Early diagnosis of autism in the community is associated with marked improvement in social symptoms within 1-2 years

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

Purpose

The 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.

Methods

We 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.

Results

The 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.

Conclusion

Our 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.

Longitudinal change in symptom severity in children with ASD: Results from the ELENA cohort

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition and understanding the changes in autism symptoms over time is crucial for tailoring support and interventions. This study therefore aimed to investigate the changes in symptom severity in a large cohort of children with ASD over a three-year follow-up period and identify factors that influence these changes. The study included 575 children diagnosed with ASD, ranging in age from 2 to 12 years, who were assessed at baseline and again 3 years later using the Autism Diagnostic Observational Schedule-2 (ADOS-2). ASD severity changes were investigated using the ADOS calibrated severity score (CSS) scores for total, social affect (SA) and restricted and repetitive behaviors (RRB). Results highlight four distinct patterns: stable high, stable low, increased, and decreased severity. The ADOS CSS total score changed for half of the sample, reflecting an increase in ASD severity for 21.9% and a decrease for 29.1% of children. For the other half, the ADOS CSS score remained stable, either high (34.4%) or low (14.6%). While the majority of previous studies reported stability in ASD severity, our findings revealed significant variability with frequent improvements in SA symptoms whereas RRBs remained stable or worsened. Our findings also showed that an improvement in SA was associated with the youngest group and early diagnosis. However, no clinical or sociodemographic factors were linked to changes in RRB, emphasizing the necessity for RRB-specific therapies. The third six-year follow-up point of the ongoing ELENA cohort study will map the long-term trajectories of the severity of ASD symptoms and their potential risk factors.

Autism in Preschool-Aged Children: The Effects of COVID-19 Lockdown

The COVID-19 lockdown affected children, especially those with autism spectrum disorder, due to the disruption in rehabilitation and educational activities. We conducted a cross-sectional study of 315 preschool-aged children, 35 of which had autism, to investigate this impact. A questionnaire was administered to explore socio-demographic status, familiar/home environment, and COVID-19 exposure. The clinical features of autistic subjects were also examined. Seven variables were considered to describe the effect of pandemic: Remote learning, Behavior changes, Home activities, Sleep habits, Night awakenings, Physical activity, Information about the virus. The lockdown had a significant impact on Remote learning, Behavior changes, and Information about the virus in participants with autism. Moreover, we found a worsening in repetitive movements, echolalia, restricted interests, and aggressive behaviors.

Use of Telehealth in Autism Spectrum Disorder Assessment in Children: Evaluation of an Online Diagnostic Protocol Including the Brief Observation of Symptoms of Autism

The COVID-19 pandemic revealed the need to develop the field of remote assessment for autism spectrum disorders (ASD). The purpose of the study was to evaluate an online assessment protocol that includes the Brief Observation of Symptoms of Autism (BOSA). The online protocol consisting of BOSA and the Autism Diagnostic Interview-Revised (ADI-R) was administered by experienced examiners to 29 children with suspected ASD. The participants were then evaluated by clinical psychologists in a standard clinical setting using the Autism Diagnostic Observation Schedule-2 (ADOS-2) and other methods, and the ASD diagnosis was confirmed or ruled out. The results show substantial to moderate inter-rater agreement between the online and face-to-face raters with the value of Cohen's Kappa = 0.66 (P < 0.001); this corresponds with 79.8% agreement. The sensitivity of the protocol was approx. 94.7%, the specificity was 70%, the positive predictive value was 85.7%, and the negative predictive value was 87.5%. Further, direct false positive or false negative diagnostic conclusions based on the online protocol were absent when the possible conclusion of "I cannot decide" was included. The items B9 Showing, B10 Spontaneous Initiation of Joint Attention, B1 Unusual Eye Contact, B3 Facial Expressions Directed to Others, and C2 Imagination/Creativity were shown to be well observable in BOSA when related to ADOS-2 scoring. The results indicate that the protocol consisting of BOSA and ADI-R administered by an experienced examiner is a promising combination of tools for remote autism assessment.

Points to consider when initiating clinical investigations in autistic paediatric populations-A White Paper

Many individuals with autism spectrum disorder (ASD) experience various degrees of impairment in social interaction and communication, restricted, repetitive behaviours, interests/activities. These impairments make a significant contribution to poorer everyday adaptive functioning. Yet, there are no pharmacological therapies to effectively treat the core symptoms of ASD. Since symptoms of ASD likely emerge from a complex interplay of vulnerabilities, environmental factors and compensatory mechanisms during the early developmental period, pharmacological interventions arguably would have the greatest impact to improve long-term outcomes when implemented at a young age. It is essential therefore, that clinical development programmes of investigational drugs in ASD include the paediatric population early on in clinical trials. Such trials need to offer the prospect of direct benefit (PDB) for participants. In most cases in drug development this prospect is supported by evidence of efficacy in adults. However, the effectiveness of treatment approaches may be age-dependent, so that clinical trials in adults may not provide sufficient evidence for a PDB in children. In this white paper, we consolidate recommendations from regulatory guidelines, as well as advice from the Food and Drug Administration, USA (FDA) and the Committee for Human Medicinal Products (CHMP) consultations on various development programmes on: 1) elements to support a PDB to participants in early paediatric clinical trials in ASD, including single-gene neurodevelopment disorders, 2) aspects of study design to allow for a PDB. This white paper is intended to be complementary to existing regulatory guidelines in guiding industry and academic sponsors in their conduct of early paediatric clinical trials in ASD.

ASD-GANNet: A Generative Adversarial Network-Inspired Deep Learning Approach for the Classification of Autism Brain Disorder

The classification of a pre-processed fMRI dataset using functional connectivity (FC)-based features is considered a challenging task because of the set of high-dimensional FC features and the small dataset size. To tackle this specific set of FC high-dimensional features and a small-sized dataset, we propose here a conditional Generative Adversarial Network (cGAN)-based dataset augmenter to first train the cGAN on computed connectivity features of NYU dataset and use the trained cGAN to generate synthetic connectivity features per category. After obtaining a sufficient number of connectivity features per category, a Multi-Head attention mechanism is used as a head for the classification. We name our proposed approach "ASD-GANNet", which is end-to-end and does not require hand-crafted features, as the Multi-Head attention mechanism focuses on the features that are more relevant. Moreover, we compare our results with the six available state-of-the-art techniques from the literature. Our proposed approach results using the "NYU" site as a training set for generating a cGAN-based synthetic dataset are promising. We achieve an overall 10-fold cross-validation-based accuracy of 82%, sensitivity of 82%, and specificity of 81%, outperforming available state-of-the art approaches. A sitewise comparison of our proposed approach also outperforms the available state-of-the-art, as out of the 17 sites, our proposed approach has better results in the 10 sites.

