Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020
Open accessAbstract
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 fut
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TL;DR
In 2020, Autism and Developmental Disabilities Monitoring Network surveillance across 11 U.S. sites estimated ASD prevalence among children aged 4 years at 21.5 per 1,000, with wide site variation and higher prevalence among boys and several racial/ethnic minority groups. Early identification by age 48 months was higher among children aged 4 years than among children aged 8 years, but evaluations and identifications fell after the March 2020 COVID-19 pandemic declaration and had not fully recovered by the end of 2020 at most sites.
Key findings
- Overall ASD prevalence among children aged 4 years was 21.5 per 1,000 (1 in 46), ranging from 12.7 per 1,000 in Utah to 46.4 in California. Prevalence was 32.3 per 1,000 among boys versus 10.4 among girls (male-to-female ratio 3.1).
- By race and ethnicity, prevalence was highest among Hispanic children (28.7 per 1,000), followed by Black (25.3), Asian/Pacific Islander (23.5), multiracial (19.3), White (16.3), and American Indian/Alaska Native children (11.1). Compared with White children, prevalence was 1.8 times as high among Hispanic, 1.6 times as high among Black, 1.4 times as high among Asian/Pacific Islander, and 1.2 times as high among multiracial children.
- Among the 58.3% of children aged 4 years with ASD who had intellectual ability information, 48.5% had an IQ score ≤70 or an examiner’s statement of intellectual disability. Among children with a documented developmental evaluation, 78.0% were evaluated by age 36 months.
- Cumulative incidence of ASD diagnosis or eligibility by age 48 months was 17.5 per 1,000 among children aged 4 years versus 11.2 among children aged 8 years (1.6 times higher), with site risk ratios from 1.3 in New Jersey and Utah to 2.0 in Tennessee. In the 6 months before the March 2020 pandemic declaration, there were 1,593 more evaluations and 1.89 more ASD identifications per 1,000 children aged 4 years than children aged 8 years had 4 years earlier; in the 6 months after, there were 217 fewer evaluations and 0.26 fewer identifications per 1,000. Suspected ASD prevalence ranged from 0.5 per 1,000 in California to 10.4 in Arkansas and increased from 2018 at five sites.
Methods
- Active surveillance was conducted in 11 sites in Arizona, Arkansas, California, Georgia, Maryland, Minnesota, Missouri, New Jersey, Tennessee, Utah, and Wisconsin among children aged 4 and 8 years in 2020. Children met the ASD case definition if records documented an ASD diagnostic statement, ASD special education eligibility, or an ASD ICD-9/ICD-10 code; suspected ASD was defined as a qualified professional’s statement of suspicion without meeting the ASD case definition.
- Data came from health and education records, with denominators from U.S. Census 2021 county-level postcensal population estimates for 2020, adjusted for subcounty school districts at three sites using public school enrollment counts. Analyses calculated prevalence, prevalence ratios, cumulative incidence, and risk ratios using the Wilson score method for 95% CIs, with Mantel-Haenszel, chi-square, or Fisher tests for selected comparisons; R version 4.2.0 was used.
- To assess COVID-19 impact, monthly counts of evaluations and earliest identification for children aged 4 years in 2016–2020 were compared with the same age windows for children aged 8 years in 2012–2016.
Limitations
- The paper states four limitations: surveillance area populations are not necessarily generalizable to entire states or the United States; data quality depends on availability and completeness of records; sites or study areas could change between study years, complicating comparisons over time; and the surveillance case definition of intellectual disability is not the same as a clinical diagnosis, with IQ measurements in young children potentially unstable.
- Evident from the text: intellectual ability information was available for only 58.3% of children aged 4 years with ASD, so co-occurring intellectual disability estimates are based on a subset. Also, comparisons between children with ASD and suspected ASD were among children seen for a developmental evaluation, so disparities in receiving evaluations could still exist.
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- Findingown data
The overall ASD prevalence among children aged 4 years across the 11 ADDM Network sites in 2020 was 21.5 per 1,000.
“The overall prevalence was 21.5 and was higher among boys than girls at every site.”
- Also stated in: § Prevalence and Characteristics of Children Aged 4 Years with ASD · plain
“ASD prevalence in the 11 sites combined was 21.5 per 1,000 (one in 46) children aged 4 years.”
Quote in the paperSubstantial claim - Also stated in: § Prevalence and Characteristics of Children Aged 4 Years with ASD · plain
- Findingown data
Compared with non-Hispanic White children, ASD prevalence among children aged 4 years in 2020 was 1.2 times as high among multiracial children.
“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.”
- Also stated in: § Demographics · plain
“Compared with White children, ASD prevalence was higher among Black children at nine sites (overall 1.6 times as high), Hispanic children at seven sites (overall 1.8 times as high), A/PI children at six sites (overall 1.4 times as high), and multiracial children at two sites (overall 1.2 times as high) (Table 3).”
Quote in the paperNot a substantial claim - Also stated in: § Demographics · plain
- Findingown data
Compared with non-Hispanic White children, ASD prevalence among children aged 4 years in 2020 was 1.4 times as high among Asian or Pacific Islander children.
“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.”
- Also stated in: § Demographics · plain
“Compared with White children, ASD prevalence was higher among Black children at nine sites (overall 1.6 times as high), Hispanic children at seven sites (overall 1.8 times as high), A/PI children at six sites (overall 1.4 times as high), and multiracial children at two sites (overall 1.2 times as high) (Table 3).”
Quote in the paperNot a substantial claim - Also stated in: § Demographics · plain
- Findingown data
Compared with non-Hispanic White children, ASD prevalence among children aged 4 years in 2020 was 1.6 times as high among non-Hispanic Black children.
“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.”
- Also stated in: § Demographics · plain
“Compared with White children, ASD prevalence was higher among Black children at nine sites (overall 1.6 times as high), Hispanic children at seven sites (overall 1.8 times as high), A/PI children at six sites (overall 1.4 times as high), and multiracial children at two sites (overall 1.2 times as high) (Table 3).”
Quote in the paperNot a substantial claim - Also stated in: § Demographics · plain
- Findingown data
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 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.”
Quote in the paperNot a substantial claim
- Findingown data
For 2020, ASD prevalence among children aged 4 years varied across ADDM Network sites, from 12.7 per 1,000 in Utah to 46.4 per 1,000 in California.
“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.”
- Also stated in: § Prevalence and Characteristics of Children Aged 4 Years with ASD · plain
“For 2020, prevalence ranged from 12.7 per 1,000 children aged 4 years in Utah to 46.4 in California (Table 1).”
Quote in the paperSubstantial claim - Also stated in: § Prevalence and Characteristics of Children Aged 4 Years with ASD · plain
- Findingown data
ASD prevalence among children aged 4 years in 2020 was higher among boys than girls at every ADDM Network site.
“The overall prevalence was 21.5 and was higher among boys than girls at every site.”
- Also stated in: § Demographics · plain
“Prevalence was significantly higher among males than females at all sites (95% CI excludes 1.0).”
Quote in the paperNot a substantial claim - Also stated in: § Demographics · plain
- Findingown data
Compared with non-Hispanic White children, ASD prevalence among children aged 4 years in 2020 was 1.8 times as high among Hispanic children.
“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.”
- Also stated in: § Demographics · plain
“Compared with White children, ASD prevalence was higher among Black children at nine sites (overall 1.6 times as high), Hispanic children at seven sites (overall 1.8 times as high), A/PI children at six sites (overall 1.4 times as high), and multiracial children at two sites (overall 1.2 times as high) (Table 3).”
Quote in the paperNot a substantial claim - Also stated in: § Demographics · plain
- Findingown data
Among children aged 4 years with ASD and a documented developmental evaluation, 78.0% were evaluated by age 36 months.
“Among children with a documented developmental evaluation, 78.0% were evaluated by age 36 months.”
Quote in the paperSubstantial claim - Findingcitation
The male-to-female ASD prevalence ratio among children aged 4 years was lower than among children aged 8 years in 2020.
“The male-to-female ratio among children aged 4 years was similar across sites, but the ratio was lower than among children aged 8 years (11).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] Maenner, 2023, Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020., MMWR Surveill Summ source
Not checked in the free run.
- Findingown data
Overall, 79.4% of children aged 4 years who met the ADDM Network ASD case definition had a recorded ASD ICD code, ranging from 22.0% to 92.7%.
“Overall, 80.2% of children who met the ADDM Network case definition had a recorded ASD diagnosis (range = 54.1%–98.3%) (Figure 1) (Supplementary Figure 1, https://stacks.cdc.gov/view/cdc/124396), 38.1% had a recorded ASD eligibility (range = 9.3%–75.1%), and 79.4% had a recorded ASD ICD code (range = 22.0%–92.7%).”
Quote in the paperNot a substantial claim - Findingown data
Black children with ASD had a higher percent of co-occurring intellectual disability than other groups at 62.7%.
“Black children with ASD had a higher percent of co-occurring intellectual disability than other groups at 62.7%.”
Quote in the paperNot a substantial claim - Findingown data
After the COVID-19 pandemic declaration, 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.
“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”
- Also stated in: § Conclusion · plain
“However, after pandemic onset, the pattern reversed, and fewer evaluations and identifications were observed among children aged 4 years.”
Quote in the paperSubstantial claim - Also stated in: § Conclusion · plain
- Findingnone given
From 2016 through February 2020, ASD evaluation and identification among the cohort of children aged 4 years was outpacing ASD evaluation and identification 4 years earlier among the cohort of children aged 8 years in 2020.
