A public health collaboration for the surveillance of autism spectrum disorders

Prevalence and Characteristics of Adolescents with Autism Spectrum Disorder in the New York-New Jersey Metropolitan Area

PURPOSE

Almost all epidemiologic studies estimating autism spectrum disorder (ASD) prevalence have focused on school-age children. This study provides the first population-based data on the prevalence and expression of ASD among adolescents in a large US metropolitan region.

METHODS

Active multiple source ASD surveillance of adolescents aged 16-years was conducted according to the Autism and Developmental Disabilities Monitoring (ADDM) Network method in a four-county New Jersey metropolitan region. Prevalence estimates are provided, characteristics are described and comparison of the distribution and characteristics of ASD is offered for this cohort, at 8 and 16-years.

RESULTS

ASD prevalence was 17.7 per 1000 (95% CI: 16.3-19.2)]. One-in-55 males and one in 172 females were identified with ASD. High-SES was positively associated with ASD and White adolescents had higher ASD prevalence (22.2 per 1000) than Hispanic adolescents (13.1 per 1000). One in four study-confirmed individuals with ASD did not have an ASD diagnosis. A majority of ASD adolescents (58.8%) had a co-occurring neuropsychiatric disorder. White and High-SES individuals had greater likelihood of co-occurring disorder. The demographic distribution and functional profile of ASD was similar in this cohort at 8 and 16-years.

CONCLUSION

Approximately one-in-55 adolescents in our area had ASD, in 2014, and one-in-4 16-year-olds with ASD was not diagnosed. A majority (3-in-5) of the adolescents with ASD had a co-occurring neuropsychiatric disorder. ASD under-identification and the high frequency of co-disorders in adolescents with ASD pose significant challenges to care and support.

Predictors of an earlier diagnosis of Autism Spectrum Disorder in children and adolescents: a systematic review (1987-2017)

Autism Spectrum Disorder (ASD) is an early onset neurodevelopmental disorder in which the first signs generally emerge at approximately 12 months of age but its diagnosis is feasible only from the age of 18 months. According to the literature, the average age of diagnosis ranges from 2.7 to 7.2 years, which raises the question of factors associated with early diagnosis as a condition for early intervention. In this systematic review, we aim to identify clinical, social, and environmental factors associated with the age at which the diagnosis of ASD is confirmed in children. A literature search was performed in the Pubmed, Web of Sciences, PsycInfo, and Cochrane databases. Among the 530 publications identified, 50 were selected according to the inclusion criteria. This review focuses on studies conducted in 21 countries using data collected over a period from 1987 to 2017. These studies were published before December 31st, 2019. The results suggest that the diagnosis of ASD occurs earlier if there is a delay in social communication or the presence of intellectual disability. There is a low level of evidence concerning associations between the age at diagnosis and sex, race, parental education, or socioeconomic status and accessibility to health care. Further studies using large and well-characterized data sets are needed to simultaneously explore clinical and socio-environmental factors involved in early diagnosis.

Prevalence and Disparities in the Detection of Autism Without Intellectual Disability

BACKGROUND

Intellectual ability predicts functional outcomes for children with autism spectrum disorder (ASD). It is essential to classify ASD children with and without intellectual disability (ID) to aid etiological research, provide services, and inform evidence-based educational and health planning.

METHODS

Using a cross-sectional study design, data from 2000 to 2016 active ASD surveillance among 8-year-olds residing in the New York-New Jersey Metropolitan Area were analyzed to determine ASD prevalence with and without ID. Multivariable Poisson regression models were used to identify trends for ASD with ID (ASD-I) and without ID (ASD-N).

