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Prevalence and Early Identification of Autism Spectrum Disorder Among Children Aged 4 and 8 Years - Autism and Developmental Disabilities Monitoring Network, 16 Sites, United States, 2022

Problem/Condition

Autism spectrum disorder (ASD).

Period Covered

2022.

Description of System

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 2022, a total of 16 sites (located in Arizona, Arkansas, California, Georgia, Indiana, Maryland, Minnesota, Missouri, New Jersey, Pennsylvania, Puerto Rico, Tennessee, Texas [two sites: Austin and Laredo], Utah, and Wisconsin) conducted surveillance for 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 2022. Children were classified as having ASD if they ever received 1) an ASD diagnostic statement in a comprehensive developmental evaluation, 2) autism special education eligibility, or 3) an ASD International Classification of Diseases, Ninth Revision (ICD-9) code in the 299 range or International Classification of Diseases, Tenth Revision (ICD-10) code of F84.0, F84.3, F84.5, F84.8, or F84.9. Children aged 4 years were classified as having suspected ASD if they did not meet the case definition for ASD but had an evaluator's suspicion of ASD documented in a comprehensive developmental evaluation.

Results

Among children aged 8 years in 2022, ASD prevalence was 32.2 per 1,000 children (one in 31) across the 16 sites, ranging from 9.7 in Texas (Laredo) to 53.1 in California. The overall observed prevalence estimate was similar to estimates calculated using Bayesian hierarchical and random effects models. ASD was 3.4 times as prevalent among boys (49.2) than girls (14.3). Overall, ASD prevalence was lower among non-Hispanic White (White) children (27.7) than among Asian or Pacific Islander (A/PI) (38.2), American Indian or Alaska Native (AI/AN) (37.5), non-Hispanic Black or African American (Black) (36.6), Hispanic or Latino (Hispanic) (33.0), and multiracial children (31.9). No association was observed between ASD prevalence and neighborhood median household income (MHI) at 11 sites; higher ASD prevalence was associated with lower neighborhood MHI at five sites.Record abstraction was completed for 15 of the 16 sites for 8,613 children aged 8 years who met the ASD case definition. Of these 8,613 children, 68.4% had a documented diagnostic statement of ASD, 67.3% had a documented autism special education eligibility, and 68.9% had a documented ASD ICD-9 or ICD-10 code. All three elements of the ASD case definition were present for 34.6% of children aged 8 years with ASD.Among 5,292 (61.4% of 8,613) children aged 8 years with ASD with information on cognitive ability, 39.6% were classified as having an intellectual disability. Intellectual disability was present among 52.8% of Black, 50.0% of AI/AN, 43.9% of A/PI, 38.8% of Hispanic, 32.7% of White, and 31.2% of multiracial children with ASD. The median age of earliest known ASD diagnosis was 47 months and ranged from 36 months in California to 69.5 months in Texas (Laredo).Cumulative incidence of ASD diagnosis or eligibility by age 48 months was higher among children born in 2018 (aged 4 years in 2022) than children born in 2014 (aged 8 years in 2022) at 13 of the 15 sites that were able to abstract records. Overall cumulative incidence of ASD diagnosis or eligibility by age 48 months was 1.7 times as high among those born in 2018 compared with those born in 2014 and ranged from 1.4 times as high in Arizona and Georgia to 3.1 times as high in Puerto Rico. Among children aged 4 years, for every 10 children meeting the case definition of ASD, one child met the definition of suspected ASD.Children with ASD who were born in 2018 had more evaluations and identification during ages 0-4 years than children with ASD who were born in 2014 during the 0-4 years age window, with an interruption in the pattern in early 2020 coinciding with onset of the COVID-19 pandemic.Overall, 66.5% of children aged 8 years with ASD had a documented autism test. Use of autism tests varied widely across sites: 24.7% (New Jersey) to 93.5% (Puerto Rico) of children aged 8 years with ASD had a documented autism test in their records. The most common tests documented for children aged 8 years were the Autism Diagnostic Observation Schedule, Autism Spectrum Rating Scales, Childhood Autism Rating Scale, Gilliam Autism Rating Scale, and Social Responsiveness Scale.

Interpretation

Prevalence of ASD among children aged 8 years was higher in 2022 than previous years. ASD prevalence was higher among A/PI, Black, and Hispanic children aged 8 years than White children aged 8 years, continuing a pattern first observed in 2020. A/PI, Black, and Hispanic children aged 8 years with ASD were also more likely than White or multiracial children with ASD to have a co-occurring intellectual disability. Identification by age 48 months was higher among children born in 2018 compared with children born in 2014, suggesting increased early identification consistent with historical patterns.

Public Health Action

Increased identification of autism, particularly among very young children and previously underidentified groups, underscores the increased demand and ongoing need for enhanced planning to provide equitable diagnostic, treatment, and support services for all children with ASD. The substantial variability in ASD identification across sites suggests opportunities to identify and implement successful strategies and practices in communities to ensure all children with ASD reach their potential.

Mega-analytic support for Early Start Denver Model, age at intervention start, and pre-intervention developmental level as factors differentiating early intervention outcomes in autism

Objective

Autism early intervention meta-analyses have provided initial answers to questions such as 'what types of interventions work' and 'for what outcomes'? However, we also want to know 'for whom' is early intervention most effective for? Mega-analysis can offer up complementary insights to meta-analyses regarding the 'what works' and 'for what', while also offering unique insights into the 'for whom' question.

Methods

Here we conduct a mega-analysis with linear mixed effect modeling on AEIR consortium early intervention datasets totaling n=645 children spanning several countries (e.g., USA, Switzerland, Italy, Israel, and Australia). Early Start Denver Model (ESDM) and other non-ESDM approaches (e.g., EIBI, NDBI, other community/treatment as usual approaches) was evaluated as contrasting intervention types. Models also evaluated intervention intensity, type, participant sex, age at intervention start, and pre-intervention developmental quotient. Subscales of Mullen Scales of Early Learning (MSEL), Vineland Adaptive Behavior Scales (VABS), and Autism Diagnostic Observation Schedule (ADOS) were utilized as outcome measures.