“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 .”
Quote in the paperSubstantial claim- Rests on an assumption: The 8-year-old cohort's evaluation and identification patterns 4 years earlier provide a valid counterfactual for what the 4-year-old cohort would have experienced absent the COVID-19 pandemic.
- Findingown data
From 2016 through February 2020, children aged 4 years in 2020 had more evaluations and identifications than the cohort aged 8 years in 2020 had during 2012-2016.
“From 2016 through February 2020, children aged 4 years in 2020 had more evaluations and identifications than the cohort aged 8 years in 2020 had during 2012–2016.”
- Also stated in: § Discussion · plain
“Children aged 4 years had consistently more evaluations and higher identification from 2016 through February 2020 than children aged 8 had during 2012–2016.”
- Also stated in: § Conclusion · plain
“Children aged 4 years in 2020 received more evaluations and ASD identification before the March 2020 COVID-19 pandemic declaration than children aged 8 years received 4 years earlier.”
Quote in the paperSubstantial claim - Also stated in: § Discussion · plain
- Findingown data
In the 6 months before the March 2020 COVID-19 pandemic declaration, children aged 4 years had 1,593 more evaluations than children aged 8 years had received 4 years earlier.
“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.”
Quote in the paperNot a substantial claim- Rests on an assumption: The 8-year-old cohort's evaluation and identification patterns 4 years earlier provide a valid counterfactual for what the 4-year-old cohort would have experienced absent the COVID-19 pandemic.
- Findingnone given
Demographic and cognitive characteristics of children aged 4 years with suspected ASD were similar to children aged 4 years with ASD.
“Demographic and cognitive characteristics of children aged 4 years with suspected ASD were similar to children aged 4 years with ASD.”
Quote in the paperNot a substantial claim - Findingown data
The percentage of co-occurring intellectual disability was similar among White, Hispanic, A/PI, and multiracial children with ASD (43%, 44.7%, 49.4%, and 40.5%, respectively).
“and among White, Hispanic, A/PI, and multiracial children with ASD (43%, 44.7%, 49.4%, and 40.5%, respectively).”
Quote in the paperNot a substantial claim - Findingown data
Race and ethnicity patterns in ASD prevalence were mostly consistent across sites.
“Race and ethnicity patterns in prevalence were mostly consistent across sites.”
Quote in the paperNot a substantial claim - Findingcitation
Fewer children had information about intellectual ability available at age 4 years (58.3%) compared with age 8 years (66.7%) in 2020.
“Fewer children had information about intellectual ability available at age 4 years (58.3%) compared with age 8 years (66.7%) in 2020 (11)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] Maenner, 2023, Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020., MMWR Surveill Summ source
Not checked in the free run.
- Findingown data
In the 6 months before the March 2020 COVID-19 pandemic declaration, children aged 4 years had 1.89 more ASD identifications per 1,000 children than children aged 8 years had received 4 years earlier.
“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.”
Quote in the paperNot a substantial claim- Rests on an assumption: The 8-year-old cohort's evaluation and identification patterns 4 years earlier provide a valid counterfactual for what the 4-year-old cohort would have experienced absent the COVID-19 pandemic.
- Findingown data
Children aged 4 years had 1.6 times higher cumulative incidence of ASD diagnosis or eligibility by age 48 months than children aged 8 years in 2020 (17.5 per 1,000 compared with 11.2).
“Children aged 4 years had 1.6 times higher cumulative incidence of ASD diagnosis or eligibility by age 48 months than children aged 8 years in 2020 (17.5 per 1,000 children compared with 11.2) (Figure 2)”
Quote in the paperSubstantial claim - Findingown data
Evaluation by age 36 months varied across sites, ranging from 66.7% in Tennessee to 87.8% in Arkansas.
“with variability across sites ranging from 66.7% in Tennessee to 87.8% in Arkansas (Table 5).”
Quote in the paperNot a substantial claim - Findingnone given
Higher numbers of evaluations and rates of identification were evident among children aged 4 years until the COVID-19 pandemic onset in 2020.
“Higher numbers of evaluations and rates of identification were evident among children aged 4 years until the COVID-19 pandemic onset in 2020.”
Quote in the paperSubstantial claim - Findingown data
Similar percentages of children with and without co-occurring intellectual disability were evaluated by age 36 months (82.4% and 85.1%).
“Similar percentages of children with and without co-occurring intellectual disability were evaluated by age 36 months (82.4% and 85.1%)”
Quote in the paperNot a substantial claim - Findingown data
In the 6 months after the March 2020 COVID-19 pandemic declaration, there were 0.26 fewer ASD identifications per 1,000 children aged 4 years than children aged 8 years had 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.”
Quote in the paperSubstantial claim- Rests on an assumption: The 8-year-old cohort's evaluation and identification patterns 4 years earlier provide a valid counterfactual for what the 4-year-old cohort would have experienced absent the COVID-19 pandemic.
- Findingown data
The pattern of higher cumulative incidence among children aged 4 compared with 8 was consistent across sites, ranging from 1.3 times as high in New Jersey and Utah to 2.0 times as high in Tennessee.
“This pattern was consistent across sites, ranging from 1.3 times as high in New Jersey and Utah to 2.0 times as high in Tennessee”
Quote in the paperNot a substantial claim - Findingown data
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 ADDM Network sites in 2020.
“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.”
- Also stated in: § Interpretation · plain
“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.”
Quote in the paperSubstantial claim - Also stated in: § Interpretation · plain
- Findingown data
For 2020, prevalence of suspected ASD among children aged 4 years ranged from 0.5 per 1,000 in California to 10.4 per 1,000 in Arkansas.
“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).”
Quote in the paperSubstantial claim - Findingown data
The percentage of children evaluated by age 36 months was lower among children without intellectual ability information (68.5%).
“but the percentage was lower among children without intellectual ability information (68.5%).”
Quote in the paperNot a substantial claim - Findingown data
A higher percentage of children with information available met the surveillance case definition for intellectual disability among children aged 4 years (48.5%) compared with children aged 8 years (37.9%).
“but a higher percentage of children with information available met the surveillance case definition for intellectual disability among children aged 4 years (48.5%) compared with children aged 8 years (37.9%).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] Maenner, 2023, Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020., MMWR Surveill Summ source
Not checked in the free run.
- Findingown data
In 2020, ASD identification by age 48 months was higher among children aged 4 years than aged 8 years at all sites.
“In 2020, ASD identification by age 48 months was higher among children aged 4 years than aged 8 years at all sites”
Quote in the paperSubstantial claim - Findingown data
The male-to-female ASD prevalence ratio among children aged 4 years was similar across ADDM Network sites in 2020.
“The male-to-female ratio among children aged 4 years was similar across sites, but the ratio was lower than among children aged 8 years (11).”
Quote in the paperNot a substantial claim - Findingown data
In the 6 months before the COVID-19 pandemic declaration in March 2020, 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.
“In the 6 months before the COVID-19 pandemic declaration in March 2020, 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”
Quote in the paperSubstantial claim - Findingown data
In the 6 months after the March 2020 COVID-19 pandemic declaration, there were 217 fewer evaluations among children aged 4 years than children aged 8 years had 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.”
Quote in the paperSubstantial claim- Rests on an assumption: The 8-year-old cohort's evaluation and identification patterns 4 years earlier provide a valid counterfactual for what the 4-year-old cohort would have experienced absent the COVID-19 pandemic.
- Findingown data
Among children with ASD with a developmental evaluation, 78% were evaluated by age 36 months.
“Among children with ASD with a developmental evaluation, 78% were evaluated by age 36 months, with variability across sites ranging from 66.7% in Tennessee to 87.8% in Arkansas (Table 5).”
Quote in the paperSubstantial claim - Findingown data
Suspected ASD prevalence among children aged 4 years increased from 2018 at five sites (Arizona, Arkansas, Maryland, New Jersey, and Utah).
“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).”
Quote in the paperNot a substantial claim - Findingown data
The percentage of co-occurring intellectual disability was similar between boys (49.2%) and girls (46.2%) with ASD.
“The percentage was similar between boys (49.2%) and girls (46.2%) with ASD (Table 4)”
Quote in the paperNot a substantial claim - Findingown data
Prevalence of suspected ASD varied widely among sites, from 0.5 per 1,000 children aged 4 years in California to 10.4 in Arkansas.
“Prevalence varied widely among sites in the prevalence of children with suspected ASD (those with a documented qualified professional’s statement indicating a suspicion of ASD but without a diagnosis, eligibility, or ICD code), from 0.5 per 1,000 children aged 4 years in California to 10.4 in Arkansas.”
Quote in the paperNot a substantial claim - Findingown data
The risk ratios for cumulative incidence of ASD diagnosis or eligibility by age 48 months comparing children aged 4 years with children aged 8 years in 2020 ranged from 1.3 in New Jersey and Utah to 2.0 in Tennessee.
“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.”
Quote in the paperSubstantial claim - Findingown data
A/PI and multiracial children were more likely to have information on intellectual disability available (67% and 71.2%) than White (57.1%) and Black children (56.4%).
“A/PI and multiracial children were more likely to have information on intellectual disability available (67% and 71.2% with information) than White (57.1%) and Black children (56.4%).”
Quote in the paperNot a substantial claim - Findingnone given
ASD prevalence was higher among boys compared with girls.
