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.

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.

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

• The global industry was valued at US$ 1.6 Bn in 2023
• It is expected to grow at a CAGR of 5.2% from 2024 to 2034 and reach US$ 2.9 Bn by the end of 2034

Market Introduction

Attribute	Detail
Market Drivers	Rise in Incidence of Autism Spectrum Disorder Rise in Research-related Initiatives by Governments and Private Agencies

Regional Outlook

Attribute	Detail
Leading Region	North America

Autism Spectrum Disorder Treatment Market Snapshot

Attribute	Detail
Market Size in 2023	US$ 1.6 Bn
Market Forecast (Value) in 2034	US$ 2.9 Bn
Growth Rate (CAGR)	5.2%
Forecast Period	2024-2034
Historical Data Available for	2020-2022
Quantitative Units	US$ Bn for Value
Market Analysis	It includes segment analysis as well as regional level analysis. Moreover, qualitative analysis includes drivers, restraints, opportunities, key trends, Porter’s Five Forces analysis, value chain analysis, and key trend analysis.
Competition Landscape	Market share analysis by company (2023) Company profiles section includes overview, product portfolio, sales footprint, key subsidiaries or distributors, strategy & recent developments, and key financials
Format	Electronic (PDF) + Excel
Market Segmentation	Drug Class Antipsychotic Drugs Selective Serotonin Reuptake Inhibitors Stimulants Sleep Medications Others Age Group Children Adults Distribution Channel Hospital Pharmacies Retail Pharmacies Online Pharmacies
Regions Covered	North America Europe Asia Pacific Latin America Middle East & Africa
Countries Covered	U.S. Canada Germany U.K. France Italy Spain China India Japan Australia & New Zealand Brazil Mexico South Africa GCC
Companies Profiled	Bristol-Myers Squibb Company Merck & Co., Inc. Novartis AG Eli Lilly and Company Pfizer Inc. Johnson & Johnson Otsuka Pharmaceutical Co., Ltd. Yamo Pharmaceuticals F. Hoffmann-La Roche Ltd. Axial Therapeutics, Inc. Curemark LLC Others
Customization Scope	Available Upon Request
Pricing	Available Upon Request

Pairwise correlations were performed, as described in Table 1, between state-level annual incidence for specific female cancers and autism prevalence (ages 3–21) from states selected on the basis of their criteria for diagnosing autism (Fig. 2). P represents combined p-values for Spearman correlations using Simes' method and bolded if P≤0.01. N represents the median number of states for which both autism and cancer data were available for analyses. Kaposi's sarcoma is omitted because there was insufficient data to conduct the analyses. Data are similar when the Pearson Correlation Coefficient is used (Table S3).

IntroductionCo-occurring conditions are common in children with autism spectrum disorder (ASD) and have important negative impacts on the children and their families. For Iraqi children, local healthcare systems tend to place more emphasis on the management of ASD itself while neglecting co-occurring conditions.ObjectivesThis study aims to investigate the prevalence, characteristics, and potential risk factors of co-occurring epilepsy, sleep, and weight issues among Iraqi children with ASD.MethodsA multicenter cross-sectional study was conducted from January 24 to August 7, 2024, including children from Imam Hussein Centre, Al-Subtain Academy for Autism and Neurodevelopmental Disorders, and Baghdad’s National Centre for Autism and Child Psychiatry. A structured questionnaire was used, including 35 items for demographic information, epilepsy, sleep problems, and weight issues.ResultsOur sample included 240 children, of whom 34 (14.2%) had co-occurring epilepsy, 178 (74.2%) had at least one sleep problem, and 104 (43.3%) were obese. Among children with epilepsy, 18 (52.9%) received their diagnosis before ASD. The most prescribed anticonvulsant, sodium valproate, was noted in 18 (52.9%) cases. Difficulty falling asleep was the most common sleep problem, affecting 97 (40.4%), while sleepwalking was reported in only 26 (10.8%). Significant differences in the body-mass index were observed based on risperidone use (adjusted p-value = 0.036, R-value = 0.163, 95% CI: 0.031, 0.288), sleep duration (r = -0.166, adjusted p-value = 0.036), and diet (adjusted p-value = 0.036, ϵ2 = 0.038, 95% CI: 0.005, 0.087). However, no significant association was demonstrated between BMI and screen time (adjusted p-value = 0.264).ConclusionCo-occurring conditions are common among children with ASD and should be assessed simultaneously. Additionally, since some of the children might be diagnosed with epilepsy first, it is important to consider co-occurring ASD in their diagnosis.