An integrated clinical approach to children at genetic risk for neurodevelopmental and psychiatric conditions: interdisciplinary collaboration and research infrastructure

BACKGROUND

A sizeable proportion of pathogenic genetic variants identified in young children tested for congenital differences are associated with neurodevelopmental psychiatric disorders (NPD). In this growing group, a genetic diagnosis often precedes the emergence of diagnosable developmental concerns. Here, we describe DAGSY (Developmental Assessment of Genetically Susceptible Youth), a novel interdisciplinary 'genetic-diagnosis-first' clinic integrating psychiatric, psychological and genetic expertise, and report our first observations and feedback from families and referring clinicians.

METHODS

We retrieved data on referral sources and indications, genetic and NPD diagnoses and recommendations for children seen at DAGSY between 2018 and 2022. Through a survey, we obtained feedback from twenty families and eleven referring clinicians.

RESULTS

159 children (mean age 10.2 years, 57.2% males) completed an interdisciplinary (psychiatry, psychology, genetic counselling) DAGSY assessment during this period. Of these, 69.8% had a pathogenic microdeletion or microduplication, 21.5% a sequence-level variant, 4.4% a chromosomal disorder, and 4.4% a variant of unknown significance with emerging evidence of pathogenicity. One in four children did not have a prior NPD diagnosis, and referral to DAGSY was motivated by their genetic vulnerability alone. Following assessment, 76.7% received at least one new NPD diagnosis, most frequently intellectual disability (24.5%), anxiety (20.7%), autism spectrum (18.9%) and specific learning (16.4%) disorder. Both families and clinicians responding to our survey expressed satisfaction, but also highlighted some areas for potential improvement.

CONCLUSIONS

DAGSY addresses an unmet clinical need for children identified with genetic variants that confer increased vulnerability for NPD and provides a crucial platform for research in this area. DAGSY can serve as a model for interdisciplinary clinics integrating child psychiatry, psychology and genetics, addressing both clinical and research needs for this emerging population.

Are the diagnostic rates of autistic females increasing? An examination of state-wide trends

BACKGROUND

Autism has been considered a 'male-dominant' condition. However, recent research suggests that autistic females are underdiagnosed, misdiagnosed, and later diagnosed. Females may also have different and more nuanced behavioral profiles. To examine diagnosis rates of females, we used 20 years of state-wide data to characterize historical trends in the diagnosis of autism in females to determine whether the proportion of females diagnosed with autism has changed over time.

METHODS

Data were drawn from 10,247 participants (males = 8,319, females = 1928) who received an autism diagnosis between 2000 and 2021 from state-wide autism centers associated with the University of North Carolina TEACCH Autism Program.

RESULTS

The rates of females diagnosed with autism increased at a greater rate compared with males. Age of diagnosis remained consistently higher for females. Late diagnosis (defined as 13+) increased over time across both males and females, however, was more commonly associated with females, particularly those with co-occurring intellectual disability.

CONCLUSIONS

Our results indicate that the proportion of females diagnosed with autism has increased steadily over a 20-year period, which likely reflects greater societal knowledge of how autism may manifest differentially in females.

Race and Ethnicity Reporting in Occupational Therapy Research Among Autistic Youth

Historically marginalized racial and ethnic groups are underrepresented in autism research broadly; however, patterns of inclusion in occupational therapy research are unknown.In this secondary data analysis, we examined race and ethnicity reporting across articles included in a systematic review of evidence related to occupational therapy practice with autistic individuals <18 years (2013-2021).Two team members reached >90% interrater coding agreement in race and ethnicity reporting across articles in ADLs/IADLs, education/work, play, sleep, and social participation in clinics, homes/communities, and schools.Intervention outcomes of ADL/IADLs (66.7%) and play (66.7%) had low rates of reporting, while social participation in schools (100%) and education/work (100%) had higher rates of reporting. Sample diversity was greatest among research in schools and most limited in clinic-based settings.Systemic racism that limits individuals' participation in occupational therapy research perpetuates racial and ethnic health inequities among autistic children and adolescents.

Autism interventions designed or adapted for the Black/African American population: A systematic review

LAY ABSTRACT

Black/African American people in the United States who have a diagnosis of autism often experience service-related disparities, including not having the same access to high-quality autism and related care (e.g. behavioral interventions), and are less likely to have sustained treatment engagement across their lifespan. While interventions to support autistic people are typically designed to be universal, there is concern that these interventions not being tailored to the Black/African American population could reduce the overall impact due to a lack of responsiveness to the needs of the Black children or families who receive the intervention. The current systematic review summarized research on interventions developed for the Black autism community, including Black children with autism and their caregivers. After a comprehensive, systematic search, eight peer-reviewed publications were identified that met the study's inclusion criteria. The majority of the interventions were tailored to Black caregivers of children with autism. Autism researchers demonstrate different strategies for engaging Black caregivers in culturally responsive ways; however, more research into these interventions is needed in order to assess their effectiveness. In addition, there are still limited interventions adapted to be culturally responsive to Black/African American autistic people. The Cultural Adaptation Checklist framework is a novel approach with promise to become the standard for adapting interventions to meet the needs of culturally diverse groups. Cultural responsiveness is an important facet in the development of interventions that produce optimal outcomes for the range of diversity in the United States and is an important step to achieving equitable autism research practices.

Combining developmental and sleep health measures for autism spectrum disorder screening: an ECHO study

BACKGROUND

Sleep problems are reported for up to 80% of autistic individuals. We examined whether parsimonious sets of items derived from the Modified Checklist for Autism in Toddlers, Revised (M-CHAT-R) and the Brief Infant Sleep Questionnaire (BISQ) are superior to the standard M-CHAT-R in predicting subsequent autism spectrum disorder (ASD) diagnoses.