RESULTS

Overall, 4661 8-year-olds were identified with ASD. Those that were ASI-I were 1505 (32.3%) and 2764 (59.3%) were ASD-N. Males were 3794 (81.4%), 946 (20.3%) were non-Hispanic Black (Black), 1230 (26.4%) were Hispanic, and 2114 (45.4%) were non-Hispanic white (white). We observed 2-fold and 5-fold increases in the prevalence of ASD-I and ASD-N, respectively, from 2000-2016. Black children were 30% less likely to be identified with ASD-N compared with white children. Children residing in affluent areas were 80% more likely to be identified with ASD-N compared with children in underserved areas. A greater proportion of children with ASD-I resided in vulnerable areas compared with children with ASD-N. Males had higher prevalence compared with females regardless of ID status; however, male-to-female ratios were slightly lower among ASD-I compared with ASD-N cases.

CONCLUSIONS

One-in-3 children with ASD had ID. Disparities in the identification of ASD without ID were observed among Black and Hispanic children as well as among children residing in underserved areas.

Defining in Detail and Evaluating Reliability of DSM-5 Criteria for Autism Spectrum Disorder (ASD) Among Children

This paper describes a process to define a comprehensive list of exemplars for seven core Diagnostic and Statistical Manual (DSM) diagnostic criteria for autism spectrum disorder (ASD), and report on interrater reliability in applying these exemplars to determine ASD case classification. Clinicians completed an iterative process to map specific exemplars from the CDC Autism and Developmental Disabilities Monitoring (ADDM) Network criteria for ASD surveillance, DSM-5 text, and diagnostic assessments to each of the core DSM-5 ASD criteria. Clinicians applied the diagnostic exemplars to child behavioral descriptions in existing evaluation records to establish initial reliability standards and then for blinded clinician review in one site (phase 1) and for two ADDM Network surveillance years (phase 2). Interrater reliability for each of the DSM-5 diagnostic categories and overall ASD classification was high (defined as very good .60-.79 to excellent ≥ .80 Kappa values) across sex, race/ethnicity, and cognitive levels for both phases. Classification of DSM-5 ASD by mapping specific exemplars from evaluation records by a diverse group of clinician raters is feasible and reliable. This framework provides confidence in the consistency of prevalence classifications of ASD and may be further applied to improve consistency of ASD diagnoses in clinical settings.

Use of selective serotonin and norepinephrine reuptake inhibitors (SNRIs) in the treatment of autism spectrum disorder (ASD), comorbid psychiatric disorders and ASD-associated symptoms: a clinical review

It is challenging to treat symptoms of autism spectrum disorder (ASD), comorbid psychiatric disorders and ASD-associated symptoms. Some of the commonly used medications to treat these can, and frequently do have serious adverse side effects. Therefore, it is important to identify medications that are effective and with fewer side effects and negative outcomes. In this review, we looked at current evidence available for using the serotonin and norepinephrine reuptake inhibitors (SNRIs) class of medications in treating some of these often difficult to treat symptoms and behaviors. An extensive literature search was conducted using EBSCO.host. Our search algorithm identified 130 articles, 6 of which were deemed to meet criteria for the purpose of this review. Each of these six articles was independently reviewed and critically appraised. As a prototype of the SNRIs family, venlafaxine was found to be a useful adjuvant in children and adults with ASD for the treatment of self-injurious behaviors, aggression, and ADHD symptoms when used in doses lower than its antidepressant dosage. However, duloxetine was not found to show any added benefit in treatment of any of the comorbid symptoms and behaviors in ASD when compared to other antidepressants. On the other hand, milnacipran was reported to produce improvements in impulsivity, hyperactivity symptoms, and social functioning through reduction of inattention of ADHD when comorbid with ASD. Overall, SNRIs were shown variable effectiveness in treatment of these comorbid symptoms and behaviors in ASD.

