The prevalence of autism spectrum disorder (ASD) among children in the United States has risen dramatically over the past two decades. In 2022, an estimated 32.2 out of every 1,000 8-year-old children were identified with ASD, marking a nearly fivefold increase from the rate of 6.7 per 1,000 children in 2000. This significant upward trend underscores the growing importance of understanding and addressing ASD in American society. Gender disparities in autism diagnosis The increase in ASD prevalence is not uniform across genders. From 2016 to 2019, male children were nearly four times more likely to be diagnosed with ASD than their female counterparts. Approximately 4.8 percent of boys aged 3 to 17 years had received an ASD diagnosis at some point in their lives, compared to only 1.3 percent of girls in the same age group. This substantial gender gap highlights the need for further research into potential biological and social factors influencing ASD diagnosis rates. Racial and ethnic variations in autism prevalence Autism prevalence also varies across racial and ethnic groups. Data from 2016 to 2019 show that non-Hispanic white children aged 3 to 17 years had an ASD prevalence of 2.9 percent, while around 3.5 percent of Hispanic children had ASD. While this statistic provides insight, it is essential to consider potential disparities in diagnosis and access to services among different racial and ethnic communities. Further research and targeted interventions may be necessary to ensure equitable identification and support for children with ASD across all populations.

The prevalence rate of autism spectrum disorder among four-year-old children in Missouri was around 24.8 per 1,000 children in 2022. Autism spectrum disorder is a developmental disability characterized by deficits in social communication and interaction as well as repetitive behavior, interest, or activity patterns. This statistic displays the estimated prevalence of autism spectrum disorder among children aged four years in select U.S. states in 2022.

These statistics present a group of measures on waiting times for autism spectrum disorder diagnostic pathways, based on the time between a referral for suspected autism and the first care contact associated with that referral. There are also multiple breakdowns based on the progression and outcomes of those referrals. Each of these measures contributes to an overall picture of waiting times for diagnostic pathways. The approach is outlined in the methodology section of this publication.

The prevalence rate of autism spectrum disorder among white, non-Hispanic eight-year-olds in Georgia was estimated to be ** per 1,000 children as of 2022. Autism spectrum disorder is a developmental disability characterized by deficits in social communication and interaction as well as repetitive behavior, interest, or activity patterns. This statistic displays the estimated prevalence of autism spectrum disorder among children aged eight years in selected U.S. states in 2022, by race/ethnicity.

This data table provides a collection of information from peer-reviewed autism prevalence studies. Information reported from each study includes the autism prevalence estimate and additional study characteristics (e.g., case ascertainment and criteria). A PubMed search was conducted to identify studies published at any time through September 2020 using the search terms: autism (title/abstract) OR autistic (title/abstract) AND prevalence (title/abstract). Data were abstracted and included if the study fulfilled the following criteria: • The study was published in English; • The study produced at least one autism prevalence estimate; and • The study was population-based (any age range) within a defined geographic area.

In the academic year of 2022/23, there were approximately ******* individuals 3- to 21-years-old with autism in the United States who were covered by the Individuals with Disabilities Education Act (IDEA). This is an increase from the previous year, when ******* individuals with autism were covered under IDEA.

The prevalence rate of autism spectrum disorder among male children aged eight years in Georgia was estimated to be around ** per 1,000 children as of 2022. Autism spectrum disorder is a developmental disability characterized by deficits in social communication and interaction as well as repetitive behavior, interest, or activity patterns. This statistic displays the estimated prevalence of autism spectrum disorder among children aged 8 years in select U.S. states in 2022, by gender.

This table provides county-level prevalence for 2018 for seven US states using linked statewide health and education data. For full methods see: Shaw KA, Williams S, Hughes MM, Warren Z, Bakian AV, Durkin MS, et al. Statewide county-level autism spectrum disorder prevalence estimates — seven U.S. states, 2018. Annals of Epidemiology. 2023 Jan 18; Available from: https://www.sciencedirect.com/science/article/pii/S1047279723000182

This report presents a new estimate of the prevalence of autism among adults aged 18 years and over. This was derived using data from the 2007 Adult Psychiatric Morbidity Survey (APMS 2007) in combination with data from a new study of the prevalence of autism among adults with learning disabilities, who are a key group to study because they could not take part in the APMS 2007 and have been found to have an increased risk of autism. The study was based on adults with learning disabilities living in private households and communal care establishments in Leicestershire, Lambeth and Sheffield. Whilst the study comprised a relatively small sample with limited geographical coverage and did not include the institutional population, it did include two non-mutually exclusive populations (people in communal care establishments and people with learning disabilities) which were not covered by the APMS 2007. The study demonstrates that autism is common among people with a learning disability and, in taking these into account, at 1.1 per cent nationally is slightly higher than the previous estimate of 1.0 per cent in the APMS 2007. Sensitivity analysis showed that the estimates for national prevalence produced by this study were relatively insensitive to inaccuracies caused by the limitations.

Prevalence Of Autism Spectrum Disorder Per 1000 Children 8 Years Old All States

Input datasets on Ohio Birth and Autism will not be made accessible to the public due to the fact that they include individual-level data with PII. Output data are all available in tabulated form within the published manuscript. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Input data can be obtained from Applications from owners of the data (Children's Hospital and Ohio Department of Health). The tabulated output data is found in the manuscript. Format: Input datasets on Ohio Birth and Autism will not be made accessible to the public due to the fact that they include individual-level data with PII. Output data are all available in tabulated form within the published manuscript (e.g., results of regression models, measures of central tendency, population characteristics, etc.). This dataset is associated with the following publication: Kaufman, J., M. Wright, G. Rice, N. Connolly, K. Bowers, and J. Anixt. AMBIENT OZONE AND FINE PARTICULATE MATTER EXPOSURES AND AUTISM SPECTRUM DISORDER IN METROPOLITAN CINCINNATI, OHIO. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 171: 218-227, (2019).