METHODS

Participants from 11 Environmental influences on Child Health Outcomes (ECHO) cohorts were included. We performed logistic LASSO regression models with 10-fold cross-validation to identify whether a combination of items derived from the M-CHAT-R and BISQ are superior to the standard M-CHAT-R in predicting ASD diagnoses.

RESULTS

The final sample comprised 1552 children. The standard M-CHAT-R had a sensitivity of 44% (95% CI: 34, 55), specificity of 92% (95% CI: 91, 94), and AUROC of 0.726 (95% CI: 0.663, 0.790). A higher proportion of children with ASD had difficulty falling asleep or resisted bedtime during infancy/toddlerhood. However, LASSO models revealed parental reports of sleep problems did not improve the accuracy of the M-CHAT-R in predicting ASD diagnosis.

CONCLUSION

While children with ASD had higher rates of sleep problems during infancy/toddlerhood, there was no improvement in ASD developmental screening through the incorporation of parent-report sleep metrics.

IMPACT

Parental-reported sleep problems are common in autism spectrum disorder (ASD). We investigated whether the inclusion of parental-reports of infant/toddler sleep patterns enhanced the effectiveness of developmental screening for autism. We reported higher rates of difficulty falling asleep and resisting bedtime during infancy and toddlerhood among children later diagnosed with ASD; however, we did not find an improvement in ASD developmental screening through the incorporation of parent-report sleep metrics. In our sample, the standard M-CHAT-R had a sensitivity of 39% among children of mothers with government insurance compared with a sensitivity of 53% among children of mothers with employer-based insurance.

The Role of Pathways Early Autism Intervention in Improving Social Skills and Respeto for Young Hispanic Autistic Children

PURPOSE

We know very little about Hispanic autistic children's response to intervention as, historically, Hispanic children are underrepresented in intervention studies. Pathways parent-mediated early autism intervention is one of the few naturalistic developmental behavioral interventions (NDBIs) that is contextually and linguistically responsive to Hispanic families. However, some child-centered NDBI strategies do not align with the Hispanic caregiving value of respeto. A child exhibiting respeto demonstrates affiliative obedience by displaying deference and respect toward adults. Furthermore, theories of the ontogeny of cultural learning suggest that certain levels of social development may be necessary to learn cultural values. The current study investigates (1) the relationship between Hispanic autistic children's social skills and affiliative obedience and (2) the efficacy of Pathways in improving affiliative obedience in Hispanic children.

METHODS

This quasi-experimental design study used preexisting standardized test data and video recordings from 26 Hispanic participants who took part in a previous Pathways efficacy study. Recordings were coded for affiliative obedience and social connectedness. Residual change variables were used to measure progress from baseline to post-intervention, and correlation and hierarchical regression analyses were conducted to analyze the data.

RESULTS

We found significant positive correlations between social skills and children's affiliative obedience for baseline and change variables. In addition, we found Pathways had a significant medium-large magnitude effect on change in affiliative obedience skills.

CONCLUSION

This study highlights the benefits of NDBI interventions that advance social development in autistic children and support Hispanic parents in enculturating their children in the value of respeto.

Screening Options in Autism Telediagnosis: Examination of TAP, M-CHAT-R, and DCI Concordance and Predictive Value in a Telediagnostic Model

Tele-assessment of autism in early childhood has increased. However, it is unclear how autism screening tools (M-CHAT-R, DCI) function as part of tele-assessment and relate to a commonly used tele-assessment instrument, the TAP. 361 families from a clinically referred sample of children (mean age: 27.63 months, sd = 4.86 months) completed the M-CHAT-R and DCI prior to a tele-assessment visit utilizing the TAP. Data was collected on demographic background, measure scores, and diagnostic outcome. No significant differences in measure scores or diagnostic findings emerged in age at referral, age group, age at diagnosis, or child sex, ethnicity, or racial background. The M-CHAT-R and DCI correlated strongly and positively. Older age was associated with lower risk scores on screening instruments. Children with autism had significantly higher scores on all screener and subdomain scores, with the exception of DCI Behavior. Subdomains of the DCI emerged as the strongest predictor of diagnostic outcome. Both the DCI total score and the M-CHAT-R significantly related to diagnostic outcome and TAP score in this tele-assessment model, regardless of child age or sex. Findings also support use of the DCI for children under 24 months of age.

Experiences of families with children diagnosed with Autism Spectrum Disorder in World Health Organization Afro-Regions: A scoping review

A scoping review on Autism Spectrum Disorder (ASD) and its impact on the families of affected children was undertaken due to insufficient information available on the diverse experiences impacting their lives. Using the Joanna Briggs Institute methodology, eligibility criteria were guided by Population (families), Concept (family experiences), and Context (African region). English-language articles were sought from a variety of databases and search engines. The publication date of the identified articles ranged from 2003 to 2021 with most published in 2020 (n = 10), and the majority using qualitative methodologies (n = 51). Most family members involved were parents (n = 51) and their ages ranged from 18 to 75 years. The families experienced various challenges related to their child with regard to education, healthcare, and the broader community including lack of support. Family coping strategies included believing in God, attending counseling sessions, adapting, and accepting the situation. Healthcare professionals should be prepared and positioned to educate families and siblings on various aspects of ASD. There is a need for active, continued research on families within most countries of World Health Organization Afro-region.

Examination of Clinical and Assessment Type Differences Between Toddlers with ASD from Multiplex and Simplex Families

Few studies have examined differences in autism spectrum disorder (ASD) phenotype between children from multiplex and simplex families at the time of diagnosis. The present study used an age- and gender-matched, community-based sample (n = 105) from the southwestern United States to examine differences in ASD symptom severity, cognitive development, and adaptive functioning. No significant differences between children from multiplex and simplex families were observed. Exploratory analysis revealed that parents underreported receptive and expressive language and fine motor skills compared to professional observation, especially among children from multiplex families. These findings suggest that diagnosticians may need to consider family structure when choosing and interpreting assessments of receptive language, expressive language, and fine motor skills.