Oral Health Preventive Program in Patients with Autism Spectrum Disorder

The aim of the study was to evaluate clinical hygienic parameters, patient collaboration, and dental habits in patients with ASD (autism spectrum disorder) before and after a tailored prevention program. A total of 100 patients (78 males and 22 females, mean age 8 ± 0.7 years old) was recruited, with ages ranging from 7 to 16 years old, and diagnoses of ASD. We evaluated the plaque index (IP), gingival index (IG), the dmft/DMFT, the frequency of tooth brushing, and the frequency of snacks for each patient. Patient behaviour was evaluated with the Frankl scale, and each patient was individually reassessed after five visits from the first one by the same operator. The t test was used to compare the parameters before and after the inclusion in the dedicated dental pathway. From T1 to T2 we found a significant improvement of the IP (p < 0.001), IG (p < 0.001), and the frequency of tooth brushing (p < 0.001). Concerning the frequency of snacks and the parameter dmft/DMFT, the differences in the observed averages were not significant (p > 0.05). The difference in collaboration between T1 and T2 evaluated by the Frankl scale was statistically significant (p < 0.001). It was found that the prevention program allowed a significant improvement in both clinical parameters and patient behaviour. The personalized digital supports can have a key role for success in familiarization and desensitization processes of patients affected by ASD, leading an increase in their collaboration.

Patterns of Special Education Eligibility and Age of First Autism Spectrum Disorder (ASD) Identification Among US Children with ASD

The study examined timing of autism spectrum disorder (ASD) identification in education versus health settings for 8-year-old children with ASD identified through records-based surveillance. The study also examined type of ASD symptoms noted within special education evaluations. Results indicated that children with records from only education sources had a median time to identification of ASD over a year later than children with records from health sources. Black children were more likely than White children to have records from only education sources. Restricted and repetitive behaviors were less frequently documented in educational evaluations resulting in developmental delay eligibility compared to specific ASD eligibility among children with ASD. Future research could explore strategies reduce age of identification in educational settings and increase equitable access to health evaluations.

Applying a public health approach to autism research: A framework for action

Most published autism research, and the funding that supports it, remains focused on basic and clinical science. However, the public health impact of autism drives a compelling argument for utilizing a public health approach to autism research. Fundamental to the public health perspective is a focus on health determinants to improve quality of life and to reduce the potential for adverse outcomes across the general population, including in vulnerable subgroups. While the public health research process can be conceptualized as a linear, 3-stage path consisting of discovery - testing - translation/dissemination/implementation, in this paper we propose an integrated, cyclical research framework to advance autism public health objectives in a more comprehensive manner. This involves discovery of primary, secondary and tertiary determinants of health in autism; and use of this evidence base to develop and test detection, intervention, and dissemination strategies and the means to implement them in 'real world' settings. The proposed framework serves to facilitate identification of knowledge gaps, translational barriers, and shortfalls in implementation; guides an iterative research cycle; facilitates purposeful integration of stakeholders and interdisciplinary researchers; and may yield more efficient achievement of improved health and well-being among persons on the autism spectrum at the population-level. LAY SUMMARY: Scientists need better ways to identify and address gaps in autism research, conduct research with stakeholders, and use findings to improve the lives of autistic people. We recommend an approach, based in public health science, to guide research in ways that might impact lives more quickly.

Prevalence of autism spectrum disorder in a large, diverse metropolitan area: Variation by sociodemographic factors

Autism spectrum disorder (ASD) prevalence estimates have varied by region. In this study, ASD prevalence, based on active case finding from multiple sources, was determined at the county and school district levels in the New Jersey metropolitan area. Among children born in 2008, residing in a four-county area and enrolled in public school in 2016, ASD prevalence was estimated to be 36 per 1000, but was significantly higher in one region-54 per 1000 and greater than 70 per 1000, in multiple school districts. Significant variation in ASD prevalence by race/ethnicity, socioeconomic status (SES), and school district size was identified. Highest prevalence was in mid-SES communities, contrary to expectation. Prevalence among Hispanic children was lower than expected, indicating a disparity in identification. Comprehensive surveillance should provide estimates at the county and town levels to appreciate ASD trends, identify disparities in detection or treatment, and explore factors influencing change in prevalence. LAY SUMMARY: We found autism prevalence to be 3.6% in New Jersey overall, but higher in one region (5.4%) and in multiple areas approaching 7.0%. We identified significant variation in autism spectrum disorder (ASD) prevalence by race/ethnicity, socioeconomic status (SES) and school district size. Mapping prevalence in smaller, well-specified, regions may be useful to better understand the true scope of ASD, disparities in ASD detection and the factors impacting ASD prevalence estimation.

