Characteristics and Co-morbidities of Autism Spectrum Disorder as Risk Factors for Severity: A National Survey in the United States

Characteristics and Co-morbidities of Autism Spectrum Disorder as Risk Factors for Severity: A National Survey in the United States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Characteristics and Co-morbidities of Autism Spectrum Disorder as Risk Factors for Severity: A National Survey in the United States Mona Salehi, Arham Ahmad, Aida Lotfi, Sasidhar Gunturu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3921934/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Autism spectrum disorder (ASD) consists of heterogeneous neurodevelopmental disorders with impairments in social communication, repetitive behaviors, and restricted interests. This condition is associated with several co-morbidities which significantly affect the quality of life. Therefore, individuals with ASD should undergo screening for common co-morbidities to enable early diagnosis and treatment. This study aimed to assess the prevalence and common socio-demographic characteristics of ASD as well as both medical and psychiatric co-morbidities and their effects on the severity of ASD. Methods Data from the National Survey of Children’s Health (NSCH) in the US from 2020 to 2021 were used in this study. We included 79,182 children and adolescents aged between 3 to 17 years for the analysis. The mean age of these individuals was 10.1 (standard deviation: 4.6), and 2,568 (3.2%) had ASD diagnosis. Results Our analysis showed that ASD is more common in males (78.7%) than females (21.3%). Additionally, ASD was associated with lower family income and a higher level of education in the family. We found that 96.4% of patients developed at least one co-morbid condition. The most common neuropsychiatric co-morbidities included developmental delay (64%), behavioral and conduct problems (57.8%), and anxiety (45.7%). While the most common medical co-morbidities were allergies (32.4%), genetic disorders (26.2%), and asthma (12.6%). The odds of intellectual disability (odds ratio, OR: 5.8), developmental delay (OR: 5.0), Down syndrome (OR: 4.5), epilepsy (OR: 3.4), cerebral palsy (OR: 3.0), vision problems (OR: 2.5), and genetic disorders (OR: 2.3) were significantly higher among severe cases. Conclusions The presence of numerous co-morbidities in individuals with ASD, often linked to increased ASD severity, underscores the critical importance of comprehensive screening, early diagnosis, and targeted treatment strategies to enhance the overall health and well-being of these individuals. Autism spectrum disorder (ASD) Prevalence Co-morbidity Socio-demographics characteristics severity Figures Figure 1 Introduction Autism Spectrum Disorder (ASD) is a neurodevelopment disorder characterized by deficits in three main areas of functioning including social interaction, communication skills, and pervasive or repetitive behaviors as delineated in the DSM-V ( 1 ). A recent study reported that 1 in 36 children in the United States have ASD, with a male-to-female ratio being 4:1 ( 2 ). ASD prevalence has been noted to be higher among youth from non-Hispanic white backgrounds as compared to non-Hispanic black and Hispanic ethnic groups ( 3 ). While diagnostic criteria for ASD can be met as early as 2 years of age, the average age for the first intervention has been reported to be after a child reaches 4 years of age. This is noteworthy because evidence suggests that parents might be capable of identifying concerns related to ASD in their children even before the child reaches 12 months of age ( 4 ). These issues could be because there aren't enough experts, evaluations take a long time, care is expensive, and healthcare providers are hesitant to make referrals. This all leads to delays and longer wait times for evaluations ( 5 ). Socioeconomic factors, such as parents' education and family income, play a significant role in how individuals with ASD experience life and get the help they need. Families with lower incomes often face challenges when trying to access specialized therapies, education support, and healthcare services, and this can affect their long-term outcomes ( 3 ). ASD is categorized into three severity levels based on the DSM-V. Level 1 requires some support, Level 2 needs substantial support, and Level 3 necessitates very substantial support. This classification helps tailor interventions to individuals' unique needs within the autism spectrum ( 1 ). These severity levels can be assigned by clinicians using clinical observations and psychological evaluations to judge an individual’s specific deficits in various domains indicating the level of support required ( 6 ). Due to its highly variable and complex genetic nature, ASD exists within a web of interrelated factors, causing individuals with ASD to often contend with a constellation of co-morbidities ( 7 ). From genetic to psychiatric to medical, some common co-morbid conditions individuals with ASD can be implicated with are Fragile X syndrome, Rett Syndrome, attention-deficit/hyperactivity disorder (ADHD), depression, anxiety, intellectual disability, schizophrenia, epilepsy, gastrointestinal disturbances, and susceptibility to infections ( 8 – 11 ). Each co-morbidity is coupled with its own level of increased overall dysfunction, closely affecting the severity of ASD and causing significant clinical impairment and additional disease burden ( 6 ). Evidence suggests that specific treatments targeted at co-morbid conditions are associated with greater improvement in functioning than nonspecific treatment ( 12 ). In this nationwide representative study, we aim to estimate parent-based prevalence of ASD among children and adolescents in the United States, along with its association to family income and parental highest level of education. We will also describe the medical and psychiatric co-morbidities, their correlation with severity levels, and socio-demographic characteristics of the affected individuals, in terms of 3 age groups (0–5, 6–11, and 12–17 years old), gender, and ethnicity. Understanding these characteristics is crucial for guiding clinicians to deliver comprehensive and tailored interventions that address the unique needs of all individuals on the spectrum. Methods Study Design This study examines the characteristics and co-morbid conditions of ASD in individuals aged 3 to 17 years, utilizing data from The National Survey of Children's Health (NSCH) for the years 2020–2021 in the United States. The NSCH is a comprehensive, nationally representative survey conducted by the Health Resources and Services Administration’s Maternal and Child Health Bureau (HRSA MCHB) within the U.S. Department of Health and Human Services ( 13 ). Its primary purpose is to evaluate the well-being, access to quality health care, familial neighborhood and education, and social contexts of youth residing in the United States, along with associated influencing factors. Procedure and Sampling in the Dataset The NSCH used 42,777 surveys in 2020 (response rate: 42.4%), and 50,892 surveys in 2021 (response rate: 40.3%)( 13 ). The analytic sample of this study included 79,182 children and adolescents aged between 3 to 17 years. An address-based sample was selected from an extract of the Census Bureau’s Master Address File (MAF) which covered 50 states and the District of Columbia. The sample frame employed flags based on administrative records to establish four distinct and non-overlapping strata ( 13 ). The survey was administered online and via mail. Randomly selected households received instructions to access the survey online, with some also receiving a paper version. Survey questions are summarized in supplemental table 1. The data collection involved a two-phase methodology, starting with a household screener and followed by a comprehensive questionnaire for a selected youth’s parents or caregivers. Various strategies were employed to enhance response rates, including clear question phrasing, multiple response mode options, cash incentives, and other interventions. Data from the NSCH 2020–2021 underwent a weighting process involving adjustments for nonresponse, post-stratification, and raking. The raking adjustment iteratively fits case weights across dimensions such as state-by-household poverty ratio, respondent's education, selected children's race/ethnicity, age group, and nationally selected children's race/ethnicity and sex. The process involved up to 100 iterations or until the weights converged to population totals ( 13 ). Further details on the selection and sampling methodology can be found on the DRC website at childhealthdata.org. Confidentiality Participation in the 2021 NSCH was voluntary, and all data collected that could potentially identify an individual person are confidential. Data are kept private in accordance with applicable law. Respondents are assured of the confidentiality of their replies in accordance with 13 U.S.C. Section 9. All access to Title 13 data from this survey is restricted to Census Bureau employees and those holding Census Bureau Special Sworn Status pursuant to 13 U.S.C. Section 23(c). In compliance with this law, all data released to the public are only in a statistical format. No information that could personally identify a respondent or household may be released. The Screener and Topical public use data files went through a thorough disclosure review process and were approved by the Census Disclosure Review Board prior to release. Data Variables and Measures Independent Variables The analysis considered various independent variables, encompassing socio-demographic factors such as age, race, ethnicity, family income classified based on federal poverty level, and highest education of adults in the household. The primary dependent variable of interest was the presence or absence of ASD, as measured by the report of parents or caregivers. They were asked, “Does this child currently have autism or ASD including Asperger's disorder and pervasive developmental disorder?” The secondary dependent variable was the severity of ASD, which was measured by asking the parents or caregivers, “Would you describe this child’s current autism or autism spectrum disorder as mild, moderate, or severe?” (Supplemental Table 1). Independent Variables Co-morbid conditions including psychiatric disorders such as ADHD, Tourette syndrome, depression, and anxiety problems as well as medical issues, including heart disorders, developmental delay, intellectual disability, behavioral and conduct problems, asthma, allergies, arthritis, cerebral palsy, diabetes, down syndrome, epilepsy, frequent/severe headaches, hearing problems, genetic disorders, and vision problems, were included in the analysis. Statistical Analysis All statistical analyses were conducted using Stata version 17.0. Continuous variables were presented as mean ± standard deviation, and categorical variables were presented as frequency (percentage). Initial comparisons between the two groups (children with and without ASD) for continuous variables were performed using t-tests. Univariate and multivariate regression models were utilized to examine the association between medical and psychiatric co-morbidities as well as socio-demographic factors and ASD. In the univariate models, each covariate's association with ASD was assessed independently. The multivariate models evaluated the association between each covariate and ASD while adjusting for all other covariates. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were generated by the regression models, indicating the increased odds of ASD associated with each covariate after controlling for other variables. Results The NSCH dataset for the years 2020–2021 comprised 93,669 participants, encompassing children and adolescents aged 0 to 17 years. Among these, 79,182 individuals aged 3 to 17 years were questioned about their ASD diagnosis and constituted the subjects of this study. The average age of the participants was 10.1 ± 4.6 (mean ± SD). Notably, 2,568 individuals, equivalent to 3.2%, had a confirmed current diagnosis of ASD (Table 1 ). Table 1 Socio-Demographic Characteristics of the Autism Spectrum Disorder (ASD) Socio-Demographic Characteristics Total ( n = 79,182) With ASD (n = 2,568) No ASD (n = 76,614) Age (years) 10.1 ± 4.6 10.7 ± 4.4 10.0 ± 4.6 Age Groups Preschool: 3–5 18,205 (23.8%) 453 ( 17.6%) 18,658 (23.6%) School: 6–10 21,566 (28.1%) 709 (27.6%) 22,275 (28.1%) Adolescents: 11–17 36,843 (48.1%) 1,406 ( 54.7%) 38,249 (48.3%) Sex Male 41,076 (51.9%) 2,022 (78.74%) 39,054 (50.98%) Female 38,106 (48.1%) 546 (21.26%) 37,560 (49.02%) Race Hispanic 10,717 (13.5%) 366 (14.3%) 10,351 (13.5%) White 52,162 (65.8%) 1,687 (65.7%) 50,475 (65.9%) Black 5,404 (6.8%) 179 (6.9%) 5,225 (6.8%) Asian 4,495 (5.7%) 104 (4.1%) 4,391 (5.7%) American Indian/ Alaska Native 524 (0.7%) 15 (0.6%) 509 (0.7%) Native Hawaiian/ Other Pacific Islander 238 (0.350 5 (0.2%) 233 (0.3%) Multi-Race 5,642 (7.1%) 212 (8.3%) 5,430 (7.1%) Federal Poverty Level ≥ 400% 31,894 (40.3%) 772 (30.1%) 31,122 (40.6%) 200% − 399% 24,101 (30.4%) 799 (31.1%) 23,302 (30.4%) 100% − 199% 13,188 (16.7%) 554 (21.6%) 12,634 (16.5%) < 100% 9,999 (12.6%) 443 (17.3%) 9,556 (12.5%) Highest education of adults in the household Less than high school 2,164 (2.7%) 63 (2.5%) 2,101 (2.7%) High school degree 10,731 (13.5%) 427 (16.6%) 10,304 (13.5%) More than high school 66,287 (83.7%) 2,078 (80.9%) 64,209 (83.8%) Multivariate analysis showed significant increases in the odds of ASD in the adolescent (11–17 year-olds) age group (odds ratio, OR: 1.5, p-value < 0.001) and multi-race ethnic group (OR: 1.2, p-value: 0.04) as well as in families with high school and higher than high school educational level (OR: 1.5, p-value: 0.005) (Table 2 ). Conversely, female gender (OR: 0.3, p-value < 0.001), higher household income (OR: 0.5, p-value < 0.001), and Asian race (OR: 0.7, p-value: 0.01) were associated with decreased odds of ASD among youths (Table 2 ). Table 2 Socio-Demographic Predictors of Autism Spectrum Disorder (ASD)based on Univariate and Multivariate Analysis Socio-Demographic Characteristics Univariate Analysis OR (95% CI) Multivariate Analysis OR (95% CI) Age Groups Preschool: 3–5 Reference Reference School: 6–10 1.3 (1.2–1.5)*** 1.3 (1.1–1.5)*** Adolescents: 11–17 1.5 (1.4–1.7)*** 1.5 (1.4–1.7)*** Sex Male Reference Reference Female 0.3(0.2–0.3)*** 0.3 (0.2–0.3)*** Race Hispanic Reference Reference White 0.9 (0.8–1.1) 1.04(0.9–1.2) Black 0.9 (0.8–1.2) 0.9(0.7–1.1) Asian 0.7(0.5–0.8)*** 0.7 (0.6–0.9)* American Indian/ Alaska Native 0.8(0.5–1.4) 0.8(0.4–1.3) Native Hawaiian/ Other Pacific Islander 0.6 (0.2–1.5) 0.6 (0.2–1.4) Multi-Race 1.1 (0.9–1.3) 1.2 (1.0-1.4)* Federal Poverty Level < 100% Reference Reference 100% − 199% 0.9 (0.8–1.1) 0.9 (0.8-1.0) 200% − 399% 0.7 (0.6–0.8)*** 0.7 (0.6–0.8)*** ≥ 400% 0.5 (0.5–0.6)*** 0.5 (0.4–0.6)*** Highest education of adults in the household Less than high school Reference Reference High school degree 1.4 (1.1–1.8)* 1.5 (1.1-2)** More than high school 1.1(0.8–1.4) 1.5 (1.1–1.9)** *: P value < 0.05, **: P value < 0.01, ***: P value < 0.001 96.4% of youth with ASD had at least one co-morbid condition. The most common neuropsychiatric co-morbid condition with ASD was developmental delay (64%), followed by behavioral and conduct problems (57.8%) and anxiety problems (45.7%); respectively (Fig. 1 ). While the most common medical co-morbidities were allergies (32.4%), genetic disorders (26.2%), and asthma (12.6%) (Fig. 1 ). The odds of co-morbid vision problems (OR: 2.3, p-value < 0.001), cerebral palsy (OR: 2.2, p-value: 0.042), frequent/ severe headaches (OR:1.7, p-value: 0.002), epilepsy (OR: 1.7, p-value: 0.014), depression (OR: 1.6, p-value < 0.001), and intellectual disability (OR:1.5, p-value: 0.001) were significantly greater in females than males, whereas ADHD (OR: 0.8, p-value: 0.007) and anxiety problems (OR: 0.35, p-value < 0.001) were significantly lower among female individuals with ASD (Table 3 ). Regarding the severity of ASD, we found that co-morbid occurrence of intellectual disability (OR: 5.8, p-value < 0.001), developmental delay (OR: 5, p-value < 0.001), epilepsy (OR: 3.4, p-value < 0.001), Down syndrome (OR: 3, p-value: 0.01),, vision problems (OR: 2.5, p-value < 0.001), behavioral and conduct problems (OR: 2.4, p-value < 0.001), genetic disorders (OR: 2.3, p-value < 0.001), hearing problems (OR: 1.6, p-value: 0.03), and anxiety problems (OR: 1.3, p-value < 0.001) may contribute to a more severe manifestation of ASD (Table 3 ). Table 3 Psychiatric and Medical Co-morbidities in Autism Spectrum Disorder (ASD), Abbreviation: M: Male, F: Female, 1: Mild ASD, 2: Moderate to severe ASD Disorders ASD (n = 2,568) Sex OR (95% CI) ASD Severity OR (95% CI) Attention-Deficit Hyperactivity Disorder (ADHD) 1,136 (44.3%)*** M: Reference F: 0.8 (0.6–0.9)** 1: Reference 2: 1.03 (0.9–1.2) Tourette Syndrome 43 (1.7%)*** M: Reference F: 0.6 (0.2–1.4) 1: Reference 2: 1.8 (0.9–3.5) Depression 425(16.7%)*** M: Reference F: 1.6 (1.3-2.0)*** 1: Reference 2: 0.9 (0.2–1.2) Anxiety Problems 1,151(45.7%)*** M: Reference F: 0.35 ( 0.3–0.4)*** 1: Reference 2: 1.3 (1.3–1.8)*** Heart Disorders 89 (3.5%) *** M: Reference F: 1.3 (0.8–2.1) 1: Reference 2: 0.9 (0.6–1.5) Developmental Delay 1,628(64.0%)*** M: Reference F: 1.02 (0.8–1.2) 1: Reference 2: 5.0 (4.1–5.9)*** Intellectual Disability 411 (16.1%) *** M: Reference F: 1.5 (1.2–1.9) ** 1: Reference 2: 5.8 (4.4–7.7)*** Behavioral and Conduct Problems 1,470 (57.8%)*** M: Reference F: 0.9 ( 0.7-1.0) 1: Reference 2: 2.4 (2.0-2.8)*** Asthma 320 (12.6%)*** M: Reference F: 1.06 (0.8–1.4) 1: Reference 2: 1.1 (0.9–1.4) Allergies 832 (32.4%)*** M: Reference F: 0.8 (0.7–1.03) 1: Reference 2: 1.3 (1.06–1.5) Arthritis 14 (0.5%)** M: Reference F: 0.6 (0.1–2.7) 1: Reference 2: 1.6 (0.5–4.8) Cerebral Palsy 30 (1.2%)*** M: Reference F: 2.2 (1.02–4.6)* 1: Reference 2: 3 (1.3-7.0)** Diabetes 25( 0.9%)*** M: Reference F: 1.2 (0.5–2.9) 1: Reference 2: 0.9( 0.4–2.1) Down Syndrome 25 (0.9% )*** M: Reference F: 2.1 (0.9–4.7) 1: Reference 2: 4.5 (1.5–13.4)** Epilepsy 107 (4.2%)*** M: Reference F: 1.7 (1.1–2.6)** 1: Reference 2: 3.4 (2.1–5.5)*** Frequent/Severe Headaches 159 (6.2%) *** M: Reference F: 1.7 (1.2–2.4)** 1: Reference 2: 1.06 (0.7–1.5) Hearing Problems 87 (3.4%)*** M: Reference F:1.2 (0.7–1.9) 1: Reference 2: 1.6 (1.03–2.5)* Genetic Disorder 669 (26.2%)*** M: Reference F: 1.2 ( 0.9–1.5) 1: Reference 2: 2.3 (1.9–2.8)*** Vision Problems 117 (4.6%)*** M: Reference F: 2.3 (1.6–3.5)*** 1: Reference 2: 2.5 (1.6–3.8)*** *: P value < 0.05, **: P value < 0.01, ***: P value < 0.001 Discussion We identified up to 2,568 (3.2%) of the youth to have a current diagnosis of ASD. ASD was found to be associated with gender, race, household income, and education level. The most common co-morbid conditions were developmental delay, behavioral and conduct problems, and anxiety problems. We found that co-morbid medical and psychiatric conditions with ASD can affect its severity. Prevalence of ASD The parent- based prevalence of ASD in US children and adolescents was 3.2%. This finding is slightly increased from the previous report of 3.14% according to the 2019–2020 NSCH survey ( 14 ). Further extrapolation backward shows that there has been a steady overall incline in prevalence over time in other previous reports. Autism and Developmental Disabilities Monitoring (ADDM) Network in 2018 reported a prevalence of 2.3% and the NSCH in 2016 reported 2.5% ( 15 , 16 ). However, conclusions must be drawn with caution from the above-noted reports as their estimates are derived from different systems reflecting different ages and populations; children aged 8 years from 11 local populations in ADDM versus 3–17 years in NSCH from populations from the entire USA ( 15 , 16 ). The variations in the prevalence of ASD may also be attributed to the inherent nature of ASD as a spectrum disorder, characterized by diverse traits that can even influence the definitions and diagnostic criteria of ASD ( 1 ). Socio-demographic associations Age-groups We analyzed the parent-reported diagnosis of ASD in three different age groups, and our analysis showed a significantly higher prevalence of ASD in the adolescent (11–17 year-olds) age group. Based on previous literature, the assignment of diagnosis becomes more reliable in older ages ( 17 ) as the older children with ASD show greater deficits in executive and social functioning compared to younger children, leading to an easier and more robust diagnosis ( 18 ). However, at younger ages, there is a possibility of missing children with ASD until they enter primary school or even later ( 19 ). This is because the social demands placed on children increase as they grow older, making particular autistic deficits more apparent ( 20 ). Gender differences We observed a male-to-female ratio of approximately 3.7 in the prevalence of ASD. This aligns with previous research, which has consistently reported male-to-female ratios ranging from 2:1 to 5:1( 21 – 24 ). The prevailing belief that ASD is more frequently identified in males compared to females has spurred the development of several hypotheses that aim to explain the characteristics and causes of ASD. These ideas include the extreme male brain theory ( 25 ), the female protective effect theory ( 17 , 24 , 26 ) and the female autism phenotype theory ( 27 – 29 ). The extreme male brain theory suggests that understanding gender differences involves considering "empathizing" and "systemizing." Evidence indicates that males tend to excel in systematization, while females have distinct cognitive traits. This theory proposes that ASD might represent an extreme male cognitive profile ( 25 ). The "female protective model/effect" stems from the consistent male predominance in ASD. It assumes that ASD risk is spread throughout the population and suggests that females have a protective factor against autism ( 24 ). Due to this protective effect, ASD affects women less often than men. High-functioning ASD has a male-to-female ratio of 7:1, while moderate to severe Intellectual Disability has a ratio of 2:1, indicating female protection even in the presence of risk factors ( 17 , 26 , 30 ). This means that females have a higher threshold for ASD risk, including genetic and environmental factors, before the condition becomes apparent ( 31 ). The