The Influence of Social and Developmental Factors on the Timing of Autism Spectrum Disorder Diagnosis of Preschool-Aged Children: Evidence from a Specialized Chilean Center

We investigated the influence of developmental and social factors on the age of autism diagnosis (AoD) in a cohort of toddlers living in Chile. A cross-sectional study was conducted among 509 preschool children diagnosed with autism spectrum disorder [M = 40.2 months (SD = 8.6), girls: 32%] in the neurodevelopmental unit of a university clinic in Santiago, Chile (2015-2023). Structural changes in the annual trend of AoD were tested. Generalized linear models (gamma distribution) with and without interaction terms were used for the multivariate analysis, adjusting for gender, residential area, year of diagnosis, developmental variables (language regression, delayed walking, and use of expressive verbal language), and primary caregiver age and education level (CEL). 95% confidence intervals of the unstandardized regression coefficients (B) were calculated using 1000 bootstrap resampling to estimate associations. AoD increased between 2021-2022 and decreased in 2023. Female gender (B = 2.72 [1.21-4.23]), no history of language regression (B = 3.97 [1.66-6.28]), and the presence of expressive verbal language at diagnosis (B = 1.57 [0.05-3.08]) were associated with higher AoD. Children whose caregivers had tertiary education were diagnosed earlier than those with ≤ 12 years of formal education. Although the influence of CEL increased with caregiver age, differences between CEL groups were significant only for caregivers aged ≥ 30 years. Improved education and early screening for clinical features of autism among healthcare professionals and the community, with a focus on young children without highly apparent developmental concerns and those from vulnerable social groups, are warranted.

Comparison between the Clancy Behavior Scale and the Modified Checklist for Autism in Toddlers in Taiwan

(1) Background: Precise diagnosis and early intervention are crucial for toddlers with autism spectrum disorder (ASD) to achieve a better prognosis. This study investigated the efficacy of the Clancy Behavior Scale (CBS) and Modified Checklist for Autism in Toddlers (M-CHAT) in detecting ASD among toddlers under 30 months of age. (2) Methods: A total of 215 toddlers (117 with ASD and 98 with development delays) aged between 18 and 29 months participated in this study. All the primary caregivers of these toddlers were recruited to complete the CBS and M-CHAT. (3) Results: The findings indicated that the accuracy of the CBS and M-CHAT was promising, and the short forms of these two instruments performed better than their full versions. The CBS:9 critical items presented a sensitivity of 0.75 and a specificity of 0.74, while the M-CHAT:14 brief items showed a sensitivity of 0.72 and a specificity of 0.85. (4) Conclusions: The diagnostic accuracy of high-risk ASD toddlers improved via the combination of CBS and M-CHAT, particularly when the information gathered from these two instruments were consistent. The findings may provide implications for enhancing the early detection of ASD.

Occupational Therapy Practice Guidelines for Autistic People Across the Lifespan

IMPORTANCE

Occupational therapy practitioners need evidence to support interventions that promote subjective well-being among autistic people and their families through optimal engagement and participation in occupations.

OBJECTIVE

These Practice Guidelines are informed by systematic reviews to expand knowledge of interventions that promote access, inclusion, engagement, and optimal participation in occupations that are meaningful to autistic people. Our intent was to foster occupational therapy practitioners' clinical decision-making and reasoning when working with autistic people and their care partners.

METHOD

These Practice Guidelines were developed on the basis of four systematic reviews, supporting evidence and literature, along with continued revisions and integration through an iterative and collaborative process.

RESULTS

A total of 98 articles were included in the systematic reviews, which are the foundation for practice recommendations in these guidelines. Forty-eight of the systematic review articles were used to inform the clinical recommendations included in these Practice Guidelines.

CONCLUSIONS AND RECOMMENDATIONS

Strong to moderate evidence indicates the need for multidisciplinary, goal-oriented interventions to support autistic people in different contexts. Although there is only emerging evidence in the inclusion of autistic people's strengths, interests, and perspectives to guide occupational therapy interventions, such practices can enhance the delivery of neurodiversity-affirming and trauma-informed practices. In addition, evidence is needed to support participation in activities of daily living (ADLs) for autistic youths. We recommend the use of strengths-based language to describe autistic people and the use of environmental adaptations, care partner education, and coaching to enhance occupational therapy service delivery. Plain-Language Summary: The literature is sparse regarding neurodiversity-affirming and trauma-informed practices for autistic youths, as well as for participation in activities of daily living (ADLs). These Practice Guidelines provide new information on positive mental health development; self-determination; ADLs, instrumental ADLs, play, and leisure occupations for children, adolescents, and adults; person-centered planning for adolescents and adults; and rest and sleep. Information on health management is also provided. Positionality Statement: This article uses the identity-first language autistic people. This nonableist language describes their strengths and abilities and is a conscious decision. This language is favored by autistic communities and self-advocates and has been adopted by health care professionals and researchers (Bottema-Beutel et al., 2021; Kenny et al., 2016). However, we respect the use of person-first language and have made a conscious decision to include research articles that have used this language.

Assessing Autism Knowledge Across the Global Landscape Using the ASK-Q

Substantial variability exists with regard to autism service provision around the world. Service disparities observed in many low- and middle-income countries may be driven, in part, by limited autism knowledge; however, measurement limitations have made it difficult to quantify autism knowledge across countries. The current study uses the autism stigma and knowledge questionnaire (ASK-Q) to quantify autism knowledge and stigma between different countries and demographics. The current study compiled data from 6830 participants collected using adapted versions of the ASK-Q administered in 13 different countries, representing four different continents. Structural equation modeling was used to examine how autism knowledge varied across country and individual factors. Results reveal cross country variability with a large, 17-point difference between the countries with the highest knowledge (Canada) and the lowest knowledge (Lebanon). As expected, countries with higher economies had higher levels of knowledge. We also documented differences based on country worldview, participant occupation, gender, age, and education level. These results help to identify specific regions and populations that might most need greater information about autism.