Results

Neither intervention intensity nor participant sex affected outcomes. ESDM showed faster growth in language and non-verbal cognition compared to non-ESDM intervention. Irrespective of intervention type, earlier intervention start was associated with increased MSEL and VABS scores and decreased ADOS severity. Growth trajectories on the MSEL also varied by pre-intervention developmental quotient, with higher quotients predicting faster growth irrespective of intervention type.

Conclusions

Age at intervention start and pre-intervention developmental quotient are important individualized factors that predict early intervention response. ESDM also impacts language, non-verbal cognition, and core autism features.

Early detection of neurodevelopmental disorders in paediatric primary care: A scoping review

BACKGROUND

Earlier detection of children at risk for neurodevelopmental disorders is critical and has longstanding repercussions if not addressed early enough.

OBJECTIVES

To explore the supporting or facilitating characteristics of paediatric primary care models of care for early detection in infants and toddlers at risk for neurodevelopmental disorders, identify practitioners involved, and describe how they align with occupational therapy's scope of practice.

METHODS

A scoping review following the Joanna Briggs Institute framework was used. PubMed Central, Cumulative Index to Nursing & Allied Health Literature, and Scopus databases were searched. The search was conducted between January and February 2022. Inclusion criteria were: children aged 0-3 years old; neurodevelopmental disorders including cerebral palsy (CP) and autism spectrum disorder (ASD); models of care used in the paediatric primary care setting and addressing concepts of timing and plasticity; peer-reviewed literature written in English; published between 2010 and 2022. Study protocol registered at https://doi.org/10.17605/OSF.IO/MD4K5.

RESULTS

We identified 1,434 publications, yielding 22 studies that met inclusion criteria. Models of care characteristics included the use of technology, education to parents and staff, funding to utilize innovative models of care, assessment variability, organizational management changes, increased visit length, earlier timeline for neurodevelopmental screening, and collaboration with current office staff or nonphysician practitioners. The top 4 providers were paediatricians, general or family practitioners, nurse/nurse practitioners, and office staff. All studies aligned with occupational therapy health promotion scope of practice and intervention approach yet did not include occupational therapy within the paediatric primary care setting.

CONCLUSIONS

No studies included occupational therapy as a healthcare provider that could be used within the paediatric primary care setting. However, all studies demonstrated models of care facilitating characteristics aligning with occupational therapy practice. Models of care facilitating characteristics identified interdisciplinary staff as a major contributor, which can include occupational therapy, to improve early detection within paediatric primary care.

Development of a school-age extension of the Modified Checklist for Autism in Toddlers through expert consensus and stakeholder input

LAY ABSTRACT

The American Academy of Pediatrics recommends universal screening to identify children at higher likelihood for autism at 18- and 24-month well-child visits. There are many children, however, that are missed during this toddler age who do not get diagnosed until much later in development, delaying access to autism-specific interventions. Currently, brief measures for universal autism screening for school-age children, however, are lacking. In this project, we adapted a commonly used autism screener for toddlers, the Modified Checklist for Autism in Toddlers, Revised, with Follow-Up (M-CHAT-R/F), to be used for school-age children. This measure, called the M-CHAT-School (M-CHAT-S), is a parent- and teacher-report questionnaire to be used to screen for autism in school-age children aged 4 to 8 years of age. M-CHAT-S was developed through feedback from autism experts, as well as interviews with parents and teachers to provide input on the items. Two versions of M-CHAT-S were developed, one for verbally fluent and one for minimally verbal school-age children. M-CHAT-S is a brief measure, with updated items to reflect changes in the way experts think and talk about autism, making it a useful measure to use for autism screening in elementary aged children. The next steps include further testing to ensure that M-CHAT-S performs well in identifying children with increased likelihood of autism, after which it will be made available to parents, educators, and other professionals.

Associations Among Race, Ethnicity, and Clinical Profiles of Young Children Evaluated for Autism in the Primary Care Setting

OBJECTIVE

Despite long-standing racial and ethnic disparities in autism spectrum (AS) diagnosis, recent research suggests that overall, greater numbers of Black and Latine children are now diagnosed with AS as compared with non-Latine White (NLW) children in some US regions. However, gaps remain in the equitable detection of Black and Latine children with AS without significant developmental impairment. The objective of this study was to determine whether the clinical profiles of young children evaluated for AS across a statewide system of early autism diagnosis in Indiana vary by race and ethnicity.

METHODS

We examined racial and ethnic differences in: (1) AS symptom severity, (2) developmental functioning, (3) adaptive functioning, and (4) behavior problems in a sample of 147 children, aged 14 to 48 months (M = 2.6 years), referred for AS evaluation.

RESULTS

Clinical profiles of young children evaluated differed significantly by race and ethnicity, with Black and Latine children exhibiting lower developmental ( p = 0.008) and adaptive abilities ( p = 0.01) and higher AS symptoms ( p = 0.03) as compared with NLW children.

CONCLUSION

Potential explanations for findings include racial and ethnic differences in family and community awareness and knowledge about AS and follow-through on evaluation referral, both driven by social determinants of health (SDOH) affecting minoritized children. Bias in screening and assessment instruments and clinician surveillance, screening, and referral practices may also underlie differences in clinical profiles of children evaluated. Future research is needed to understand the SDOH that influence AS detection and diagnosis to improve equitable access to early diagnosis and intervention.

Differences in regional brain structure in toddlers with autism are related to future language outcomes

Language and social symptoms improve with age in some autistic toddlers, but not in others, and such outcome differences are not clearly predictable from clinical scores alone. Here we aim to identify early-age brain alterations in autism that are prognostic of future language ability. Leveraging 372 longitudinal structural MRI scans from 166 autistic toddlers and 109 typical toddlers and controlling for brain size, we find that, compared to typical toddlers, autistic toddlers show differentially larger or thicker temporal and fusiform regions; smaller or thinner inferior frontal lobe and midline structures; larger callosal subregion volume; and smaller cerebellum. Most differences are replicated in an independent cohort of 75 toddlers. These brain alterations improve accuracy for predicting language outcome at 6-month follow-up beyond intake clinical and demographic variables. Temporal, fusiform, and inferior frontal alterations are related to autism symptom severity and cognitive impairments at early intake ages. Among autistic toddlers, brain alterations in social, language and face processing areas enhance the prediction of the child's future language ability.

