Early Identification Of Autism Spectrum Disorder Research Articles

ESTRATÉGIAS DA ANÁLISE DO COMPORTAMENTO APLICADA PARA DESENVOLVER A INTERAÇÃO SOCIAL EM CRIANÇAS AUTISTAS

Autism Spectrum Disorder (ASD) has evolved from a psychiatric classification to a neurodevelopmental disorder, reflecting advances in scientific understanding and diagnostic approaches. The DSM-5, with its more sensitive criteria, facilitates early identification of ASD, which is crucial for effective interventions. Applied Behavior Analysis (ABA) is a proven approach for enhancing social skills and reducing problematic behaviors in children with ASD. By employing techniques such as positive reinforcement and modeling, ABA fosters individualized communication and social interaction, providing significant benefits for both children and their families. Integrating playful resources and innovative strategies can complement interventions, promoting greater autonomy and self-awareness in autistic children. Ongoing advancements in ASD diagnosis and treatment, combined with scientific innovation, are essential for improving the quality of life for children and their families.

Performance of the Social Responsiveness Scale-2 for the Assessment of Autistic Behaviors in a Sample of Canadian Preschool-Aged Children.

Behavioral traits of autism spectrum disorder (ASD) typically present in early childhood, underscoring the importance of screening tools for the early identification of ASD. The current study compared scores on the Social Responsiveness Scale-Second Edition (SRS-2) Preschool Form between the US standardization sample (n = 247) and a Canadian cohort of preschool-aged children (n = 595) recruited from the Maternal-Infant Research on Environmental Chemicals (MIREC) study. In the MIREC sample, we examined whether ASD-like traits are correlated with sociodemographic characteristics and child intellectual abilities, and how maternal ratings of social skills assessed by the SRS-2 are associated with maternal ratings of general problem behaviors. Mean total SRS-2 raw score was significantly lower in the MIREC sample (mean = 29.7, SD = 15.8) compared to the US standardization sample (mean = 41.9, SD = 26.0). Total raw score in the US standardization sample did not significantly differ between males (mean = 40.6, SD = 23.1) and females (mean = 42.8, SD = 28.7), whereas in the MIREC sample the total raw score was significantly higher among males (mean = 33.0, SD = 17.1) than females (mean = 26.6, SD = 13.9). A significantly larger proportion of the MIREC sample was White, younger in age, and had more educated parents compared to the US standardization sample. ASD-like traits were correlated with lower intellectual abilities, a less enriched home environment, more behavioral problems, and poorer adaptive skills. SRS-2 Preschool Form scores were significantly lower in the Canadian sample compared to the US standardization sample, which may reflect demographic differences between the two groups. Girls may be under-identified when SRS-2 Preschool Form norms are used for screening ASD.

Early identification of autism spectrum disorder based on machine learning with eye-tracking data

BackgroundEarly identification of autism spectrum disorder (ASD) improves long-term outcomes, yet significant diagnostic delays persist. MethodsA retrospective cohort of 449 children (ASD: 246, typically developing [TD]: 203) was used for model development. Eye-movement data were collected from the participants watching videos that featured eye-tracking paradigms for assessing social and non-social cognition. Five machine learning algorithms, namely random forest, support vector machine, logistic regression, artificial neural network, and extreme gradient boosting, were trained to classify children with ASD and TD. The best-performing algorithm was selected to build the final model which was further evaluated in a prospective cohort of 80 children. The Shapley values interpreted important eye-tracking features. ResultsRandom forest outperformed other algorithms during model development and achieved an area under the curve of 0.849 (< 3 years: 0.832, ≥ 3 years: 0.868) on the external validation set. Of the ten most important eye-tracking features, three measured social cognition, and the rest were related to non-social cognition. A deterioration in model performance was observed using only the social or non-social cognition-related eye-tracking features. LimitationsThe sample size of this study, although larger than that of existing studies of ASD based on eye-tracking data, was still relatively small compared to the number of features. ConclusionsMachine learning models based on eye-tracking data have the potential to be cost- and time-efficient digital tools for the early identification of ASD. Eye-tracking phenotypes related to social and non-social cognition play an important role in distinguishing children with ASD from TD children.

Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing

PurposeThe main aim of our study was to explore the utility of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD). The study primarily focused on using machine learning (ML) and deep learning (DL) models to detect ASD potential cases by analyzing text inputs, especially from social media platforms like Twitter. This is to overcome the ongoing challenges in ASD diagnosis, such as the requirement for specialized professionals and extensive resources. Timely identification, particularly in children, is essential to provide immediate intervention and support, thereby improving the quality of life for affected individuals.MethodsWe employed natural language processing (NLP) techniques along with ML models like decision trees, extreme gradient boosting (XGB), k-nearest neighbors algorithm (KNN), and DL models such as recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), bidirectional encoder representations from transformers (BERT and BERTweet). We extracted a dataset of 404,627 tweets from Twitter users using the platform’s API and classified them based on whether they were written by individuals claiming to have ASD (ASD users) or by those without ASD (non-ASD users). From this dataset, we used a subset of 90,000 tweets (45,000 from each classification group) for the training and testing of these models.ResultsThe application of our AI models yielded promising results, with the predictive model reaching an accuracy of almost 88% when classifying texts that potentially originated from individuals with ASD.ConclusionOur research demonstrated the potential of using AI, particularly DL models, in enhancing the accuracy of ASD detection and diagnosis. This innovative approach signifies the critical role AI can play in advancing early diagnostic techniques, enabling better patient outcomes and underlining the importance of early identification of ASD, especially in children.

Altered Gut Microbiota as Potential Biomarkers for Autism Spectrum Disorder in Early Childhood

Gastrointestinal (GI) disorders are widely recorded in autism spectrum disorder (ASD), and ASD with GI symptoms is a vital subtype of this disease. Growing evidence suggests altered gut microbiota biomarkers in ASD, but little is known about the gut microbiota of individuals with ASD with GI Symptoms, particularly in early childhood. In our study, the gut microbiota of 36 individuals with ASD along with GI symptoms and 40 typically developing (TD) children were compared using 16S rRNA gene sequencing. The microbial diversity and composition were found to differ between the two groups. Compared to TD, the gut microbiota of ASD patients with GI symptoms exhibited decreased alpha diversity and depletion of butyrate-producing bacteria (e.g., Faecalibacterium and Coprococcus). In addition, microbial functional analysis showed abnormality in several gut metabolic models and gut brain models of ASD with GI symptoms, including short-chain fatty acid (SCFA) synthesis/degradation and neurotoxin-related p-cresol degradation, which are closely associated with ASD-related behaviors in animal models. Furthermore, we constructed a Support Vector Machine classification model, which robustly discriminated individuals with ASD and GI symptoms from TD individuals in a validation set (AUC = 0.88). Our findings provide a deep insight into the roles of the disturbed gut ecosystem in individuals with ASD and GI symptoms aged 3–6 years. Our classification model supports gut microbiota as a potential biomarker for the early identification of ASD and interventions targeting particular gut-beneficial microbiota.

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.

An Enhanced Prediction Model For Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a behavioural condition that affects the child’s social interaction, communication, and behaviour. The early identification of ASD is critical for the effective and timely therapies. This study presents an enhanced prediction model for Autism Spectrum Disorder (ASD). It is based on facial features extracted from the face image. Mallat’s multi-resolution algorithm is employed in this work for extracting facial features. Two distance based classifiers such as Euclidean Distance Classifier (EDC) and Absolute Distance Classifier (ADC) are employed for the ASD prediction. The proposed ASD prediction system is evaluated on face images of autistic and non-autistic children. The database is obtained from the Kaggle data repository. A total of 2940 facial images (1470 autistic and 1470 non-autistic) are employed for performance analysis. Experimental results show that the proposed ASD prediction system provides promising results with an accuracy of 97.01% by EDC and 96.87% by ADC classifiers. Keywords Autism spectrum disorder, Computer aided diagnosis, Machine learning, Image based diagnosis, Prediction system.

