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Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018.
Open accessAbstract
PROBLEM/CONDITION: Autism spectrum disorder (ASD). PERIOD COVERED: 2018. DESCRIPTION OF SYSTEM: The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts active surveillance of ASD. This report focuses on the prevalence and characteristics of ASD among children aged 8 years in 2018 whose parents or guardians lived in 11 ADDM Network sites in the United States (Arizona, Arkansas, California, Georgia, Maryland, Minnesota, Missouri, New Jersey, Tennessee, Utah, and Wisconsin). To ascertain ASD among children aged 8 years, ADDM Network staff review and abstract developmental evaluations and records from community medical and educational service providers. In 2018, children met the case definition if their records documented 1) an ASD diagnostic statement in an evaluation (diagnosis), 2) a special education classification of ASD (eligibility), or 3) an ASD International Classification of Diseases (ICD) code. RESULTS: For 2018, across all 11 ADDM sites, ASD prevalence per 1,000 children aged 8 years ranged from 16.5 in Missouri to 38.9 in California. The overall ASD prevalence was 23.0 per 1,000 (one in 44) children aged 8 years, and ASD was 4.2 times as prevalent among boys as among girls. Overall ASD prevalence was similar across racial and ethnic groups, except American Indian/Alaska Native children had higher ASD prevalence than non-Hispanic White (White) children (29.0 versus 21.2 per 1,000 children aged 8 years). At multiple sites, Hispanic children had lower ASD prevalence than White children (Arizona, Arkansas, Georgia, and Utah), and non-Hispanic Black (Black) children (Georgia and Minnesota). The associations between ASD prevalence and neighborhood-level median household income varied by site. Among the 5,058 children who met the ASD case definition, 75.8% had a diagnostic statement of ASD in an evaluation, 18.8% had an ASD special education classification or eligibility and no ASD diagnostic statement, and 5.4% had an ASD ICD code only. ASD prevalence per 1,000 children aged 8 years that was based exclusively on documented ASD diagnostic statements was 17.4 overall (range: 11.2 in Maryland to 29.9 in California). The median age of earliest known ASD diagnosis ranged from 36 months in California to 63 months in Minnesota. Among the 3,007 children with ASD and data on cognitive ability, 35.2% were classified as having an intelligence quotient (IQ) score ≤70. The percentages of children with ASD with IQ scores ≤70 were 49.8%, 33.1%, and 29.7% among Black, Hispanic, and White children, respectively. Overall, children with ASD and IQ scores ≤70 had earlier median ages of ASD diagnosis than children with ASD and IQ scores >70 (44 versus 53 months). INTERPRETATION: In 2018, one in 44 children aged 8 years was estimated to have ASD, and prevalence and median age of identification varied widely across sites. Whereas overall ASD prevalence was similar by race and ethnicity, at certain sites Hispanic children were less likely to be identified as having ASD than White or Black children. The higher proportion of Black children compared with White and Hispanic children classified as having intellectual disability was consistent with previous findings. PUBLIC HEALTH ACTION: The variability in ASD prevalence and community ASD identification practices among children with different racial, ethnic, and geographical characteristics highlights the importance of research into the causes of that variability and strategies to provide equitable access to developmental evaluations and services. These findings also underscore the need for enhanced infrastructure for diagnostic, treatment, and support services to meet the needs of all children.
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In the citing paper
“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)”
In the citing paper
“Surveillance was conducted using the same surveillance methods and case definitions used in 2018 and 2020 (6,13).”
In the citing paper
“No evidence of heterogeneity of the male-to-female ratio among children aged 4 years across sites was observed; however, the overall male-to-female ratio for children aged 4 years was lower than the 4.2 male-to-female ratio for children aged 8 years (11).”
In the citing paper
“The site in California had the highest cumulative incidence of ASD by age 48 months and the fewest cases of suspected ASD and was the only site where prevalence among children aged 4 years was higher than among children aged 8 years (11).”
In the citing paper
“The combined prevalence of ASD and suspected ASD (19.7 per 1,000 children aged 4 years) was still lower than ASD prevalence observed among children aged 8 years (23.0 per 1,000 children aged 8 years) (11).”
In the citing paper
“Children with co-occurring intellectual disability are more likely to be identified earlier than children without intellectual disability, as indicated by an earlier median age at identification among this group (11) as well as the consistently higher proportion of children with intellectual disability at age 4 years (8,13) than at age 8 years (9,11,17).”
In the citing paper
“Consistent with findings among children aged 8 years (11), a higher proportion of Black children aged 4 years with ASD have co-occurring intellectual disability.”
In the citing paper
“The 2018 estimate for children aged 4 years was lower than the 23.0 per 1,000 ASD prevalence among children aged 8 years (11), a finding consistent with previous reports (8,13).”