In 2019, around 13 percent of U.S. adults aged 30 to 49 years believed certain vaccines cause autism in children. This statistic shows the percentage of U.S. adults who thought certain vaccines cause autism in children as of 2019, by age.

ObjectivesEarly identification and timely intervention is critical for young children with autism spectrum disorder (ASD). The current study aims to explore potential disparities in static and dynamic intrinsic brain function in preschoolers with ASD, and uncover underlying neural underpinnings that can be used for facilitating the identification of ASD.Materials and methodsStatic and dynamic amplitude of low-frequency fluctuations (ALFF) of 73 ASD preschoolers and 43 age-matched typically developing individuals (TDs) were extracted and compared to identify differences in intrinsic brain local connectivity associated with ASD. The dynamic ALFF (dALFF) utilized a sliding window technique that integrates static ALFF (sALFF) to gauge the variance of local brain activity over time. A receiver operating characteristic (ROC) analysis was conducted to evaluate the potential diagnostic capability of the sALFF and dALFF metrics in identifying ASD.ResultsCompared with TDs, ASD preschoolers exhibited lower levels of sALFF in the left middle temporal gyrus, medial orbitofrontal cortex, precuneus and reduced dALFF values in the left inferior orbitofrontal cortex, middle temporal gyrus. ROC analysis indicated that sALFF and dALFF could distinguish preschoolers with ASD from TDs with the areas under the curve (AUC) of 0.848 and 0.744 (p 

Latest monthly statistics on Learning Disabilities and Autism (LDA) patients from the Assuring Transformation (AT) collection and Mental Health Services Data Set (MHSDS). Data on inpatients with learning disabilities and/or autism are being collected both within the AT collection and MHSDS. There are differences in the inpatient figures between the AT and MHSDS data sets and work has been ongoing to better understand these. LDA data from MHSDS are experimental statistics, however, while impacts from the cyber incident are still present they will be considered to be management information. From October 2021, LDA MHSDS data has been collected under MHSDS version 5. A number of comparators are published each month to assess the differences in reporting between the collections. These can be found in the MHSDS datasets section. From 1 July 2022, Integrated Care Boards were established within Integrated Care Systems data and replaced Sustainability and Transformation Plans (STPs). Clinical Commissioning Groups have been replaced by sub-Integrated Care Boards. Data for the AT collection is now submitted by sub-Integrated Care Boards. This has resulted in some renaming within tables and the inclusion of a new Table 5.1b with a patient breakdown by submitting organisation. Patients by originating organisation and commissioning type are still available in Table 5.1a. Data in the tables are now presented by the current organisational structures. Old organisational structures have been mapped to new structures in any time series. As of 23rd May 2024, restraints data for MHSDS February 2024 has been added to the 'Learning disability services monthly statistics from MHSDS: Data tables' page. This is available within Tables 15-18 of v2 of the Data tables as well as within v2 of the csv file.

Supporting Tables. Table S1 Epilepsy Diagnosis by Age among Individuals with Autism Spectrum Disorder, Genetic Collaborative Samples. The prevalence of epilepsy was significantly higher in older children in all of the genetic collaborative samples. Table S2 Epilepsy Diagnosis by Gender among Individuals with Autism Spectrum Disorder, Genetic Collaborative Samples. The prevalence of epilepsy was higher in females with ASD in all of the genetic collaborative samples, but this difference only reached statistical significance in the AGRE sample. Table S3 Epilepsy Diagnosis by History of Developmental Regression among Individuals with Autism Spectrum Disorder, Genetic Collaborative Samples. The prevalence of epilepsy was higher in individuals with a history of developmental regression in all of the genetic collaborative samples. Table S4 Epilepsy Diagnosis by Language among Individuals with Autism Spectrum Disorder, Genetic Collaborative Samples. The prevalence of epilepsy was significantly higher in individuals with fewer than 5 words in all of the genetic collaborative samples. Table S5 Epilepsy Diagnosis by Cognitive Ability among Individuals with Autism Spectrum Disorder, Genetic Collaborative Samples. Individuals with epilepsy had significantly lower cognitive ability in all of the genetic collaborative samples. Table S6 Epilepsy Diagnosis by Intellectual Disability among Individuals with Autism Spectrum Disorder, Genetic Collaborative Samples. The prevalence of epilepsy was higher in individuals with intellectual disability in all of the genetic collaborative samples. Table S7 Epilepsy Diagnosis by Adaptive Functioning among Individuals with Autism Spectrum Disorder, Genetic Collaborative Samples. Individuals with epilepsy had significantly lower adaptive functioning in all of the genetic collaborative samples. Table S8 Epilepsy Diagnosis by Autism Severity among Individuals with Autism Spectrum Disorder, Genetic Collaborative Samples. Individuals with epilepsy had higher mean ADOS Calibrated Severity scores in all of the genetic collaborative samples. Table S9 Logistic Regression Modeling the Odds of an Epilepsy Diagnosis by Demographic and Clinical Characteristics, Individual Genetic Collaborative Samples. Logistic regression model findings were similar in participants of the individual genetic collaborative samples to the results from the combined sample. Table S10 Cross-Validation of Parent Report Epilepsy Diagnosis on the ADI-R with Report of Non-Febrile Seizures based on Medical History, Subset of Genetic Collaborative Study Participants (n = 2,525). There was good agreement between parent report of epilepsy diagnosis on the ADI-R and medical history. (DOCX)