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.

Embryonic origin of two ASD subtypes of social symptom severity: the larger the brain cortical organoid size, the more severe the social symptoms

BACKGROUND

Social affective and communication symptoms are central to autism spectrum disorder (ASD), yet their severity differs across toddlers: Some toddlers with ASD display improving abilities across early ages and develop good social and language skills, while others with "profound" autism have persistently low social, language and cognitive skills and require lifelong care. The biological origins of these opposite ASD social severity subtypes and developmental trajectories are not known.

METHODS

Because ASD involves early brain overgrowth and excess neurons, we measured size and growth in 4910 embryonic-stage brain cortical organoids (BCOs) from a total of 10 toddlers with ASD and 6 controls (averaging 196 individual BCOs measured/subject). In a 2021 batch, we measured BCOs from 10 ASD and 5 controls. In a 2022 batch, we  tested replicability of BCO size and growth effects by generating and measuring an independent batch of BCOs from 6 ASD and 4 control subjects. BCO size was analyzed within the context of our large, one-of-a-kind social symptom, social attention, social brain and social and language psychometric normative datasets ranging from N = 266 to N = 1902 toddlers. BCO growth rates were examined by measuring size changes between 1- and 2-months of organoid development. Neurogenesis markers at 2-months were examined at the cellular level. At the molecular level, we measured activity and expression of Ndel1; Ndel1 is a prime target for cell cycle-activated kinases; known to regulate cell cycle, proliferation, neurogenesis, and growth; and known to be involved in neuropsychiatric conditions.

RESULTS

At the BCO level, analyses showed BCO size was significantly enlarged by 39% and 41% in ASD in the 2021 and 2022 batches. The larger the embryonic BCO size, the more severe the ASD social symptoms. Correlations between BCO size and social symptoms were r = 0.719 in the 2021 batch and r = 0. 873 in the replication 2022 batch. ASD BCOs grew at an accelerated rate nearly 3 times faster than controls. At the cell level, the two largest ASD BCOs had accelerated neurogenesis. At the molecular level, Ndel1 activity was highly correlated with the growth rate and size of BCOs. Two BCO subtypes were found in ASD toddlers: Those in one subtype had very enlarged BCO size with accelerated rate of growth and neurogenesis; a profound autism clinical phenotype displaying severe social symptoms, reduced social attention, reduced cognitive, very low language and social IQ; and substantially altered growth in specific cortical social, language and sensory regions. Those in a second subtype had milder BCO enlargement and milder social, attention, cognitive, language and cortical differences.

LIMITATIONS

Larger samples of ASD toddler-derived BCO and clinical phenotypes may reveal additional ASD embryonic subtypes.

CONCLUSIONS

By embryogenesis, the biological bases of two subtypes of ASD social and brain development-profound autism and mild autism-are already present and measurable and involve dysregulated cell proliferation and accelerated neurogenesis and growth. The larger the embryonic BCO size in ASD, the more severe the toddler's social symptoms and the more reduced the social attention, language ability, and IQ, and the more atypical the growth of social and language brain regions.

Metabolic network analysis of pre-ASD newborns and 5-year-old children with autism spectrum disorder

Classical metabolomic and new metabolic network methods were used to study the developmental features of autism spectrum disorder (ASD) in newborns (n = 205) and 5-year-old children (n = 53). Eighty percent of the metabolic impact in ASD was caused by 14 shared biochemical pathways that led to decreased anti-inflammatory and antioxidant defenses, and to increased physiologic stress molecules like lactate, glycerol, cholesterol, and ceramides. CIRCOS plots and a new metabolic network parameter,V ° net, revealed differences in both the kind and degree of network connectivity. Of 50 biochemical pathways and 450 polar and lipid metabolites examined, the developmental regulation of the purine network was most changed. Purine network hub analysis revealed a 17-fold reversal in typically developing children. This purine network reversal did not occur in ASD. These results revealed previously unknown metabolic phenotypes, identified new developmental states of the metabolic correlation network, and underscored the role of mitochondrial functional changes, purine metabolism, and purinergic signaling in autism spectrum disorder.

Association Between Family Income and Positive Developmental Screening Using the Infant Toddler Checklist at the 18-Month Health Supervision Visit

OBJECTIVE

To examine the associations between several potential predictors (child biologic, social, and family factors) and a positive screen for developmental delay using the Infant Toddler Checklist (ITC) at the 18-month health supervision visit in primary care.

METHODS

This was a cross-sectional study of healthy children attending an 18-month health supervision visit in primary care. Parents completed a standardized questionnaire, addressing child, social, and family characteristics, and the ITC. Logistic regression analyses were used to assess the associations between predictors and a positive ITC.

RESULTS

Among 2188 participants (45.5% female; mean age, 18.2 months), 285 (13%) had a positive ITC and 1903 (87%) had a negative ITC. The aOR for a positive ITC for male compared with female sex was 2.15 (95% CI, 1.63-2.83; P < .001). The aOR for birthweight was 0.65 per 1 kg increase (95% CI, 0.53-0.80; P < .001). The aOR for a family income of <$40,000 compared with ≥$150,000 was 3.50 (95% CI, 2.22-5.53; P < .001), and the aOR for family income between $40,000-$79,999 compared with ≥$150,000 was 1.88 (95% CI, 1.26-2.80; P = .002).

CONCLUSIONS

Screening positive on the ITC may identify children at risk for the double jeopardy of developmental delay and social disadvantage and allow clinicians to intervene through monitoring, referral, and resource navigation for both child development and social needs.

TRIAL REGISTRATION

Clinicaltrials.gov (NCT01869530).

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.

