Prevalence of Autism Spectrum Disorder among Rural, Urban, and Tribal Children (1-10 Years of Age).
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ABSTRACT:
SUBMITTER: Raina SK
PROVIDER: S-EPMC5488556 | biostudies-other | 2017 Jul-Sep
REPOSITORIES: biostudies-other
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Project description:Daily living skills (DLS), such as personal hygiene, meal preparation, and money management, are important to independent living. Research suggests that many individuals with autism spectrum disorder exhibit impairments in daily living skills relative to their cognitive skills. This study examined predictors of daily living skills attainment and trajectories of daily living skills in a longitudinal sample referred for possible autism spectrum disorder and followed from 2 to 21?years of age. Consistent with previous studies, participants with autism spectrum disorder and nonspectrum diagnoses showed continual development of daily living skills throughout childhood and adolescence. Early childhood nonverbal mental age was the strongest predictor of daily living skills attainment for both diagnostic groups. Group-based modeling suggested two distinct trajectories of daily living skills development for participants with autism spectrum disorder. Skill levels for both groups of young adults with autism spectrum disorder remained considerably below age level expectations. Whereas the "High-DLS" group gained approximately 12?years in daily living skills from T2 to T21, the "Low-DLS" group's daily living skills improved 3-4?years over the 16- to 19-year study period. Nonverbal mental age, receptive language, and social-communication impairment at 2?years predicted High- versus Low-DLS group membership. Receiving greater than 20?h of parent-implemented intervention before age 3 was also associated with daily living skills trajectory. Results suggest that daily living skills should be a focus of treatment plans for individuals with autism spectrum disorder, particularly adolescents transitioning to young adulthood.
| S-EPMC4912002 | biostudies-literature
Project description:BackgroundLow-resource rural communities face significant challenges regarding availability and adequacy of evidence-based services.PurposesWith respect to accessing evidence-based services for Autism Spectrum Disorder (ASD), this brief report summarizes needs of rural citizens in the South-Central Appalachian region, an area notable for persistent health disparities.MethodsA mixed-methods approach was used to collect quantitative and qualitative data during focus groups with 33 service providers and 15 caregivers of children with ASD in rural southwest Virginia.ResultsResults supported the barriers of availability and affordability of ASD services in this region, especially relating to the need for more ASD-trained providers, better coordination and navigation of services, and addition of programs to assist with family financial and emotional stressors. Results also suggested cultural attitudes related to autonomy and trust towards outside professionals that may prevent families from engaging in treatment.ImplicationsRelevant policy recommendations are discussed related to provider incentives, insurance coverage, and telehealth. Integration of autism services into already existing systems and multicultural sensitivity of providers are also implicated.
| S-EPMC9138840 | biostudies-literature
Project description:PROBLEM/CONDITION:Autism spectrum disorder (ASD). PERIOD COVERED:2016. DESCRIPTION OF SYSTEM:The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance program that provides estimates of the prevalence of ASD among children aged 8 years whose parents or guardians live in 11 ADDM Network sites in the United States (Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin). Surveillance is conducted in two phases. The first phase involves review and abstraction of comprehensive evaluations that were completed by medical and educational service providers in the community. In the second phase, experienced clinicians who systematically review all abstracted information determine ASD case status. The case definition is based on ASD criteria described in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. RESULTS:For 2016, across all 11 sites, ASD prevalence was 18.5 per 1,000 (one in 54) children aged 8 years, and ASD was 4.3 times as prevalent among boys as among girls. ASD prevalence varied by site, ranging from 13.1 (Colorado) to 31.4 (New Jersey). Prevalence estimates were approximately identical for non-Hispanic white (white), non-Hispanic black (black), and Asian/Pacific Islander children (18.5, 18.3, and 17.9, respectively) but lower for Hispanic children (15.4). Among children with ASD for whom data on intellectual or cognitive functioning were available, 33% were classified as having intellectual disability (intelligence quotient [IQ] ?70); this percentage was higher among girls than boys (39% versus 32%) and among black and Hispanic than white children (47%, 36%, and 27%, respectively) [corrected]. Black children with ASD were less likely to have a first evaluation by age 36 months than were white children with ASD (40% versus 45%). The overall median age at earliest known ASD diagnosis (51 months) was similar by sex and racial and ethnic groups; however, black children with IQ ?70 had a later median age at ASD diagnosis than white children with IQ ?70 (48 months versus 42 months). INTERPRETATION:The prevalence of ASD varied considerably across sites and was higher than previous estimates since 2014. Although no overall difference in ASD prevalence between black and white children aged 8 years was observed, the disparities for black children persisted in early evaluation and diagnosis of ASD. Hispanic children also continue to be identified as having ASD less frequently than white or black children. PUBLIC HEALTH ACTION:These findings highlight the variability in the evaluation and detection of ASD across communities and between sociodemographic groups. Continued efforts are needed for early and equitable identification of ASD and timely enrollment in services.