Autism Severity and its Relationship to Disability

Autism severity is currently defined and measured based exclusively on the severity levels of the two core symptom domains: social-communication and restricted or repetitive patterns of behaviors and interests. Autistic individuals, however, are often diagnosed with other medical, developmental, and psychological co-occurring conditions. These additional challenges such as intellectual disability, limited expressive and/or receptive language, and anxiety disorders, can have a tremendous impact on the day-to-day lives of autistic individuals, for both their adaptive functioning as well as their sense of wellbeing. Furthermore, the initial presentation of core symptoms and their likelihood of changing over time are influenced by the presence of such co-occurring conditions. In order to truly understand how a person's autism impacts their life, both core symptoms as well as other challenges should be considered. This approach was recently taken by The Lancet Commission on the future of care and clinical research in autism, which proposed the term "profound autism" for a subgroup of individuals presenting with high core symptom severity, co-occurring intellectual disability, and little or no language, who require extensive long-term care. Considering other individual factors such as daily living skills, specific support needs and environmental resources would also enhance the evaluation of disability in autistic individuals. As currently employed in the assessment of intellectual disability, a multidimensional approach to autism could provide a more comprehensive system for classification of impairment. At present, however, there is no formal way to designate the combined effect of these different aspects of autism on a person's life. A comprehensive outlook that acknowledges impairments, capabilities, co-occurring conditions, and environmental factors would be useful for identifying subgroups of individuals as well as for determining individual needs and strengths in clinical assessments. Lay Summary: The severity of a person's autism is currently defined based on the severity of their core autism symptoms: impaired social-communication and the presence of restricted or repetitive patterns of behaviors and interests. But autistic people often face additional challenges such as intellectual disability, epilepsy, and anxiety disorder, that considerably impact their everyday life, wellbeing, and the need for support. A more complete view of autism severity, one that includes core symptoms as well as additional challenges, could help identify meaningful sub-groups of autistic individuals and could be useful in clinical care. Appeared originally in Autism Res 2023; 16:685-696.

Identification of autism spectrum disorder based on electroencephalography: A systematic review

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and repetitive and stereotyped behaviors. According to the World Health Organization, about 1 in 100 children worldwide has autism. With the global prevalence of ASD, timely and accurate diagnosis has been essential in enhancing the intervention effectiveness for ASD children. Traditional ASD diagnostic methods rely on clinical observations and behavioral assessment, with the disadvantages of time-consuming and lack of objective biological indicators. Therefore, automated diagnostic methods based on machine learning and deep learning technologies have emerged and become significant since they can achieve more objective, efficient, and accurate ASD diagnosis. Electroencephalography (EEG) is an electrophysiological monitoring method that records changes in brain spontaneous potential activity, which is of great significance for identifying ASD children. By analyzing EEG data, it is possible to detect abnormal synchronous neuronal activity of ASD children. This paper gives a comprehensive review of the EEG-based ASD identification using traditional machine learning methods and deep learning approaches, including their merits and potential pitfalls. Additionally, it highlights the challenges and the opportunities ahead in search of more effective and efficient methods to automatically diagnose autism based on EEG signals, which aims to facilitate automated ASD identification.

Large increase in ASD prevalence in Israel between 2017 and 2021

Accurate estimation of annual changes in autism spectrum disorders (ASD) prevalence is critical for planning the expansion of diagnostic, education, and intervention services at an adequate rate. Previous studies from Israel have reported that ASD prevalence among 8-year-old children has increased from estimates of 0.3% in 2008 to 0.65% in 2015 and 1.3% in 2018. Here, we analyzed data acquired from the National Insurance Institute of Israeli (NII), a governmental organization that approves and monitors all ASD children who receive welfare services in Israel, and Clalit Health Services (CHS), the largest Health Maintenance Organization in Israel that provides health services to ~52% of the population. Data from both sources included annual data files from 2017 to 2021 containing the number of ASD cases per year of birth for 1-17-year-old children. This allowed us to estimate annual ASD prevalence among 3.5 million children born between 2000 and 2020 in Israel. Both data sources revealed a nearly two-fold increase in ASD prevalence among 1-17-year-old children from 2017 to 2021. Estimated prevalence rates differed across age groups with 2-3-year-old (day-care) children increasing from 0.27% to 1.19% (>4 fold change), 4-6-year-old (pre-school) children increasing from 0.8% to 1.83%, and 8-year-old children increasing from 0.82% to 1.56% in NII data. These results demonstrate that autism prevalence continues to increase in Israel with a shift towards diagnosis at earlier ages. These findings highlight the challenge facing health and education service providers in meeting the needs of a rapidly growing autism population.

The Importance of Language Delays as an Early Indicator of Subsequent ASD Diagnosis in Public Healthcare Settings

Previous studies have reported that ASD children with more severe symptoms are diagnosed earlier. However, previous studies in community settings have mostly relied on retrospective parental reports without the use of quantitative standardized test scores. Here, we evaluated the association of language, cognitive, and ASD severity standardized scores with the age of diagnosis in 1-6-year-old children diagnosed in a public healthcare setting. The results revealed that language scores were the strongest variable associated with the age of diagnosis, explaining ~ 30% of the variability across children. Indeed, all children diagnosed before 30-months of age exhibited moderate-to-severe language delays. These results further substantiate the prominence of language delay as a highly visible symptom associated with earlier ASD diagnosis in community clinical settings.

Improving autism identification and support for individuals assigned female at birth: clinical suggestions and research priorities

Emerging evidence suggests that the higher prevalence of autism in individuals who are assigned male than assigned female at birth results from both biological factors and identification biases. Autistic individuals who are assigned female at birth (AFAB) and those who are gender diverse experience health disparities and clinical inequity, including late or missed diagnosis and inadequate support. In this Viewpoint, an international panel of clinicians, scientists, and community members with lived experiences of autism reviewed the challenges in identifying autism in individuals who are AFAB and proposed clinical and research directions to promote the health, development, and wellbeing of autistic AFAB individuals. The recognition challenges stem from the interplay between cognitive differences and nuanced or different presentations of autism in some AFAB individuals; expectancy, gender-related, and autism-related biases held by clinicians; and social determinants. We recommend that professional development for clinicians be supported by health-care systems, professional societies, and governing bodies to improve equitable access to assessment and earlier identification of autism in AFAB individuals. Autistic AFAB individuals should receive tailored support in education, identity development, health care, and social and professional sense of belonging.

In Context: A Developmental Model of Reward Processing, With Implications for Autism and Sensitive Periods

OBJECTIVE

Differences in reward processing have been associated with numerous psychiatric disorders, including autism and attention-deficit/hyperactivity disorder (ADHD). Many attempts to understand reward processing characterize differences in clinical populations after disorder onset; however, divergence may begin much earlier. In fact, the typical developmental progression of reward processing in infancy and early childhood is poorly understood. We re-conceptualize classic infant developmental constructs such as preferential looking into a Six-Component Developmental Model of Reward Processing: an infant- and young child-focused framework to guide research and assessment of reward processing across development.