Initial diagnostic impressions of trainees during autism evaluations: High specificity but low sensitivity

Reducing the age of first autism diagnosis facilitates access to critical early intervention services. A current "waitlist crisis" for autism diagnostic evaluation thus demands that we consider novel use of available clinical resources. Previous work has found that expert autism clinicians can identify autism in young children with high specificity after only a brief observation; rapid identification by non-experts remains untested. In the current study, 252 children ages 12-53 months presented for a comprehensive autism diagnostic evaluation. We found that junior clinicians in training to become autism specialists (n = 29) accurately determined whether or not a young child would be diagnosed with autism in the first five minutes of the clinic visit in 75% of cases. Specificity of brief observations was high (0.92), suggesting that brief observations may be an effective tool for triaging young children toward autism-specific interventions. In contrast, the lower negative predictive value (0.71) of brief observations, suggest that they should not be used to rule out autism. When trainees expressed more confidence in their initial impression, their impression was more likely to match the final diagnosis. These findings add to a body of literature showing that clinical observations of suspected autism should be taken seriously, but lack of clinician concern should not be used to rule out autism or overrule other indicators of likely autism, such as parent concern or a positive screening result.

Consistency between parent report and direct assessment of development in toddlers with autism spectrum disorder and other delays: Does sex assigned at birth matter?

The current study examined differences between parent report and diagnostician direct assessment of receptive language, expressive language, and fine motor abilities in toddlers with autism spectrum disorder (ASD) and other delays. Additionally, this study examined whether parent-diagnostician consistency varied by child diagnosis and sex assigned at birth (SAB). Initial mixed analysis of variances (ANOVAs) were conducted using data from a sample of 646 toddlers to examine whether parent-diagnostician consistency differed by child diagnosis. Matched samples (using child age, SAB, and nonverbal IQ) were then created within each diagnostic group and mixed ANOVAs were conducted to examine if consistency was similar in matched diagnostic subsamples and whether it differed by SAB. Findings from the full sample mostly replicated previous research that has documented consistency between parent report and direct observation regardless of child diagnosis. However, when examined in matched diagnostic subgroups, more nuanced patterns were observed. Parent report of receptive language was lower in ASD and ASD features subgroups and parent report of fine motor skills was lower than direct observation in the ASD, ASD features, and developmental delay groups. When examining the moderating effect of SAB, only expressive language was impacted for children in the ASD group. Results indicate the importance of considering child demographic characteristics and that child SAB may impact parent report and/or diagnostician perception of expressive language.

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

Problem/Condition

Autism spectrum disorder (ASD).

Period Covered

2020.

Description of System

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.

Results

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.

Interpretation

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.

Public Health Action

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.

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

Problem/Condition

Autism spectrum disorder (ASD).

Period Covered

2020.

Description of System

The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance program that provides estimates of the prevalence of ASD among children aged 8 years. In 2020, there were 11 ADDM Network sites across the United States (Arizona, Arkansas, California, Georgia, Maryland, Minnesota, Missouri, New Jersey, Tennessee, Utah, and Wisconsin). To ascertain ASD among children aged 8 years, ADDM Network staff review and abstract developmental evaluations and records from community medical and educational service providers. A child met the case definition if their record documented 1) an ASD diagnostic statement in an evaluation, 2) a classification of ASD in special education, or 3) an ASD International Classification of Diseases (ICD) code.

Results

For 2020, across all 11 ADDM sites, ASD prevalence per 1,000 children aged 8 years ranged from 23.1 in Maryland to 44.9 in California. The overall ASD prevalence was 27.6 per 1,000 (one in 36) children aged 8 years and was 3.8 times as prevalent among boys as among girls (43.0 versus 11.4). Overall, ASD prevalence was lower among non-Hispanic White children (24.3) and children of two or more races (22.9) than among non-Hispanic Black or African American (Black), Hispanic, and non-Hispanic Asian or Pacific Islander (A/PI) children (29.3, 31.6, and 33.4 respectively). ASD prevalence among non-Hispanic American Indian or Alaska Native (AI/AN) children (26.5) was similar to that of other racial and ethnic groups. ASD prevalence was associated with lower household income at three sites, with no association at the other sites.Across sites, the ASD prevalence per 1,000 children aged 8 years based exclusively on documented ASD diagnostic statements was 20.6 (range = 17.1 in Wisconsin to 35.4 in California). Of the 6,245 children who met the ASD case definition, 74.7% had a documented diagnostic statement of ASD, 65.2% had a documented ASD special education classification, 71.6% had a documented ASD ICD code, and 37.4% had all three types of ASD indicators. The median age of earliest known ASD diagnosis was 49 months and ranged from 36 months in California to 59 months in Minnesota.Among the 4,165 (66.7%) children with ASD with information on cognitive ability, 37.9% were classified as having an intellectual disability. Intellectual disability was present among 50.8% of Black, 41.5% of A/PI, 37.8% of two or more races, 34.9% of Hispanic, 34.8% of AI/AN, and 31.8% of White children with ASD. Overall, children with intellectual disability had earlier median ages of ASD diagnosis (43 months) than those without intellectual disability (53 months).

Interpretation

For 2020, one in 36 children aged 8 years (approximately 4% of boys and 1% of girls) was estimated to have ASD. These estimates are higher than previous ADDM Network estimates during 2000-2018. For the first time among children aged 8 years, the prevalence of ASD was lower among White children than among other racial and ethnic groups, reversing the direction of racial and ethnic differences in ASD prevalence observed in the past. Black children with ASD were still more likely than White children with ASD to have a co-occurring intellectual disability.

Public Health Action

The continued increase among children identified with ASD, particularly among non-White children and girls, highlights the need for enhanced infrastructure to provide equitable diagnostic, treatment, and support services for all children with ASD. Similar to previous reporting periods, findings varied considerably across network sites, indicating the need for additional research to understand the nature of such differences and potentially apply successful identification strategies across states.