In the citing paper
“Adding the children identified with ASD to those suspected of having ASD at age 4 years is still less than the prevalence of ASD by age 8 years at all sites except California (11).”
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“before a child turns three years old is possible and has increased [3,4].”
BackgroundMethodMesleh, A. G., Ehtewish, H., de la Fuente, A., Shammari, H. A., Ghazal, I., Al‐Faraj, F., Alshaban, F. A. W., Abdesselem, H., Emara, M. M., Alajez, N. M., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24087443 · Cited by 14
“7% [6], and in the United States, ASD cases are reported in 1 in every 44 children [7].”
BackgroundHenderson, T. B., Ludden, B. J., & Romero, R. A. A. (2023) · Behavior Analysis in Practice · doi:10.1007/s40617-023-00787-z · Cited by 12
A Scoping Review of Diagnosis of Autism Spectrum Disorder in Primary Care
Johnson, N. L., Fial, A., Hecke, A. V. V., Whitmore, K. E., Meyer, K., Peña, S., Carlson, M., & Koth, K. A. (2023) · Journal of Pediatric Health Care · doi:10.1016/j.pedhc.2023.04.003 · Cited by 12
Holingue, C., Pfeiffer, D. L., Ludwig, N. N., Reetzke, R., Hong, J. S., Kalb, L. G., & Landa, R. J. (2023) · Autism Research · doi:10.1002/aur.2972 · Cited by 12
“Roughly 1 in 3 autistic children have ID (Maenner et al., 2021).”
The genetics of autism spectrum disorder in an East African familial cohort
Tuncay, İ. O., DeVries, D., Gogate, A., Kaur, K. J., Kumar, A., Xing, C., Goodspeed, K., Seyoum-Tesfa, L., & Chahrour, M. H. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100322 · Cited by 13
“ASD affects 1 in 44 children in the United States (US).(7) The reported prevalence is geographically variable within the US, presumably due to decreased surveillance where access to assessment, diagnosis, and services are limited.”
BackgroundYoon, S., Santos, M. D., Forrest, M. P., Pratt, C. P., Khalatyan, N., Mohler, P. J., Savas, J. N., & Penzes, P. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112784 · Cited by 13
Tian, L., Ma, S., Li, Y., Zhao, M., Xu, C., Wang, C., Zhang, X., & Gao, L. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1188648 · Cited by 12
Yang, Y., Wang, H., Hai-ping, X., Yao, M., & Yu, D. (2023) · Autism Research · doi:10.1002/aur.3006 · Cited by 12
“Over the past 10 years, there has been a sharp rise in the prevalence of ASD, which was estimated to be 1 in 44 for US children aged 8 in 2018 (Maenner et al., 2021).”
BackgroundHarker, S. A., Al-Hassan, L., Huentelman, M. J., Braden, B. B., & Lewis, C. R. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms242115988 · Cited by 11
Stephens, A. M., Briggs, A. M., Loder‐Lafferty, B. H., Barr, J., & Miller, A. (2023) · Behavior Analysis in Practice · doi:10.1007/s40617-023-00896-9 · Cited by 12
“The Autism and Developmental Disabilities Monitoring Network recently estimated that autism spectrum disorder (ASD) now affects 1 in 44 of the nation’s children (Maenner et al., 2021).”
Powell, T., Parker, J., Kitson, H., & Rogalewski, M. (2024) · Autism in Adulthood · doi:10.1089/aut.2023.0031 · Cited by 10
Woodhouse, E., Hollingdale, J., Davies, L., Al‐Attar, Z., Young, S., Vinter, L. P., Agyemang, K., Bartlett, C., Berryessa, C. M., Chaplin, E., et al. (2024) · BMC Medicine · doi:10.1186/s12916-024-03320-3 · Cited by 20
“Reported diagnosis rates are three times higher in biological males than females [10, 11] but the biological male:female ratio is debated due to potential sex differences in detection, referral and assessment.”
BackgroundInfluentialLiu, J., Girault, J. B., Nishino, T., Shen, M. D., Kim, S. H., Burrows, C. A., Elison, J. T., Marrus, N. M., Wolff, J. J., Botteron, K. N., et al. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae092 · Cited by 13
Wang, L., Qin, Y., Yang, S., Jin, D., Zhu, Y., Li, X., Li, W., Wang, Y., & Jin, C. (2024) · Human Brain Mapping · doi:10.1002/hbm.26750 · Cited by 13
Hirai, T., Umeda, N., Harada, T., Okumura, A., Nakayasu, C., Ohto‐Nakanishi, T., Tsuchiya, K. J., Nishimura, T., & Matsuzaki, H. (2024) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13710 · Cited by 16
“…factors (interleukin-1RA, tumor necrosis factor-α , serpin E1, vascular cell adhesion molecule 1, vascular endothelial growth factor D, epidermal growth factor, and colony-stimulating factors 1 and 2) in cord blood plasma and maternal mid-pregnancy plasma were associated with increased ASD risk.”