“ASD prevalence was higher among boys compared with girls”
Quote in the paperSubstantial claim - Findingown data
Prevalence of ASD among children aged 4 years increased 26%, from 17.0 per 1,000 children in 2018 to 21.5 in 2020.
“Prevalence of ASD among children aged 4 years increased 26%, from 17.0 per 1,000 children in 2018 to 21.5 in 2020.”
- Also stated in: § Conclusion · plain
“Findings from the ADDM Network 2020 surveillance year include a higher prevalence of ASD among children aged 4 years than in 2018.”
Quote in the paperSubstantial claim - Also stated in: § Conclusion · plain
- Findingown data
ASD prevalence was higher among Hispanic, Black, and A/PI children compared with White and AI/AN children.
“and among Hispanic, Black, and A/PI children compared with White and AI/AN children.”
Quote in the paperSubstantial claim - Interpretationown data
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.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: The 8-year-old cohort's evaluation and identification patterns 4 years earlier provide a valid counterfactual for what the 4-year-old cohort would have experienced absent the COVID-19 pandemic.
- Study design and conductcitation
All sites had access to health and education records in 2020.
“All sites had access to health and education records in 2020 (11).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [11] Maenner, 2023, Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020., MMWR Surveill Summ source
Not checked in the free run.
- Interpretationown data
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.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: The 8-year-old cohort's evaluation and identification patterns 4 years earlier provide a valid counterfactual for what the 4-year-old cohort would have experienced absent the COVID-19 pandemic.
- Interpretationown data
A large drop-off in progress among children aged 4 years was evident beginning with the pandemic onset in March 2020.
“Despite the encouraging patterns, a large drop-off in progress among children aged 4 years was evident beginning with the pandemic onset in March 2020.”
Quote in the paperSubstantial claim - Interpretationown data
The higher identification among children aged 4 than 8 indicates progress in early identification across the ADDM Network.
“indicating progress in early identification across the ADDM Network.”
Quote in the paperSubstantial claim - Interpretationreasoning
The wide variation in prevalence across sites likely reflects community factors influencing early ASD identification.
“This likely reflects community factors influencing early ASD identification.”
Quote in the paperSubstantial claim - Interpretationown data
ADDM Network data for 2020 show differences across sites that suggest differences in early identification practices or policies.
“ADDM Network data for 2020 show differences across sites that suggest differences in early identification practices or policies.”
Quote in the paperSubstantial claim - Interpretationreasoning
The increase in suspected ASD prevalence at five sites could be linked to challenges in evaluation and follow-up of children suspected of having ASD during the pandemic.
“Suspected ASD prevalence in 2020 increased from 2018 at five sites and could be linked to challenges in evaluation and follow-up of children suspected of having ASD during the pandemic.”
Quote in the paperNot a substantial claim - Interpretationown data
The higher cumulative incidence of ASD identification by age 48 months among children aged 4 years compared with children aged 8 years indicates more early identification in the younger group.
“The higher cumulative incidence of ASD identification by age 48 months among children aged 4 years compared with children aged 8 years indicates more early identification in the younger group.”
Quote in the paperSubstantial claim
- May combine two claims. 34
- Classified as probably not a substantial claim. 17
May combine two claims.
Findingown dataIn 2020, overall ASD prevalence among children aged 4 years was 32.3 per 1,000 among boys and 10.4 per 1,000 among girls, a male-to-female prevalence ratio of 3.1.
“For 2020, prevalence was 32.3 per 1,000 boys aged 4 years, which was 3.1 times as high as among girls (10.4) (Table 2).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataOverall, 80.2% of children aged 4 years who met the ADDM Network ASD case definition had a recorded ASD diagnosis, ranging from 54.1% to 98.3%.
“Overall, 80.2% of children who met the ADDM Network case definition had a recorded ASD diagnosis (range = 54.1%–98.3%) (Figure 1) (Supplementary Figure 1, https://stacks.cdc.gov/view/cdc/124396), 38.1% had a recorded ASD eligibility (range = 9.3%–75.1%), and 79.4% had a recorded ASD ICD code (range = 22.0%–92.7%).”
Quote in the paperSubstantial claimMay combine two claims.
FindingcitationPrevalence of suspected ASD increased from 2018 at five sites (Arizona, Arkansas, Maryland, New Jersey, and Utah) and was similar between years at other sites.
“Prevalence of suspected ASD increased from 2018 at five sites (Arizona, Arkansas, Maryland, New Jersey, and Utah) and was similar between years at other sites (7)”
- Also stated in: § Conclusion · plain
“Identification of children with suspected ASD also increased at certain sites compared with 2018 data.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [7] Shaw, 2021, Early identification of autism spectrum disorder among children aged 4 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018., MMWR Surveill Summ source
Not checked in the free run.
- Also stated in: § Conclusion · plain
May combine two claims.
Findingown dataIn 2020, among children aged 4 years, ASD prevalence was 28.7 per 1,000 among Hispanic children, 25.3 among Black children, 23.5 among A/PI children, 19.3 among multiracial children, 16.3 among White children, and 11.1 among AI/AN children.
“Among race and ethnicity groups, Hispanic children had the highest prevalence of ASD at 28.7 among children aged 4 years, followed by Black children (25.3), A/PI children (23.5), multiracial children (19.3), White children (16.3), and AI/AN children (11.1) (Table 3).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataChildren with ASD and children with suspected ASD were similar by sex, race and ethnicity, intellectual disability, and evaluation by age 36 months.
“Children with ASD and children with suspected ASD were similar by sex, race and ethnicity, intellectual disability, and evaluation by age 36 months”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAlthough every site experienced a period of lower evaluation and identification among children aged 4 years, certain sites showed movement toward recovery to prepandemic levels.
“Although every site experienced a period of lower evaluation and identification among children aged 4 years, certain sites showed movement toward recovery to prepandemic levels.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataCompared with White children, ASD prevalence was higher among Black children at nine sites, Hispanic children at seven sites, A/PI children at six sites, and multiracial children at two sites.
“Compared with White children, ASD prevalence was higher among Black children at nine sites (overall 1.6 times as high), Hispanic children at seven sites (overall 1.8 times as high), A/PI children at six sites (overall 1.4 times as high), and multiracial children at two sites (overall 1.2 times as high) (Table 3).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAmong children aged 4 years with ASD, 74.4% had more than one component of the ADDM Network case definition in their records, 23.3% had all three, and 9.8% had an ASD ICD code only.
“A majority of children (74.4%) had more than one component of the case definition in their records and 23.3% had all three; 9.8% had an ASD ICD code only (Figure 1).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataPatterns of evaluations and ASD identification after pandemic onset varied by site, with certain sites (e.g., Georgia, Minnesota, and Utah) having sustained declines in evaluation and identification through the end of the year, but other sites (e.g, Missouri and Tennessee) showing resurgences closer to prepandemic levels.
“Patterns of evaluations and ASD identification after pandemic onset varied by site, with certain sites (e.g., Georgia, Minnesota, and Utah) having sustained declines in evaluation and identification through the end of the year, but other sites (e.g, Missouri and Tennessee) showing resurgences closer to prepandemic levels”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAmong children with data on intellectual ability, 48.5% of children with ASD met the surveillance case definition of having co-occurring intellectual disability, with a range from 24.7% in California to 67.3% in Arkansas.
“Among children with data on intellectual ability, 48.5% of children with ASD met the surveillance case definition of having co-occurring intellectual disability (range = 24.7% in California to 67.3% in Arkansas).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataOverall, 58.3% of children aged 4 years with ASD had information on intellectual ability available, ranging from 38.6% in Wisconsin to 89.8% in Arkansas.
“Overall, 58.3% of children had information on intellectual ability available (range = 38.6% in Wisconsin to 89.8% in Arkansas) (Table 4).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataEvaluation by age 36 months was similar by sex and by racial and ethnic groups except for being lower among Black (75.9%) compared with Hispanic (79.8%) children.
“Evaluation by age 36 months was similar by sex and by racial and ethnic groups except for being lower among Black (75.9%) compared with Hispanic (79.8%) children.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataPrevalence among AI/AN children was similar to White children, but Black, Hispanic, A/PI, and multiracial children had an ASD prevalence 2.3, 2.6, 2.1, and 1.7 times as high as AI/AN children, respectively.
“Prevalence among AI/AN children was similar to White children, but Black, Hispanic, A/PI, and multiracial children had an ASD prevalence 2.3, 2.6, 2.1, and 1.7 times as high as AI/AN children, respectively.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataPatterns 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.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: The 8-year-old cohort's evaluation and identification patterns 4 years earlier provide a valid counterfactual for what the 4-year-old cohort would have experienced absent the COVID-19 pandemic.
May combine two claims.
FindingcitationIn 2020, ASD prevalence among children aged 4 years was 24.3% higher among boys and 34.5% higher among girls than prevalence in 2018.
“Prevalence in 2020 among children aged 4 years was 24.3% higher among boys and 34.5% higher among girls than prevalence in 2018 (7), but the male-to-female ratios were similar between years.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [7] Shaw, 2021, Early identification of autism spectrum disorder among children aged 4 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018., MMWR Surveill Summ source
Not checked in the free run.
May combine two claims.
Findingown dataOverall, 38.1% of children aged 4 years who met the ADDM Network ASD case definition had a recorded ASD special education eligibility, ranging from 9.3% to 75.1%.