The Role of Maternal Immune Activation in the Pathogenesis of Autism: A Review of the Evidence, Proposed Mechanisms and Implications for Treatment

Autism spectrum disorder (ASD) is a neurodevelopmental disease that is characterized by a deficit in social interactions and communication, as well as repetitive and restrictive behaviors. Increasing lines of evidence suggest an important role for immune dysregulation and/or inflammation in the development of ASD. Recently, a relationship between inflammation, oxidative stress, and mitochondrial dysfunction has been reported in the brain tissue of individuals with ASD. Some recent studies have also reported oxidative stress and mitochondrial abnormalities in animal models of maternal immune activation (MIA). This review is focused on the hypothesis that MIA induces microglial activation, oxidative stress, and mitochondrial dysfunction, a deleterious trio in the brain that can lead to neuroinflammation and neurodevelopmental pathologies in offspring. Infection during pregnancy activates the mother’s immune system to release proinflammatory cytokines, such as IL-6, TNF-α, and others. Furthermore, these cytokines can directly cross the placenta and enter the fetal circulation, or activate resident immune cells, resulting in an increased production of proinflammatory cytokines, including IL-6. Proinflammatory cytokines that cross the blood–brain barrier (BBB) may initiate a neuroinflammation cascade, starting with the activation of the microglia. Inflammatory processes induce oxidative stress and mitochondrial dysfunction that, in turn, may exacerbate oxidative stress in a self-perpetuating vicious cycle that can lead to downstream abnormalities in brain development and behavior.

The influence of race on parental beliefs and concerns during an autism diagnosis: A mixed-method analysis

LAY ABSTRACT

The goal of this study was to examine if there were differences between races in parental concern and belief about autism spectrum disorder (ASD) and the perspectives of clinicians. We studied 489 children with ASD who were having their first evaluation at an ASD clinic. Parents of White children most often believed that their child had ASD. However, White children whose parents believed the child had ASD were less severe in their symptoms. Parents of Black/African American or Hispanic children were more likely to have concerns about communication than parents of White children. In Hispanic families, parental concern about social communication was related to more severe symptoms in children. We discuss the implications of our findings for diagnosis.

Age of Initial Identification of Autism Spectrum Disorder in a Diverse Urban Sample

This paper examines age of autism spectrum disorder (ASD) identification and related factors in a diverse urban sample, focusing on ASD identification in the East African Somali community. The overall average age of initial ASD identification was 4.8 years. Somali children received an initial clinical diagnosis of Autistic Disorder later than White children, and Somali children diagnosed with ASD born outside of Minnesota (MN) received their first comprehensive evaluation later than Somali children diagnosed with ASD born in MN. Most children had noted developmental concerns before age 3, with no significant racial or ethnic differences in those concerns. The current study contributes to a limited number of studies on early ASD identification in culturally and linguistically diverse populations.

Oral Health Status of Children with Autism in Central Italy

Children with autism spectrum disorder (ASD) have significantly higher prevalence and caries severity compared to the average population. Knowledge about the oral health indices of children with this mental disorder is key to designing efficient plans of intervention. This paper reports the results of a study on the oral health status of children with ASD in central Italy. This is the first study of this type in Italy. The sample consists of 229 autistic children aged between 5 and 14 years, attending the Unit of Special Needs Policlinico Umberto I in Rome. Each patient received an intraoral examination to investigate decayed, missing, and filled teeth as well as periodontal status. Information on demographic attributes, dietary habits, medical history, and child’s cooperativeness at the first visit was also recorded. Of the participants, 79.26% presented signs of gingivitis and about 90% of them had plaque. Caries prevalence was 66.38%. The average of the total number of decayed, missing, and filled teeth in the permanent and primary dentition was 2.91. Among the factors considered, only dietary habits and the periodontal indices showed statistically significant association with caries prevalence and caries severity. Despite the selection bias, that prevents us to interpret the results presented as epidemiological evidence, our study suggests that children with ASD in central Italy represent a population at risk.