Atypical functional connectivity of temporal cortex with precuneus and visual regions may be an early-age signature of ASD

BACKGROUND

Social and language abilities are closely intertwined during early typical development. In autism spectrum disorder (ASD), however, deficits in social and language development are early-age core symptoms. We previously reported that superior temporal cortex, a well-established social and language region, shows reduced activation to social affective speech in ASD toddlers; however, the atypical cortical connectivity that accompanies this deviance remains unknown.

METHODS

We collected clinical, eye tracking, and resting-state fMRI data from 86 ASD and non-ASD subjects (mean age 2.3 ± 0.7 years). Functional connectivity of left and right superior temporal regions with other cortical regions and correlations between this connectivity and each child's social and language abilities were examined.

RESULTS

While there was no group difference in functional connectivity, the connectivity between superior temporal cortex and frontal and parietal regions was significantly correlated with language, communication, and social abilities in non-ASD subjects, but these effects were absent in ASD subjects. Instead, ASD subjects, regardless of different social or nonsocial visual preferences, showed atypical correlations between temporal-visual region connectivity and communication ability (r(49) = 0.55, p < 0.001) and between temporal-precuneus connectivity and expressive language ability (r(49) = 0.58, p < 0.001).

LIMITATIONS

The distinct connectivity-behavior correlation patterns may be related to different developmental stages in ASD and non-ASD subjects. The use of a prior 2-year-old template for spatial normalization may not be optimal for a few subjects beyond this age range.

CONCLUSIONS

Superior temporal cortex is known to have reduced activation to social affective speech in ASD at early ages, and here we find in ASD toddlers that it also has atypical connectivity with visual and precuneus cortices that is correlated with communication and language ability, a pattern not seen in non-ASD toddlers. This atypicality may be an early-age signature of ASD that also explains why the disorder has deviant early language and social development. Given that these atypical connectivity patterns are also present in older individuals with ASD, we conclude these atypical connectivity patterns persist across age and may explain why successful interventions targeting language and social skills at all ages in ASD are so difficult to achieve.

Predictive Validity of the Infant Toddler Checklist in Primary Care at the 18-month Visit and School Readiness at 4 to 6 Years

OBJECTIVE

The American Academy of Pediatrics recommends developmental surveillance and screening in early childhood in primary care. The 18-month visit may be an ideal time for identification of children with delays in language and communication, or symptoms of autism spectrum disorder (ASD). Little is known about the predictive validity of developmental screening tools administered at 18 months. Our objective was to examine the predictive validity of the Infant Toddler Checklist (ITC) at the 18-month health supervision visit, using school readiness at kindergarten age as the criterion measure.

METHODS

We designed a prospective cohort study, recruiting in primary care in Toronto, Canada. Parents completed the ITC at the 18-month visit. Teachers completed the Early Development Instrument (EDI) when the children were in Kindergarten, age 4-6 years. We calculated screening test properties with 95% confidence intervals (CIs). We used multivariable logistic and linear regression analyses adjusted for important covariates.

RESULTS

Of 293 children (mean age 18 months), 30 (10.2%) had a positive ITC including: concern for speech delay (n = 11, 3.8%), concern for other communication delay (n = 13, 4.4%), and concern for both (n = 6, 2.0%). At follow-up (mean age 5 years), 54 (18.4%) had overall EDI vulnerability, 19 (6.5%) had vulnerability on the 2 EDI communication domains. The ITC sensitivity ranged from 11% to 32%, specificity from 91% to 96%, false positive rates from 4% to 9%, PPV from 16% to 35%, NPV from 83% to 95%. A positive ITC screen and ITC concern for speech delay were associated with lower scores in EDI communication skills and general knowledge (β = -1.08; 95% CI: -2.10, -0.17; β = -2.35; 95% CI: -3.63, -1.32) and EDI language and cognitive development (β = -0.62; 95% CI: -1.25, -0.18; β = -1.22; 95% CI: -2.11, -0.58).

CONCLUSIONS

The ITC demonstrated high specificity suggesting that most children with a negative ITC screen will demonstrate school readiness at 4-6 years, and low false positive rates, minimizing over-diagnosis. The ITC had low sensitivity highlighting the importance of ongoing developmental surveillance and screening.

Level of Attention to Motherese Speech as an Early Marker of Autism Spectrum Disorder

Importance

Caregivers have long captured the attention of their infants by speaking in motherese, a playful speech style characterized by heightened affect. Reduced attention to motherese in toddlers with autism spectrum disorder (ASD) may be a contributor to downstream language and social challenges and could be diagnostically revealing.

Objective

To investigate whether attention toward motherese speech can be used as a diagnostic classifier of ASD and is associated with language and social ability.

Design, Setting, and Participants

This diagnostic study included toddlers aged 12 to 48 months, spanning ASD and non-ASD diagnostic groups, at a research center. Data were collected from February 2018 to April 2021 and analyzed from April 2021 to March 2022.

Exposures

Gaze-contingent eye-tracking test.

Main Outcomes and Measures

Using gaze-contingent eye tracking wherein the location of a toddler's fixation triggered a specific movie file, toddlers participated in 1 or more 1-minute eye-tracking tests designed to quantify attention to motherese speech, including motherese vs traffic (ie, noisy vehicles on a highway) and motherese vs techno (ie, abstract shapes with music). Toddlers were also diagnostically and psychometrically evaluated by psychologists. Levels of fixation within motherese and nonmotherese movies and mean number of saccades per second were calculated. Receiver operating characteristic (ROC) curves were used to evaluate optimal fixation cutoff values and associated sensitivity, specificity, positive predictive value (PPV), and negative predictive value. Within the ASD group, toddlers were stratified based on low, middle, or high levels of interest in motherese speech, and associations with social and language abilities were examined.