| S-EPMC7119644 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2018-02-27 | GSE111176 | GEO
Project description:There has been increasing evidence of White Matter (WM) microstructural disintegrity and connectome disruption in Autism Spectrum Disorder (ASD). We evaluated the effects of age on WM microstructure by examining Diffusion Tensor Imaging (DTI) metrics and connectome Edge Density (ED) in a large dataset of ASD and control patients from different age cohorts. N = 583 subjects from four studies from the National Database of Autism Research were included, representing four different age groups: (1) A Longitudinal MRI Study of Infants at Risk of Autism [infants, median age: 7 (interquartile range 1) months, n = 155], (2) Biomarkers of Autism at 12 months [toddlers, 32 (11)m, n = 102], (3) Multimodal Developmental Neurogenetics of Females with ASD [adolescents, 13.1 (5.3) years, n = 230], (4) Atypical Late Neurodevelopment in Autism [young adults, 19.1 (10.7)y, n = 96]. For each subject, we created Fractional Anisotropy (FA), Mean- (MD), Radial- (RD), and Axial Diffusivity (AD) maps as well as ED maps. We performed voxel-wise and tract-based analyses to assess the effects of age, ASD diagnosis and sex on DTI metrics and connectome ED. We also optimized, trained, tested, and validated different combinations of machine learning classifiers and dimensionality reduction algorithms for prediction of ASD diagnoses based on tract-based DTI and ED metrics. There is an age-dependent increase in FA and a decline in MD and RD across WM tracts in all four age cohorts, as well as an ED increase in toddlers and adolescents. After correction for age and sex, we found an ASD-related decrease in FA and ED only in adolescents and young adults, but not in infants or toddlers. While DTI abnormalities were mostly limited to the corpus callosum, connectomes showed a more widespread ASD-related decrease in ED. Finally, the best performing machine-leaning classification model achieved an area under the receiver operating curve of 0.70 in an independent validation cohort. Our results suggest that ASD-related WM microstructural disintegrity becomes evident in adolescents and young adults-but not in infants and toddlers. The ASD-related decrease in ED demonstrates a more widespread involvement of the connectome than DTI metrics, with the most striking differences being localized in the corpus callosum.
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Project description:Autism spectrum disorder is a construct used to describe individuals with a specific combination of impairments in social communication and repetitive behaviours, highly restricted interests and/or sensory behaviours beginning early in life. The worldwide prevalence of autism is just under 1%, but estimates are higher in high-income countries. Although gross brain pathology is not characteristic of autism, subtle anatomical and functional differences have been observed in post-mortem, neuroimaging and electrophysiological studies. Initially, it was hoped that accurate measurement of behavioural phenotypes would lead to specific genetic subtypes, but genetic findings have mainly applied to heterogeneous groups that are not specific to autism. Psychosocial interventions in children can improve specific behaviours, such as joint attention, language and social engagement, that may affect further development and could reduce symptom severity. However, further research is necessary to identify the long-term needs of people with autism, and treatments and the mechanisms behind them that could result in improved independence and quality of life over time. Families are often the major source of support for people with autism throughout much of life and need to be considered, along with the perspectives of autistic individuals, in both research and practice.
| S-EPMC8900942 | biostudies-literature
Project description:The aim of this study was to identify ASD vulnerability components, explore possible vulnerability subgroups, and evaluate environmental contribution to phenotypic variability. We performed PCA and cluster analysis using ASD risk factor data. We found that PC1 had a higher correlation with psychosocial stress (maternal stress, maternal education, and social class), and PC2 a higher correlation with biological factors (Psychiatric Family History and Gestational Complications). Clustering analysis of PCA scores using bootstrap approach showed two clusters. Comparing the methylome between clusters we found 11.879 DMPs (p<0.05), and their CpG sites were enriched in VMRs, most showing environmental and genetic influences. Hypermethylated probes presented higher rates in different regulatory regions associated with functional SNPs, indicating that the subgroups may have different affected regulatory regions and their liability to disease explained by common variations. Vulnerability score moderated by epigenetic clock was associated with Vineland Total score (p=0.0036, adjR2=0.31), suggesting risk factors with stress burden can influence phenotype.