Early Screening of Autism Spectrum Disorder Diagnoses of Children Using Artificial Intelligence

In today’s society, with fast-growing case rates, medical expenditures, social implications, and lengthy waiting periods after the first screening, there is a need for early screening that is both simple and effective for children who may be at risk for autism spectrum disorder (ASD). This is of utmost significance in light of the significant rise in the case rates of ASDs, as well as the associated medical expenses and social effects, in the contemporary world. In this study, utilizing methods from machine learning, a system was constructed, which was effective in obtaining high performance for identifying the early indicators of ASD in children. The study was carried out by the authors of this paper. The purpose of this research is to categorize ASD data in order to give a fast, easily available, and simple method for supporting the early identification of ASD. It was suggested to use machine learning methods, such as k-nearest neighbors, linear discriminant analysis, the support vector machine (SVM) method, and the random forests (RF) method, to divide populations into those who have ASD and those who do not have it. These machine learning algorithms were examined and tested using standard data collected from the machine learning repository, which contains two classes: normal and autism. The dataset was split into a training portion of 80% and a testing portion of 20%. In their separate testing, both the SVM and RF algorithms achieved a level of accuracy that was exceptional (100%). In addition, the sensitivity analysis method was used to estimate the amount of inaccuracy that would be present between the values that were intended to be achieved and the values that were predicted. The findings of the sensitivity analysis revealed that both SVM and RF had an R 2 = 100% in both the phases. When the results obtained were compared with those of the current systems, it was found that the suggested algorithms performed better than that of existing systems. It is very important to diagnose ASD as early as possible. The machine learning algorithms obtained a high level of accuracy in the diagnosis of ASD. When it comes to the categorization of ASD data, the SVM and RF approaches exhibit the best results among the two different classification approaches.

Why not try to predict autism spectrum disorder with crucial biomarkers in cuproptosis signaling pathway?

The exact pathogenesis of autism spectrum disorder (ASD) is still unclear, yet some potential mechanisms may not have been evaluated before. Cuproptosis is a novel form of regulated cell death reported this year, and no study has reported the relationship between ASD and cuproptosis. This study aimed to identify ASD in suspected patients early using machine learning models based on biomarkers of the cuproptosis pathway. We collected gene expression profiles from brain samples from ASD model mice and blood samples from humans with ASD, selected crucial genes in the cuproptosis signaling pathway, and then analysed these genes with different machine learning models. The accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curves of the machine learning models were estimated in the training, internal validation, and external validation cohorts. Differences between models were determined with Bonferroni's test. The results of screening with the Boruta algorithm showed that FDX1, DLAT, LIAS, and ATP7B were crucial genes in the cuproptosis signaling pathway for ASD. All selected genes and corresponding proteins were also expressed in the human brain. The k-nearest neighbor, support vector machine and random forest models could identify approximately 72% of patients with ASD. The artificial neural network (ANN) model was the most suitable for the present data because the accuracy, sensitivity, and specificity were 0.90, 1.00, and 0.80, respectively, in the external validation cohort. Thus, we first report the prediction of ASD in suspected patients with machine learning methods based on crucial biomarkers in the cuproptosis signaling pathway, and these findings may contribute to investigations of the potential pathogenesis and early identification of ASD.

The impact of educational rewards on the diagnosis of autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects social interactions and communication. The prevalence of ASD has risen dramatically in recent years, but the underlying factors leading to this rise are not clear. In this paper, we test whether changes in state-level educational policy that impact school-level resources are associated with the rise in ASD diagnostic prevalence. Early identification of ASD can improve an array of outcomes for children, and school systems play an important role with identification of the condition. It is plausible that children attending schools with better resources from state governments are more likely to receive an ASD diagnosis and presumably appropriate services. We focus on one educational policy in particular, state-level rewards, which consist of a monetary transfer from state governments to school districts. To test the impact of educational rewards on ASD diagnosis, we rely on policy variation across states and time and estimate both two-way fixed effects (TWFE) models alongside recently advanced methods in the difference-in-differences (DiD) literature. Under a baseline TWFE specification we estimate that rewards policies are associated with a 18.46% increase in ASD diagnosis. Further, using DiD methods that account for bias in settings of differential policy timing, we find that the magnitude of the effect increases to 24.8%. We believe these findings to be suggestive evidence that educational rewards policies improved the likelihood of detection and diagnosis of ASD.

