Parental Socioeconomic Status and Autism Spectrum Disorder in Offspring: A Population-Based Cohort Study in Taiwan

2020 ◽  
Author(s):  
Tsung Yu ◽  
Yueh-Ju Lien ◽  
Fu-Wen Liang ◽  
Pao-Lin Kuo
Keyword(s):  
Autism Spectrum Disorder ◽  
Socioeconomic Status ◽  
Cox Regression ◽  
International Classification Of Diseases ◽  
The United States ◽  
Population Based ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Cox Regression Analysis ◽  
Universal Healthcare

Abstract Studies from the United States have shown increasing incidence of autism spectrum disorder (ASD) with increasing socioeconomic status (SES), whereas in Scandinavian countries, no such relation was identified. We investigated how ASD risk in offspring varied according to parental SES in Taiwan, where we have universal healthcare. Through linking birth reporting data and data from Taiwan’s National Health Insurance program, we studied 706,111 singleton births from 2004 to 2007 and followed them until 2015. Parental SES was determined by monthly salary at the time of childbirth, and child neuropsychiatric outcomes were defined using International Classification of Diseases codes. We identified 7,323 ASD cases and 7,438 intellectual disability (ID) cases; 17% of ASD cases had co-occurring ID. In multivariable Cox regression analysis, higher SES was independently associated with higher risk of ASD after we took into account urbanization levels, child sex, parental age and other covariates. By contrast, higher SES was independently associated with lower risk of ID. Besides the SES disparity in ASD case ascertainment and in the access to healthcare, findings from Taiwan suggest that other social, environmental, biological and immunological factors linked with parental SES levels may contribute to the positive relation of SES and ASD risk.

scholarly journals Genotype-Based Deep Learning in Autism Spectrum Disorder: Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants (Preprint)

2020 ◽  
Author(s):  
Haishuai Wang ◽  
Paul Avillach
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
The United States ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Screening Tools ◽  
Common Variants ◽  
Autism Screening

BACKGROUND In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. OBJECTIVE Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. METHODS After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. RESULTS The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic from nonautistic individuals. Our classifier demonstrated a significant improvement over standard autism screening tools by average 13% in terms of classification accuracy. CONCLUSIONS Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

scholarly journals Timing of Identification Among Children With an Autism Spectrum Disorder: Findings From a Population-Based Surveillance Study

2009 ◽  
Vol 48 (5) ◽  
pp. 474-483 ◽  
Author(s):  
Paul T. Shattuck ◽  
Maureen Durkin ◽  
Matthew Maenner ◽  
Craig Newschaffer ◽  
David S. Mandell ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Population Based ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Surveillance Study

scholarly journals Social (pragmatic) communication disorders and autism spectrum disorder

2015 ◽  
Vol 101 (8) ◽  
pp. 745-751 ◽  
Author(s):  
Gillian Baird ◽  
Courtenay Frazier Norbury
Keyword(s):  
Autism Spectrum Disorder ◽  
Language Impairment ◽  
Communication Disorders ◽  
International Classification Of Diseases ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Pragmatic Language ◽  
Clinical Disorder ◽  
Dsm 5 ◽  
Pragmatic Communication

Changes have been made to the diagnostic criteria for autism spectrum disorder (ASD) in the recent revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), and similar changes are likely in the WHO International Classification of Diseases (ICD-11) due in 2017. In light of these changes, a new clinical disorder, social (pragmatic) communication disorder (SPCD), was added to the neurodevelopmental disorders section of DSM-5. This article describes the key features of ASD, SPCD and the draft ICD-11 approach to pragmatic language impairment, highlighting points of overlap between the disorders and criteria for differential diagnosis.

164 Hispanic Ethnicity and Socioeconomic Status are Independently Associated with Improved Prognosis in Glioblastoma Patients

Neurosurgery ◽  
2017 ◽  
Vol 64 (CN_suppl_1) ◽  
pp. 241-241
Author(s):  
Kevin J Moore ◽  
Angela Richardson ◽  
Tulay Koru-Sengul ◽  
Michael E Ivan
Keyword(s):  
Socioeconomic Status ◽  
Race And Ethnicity ◽  
Cox Regression ◽  
The United States ◽  
Population Based ◽  
Epidemiologic Studies ◽  
Hispanic Ethnicity ◽  
Cancer Data ◽  
Hispanic Patients

Abstract INTRODUCTION Significant racial and social disparities have previously been identified in outcomes from glioblastoma. Although some epidemiologic studies have shown Hispanic ethnicity to be protective, other studies have not replicated this finding. As many studies do not consider race separately from ethnicity, the role of Hispanic ethnicity in glioblastoma survival is not well understood. Florida has one of the largest Hispanic populations in the United States. Using a population-based cancer database, this study examines sociodemographic and survival disparities in glioblastoma patients. METHODS Data from the Florida Cancer Data System (FCDS) and the US Census were linked for adult (>18 yrs) glioblastoma patients to determine disease burden and survival. A multivariable Cox regression model was used to model patient survival adjusting for sociodemographic, tumor, and clinical characteristics. Adjusted hazard ratios (aHR) and 95% confidence intervals (95% CI) were calculated for overall sample. All statistical analyses were completed with SAS v.9.4. RESULTS >In total, 16,180 Florida adults were diagnosed with glioblastoma between 1981 and 2013. The majority were male (56.0%) and white (93.0%), and 11.2% of glioblastoma patients identified as Hispanic with 2.4% self-identifying as Cuban. Hispanics had significantly better survival compared to non-Hispanics (aHR 0.84; 95% CI 0.78 0.90). Current smokers fared significantly worse (aHR 1.11; 95% CI 1.04 1.18). Higher socioeconomic status was also associated with increased survival (aHR 0.91, 95% CI 0.84 0.99). Younger age at diagnosis, surgical resection, chemotherapy, radiation therapy, and female sex were also associated with significantly improved outcomes. CONCLUSION This study demonstrates clear sociodemographic and survival disparities for glioblastoma patients. This analysis considers race and ethnicity as two distinct variables and shows improved survival outcomes for Hispanic patients. Additionally patients from neighborhoods with higher socioeconomic status have increased survival. Further analysis is needed to assess the role of histologic and molecular subtypes in these ethnic groups.

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