Results

A total of 653 toddlers were included (mean [SD] age, 26.45 [8.37] months; 480 males [73.51%]). Unlike toddlers without ASD, who almost uniformly attended to motherese speech with a median level of 82.25% and 80.75% across the 2 tests, among toddlers with ASD, there was a wide range, spanning 0% to 100%. Both the traffic and techno paradigms were effective diagnostic classifiers, with large between-group effect sizes (eg, ASD vs typical development: Cohen d, 1.0 in the techno paradigm). Across both paradigms, a cutoff value of 30% or less fixation on motherese resulted in an area under the ROC curve (AUC) of 0.733 (95% CI, 0.693-0.773) and 0.761 (95% CI, 0.717-0.804), respectively; specificity of 98% (95% CI, 95%-99%) and 96% (95% CI, 92%-98%), respectively; and PPV of 94% (95% CI, 86%-98%). Reflective of heterogeneity and expected subtypes in ASD, sensitivity was lower at 18% (95% CI, 14%-22%) and 29% (95% CI, 24%-34%), respectively. Combining metrics increased the AUC to 0.841 (95% CI, 0.805-0.877). Toddlers with ASD who showed the lowest levels of attention to motherese speech had weaker social and language abilities.

Conclusions and Relevance

In this diagnostic study, a subset of toddlers showed low levels of attention toward motherese speech. When a cutoff level of 30% or less fixation on motherese speech was used, toddlers in this range were diagnostically classified as having ASD with high accuracy. Insight into which toddlers show unusually low levels of attention to motherese may be beneficial not only for early ASD diagnosis and prognosis but also as a possible therapeutic target.

A predictive ensemble classifier for the gene expression diagnosis of ASD at ages 1 to 4 years

Autism Spectrum Disorder (ASD) diagnosis remains behavior-based and the median age of diagnosis is ~52 months, nearly 5 years after its first-trimester origin. Accurate and clinically-translatable early-age diagnostics do not exist due to ASD genetic and clinical heterogeneity. Here we collected clinical, diagnostic, and leukocyte RNA data from 240 ASD and typically developing (TD) toddlers (175 toddlers for training and 65 for test). To identify gene expression ASD diagnostic classifiers, we developed 42,840 models composed of 3570 gene expression feature selection sets and 12 classification methods. We found that 742 models had AUC-ROC ≥ 0.8 on both Training and Test sets. Weighted Bayesian model averaging of these 742 models yielded an ensemble classifier model with accurate performance in Training and Test gene expression datasets with ASD diagnostic classification AUC-ROC scores of 85-89% and AUC-PR scores of 84-92%. ASD toddlers with ensemble scores above and below the overall ASD ensemble mean of 0.723 (on a scale of 0 to 1) had similar diagnostic and psychometric scores, but those below this ASD ensemble mean had more prenatal risk events than TD toddlers. Ensemble model feature genes were involved in cell cycle, inflammation/immune response, transcriptional gene regulation, cytokine response, and PI3K-AKT, RAS and Wnt signaling pathways. We additionally collected targeted DNA sequencing smMIPs data on a subset of ASD risk genes from 217 of the 240 ASD and TD toddlers. This DNA sequencing found about the same percentage of SFARI Level 1 and 2 ASD risk gene mutations in TD (12 of 105) as in ASD (13 of 112) toddlers, and classification based only on the presence of mutation in these risk genes performed at a chance level of 49%. By contrast, the leukocyte ensemble gene expression classifier correctly diagnostically classified 88% of TD and ASD toddlers with ASD risk gene mutations. Our ensemble ASD gene expression classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; out-performs a risk gene mutation classifier; and has potential for clinical translation.

Language, Social, and Face Regions Are Affected in Toddlers with Autism and Predictive of Language Outcome

Identifying prognostic early brain alterations is crucial for autism spectrum disorder (ASD). Leveraging structural MRI data from 166 ASD and 109 typical developing (TD) toddlers and controlling for brain size, we found that, compared to TD, ASD toddlers showed larger or thicker lateral temporal regions; smaller or thinner frontal lobe and midline structures; larger callosal subregion volume; and smaller cerebellum. Most of these differences were replicated in an independent cohort of 38 ASD and 37 TD toddlers. Moreover, the identified brain alterations were related to ASD symptom severity and cognitive impairments at intake, and, remarkably, they improved the accuracy for predicting later language outcome beyond intake clinical and demographic variables. In summary, brain regions involved in language, social, and face processing were altered in ASD toddlers. These early-age brain alterations may be the result of dysregulation in multiple neural processes and stages and are promising prognostic biomarkers for future language ability.

Implementing the Get SET Early Model in a Community Setting to Lower the Age of ASD Diagnosis

OBJECTIVE

The objective of this study was to implement a validated, university-based early detection program, the Get SET Early model, in a community-based setting. Get SET was developed to improve Screening, Evaluation, and Treatment referral practices. Specifically, its purpose was to lower the age of diagnosis and enable toddlers with autism spectrum disorder (ASD) to begin treatment by 36 months.

METHODS

One hundred nine pediatric health care providers were recruited to administer the Communication and Symbolic Behavior Scales Developmental Profile Infant-Toddler Checklist at 12-month, 18-month, and 24-month well-baby visits and referred toddlers whose scores indicated the need for a developmental evaluation. Licensed psychologists were trained to provide diagnostic evaluations to toddlers as young as 12 months. Mean age of diagnosis was compared with current population rates.

RESULTS

In 4 years, 45,504 screens were administered at well-baby visits, and 648 children were evaluated at least 1 time. The overall median age for ASD diagnosis was 22 months, which is significantly lower than the median age reported by the CDC (57 months). For children screened at 12 months, the age of first diagnosis was significantly lower at 15 months. Of the 350 children who completed at least 1 follow-up evaluation, 323 were diagnosed with ASD or another delay, and 239 (74%) were enrolled in a treatment program.

CONCLUSION

Toddlers with ASD were diagnosed nearly 3 years earlier than the most recent CDC report, which allowed children to start a treatment program by 36 months. Overall, Get SET Early was an effective strategy for improving the current approach to screening, evaluation, and treatment. Efforts to demonstrate sustainability are underway.