Factors affecting knowledge of autism spectrum disorder among pediatric residents in eastern China: a cross-sectional study

BackgroundThere is a global increase in the prevalence of autism spectrum disorder (ASD). Early identification of ASD in children and intervention are key aspects in the management of ASD. However, early identification is partly dependent on knowledge on ASD among pediatricians. This study analyzed the extent of ASD knowledge and its underlying factors among pediatric residents in eastern China, to provide a reference for medical education reforms.MethodsThe study employed the Knowledge about Childhood Autism among Health Workers questionnaire. A total of 138 pediatric residents participated in the survey. Descriptive statistics were used to describe demographic characteristics and the four domains of the questionnaire. Univariate analysis was employed to assess impacts of the demographic characteristics on the questionnaire scores. On the other hand, multivariate regression analysis was used to analyze the correlation between the participants’ demographic characteristics and the questionnaire scores.ResultsThe average ASD cognitive score of 138 respondents was 13.38 ± 4.48. The ASD cognitive scores in female pediatric residents were higher compared to that in males (p < 0.05). Residents who had obtained professional doctor qualification certificate were more than those without professional doctor qualification certificate (p < 0.05). The ASD knowledge in the group which did not have rotation in both departments was lower than in the group which had rotation in both departments (p < 0.05) as well as the group that had rotation in developmental and behavioral pediatrics department only (p < 0.05). Our multivariate linear regression model demonstrated significant statistical differences (p < 0.05), and showed that gender and systematic exposure to ASD knowledge had significant effects on cognitive scores (p < 0.05).ConclusionMost participants had relatively low levels of awareness and knowledge about ASD, especially on ASD comorbidities and age of onset. Women, systematic learning of ASD knowledge in medical school, successful passing of the physician examination, and rotation in the developmental and behavioral pediatrics (DBP) department significantly influence the levels of ASD awareness and knowledge. It is, therefore, important to strengthen ASD education in medical students at the university level and make rotation in the DBP department a requisite for pediatric trainees.

Early Developmental Trends in High-Risk Neonates Later Diagnosed With Autism Spectrum Disorder.

We hypothesized that clinical data from a neonatal intensive care unit (NICU) infant developmental follow-up clinic would identify early manifestations of autism spectrum disorder (ASD). One hundred forty-four infants were identified; 72 later diagnosed with ASD and 72 controls. Retrospective chart review provided data from the Test of Infant Motor Performance (TIMP) and the Bayley Scales of Infant and Toddler Development, third edition (Bayley-III), between 8 and 26 months of age. Between-group comparisons indicated no significant group difference in TIMP scores; however, Bayley-III scaled scores differed between the groups at 2 administration times. The within-group Bayley-III change scores declined significantly more for the ASD group in cognitive and communication subtests. High-risk neonates, due to prematurity or morbidity, later diagnosed with ASD demonstrated statistically significant differences, including a more precipitous drop in Bayley-III scores over time. Early, longitudinal developmental surveillance for neonates at risk of ASD is critical. What this adds to the evidence: Early identification of ASD is critical to optimize developmental outcomes in young children, including infants born prematurely or with neonatal morbidity, who are perceived to have an increased risk for ASD. Despite these findings, minimal research has been conducted to evaluate the utility of commonly administered norm-referenced developmental surveillance instruments to identify possible early signs of ASD in this high-risk population due to prematurity or neonatal morbidity and not familial association. The present study analyzed retrospectively collected clinical data from a NICU developmental follow-up clinic for 144 infants, 72 of which were later diagnosed with ASD and 72 sex- and gestational age-matched controls. Results demonstrated statistically significant poorer Bayley-III outcomes for the ASD group compared with controls at 2 different study time points, including a more precipitous drop in Bayley-III scaled scores over time. This study highlights the importance of early and longitudinal developmental surveillance for high-risk neonates at risk of ASD.