Disparities in Documented Diagnoses of Autism Spectrum Disorder Based on Demographic, Individual, and Service Factors

The objectives of our study were to (a) report how many children met an autism spectrum disorder (ASD) surveillance definition but had no clinical diagnosis of ASD in health or education records and (b) evaluate differences in demographic, individual, and service factors between children with and without a documented ASD diagnosis. ASD surveillance was conducted in selected areas of Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin. Children were defined as having ASD if sufficient social and behavioral deficits and/or an ASD diagnosis were noted in health and/or education records. Among 4,498 children, 1,135 (25%) had ASD indicators without having an ASD diagnosis. Of those 1,135 children without a documented ASD diagnosis, 628 (55%) were not known to receive ASD services in public school. Factors associated with not having a clinical diagnosis of ASD were non-White race, no intellectual disability, older age at first developmental concern, older age at first developmental evaluation, special education eligibility other than ASD, and need for fewer supports. These results highlight the importance of reducing disparities in the diagnosis of children with ASD characteristics so that appropriate interventions can be promoted across communities. Autism Res 2020, 13: 464-473. © 2019 International Society for AutismResearch,Wiley Periodicals, Inc. LAY SUMMARY: Children who did not have a clinical diagnosis of autism spectrum disorder (ASD) documented in health or education records were more likely to be non-White and have fewer developmental problems than children with a clinical diagnosis of ASD. They were brought to the attention of healthcare providers at older ages and needed fewer supports than children with a clinical diagnosis of ASD. All children with ASD symptoms who meet diagnostic criteria should be given a clinical diagnosis so they can receive treatment specific to their needs.

Concordance between a U.S. Educational Autism Classification and the Autism Diagnostic Observation Schedule

States in the United States differ in how they determine special education eligibility for autism services. Few states include an autism-specific diagnostic tool in their evaluation. In research, the Autism Diagnostic Observation Schedule (ADOS for first edition, ADOS-2 for second edition) is considered the gold-standard autism assessment. The purpose of this study was to estimate the proportion of children with an educational classification of autism who exceed the ADOS/ADOS-2 threshold for autism spectrum (concordance rate). Data were drawn from 4 school-based studies across 2 sites (Philadelphia, Pennsylvania, and San Diego, California). Participants comprised 627 children (2-12 years of age; 83% male) with an autism educational classification. Analyses included (a) calculating the concordance rate between educational and ADOS/ADOS-2 classifications and (b) estimating the associations between concordance and child's cognitive ability, study site, and ADOS/ADOS-2 administration year using logistic regression. More San Diego participants (97.5%, all assessed with the ADOS-2) met ADOS/ADOS-2 classification than did Philadelphia participants assessed with the ADOS-2 (92.2%) or ADOS (82.9%). Children assessed more recently were assessed with the ADOS-2; this group was more likely to meet ADOS/ADOS-2 classification than the group assessed longer ago with the ADOS. Children with higher IQ were less likely to meet ADOS/ADOS-2 classification. Most children with an educational classification of autism meet ADOS/ADOS-2 criteria, but results differ by site and by ADOS version and/or recency of assessment. Educational classification may be a reasonable but imperfect measure to include children in community-based trials.

Relationship Between Advanced Maternal Age and Timing of First Developmental Evaluation in Children with Autism

OBJECTIVE

Mothers of advanced maternal age (AMA) at childbirth (age ≥35 years) may have different perceptions of autism spectrum disorder (ASD) risk, independent of sociodemographic factors, that may affect ASD identification. We aimed to estimate associations between AMA and both age of a child's first evaluation noting developmental concerns and time from first evaluation to first ASD diagnosis.