“Overall, 80.2% of children who met the ADDM Network case definition had a recorded ASD diagnosis (range = 54.1%–98.3%) (Figure 1) (Supplementary Figure 1, https://stacks.cdc.gov/view/cdc/124396), 38.1% had a recorded ASD eligibility (range = 9.3%–75.1%), and 79.4% had a recorded ASD ICD code (range = 22.0%–92.7%).”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationCalifornia continued to report high levels of early ASD identification, with higher prevalence at age 4 years than 8 years in 2020.
“California continued to report high levels of early ASD identification, with higher prevalence at age 4 years than 8 years in 2020 (11).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] Maenner, 2023, Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020., MMWR Surveill Summ source
Not checked in the free run.
May combine two claims.
Findingnone givenFindings were similar to 2018 by presence of co-occurring intellectual disability and evaluation by age 36 months.
“Findings were similar to 2018 by presence of co-occurring intellectual disability and evaluation by age 36 months.”
Quote in the paperSubstantial claimMay combine two claims.
FindingreasoningSuspected ASD was highest in the three states (Utah, Arizona, and Arkansas) with the lowest ASD prevalence, and two of the three states with the highest prevalence (California and Tennessee) had the lowest suspected ASD prevalence.
“Suspected ASD was highest in the three states (Utah, Arizona, and Arkansas) with the lowest ASD prevalence, and two of the three states with the highest prevalence (California and Tennessee) had the lowest suspected ASD prevalence.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationPrevalence among girls surpassed 10 per 1,000 children aged 4 years for the first time in ADDM Network surveillance (also observed among children aged 8 years for the first time).
“Prevalence among girls surpassed 10 per 1,000 children aged 4 years for the first time in ADDM Network surveillance (also observed among children aged 8 years for the first time) (11).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] Maenner, 2023, Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020., MMWR Surveill Summ source
Not checked in the free run.
May combine two claims.
Findingown dataPrevalence varied widely across sites in 2020, with prevalence 265% higher in California than in Utah, the site with the lowest prevalence.
“However, prevalence varied widely across sites in 2020, with prevalence 265% higher in California than in Utah, the site with the lowest prevalence.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationThe higher prevalence of ASD among Black, Hispanic, and A/PI versus White children aged 4 years first reported in 2018 continued in 2020 and also was seen among children aged 8 years.
“The higher prevalence of ASD among Black, Hispanic, and A/PI versus White children aged 4 years first reported in 2018 continued in 2020 and also was seen among children aged 8 years (11).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] Maenner, 2023, Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020., MMWR Surveill Summ source
Not checked in the free run.
May combine two claims.
Findingown dataA larger increase in prevalence from 2018 for girls compared with boys was observed; however, the male-to-female ratio was similar across years.
“A larger increase in prevalence from 2018 for girls compared with boys was observed; however, the male-to-female ratio was similar across years.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationSurveillance in 2020 was conducted using the same methods as in 2018.
“Surveillance was conducted using the same methods as in 2018 (7,10).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [7] Shaw, 2021, Early identification of autism spectrum disorder among children aged 4 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018., MMWR Surveill Summ source
Not checked in the free run.
- cites [10] Maenner, 2021, Comparison of two case definitions for ascertaining prevalence of autism spectrum disorder among 8-Year-old children., Am J Epidemiol source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductnone givenPrevalence and risk ratios were considered significant when 95% CIs did not include 1.0.
“Prevalence and risk ratios were considered significant when 95% CIs did not include 1.0.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataAt 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.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Higher cumulative incidence of ASD by age 48 months among children aged 4 years compared with children aged 8 years in 2020 reflects improvements in early identification rather than other cohort differences.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenAll ADDM Network sites functioned as public health authorities under HIPAA and met applicable local institutional review board, privacy, and confidentiality requirements under 45 CFR 46.
“All sites functioned as public health authorities under the Health Insurance Portability and Accountability Act of 1996 Privacy Rule and met applicable local institutional review board, privacy, and confidentiality requirements under 45 CFR 46.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataNine sites had data agreements with education sources covering 100% of their study areas, Missouri had agreements covering 50.3%, and Georgia covering 97.6%.
“Nine sites had data agreements in place with education sources covering 100% of their study areas, Missouri had agreements with education data sources covering 50.3%, and Georgia with education data sources covering 97.6% of its study area.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataPrevalence estimates with a relative SE ≥30% were suppressed because of limited statistical precision.
“Prevalence estimates with a relative SE ≥30% were suppressed because of limited statistical precision.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataA wide range of prevalence of ASD by age 4 years was observed, suggesting differences in early ASD identification practices among communities.
“A wide range of prevalence of ASD by age 4 years was observed, suggesting differences in early ASD identification practices among communities.”
Quote in the paperSubstantial claim- Rests on an assumption: Differences in ASD prevalence by age 4 years across communities reflect differences in early ASD identification practices rather than differences in the true occurrence of ASD.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationIncreased use of telehealth approaches could reflect the movement toward recovery by enabling remote evaluation and identification of children with ASD.
“This could reflect increased use of telehealth approaches that enabled remote evaluation and identification of children with ASD (12–16).”
Quote in the paperNot a substantial claim0 of 5 sources checked- cites [12] Corona L, Hine J, Ncholson A, TELE-ASD-PEDS: a telemedicine-based ASD evaluation tool for toddlers and young children. Nashville, TN: Vanderbilt University Medical Center; 2020. https://vkc.vumc.org/vkc/triad/tele-asd-peds
Not checked in the free run.
- cites [13] Wagner, 2021, Use of the TELE-ASD-PEDS for autism evaluations in response to COVID-19: preliminary outcomes and clinician acceptability., J Autism Dev Disord source
Not checked in the free run.
- cites [14] Dow, 2022, The brief observation of symptoms of autism (BOSA): development of a new adapted assessment measure for remote telehealth administration through COVID-19 and beyond., J Autism Dev Disord source
Not checked in the free run.
- cites [15] Dow, 2017, Psychometric analysis of the Systematic Observation of Red Flags for autism spectrum disorder in toddlers., Autism source
Not checked in the free run.
- cites [16] Dow, 2020, Screening for autism spectrum disorder in a naturalistic home setting using the Systematic Observation of Red Flags (SORF) at 18–24 months., Autism Res source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenSurveillance area populations are not necessarily generalizable to the entire state or United States.
“First, surveillance area populations are not necessarily generalizable to the entire state or United States.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningVariability in state-level procedures for early intervention could contribute to differences in prevalence among sites.
“Variability in state-level procedures for early intervention also could contribute to differences in prevalence among sites.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationArkansas code required agreement by three professionals in specified categories to receive an ASD diagnosis until the code was revised in 2021 to two "qualified professionals," easing the requirements for identification.
“For example, Arkansas code required agreement by three professionals in specified categories to receive an ASD diagnosis until the code was revised in 2021 to two “qualified professionals” (17), easing the requirements for identification.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [17] Bill A. State of Arkansas 93rd General Assembly HB1545. 2021 Apr 12;5.
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThe potential complication from changing sites is mitigated by comparing ASD identification between children aged 4 and 8 years in the same study area during the surveillance period.
“The latter is mitigated by comparing ASD identification between children aged 4 and 8 years in the same study area during the surveillance period.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningSustained decreases could reflect extended remote learning through the end of 2020 in certain sites such as Minnesota, where many children are identified in schools.
“Sustained decreases also could reflect extended remote learning through the end of 2020 in certain sites such as in Minnesota, where many children are identified in schools (Figure 1).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenData quality is dependent on data availability and completeness of records at data sources.
“Second, data quality is dependent on data availability and completeness of records at data sources.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningDemographic characteristics reflect what is documented in records; sex and race and ethnicity estimates could be higher or lower if administrative records are different from individual or family preferences.
“For example, demographic characteristics reflect what is documented in records; sex and race and ethnicity estimates could be higher or lower if administrative records are different from individual or family preferences.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationASD is beginning to be identified more in lower-SES groups than previously; however, barriers to services still exist for lower-SES families of children with ASD (e.g., transportation, childcare, and job accommodations).
“ASD is beginning to be identified more in lower-SES groups than previously (7,11); however, barriers to services still exist for lower-SES families of children with ASD (e.g., transportation, childcare, and job accommodations) (18–26).”
Quote in the paperNot a substantial claim0 of 10 sources checked- cites [7] Shaw, 2021, Early identification of autism spectrum disorder among children aged 4 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018., MMWR Surveill Summ source
Not checked in the free run.
- cites [11] Maenner, 2023, Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020., MMWR Surveill Summ source
Not checked in the free run.
- cites [18] Smith, 2020, Disparities in service use among children with autism: a systematic review., Pediatrics source
Not checked in the free run.
- cites [19] Bilaver, 2021, Understanding racial and ethnic disparities in autism-related service use among Medicaid-enrolled children., J Autism Dev Disord source
Not checked in the free run.
- cites [20] Magaña, 2012, Racial and ethnic disparities in quality of health care among children with autism and other developmental disabilities., Intellect Dev Disabil source
Not checked in the free run.
- cites [21] Magaña, 2013, Access to diagnosis and treatment services among latino children with autism spectrum disorders., Intellect Dev Disabil source
Not checked in the free run.
- cites [22] Flores, 2008, Racial and ethnic disparities in medical and dental health, access to care, and use of services in US children., Pediatrics source
Not checked in the free run.
- cites [23] Hodgkinson, 2017, Improving mental health access for low-income children and families in the primary care setting., Pediatrics source
Not checked in the free run.
