoods. Children with intellectual disability had a lower percent of any ASD test (81.7%) and 'gold standard' tests (52.6%) compared to children without intellectual disability (84.0% and 57.6%, respectively). Autism spectrum disorder testing practices vary widely by site and differ by race and presence of co-occurring intellectual disability, suggesting opportunities to standardise and/or improve autism spectrum disorder identification practices.

Health Conditions, Education Services, and Transition Planning for Adolescents With Autism.

Our objectives with this study were to describe the frequency of selected cooccurring health conditions and individualized education program (IEP) services and post-high school transition planning for adolescents with autism spectrum disorder and identify disparities by sex, intellectual ability, race or ethnicity, and geographic area. The study sample included 1787 adolescents born in 2004 who were identified as having autism through a health and education record review through age 16 years in 2020. These adolescents were part of a longitudinal population-based surveillance birth cohort from the Autism and Developmental Disabilities Monitoring Network from 2004 to 2020 in 5 US catchment areas. Attention deficit hyperactivity disorder (47%) and anxiety (39%) were the most common cooccurring health conditions. Anxiety was less commonly identified for those with intellectual disability than those without. It was also less commonly identified among Black adolescents compared with White or Hispanic adolescents. There was wide variation across Autism and Developmental Disabilities Monitoring Network sites in the provision of school-based IEP services. Students with intellectual disability were less likely to receive school-based mental health services and more likely to have a goal for postsecondary independent living skills compared with those without intellectual disability. A total of 37% of students did not participate in standardized testing. We identified disparities in the identification of cooccurring conditions and school-based IEP services, practices, and transition planning. Working with pediatric health and education providers, families, and adolescents with autism will be important to identify contributing factors and to focus efforts to reduce disparities in the supports and services adolescents with autism have access to and receive.

A Systematic Literature Review of Racial Disproportionality in Autism in the U.S.

In recent years, the Autism and Developmental Disabilities Monitoring Network has observed a shift in racial disparities in autism. To delineate the historical shift of racial disproportionality in US autism prevalence, our literature review examines three key topics: publication trends concerning racial disproportionality in autism, discernible national and state-level patterns, and underlying factors contributing to the disproportionality. Using the PRISMA framework, we synthesized 24 empirical studies on racial disproportionality in autism and its change over time. These studies explored national patterns and spatiotemporal variations to provide a comprehensive understanding of racial disparities in autism. Studies indicated similar national patterns for Black and Asian racial groups; both groups had had mixed results around the turn of the millennium. By 2007, the Asian group was overrepresented again. Hispanic and Native American groups have consistently been underrepresented. However, significant spatiotemporal variations were found, suggesting that these disparities might reflect inherent inequalities within the current identification and classification system. The patterns of racial disproportionality in autism seem to be influenced by numerous factors. These include varying state definitions of autism, disparities in resource distribution, differences in symptom recognition across cultures, service preferences, cultural mismatches between professionals and families, and prevailing biases and stigmas, as revealed by the reviewed studies. These findings prompt a closer look into the causes and implications of these disparities, offering the underlying issues within the current diagnostic system and highlighting the need for further research to ensure equal educational opportunities regardless of disabilities and race/ethnicity.

From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism.

Autism Spectrum Disorder (ASD) poses significant challenges to society and science due to its impact on communication, social interaction, and repetitive behavior patterns in affected children. The Autism and Developmental Disabilities Monitoring (ADDM) Network continuously monitors ASD prevalence and characteristics. In 2020, ASD prevalence was estimated at 1 in 36 children, with higher rates than previous estimates. This study focuses on ongoing ASD research conducted by Erciyes University. Serum samples from 45 ASD patients and 21 unrelated control participants were analyzed to assess the expression of 372 microRNAs (miRNAs). Six miRNAs (miR-19a-3p, miR-361-5p, miR-3613-3p, miR-150-5p, miR-126-3p, and miR-499a-5p) exhibited significant downregulation in all ASD patients compared to healthy controls. The current study endeavors to identify dependable diagnostic biomarkers for ASD, addressing the pressing need for non-invasive, accurate, and cost-effective diagnostic tools, as current methods are subjective and time-intensive. A pivotal discovery in this study is the potential diagnostic value of miR-126-3p, offering the promise of earlier and more accurate ASD diagnoses, potentially leading to improved intervention outcomes. Leveraging machine learning, such as the K-nearest neighbors (KNN) model, presents a promising avenue for precise ASD diagnosis using miRNA biomarkers.