METHOD

The extant developmental literature including recent textbooks, systematic reviews, and meta-analyses was reviewed to build a conceptual framework. We describe experimental paradigms to assess each developmental component of reward processing longitudinally from infancy. A timeline of each component's emergence was estimated.

RESULTS

Six components of reward processing were identified-association, discrimination, preference/valuation, effort, anticipation, and response. Selected evidence suggests emergence between birth and 6 months. Application of this model to autism led to a reinterpretation of existing disparate results, and illuminated a path to study the developmental processes underlying a popular hypothesis of autism, the motivation hypothesis. Current evidence further suggests that a sensitive period may exist for the emergence of reward processing.

CONCLUSION

The proposed framework offers a useful reconceptualization of the extant literature. Future longitudinal work using the suggested experimental paradigms with high-risk populations could elucidate the developmental trajectory of the components and timing of potential sensitive period(s) for each component.

Association Between Early Developmental Milestones and Autism Spectrum Disorder

Early diagnosis and treatment of autism spectrum disorder (ASD) has been shown to lead to better prognosis. Here, we examined the association of commonly measured early developmental milestones (DMs) with later diagnosis of ASD. We conducted a case-control study of 280 children with ASD (cases) and 560 typically developed children (controls) matched to cases by date of birth, sex, and ethnicity in a control/case ratio of 2:1. Both cases and controls were ascertained from all children whose development was monitored at mother-child health clinics (MCHCs) in southern Israel. DM failure rates during the first 18 months of life in three developmental categories (motor, social, and verbal) were compared between cases and controls. Conditional logistic regression models were used to assess the independent association of specific DMs with the risk of ASD, while adjusting for demographic and birth characteristics.Significant case-control differences in DM failure rates were observed as early as 3 months of age (p < 0.001), and these differences increased with age. Specifically, cases were 2.4 times more likely to fail ≥ 1 DM at 3 months (aOR = 2.39; 95%CI = 1.41-4.06), and 15.3 times more likely to fail ≥ 3 DMs at 18 months (aOR = 15.32; 95%CI = 7.75-30.28). The most notable DM-ASD association was observed for social DM failure at 9-12 months (aOR = 4.59; 95%CI = 2.59-8.13). Importantly, the sex or ethnicity of the participants did not affect these DM-ASD associations. Our findings highlight the potential role of DMs as early signs of ASD that could facilitate earlier referral and diagnosis of ASD.

IcoConv : Explainable brain cortical surface analysis for ASD classification

In this study, we introduce a novel approach for the analysis and interpretation of 3D shapes, particularly applied in the context of neuroscientific research. Our method captures 2D perspectives from various vantage points of a 3D object. These perspectives are subsequently analyzed using 2D Convolutional Neural Networks (CNNs), uniquely modified with custom pooling mechanisms. We sought to assess the efficacy of our approach through a binary classification task involving subjects at high risk for Autism Spectrum Disorder (ASD). The task entailed differentiating between high-risk positive and high-risk negative ASD cases. To do this, we employed brain attributes like cortical thickness, surface area, and extra-axial cerebral spinal measurements. We then mapped these measurements onto the surface of a sphere and subsequently analyzed them via our bespoke method. One distinguishing feature of our method is the pooling of data from diverse views using our icosahedron convolution operator. This operator facilitates the efficient sharing of information between neighboring views. A significant contribution of our method is the generation of gradient-based explainability maps, which can be visualized on the brain surface. The insights derived from these explainability images align with prior research findings, particularly those detailing the brain regions typically impacted by ASD. Our innovative approach thereby substantiates the known understanding of this disorder while potentially unveiling novel areas of study.

Autism severity and its relationship to disability

Autism severity is currently defined and measured based exclusively on the severity levels of the two core symptom domains: social-communication and restricted or repetitive patterns of behaviors and interests. Autistic individuals, however, are often diagnosed with other medical, developmental, and psychological co-occurring conditions. These additional challenges such as intellectual disability, limited expressive and/or receptive language, and anxiety disorders, can have a tremendous impact on the day-to-day lives of autistic individuals, for both their adaptive functioning as well as their sense of wellbeing. Furthermore, the initial presentation of core symptoms and their likelihood of changing over time are influenced by the presence of such co-occurring conditions. In order to truly understand how a person's autism impacts their life, both core symptoms as well as other challenges should be considered. This approach was recently taken byThe Lancet Commission on the future of care and clinical research in autism, which proposed the term "profound autism" for a subgroup of individuals presenting with high core symptom severity, co-occurring intellectual disability, and little or no language, who require extensive long-term care. Considering other individual factors such as daily living skills, specific support needs and environmental resources would also enhance the evaluation of disability in autistic individuals. As currently employed in the assessment of intellectual disability, a multidimensional approach to autism could provide a more comprehensive system for classification of impairment. At present, however, there is no formal way to designate the combined effect of these different aspects of autism on a person's life. A comprehensive outlook that acknowledges impairments, capabilities, co-occurring conditions, and environmental factors would be useful for identifying subgroups of individuals as well as for determining individual needs and strengths in clinical assessments.

ECHO (Extension for Community Healthcare Outcomes) Autism STAT: A Diagnostic Accuracy Study of Community-Based Primary Care Diagnosis of Autism Spectrum Disorder

OBJECTIVE

Children can be reliably diagnosed with autism spectrum disorder (ASD) by a highly trained clinician as early as 12 to 24 months of age, but recent estimates indicate that the average age of diagnosis is 4.4 years. We hypothesized that trained primary care physicians and practitioners can reliably and accurately diagnose children 14 to 48 months with unambiguous symptoms of ASD.

METHODS

Through this diagnostic accuracy study, 20 patients diagnosed with ASD by clinicians trained through the ECHO (Extension for Community Healthcare Outcomes) Autism STAT program participated in an independent gold-standard evaluation at a regional autism center. Caregiver perceptions of the diagnostic process were also assessed.

RESULTS

Of the 20 patients who received a diagnosis of ASD by a trained clinician and completed the study, 19 diagnoses were confirmed by a gold-standard evaluation. Caregivers indicated that undergoing diagnosis in their local community rather than an autism specialty center was helpful (4.8/5 on a 5-point Likert scale, n = 19). Results of this study demonstrate that primary care clinicians can be trained to reliably diagnose ASD in children 14 to 48 months with unambiguous symptoms.