female autism phenotype theories suggest a unique expression of autism in females, but current autism research is male-centric ( 32 – 34 ). Evidence shows that females with ASD have stronger social desires and are more likely to form friendships than males with ASD ( 27 – 29 ). Other sociodemographic factors We found the highest odds of ASD in multi-race youth, as well as youth with high school and higher than high school caregiver educational level. There are some differences between our study findings and Centers for Disease Control and Prevention (CDC) report of racial differences of youth with ASD, as they found the highest prevalence of ASD in youth with Asian/pacific islander descents ( 35 ), which could be the result of different methodologies and instruments. Research has shown that the prevalence of ASD was initially higher in white children compared to black or Hispanic children but gradually equalized by 2016 and 2018 ( 15 , 35 ). Recent reports in 2020 marked the first time ASD rates were lower in white children than in other groups at age 8, and a similar trend was observed among 4-year-olds in 2018 ( 15 , 35 ). This shift may be due to better screening, increased awareness, and improved services for historically marginalized populations ( 35 ). Youth with caregivers having at least a high school education are more likely to have ASD, a finding consistent with other studies ( 36 , 37 ). Diagnostic odds ratios for parental education and income are complex. Families with lower educational attainment have a reduced likelihood of receiving an ASD diagnosis, potentially linked to inequities in healthcare access and outcomes. Lower-educated families may face limited healthcare services, decreasing their chances of obtaining a medical diagnosis for their child ( 36 ). We found that ASD in children and adolescents is higher among families with lower than 100% FPL income. The ADDM Network Reported that the prevalence of ASD in 2020 exhibited a correlation with lower socioeconomic status ( 35 ). Although some previous studies reported contrast findings ( 3 ), These findings offers further evidence of improved ASD identification for youth regardless of their socioeconomic level. As research continues to grow on better identification methods, attention may shift towards understanding the factors, such as socioeconomic determinants of health, that could lead to higher rates of detected impairment in specific communities ( 35 ). Co-morbid conditions and severity of ASD Over 96% of ASD patients had at least one co-morbid condition. We found the most common co-morbid conditions with ASD to be developmental delay, behavioral and conduct problems, anxiety problems, allergies, genetic disorders, and asthma. Simonof et al. ( 38 ) found that 70% of children with ASD in a population-derived cohort had at least one co-morbid psychiatric disorder. They found the most common co-occurring psychiatric disorders with ASD to be social anxiety disorder, ADHD, and oppositional defiant disorder ( 38 ). Similarly, Leyfer et al. found the most prevalent co-morbid psychiatric conditions in autistic children to be specific phobia, obsessive-compulsive disorder (OCD), and ADHD, respectively ( 12 ). Regarding medical co-morbidities, previous literature reported higher rates of allergies including skin allergies, food allergies, and asthma as well as gastrointestinal problems ( 39 ). We found that co-morbid occurrence of intellectual disability, developmental delay, Down syndrome, epilepsy, vision problems, behavioral and conduct problems, genetic disorders, hearing problems, and anxiety problems was associated with a more severe form of ASD. There is a gap in the literature exploring the correlation between co-morbidities and the severity of ASD. Jang et al. studied a group of children and adolescents with ASD and found that youth with more severe symptoms of ASD are more likely to have concurrent psychiatric conditions ( 40 ). Leyfer et al. also reported an association between functional impairment and psychiatric co-morbidities in ASD ( 12 ). Konstantareas et al. studied a group of youth with ASD and concurrent ear problems including hearing loss and ear infections and found that co-morbid ear problems are positively correlated with the severity of autistic symptomatology ( 41 ). Consistent with our findings, previous literature also reported that neurological co-morbid conditions such as intellectual disability and epilepsy are associated with more severe forms of ASD ( 7 , 42 , 43 ). These findings suggest that individuals with ASD face extra challenges when dealing with accompanying co-morbid conditions, which is clinically important as earlier screening for co-morbidity can aid in establishing additional priorities for assessment and more effective treatment options ( 40 ). Conclusions In conclusion, our findings suggest that male adolescents, multi-race individuals, and those with higher-educated families as well as lower family income levels may have increased odds of ASD, while females, higher-income households, and Asian race may have lower odds. Importantly, many individuals with ASD have co-morbid conditions, particularly developmental delay and behavioral issues which can affect the severity of ASD. Identifying and addressing these co-morbidities early is crucial for clinical management and support. Limitations The major limitation of this study is the lack of a valid diagnostic instrument and parent-based reports which can be a source of bias. Declarations Funding sources This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors. Disclosure The authors report no proprietary or commercial interest in any product mentioned or concepts discussed in this article. Ethics approval Ethical approval of the original NSCH survey for all procedures is obtained from the National Center for Health Statistics Research Ethics Review Board. References Association AP (2013) Diagnostic and Statistical Manual of Mental Disorders, 5th edn) edn. American Psy-chiatric Association, Retrieved from http://psychiatryonlineorg/doi/book/101176/appi books 9780890425596 Christensen DL (2016) Prevalence and characteristics of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2012. MMWR Surveillance summaries. ;65 Durkin MS, Maenner MJ, Baio J, Christensen D, Daniels J, Fitzgerald R et al (2017) Autism spectrum disorder among US children (2002–2010): Socioeconomic, racial, and ethnic disparities. Am J Public Health 107(11):1818–1826 Gordon-Lipkin E, Foster J, Peacock G (2016) Whittling down the wait time: exploring models to minimize the delay from initial concern to diagnosis and treatment of autism spectrum disorder. Pediatr Clin 63(5):851–859 McNally Keehn R, Ciccarelli M, Szczepaniak D, Tomlin A, Lock T, Swigonski N (2020) A statewide tiered system for screening and diagnosis of autism spectrum disorder. Pediatrics. ;146(2) Ellison K, Bundy MB, Gore J, Wygant D (2019) Exploration of the DSM-5’s Autism Spectrum Disorder severity level specifier and prediction of autism severity. Exceptionality 27(4):289–298 Doshi-Velez F, Ge Y, Kohane I (2014) Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis. Pediatrics 133(1):e54–e63 Ingason A, Rujescu D, Cichon S, Sigurdsson E, Sigmundsson T, Pietiläinen O et al (2011) Copy number variations of chromosome 16p13. 1 region associated with schizophrenia. Mol Psychiatry 16(1):17–25 Al-Beltagi M (2021) Autism medical comorbidities. World J Clin Pediatr 10(3):15 Consortium C-DGPG (2013) Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381(9875):1371–1379 Tabares-Seisdedos R, Rubenstein J (2009) Chromosome 8p as a potential hub for developmental neuropsychiatric disorders: implications for schizophrenia, autism and cancer. Mol Psychiatry 14(6):563–589 Leyfer OT, Folstein SE, Bacalman S, Davis NO, Dinh E, Morgan J et al (2006) Comorbid psychiatric disorders in children with autism: Interview development and rates of disorders. J Autism Dev Disord 36:849–861 (NSCH) Tnsocsh. Data Resource Center for Child & Adolescent Health (n.d.). 2020–2021 Li Q, Li Y, Liu B, Chen Q, Xing X, Xu G et al (2022) Prevalence of autism spectrum disorder among children and adolescents in the United States from 2019 to 2020. JAMA Pediatr 176(9):943–945 Maenner MJ, Shaw KA, Bakian AV, Bilder DA, Durkin MS, Esler A et al (2021) Prevalence and characteristics of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2018. MMWR Surveillance Summaries 70(11):1 Kogan MD, Vladutiu CJ, Schieve LA, Ghandour RM, Blumberg SJ, Zablotsky B et al (2018) The prevalence of parent-reported autism spectrum disorder among US children. Pediatrics. ;142(6) Fombonne E (2009) Epidemiology of pervasive developmental disorders. Pediatr Res 65(6):591–598 Rosenthal M, Wallace GL, Lawson R, Wills MC, Dixon E, Yerys BE et al (2013) Impairments in real-world executive function increase from childhood to adolescence in autism spectrum disorders. Neuropsychology 27(1):13 Fombonne E, MacFarlane H, Salem AC (2021) Epidemiological surveys of ASD: advances and remaining challenges. J Autism Dev Disord 51:4271–4290 Talantseva OI, Romanova RS, Shurdova EM, Dolgorukova TA, Sologub PS, Titova OS et al (2023) The global prevalence of autism spectrum disorder: A three-level meta-analysis. Front Psychiatry 14:1071181 Loomes R, Hull L, Mandy WPL (2017) What is the male-to-female ratio in autism spectrum disorder? A systematic review and meta-analysis. J Am Acad Child Adolesc Psychiatry 56(6):466–474 Lord C, Brugha TS, Charman T, Cusack J, Dumas G, Frazier T et al (2020) Autism spectrum disorder. Nat reviews Disease primers 6(1):1–23 Halladay AK, Bishop S, Constantino JN, Daniels AM, Koenig K, Palmer K et al (2015) Sex and gender differences in autism spectrum disorder: summarizing evidence gaps and identifying emerging areas of priority. Mol autism 6(1):1–5 Werling DM, Geschwind DH (2013) Understanding sex bias in autism spectrum disorder. Proceedings of the National Academy of Sciences. ;110(13):4868-9 Baron-Cohen S, Knickmeyer RC, Belmonte MK (2005) Sex differences in the brain: implications for explaining autism. Science 310(5749):819–823 Stevenson CL, Krantz PJ, McClannahan LE (2000) Social interaction skills for children with autism: A script-fading procedure for nonreaders. Behav Interventions: Theory Pract Residential Community‐Based Clin Programs 15(1):1–20 Bargiela S, Steward R, Mandy W (2016) The experiences of late-diagnosed women with autism spectrum conditions: An investigation of the female autism phenotype. J Autism Dev Disord 46:3281–3294 Head AM, McGillivray JA, Stokes MA (2014) Gender differences in emotionality and sociability in children with autism spectrum disorders. Mol autism 5(1):1–9 Sedgewick F, Hill V, Yates R, Pickering L, Pellicano E (2016) Gender differences in the social motivation and friendship experiences of autistic and non-autistic adolescents. J Autism Dev Disord 46:1297–1306 Jacquemont S, Coe BP, Hersch M, Duyzend MH, Krumm N, Bergmann S et al (2014) A higher mutational burden in females supports a female protective model in neurodevelopmental disorders. Am J Hum Genet 94(3):415–425 Werling DM (2016) The role of sex-differential biology in risk for autism spectrum disorder. Biology sex differences 7:1–18 Lai