Early identification of autism in Egyptian children using Arabic version of checklist for autism in toddlers-23 (CHAT-23)

Background: Screening is the first important step in the diagnostic process. There is strong evidence that early diagnosis and management of autism spectrum disorder (ASD) can lead to a better prognosis. The purpose of this study was to develop an Arabic version of the Chinese Checklist for Autism in Toddlers-23 (CHAT-23) to distinguish children with ASD in the Egyptian community. Methods: The Arabic CHAT-23 checklist was applied to 100 Egyptian children with mental age 18-24 months including 30 autistic and 70 typically-developing children. Results and conclusion: Arabic CHAT-23 checklist is a valid and reliable tool for early identification of ASD in Egyptian children with high sensitivity (93.3%) and specificity (97%). Children failing any 15 of all 23 questions of the parental questionnaire should be observed with observational items. Failing any 3 of the 4 observational items would suggest ASD.

Disparities in Early Intervention Program Participation by Children With Autism Spectrum Disorder in a US Metropolitan Area, 2006 to 2016

Multiple studies have shown the value of early interventions for autism spectrum disorder (ASD). In the US, the Early Intervention Program (EIP) is mandated by law (Part C of the Individuals With Disabilities Education Act [IDEA]) to provide services to all young children with delays or disabilities. However, the extent to which children with ASD participate in this key service system is unknown. To evaluate EIP use by children with ASD from 2006 to 2016 and to describe the factors associated with EIP participation. This cross-sectional study used repeated data collected from 2006 to 2016 by active ASD surveillance of the New York-New Jersey metropolitan area as reported in the New Jersey Autism Study. The New Jersey Autism Study identified 4050 children aged 8 years with ASD from 2006 to 2016. Demographic and clinical data were collected and participation in an EIP was assessed through active surveillance. Data were analyzed from June to December 2021. Sociodemographic factors associated with the outcome of EIP participation. Participation in an EIP assessed at age 8 years. Demographic, ecological, and clinical factors, as well as temporal patterns, were examined by using standard and multilevel logistic regression models. Among 4050 children aged 8 years with ASD by active surveillance, 1887 (46.6%) received EIP services. Of these children, 3303 (81.6%) were boys; 1105 (27.3%) were Hispanic, 801 (19.8%) were non-Hispanic Black, 1816 (44.8%) were non-Hispanic White, and 328 (8.1%) were non-Hispanic other (included Alaska Native or American Indian and Asian or Pacific Islander). In adjusted regression models, non-Hispanic Black children with ASD had lower odds of EIP participation (adjusted odds ratio [AOR], 0.67; 95% CI, 0.54-0.84) compared with their non-Hispanic White peers, and children residing in affluent areas had higher odds of receiving EIP services (AOR, 1.71; 95% CI, 1.36-2.15) compared with children residing in underserved areas. Children with ASD born in 2008 had higher odds of EIP participation than children born in 1998 (AOR, 2.64; 95% CI, 2.07-3.36). Early identification of ASD is an important public health priority and receipt of EIP services may improve ASD outcomes. Approximately half of the population of children aged 8 years with ASD received EIP services between 2006 and 2016, and EIP participation by children with ASD increased during the 10-year period. However, receipt of EIP services was marked by strong socioeconomic status- and race and ethnicity-based disparities. Universal ASD screening and additional strategies are needed to address disparities and to increase access to EIP services.

Progress and Disparities in Early Identification of Autism Spectrum Disorder: Autism and Developmental Disabilities Monitoring Network, 2002-2016.