METHODS

We used data for 8-year-olds identified with ASD in the 2008 to 2012 Autism and Developmental Disabilities Monitoring Network. We estimated differences in age at first evaluation noting developmental concerns and time to first ASD diagnosis by AMA using quantile and Cox regression.

RESULTS

Of 10,358 children with ASD, 19.7% had mothers of AMA. AMA was associated with higher educational attainment and previous live births compared with younger mothers. In unadjusted analyses, AMA was associated with earlier first evaluation noting developmental concerns (median 37 vs 40 mo) and patterns in time to first evaluation (hazard ratio: 1.12, 95% confidence interval: 1.06-1.18). Associations between AMA and evaluation timing diminished and were no longer significant after adjustment for socioeconomic and demographic characteristics. Children's intellectual disability did not modify associations between AMA and timing of evaluations.

CONCLUSION

Advanced maternal age is a sociodemographic factor associated with younger age of first evaluation noting developmental concerns in children with ASD, but AMA was not independently associated likely, because it is a consequence or cofactor of maternal education and other sociodemographic characteristics. AMA may be a demographic factor to consider when aiming to screen and evaluate children at risk for ASD.

Clinical Profile of Autism Spectrum Disorder in a Pediatric Population from Northern Mexico

Autism spectrum disorder (ASD) is a neurodevelopmental condition classified based on needs of support, in order to address impairments in the areas of social communication and restricted and repetitive behavior. The aim of this work is to describe the main clinical features of the ASD severity levels in a group of Mexican pediatric patients. The results show firstly that this condition was more frequent in males than females. Secondly, an inverse relationship was found between the intellectual coefficient and the level of severity of the disorder. Thirdly, deficits in social reciprocity and communication were more evident in Level 3, than in Levels 1 and 2, while the difference was less evident in restricted and repetitive patterns of behavior.

A comparison of machine learning algorithms for the surveillance of autism spectrum disorder

Objective

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.

Materials and methods

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.

Results

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.

Discussion

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.

Conclusion

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.

Vasopressin in the Amelioration of Social Functioning in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a developmental disability described by diagnostic criteria that comprise deficits in social communication and the existence of repetitive, restricted patterns of behavior, interests, or activities that can last throughout life. Many preclinical studies show the importance of arginine vasopressin (AVP) physiology in social functioning in several mammalian species. Currently, there is a trend to investigate more specific pharmacological agents to improve social functioning in patients with ASD. Neurobiological systems that are crucial for social functioning are the most encouraging conceivable signaling pathways for ASD therapeutic discovery. The AVP signaling pathway is one of the most promising. The purpose of this commentary is to detail the evidence on the use of AVP as an agent that can improve social functioning. The pharmacologic aspects of the drug as well as its potential to ameliorate social functioning characteristics in human and animal studies are described in this manuscript. AVP, especially in its inhaled form, seems to be safe and beneficial in improving social functioning including in children with autism. Larger randomized studies are required to implement a long awaited safe and feasible treatment in people with a deficiency in social functioning.

Utilization and medical costs of outpatient rehabilitation among children with autism spectrum conditions in Taiwan

Background

We examined the utilization of rehabilitation resources among children with autism spectrum condition (ASC), a neurodevelopmental condition, in Taiwan.

Methods

We derived from the National Health Insurance Research Database of Taiwan data pertaining to 3- to 12-year-old children for the period 2008–2010. Based on diagnoses executed in accordance with the International Classification of Diseases, Ninth Revision, Clinical Modification, we classified these data into the ASC and non-ASC groups and analyzed them through multiple linear regression model, negative binomial model, independent sample t testing, and χ² testing.