Cultivating Insight: Detecting Autism Spectrum Disorder through Residual Attention Network in Facial Image Analysis

Revolutionizing Autism Spectrum Disorder Identification through Deep Learning: Unveiling Facial Activation Patterns. In this study, our primary objective is to harness the power of deep learning algorithms for the precise identification of individuals with autism spectrum disorder (ASD) solely from facial image datasets. Our investigation centers around the utilization of face activation patterns, aiming to uncover novel insights into the distinctive facial features of ASD patients. To accomplish this, we meticulously examined facial imaging data from a global and multidisciplinary repository known as the Autism Face Imaging Data Exchange. Autism spectrum disorder is characterized by inherent social deficits and manifests in a spectrum of diverse symptomatic scenarios. Recent data from the Centers for Disease Control (CDC) underscores the significance of this disorder, indicating that approximately 1 in 54 children are impacted by ASD, according to estimations from the CDC's Autism and Developmental Disabilities Monitoring Network (ADDM). Our research delved into the intricate functional connectivity patterns that objectively distinguish ASD participants, focusing on their facial imaging data. Through this investigation, we aimed to uncover the latent facial patterns that play a pivotal role in the classification of ASD cases. Our approach introduces a novel module that enhances the discriminative potential of standard convolutional neural networks (CNNs), such as ResNet-50, thus significantly advancing the state-of-the-art. Our model achieved an impressive accuracy rate of 99% in distinguishing between ASD patients and control subjects within the dataset. Our findings illuminate the specific facial expression domains that contribute most significantly to the differentiation of ASD cases from typically developing individuals, as inferred from our deep learning methodology. To validate our approach, we conducted real-time video testing on diverse children, achieving an outstanding accuracy score of 99.90% and an F1 score of 99.67%. Through this pioneering work, we not only offer a cutting-edge approach to ASD identification but also contribute to the understanding of the underlying facial activation patterns that hold potential for transforming the diagnostic landscape of autism spectrum disorder.

Environmental Factors Related to Quality of Life of Parents of Children with Autism Spectrum Disorder : A Scoping Review

Introduction: Concerns about the quality of life (QoL) of parents of children with autism spectrum disorder have been raised by previous study. Policy decision-makers and healthcare professionals can be better informed about the environmental factors associated with parental QoL, which will enhance outcomes for both parents and children. The objective of this review is to perform a comprehensive review that assessed the environmental relevant factors associated with quality of life (QoL) of parents of ASD children (under 18 years old). Methodology: To identify the environmental factors associated with parental QoL, a scoping review was conducted using the Arksey and O'Malley original and enhanced framework. Three electronic data bases (PubMed, Scopus & WOS) and reference list checks were used in the search. Results: Social support, family support, residential condition and access to services were all factors linked to parental quality of life in this group. Conclusion: The review clarified environmental factors as one of variables that have association with parental QoL. The results of this analysis also highlighted potential areas of support that have implications on future policy and practices. Keywords : environmental factors, autism spectrum disorder, quality of life, parents, children References CDC. Prevalence of autism spectrum disorders--Autism and Developmental Disabilities Monitoring Network, 14 sites, United States, 2008. MMWR Surveill Summ. 2012;61(3):1-19. CDC. Prevalence of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2010. MMWR Surveill Summ. 2014;63(2):1-21. Elsabbagh M, Divan G, Koh Y-J, Kim YS, Kauchali S, Marcín C, et al. Global Prevalence of Autism and Other Pervasive Developmental Disorders. Autism Research. 2012;5(3):160-79. McTiernan A, Leader G, Healy O, Mannion A. Analysis of risk factors and early predictors of challenging behavior for children with autism spectrum disorder. Research in Autism Spectrum Disorders. 2011;5(3):1215-22. Simonoff E, Pickles A, Charman T, Chandler S, Loucas T, Baird G. Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample. J Am Acad Child Adolesc Psychiatry. 2008;47(8):921-9. Bolton PF. Medical conditions in autism spectrum disorders. Journal of Neurodevelopmental Disorders. 2009;1(2):102-13. Khanna R, Madhavan SS, Smith MJ, Patrick JH, Tworek C, Becker-Cottrill B. Assessment of Health-Related Quality of Life Among Primary Caregivers of Children with Autism Spectrum Disorders. Journal of Autism and Developmental Disorders. 2011;41(9):1214-27.