BackgroundWhole-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
Kang, N., Sargsyan, S., Chough, I., Petrick, L. M., Liao, J., Wu, C., Pavlovic, N., Lurmann, F., Martinez, M. P., McConnell, R., et al. (2024) · Environmental Pollution · doi:10.1016/j.envpol.2024.124729 · Cited by 11
Unraveling the relative abundance of psychobiotic bacteria in children with Autism Spectrum Disorder
Darwesh, M.-A. K., Bakr, W., Omar, T. E. I., El‐Kholy, M. A., & Azzam, N. F. A. E. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-72962-3 · Cited by 13
Smith-Young, J., Pike, A. D., Swab, M., & Chafe, R. (2024) · JBI Evidence Synthesis · doi:10.11124/jbies-23-00332 · Cited by 14
Kasitipradit, K., Thongkorn, S., Kanlayaprasit, S., Saeliw, T., Lertpeerapan, P., Panjabud, P., Jindatip, D., Hu, V. W., Kikkawa, T., Osumi, N., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-86392-2 · Cited by 14
Karpur, A., Vasudevan, V., Frazier, T. W., & Shih, A. J. (2022) · Disability and health journal · doi:10.1016/j.dhjo.2022.101323 · Cited by 14
Buranova, N., Dampf, M., Stevenson, B., & Sohl, K. (2022) · Clinical Pediatrics · doi:10.1177/00099228221090710 · Cited by 12
Autism spectrum disorder: Comorbidity and demographics in a clinical sample
Kılıçaslan, F., & Tufan, A. E. (2022) · International Journal of Developmental Disabilities · doi:10.1080/20473869.2022.2074242 · Cited by 12
“Although ASD can be reliably diagnosed as early as two years old, most children are still being diagnosed after four years of age (Maenner et al. 2021).”
InfluentialLin, Y., Wang, G., Yang, Y., Jin, X., Huang, H., Zhang, Y., & Jin, Z. (2022) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-022-05603-1 · Cited by 15
“However, the average age of diagnosis is about 4 years old in United States(Maenner et al., 2021) and ASD patients are most often diagnosed at older ages in low- and middle- income countries(Rudra et al., 2017).”
BackgroundInfluentialJoshi, G., & Wilens, T. E. (2022) · Child and Adolescent Psychiatric Clinics of North America · doi:10.1016/j.chc.2022.03.012 · Cited by 13
Differences in Prediction May Underlie Language Disorder in Autism
Weismer, S. E., & Saffran, J. R. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.897187 · Cited by 14
Shanghai Autism Early Development: An Integrative Chinese ASD Cohort
Dai, Y., Liu, Y., Zhang, L., Ren, T., Wang, H., Yu, J., Liu, X., Chen, Z., Deng, L., Tao, M., et al. (2022) · Neuroscience Bulletin · doi:10.1007/s12264-022-00904-y · Cited by 12
Venker, C. E., & Johnson, J. R. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.929589 · Cited by 12
Ruble, L. A., Ogle, L. N., & McGrew, J. H. (2022) · Psychology in the Schools · doi:10.1002/pits.22800 · Cited by 13
Distinct social attention profiles in preschoolers with autism contrasted to fragile X syndrome
Wall, C. A., Shic, F., Varanasi, S., & Roberts, J. E. (2022) · Autism Research · doi:10.1002/aur.2857 · Cited by 13
“Thirty-one percent of those with ASD have co-occurring intellectual disability (ID) (Maenner et al., 2021), and yet, despite the growing body of research utilizing eye-tracking methods to understand social attention in ASD, very little of this work has included children with co-occurring ID…”
BackgroundInfluentialBurman, D., Ramanujam, K., Manzar, M. D., Chattu, V. K., Spence, D. W., Zaki, N. F. W., Jahrami, H., & Pandi-Perumal, S. R. (2023) · Sleep and Vigilance · doi:10.1007/s41782-022-00222-3 · Cited by 12
Gandhi, T., Liu, C., Adeyelu, T. T., Canepa, C. R., & Lee, C. C. (2023) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2023.1114789 · Cited by 13
Non-invasive brain stimulation for patient with autism: a systematic review and meta-analysis
Liu, A., Gong, C., Wang, B., Sun, J., & Jiang, Z. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1147327 · Cited by 16
“According to 2020, Recent data from the Centers for Disease Control and Prevention show that The overall ASD prevalence aged 8 years rises to 1 in 44, which is already more than the sum of the world’s three major diseases (AIDS, cancer, diabetes) (5).”