The nosology and epidemiology of Autism has undergone transformation following consolidation of once disparate disorders under the umbrella diagnostic, autism spectrum disorders. Despite this re-conceptualization, research initiatives, including the NIMH’s Research Domain Criteria and Precision Medicine, highlight the need to bridge psychiatric and psychological classification methodologies with biomedical techniques. Combining traditional bibliometric co-word techniques, with tenets of graph theory and network analysis, this article provides an objective thematic review of research between 1994 and 2015 to consider evolution and focus. Results illustrate growth in Autism research since 2006, with nascent focus on physiology. However, modularity and citation analytics demonstrate dominance of subjective psychological or psychiatric constructs, which may impede progress in the identification and stratification of biomarkers as endorsed by new research initiatives.

The t and p are statistical and corrected probability values, respectively.

The t and p are statistical and corrected probability values, respectively.

The metrics |Ti|, S, and the ratio of p/p0 averaged over subjects.

Purpose: The purpose of this study was to investigate comorbidity prevalence and patterns in childhood apraxia of speech (CAS) and their relationship to severity. Method: In this retroactive cross-sectional study, medical records for 375 children with CAS (Mage = 4;9 [years;months], SD = 2;9) were examined for comorbid conditions. The total number of comorbid conditions and the number of communication-related comorbidities were regressed on CAS severity as rated by speech-language pathologists during diagnosis. The relationship between CAS severity and the presence of four common comorbid conditions was also examined using ordinal or multinomial regressions. Results: Overall, 83 children were classified with mild CAS; 35, with moderate CAS; and 257, with severe CAS. Only one child had no comorbidities. The average number of comorbid conditions was 8.4 (SD = 3.4), and the average number of communication-related comorbidities was 5.6 (SD = 2.2). Over 95% of children had comorbid expressive language impairment. Children with comorbid intellectual disability (78.1%), receptive language impairment (72.5%), and nonspeech apraxia (37.3%; including limb, nonspeech oromotor, and oculomotor apraxia) were significantly more likely to have severe CAS than children without these comorbidities. However, children with comorbid autism spectrum disorder (33.6%) were no more likely to have severe CAS than children without autism. Conclusions: Comorbidity appears to be the rule, rather than the exception, for children with CAS. Comorbid intellectual disability, receptive language impairment, and nonspeech apraxia confer additional risk for more severe forms of CAS. Findings are limited by being from a convenience sample of participants but inform future models of comorbidity. Supplemental Material S1. Table of comorbid conditions, categories, and rates of occurrence. Chenausky, K. V., Baas, B., Stoeckel, R., Brown, T., Green, J. R., Runke, C., Schimmenti, L., & Clark, H. (2023). Comorbidity and severity in childhood apraxia of speech: A retrospective chart review. Journal of Speech, Language, and Hearing Research, 66(3), 791–803. https://doi.org/10.1044/2022_JSLHR-22-00436

MR analysis results regarding gene family members show that OR > 1 indicates a risk factor, while OR < 1 indicates a protective factor.

In the TWAS analysis results, the genes within the top 20 p-values, ARHGAP27 and MAPT, reached the significance threshold.