- cites [24] Wieckowski, 2022, Addressing current barriers to autism diagnoses through a tiered diagnostic approach involving pediatric primary care providers., Autism Res source
Not checked in the free run.
- cites [25] Drahota, 2020, Service deserts and service oases: Utilizing geographic information systems to evaluate service availability for individuals with autism spectrum disorder., Autism source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningThese comparisons of characteristics between children with suspected ASD and children with ASD were among children seen for a developmental evaluation; disparities in receiving evaluations could still exist.
“but these comparisons were among children seen for a developmental evaluation; disparities in receiving evaluations could still exist.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe surveillance case definition of intellectual disability is not the same as a clinical diagnosis; IQ measurements in young children might lack stability and children might not ultimately receive a diagnosis of intellectual disability.
“Finally, the surveillance case definition of intellectual disability is not the same as a clinical diagnosis; IQ measurements in young children might lack stability and children might not ultimately receive a diagnosis of intellectual disability.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe higher occurrence of ASD among Black, Hispanic, and A/PI groups possibly suggests higher risk for ASD (e.g., perinatal or environmental factors) in populations that were previously masked by socioeconomic disparities in accessing ASD identification and treatment services.
“The higher occurrence of ASD among these groups possibly suggests higher risk for ASD (e.g., perinatal or environmental factors) in populations that were previously masked by socioeconomic disparities in accessing ASD identification and treatment services.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningHigher levels of suspected ASD could reflect community requirements for ASD diagnosis.
“Higher levels of suspected ASD could reflect community requirements for ASD diagnosis.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenSites or study areas could change between study years, which could complicate comparisons over time.
“Third, sites or study areas could change between study years, which could complicate comparisons over time.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationLocal factors likely contribute to early identification at the California site, such as hundreds of pediatricians participating in the Get SET Early model and a state-funded regional center that provides evaluations and service coordination for persons with disabilities and their families.
“Local factors likely contribute to early identification at the California site, such as hundreds of pediatricians participating in the Get SET Early model (27) and a state-funded regional center that provides evaluations and service coordination for persons with disabilities and their families”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27] Pierce, 2021, Get SET early to identify and treatment refer autism spectrum disorder at 1 year and discover factors that influence early diagnosis., J Pediatr source
Not checked in the free run.
The paper relies on this without support.
The 8-year-old cohort's evaluation and identification patterns 4 years earlier provide a valid counterfactual for what the 4-year-old cohort would have experienced absent the COVID-19 pandemic.
“To compare the same age windows by calendar month, the numbers of evaluations and incidence of identification from 2012 (year 0) through 2016 (year 4) for children aged 8 years was subtracted from the same months during 2016 (year 0) through 2020 (year 4) for children aged 4 years.”
The paper relies on this without support.
Differences in ASD prevalence by age 4 years across communities reflect differences in early ASD identification practices rather than differences in the true occurrence of ASD.
“A wide range of prevalence of ASD by age 4 years was observed, suggesting differences in early ASD identification practices among communities.”
The paper relies on this without support.
Higher cumulative incidence of ASD by age 48 months among children aged 4 years compared with children aged 8 years in 2020 reflects improvements in early identification rather than other cohort differences.
“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.”
The paper relies on this without support.
Earlier identification of ASD leads to better outcomes.
“Because earlier identification could lead to better outcomes, follow-up of children suspected but not confirmed of having ASD could help minimize delays in identification and intervention.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Although prevalence has been lower among children aged 4 years than aged 8 years in each reporting year, comparing cumulative incidence of identification by age 48 months has consistently indicated higher rates of ASD identification in younger cohorts.
“Although prevalence has been lower among children aged 4 years than aged 8 years in each reporting year, comparing cumulative incidence of identification by age 48 months has consistently indicated higher rates of ASD identification in younger cohorts (i.e., children born more recently) (9–11).”
A report that presented data collected in 2020 found interruptions to early identification during the onset of the COVID-19 pandemic.
“A report that presented data collected in 2020 found interruptions to early identification during the onset of the COVID-19 pandemic (11).”
California has had the highest prevalence among children aged 4 years and 8 years since joining the ADDM Network in 2018.
“California has had the highest prevalence among children aged 4 years and 8 years since joining the ADDM Network in 2018 (6,10,11,13)”
Cites
Maenner, M. J., Warren, Z., Williams, A. R., Amoakohene, E., Bakian, A. V., Bilder, D. A., Durkin, M. S., Fitzgerald, R. T., Furnier, S. M., Hughes, M. M., et al. (2023) · MMWR. Surveillance Summaries · doi:10.15585/mmwr.ss7202a1 · Cited by 2,706 · see citing works
Self-citationIdentification, Evaluation, and Management of Children With Autism Spectrum Disorder
Hyman, S. L., Levy, S. E., Myers, S. M., COUNCIL ON CHILDREN WITH DISABILITIES, S. O. D. A. B. P., Kuo, D. Z., Apkon, S., Davidson, L. F., Ellerbeck, K. A., Foster, J. E. A., Noritz, G. H., et al. (2020) · Pediatrics · doi:10.1542/peds.2019-3447 · Cited by 1,548 · see citing works
Long-Term Outcomes of Early Intervention in 6-Year-Old Children With Autism Spectrum Disorder
Estes, A., Munson, J., Rogers, S. J., Greenson, J., Winter, J., & Dawson, G. (2015) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2015.04.005 · Cited by 540 · see citing works
Flores, G., & Tomany-Korman, S. C. (2008) · Pediatrics · doi:10.1542/peds.2007-1243 · Cited by 492 · see citing works
Improving mental health access for low-income children and families in the primary care setting.
Hodgkinson, S., Godoy, L., Beers, L. S., & Lewin, A. (2017) · Pediatrics · doi:10.1542/peds.2015-1175 · Cited by 451 · see citing works
Anderson, D. K., Liang, J. W., & Lord, C. (2014) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12178 · Cited by 401 · see citing works
Access to Diagnosis and Treatment Services Among Latino Children With Autism Spectrum Disorders
Magaña, S., Lopez, K., Aguinaga, A., & Morton, H. (2013) · Intellectual and Developmental Disabilities · doi:10.1352/1934-9556-51.3.141 · Cited by 375 · see citing works
Christensen, D. L., Maenner, M. J., Bilder, D., Constantino, J. N., Daniels, J., Durkin, M. S., Fitzgerald, R. T., Kurzius-Spencer, M., Pettygrove, S. D., Robinson, C., et al. (2019) · MMWR. Surveillance Summaries · doi:10.15585/mmwr.ss6802a1 · Cited by 344 · see citing works
Self-citationShaw, K. A., Maenner, M. J., Baio, J., EdS1, Washington, A., Christensen, D. L., Wiggins, L. D., Pettygrove, S., Andrews, J. G., White, T., et al. (2020) · MMWR. Surveillance Summaries · doi:10.15585/mmwr.ss6903a1 · Cited by 286 · see citing works
Self-citationDisparities in Diagnosis and Treatment of Autism in Latino and Non-Latino White Families
Zuckerman, K. E., Lindly, O. J., Reyes, N., Chavez, A. E., Macias, K., Smith, K. N., & Reynolds, A. (2017) · PEDIATRICS · doi:10.1542/peds.2016-3010 · Cited by 285 · see citing works
Magaña, S., Parish, S. L., Rose, R. A., Timberlake, M., & Swaine, J. G. (2012) · Intellectual and Developmental Disabilities · doi:10.1352/1934-9556-50.4.287 · Cited by 255 · see citing works
Disparities in service use among children with autism: a systematic review.
Smith, K. A., Gehricke, J.-G., Iadarola, S., Wolfe, A., & Kuhlthau, K. A. (2020) · Pediatrics · doi:10.1542/peds.2019-1895g · Cited by 254 · see citing works
Shaw, K. A., Maenner, M. J., Bakian, A. V., Bilder, D. A., Durkin, M. S., Furnier, S. M., Hughes, M. M., Patrick, M., Pierce, K., Salinas, A., et al. (2021) · MMWR. Surveillance Summaries · doi:10.15585/mmwr.ss7010a1 · Cited by 165 · see citing works
Self-citationGabbay-Dizdar, N., Ilan, M., Meiri, G., Faroy, M., Michaelovski, A., Flusser, H., Menashe, I., Koller, J., Zachor, D. A., & Dinstein, I. (2021) · Autism · doi:10.1177/13623613211049011 · Cited by 150 · see citing works
Wagner, L., Corona, L. L., Weitlauf, A. S., Marsh, K. L., Berman, A. F., Broderick, N. A., Francis, S., Hine, J., Nicholson, A., Stone, C., et al. (2021) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-020-04767-y · Cited by 138 · see citing works
Self-citationDow, D., Holbrook, A., Toolan, C., McDonald, N., Sterrett, K., Rosen, N., Kim, S. H., & Lord, C. (2022) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-021-05395-w · Cited by 99 · see citing works
Drahota, A., Sadler, R., Hippensteel, C., Ingersoll, B., & Bishop, L. (2020) · Autism · doi:10.1177/1362361320931265 · Cited by 85 · see citing works
Bilaver, L. A., Sobotka, S. A., & Mandell, D. S. (2021) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-020-04797-6 · Cited by 67 · see citing works
Pierce, K., Gazestani, V., Bacon, E., Courchesne, E., Cheng, A., Barnes, C. C., Nalabolu, S., Cha, D., Arias, S., Lopez, L., et al. (2021) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2021.04.041 · Cited by 66 · cited 1× · see citing works
“Local factors likely contribute to early identification at the California site, such as hundreds of pediatricians participating in the Get SET Early model (27) and a state-funded regional center that provides evaluations and service coordination for persons with disabilities and their families (https://www.”