Prevalence and Characteristics of Adolescents with Autism Spectrum Disorder in the New York-New Jersey Metropolitan Area.

Almost all epidemiologic studies estimating autism spectrum disorder (ASD) prevalence have focused on school-age children. This study provides the first population-based data on the prevalence and expression of ASD among adolescents in a large US metropolitan region. Active multiple source ASD surveillance of adolescents aged 16-years was conducted according to the Autism and Developmental Disabilities Monitoring (ADDM) Network method in a four-county New Jersey metropolitan region. Prevalence estimates are provided, characteristics are described and comparison of the distribution and characteristics of ASD is offered for this cohort, at 8 and 16-years. ASD prevalence was 17.7 per 1000 (95% CI: 16.3-19.2)]. One-in-55 males and one in 172 females were identified with ASD. High-SES was positively associated with ASD and White adolescents had higher ASD prevalence (22.2 per 1000) than Hispanic adolescents (13.1 per 1000). One in four study-confirmed individuals with ASD did not have an ASD diagnosis. A majority of ASD adolescents (58.8%) had a co-occurring neuropsychiatric disorder. White and High-SES individuals had greater likelihood of co-occurring disorder. The demographic distribution and functional profile of ASD was similar in this cohort at 8 and 16-years. Approximately one-in-55 adolescents in our area had ASD, in 2014, and one-in-4 16-year-olds with ASD was not diagnosed. A majority (3-in-5) of the adolescents with ASD had a co-occurring neuropsychiatric disorder. ASD under-identification and the high frequency of co-disorders in adolescents with ASD pose significant challenges to care and support.

The Intersection of Systemic, Child, and Evaluation Factors in the Prediction of Autism Special Education Eligibility; Examining the Role of Race and Ethnicity.

Though there is evidence autism identification has been inequitable for populations who are culturally and linguistically minoritized, there is limited research that explains the issue of disproportionality and factors contributing to its occurrence, especially within an educational setting. To explore contributors to racial/ethnic disparities in autism special education eligibility, the current investigation evaluated child and evaluation characteristics as they relate to the absence of autism eligibility. Data were obtained from the Autism and Developmental Disabilities Monitoring (ADDM) Network Study and included children with behavioral characteristics consistent with autism and educational evaluation records. Despite documented characteristics consistent with autism, only 72% of the sample received educational services under autism eligibility. To characterize children without autism eligibility, hierarchical logistic regression was used to evaluate factors documented in evaluation records predicting the absence of autism eligibility. Factors influencing autism eligibility included behavioral characteristics documented, evaluation components completed, intellectual ability, and clinical diagnoses present. There was no unique contribution of race/ethnicity in predicting the absence of autism eligibility when accounting for these previous predictors, but many of these predictors differed by racial/ethnic group. Disproportionality in autism may be the manifestation of inequitable evaluation experiences, including experiencing less comprehensive evaluations, and not receiving an autism specific assessment. Though race/ethnicity did not uniquely contribute to the absence of autism eligibility above and beyond those combined factors, it is important to evaluate and reduce inequities experienced within the autism identification process for populations who are culturally and linguistically minoritized.

Individualized Education Programs and Transition Planning for Adolescents With Autism.

The study objectives were to examine the contents of individualized education programs (IEPs) of adolescents with autism spectrum disorder (ASD), including postsecondary transition goals, services, and changes in special education classification over time. This study involved a longitudinal population-based surveillance cohort from the Autism Developmental Disabilities Monitoring Network from 2002 to 2018 in 3 catchment areas in the United States. The sample included 322 adolescents who were born in 2002, identified with ASD, and had an IEP available for review at ages 15-16 years. We found that 297 (92%) adolescents with ASD had an IEP including a transition plan. Those without intellectual disability (ID) were more likely to have postsecondary education and employment goals and have those goals be to pursue higher education or competitive employment compared with those with ID. Forty-one percent of adolescents with ASD had a postsecondary living arrangement goal. Although 28% of adolescents with ASD received school-based mental health services, none of these adolescents were Black; additionally, 15% of those with ID received mental health services compared with 34% without ID. The percentage of adolescents with ASD served under an autism classification increased from 44% at age 8 years to 62% by age 16. We identified gaps and disparities in school-based postsecondary transition planning. Working with education partners, families, and adolescents will be important to identify what challenges contribute to these findings and what supports are needed to improve the equity and quality of the transition planning process for adolescents with ASD so they are prepared for adulthood.