Future Prospects for Epigenetics in Autism Spectrum Disorder

Despite decades of investigation into the genetics of autism spectrum disorder (ASD), a current consensus in the field persists that ASD risk is too heterogeneous to be diagnosed by a single set of genetic variants. As such, ASD research has broadened to include assessment of other molecular biomarkers implicated in the condition that may be reflective of environmental exposures or gene by environment interactions. Epigenetic variance, and specifically differential DNA methylation, have emerged as areas of particularly high interest to ASD, as the epigenetic markers from specific chromatin loci collectively can reflect influences of multiple genetic and environmental factors and can also result in differential gene expression patterns. This review examines recent studies of the ASD epigenome, detailing common gene pathways found to be differentially methylated in people with ASD, and considers how these discoveries may inform our understanding of ASD etiology. We also consider future applications of epigenetics in ASD research and clinical practice, focusing on substratification, biomarker development, and experimental preclinical models of ASD that test causality. In combination with other -omics approaches, epigenomics allows an improved conceptualization of the multifactorial nature of ASD, and opens future lines of inquiry for both basic research and clinical practice.

Detecting Autism Spectrum Disorder Using Spectral Analysis of Electroretinogram and Machine Learning: Preliminary results

Autism spectrum disorder (ASD) is a neurodevelopmental condition that impacts language, communication and social interactions. The current diagnostic process for ASD is based upon a detailed multidisciplinary assessment. Currently no clinical biomarker exists to help in the diagnosis and monitoring of this condition that has a prevalence of approximately 1%. The electroretinogram (ERG), is a clinical test that records the electrical response of the retina to light. The ERG is a promising way to study different neurodevelopmental and neurodegenerative disorders, including ASD. In this study, we have proposed a machine learning based method to detect ASD from control subjects using the ERG waveform. We collected ERG signals from 47 control (CO) and 96 ASD individuals. We analyzed ERG signals both in the time and the spectral domain to gain insight into the statistically significant discriminating features between CO and ASD individuals. We evaluated the machine learning (ML) models using a subject independent cross validation-based approach. Time-domain features were able to detect ASD with a maximum 65% accuracy. The classification accuracy of our best ML model using time-domain and spectral features was 86%, with 98% sensitivity. Our preliminary results indicate that spectral analysis of ERG provides helpful information for the classification of ASD.

Predictive validity of the Infant Toddler Checklist in primary care at the 18-month visit and developmental diagnosis at 3-5 years: a prospective cohort study

OBJECTIVE

There is international variation in recommendations regarding developmental screening and growing recognition of the low sensitivity of commonly used developmental screening tools. Our objective was to examine the predictive validity of the Infant Toddler Checklist (ITC) at 18 months to predict a developmental diagnosis at 3-5 years, in a primary care setting.

METHODS

We designed a prospective cohort study, recruiting in primary care in Toronto, Canada. Parents completed the ITC at the 18-month visit and reported developmental diagnosis at 3-5 years (developmental delay, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), learning problem). We calculated screening test properties with 95% CIs. We used multivariable logistic regression analyses adjusted for important covariates.

RESULTS

In the final sample (n=488), mean age at screening was 18.5 (SD 1.1) months, and at follow-up was 46.6 (SD 10.0) months. At screening, 46 (9.4%) had a positive ITC. At follow-up, 26 (5.3%) had a developmental diagnosis, including: developmental delay (n=22), ASD (n=4), ADHD (n=1), learning problem (n=1); parents of two children each reported two diagnoses (total of 28 diagnoses). Of four children with a diagnosis of ASD at follow-up, three had a positive ITC at 18 months. The ITC specificity (92%, 95% CI: 89% to 94%) and negative predictive value (96%, 95% CI: 95% to 97%) were high; false positive rate was low (8%, 95% CI: 6% to 11%); sensitivity was low (31%, 95% CI: 14% to 52%). There was a strong association between a positive ITC at 18 months and later developmental diagnosis (adjusted OR 4.48, 95% CI: 1.72 to 11.64; p=0.002).

CONCLUSION

The ITC had high specificity, high negative predictive value, low false positive rate, and identified children with later developmental delay and ASD. The ITC had low sensitivity, similar to other screening tools underscoring the importance of continuous developmental surveillance at all health supervision visits.

Large scale validation of an early-age eye-tracking biomarker of an autism spectrum disorder subtype

Few clinically validated biomarkers of ASD exist which can rapidly, accurately, and objectively identify autism during the first years of life and be used to support optimized treatment outcomes and advances in precision medicine. As such, the goal of the present study was to leverage both simple and computationally-advanced approaches to validate an eye-tracking measure of social attention preference, the GeoPref Test, among 1,863 ASD, delayed, or typical toddlers (12-48 months) referred from the community or general population via a primary care universal screening program. Toddlers participated in diagnostic and psychometric evaluations and the GeoPref Test: a 1-min movie containing side-by-side dynamic social and geometric images. Following testing, diagnosis was denoted as ASD, ASD features, LD, GDD, Other, typical sibling of ASD proband, or typical. Relative to other diagnostic groups, ASD toddlers exhibited the highest levels of visual attention towards geometric images and those with especially high fixation levels exhibited poor clinical profiles. Using the 69% fixation threshold, the GeoPref Test had 98% specificity, 17% sensitivity, 81% PPV, and 65% NPV. Sensitivity increased to 33% when saccades were included, with comparable validity across sex, ethnicity, or race. The GeoPref Test was also highly reliable up to 24 months following the initial test. Finally, fixation levels among twins concordant for ASD were significantly correlated, indicating that GeoPref Test performance may be genetically driven. As the GeoPref Test yields few false positives (~ 2%) and is equally valid across demographic categories, the current findings highlight the ability of the GeoPref Test to rapidly and accurately detect autism before the 2nd birthday in a subset of children and serve as a biomarker for a unique ASD subtype in clinical trials.

Neural responses to affective speech, including motherese, map onto clinical and social eye tracking profiles in toddlers with ASD

Affective speech, including motherese, captures an infant's attention and enhances social, language and emotional development. Decreased behavioural response to affective speech and reduced caregiver-child interactions are early signs of autism in infants. To understand this, we measured neural responses to mild affect speech, moderate affect speech and motherese using natural sleep functional magnetic resonance imaging and behavioural preference for motherese using eye tracking in typically developing toddlers and those with autism. By combining diverse neural-clinical data using similarity network fusion, we discovered four distinct clusters of toddlers. The autism cluster with the weakest superior temporal responses to affective speech and very poor social and language abilities had reduced behavioural preference for motherese, while the typically developing cluster with the strongest superior temporal response to affective speech showed the opposite effect. We conclude that significantly reduced behavioural preference for motherese in autism is related to impaired development of temporal cortical systems that normally respond to parental affective speech.

Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018

Problem/Condition

Autism spectrum disorder (ASD).

Period Covered

2018.

Description of System

The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts active surveillance of ASD. This report focuses on the prevalence and characteristics of ASD among children aged 8 years in 2018 whose parents or guardians lived in 11 ADDM Network sites in the United States (Arizona, Arkansas, California, Georgia, Maryland, Minnesota, Missouri, New Jersey, Tennessee, Utah, and Wisconsin). To ascertain ASD among children aged 8 years, ADDM Network staff review and abstract developmental evaluations and records from community medical and educational service providers. In 2018, children met the case definition if their records documented 1) an ASD diagnostic statement in an evaluation (diagnosis), 2) a special education classification of ASD (eligibility), or 3) an ASD International Classification of Diseases (ICD) code.

Results

For 2018, across all 11 ADDM sites, ASD prevalence per 1,000 children aged 8 years ranged from 16.5 in Missouri to 38.9 in California. The overall ASD prevalence was 23.0 per 1,000 (one in 44) children aged 8 years, and ASD was 4.2 times as prevalent among boys as among girls. Overall ASD prevalence was similar across racial and ethnic groups, except American Indian/Alaska Native children had higher ASD prevalence than non-Hispanic White (White) children (29.0 versus 21.2 per 1,000 children aged 8 years). At multiple sites, Hispanic children had lower ASD prevalence than White children (Arizona, Arkansas, Georgia, and Utah), and non-Hispanic Black (Black) children (Georgia and Minnesota). The associations between ASD prevalence and neighborhood-level median household income varied by site. Among the 5,058 children who met the ASD case definition, 75.8% had a diagnostic statement of ASD in an evaluation, 18.8% had an ASD special education classification or eligibility and no ASD diagnostic statement, and 5.4% had an ASD ICD code only. ASD prevalence per 1,000 children aged 8 years that was based exclusively on documented ASD diagnostic statements was 17.4 overall (range: 11.2 in Maryland to 29.9 in California). The median age of earliest known ASD diagnosis ranged from 36 months in California to 63 months in Minnesota.

Among the 3,007 children with ASD and data on cognitive ability, 35.2% were classified as having an intelligence quotient (IQ) score ≤70. The percentages of children with ASD with IQ scores ≤70 were 49.8%, 33.1%, and 29.7% among Black, Hispanic, and White children, respectively. Overall, children with ASD and IQ scores ≤70 had earlier median ages of ASD diagnosis than children with ASD and IQ scores >70 (44 versus 53 months).

Interpretation

In 2018, one in 44 children aged 8 years was estimated to have ASD, and prevalence and median age of identification varied widely across sites. Whereas overall ASD prevalence was similar by race and ethnicity, at certain sites Hispanic children were less likely to be identified as having ASD than White or Black children. The higher proportion of Black children compared with White and Hispanic children classified as having intellectual disability was consistent with previous findings.

Public Health Action

The variability in ASD prevalence and community ASD identification practices among children with different racial, ethnic, and geographical characteristics highlights the importance of research into the causes of that variability and strategies to provide equitable access to developmental evaluations and services. These findings also underscore the need for enhanced infrastructure for diagnostic, treatment, and support services to meet the needs of all children.

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

PROBLEM/CONDITION

Autism spectrum disorder (ASD).

PERIOD COVERED

2018.

DESCRIPTION OF SYSTEM

The 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.

RESULTS

For 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).

INTERPRETATION

Groups 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 ACTION

Children 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.

Pre-treatment clinical and gene expression patterns predict developmental change in early intervention in autism

Early detection and intervention are believed to be key to facilitating better outcomes in children with autism, yet the impact of age at treatment start on the outcome is poorly understood. While clinical traits such as language ability have been shown to predict treatment outcome, whether or not and how information at the genomic level can predict treatment outcome is unknown. Leveraging a cohort of toddlers with autism who all received the same standardized intervention at a very young age and provided a blood sample, here we find that very early treatment engagement (i.e., <24 months) leads to greater gains while controlling for time in treatment. Pre-treatment clinical behavioral measures predict 21% of the variance in the rate of skill growth during early intervention. Pre-treatment blood leukocyte gene expression patterns also predict the rate of skill growth, accounting for 13% of the variance in treatment slopes. Results indicated that 295 genes can be prioritized as driving this effect. These treatment-relevant genes highly interact at the protein level, are enriched for differentially histone acetylated genes in autism postmortem cortical tissue, and are normatively highly expressed in a variety of subcortical and cortical areas important for social communication and language development. This work suggests that pre-treatment biological and clinical behavioral characteristics are important for predicting developmental change in the context of early intervention and that individualized pre-treatment biology related to histone acetylation may be key.

Atypical genomic cortical patterning in autism with poor early language outcome

Cortical regionalization develops via genomic patterning along anterior-posterior (A-P) and dorsal-ventral (D-V) gradients. Here, we find that normative A-P and D-V genomic patterning of cortical surface area (SA) and thickness (CT), present in typically developing and autistic toddlers with good early language outcome, is absent in autistic toddlers with poor early language outcome. Autistic toddlers with poor early language outcome are instead specifically characterized by a secondary and independent genomic patterning effect on CT. Genes involved in these effects can be traced back to midgestational A-P and D-V gene expression gradients and different prenatal cell types (e.g., progenitor cells and excitatory neurons), are functionally important for vocal learning and human-specific evolution, and are prominent in prenatal coexpression networks enriched for high-penetrance autism risk genes. Autism with poor early language outcome may be explained by atypical genomic cortical patterning starting in prenatal development, which may detrimentally affect later regional functional specialization and circuit formation.