To study the national prevalence of 10 developmental disabilities in US children aged 3 to 17 years and explore changes over time by associated demographic and socioeconomic characteristics, using ...the National Health Interview Survey.
Data come from the 2009 to 2017 National Health Interview Survey, a nationally representative survey of the civilian noninstitutionalized population. Parents reported physician or other health care professional diagnoses of attention-deficit/hyperactivity disorder; autism spectrum disorder; blindness; cerebral palsy; moderate to profound hearing loss; learning disability; intellectual disability; seizures; stuttering or stammering; and other developmental delays. Weighted percentages for each of the selected developmental disabilities and any developmental disability were calculated and stratified by demographic and socioeconomic characteristics.
From 2009 to 2011 and 2015 to 2017, there were overall significant increases in the prevalence of any developmental disability (16.2%-17.8%,
< .001), attention-deficit/hyperactivity disorder (8.5%-9.5%,
< .01), autism spectrum disorder (1.1%-2.5%,
< .001), and intellectual disability (0.9%-1.2%,
< .05), but a significant decrease for any other developmental delay (4.7%-4.1%,
< .05). The prevalence of any developmental disability increased among boys, older children, non-Hispanic white and Hispanic children, children with private insurance only, children with birth weight ≥2500 g, and children living in urban areas and with less-educated mothers.
The prevalence of developmental disability among US children aged 3 to 17 years increased between 2009 and 2017. Changes by demographic and socioeconomic subgroups may be related to improvements in awareness and access to health care.
Objective: This study aimed to investigate the longitudinal course of daily living skills in a large, community-based sample of adolescents and adults with autism spectrum disorders (ASD) over a ...10-year period. Method: Adolescents and adults with ASD (n = 397) were drawn from an ongoing, longitudinal study of individuals with ASD and their families. A comparison group of 167 individuals with Down syndrome (DS) were drawn from a linked longitudinal study. The Waisman Activities of Daily Living Scale was administered four times over a 10-year period. Results: We used latent growth curve modeling to examine change in daily living skills. Daily living skills improved for the individuals with ASD during adolescence and their early 20s, but plateaued during their late 20s. Having an intellectual disability was associated with lower initial levels of daily living skills and a slower change over time. Individuals with DS likewise gained daily living skills over time, but there was no significant curvature in the change. Conclusions: Future research should explore what environmental factors and interventions may be associated with continued gains in daily living skills for adults with ASD. (Contains 3 tables and 3 figures.)
This study was designed to evaluate the hypothesis that the prevalence of autism spectrum disorder (ASD) among children in the United States is positively associated with socioeconomic status (SES).
...A cross-sectional study was implemented with data from the Autism and Developmental Disabilities Monitoring Network, a multiple source surveillance system that incorporates data from educational and health care sources to determine the number of 8-year-old children with ASD among defined populations. For the years 2002 and 2004, there were 3,680 children with ASD among a population of 557,689 8-year-old children. Area-level census SES indicators were used to compute ASD prevalence by SES tertiles of the population.
Prevalence increased with increasing SES in a dose-response manner, with prevalence ratios relative to medium SES of 0.70 (95% confidence interval CI 0.64, 0.76) for low SES, and of 1.25 (95% CI 1.16, 1.35) for high SES, (P<0.001). Significant SES gradients were observed for children with and without a pre-existing ASD diagnosis, and in analyses stratified by gender, race/ethnicity, and surveillance data source. The SES gradient was significantly stronger in children with a pre-existing diagnosis than in those meeting criteria for ASD but with no previous record of an ASD diagnosis (p<0.001), and was not present in children with co-occurring ASD and intellectual disability.
The stronger SES gradient in ASD prevalence in children with versus without a pre-existing ASD diagnosis points to potential ascertainment or diagnostic bias and to the possibility of SES disparity in access to services for children with autism. Further research is needed to confirm and understand the sources of this disparity so that policy implications can be drawn. Consideration should also be given to the possibility that there may be causal mechanisms or confounding factors associated with both high SES and vulnerability to ASD.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
We computed ASD prevalence and 95% confidence intervals (CIs) from population-based surveillance, census, and survey data. We defined SES categories by using area-level education, income, and poverty indicators. We ascertained ASD in 13 396 of 1 308 641 8-year-old children under surveillance.
The prevalence of ASD increased with increasing SES during each surveillance year among White, Black, and Hispanic children. The prevalence difference between high- and low-SES groups was relatively constant over time (3.9/1000 95% CI = 3.3, 4.5 in 2002 and 4.1/1000 95% CI = 3.6, 4.6 in the period 2006-2010). Significant racial/ethnic differences in ASD prevalence remained after stratification by SES.