Potential Application of Virtual Reality in ASD Intervention

Autism spectrum disorder (ASD) is one of the most widespread developmental mental illnesses. According to the CDC’s Autism and Developmental Disabilities Monitoring (ADDM) Network, about 1 in 44 children in United States has been diagnosed with ASD, and the number is still increasing. The current treatment options show many problems and limitations, and Virtual reality (VR) appears to be an innovative approach that may overcome these limitations. The survey in this study asked 57 parents with autistic children to rate their children’s ability, behavioral observations, and level of concern. The level of optimism regarding the potential of Virtual Reality was also compared between the ASD group and the control group, indicating that parents with ASD children are more pessimistic towards the VR treatment. There are several factors might contribute to their pessimism, such as more pressure and worse mental conditions in real life, the ineffectiveness of current treatment, and financial burdens. The result is consistent with previous research that parents with ASD children face a lot of challenges and the current autism interventions appear to be not very effective in dealing with core symptoms and co-occurring problems. Therefore, virtual Reality intervention has great potential for improving social and interaction skills, saving labor and time, and generalizing the improvement in VR programs to the real world.

Social vulnerability and prevalence of Autism Spectrum Disorder, Metropolitan Atlanta Developmental Disabilities Surveillance Program (MADDSP)

The Metropolitan Atlanta Developmental Disabilities Surveillance Program tracks autism spectrum disorder (ASD) in selected counties within Georgia as part of the Autism and Developmental Disabilities Monitoring (ADDM)Network. ADDM Network analyses have historically found a higher prevalence of ASD in areas of higher socioeconomic status. We linked 2018 data from Centers for Disease Control and Prevention's Social Vulnerability Index (SVI) to two Metropolitan Atlanta Developmental Disabilities Surveillance Program counties by census tract, grouped census tracts into tertiles representing low, medium, and high social vulnerability, and calculated ASD prevalence for each tertile, overall and by each of four SVI themes. We found that overall prevalence was higher in areas of low compared to high vulnerability for the socioeconomic status and transportation themes, and in areas of medium compared to high vulnerability for all themes. This pattern was consistent among males but varied for females and by race or ethnicity. Linking ASD prevalence to SVI metrics can improve the understanding of inequities among children with ASD in racial and ethnic minority groups or those living in low-resource settings. These methods can be applied to other ADDM Network surveillance sites and public health surveillance programs.

The Prevalence and Characteristics of Children With Profound Autism, 15 Sites, United States, 2000-2016.

Autism spectrum disorder (autism) is a heterogeneous condition that poses challenges in describing the needs of individuals with autism and making prognoses about future outcomes. We applied a newly proposed definition of profound autism to surveillance data to estimate the percentage of children with autism who have profound autism and describe their sociodemographic and clinical characteristics. We analyzed population-based surveillance data from the Autism and Developmental Disabilities Monitoring Network for 20 135 children aged 8 years with autism during 2000-2016. Children were classified as having profound autism if they were nonverbal, were minimally verbal, or had an intelligence quotient <50. The percentage of 8-year-old children with profound autism among those with autism was 26.7%. Compared with children with non-profound autism, children with profound autism were more likely to be female, from racial and ethnic minority groups, of low socioeconomic status, born preterm or with low birth weight; have self-injurious behaviors; have seizure disorders; and have lower adaptive scores. In 2016, the prevalence of profound autism was 4.6 per 1000 8-year-olds. The prevalence ratio (PR) of profound autism was higher among non-Hispanic Asian/Native Hawaiian/Other Pacific Islander (PR = 1.55; 95 CI, 1.38-1.73), non-Hispanic Black (PR = 1.76; 95% CI, 1.67-1.86), and Hispanic (PR = 1.50; 95% CI, 0.88-1.26) children than among non-Hispanic White children. As the population of children with autism continues to change, describing and quantifying the population with profound autism is important for planning. Policies and programs could consider the needs of people with profound autism across the life span to ensure their needs are met.

Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020.

Autism spectrum disorder (ASD). 2020. The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance program that provides estimates of the prevalence of ASD among children aged 8 years. In 2020, there were 11 ADDM Network sites across the United States (Arizona, Arkansas, California, Georgia, Maryland, Minnesota, Missouri, New Jersey, Tennessee, Utah, and Wisconsin). To ascertain ASD among children aged 8 years, ADDM Network staff review and abstract developmental evaluations and records from community medical and educational service providers. A child met the case definition if their record documented 1) an ASD diagnostic statement in an evaluation, 2) a classification of ASD in special education, or 3) an ASD International Classification of Diseases (ICD) code. For 2020, across all 11 ADDM sites, ASD prevalence per 1,000 children aged 8 years ranged from 23.1 in Maryland to 44.9 in California. The overall ASD prevalence was 27.6 per 1,000 (one in 36) children aged 8 years and was 3.8 times as prevalent among boys as among girls (43.0 versus 11.4). Overall, ASD prevalence was lower among non-Hispanic White children (24.3) and children of two or more races (22.9) than among non-Hispanic Black or African American (Black), Hispanic, and non-Hispanic Asian or Pacific Islander (A/PI) children (29.3, 31.6, and 33.4 respectively). ASD prevalence among non-Hispanic American Indian or Alaska Native (AI/AN) children (26.5) was similar to that of other racial and ethnic groups. ASD prevalence was associated with lower household income at three sites, with no association at the other sites.Across sites, the ASD prevalence per 1,000 children aged 8 years based exclusively on documented ASD diagnostic statements was 20.6 (range=17.1 in Wisconsin to 35.4 in California). Of the 6,245 children who met the ASD case definition, 74.7% had a documented diagnostic statement of ASD, 65.2% had a documented ASD special education classification, 71.6% had a documented ASD ICD code, and 37.4% had all three types of ASD indicators. The median age of earliest known ASD diagnosis was 49 months and ranged from 36 months in California to 59 months in Minnesota.Among the 4,165 (66.7%) children with ASD with information on cognitive ability, 37.9% were classified as having an intellectual disability. Intellectual disability was present among 50.8% of Black, 41.5% of A/PI, 37.8% of two or more races, 34.9% of Hispanic, 34.8% of AI/AN, and 31.8% of White children with ASD. Overall, children with intellectual disability had earlier median ages of ASD diagnosis (43 months) than those without intellectual disability (53 months). For 2020, one in 36 children aged 8 years (approximately 4% of boys and 1% of girls) was estimated to have ASD. These estimates are higher than previous ADDM Network estimates during 2000-2018. For the first time among children aged 8 years, the prevalence of ASD was lower among White children than among other racial and ethnic groups, reversing the direction of racial and ethnic differences in ASD prevalence observed in the past. Black children with ASD were still more likely than White children with ASD to have a co-occurring intellectual disability. The continued increase among children identified with ASD, particularly among non-White children and girls, highlights the need for enhanced infrastructure to provide equitable diagnostic, treatment, and support services for all children with ASD. Similar to previous reporting periods, findings varied considerably across network sites, indicating the need for additional research to understand the nature of such differences and potentially apply successful identification strategies across states.

Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020

Autism spectrum disorder (ASD). 2020. The Autism and Developmental Disabilities Monitoring Network is an active surveillance program that estimates prevalence and characteristics of ASD and monitors timing of ASD identification among children aged 4 and 8 years. In 2020, a total of 11 sites (located in Arizona, Arkansas, California, Georgia, Maryland, Minnesota, Missouri, New Jersey, Tennessee, Utah, and Wisconsin) conducted surveillance of ASD among children aged 4 and 8 years and suspected ASD among children aged 4 years. Surveillance included children who lived in the surveillance area at any time during 2020. Children were classified as having ASD if they ever received 1) an ASD diagnostic statement in an evaluation, 2) a special education classification of autism (eligibility), or 3) an ASD International Classification of Diseases (ICD) code (revisions 9 or 10). Children aged 4 years were classified as having suspected ASD if they did not meet the case definition for ASD but had a documented qualified professional's statement indicating a suspicion of ASD. This report focuses on children aged 4 years in 2020 compared with children aged 8 years in 2020. For 2020, ASD prevalence among children aged 4 years varied across sites, from 12.7 per 1,000 children in Utah to 46.4 in California. The overall prevalence was 21.5 and was higher among boys than girls at every site. Compared with non-Hispanic White children, ASD prevalence was 1.8 times as high among Hispanic, 1.6 times as high among non-Hispanic Black, 1.4 times as high among Asian or Pacific Islander, and 1.2 times as high among multiracial children. Among the 58.3% of children aged 4 years with ASD and information on intellectual ability, 48.5% had an IQ score of ≤70 on their most recent IQ test or an examiner's statement of intellectual disability. Among children with a documented developmental evaluation, 78.0% were evaluated by age 36 months. Children aged 4 years had a higher cumulative incidence of ASD diagnosis or eligibility by age 48 months compared with children aged 8 years at all sites; risk ratios ranged from 1.3 in New Jersey and Utah to 2.0 in Tennessee. In the 6 months before the March 2020 COVID-19 pandemic declaration by the World Health Organization, there were 1,593 more evaluations and 1.89 more ASD identifications per 1,000 children aged 4 years than children aged 8 years received 4 years earlier. After the COVID-19 pandemic declaration, this pattern reversed: in the 6 months after pandemic onset, there were 217 fewer evaluations and 0.26 fewer identifications per 1,000 children aged 4 years than children aged 8 years received 4 years earlier. Patterns of evaluation and identification varied among sites, but there was not recovery to pre-COVID-19 pandemic levels by the end of 2020 at most sites or overall. For 2020, prevalence of suspected ASD ranged from 0.5 (California) to 10.4 (Arkansas) per 1,000 children aged 4 years, with an increase from 2018 at five sites (Arizona, Arkansas, Maryland, New Jersey, and Utah). Demographic and cognitive characteristics of children aged 4 years with suspected ASD were similar to children aged 4 years with ASD. A wide range of prevalence of ASD by age 4 years was observed, suggesting differences in early ASD identification practices among communities. At all sites, cumulative incidence of ASD by age 48 months among children aged 4 years was higher compared with children aged 8 years in 2020, indicating improvements in early identification of ASD. Higher numbers of evaluations and rates of identification were evident among children aged 4 years until the COVID-19 pandemic onset in 2020. Sustained lower levels of ASD evaluations and identification seen at a majority of sites after the pandemic onset could indicate disruptions in typical practices in evaluations and identification for health service providers and schools through the end of 2020. Sites with more recovery could indicate successful strategies to mitigate service interruption, such as pivoting to telehealth approaches for evaluation. From 2016 through February of 2020, ASD evaluation and identification among the cohort of children aged 4 years was outpacing ASD evaluation and identification 4 years earlier (from 2012 until March 2016) among the cohort of children aged 8 years in 2020 . From 2016 to March 2020, ASD evaluation and identification among the cohort of children aged 4 years was outpacing that among children aged 8 years in 2020 from 2012 until March 2016. The disruptions in evaluation that coincided with the start of the COVID-19 pandemic and the increase in prevalence of suspected ASD in 2020 could have led to delays in ASD identification and interventions. Communities could evaluate the impact of these disruptions as children in affected cohorts age and consider strategies to mitigate service disruptions caused by future public health emergencies.

Identification of Autism Spectrum Disorder using Residual Attention Network for Facial Image Analysis.

The goal of the present paper is to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from a face image dataset based solely on the patient's face activation patterns. We investigated ASD Patients' face imaging data from a worldwide multispecialty database known as Autism Face Imaging Data Exchange. ASD is a brain-based disorder characterized by social deficiency and the symptoms are different scenarios. According to recent Centers for Disease Control data, ASD affects About 1 in 54 children who have been identified with autism spectrum disorder (ASD) according to estimates from CDC’s Autism and Developmental Disabilities Monitoring (ADDM) Network. We investigated patterns of functional connectivity that objectively identify ASD participants from functional face imaging data and attempted to unveil the facial patterns that emerged from the classification. With the proposed module, standard CNNs are made, like ResNet-50 have more discriminative power for deep face recognition, and results improved the state-of-the-art by achieving 99% accuracy in the identification of ASD versus control patients in the dataset. We present the results and identify the areas of facial expressions that contributed most to differentiating ASD from typically developing controls as per our deep learning method. For verification purposes, the videos collected in real-time manually from different children we retested and an accuracy score of 99.90% and an F1 score of 99.67% were achieved.