Early identification can improve outcomes for children with autism spectrum disorder (ASD). We sought to assess changes in early ASD identification over time and by co-occurring intellectual disability (ID) and race/ethnicity. Using data for 2002-2016 from a biennial population-based ASD surveillance program among 8-year-old children in the United States, we defined identification as a child's earliest recorded ASD diagnosis or special education eligibility. Unidentified children had characteristics meeting the ASD surveillance case definition but no recorded identification by age 8 years. We calculated median age at identification among identified children, median age at identification including unidentified children, and cumulative incidence of identification by age 48 months. ASD identification by age 48 months was 4 times (95% CI: 3.6-4.3) as likely in 2016 as in 2002, with the largest increases among children without ID. Median age at ASD identification among identified children decreased 3 months during this time. Children of every race/ethnicity were more likely to be identified over time. There were racial disparities stratified by ID: in 2016, Black and Hispanic children without ID were less likely to be identified with ASD than were White children (both groups risk ratio: 0.7; 95% CI: 0.5-0.8), but Black children were 1.5 times (95% CI: 1.3-1.9) as likely as White children to be identified with ASD and ID. Substantial progress has been made to identify more children with ASD early, despite minimal decrease in median age at diagnosis. Considerable disparities remain in early ASD identification by race/ethnicity and co-occurring intellectual disability.

Clinical considerations when conducting diagnostic evaluations to identify autism spectrum disorder in young children

Objective This paper provides clinical guidance supported by the literature on conducting diagnostic evaluations for young children suspected of having autism spectrum disorder (ASD). Method Authors provided a summary of clinical recommendations and evidence-based strategies for providing diagnostic evaluations for young children suspected of having ASD that are supported by the existing literature. Results ASD is a complex condition that typically presents in the first couple years of a child’s life. While many children can be accurately diagnosed by age 2, the majority of children are diagnosed much later. This article provides recommendations for evidence-based clinical practices to support accurate early identification and diagnosis in young children with autism. Recommended practices and measures for screening, comprehensive diagnostic evaluation, and differential diagnosis of ASD in early childhood are discussed. Conclusions Early identification of ASD is important to promote improved outcomes related to early intervention. There are a variety of tools and practices available to support neuropsychologists in providing accurate early diagnosis and appropriate developmental monitoring for children suspected of ASD and related neurodevelopmental conditions. There is a need for continued efforts regarding awareness of ASD in early childhood and targeted training in ASD for neuropsychologists and other disciplines who support families of young children with disabilities.

Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018.