CONCLUSION

Diagnosis in the primary care setting may lead to earlier diagnosis and quicker connection to evidence-based therapies and interventions. Given the potential impact of increasing access to high-quality diagnostic services, the role of primary care clinicians in the diagnosis of ASD should be further evaluated.

Evaluation of the Lebanese Adults' Knowledge Regarding Autism Spectrum Disorder

The daily functioning and overall well-being of people with ASD depends largely on understanding how the wider public views ASD. Indeed, an increased level of ASD knowledge in the general population may result in earlier diagnosis, earlier intervention, and better overall outcomes. The present study aimed to examine the current state of ASD knowledge, beliefs, and sources of information in a Lebanese general population sample, to identify the factors that could influence this knowledge. A total of 500 participants were involved in this cross-sectional study, which was conducted in Lebanon between May 2022 and August 2022 using the Autism Spectrum Knowledge scale, General Population version (ASKSG). Overall, the participants' understanding of autism spectrum disorder was low, with a mean score of 13.8 (6.69) out of 32, or 43.1%. The highest knowledge score was found for items related to knowledge of the symptoms and associated behaviors (52%). However, the level of knowledge regarding the etiology and prevalence, assessment and diagnosis, treatment, outcomes, and prognosis of the disease was low (29%, 39.2%, 46%, and 43.4%, respectively). Moreover, age, gender, place of residence, sources of information, and ASD case were all statistically significant predictors of ASD knowledge (p < 0.001, p < 0.001, and p = 0.012, p < 0.001, p < 0.001, respectively). The general public in Lebanon perceive a lack of awareness and insufficient knowledge regarding ASD. This results in delayed identification and intervention, leading to unsatisfactory outcomes in patients. Raising awareness about autism among parents, teachers, and healthcare professionals should be a top priority.

Reexamining Pathways Early Autism Intervention in Children Before and After the Third Birthday: A Randomized Control Trial

We reexamined the efficacy of Pathways early autism intervention using generalized measures of social communication and language skills administered by an unfamiliar adult in a novel environment. Generalized measures improve on sources of measurement bias. Sixty-seven autistic children blocked on age (under versus over 3 years) were randomly assigned to 15 weeks of Pathways or services-as-usual. Age moderated the effects of Pathways for social communication. Specifically, Pathways had a significantly large effect for children under 3 and a small effect that approached significance for children over 3. Pathways also had a small effect on expressive speech/language skills. Results replicate previous findings of the efficacy of Pathways on proximal and distal skills and support the importance of early intervention.

Access to Care After Autism Diagnosis During the COVID-19 Pandemic: A Quality Improvement Project

OBJECTIVE

The aim of this study was to identify barriers and improve access to services for children diagnosed with autism spectrum disorder (ASD) during the COVID-19 pandemic.

METHODS

Sixty-two patients diagnosed with ASD between March 23 and June 30, 2020, at a large urban safety net hospital were identified by chart review. Patients were called from January to March 2021 and queried regarding access to services. Brief interventions were provided as part of a modified Plan-Do-Study-Act approach. A second chart review was conducted between April and May 2021 to determine whether families accessed any additional services.

RESULTS

At the time of initial evaluation, 12 patients (19%) had been able to access all recommended services, 32 (52%) had been able to access some recommended services, and 9 (15%) were not able to access any services. Service levels for 9 families (15%) were undetermined. Sixteen patients (26%) lacked any follow-up with their developmental behavioral pediatrician since their initial evaluation. Thirty patients (48%) reported changes to their individualized educational program or early intervention services, and 17 patients (27%) were receiving in-home applied behavior analysis. After the brief intervention, 13 patients had accessed new services, and 9 patients previously lost to follow-up had completed an appointment.

CONCLUSION

Patients diagnosed with ASD in the spring of 2020 had significant difficulty accessing community services. The transition to telemedicine exacerbated this hospital's difficulty contacting patients for follow-up. Additional research is needed to fully characterize the disruptions to services experienced by patients diagnosed with ASD during the pandemic and to increase accessibility and equity of care.

Children with autism exhibit similar longitudinal changes in core symptoms when placed in special or mainstream education settings

LAY ABSTRACT

Today, children with autism spectrum disorder (ASD) are placed in mainstream or special education settings somewhat arbitrarily with no clear clinical recommendations. Here, we compared changes in core ASD symptoms, as measured by the Autism Diagnostic Observation Schedule, 2nd edition (ADOS-2) clinical assessment, across ASD preschool children placed in special or mainstream education. Longitudinal changes in ADOS-2 scores did not differ significantly across settings over a 1- to 2-year period. While some children improved in core ASD symptoms, others deteriorated in both settings. This highlights the need to identify specific criteria for establishing meaningful placement recommendations.

Examination of the impact of the Get SET Early program on equitable access to care within the screen-evaluate-treat chain in toddlers with autism spectrum disorder

LAY ABSTRACT

Delays in autism spectrum disorder identification and access to care could impact developmental outcomes. Although trends are encouraging, children from historically underrepresented minority backgrounds are often identified at later ages and have reduced engagement in services. It is unclear if disparities exist all along the screen-evaluation-treatment chain, or if early detection programs such as Get SET Early that standardize, these steps are effective at ameliorating disparities. As part of the Get SET Early model, primary care providers administered a parent-report screen at well-baby examinations, and parents designated race, ethnicity, and developmental concerns. Toddlers who scored in the range of concern, or whose primary care provider had concerns, were referred for an evaluation. Rates of screening and evaluation engagement within ethnic/racial groups were compared to US Census data. Age at screen, evaluation, and treatment engagement and quantity was compared across groups. Statistical models examined whether key factors such as parent concern were associated with ethnicity or race. No differences were found in the mean age at the first screen, evaluation, or initiation or quantity of behavioral therapy between participants. However, children from historically underrepresented minority backgrounds were more likely to fall into the range of concern on the parent-report screen, their parents expressed developmental concerns more often, and pediatricians were more likely to refer for an evaluation than their White/Not Hispanic counterparts. Overall results suggest that models that support transparent tracking of steps in the screen-evaluation-treatment chain and service referral pipelines may be an effective strategy for ensuring equitable access to care for all children.

Concordance of Diagnosis of Autism Spectrum Disorder Made by Pediatricians vs a Multidisciplinary Specialist Team

IMPORTANCE

Wait times for autism spectrum disorder (ASD) diagnosis are lengthy because of inadequate supply of specialist teams. General pediatricians may be able to diagnose some cases of ASD, thereby reducing wait times.