BackgroundSelf-citationDow, D., Day, T. N., Kutta, T. J., Nottke, C., & Wetherby, A. M. (2020) · Autism Research · doi:10.1002/aur.2226 · Cited by 65 · see citing works
Dow, D., Guthrie, W., Stronach, S. T., & Wetherby, A. M. (2017) · Autism · doi:10.1177/1362361316636760 · Cited by 62 · see citing works
Wieckowski, A. T., Zuckerman, K. E., Broder‐Fingert, S., & Robins, D. L. (2022) · Autism Research · doi:10.1002/aur.2832 · Cited by 38 · see citing works
Maenner, M. J., Graves, S. J., Peacock, G., Honein, M. A., Boyle, C. A., & Dietz, P. M. (2021) · American Journal of Epidemiology · doi:10.1093/aje/kwab106 · Cited by 18 · see citing works
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Maenner, M. J., Warren, Z., Williams, A. R., Amoakohene, E., Bakian, A. V., Bilder, D. A., Durkin, M. S., Fitzgerald, R. T., Furnier, S. M., Hughes, M. M., et al. (2023) · MMWR. Surveillance Summaries · doi:10.15585/mmwr.ss7202a1 · Cited by 2,706 · see citing works
Self-citationShaw, K. A., Williams, S., Patrick, M. E., Valencia-Prado, M., Durkin, M. S., Howerton, E. M., Ladd-Acosta, C. M., Pas, E. T., Bakian, A. V., Bartholomew, P., et al. (2025) · MMWR. Surveillance Summaries · doi:10.15585/mmwr.ss7402a1 · Cited by 867 · see citing works
Self-citationThe Reach and Accuracy of Information on Autism on TikTok
Aragon-Guevara, D., Castle, G., Sheridan, E., & Vivanti, G. (2023) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-023-06084-6 · Cited by 116 · see citing works
The global prevalence of autism spectrum disorder in children: a systematic review and meta-analysis
Issac, A., Halemani, K., Shetty, A. P., Thimmappa, L., Vijay, V. R., Koni, K., Mishra, P., & Kapoor, V. (2025) · Osong Public Health and Research Perspectives · doi:10.24171/j.phrp.2024.0286 · Cited by 128 · see citing works
Hung, L. Y., & Margolis, K. G. (2024) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-023-00857-1 · Cited by 96 · see citing works
Huda, E., Hawker, P. J., Cibralic, S., John, J. R., Hussain, A., Diaz, A. M., & Eapen, V. (2024) · BMC Pediatrics · doi:10.1186/s12887-024-05067-5 · Cited by 92 · see citing works
Wu, J., Zhang, J., Chen, X., Wettschurack, K. W., Que, Z., Deming, B. A., Olivero-Acosta, M. I., Cui, N., Eaton, M., Zhao, Y., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02518-4 · Cited by 85 · see citing works
Johnson, C. R., Barto, L., Worley, S., Rothstein, R., & Alder, M. L. (2023) · Sleep Medicine · doi:10.1016/j.sleep.2023.08.033 · Cited by 55 · see citing works
Manter, M. A., Birtwell, K. B., Bath, J., Friedman, N. D. B., Keary, C. J., Neumeyer, A. M., Palumbo, M. L., Thom, R. P., Stonestreet, E., Brooks, H., et al. (2025) · BMC Medicine · doi:10.1186/s12916-024-03814-0 · Cited by 51 · see citing works
Gut microbiome and serum amino acid metabolome alterations in autism spectrum disorder
Chang, X., Zhang, Y., Chen, X., Li, S., Mei, H., Xiao, H., Ma, X., Liu, Z., & Li, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-54717-2 · Cited by 44 · see citing works
Duque-Cartagena, T., Dalla, M. D. B., Mundstock, E., Neto, F. K., Espinoza, S. A. R., de Moura, S. K., Zanirati, G., Padoin, A. V., Piñeros, J. G., Stein, A. T., et al. (2024) · BMC Public Health · doi:10.1186/s12889-024-19742-w · Cited by 39 · see citing works
Kaplan, Z. B., Pearce, E. N., Lee, S. Y., Shin, H., & Schmidt, R. J. (2023) · Thyroid · doi:10.1089/thy.2023.0391 · Cited by 34 · see citing works
Song, J., Reilly, M., & Reichow, B. (2024) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-023-06198-x · Cited by 30 · see citing works
Large increase in ASD prevalence in Israel between 2017 and 2021
Dinstein, I., Solomon, S., Zats, M., Shusel, R., Lottner, R., Gershon, B. B., Meiri, G., Menashe, I., & Shmueli, D. (2024) · Autism Research · doi:10.1002/aur.3085 · Cited by 35 · see citing works
Familial confounding in the associations between maternal health and autism
Khachadourian, V., Arildskov, E. S., Grove, J., O’Reilly, P. F., Buxbaum, J. D., Reichenberg, A., Sandin, S., Croen, L., Schendel, D., Hansen, S. N., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03479-5 · Cited by 30 · see citing works
Román, P., Ruiz‐González, C., Rueda‐Ruzafa, L., Cardona, D., Requena, M., & Alarcón, R. (2024) · Medicina · doi:10.3390/medicina60030479 · Cited by 27 · see citing works
Qin, Q., Fan, L., Zeng, X., Zheng, D., Wang, H., Li, M., Jiang, Y., Wang, H., Liu, H., Liang, S., et al. (2025) · Journal of Nanobiotechnology · doi:10.1186/s12951-025-03250-z · Cited by 24 · see citing works
Wang, X.-N., Zhang, T., Han, B.-C., Luo, W., Liu, W.-H., Yang, Z., Disi, A., Sun, Y., & Yang, J.-C. (2024) · Current Medical Science · doi:10.1007/s11596-024-2938-3 · Cited by 22 · see citing works
Autistic and transgender/gender diverse people’s experiences of health and healthcare
Green, K., Weir, E. M., Wright, L., Allison, C. L., & Baron‐Cohen, S. (2025) · Molecular Autism · doi:10.1186/s13229-024-00634-0 · Cited by 18 · see citing works
Self-citationNakamura, T., Yoshihara, T., Tanegashima, C., Kadota, M., Kobayashi, Y. O., Honda, K., Ishiwata, M., Ueda, J., Hara, T., Nakanishi, M., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02479-8 · Cited by 19 · see citing works
Nyquist, C. B., Torgersen, L., David, L. W., Diseth, T. H., Magnus, P. M., Biele, G., & Wæhre, A. (2024) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-024-02508-5 · Cited by 17 · see citing works
RetractedSznajder, Ł. J., Khan, M., Ciesiołka, A., Tadross, M., Nutter, C. A., Taylor, K., Pearson, C. E., Lewis, M. H., Hines, R. M., Swanson, M. S., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01943-0 · Cited by 17 · see citing works
Dell’Osso, L., Nardi, B., Bonelli, C., Amatori, G., Pereyra, M. A., Massimetti, E., Cremone, I. M., Pini, S., & Carpita, B. (2024) · Brain Sciences · doi:10.3390/brainsci14010064 · Cited by 18 · see citing works
Autistic traits associated with dichotomic thinking mediated by intolerance of uncertainty
Suzuki, N., & Hirai, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-41164-8 · Cited by 16 · see citing works
A 3D approach to understanding heterogeneity in early developing autisms
Mandelli, V., Severino, I., Eyler, L. T., Pierce, K., Courchesne, E., & Lombardo, M. V. (2024) · Molecular Autism · doi:10.1186/s13229-024-00613-5 · Cited by 16 · see citing works
Self-citationZhang, C., Chen, Y., Hou, F., Li, Y., Wang, W., Guo, L., Zhang, C., Li, L., & Lu, C. (2025) · Nutrients · doi:10.3390/nu17091602 · Cited by 15 · see citing works
Metwally, A. M., El-Din, E. M. S., Sami, S. M., Raouf, E. R. A., Sallam, S. F., Elsaeid, A., El‐Saied, M. M., Ashaat, E. A., Fathy, A. M., El-Hariri, H. M., et al. (2025) · Molecular Autism · doi:10.1186/s13229-025-00665-1 · Cited by 16 · see citing works
Yuwattana, W., Saeliw, T., van Erp, M. L., Poolcharoen, C., Kanlayaprasit, S., Trairatvorakul, P., Chonchaiya, W., Hu, V. W., & Sarachana, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-95291-5 · Cited by 15 · see citing works
Huang, M., Wang, J., Liu, W., & Zhou, H. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1349409 · Cited by 15 · see citing works
A Systematic Literature Review of Racial Disproportionality in Autism in the U.S.
Kim, H., Karakaya, M. F., Skinner, M., & Baker, D. (2024) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-024-06247-z · Cited by 15 · see citing works
The early life exposome and autism risk: a role for the maternal microbiome?