The global Autism Spectrum Disorder (ASD) market, valued at $7.78 billion in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 7.6% from 2025 to 2033. This expansion is driven by several key factors. Increased awareness and early diagnosis of ASD are leading to higher treatment rates. Advances in therapeutic interventions, including both pharmacological and non-pharmacological approaches, offer improved outcomes for individuals with ASD and their families. Furthermore, the growing prevalence of ASD globally, coupled with increased investment in research and development of new treatments, fuels market growth. The market is segmented by therapy type (pharmacological and non-pharmacological) and age group (pediatric and adult), reflecting the diverse needs of the ASD population. Pharmacological therapies currently hold a larger market share, due to the established efficacy of certain medications in managing ASD-related symptoms. However, the non-pharmacological segment is experiencing significant growth, driven by rising interest in behavioral therapies and other holistic approaches. The adult segment shows increasing demand as individuals with ASD live longer and require continued support. Regionally, North America and Europe currently dominate the market due to established healthcare infrastructure, higher awareness, and greater access to specialized services. However, Asia-Pacific is expected to witness significant growth in the coming years, fueled by rising disposable incomes, improved healthcare access, and increasing diagnostic capabilities. Competitive pressures within the market are intense, with leading companies focusing on developing innovative therapies, expanding their geographical reach, and strengthening their market positioning through strategic collaborations and acquisitions. Challenges remain, including the high cost of treatments, accessibility issues in certain regions, and the need for more effective treatments for specific ASD-related symptoms. Despite these challenges, the long-term outlook for the ASD market remains positive, driven by sustained investment in research, growing awareness, and an increasing focus on improving the lives of individuals with ASD.

National Database for Autism Research (NDAR) is an extensible, scalable informatics platform for autism spectrum disorder-relevant data at all levels of biological and behavioral organization (molecules, genes, neural tissue, behavioral, social and environmental interactions) and for all data types (text, numeric, image, time series, etc.). NDAR was developed to share data across the entire ASD field and to facilitate collaboration across laboratories, as well as interconnectivity with other informatics platforms. NDAR Homepage: http://ndar.nih.gov/

Machine learning algorithms have been widely applied in diagnostic tools for autism spectrum disorder (ASD), revealing an altered brain connectivity. However, little is known about whether an magnetic resonance imaging (MRI)-based brain network is related to the severity of ASD symptoms in a large-scale cohort. We propose a graph convolution neural network-based framework that can generate sparse hierarchical graph representations for functional brain connectivity. Instead of assigning initial features for each node, we utilized a feature extractor to derive node features and the extracted representations can be fed to a hierarchical graph self-attention framework to effectively represent the entire graph. By incorporating connectivity embeddings in the feature extractor, we propose adjacency embedding networks to characterize the heterogeneous representations of the brain connectivity. Our proposed model variants outperform the benchmarking model with different configurations of adjacency embedding networks and types of functional connectivity matrices. Using this approach with the best configuration (SHEN atlas for node definition, Tikhonov correlation for connectivity estimation, and identity-adjacency embedding), we were able to predict individual ASD severity levels with a meaningful accuracy: the mean absolute error (MAE) and correlation between predicted and observed ASD severity scores resulted in 0.96, and r = 0.61 (P < 0.0001), respectively. To obtain a better understanding on how to generate better representations, we investigate the relationships between the extracted feature embeddings and the graph theory-based nodal measurements using canonical correlation analysis. Finally, we visualized the model to identify the most contributive functional connections for predicting ASD severity scores.

BackgroundTo explore the geographical pattern and temporal trend of autism spectrum disorders (ASD) epidemiology from 1990 to 2019, and perform a bibliometric analysis of risk factors for ASD.MethodsIn this study, ASD epidemiology was estimated with prevalence, incidence, and disability-adjusted life-years (DALYs) of 204 countries and territories by sex, location, and sociodemographic index (SDI). Age-standardized rate (ASR) and estimated annual percentage change (EAPC) were used to quantify ASD temporal trends. Besides, the study performed a bibliometric analysis of ASD risk factors since 1990. Publications published were downloaded from the Web of Science Core Collection database, and were analyzed using CiteSpace.ResultsGlobally, there were estimated 28.3 million ASD prevalent cases (ASR, 369.4 per 100,000 populations), 603,790 incident cases (ASR, 9.3 per 100,000 populations) and 4.3 million DALYs (ASR, 56.3 per 100,000 populations) in 2019. Increases of autism spectrum disorders were noted in prevalent cases (39.3%), incidence (0.1%), and DALYs (38.7%) from 1990 to 2019. Age-standardized rates and EAPC showed stable trend worldwide over time. A total of 3,991 articles were retrieved from Web of Science, of which 3,590 were obtained for analysis after removing duplicate literatures. “Rehabilitation”, “Genetics & Heredity”, “Nanoscience & Nanotechnology”, “Biochemistry & Molecular biology”, “Psychology”, “Neurosciences”, and “Environmental Sciences” were the hotspots and frontier disciplines of ASD risk factors.ConclusionsDisease burden and risk factors of autism spectrum disorders remain global public health challenge since 1990 according to the GBD epidemiological estimates and bibliometric analysis. The findings help policy makers formulate public health policies concerning prevention targeted for risk factors, early diagnosis and life-long healthcare service of ASD. Increasing knowledge concerning the public awareness of risk factors is also warranted to address global ASD problem.