Autism Spectrum Disorder Among US Children (2002–2010): Socioeconomic, Racial, and Ethnic Disparities

Objectives. To describe the association between indicators of socioeconomic status (SES) and the prevalence of autism spectrum disorder (ASD) in the United States during the period 2002 to 2010, when overall ASD prevalence among children more than doubled, and to determine whether SES disparities account for ongoing racial and ethnic disparities in ASD prevalence

Comparison of autism spectrum disorder surveillance status based on two different diagnostic schemes: Findings from the Metropolitan Atlanta Developmental Disabilities Surveillance Program, 2012.

For the first time, the Autism and Developmental Disabilities Monitoring Network (ADDM) at the Centers for Disease Control and Prevention (CDC) reported prevalence estimates based on two different diagnostic schemes in the 2014 surveillance period. Results found substantial agreement between surveillance case status based on Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition-Text Revision (DSM-IV-TR) criteria and DSM-5 criteria ASD (kappa = 0.85). No study has replicated this agreement in another independent sample of surveillance records. The objectives of this study were to (1) replicate agreement between surveillance status based on DSM-IV-TR criteria and DSM-5 criteria for ASD, (2) quantify the number of children who met surveillance status based on only DSM-IV-TR criteria and only DSM-5 criteria for ASD, and (3) evaluate differences in characteristics of these latter two groups of children. The study sample was 8-year-old children who had health and education records reviewed for ASD surveillance in metropolitan Atlanta, GA in the 2012 surveillance year. Results found substantial agreement between child's surveillance status using DSM-IV-TR criteria and DSM-5 criteria for ASD (kappa = 0.80). There were no differences in child race/ethnicity, child sex, or intellectual disability between surveillance status defined by DSM-IV-TR criteria and that defined by DSM-5 criteria. Children who met surveillance status based on DSM-IV-TR criteria, but not DSM-5 criteria, were more likely to have developmental concerns and evaluations in the first three years. Children who met surveillance status based on DSM-5 criteria, but not DSM-IV-TR criteria, were more likely to have been receiving autism-related services or previously diagnosed with ASD. These results suggest that surveillance status of ASD based on DSM-5 criteria is largely comparable to that based on DSM-IV-TR criteria, and identifies children with similar demographic and intellectual characteristics

Critical congenital heart disease newborn screening implementation: lessons learned

Introduction The purpose of this article is to present the collective experiences of six federally-funded critical congenital heart disease (CCHD) newborn screening implementation projects to assist federal and state policy makers and public health to implement CCHD screening. Methods A qualitative assessment and summary from six demonstration project grantees and other state representatives involved in the implementation of CCHD screening programs are presented in the following areas: legislation, provider and family education, screening algorithms and interpretation, data collection and quality improvement, telemedicine, home and rural births, and neonatal intensive care unit populations. Results The most common challenges to implementation include: lack of uniform legislative and statutory mandates for screening programs, lack of funding/resources, difficulty in screening algorithm interpretation, limited availability of pediatric echocardiography, and integrating data collection and reporting with existing newborn screening systems. Identified solutions include: programs should consider integrating third party insurers and other partners early in the legislative/statutory process; development of visual tools and language modification to assist in the interpretation of algorithms, training programs for adult sonographers to perform neonatal echocardiography, building upon existing newborn screening systems, and using automated data transfer mechanisms. Discussion Continued and expanded surveillance, research, prevention and education efforts are needed to inform screening programs, with an aim to reduce morbidity, mortality and other adverse consequences for individuals and families affected by CCHD

Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder

<div><p>The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts population-based surveillance of autism spectrum disorder (ASD) among 8-year old children in multiple US sites. To classify ASD, trained clinicians review developmental evaluations collected from multiple health and education sources to determine whether the child meets the ASD surveillance case criteria. The number of evaluations collected has dramatically increased since the year 2000, challenging the resources and timeliness of the surveillance system. We developed and evaluated a machine learning approach to classify case status in ADDM using words and phrases contained in children’s developmental evaluations. We trained a random forest classifier using data from the 2008 Georgia ADDM site which included 1,162 children with 5,396 evaluations (601 children met ADDM ASD criteria using standard ADDM methods). The classifier used the words and phrases from the evaluations to predict ASD case status. We evaluated its performance on the 2010 Georgia ADDM surveillance data (1,450 children with 9,811 evaluations; 754 children met ADDM ASD criteria). We also estimated ASD prevalence using predictions from the classification algorithm. Overall, the machine learning approach predicted ASD case statuses that were 86.5% concordant with the clinician-determined case statuses (84.0% sensitivity, 89.4% predictive value positive). The area under the resulting receiver-operating characteristic curve was 0.932. Algorithm-derived ASD “prevalence” was 1.46% compared to the published (clinician-determined) estimate of 1.55%. Using only the text contained in developmental evaluations, a machine learning algorithm was able to discriminate between children that do and do not meet ASD surveillance criteria at one surveillance site.</p></div

Histograms of prediction scores (x-axis) compared to clinician-assigned surveillance case definition (blue: autism spectrum disorder (ASD), red: non-ASD).

<p>Horizontal bar represents classification score threshold. Upper panel: classifications for 2008 (training) data. Bottom panel: 2010 (test) data, discordant classifications are highlighted in a lighter shade of blue or red.</p

The Role of Socio-economic Status and Perinatal Factors in Racial Disparities in the Risk of Cerebral Palsy

Aim: To determine whether racial disparities in cerebral palsy (CP) risk among US children persist after controlling for socio-economic status (SES) (here indicated by maternal education) and perinatal risk factors. Method: A population-based birth cohort study was conducted using the Autism and Developmental Disabilities Monitoring Network surveillance and birth data for 8-year-old children residing in multi-county areas in Alabama, Georgia, Missouri, and Wisconsin between 2002 and 2008. The birth cohort comparison group included 458 027 children and the case group included 1570 children with CP, 1202 with available birth records. χ2 tests were performed to evaluate associations and logistic regression was used to calculate relative risks (RR) and adjusted odds ratios (OR) with 95% confidence intervals (CI). Results: The risk of spastic CP was more than 50% higher for black versus white children (RR 1.52, 95% CI 1.33–1.73), and this greater risk persisted after adjustment for SES (OR 1.35, 95% CI 1.18–1.55), but not after further adjustment for preterm birth and size for gestational age. The protective effect of maternal education remained after adjustment for race/ethnicity and perinatal factors. Interpretation: Maternal education appears to independently affect CP risk but does not fully explain existing racial disparities in CP prevalence in the US

Comparison between clinician-assigned surveillance autism spectrum disorder (ASD) case status and predictions from random forest algorithm.

<p>Comparison between clinician-assigned surveillance autism spectrum disorder (ASD) case status and predictions from random forest algorithm.</p

Characteristics of children, by algorithm-clinician concordance on autism spectrum disorder (ASD) case status.

<p>Characteristics of children, by algorithm-clinician concordance on autism spectrum disorder (ASD) case status.</p

Autism spectrum disorder (ASD) prevalence per 1,000 children (with 95% confidence interval) for 2010 Georgia Autism and Developmental Disabilities Monitoring Network site: comparison between published and algorithm-derived estimates.

<p>Autism spectrum disorder (ASD) prevalence per 1,000 children (with 95% confidence interval) for 2010 Georgia Autism and Developmental Disabilities Monitoring Network site: comparison between published and algorithm-derived estimates.</p