Gesù, C. M. D., & Buffington, S. A. (2024) · Gut Microbes · doi:10.1080/19490976.2024.2385117 · Cited by 16 · see citing works
Hu, C., Li, H., Cui, J. J., Li, Y., Zhang, F., Li, H., Luo, X., & Hao, Y. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03452-x · Cited by 13 · see citing works
Dysregulation of the mTOR-FMRP pathway and synaptic plasticity in an environmental model of ASD
Hilal, M. L., Rosina, E., Pedini, G., Restivo, L., & Bagni, C. (2025) · Molecular Psychiatry · doi:10.1038/s41380-024-02805-0 · Cited by 13 · see citing works
Whole-genome sequencing identifies novel genes for autism in Chinese trios
Chang, S., Liu, J. J., Zhao, Y., Pang, T., Zheng, X., Song, Z., Zhang, A., Gao, X., Luo, L., Guo, Y., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2564-8 · Cited by 13 · see citing works
Shi, H., & Hirai, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-73628-w · Cited by 12 · see citing works
Sesay, M. M., McCracken, C., Stewart, C. C., Simon, G. E., Penfold, R. B., Ahmedani, B. K., Rossom, R. C., Lu, C. Y., Beck, A. L., Coleman, K. J., et al. (2024) · Autism · doi:10.1177/13623613231220687 · Cited by 11 · see citing works
Neurobiology of cognitive abilities in early childhood autism
Denisova, K. (2024) · JCPP Advances · doi:10.1002/jcv2.12214 · Cited by 13 · see citing works
Zhao, Y., Lu, F., Ding, R., Zhu, D., Zhang, R., Sun, S., He, P., & Zheng, X. (2024) · Autism · doi:10.1177/13623613241290388 · Cited by 12 · see citing works
Zhang, L., Zhang, C., Yuan, X., & Ji, Y. (2025) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-025-02696-8 · Cited by 11 · see citing works
Wormwood, K. L., Charette, L., Ryan, J. P., Darie, C. C., & Woods, A. G. (2023) · The Protein Journal · doi:10.1007/s10930-023-10146-0 · Cited by 11 · see citing works
Peterson, T., Dodson, J., & Strale, F. (2024) · Cureus · doi:10.7759/cureus.56226 · Cited by 10 · see citing works
Meta-analysis of Psychotherapy for Autistic Youth
Rosenau, K. A., Kim, J., Cho, A. C., SELTZER, M. H., Ugueto, A. M., Weisz, J. R., & Wood, J. J. (2024) · Child Psychiatry & Human Development · doi:10.1007/s10578-024-01686-2 · Cited by 10 · see citing works
Xu, X., Li, Y., Ding, N., Zang, Y., Sun, S., Shen, G., & Song, X. (2024) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2024.111901 · Cited by 10 · see citing works
Zhao, Y., Lan, T., Zhong, G., Hagen, J., Pan, H., Chung, W. K., & Shen, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-59937-2 · Cited by 10 · see citing works
Campanale, A., Siniscalco, D., & Marzo, V. D. (2025) · Journal of Biomedical Science · doi:10.1186/s12929-025-01145-7 · Cited by 11 · see citing works
A Scoping Review of Tools to Assess Diet in Children and Adolescents with Autism Spectrum Disorder
Gabucio, L. M. C., Ojeda-Belokon, C., Collado, L. T., & de la Hera, M. G. (2023) · Nutrients · doi:10.3390/nu15173748 · Cited by 10 · see citing works
Marcilla-Jordá, M., Grande, C., Coelho, V., Rubio‐Belmonte, C., & Ipola, M. M. (2024) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-024-06337-y · Cited by 9 · see citing works
Johnson, C. R., Barto, L., Worley, S., Rothstein, R., & Wenzell, M. L. (2024) · Sleep Medicine · doi:10.1016/j.sleep.2024.04.022 · Cited by 9 · see citing works
Assessment of Expression of lncRNAs in Autistic Patients
Sane, S., Ebrahimi, V., Shirvani-Farsani, Z., & Ghafouri‐Fard, S. (2024) · Journal of Molecular Neuroscience · doi:10.1007/s12031-024-02258-8 · Cited by 9 · see citing works
Ham, A., Chang, A. Y., Li, H., Bain, J., Goldman, J. E., Sulzer, D., Veenstra-VanderWeele, J. M., & Tang, G. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02741-z · Cited by 16 · see citing works
Su, W.-C., Mutersbaugh, J., Huang, W.-L., Bhat, A. N., & Gandjbakhche, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-81652-z · Cited by 11 · see citing works
Nazari, S., Ramos‐Cabo, S., Nalabolu, S. R., Barnes, C. C., Andreason, C., Zahiri, J., Esquivel, A., Arias, S. J., Grzybowski, A., Lombardo, M. V., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02132-6 · Cited by 10 · see citing works
Self-citationLiu, Y., Wang, H., & Ding, Y. (2023) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-023-00592-w · Cited by 9 · see citing works
Cui, T., Liu, Z., Li, Z., Han, Y., Xiong, W., Qu, Z., & Zhang, X. (2024) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2024.01.001 · Cited by 9 · see citing works
Análise da prevalência do Transtorno do Espectro Autista em crianças nos últimos 10 anos
Oliveira, G. T. Q., Schmidt, L. M., & Coelho, E. D. S. (2024) · Revista Eletrônica Acervo Saúde · doi:10.25248/reas.e15551.2024 · Cited by 8 · see citing works
Sensorimotor variability distinguishes early features of cognition in toddlers with autism
Denisova, K., & Wolpert, D. M. (2024) · iScience · doi:10.1016/j.isci.2024.110685 · Cited by 8 · see citing works
Portillo, N. L., Thammathorn, L. P., Buitrago, L. M. M., Carter, A. S., Sheldrick, R., & Eisenhower, A. (2024) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-024-06613-x · Cited by 8 · see citing works
Wang, T., Yang, X., Mohamed, Z. A., & Jia, F. (2025) · European Journal of Pediatrics · doi:10.1007/s00431-025-05989-x · Cited by 8 · see citing works
Molecular basis of autism spectrum disorders
Desai, R., Reddy, S., Truong, M., & Thankam, F. G. (2025) · Molecular Biology Reports · doi:10.1007/s11033-025-10604-1 · Cited by 8 · see citing works
Roth, N. M., Delgado‐López, C., Wiggins, L. D., Muñoz, N. N., Mulkey, S. B., Nieves-Ferrer, L., Woodworth, K. R., Rosario, G. M., Huertas, M. M., Moore, C. A., et al. (2023) · MMWR Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm7229a5 · Cited by 8 · see citing works
Furnier, S. M., Gangnon, R. E., Daniels, J. L., Weismer, S. E., Nadler, C. B., Pazol, K., Reyes, N., Rosenberg, S. A., Rubenstein, E., Wiggins, L. D., et al. (2024) · Autism Research · doi:10.1002/aur.3107 · Cited by 8 · see citing works
Self-citationTeo, T., Koh, H. C., Zambri, N. M., Zaccario, M. L., Sossin, K. M., & Wong, C. M. (2024) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-024-01957-7 · Cited by 8 · see citing works
Girault, J. B., Nishino, T., Talović, M., Nebel, M. B., Reynolds, M., Burrows, C. A., Elison, J. T., Lee, C.-M., Snyder, A. Z., Shen, M. D., et al. (2025) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-025-09613-9 · Cited by 8 · see citing works
Zhao, X., Kuo, F., Chen, X., Li, D., & Duan, H. (2025) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-025-06845-5 · Cited by 8 · see citing works
Huang, Z., Wei, A., Yuan, H., Huang, S., Chen, X., Han, Y., & Li, X. (2025) · BMC Pediatrics · doi:10.1186/s12887-025-05922-z · Cited by 7 · see citing works
Cui, T., Zhang, J., Han, Y., Su, Y., Li, Z., & Zhang, X. (2023) · European Journal of Clinical Nutrition · doi:10.1038/s41430-023-01365-w · Cited by 7 · see citing works
Carty, A., Green, R. R., Goodman, C. V., McLaughlin, J. R., Hu, H., Lanphear, B. P., Muckle, G., & Till, C. (2024) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-024-06487-z · Cited by 7 · see citing works
Parent Facilitation of Child Emotion Regulation in ASD: An Ecological Momentary Assessment Study
Rovane, A. K., Hock, R., Yang, C., & Hills, K. J. (2024) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-024-06541-w · Cited by 7 · see citing works
Fadeev, K. A., Reyes, I. V. R., Goiaeva, D. E., Obukhova, T. S., Ovsiannikova, T. M., Prokofyev, A. O., Rytikova, A. M., Novikov, A. Y., Kozunov, V. V., Stroganova, T. A., et al. (2024) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-024-09585-2 · Cited by 7 · see citing works
Gestational autoantibody exposure impacts early brain development in a rat model of MAR autism
McLellan, J., Iosif, A., Cichewicz, K., Canales, C. P., Rahbarian, D., Corea, M., Bauman, M. D., Nord, A. S., & de Water, J. V. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-02907-3 · Cited by 7 · see citing works
Shi, S., Song, S., Wang, H., Li, P., & Zhang, X. (2025) · BMC Pediatrics · doi:10.1186/s12887-025-05921-0 · Cited by 6 · see citing works
Jones, D. R., & Sasson, N. J. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1263525 · Cited by 7 · see citing works
Ou, J., Dong, H., Dai, S. Y., Hou, Y., Wang, Y., Lu, X., Xun, G., Xia, K., Zhao, J., & Shen, Y. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1291356 · Cited by 6 · see citing works