2021-10-01 | GSE164563 | GEO
Project description:Recent longitudinal neuroimaging and neurophysiological studies have shown that tracking relative age-related changes in neural signals, rather than a static snapshot of a neural measure, could offer higher sensitivity for discriminating typically developing (TD) individuals from those with autism spectrum disorder (ASD). It is not clear, however, which aspects of age-related changes (trajectories) would be optimal for identifying atypical brain development in ASD. Using a large cross-sectional data set (Autism Brain Imaging Data Exchange [ABIDE] repository; releases I and II), we aimed to explore age-related changes in cortical thickness (CT) in TD and ASD populations (age range 6-30 years old). Cortical thickness was estimated from T1-weighted MRI images at three scales of spatial coarseness (three parcellations with different numbers of regions of interest). For each parcellation, three polynomial models of age-related changes in CT were tested. Specifically, to characterize alterations in CT trajectories, we compared the linear slope, curvature, and aberrancy of CT trajectories across experimental groups, which was estimated using linear, quadratic, and cubic polynomial models, respectively. Also, we explored associations between age-related changes with ASD symptomatology quantified as the Autism Diagnostic Observation Schedule (ADOS) scores. While no overall group differences in cortical thickness were observed across the entire age range, ASD and TD populations were different in terms of age-related changes, which were located primarily in frontal and tempo-parietal areas. These atypical age-related changes were also associated with ADOS scores in the ASD group and used to predict ASD from TD development. These results indicate that the curvature is the most reliable feature for localizing brain areas developmentally atypical in ASD with a more pronounced effect with symptomatology and is the most sensitive in predicting ASD development.
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Project description:To assess the clinical impact of splice-altering noncoding mutations in autism spectrum disorder (ASD), we used a deep learning framework (SpliceAI) to predict the splice-altering potential of de novo mutations in 3,953 individuals with ASD from the Simons Simplex Collection. To validate these predictions, we selected 36 individuals that harbored predicted de-novo cryptic splice mutations; each individual represented the only case of autism within their immediate family. We obtained peripheral blood-derived lymphoblastoid cell lines (LCLs) and performed high-depth mRNA sequencing (approximately 350 million 150 bp single-end reads per sample). We used OLego to align the reads against a reference created from hg19 by substituting de novo variants of each individual with the corresponding alternate allele.
2018-11-25 | E-MTAB-7351 | biostudies-arrayexpress
Project description:Findings from observational and experimental studies suggest that maternal inflammation during pregnancy is associated with autism spectrum disorder (ASD). We report the first study in humans to examine this association in a large prospective birth cohort. We studied 788 mother-child pairs from the Seychelles Child Development Study Nutrition Cohort 2. Thirteen inflammatory markers were measured in mothers' serum at 28 weeks' gestation, along with the sum of T-helper 1 (Th1) and 2 (Th2) cytokines. The Social Communication Questionnaire (SCQ) and Social Responsiveness Scale (SRS) were administered at age 7 years to obtain information on ASD phenotype. We evaluated associations between maternal inflammatory markers and ASD phenotype using multivariable linear regression. For the SCQ, increased MCP-1 (a chemokine that is upregulated in response to pro-inflammatory cytokines) was associated with fewer ASD symptoms (B?=?-?0.40; 95% CI?=?-?0.72, -?0.09). Increased IL-4 (a cytokine that is typically associated with an enhanced anti-inflammatory response) was associated with more ASD symptoms (B?=?2.10; 95% CI?=?0.78, 3.43). For the SRS, higher concentrations of the anti-inflammatory cytokine IL-10 were associated with fewer ASD symptoms (B?=?-?0.18; 95% CI?=?-?0.35, -?0.01), but only after removal of outliers. No associations were observed for other markers. These findings suggest that a shift in the maternal immune balance during pregnancy may be associated with ASD symptomatology. While the use of well-established measures that capture ASD phenotypic variability is a strength of the study, measurement of peripheral immune markers only once during gestation is a limitation. Our results should be confirmed using maternal immune markers measured throughout gestation.
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