M-C, Baron-Cohen S (2015) Identifying the lost generation of adults with autism spectrum conditions. Lancet Psychiatry 2(11):1013–1027 Hiller RM, Young RL, Weber N (2014) Sex differences in autism spectrum disorder based on DSM-5 criteria: evidence from clinician and teacher reporting. J Abnorm Child Psychol 42:1381–1393 Mandy W, Chilvers R, Chowdhury U, Salter G, Seigal A, Skuse D (2012) Sex differences in autism spectrum disorder: evidence from a large sample of children and adolescents. J Autism Dev Disord 42:1304–1313 Maenner MJ, Warren Z, Williams AR, Amoakohene E, Bakian AV, Bilder DA et al (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 Surveillance Summaries 72(2):1 Federico A, Zgodic A, Flory K, Hantman RM, Eberth JM, Mclain AC et al (2023) Predictors of Autism Spectrum Disorder and ADHD: Results from the National Survey of Children's Health. Disabil Health J. :101512 Shelton JF, Tancredi DJ, Hertz-Picciotto I (2010) Independent and dependent contributions of advanced maternal and paternal ages to autism risk. Autism Res 3(1):30–39 Simonoff E, Pickles A, Charman T, Chandler S, Loucas T, Baird G (2008) Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample. J Am Acad Child Adolesc Psychiatry 47(8):921–929 Isaksen J, Bryn V, Diseth TH, Heiberg A, Schjølberg S, Skjeldal OH (2013) Children with autism spectrum disorders–the importance of medical investigations. Eur J Pediatr Neurol 17(1):68–76 Jang J, Matson JL (2015) Autism severity as a predictor of comorbid conditions. J Dev Phys Disabil 27:405–415 Konstantareas MM, Homatidis S (1987) Brief report: Ear infections in autistic and normal children. J Autism Dev Disord 17(4):585–594 Mouridsen SE, Rich B, Isager T (1999) Epilepsy in disintegrative psychosis and infantile autism: a long-term validation study. Dev Med Child Neurol 41(2):110–114 Tuchman R, Rapin I (2002) Epilepsy in autism. Lancet Neurol 1(6):352–358 PubMed PMID: 12849396. Epub 2003/07/10. eng Additional Declarations No competing interests reported. Supplementary Files SupplementalTable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3921934","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270758240,"identity":"627a7ad9-20ff-4ab4-a343-c919134a6ae1","order_by":0,"name":"Mona Salehi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYPACCwYDZsbGBwkGNkAOY+MBIrRIALUwNxs8KEgDaWkgUgsDe5vkgw+HwVy8WnTbew9+/FEjIWfOztgmkWBw3m5t+2GgLTU20bi0mJ05lyzNc0zC2LKZsdkiweB28rYziUAtx9JyG3BpuZFjIM3AJpG44TBj4w2QFrMDQC2MDYfxaTH++eOfRD1QSwPQYeeSzc4/JKjFTIIX5IvDjE1A8oCd2Q1Ctpw5Y2bN2ydhCLSl2SDBIDnB7AbQlgR8fjneY3zzxzcbeYPzxx8+/PHHzt7sfPrDBx9qbHBqwQCJYJUJxCoHAXtSFI+CUTAKRsHIAACi+WaO/UNMvAAAAABJRU5ErkJggg==","orcid":"","institution":"Johns Hopkins University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Mona","middleName":"","lastName":"Salehi","suffix":""},{"id":270758241,"identity":"c915553f-65f9-4ac3-a7f4-22d4d0893690","order_by":1,"name":"Arham Ahmad","email":"","orcid":"","institution":"Bronx Care Health System","correspondingAuthor":false,"prefix":"","firstName":"Arham","middleName":"","lastName":"Ahmad","suffix":""},{"id":270758242,"identity":"5cf33a13-4286-4261-80b8-dc4db81c3a3e","order_by":2,"name":"Aida Lotfi","email":"","orcid":"","institution":"Jönköping University","correspondingAuthor":false,"prefix":"","firstName":"Aida","middleName":"","lastName":"Lotfi","suffix":""},{"id":270758243,"identity":"f65b4f39-21b1-4b67-a952-ea392ae217d7","order_by":3,"name":"Sasidhar Gunturu","email":"","orcid":"","institution":"Bronx Care Health System","correspondingAuthor":false,"prefix":"","firstName":"Sasidhar","middleName":"","lastName":"Gunturu","suffix":""}],"badges":[],"createdAt":"2024-02-02 19:46:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3921934/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3921934/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50749439,"identity":"2f701934-f12f-42ae-8bbe-2fbb6db2aee6","added_by":"auto","created_at":"2024-02-06 17:25:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":193657,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of co-morbid conditions in ASD\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3921934/v1/43f3d71ca313715d38d32992.png"},{"id":51505581,"identity":"d9931acf-7731-422b-bf0e-9f2bcf2c40bd","added_by":"auto","created_at":"2024-02-22 19:07:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":510098,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3921934/v1/83dcb264-b1bb-4156-ad1e-ca0aefc967b5.pdf"},{"id":50749438,"identity":"3e7f8ae8-c653-4fa1-87c6-da8c21ee2db4","added_by":"auto","created_at":"2024-02-06 17:25:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14020,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3921934/v1/c32f0344940e4eefb42453bc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characteristics and Co-morbidities of Autism Spectrum Disorder as Risk Factors for Severity: A National Survey in the United States","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAutism Spectrum Disorder (ASD) is a neurodevelopment disorder characterized by deficits in three main areas of functioning including social interaction, communication skills, and pervasive or repetitive behaviors as delineated in the DSM-V (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). A recent study reported that 1 in 36 children in the United States have ASD, with a male-to-female ratio being 4:1 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). ASD prevalence has been noted to be higher among youth from non-Hispanic white backgrounds as compared to non-Hispanic black and Hispanic ethnic groups (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). While diagnostic criteria for ASD can be met as early as 2 years of age, the average age for the first intervention has been reported to be after a child reaches 4 years of age. This is noteworthy because evidence suggests that parents might be capable of identifying concerns related to ASD in their children even before the child reaches 12 months of age (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These issues could be because there aren't enough experts, evaluations take a long time, care is expensive, and healthcare providers are hesitant to make referrals. This all leads to delays and longer wait times for evaluations (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Socioeconomic factors, such as parents' education and family income, play a significant role in how individuals with ASD experience life and get the help they need. Families with lower incomes often face challenges when trying to access specialized therapies, education support, and healthcare services, and this can affect their long-term outcomes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eASD is categorized into three severity levels based on the DSM-V. Level 1 requires some support, Level 2 needs substantial support, and Level 3 necessitates very substantial support. This classification helps tailor interventions to individuals' unique needs within the autism spectrum (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). These severity levels can be assigned by clinicians using clinical observations and psychological evaluations to judge an individual\u0026rsquo;s specific deficits in various domains indicating the level of support required (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Due to its highly variable and complex genetic nature, ASD exists within a web of interrelated factors, causing individuals with ASD to often contend with a constellation of co-morbidities (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). From genetic to psychiatric to medical, some common co-morbid conditions individuals with ASD can be implicated with are Fragile X syndrome, Rett Syndrome, attention-deficit/hyperactivity disorder (ADHD), depression, anxiety, intellectual disability, schizophrenia, epilepsy, gastrointestinal disturbances, and susceptibility to infections (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Each co-morbidity is coupled with its own level of increased overall dysfunction, closely affecting the severity of ASD and causing significant clinical impairment and additional disease burden (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Evidence suggests that specific treatments targeted at co-morbid conditions are associated with greater improvement in functioning than nonspecific treatment (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this nationwide representative study, we aim to estimate parent-based prevalence of ASD among children and adolescents in the United States, along with its association to family income and parental highest level of education. We will also describe the medical and psychiatric co-morbidities, their correlation with severity levels, and socio-demographic characteristics of the affected individuals, in terms of 3 age groups (0\u0026ndash;5, 6\u0026ndash;11, and 12\u0026ndash;17 years old), gender, and ethnicity. Understanding these characteristics is crucial for guiding clinicians to deliver comprehensive and tailored interventions that address the unique needs of all individuals on the spectrum.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study examines the characteristics and co-morbid conditions of ASD in individuals aged 3 to 17 years, utilizing data from The National Survey of Children's Health (NSCH) for the years 2020\u0026ndash;2021 in the United States. The NSCH is a comprehensive, nationally representative survey conducted by the Health Resources and Services Administration\u0026rsquo;s Maternal and Child Health Bureau (HRSA MCHB) within the U.S. Department of Health and Human Services (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Its primary purpose is to evaluate the well-being, access to quality health care, familial neighborhood and education, and social contexts of youth residing in the United States, along with associated influencing factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eProcedure and Sampling in the Dataset\u003c/h2\u003e \u003cp\u003eThe NSCH used 42,777 surveys in 2020 (response rate: 42.4%), and 50,892 surveys in 2021 (response rate: 40.3%)(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The analytic sample of this study included 79,182 children and adolescents aged between 3 to 17 years. An address-based sample was selected from an extract of the Census Bureau\u0026rsquo;s Master Address File (MAF) which covered 50 states and the District of Columbia. The sample frame employed flags based on administrative records to establish four distinct and non-overlapping strata (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe survey was administered online and via mail. Randomly selected households received instructions to access the survey online, with some also receiving a paper version. Survey questions are summarized in supplemental table 1. The data collection involved a two-phase methodology, starting with a household screener and followed by a comprehensive questionnaire for a selected youth\u0026rsquo;s parents or caregivers. Various strategies were employed to enhance response rates, including clear question phrasing, multiple response mode options, cash incentives, and other interventions.