MethodChang, J., & Goin‐Kochel, R. P. (2020) · Autism Research · doi:10.1002/aur.2339 · Cited by 12
Juvenile handling rescues autism-related effects of prenatal exposure to valproic acid
Seiffe, A., Ramírez, M. F., Sempé, L., & Depino, A. M. (2022) · Scientific Reports · doi:10.1038/s41598-022-11269-7 · Cited by 13
“223; juvenile treatment × time: F (4, 192) = 0.”
BackgroundInfluentialThe Global Burden of Disease Epidemiology—When Big Data Impute the Nonexistent
Roman-Urrestarazu, A., & van Kessel, R. (2024) · JAMA Pediatrics · doi:10.1001/jamapediatrics.2023.6507 · Cited by 11
Taha, H. A., Issa, A., Muhanna, Z., Al-Shehab, M., Wadi, T., Awamleh, S., Ateiwi, Y. A., Abusido, M., & Berggren, V. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1648118 · Cited by 13
Williams, Z. J. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.9503 · Cited by 11
Reetzke, R., Singh, V., Hong, J. S., Holingue, C., Kalb, L. G., Ludwig, N. N., Menon, D. U., Pfeiffer, D. L., & Landa, R. J. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.936392 · Cited by 20
“, 2016), and increasing prevalence (1 in 44; Maenner, 2021).”
BackgroundInfluentialAller, T. B., Barrett, T. S., Levin, M. E., & McClain, M. B. (2022) · Journal of Contextual Behavioral Science · doi:10.1016/j.jcbs.2022.09.001 · Cited by 14
Metadata
- Authors
- Maenner, M. J., Shaw, K. A., Bakian, A. V., Bilder, D. A., Durkin, M. S., Esler, A., Furnier, S. M., Hallas, L., Hall-Lande, J., Hudson, A., Hughes, M. M., Patrick, M., Pierce, K., Poynter, J. N., Salinas, A., Shenouda, J., Vehorn, A., Warren, Z., Constantino, J. N., DiRienzo, M., Fitzgerald, R. T., Grzybowski, A., Spivey, M. H., Pettygrove, S., Zahorodny, W., Ali, A., Andrews, J. G., Baroud, T., Gutierrez, J., Hewitt, A., Lee, L.-C., Lopez, M., Mancilla, K. C., McArthur, D., Schwenk, Y. D., Washington, A., Williams, S., & Cogswell, M. E.
- Affiliations
- Centers for Disease Control and Prevention (US)
- University of Utah (US)
- University of Wisconsin–Madison (US)
- University of Minnesota (US)
- University of Arkansas for Medical Sciences (US)
- University of California San Diego (US)
- Rutgers New Jersey Medical School (US)
- Vanderbilt University Medical Center (US)
- Washington University in St. Louis (US)
- Johns Hopkins University (US)
- University of Arizona (US)
- United States Department of Health and Human Services (US)
- Corresponding author
- Maenner, Matthew J.
- ORCID
- Maenner, Matthew J.
- Bakian, Amanda V.
- Bilder, Deborah A.
- Durkin, Maureen S.
- Esler, Amy
- Furnier, Sarah M.
- Hughes, Michelle M.
- Patrick, Mary
- Pierce, Karen
- Poynter, Jenny N.
- Shenouda, Josephine
- Warren, Zachary
- Constantino, John N.
- DiRienzo, Monica
- Fitzgerald, Robert T.
- Pettygrove, Sydney
- Zahorodny, Walter
- Ali, Akilah
- Andrews, Jennifer G.
- Lee, Li-Ching
- Lopez, Maya
- McArthur, Dedria
- Washington, Anita
- Cogswell, Mary E.
- Venue
- MMWR. Surveillance Summaries
- Published
- 2021-12-03
- Volume
- 70
- Issue
- 11
- Pages
- 1-16
- ISSN
- 1545-8636 (ISSN-L)
- 1546-0738
- PMC
- PMC8639024
- OpenAlex
- W3216151710
- Cited by
- 1,818
- References
- 37
- Citations by year
- 2026: 101 · 2025: 270 · 2024: 438 · 2023: 553 · 2022: 438 · 2021: 8
- FWCI
- 193.23
- 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)
- 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, special education eligibility, ICD-10 codes, sex differences, racial and ethnic disparities, American Indian and Alaska Native children, median household income, neighborhood socioeconomic status, United States states and territories
- MeSH terms
- Autism Spectrum Disorder, Race Factors, Child, Ethnicity, Female, Geography, Humans, Male, Population Surveillance, United States, Prevalence, Racial Groups, Health Status Disparities, Epidemiological Monitoring
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