Problem/ConditionAutism spectrum disorder (ASD).Period Covered2018.Description of SystemThe Autism and Developmental Disabilities Monitoring Network is an active surveillance program that estimates ASD prevalence and monitors timing of ASD identification among children aged 4 and 8 years. This report focuses on children aged 4 years in 2018, who were born in 2014 and had a parent or guardian who lived in the surveillance area in one of 11 sites (Arizona, Arkansas, California, Georgia, Maryland, Minnesota, Missouri, New Jersey, Tennessee, Utah, and Wisconsin) at any time during 2018. Children were classified as having ASD if they ever received 1) an ASD diagnostic statement (diagnosis) in an evaluation, 2) a special education classification of ASD (eligibility), or 3) an ASD International Classification of Diseases (ICD) code. Suspected ASD also was tracked among children aged 4 years. Children who did not meet the case definition for ASD were classified as having suspected ASD if their records contained a qualified professional’s statement indicating a suspicion of ASD.ResultsFor 2018, the overall ASD prevalence was 17.0 per 1,000 (one in 59) children aged 4 years. Prevalence varied from 9.1 per 1,000 in Utah to 41.6 per 1,000 in California. At every site, prevalence was higher among boys than girls, with an overall male-to-female prevalence ratio of 3.4. Prevalence of ASD among children aged 4 years was lower among non-Hispanic White (White) children (12.9 per 1,000) than among non-Hispanic Black (Black) children (16.6 per 1,000), Hispanic children (21.1 per 1,000), and Asian/Pacific Islander (A/PI) children (22.7 per 1,000). Among children aged 4 years with ASD and information on intellectual ability, 52% met the surveillance case definition of co-occurring intellectual disability (intelligence quotient ≤70 or an examiner’s statement of intellectual disability documented in an evaluation). Of children aged 4 years with ASD, 72% had a first evaluation at age ≤36 months. Stratified by census-tract–level median household income (MHI) tertile, a lower percentage of children with ASD and intellectual disability was evaluated by age 36 months in the low MHI tertile (72%) than in the high MHI tertile (84%). Cumulative incidence of ASD diagnosis or eligibility received by age 48 months was 1.5 times as high among children aged 4 years (13.6 per 1,000 children born in 2014) as among those aged 8 years (8.9 per 1,000 children born in 2010). Across MHI tertiles, higher cumulative incidence of ASD diagnosis or eligibility received by age 48 months was associated with lower MHI. Suspected ASD prevalence was 2.6 per 1,000 children aged 4 years, meaning for every six children with ASD, one child had suspected ASD. The combined prevalence of ASD and suspected ASD (19.7 per 1,000 children aged 4 years) was lower than ASD prevalence among children aged 8 years (23.0 per 1,000 children aged 8 years).InterpretationGroups with historically lower prevalence of ASD (non-White and lower MHI) had higher prevalence and cumulative incidence of ASD among children aged 4 years in 2018, suggesting progress in identification among these groups. However, a lower percentage of children with ASD and intellectual disability in the low MHI tertile were evaluated by age 36 months than in the high MHI group, indicating disparity in timely evaluation. Children aged 4 years had a higher cumulative incidence of diagnosis or eligibility by age 48 months compared with children aged 8 years, indicating improvement in early identification of ASD. The overall prevalence for children aged 4 years was less than children aged 8 years, even when prevalence of children suspected of having ASD by age 4 years is included. This finding suggests that many children identified after age 4 years do not have suspected ASD documented by age 48 months.Public Health ActionChildren born in 2014 were more likely to be identified with ASD by age 48 months than children born in 2010, indicating increased early identification. However, ASD identification among children aged 4 years varied by site, suggesting opportunities to examine developmental screening and diagnostic practices that promote earlier identification. Children aged 4 years also were more likely to have co-occurring intellectual disability than children aged 8 years, suggesting that improvement in the early identification and evaluation of developmental concerns outside of cognitive impairments is still needed. Improving early identification of ASD could lead to earlier receipt of evidence-based interventions and potentially improve developmental outcomes.

Considerations for the identification of autism spectrum disorder in children with vision or hearing impairment: A critical review of the literature and recommendations for practice

Objective: There is higher risk for autism spectrum disorder (ASD) across many pediatric neurological conditions characterized by vision impairment or hearing loss. Early and accurate identification of ASD is imperative in promoting access to appropriate and early evidenced-based intervention; however, differential diagnosis can be particularly challenging in children with sensory impairment given the heterogeneity of ASD combined with the impact of vision impairment or hearing loss/deafness on development and behavior. A neuropsychologist’s unique expertise and appreciation of the interplay between sensory and behavioral manifestations can be valuable for making an early and accurate ASD diagnosis in children who are blind/visually impaired or deaf/hard-of-hearing. This article highlights clinical considerations when identifying ASD within the context of vision impairment or hearing loss/deafness. Method: We discuss clinical considerations for the early identification of ASD in children who are blind/visually impaired and deaf/hard-of-hearing. Information presented in the article is based on a critical review of the literature and the expertise of the author group. Conclusion: Ongoing development of clinical expertise and evidence-based assessment methods are important when informing the early differential diagnosis of ASD in individuals with sensory impairment. Accurate identification is also vital for the development of targeted interventions across the lifespan.