\u003c/p\u003e \u003cp\u003eData from the NSCH 2020\u0026ndash;2021 underwent a weighting process involving adjustments for nonresponse, post-stratification, and raking. The raking adjustment iteratively fits case weights across dimensions such as state-by-household poverty ratio, respondent's education, selected children's race/ethnicity, age group, and nationally selected children's race/ethnicity and sex. The process involved up to 100 iterations or until the weights converged to population totals (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Further details on the selection and sampling methodology can be found on the DRC website at childhealthdata.org.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eConfidentiality\u003c/h2\u003e \u003cp\u003eParticipation in the 2021 NSCH was voluntary, and all data collected that could potentially identify an individual person are confidential. Data are kept private in accordance with applicable law. Respondents are assured of the confidentiality of their replies in accordance with 13 U.S.C. Section 9. All access to Title 13 data from this survey is restricted to Census Bureau employees and those holding Census Bureau Special Sworn Status pursuant to 13 U.S.C. Section 23(c). In compliance with this law, all data released to the public are only in a statistical format. No information that could personally identify a respondent or household may be released. The Screener and Topical public use data files went through a thorough disclosure review process and were approved by the Census Disclosure Review Board prior to release.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Variables and Measures\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eIndependent Variables\u003c/h2\u003e \u003cp\u003eThe analysis considered various independent variables, encompassing socio-demographic factors such as age, race, ethnicity, family income classified based on federal poverty level, and highest education of adults in the household. The primary dependent variable of interest was the presence or absence of ASD, as measured by the report of parents or caregivers. They were asked, \u0026ldquo;Does this child currently have autism or ASD including Asperger's disorder and pervasive developmental disorder?\u0026rdquo; The secondary dependent variable was the severity of ASD, which was measured by asking the parents or caregivers, \u0026ldquo;Would you describe this child\u0026rsquo;s current autism or autism spectrum disorder as mild, moderate, or severe?\u0026rdquo; (Supplemental Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIndependent Variables\u003c/h2\u003e \u003cp\u003eCo-morbid conditions including psychiatric disorders such as ADHD, Tourette syndrome, depression, and anxiety problems as well as medical issues, including heart disorders, developmental delay, intellectual disability, behavioral and conduct problems, asthma, allergies, arthritis, cerebral palsy, diabetes, down syndrome, epilepsy, frequent/severe headaches, hearing problems, genetic disorders, and vision problems, were included in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using Stata version 17.0. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables were presented as frequency (percentage). Initial comparisons between the two groups (children with and without ASD) for continuous variables were performed using t-tests. Univariate and multivariate regression models were utilized to examine the association between medical and psychiatric co-morbidities as well as socio-demographic factors and ASD. In the univariate models, each covariate's association with ASD was assessed independently. The multivariate models evaluated the association between each covariate and ASD while adjusting for all other covariates. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were generated by the regression models, indicating the increased odds of ASD associated with each covariate after controlling for other variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe NSCH dataset for the years 2020\u0026ndash;2021 comprised 93,669 participants, encompassing children and adolescents aged 0 to 17 years. Among these, 79,182 individuals aged 3 to 17 years were questioned about their ASD diagnosis and constituted the subjects of this study. The average age of the participants was 10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). Notably, 2,568 individuals, equivalent to 3.2%, had a confirmed current diagnosis of ASD (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-Demographic Characteristics of the Autism Spectrum Disorder (ASD)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocio-Demographic Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e( n\u0026thinsp;=\u0026thinsp;79,182)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWith ASD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,568)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo ASD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;76,614)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreschool:\u003c/p\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,205 (23.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e453 ( 17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18,658 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchool:\u003c/p\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,566 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e709 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22,275 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdolescents:\u003c/p\u003e \u003cp\u003e11\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36,843 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,406 ( 54.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38,249 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41,076 (51.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,022 (78.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39,054 (50.98%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38,106 (48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e546 (21.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37,560 (49.02%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,717 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e366 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,351 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52,162 (65.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,687 (65.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50,475 (65.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,404 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,225 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,495 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,391 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmerican Indian/ Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e524 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e509 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNative Hawaiian/ Other Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e233 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,642 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,430 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFederal Poverty Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;400%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31,894 (40.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e772 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31,122 (40.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200% \u0026minus;\u0026thinsp;399%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24,101 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e799 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23,302 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100% \u0026minus;\u0026thinsp;199%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,188 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e554 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12,634 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,999 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e443 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,556 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHighest education of adults in the household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,164 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,101 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,731 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e427 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,304 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66,287 (83.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,078 (80.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64,209 (83.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariate analysis showed significant increases in the odds of ASD in the adolescent (11\u0026ndash;17 year-olds) age group (odds ratio, OR: 1.5, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and multi-race ethnic group (OR: 1.2, p-value: 0.04) as well as in families with high school and higher than high school educational level (OR: 1.5, p-value: 0.005) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Conversely, female gender (OR: 0.3, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher household income (OR: 0.5, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Asian race (OR: 0.7, p-value: 0.01) were associated with decreased odds of ASD among youths (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-Demographic Predictors of Autism Spectrum Disorder (ASD)based on Univariate and Multivariate Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocio-Demographic Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge Groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreschool:\u003c/p\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchool:\u003c/p\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (1.2\u0026ndash;1.5)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3 (1.1\u0026ndash;1.5)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdolescents:\u003c/p\u003e \u003cp\u003e11\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (1.4\u0026ndash;1.7)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5 (1.4\u0026ndash;1.7)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3(0.2\u0026ndash;0.3)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 (0.2\u0026ndash;0.3)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.8\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04(0.9\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.8\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9(0.7\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7(0.5\u0026ndash;0.8)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 (0.6\u0026ndash;0.9)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmerican Indian/ Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8(0.5\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8(0.4\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNative Hawaiian/ Other Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 (0.2\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6 (0.2\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.9\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 (1.0-1.4)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFederal Poverty Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100% \u0026minus;\u0026thinsp;199%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.8\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 (0.8-1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200% \u0026minus;\u0026thinsp;399%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7 (0.6\u0026ndash;0.8)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 (0.6\u0026ndash;0.8)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;400%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.5\u0026ndash;0.6)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 (0.4\u0026ndash;0.6)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHighest education of adults in the household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4 (1.1\u0026ndash;1.8)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5 (1.1-2)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1(0.8\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5 (1.1\u0026ndash;1.9)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*: P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **: P value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***: P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e96.4% of youth with ASD had at least one co-morbid condition. The most common neuropsychiatric co-morbid condition with ASD was developmental delay (64%), followed by behavioral and conduct problems (57.8%) and anxiety problems (45.7%); respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). While the most common medical co-morbidities were allergies (32.4%), genetic disorders (26.2%), and asthma (12.6%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe odds of co-morbid vision problems (OR: 2.3, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cerebral palsy (OR: 2.2, p-value: 0.042), frequent/ severe headaches (OR:1.7, p-value: 0.002), epilepsy (OR: 1.7, p-value: 0.014), depression (OR: 1.6, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and intellectual disability (OR:1.5, p-value: 0.001) were significantly greater in females than males, whereas ADHD (OR: 0.8, p-value: 0.007) and anxiety problems (OR: 0.35, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly lower among female individuals with ASD (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the severity of ASD, we found that co-morbid occurrence of intellectual disability (OR: 5.8, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), developmental delay (OR: 5, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), epilepsy (OR: 3.4, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Down syndrome (OR: 3, p-value: 0.01),, vision problems (OR: 2.5, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), behavioral and conduct problems (OR: 2.4, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), genetic disorders (OR: 2.3, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hearing problems (OR: 1.6, p-value: 0.03), and anxiety problems (OR: 1.3, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) may contribute to a more severe manifestation of ASD (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePsychiatric and Medical Co-morbidities in Autism Spectrum Disorder (ASD), Abbreviation: M: Male, F: Female, 1: Mild ASD, 2: Moderate to severe ASD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisorders\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASD (n\u0026thinsp;=\u0026thinsp;2,568)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASD Severity\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttention-Deficit Hyperactivity Disorder (ADHD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,136 (44.3%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 0.8 (0.6\u0026ndash;0.9)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 1.03 (0.9\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTourette Syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43 (1.7%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 0.6 (0.2\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 1.8 (0.9\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e425(16.7%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 1.6 (1.3-2.0)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 0.9 (0.2\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety Problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,151(45.7%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 0.35 ( 0.3\u0026ndash;0.4)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 1.3 (1.3\u0026ndash;1.8)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart Disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89 (3.5%) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 1.3 (0.8\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 0.9 (0.6\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevelopmental Delay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,628(64.0%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 1.02 (0.8\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 5.0 (4.1\u0026ndash;5.9)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual Disability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e411 (16.1%) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 1.5 (1.2\u0026ndash;1.9) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 5.8 (4.4\u0026ndash;7.7)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral and Conduct Problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,470 (57.8%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 0.9 ( 0.7-1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 2.4 (2.0-2.8)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e320 (12.6%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 1.06 (0.8\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 1.1 (0.9\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllergies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e832 (32.4%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 0.8 (0.7\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 1.3 (1.06\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (0.5%)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 0.6 (0.1\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 1.6 (0.5\u0026ndash;4.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral Palsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (1.2%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 2.2 (1.02\u0026ndash;4.6)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 3 (1.3-7.0)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25( 0.9%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 1.2 (0.5\u0026ndash;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 0.9( 0.4\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDown Syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (0.9% )***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 2.1 (0.9\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 4.5 (1.5\u0026ndash;13.4)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpilepsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107 (4.2%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 1.7 (1.1\u0026ndash;2.6)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 3.4 (2.1\u0026ndash;5.5)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequent/Severe Headaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159 (6.2%) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 1.7 (1.2\u0026ndash;2.4)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 1.06 (0.7\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHearing Problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87 (3.4%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF:1.2 (0.7\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 1.6 (1.03\u0026ndash;2.5)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenetic Disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e669 (26.2%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 1.2 ( 0.9\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 2.3 (1.9\u0026ndash;2.8)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVision Problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117 (4.6%)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM: Reference\u003c/p\u003e \u003cp\u003eF: 2.3 (1.6\u0026ndash;3.5)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1: Reference\u003c/p\u003e \u003cp\u003e2: 2.5 (1.6\u0026ndash;3.8)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*: P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **: P value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***: P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe identified up to 2,568 (3.2%) of the youth to have a current diagnosis of ASD. ASD was found to be associated with gender, race, household income, and education level. The most common co-morbid conditions were developmental delay, behavioral and conduct problems, and anxiety problems. We found that co-morbid medical and psychiatric conditions with ASD can affect its severity.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of ASD\u003c/h2\u003e \u003cp\u003eThe parent- based prevalence of ASD in US children and adolescents was 3.2%. This finding is slightly increased from the previous report of 3.14% according to the 2019\u0026ndash;2020 NSCH survey (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Further extrapolation backward shows that there has been a steady overall incline in prevalence over time in other previous reports. Autism and Developmental Disabilities Monitoring (ADDM) Network in 2018 reported a prevalence of 2.3% and the NSCH in 2016 reported 2.5% (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, conclusions must be drawn with caution from the above-noted reports as their estimates are derived from different systems reflecting different ages and populations; children aged 8 years from 11 local populations in ADDM versus 3\u0026ndash;17 years in NSCH from populations from the entire USA (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The variations in the prevalence of ASD may also be attributed to the inherent nature of ASD as a spectrum disorder, characterized by diverse traits that can even influence the definitions and diagnostic criteria of ASD (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSocio-demographic associations\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eAge-groups\u003c/h2\u003e \u003cp\u003eWe analyzed the parent-reported diagnosis of ASD in three different age groups, and our analysis showed a significantly higher prevalence of ASD in the adolescent (11\u0026ndash;17 year-olds) age group. Based on previous literature, the assignment of diagnosis becomes more reliable in older ages (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) as the older children with ASD show greater deficits in executive and social functioning compared to younger children, leading to an easier and more robust diagnosis (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, at younger ages, there is a possibility of missing children with ASD until they enter primary school or even later (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This is because the social demands placed on children increase as they grow older, making particular autistic deficits more apparent (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGender differences\u003c/h2\u003e \u003cp\u003eWe observed a male-to-female ratio of approximately 3.7 in the prevalence of ASD. This aligns with previous research, which has consistently reported male-to-female ratios ranging from 2:1 to 5:1(\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The prevailing belief that ASD is more frequently identified in males compared to females has spurred the development of several hypotheses that aim to explain the characteristics and causes of ASD. These ideas include the extreme male brain theory (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), the female protective effect theory (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and the female autism phenotype theory (\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The extreme male brain theory suggests that understanding gender differences involves considering \"empathizing\" and \"systemizing.