OBJECTIVE

To determine the accuracy of ASD diagnostic assessments conducted by general pediatricians compared with a multidisciplinary team (MDT).

DESIGN, SETTING, AND PARTICIPANTS

This prospective diagnostic study was conducted in and a specialist assessment center in Toronto, Ontario, Canada, and Ontario general pediatrician practices from June 2016 to March 2020. Children were younger than 5.5 years, referred with a developmental concern, and without an existing ASD diagnosis. Data analysis was performed from October 2021 to February 2022.

EXPOSURES

The pediatrician and MDT each conducted blinded assessments and recorded a decision as to whether the child had ASD.

MAIN OUTCOMES AND MEASURES

Main outcomes included sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A logistic regression was performed to identify factors associated with accurate pediatrician assessment for children with or without an ASD diagnosis.

RESULTS

Seventeen pediatricians (12 women [71%]) participated in the study and referred 106 children (79 boys [75%]; mean [SD] age, 41.9 [13.3] months). Sixty participants (57%) were from minoritized racial and ethnic groups (eg, Black, Asian, Hispanic, Middle Eastern, and multiracial). Seventy-two participants (68%) received a diagnosis of ASD by the MDT. Sensitivity and specificity of the pediatrician assessments compared with MDT were 0.75 (95% CI, 0.67-0.83) and 0.79 (95% CI, 0.62-0.91), respectively. The PPV of the pediatrician assessments was 0.89 (95% CI, 0.80-0.94) (ie, 89% agreement with the MDT), and NPV was 0.60 (95% CI, 0.49-0.70) (ie, 60% agreement with the MDT). Higher pediatrician certainty (odds ratio [OR], 3.33; 95% CI, 1.71-7.34; P = .001) was associated with increased diagnostic accuracy for children with ASD. Lower accuracy was seen for children with higher Visual Reception subscale developmental skills (OR, 0.93; 95% CI, 0.89-0.97; P = .001), speaking abilities (OR, 0.17; 95% CI, 0.03-0.67; P = .03), and White race (OR, 0.32; 95% CI, 0.10-0.97; P = .04). Age, gender, and Autism Diagnostic Observation Schedule, 2nd Edition composite scores were not significantly associated with the accuracy of assessments. All 7 children with a sibling with ASD received an accurate diagnosis; otherwise, no significant factors were identified for accuracy in children without ASD.

CONCLUSIONS AND RELEVANCE

This study of concordance of autism assessment between pediatricians and an expert MDT in young children found high accuracy when general pediatricians felt confident and lower accuracy when ruling out ASD. These findings suggest that children with co-occurring delays may be potential candidates for community assessment.

Has Machine Learning Enhanced the Diagnosis of Autism Spectrum Disorder?

Autism spectrum disorder (ASD) is a complex neurological condition that limits an individual's capacity for communication and learning throughout their life. Although symptoms of Autism can be diagnosed in individuals of different ages, it is labeled as a developmental disorder because symptoms typically start to show up in the initial 2 years of childhood. Autism has no single known cause but multiple factors contribute to its etiology in children. Because symptoms and severity of ASD vary in every individual, there could be many causes. Detection of ASD in the early stages is crucial for providing a path for rehabilitation that enhances the quality of life and integrates the ASD person into the social, family, and professional spheres. Assessment of ASD includes experienced observers in neutral environments, which brings constraints and biases to a lack of credibility and fails to accurately reflect performance in terms of real-world scenarios. To get around these limitations, the conducted review offers a thorough analysis of the impact on the individual and the ones living around them and most recent research on how these techniques are implemented in the diagnosis of ASD. As a result of improvements in technology, assessments now include processing unconventional data than can be collected from measurements arising out of laboratory chemistry or of electrophysiological origin. Examples of these technologies include virtual reality and sensors including eye-tracking imaging. Studies have been conducted towards recognition of emotion and brain networks to identify functional connectivity and discriminate between people with ASD and people who are thought to be typically developing. Diagnosis of Autism has recently made substantial use of long short term memory (LSTM), convolutional neural network (CNN) and its variants, the random forest (RF) and naive Bayes (NB) machine learning techniques. It is hoped that researchers will develop methodologies that increase the probability of identification of ASD in its varied forms and contribute towards improved lifestyle for patients with ASD and those affected by the pathology.

Relationship between cognitive ability and predictors for age at the time of autism spectrum disorder diagnosis

The rising prevalence of autism spectrum disorder (ASD) has increased awareness of the need for early diagnosis and intervention; however, not all children are diagnosed at preschool age. In the literature, findings regarding factors that may play a role in later diagnosis are inconsistent. Presence or absence of intellectual disability is a possible factor. This study aimed to identify factors associated with later diagnosis of ASD among children grouped according to low and high cognitive levels. Data of children who were previously diagnosed with ASD between 2002 and 2016 at a national autism center were analyzed. Better cognitive ability, having more siblings, and previous diagnosis of attention-deficit/hyperactivity disorder were predictors of later diagnosis among the entire cohort. Children exhibiting lower cognitive levels (LC-ASD; DQ/IQ < 70; n = 209) were diagnosed much earlier. Among this group, later ASD diagnosis was predicted by poorer adaptive behavior, more severe restricted and repetitive behaviors, and previous diagnosis of epilepsy. In the higher cognitive level group (HC-ASD; DQ/IQ ≥ 70; n = 321), familial variables and more severe social impairments were predictors of later ASD diagnosis. Clinicians should be aware of variables that may affect ASD diagnostic age among children with varying cognitive abilities.

"On-Time Autism Intervention": A Diagnostic Practice Framework to Accelerate Access

It is well-documented that autism can be reliably diagnosed by age two and that early signs emerge most often between 18 and 24 months. However, despite the increased awareness and focus on early diagnosis, the average age of diagnosis is over 4 years old; even later for Black children and those who are Medicaid-eligible. In this paper, we will propose a framework for accurate and accelerated autism diagnosis for children before age three. The proposed framework emphasizes a collaborative diagnostic process, which relies heavily on Birth to Three provider knowledge and expertise. Considerations for next steps are presented. This approach could increase access to diagnosis of young children soon after first signs of autism emerge.