Implementing and evaluating Social Attention and Communication Surveillance (SACS) to prospectively identify autism in very young children in Nepal

BackgroundAlthough Autism Spectrum Disorder (ASD) can be identified as early as 12 months and diagnosed by 24 months, the mean age of diagnosis of ASD in Nepal is ∼58 months, with children missing the opportunity for early intervention. AimsThis study implemented and evaluated a Nepali version of the Social Attention and Communication Surveillance (SACS-N) tool, to identify children between 11–30 months who are at “high likelihood” of ASD in a local community in Nepal. MethodsSixty Female Community Health Volunteers (FCHVs) were trained to monitor and identify the early signs of ASD using SACS-N. ResultsOverall, 1926 children were monitored with the SACS-N, with 11 children (0.57 %) referred for further assessments at 11–15 months (n = 4), 16–21 months (n = 4), and 22–30 months (n = 3). Of these children, 10 children had a developmental disorder, including ASD and Global Developmental Delay; no information was available on one child who migrated. Hence, the positive predictive value (PPV) of SACS-N for all developmental disorders was 100 %. Of seven children attending a gold standard diagnostic/ developmental assessment, three had ASD (43 % PPV for ASD). The estimated prevalence of ASD ranged between 0.16 % to 0.26 %. ConclusionCommunity-based developmental monitoring of ASD and other developmental delays by FCHVs is a feasible, cost-effective and sustainable approach to promoting early identification of ASD in Nepal. Further training and awareness of autism is needed for more accurate and increased referral rates using the SACS-N, including regular supervision of FCHVs.

Effect of Family Navigation on Diagnostic Ascertainment Among Children at Risk for Autism

Early identification of autism spectrum disorder (ASD) is associated with improved cognitive and behavioral outcomes. Targeted strategies are needed to support equitable access to diagnostic services to ensure that children from low-income and racial/ethnic minority families receive the benefits of early ASD identification and treatment. To test the efficacy of family navigation (FN), an individually tailored, culturally informed care management strategy, to increase the likelihood of achieving diagnostic ascertainment among young children at risk for ASD. This randomized clinical trial of 249 families of children aged 15 to 27 months who had positive screening results for possible ASD was conducted in 11 urban primary care sites in 3 cities. Data collection occurred from February 24, 2015, through November 5, 2018. Statistical analysis was performed on an intent-to-treat basis from November 5, 2018, to July 27, 2020. Families were randomized to FN or conventional care management (CCM). Families receiving FN were assigned a navigator who conducted community-based outreach to families to address structural barriers to care and support engagement in recommended services. Families receiving CCM were assigned to a care manager, who did limited telephone outreach. Families received FN or CCM after positive initial screening results and for 100 days after diagnostic ascertainment. The primary outcome, diagnostic ascertainment, was measured as the number of days from randomization to completion of the child's clinical developmental evaluation, when a diagnosis of ASD or other developmental disorder was determined. Among 250 families randomized, 249 were included in the primary analysis (174 boys [69.9%]; mean [SD] age, 22.0 [3.5] months; 205 [82.3%] publicly insured; 233 [93.6%] non-White). Children who received FN had a greater likelihood of reaching diagnostic ascertainment over the course of 1 year (FN, 108 of 126 [85.7%]; CCM, 94 of 123 [76.4%]; unadjusted hazard ratio [HR], 1.39 [95% CI, 1.05-1.84]). Site (Boston, New Haven, and Philadelphia) and ethnicity (Hispanic vs non-Hispanic) moderated the effect of FN (treatment × site interaction; P = .03; Boston: HR, 2.07 [95% CI, 1.31-3.26]; New Haven: HR, 1.91 [95% CI, 0.94-3.89]; and Philadelphia: HR, 0.91 [95% CI, 0.60-1.37]) (treatment × ethnicity interaction; P < .001; Hispanic families: HR, 2.81 [95% CI, 2.23-3.54] vs non-Hispanic families: HR, 1.49 [95% CI, 1.45-1.53]). The magnitude of FN's effect was significantly greater among Hispanic families than among non-Hispanic families (diagnostic ascertainment among Hispanic families: FN, 90.9% [30 of 33], and CCM, 53.3% [16 of 30]; vs non-Hispanic families: FN, 89.7% [35 of 39], and CCM, 77.5% [31 of 40]). Family navigation improved the likelihood of diagnostic ascertainment among children from racial/ethnic minority, low-income families who were detected as at risk for ASD in primary care. Results suggest differential effects of FN by site and ethnicity. ClinicalTrials.gov Identifier: NCT02359084.