Woolsey, C., Bisht, P., Rothman, J. M., & Gondy Leroy, A. (2024) · PubMed Central · doi:10.48550/arxiv.2405.06695 · Cited by 6 · see citing works
Lopez, K. (2024) · Healthcare · doi:10.3390/healthcare12232381 · Cited by 7 · see citing works
Adapting individual placement and support for unemployed adults with autism spectrum disorder
Florence, A. C., Elwyn, G. J., Mueser, K. T., McGurk, S. R., Liebmann, E. P., McLaren, J., & Drake, R. E. (2025) · Journal of Vocational Rehabilitation · doi:10.1177/10522263241310028 · Cited by 6 · see citing works
Data-Driven Characterization of Individuals With Delayed Autism Diagnosis
Aizenberg, D., Shalev, I., Uzefovsky, F., & Eran, A. (2025) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2024.6075 · Cited by 5 · see citing works
Wenzell, M., Ievers-Landis, C., Kim, S., DeSimio, S., Neudecker, M., Aaron, S. P., Wierenga, K., Miao, M., & Williamson, A. A. (2025) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-025-06807-x · Cited by 5 · see citing works
Hashemi, F., Hoepner, L., Hashemi, H., Hoseini, M., Omeragić, E., Hamidinejad, F. S., Haluza, D., Mititelu, M., & Guo, C. (2025) · Environmental Pollution · doi:10.1016/j.envpol.2025.126700 · Cited by 6 · see citing works
Neurodevelopmental Profiles of 4-Year-Olds in the Navajo Birth Cohort Study
Rennie, B. J., Bishop, S. L., LEVENTHAL, B. L., Zheng, S., Geib, E. F., Kim, Y. S., Burnette, C. P., Salzman, E., Nozadi, S. S., Kim, H., et al. (2023) · JAACAP Open · doi:10.1016/j.jaacop.2023.06.003 · Cited by 6 · see citing works
Rooks-Ellis, D. L., Scheibel, G., Mason, C. A., & Tu, S. (2023) · Journal of Early Intervention · doi:10.1177/10538151231218958 · Cited by 6 · see citing works
Pais, M., Sereno, J., Tomé, V. A., Fonseca, C., Seco, C., Ribeiro, I., Martins, J., Fortuna, A. C., Abrunhosa, A., Pinto, L., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03464-7 · Cited by 5 · see citing works
Howerton, E. M., Morrill, V. N., Schrott, R., Daniels, J. A., Song, A., Benke, K. S., Volk, H. E., Farzadegan, H., Alexander, A. A., Tapia, A. L., et al. (2025) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-025-09637-1 · Cited by 5 · see citing works
Self-citationPak, N. S., Peredo, T. N., Ucero, A. P. M., & Kaiser, A. P. (2024) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-024-06322-5 · Cited by 5 · see citing works
Cui, K., Li, L., Li, K., Xiao, W., & Wang, Q. (2024) · NeuroToxicology · doi:10.1016/j.neuro.2024.07.012 · Cited by 5 · see citing works
Harbin, S. G., Hugh, M. L., Tagavi, D. M., Bravo, A., Joshi, M., Kiche, S. W., Michael, O. G., & Locke, J. J. (2024) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-024-06531-y · Cited by 6 · see citing works
Loo, K. K., Yang, S.-J., & Cheng, J. C. (2024) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2024.114434 · Cited by 6 · see citing works
Social first impressions and perceived gender in autistic and non-autistic youth
Baer, M., Cola, M., Knox, A., Lyons, M., Schillinger, S., Lee, A., Worth, B. F., Parish‐Morris, J., & Grossman, R. B. (2025) · Scientific Reports · doi:10.1038/s41598-025-89083-0 · Cited by 6 · see citing works
Reuben, K. E., Arias, J. J., Self‐Brown, S., & Thomas, E. V. (2025) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-025-06905-w · Cited by 5 · see citing works
Liu, S., Liu, X., Duan, Y., Huang, L., Ye, T., Gu, N., Tan, T., Zhang, Z., & Sun, J. (2025) · Journal of Nanobiotechnology · doi:10.1186/s12951-025-03557-x · Cited by 5 · see citing works
Khalil, A., Yatcilla, J., Christie, N., & Zhou, X. (2025) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-025-02555-x · Cited by 6 · see citing works
Bjørklund, G., Meguid, N. A., Hemimi, M., Sahakyan, E., Fereshetyan, K., & Yenkoyan, K. B. (2024) · Molecular Neurobiology · doi:10.1007/s12035-024-04099-3 · Cited by 5 · see citing works
Simcoe, K., Stainbrook, J. A., Chazin, K. T., Schnelle, E., Wagner, L., Hooper, M. A., Juárez, A. O. R., & Warren, Z. E. (2024) · Autism · doi:10.1177/13623613241273081 · Cited by 5 · see citing works
Self-citationPark, H., Jung, S. B., Byun, Y. H., Ahn, D., Park, C. W., Byun, S., & Huang, Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713965 · Cited by 4 · see citing works
Kreiser, N., & Segal, O. (2025) · Autism & Developmental Language Impairments · doi:10.1177/23969415251355462 · Cited by 4 · see citing works
Russell, L., Tinker, S. C., Shaw, K. A., Maenner, M. J., DiRienzo, M., Kirby, A. V., Howerton, E. M., Vanegas, S. B., & Lopez, M. L. C. (2026) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-026-07292-6 · Cited by 4 · see citing works
Self-citationBarn med autism, tidig intervention, och kvalitet i inkluderande förskolemiljö
Bejnö, H. (2025) · doi:10.16993/bct.h · Cited by 4 · see citing works
Advances in magnetic resonance imaging of the developing brain and its applications in pediatrics
Chen, R., Li, M., Zhao, Z., Xu, H., Ning, C.-L., Lu, J., Zhu, Q.-F., Huang, Y., Zhao, R., Zhang, L.-X., et al. (2025) · World Journal of Pediatrics · doi:10.1007/s12519-025-00905-7 · Cited by 4 · see citing works
Fogelström, A., Skoglund, C. B., Hagel, E., Wester, T., Granström, A. L., & Burgos, C. M. (2025) · Pediatric Surgery International · doi:10.1007/s00383-025-06201-9 · Cited by 4 · see citing works
Chen, H., Li, M., Yao, P., Nie, Z., Xu, L., Zhang, Y., Xu, X., Shen, C., Huang, J., Liu, Y., et al. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-07389-9 · Cited by 4 · see citing works
- Authors
- Shaw, K. A., Bilder, D. A., McArthur, D., Williams, A. R., Amoakohene, E., Bakian, A. V., Durkin, M. S., Fitzgerald, R. T., Furnier, S. M., Hughes, M. M., Pas, E. T., Salinas, A., Warren, Z., Williams, S., Esler, A., Grzybowski, A., Ladd-Acosta, C. M., Patrick, M., Zahorodny, W., Green, K. K., Hall-Lande, J., Lopez, M., Mancilla, K. C., Nguyen, R. H. N., Pierce, K., Schwenk, Y. D., Shenouda, J., Sidwell, K., Vehorn, A., DiRienzo, M., Gutierrez, J., Hallas, L., Hudson, A., Spivey, M. H., Pettygrove, S., Washington, A., & Maenner, M. J.
- Affiliations
- Centers for Disease Control and Prevention (US)
- University of Utah (US)
- University of Wisconsin–Madison (US)
- Washington University in St. Louis (US)
- Johns Hopkins University (US)
- Vanderbilt University Medical Center (US)
- University of Minnesota (US)
- University of California San Diego (US)
- Rutgers New Jersey Medical School (US)
- University of Arkansas for Medical Sciences (US)
- University of Arizona (US)
- Corresponding author
- Shaw, Kelly A.
- ORCID
- Bilder, Deborah A.
- McArthur, Dedria
- Williams, Ashley Robinson
- Bakian, Amanda V.
- Durkin, Maureen S.
- Fitzgerald, Robert T.
- Furnier, Sarah M.
- Hughes, Michelle M.
- Pas, Elise T.
- Warren, Zachary
- Esler, Amy
- Ladd-Acosta, Christine M.
- Patrick, Mary
- Zahorodny, Walter
- Lopez, Maya
- Nguyen, Ruby H.N.
- Pierce, Karen
- Shenouda, Josephine
- DiRienzo, Monica
- Pettygrove, Sydney
- Washington, Anita
- Maenner, Matthew J.
- Venue
- MMWR. Surveillance Summaries
- Published
- 2023-03-24
- Volume
- 72
- Issue
- 1
- Pages
- 1-15
- ISSN
- 1545-8636 (ISSN-L)
- 1546-0738
- PMC
- PMC10042615
- OpenAlex
- W4360599455
- Semantic Scholar
- f35fc0eac2ebcd6950ecee824df6cc887c3d5566
- Cited by
- 223
- References
- 23
- Citations by year
- 2026: 17 · 2025: 107 · 2024: 81 · 2023: 18
- FWCI
- 33.32
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- public-domain
- Locations
- MMWR Surveillance Summaries (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- UA Campus Repository (The University of Arizona) (submittedVersion, public-domain)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50HD103537)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Health Sciences › Medicine › Psychiatry and Mental health › Child Nutrition and Feeding Issues
- Social Sciences › Psychology › Clinical Psychology › Family and Disability Support Research
- Keywords
- autism spectrum disorder, autism prevalence, early detection, special education eligibility, intellectual disability, sex differences, racial and ethnic disparities, developmental evaluation, cumulative incidence
- MeSH terms
- Autism Spectrum Disorder, COVID-19, Autistic Disorder, Child, Developmental Disabilities, Female, Humans, Male, Population Surveillance, United States, Utah, Prevalence, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-06.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-06.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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