A positive SES gradient in ASD prevalence according to US surveillance data prevailed between 2002 and 2010, and racial and ethnic disparities in prevalence persisted during this time among low-SES children.
The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random ...forests methods have shown promise in speeding up this process, but they lag behind human classification accuracy by about 5%. We explore whether more recently available document classification algorithms can close this gap.
Using data gathered from a single surveillance site, we applied 8 supervised learning algorithms to predict whether children meet the case definition for ASD based solely on the words in their evaluations. We compared the algorithms' performance across 10 random train-test splits of the data, using classification accuracy, F1 score, and number of positive calls to evaluate their potential use for surveillance.
Across the 10 train-test cycles, the random forest and support vector machine with Naive Bayes features (NB-SVM) each achieved slightly more than 87% mean accuracy. The NB-SVM produced significantly more false negatives than false positives (P = 0.027), but the random forest did not, making its prevalence estimates very close to the true prevalence in the data. The best-performing neural network performed similarly to the random forest on both measures.
The random forest performed as well as more recently available models like the NB-SVM and the neural network, and it also produced good prevalence estimates. NB-SVM may not be a good candidate for use in a fully-automated surveillance workflow due to increased false negatives. More sophisticated algorithms, like hierarchical convolutional neural networks, may not be feasible to train due to characteristics of the data. Current algorithms might perform better if the data are abstracted and processed differently and if they take into account information about the children in addition to their evaluations.
Deep learning models performed similarly to traditional machine learning methods at predicting the clinician-assigned case status for CDC's autism surveillance system. While deep learning methods had limited benefit in this task, they may have applications in other surveillance systems.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Objectives:
Autism spectrum disorder (autism) is a heterogeneous condition that poses challenges in describing the needs of individuals with autism and making prognoses about future outcomes. We ...applied a newly proposed definition of profound autism to surveillance data to estimate the percentage of children with autism who have profound autism and describe their sociodemographic and clinical characteristics.
Methods:
We analyzed population-based surveillance data from the Autism and Developmental Disabilities Monitoring Network for 20 135 children aged 8 years with autism during 2000-2016. Children were classified as having profound autism if they were nonverbal, were minimally verbal, or had an intelligence quotient <50.
Results:
The percentage of 8-year-old children with profound autism among those with autism was 26.7%. Compared with children with non–profound autism, children with profound autism were more likely to be female, from racial and ethnic minority groups, of low socioeconomic status, born preterm or with low birth weight; have self-injurious behaviors; have seizure disorders; and have lower adaptive scores. In 2016, the prevalence of profound autism was 4.6 per 1000 8-year-olds. The prevalence ratio (PR) of profound autism was higher among non-Hispanic Asian/Native Hawaiian/Other Pacific Islander (PR = 1.55; 95 CI, 1.38-1.73), non-Hispanic Black (PR = 1.76; 95% CI, 1.67-1.86), and Hispanic (PR = 1.50; 95% CI, 0.88-1.26) children than among non-Hispanic White children.
Conclusions:
As the population of children with autism continues to change, describing and quantifying the population with profound autism is important for planning. Policies and programs could consider the needs of people with profound autism across the life span to ensure their needs are met.
This study evaluated independent effects of maternal and paternal age on risk of autism spectrum disorder. A case-cohort design was implemented using data from 10 US study sites participating in the ...Centers for Disease Control and Prevention's Autism and Developmental Disabilities Monitoring Network. The 1994 birth cohort included 253,347 study-site births with complete parental age information. Cases included 1,251 children aged 8 years with complete parental age information from the same birth cohort and identified as having an autism spectrum disorder based on Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision criteria. After adjustment for the other parent's age, birth order, maternal education, and other covariates, both maternal and paternal age were independently associated with autism (adjusted odds ratio for maternal age ≥35 vs. 25–29 years = 1.3, 95% confidence interval: 1.1, 1.6; adjusted odds ratio for paternal age ≥40 years vs. 25–29 years = 1.4, 95% confidence interval: 1.1, 1.8). Firstborn offspring of 2 older parents were 3 times more likely to develop autism than were third- or later-born offspring of mothers aged 20–34 years and fathers aged <40 years (odds ratio = 3.1, 95% confidence interval: 2.0, 4.7). The increase in autism risk with both maternal and paternal age has potential implications for public health planning and investigations of autism etiology.
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