\" Evidence indicates that males tend to excel in systematization, while females have distinct cognitive traits. This theory proposes that ASD might represent an extreme male cognitive profile (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The \"female protective model/effect\" stems from the consistent male predominance in ASD. It assumes that ASD risk is spread throughout the population and suggests that females have a protective factor against autism (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Due to this protective effect, ASD affects women less often than men. High-functioning ASD has a male-to-female ratio of 7:1, while moderate to severe Intellectual Disability has a ratio of 2:1, indicating female protection even in the presence of risk factors (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This means that females have a higher threshold for ASD risk, including genetic and environmental factors, before the condition becomes apparent (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The female autism phenotype theories suggest a unique expression of autism in females, but current autism research is male-centric (\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Evidence shows that females with ASD have stronger social desires and are more likely to form friendships than males with ASD (\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eOther sociodemographic factors\u003c/h2\u003e \u003cp\u003eWe found the highest odds of ASD in multi-race youth, as well as youth with high school and higher than high school caregiver educational level. There are some differences between our study findings and Centers for Disease Control and Prevention (CDC) report of racial differences of youth with ASD, as they found the highest prevalence of ASD in youth with Asian/pacific islander descents (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), which could be the result of different methodologies and instruments. Research has shown that the prevalence of ASD was initially higher in white children compared to black or Hispanic children but gradually equalized by 2016 and 2018 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Recent reports in 2020 marked the first time ASD rates were lower in white children than in other groups at age 8, and a similar trend was observed among 4-year-olds in 2018 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). This shift may be due to better screening, increased awareness, and improved services for historically marginalized populations (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Youth with caregivers having at least a high school education are more likely to have ASD, a finding consistent with other studies (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Diagnostic odds ratios for parental education and income are complex. Families with lower educational attainment have a reduced likelihood of receiving an ASD diagnosis, potentially linked to inequities in healthcare access and outcomes. Lower-educated families may face limited healthcare services, decreasing their chances of obtaining a medical diagnosis for their child (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that ASD in children and adolescents is higher among families with lower than 100% FPL income. The ADDM Network Reported that the prevalence of ASD in 2020 exhibited a correlation with lower socioeconomic status (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Although some previous studies reported contrast findings (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), These findings offers further evidence of improved ASD identification for youth regardless of their socioeconomic level. As research continues to grow on better identification methods, attention may shift towards understanding the factors, such as socioeconomic determinants of health, that could lead to higher rates of detected impairment in specific communities (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCo-morbid conditions and severity of ASD\u003c/h2\u003e \u003cp\u003eOver 96% of ASD patients had at least one co-morbid condition. We found the most common co-morbid conditions with ASD to be developmental delay, behavioral and conduct problems, anxiety problems, allergies, genetic disorders, and asthma. Simonof et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) found that 70% of children with ASD in a population-derived cohort had at least one co-morbid psychiatric disorder. They found the most common co-occurring psychiatric disorders with ASD to be social anxiety disorder, ADHD, and oppositional defiant disorder (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Similarly, Leyfer et al. found the most prevalent co-morbid psychiatric conditions in autistic children to be specific phobia, obsessive-compulsive disorder (OCD), and ADHD, respectively (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Regarding medical co-morbidities, previous literature reported higher rates of allergies including skin allergies, food allergies, and asthma as well as gastrointestinal problems (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that co-morbid occurrence of intellectual disability, developmental delay, Down syndrome, epilepsy, vision problems, behavioral and conduct problems, genetic disorders, hearing problems, and anxiety problems was associated with a more severe form of ASD. There is a gap in the literature exploring the correlation between co-morbidities and the severity of ASD. Jang et al. studied a group of children and adolescents with ASD and found that youth with more severe symptoms of ASD are more likely to have concurrent psychiatric conditions (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Leyfer et al. also reported an association between functional impairment and psychiatric co-morbidities in ASD (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Konstantareas et al. studied a group of youth with ASD and concurrent ear problems including hearing loss and ear infections and found that co-morbid ear problems are positively correlated with the severity of autistic symptomatology (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Consistent with our findings, previous literature also reported that neurological co-morbid conditions such as intellectual disability and epilepsy are associated with more severe forms of ASD (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). These findings suggest that individuals with ASD face extra challenges when dealing with accompanying co-morbid conditions, which is clinically important as earlier screening for co-morbidity can aid in establishing additional priorities for assessment and more effective treatment options (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our findings suggest that male adolescents, multi-race individuals, and those with higher-educated families as well as lower family income levels may have increased odds of ASD, while females, higher-income households, and Asian race may have lower odds. Importantly, many individuals with ASD have co-morbid conditions, particularly developmental delay and behavioral issues which can affect the severity of ASD. Identifying and addressing these co-morbidities early is crucial for clinical management and support.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe major limitation of this study is the lack of a valid diagnostic instrument and parent-based reports which can be a source of bias.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no proprietary or commercial interest in any product mentioned or concepts discussed in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval of the original NSCH survey for all procedures is obtained from the National Center for Health Statistics Research Ethics Review Board.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAssociation AP (2013) Diagnostic and Statistical Manual of Mental Disorders, 5th edn) edn. American Psy-chiatric Association, Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://psychiatryonlineorg/doi/book/101176/appi\u003c/span\u003e\u003cspan address=\"http://psychiatryonlineorg/doi/book/101176/appi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e books 9780890425596\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristensen DL (2016) Prevalence and characteristics of autism spectrum disorder among children aged 8 years\u0026mdash;autism and developmental disabilities monitoring network, 11 sites, United States, 2012. MMWR Surveillance summaries. ;65\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDurkin MS, Maenner MJ, Baio J, Christensen D, Daniels J, Fitzgerald R et al (2017) Autism spectrum disorder among US children (2002\u0026ndash;2010): Socioeconomic, racial, and ethnic disparities. Am J Public Health 107(11):1818\u0026ndash;1826\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGordon-Lipkin E, Foster J, Peacock G (2016) Whittling down the wait time: exploring models to minimize the delay from initial concern to diagnosis and treatment of autism spectrum disorder. Pediatr Clin 63(5):851\u0026ndash;859\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcNally Keehn R, Ciccarelli M, Szczepaniak D, Tomlin A, Lock T, Swigonski N (2020) A statewide tiered system for screening and diagnosis of autism spectrum disorder. Pediatrics. ;146(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllison K, Bundy MB, Gore J, Wygant D (2019) Exploration of the DSM-5\u0026rsquo;s Autism Spectrum Disorder severity level specifier and prediction of autism severity. Exceptionality 27(4):289\u0026ndash;298\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoshi-Velez F, Ge Y, Kohane I (2014) Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis. Pediatrics 133(1):e54\u0026ndash;e63\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIngason A, Rujescu D, Cichon S, Sigurdsson E, Sigmundsson T, Pietil\u0026auml;inen O et al (2011) Copy number variations of chromosome 16p13. 