Adolescents With Autism Spectrum Disorder: Diagnostic Patterns, Co-occurring Conditions, and Transition Planning

PurposeThe objectives of this study were to describe child characteristics associated with later autism spectrum disorder (ASD) identification and the health status and educational transition plans of adolescents with ASD. MethodsLongitudinal population-based surveillance cohort from the Autism Developmental Disabilities Monitoring Network during 2002–2018 in five catchment areas in the United States. Participants included 3,148 children born in 2002 whose records were first reviewed for ASD surveillance in 2010. ResultsOf the 1,846 children identified in the community as an ASD case, 11.6% were first identified after age 8 years. Children who were more likely to have ASD identified at older ages were Hispanic; were born with low birth weight; were verbal; had high intelligence quotient or adaptive scores; or had certain co-occurring neuropsychological conditions by age 8 years. By age 16 years, neuropsychological conditions were common with more than half of the adolescents with ASD having a diagnosis of attention-deficit/hyperactivity disorder or anxiety. Intellectual disability (ID) status was unchanged for the majority (>80%) of children from ages 8–16 years. A transition plan was completed for over 94% of adolescents, but disparities were observed in planning by ID status. DiscussionA high percentage of adolescents with ASD have co-occurring neuropsychological conditions, markedly higher than at age 8. While most adolescents had transition planning, this occurred less often for those with ID. Ensuring access to services for all people with ASD during adolescence and transition to adulthood may help to promote overall health and quality of life.

Statewide county-level autism spectrum disorder prevalence estimates—seven U.S. states, 2018

Autism spectrum disorder (ASD) prevalence information is necessary for identifying community needs such as addressing disparities in identification and services. Seven Autism and Developmental Disabilities Monitoring (ADDM) Network sites participated in a pilot project to link statewide health and education data to generate statewide and county-level prevalence estimates for a broader age range for their states for the first time. Statewide prevalence of ASD for ages 3-21 years in 2018 ranged from 1.5% in Tennessee and Wisconsin to 2.3% in Arizona. The median county-level prevalence of ASD was 1.4% of residents ages 3-21 years. More boys than girls had ASD at all sites, and prevalence was lower among non-Hispanic Black, Hispanic, Asian/Pacific Islander, and American Indian/Alaska Native residents compared to non-Hispanic White residents at most sites. ASD prevalence estimates for children aged 8 years were similar to 2018 ADDM Network estimates that used record review to provide more in-depth information, but showed greater variation for children aged 4 years. Linkage of statewide data sets provides less detailed but actionable local information when more resource-intensive methods are not possible.

An Evidence-based Practice Project to Improve Sleep for Individuals with Autism

According to the Centers for Disease Control and Prevention Autism and Developmental Disabilities Monitoring (2023), autism spectrum disorder (ASD) is diagnosed in approximately 1 in 36 children. Common signs of ASD are difficulties with social interaction and communication, repetitive behaviors, delayed cognitive skills, and unusual sleep habits (Centers for Disease Control and Prevention, 2023). Sleep disorders are present in 80% of autistic children and 50% of autistic adults (Lane et al., 2022). Common sleep issues reported are prolonged sleep latency, reduced duration of sleep, and waking during sleep (Lane et al., 2022). These common sleep issues have been associated with an increase in physical aggression, inattention, oppositional behavior, tantrums, self-injury, and mood variability in the ASD population (Seo, 2021). Weighted devices such as weighted blankets, compression t-shirts, and garments are becoming a popular discussion for improving sleep for individuals with ASD. In theory, weighted devices such as weighted blankets create a deep pressure sensation that decreases one’s physiologic level of stress and arousal, potentially improveing sleep quality (Bolic Baric et al., 2021). Despite the increasing popularity of weighted devices, it remains unclear how effective these weighted devices are in reducing sleep disturbances for individuals with ASD.

For ASD, Psychiatrists Must Continuously Monitor Patients, Assess Therapies

For ASD, Psychiatrists Must Continuously Monitor Patients, Assess Therapies