1 region associated with schizophrenia. Mol Psychiatry 16(1):17\u0026ndash;25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Beltagi M (2021) Autism medical comorbidities. World J Clin Pediatr 10(3):15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConsortium C-DGPG (2013) Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381(9875):1371\u0026ndash;1379\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabares-Seisdedos R, Rubenstein J (2009) Chromosome 8p as a potential hub for developmental neuropsychiatric disorders: implications for schizophrenia, autism and cancer. Mol Psychiatry 14(6):563\u0026ndash;589\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeyfer OT, Folstein SE, Bacalman S, Davis NO, Dinh E, Morgan J et al (2006) Comorbid psychiatric disorders in children with autism: Interview development and rates of disorders. J Autism Dev Disord 36:849\u0026ndash;861\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e(NSCH) Tnsocsh. Data Resource Center for Child \u0026amp; Adolescent Health (n.d.). 2020\u0026ndash;2021\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Q, Li Y, Liu B, Chen Q, Xing X, Xu G et al (2022) Prevalence of autism spectrum disorder among children and adolescents in the United States from 2019 to 2020. JAMA Pediatr 176(9):943\u0026ndash;945\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaenner MJ, Shaw KA, Bakian AV, Bilder DA, Durkin MS, Esler A et al (2021) Prevalence and characteristics of autism spectrum disorder among children aged 8 years\u0026mdash;autism and developmental disabilities monitoring network, 11 sites, United States, 2018. MMWR Surveillance Summaries 70(11):1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKogan MD, Vladutiu CJ, Schieve LA, Ghandour RM, Blumberg SJ, Zablotsky B et al (2018) The prevalence of parent-reported autism spectrum disorder among US children. Pediatrics. ;142(6)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFombonne E (2009) Epidemiology of pervasive developmental disorders. Pediatr Res 65(6):591\u0026ndash;598\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenthal M, Wallace GL, Lawson R, Wills MC, Dixon E, Yerys BE et al (2013) Impairments in real-world executive function increase from childhood to adolescence in autism spectrum disorders. Neuropsychology 27(1):13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFombonne E, MacFarlane H, Salem AC (2021) Epidemiological surveys of ASD: advances and remaining challenges. J Autism Dev Disord 51:4271\u0026ndash;4290\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalantseva OI, Romanova RS, Shurdova EM, Dolgorukova TA, Sologub PS, Titova OS et al (2023) The global prevalence of autism spectrum disorder: A three-level meta-analysis. Front Psychiatry 14:1071181\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoomes R, Hull L, Mandy WPL (2017) What is the male-to-female ratio in autism spectrum disorder? A systematic review and meta-analysis. J Am Acad Child Adolesc Psychiatry 56(6):466\u0026ndash;474\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLord C, Brugha TS, Charman T, Cusack J, Dumas G, Frazier T et al (2020) Autism spectrum disorder. Nat reviews Disease primers 6(1):1\u0026ndash;23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalladay AK, Bishop S, Constantino JN, Daniels AM, Koenig K, Palmer K et al (2015) Sex and gender differences in autism spectrum disorder: summarizing evidence gaps and identifying emerging areas of priority. Mol autism 6(1):1\u0026ndash;5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWerling DM, Geschwind DH (2013) Understanding sex bias in autism spectrum disorder. Proceedings of the National Academy of Sciences. ;110(13):4868-9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaron-Cohen S, Knickmeyer RC, Belmonte MK (2005) Sex differences in the brain: implications for explaining autism. Science 310(5749):819\u0026ndash;823\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevenson CL, Krantz PJ, McClannahan LE (2000) Social interaction skills for children with autism: A script-fading procedure for nonreaders. Behav Interventions: Theory Pract Residential Community‐Based Clin Programs 15(1):1\u0026ndash;20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBargiela S, Steward R, Mandy W (2016) The experiences of late-diagnosed women with autism spectrum conditions: An investigation of the female autism phenotype. J Autism Dev Disord 46:3281\u0026ndash;3294\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHead AM, McGillivray JA, Stokes MA (2014) Gender differences in emotionality and sociability in children with autism spectrum disorders. Mol autism 5(1):1\u0026ndash;9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSedgewick F, Hill V, Yates R, Pickering L, Pellicano E (2016) Gender differences in the social motivation and friendship experiences of autistic and non-autistic adolescents. J Autism Dev Disord 46:1297\u0026ndash;1306\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJacquemont S, Coe BP, Hersch M, Duyzend MH, Krumm N, Bergmann S et al (2014) A higher mutational burden in females supports a female protective model in neurodevelopmental disorders. Am J Hum Genet 94(3):415\u0026ndash;425\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWerling DM (2016) The role of sex-differential biology in risk for autism spectrum disorder. Biology sex differences 7:1\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLai M-C, Baron-Cohen S (2015) Identifying the lost generation of adults with autism spectrum conditions. Lancet Psychiatry 2(11):1013\u0026ndash;1027\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiller RM, Young RL, Weber N (2014) Sex differences in autism spectrum disorder based on DSM-5 criteria: evidence from clinician and teacher reporting. J Abnorm Child Psychol 42:1381\u0026ndash;1393\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMandy W, Chilvers R, Chowdhury U, Salter G, Seigal A, Skuse D (2012) Sex differences in autism spectrum disorder: evidence from a large sample of children and adolescents. J Autism Dev Disord 42:1304\u0026ndash;1313\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaenner MJ, Warren Z, Williams AR, Amoakohene E, Bakian AV, Bilder DA et al (2023) Prevalence and characteristics of autism spectrum disorder among children aged 8 years\u0026mdash;Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020. MMWR Surveillance Summaries 72(2):1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFederico A, Zgodic A, Flory K, Hantman RM, Eberth JM, Mclain AC et al (2023) Predictors of Autism Spectrum Disorder and ADHD: Results from the National Survey of Children's Health. Disabil Health J. :101512\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShelton JF, Tancredi DJ, Hertz-Picciotto I (2010) Independent and dependent contributions of advanced maternal and paternal ages to autism risk. Autism Res 3(1):30\u0026ndash;39\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimonoff E, Pickles A, Charman T, Chandler S, Loucas T, Baird G (2008) Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample. J Am Acad Child Adolesc Psychiatry 47(8):921\u0026ndash;929\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsaksen J, Bryn V, Diseth TH, Heiberg A, Schj\u0026oslash;lberg S, Skjeldal OH (2013) Children with autism spectrum disorders\u0026ndash;the importance of medical investigations. Eur J Pediatr Neurol 17(1):68\u0026ndash;76\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJang J, Matson JL (2015) Autism severity as a predictor of comorbid conditions. J Dev Phys Disabil 27:405\u0026ndash;415\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKonstantareas MM, Homatidis S (1987) Brief report: Ear infections in autistic and normal children. J Autism Dev Disord 17(4):585\u0026ndash;594\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMouridsen SE, Rich B, Isager T (1999) Epilepsy in disintegrative psychosis and infantile autism: a long-term validation study. Dev Med Child Neurol 41(2):110\u0026ndash;114\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuchman R, Rapin I (2002) Epilepsy in autism. Lancet Neurol 1(6):352\u0026ndash;358 PubMed PMID: 12849396. Epub 2003/07/10. eng\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Autism spectrum disorder (ASD), Prevalence, Co-morbidity, Socio-demographics, characteristics, severity","lastPublishedDoi":"10.21203/rs.3.rs-3921934/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3921934/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAutism spectrum disorder (ASD) consists of heterogeneous neurodevelopmental disorders with impairments in social communication, repetitive behaviors, and restricted interests. This condition is associated with several co-morbidities which significantly affect the quality of life. Therefore, individuals with ASD should undergo screening for common co-morbidities to enable early diagnosis and treatment. This study aimed to assess the prevalence and common socio-demographic characteristics of ASD as well as both medical and psychiatric co-morbidities and their effects on the severity of ASD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from the National Survey of Children\u0026rsquo;s Health (NSCH) in the US from 2020 to 2021 were used in this study. We included 79,182 children and adolescents aged between 3 to 17 years for the analysis. The mean age of these individuals was 10.1 (standard deviation: 4.6), and 2,568 (3.2%) had ASD diagnosis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur analysis showed that ASD is more common in males (78.7%) than females (21.3%). Additionally, ASD was associated with lower family income and a higher level of education in the family. We found that 96.4% of patients developed at least one co-morbid condition. The most common neuropsychiatric co-morbidities included developmental delay (64%), behavioral and conduct problems (57.8%), and anxiety (45.7%). While the most common medical co-morbidities were allergies (32.4%), genetic disorders (26.2%), and asthma (12.6%). The odds of intellectual disability (odds ratio, OR: 5.8), developmental delay (OR: 5.0), Down syndrome (OR: 4.5), epilepsy (OR: 3.4), cerebral palsy (OR: 3.0), vision problems (OR: 2.5), and genetic disorders (OR: 2.3) were significantly higher among severe cases.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe presence of numerous co-morbidities in individuals with ASD, often linked to increased ASD severity, underscores the critical importance of comprehensive screening, early diagnosis, and targeted treatment strategies to enhance the overall health and well-being of these individuals.\u003c/p\u003e","manuscriptTitle":"Characteristics and Co-morbidities of Autism Spectrum Disorder as Risk Factors for Severity: A National Survey in the United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 17:25:04","doi":"10.21203/rs.3.rs-3921934/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"73fc5f40-3cf5-4e3d-bbfc-6cb4cd89c3db","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-22T18:59:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-06 17:25:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3921934","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3921934","identity":"rs-3921934","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}