Results

Compared with the non-ASC group, the ASC group exhibited higher utilization of rehabilitation resources. Because hospitals are constrained by overall expenditure limits, expenditure on rehabilitation resources has plateaued, preventing any increase in the utilization of rehabilitation resources. In our ASC group, preschool-aged children significantly outnumbered (p < 0.001) school-aged children. When stratified by the hospital level, district hospitals reported the highest utilization (p < 0.001). When stratified by region, the highest utilization was in Taipei, whereas the lowest was in the East region (p < 0.001). The total annual cost, average frequency of visits, utilization of rehabilitation resources, and average cost were all affected by such elements as patient demographics, hospital type and location (p < 0.001).

Conclusions

For improving treatment outcomes among children with ASC and decreasing treatment expenditure, policies that promote the timely ASC detection and treatment should be implemented.

Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 4 Years - Early Autism and Developmental Disabilities Monitoring Network, Seven Sites, United States, 2010, 2012, and 2014

Problem/Condition

Autism spectrum disorder (ASD) is estimated to affect up to 3% of children in the United States. Public health surveillance for ASD among children aged 4 years provides information about trends in prevalence, characteristics of children with ASD, and progress made toward decreasing the age of identification of ASD so that evidence-based interventions can begin as early as possible.

Period Covered

2010, 2012, and 2014.

Description of System

The Early Autism and Developmental Disabilities Monitoring (Early ADDM) Network is an active surveillance system that provides biennial estimates of the prevalence and characteristics of ASD among children aged 4 years whose parents or guardians lived within designated sites. During surveillance years 2010, 2012, or 2014, data were collected in seven sites: Arizona, Colorado, Missouri, New Jersey, North Carolina, Utah, and Wisconsin. The Early ADDM Network is a subset of the broader ADDM Network (which included 13 total sites over the same period) that has been conducting ASD surveillance among children aged 8 years since 2000. Each Early ADDM site covers a smaller geographic area than the broader ADDM Network. Early ADDM ASD surveillance is conducted in two phases using the same methods and project staff members as the ADDM Network. The first phase consists of reviewing and abstracting data from children’s records, including comprehensive evaluations performed by community professionals. Sources for these evaluations include general pediatric health clinics and specialized programs for children with developmental disabilities. In addition, special education records (for children aged ≥3 years) were reviewed for Arizona, Colorado, New Jersey, North Carolina, and Utah, and early intervention records (for children aged 0 to <3 years) were reviewed for New Jersey, North Carolina, Utah, and Wisconsin; in Wisconsin, early intervention records were reviewed for 2014 only. The second phase involves a review of the abstracted evaluations by trained clinicians using a standardized case definition and method. A child is considered to meet the surveillance case definition for ASD if one or more comprehensive evaluations of that child completed by a qualified professional describes behaviors consistent with the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR) diagnostic criteria for any of the following conditions: autistic disorder, pervasive developmental disorder–not otherwise specified (PDD-NOS, including atypical autism), or Asperger disorder (2010, 2012, and 2014). For 2014 only, prevalence estimates based on surveillance case definitions according to DSM-IV-TR and the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) were compared. This report provides estimates of overall ASD prevalence and prevalence by sex and race/ethnicity; characteristics of children aged 4 years with ASD, including age at first developmental evaluation, age at ASD diagnosis, and cognitive function; and trends in ASD prevalence and characteristics among Early ADDM sites with data for all 3 surveillance years (2010, 2012, and 2014), including comparisons with children aged 8 years living in the same geographic area. Analyses of time trends in ASD prevalence are restricted to the three sites that contributed data for all 3 surveillance years with consistent data sources (Arizona, Missouri, and New Jersey).

Results

The overall ASD prevalence was 13.4 per 1,000 children aged 4 years in 2010, 15.3 in 2012, and 17.0 in 2014 for Early ADDM sites with data for the specific years. ASD prevalence was determined using a surveillance case definition based on DSM-IV-TR. Within each surveillance year, ASD prevalence among children aged 4 years varied across surveillance sites and was lowest each year for Missouri (8.5, 8.1, and 9.6 per 1,000, for 2010, 2012, and 2014, respectively) and highest each year for New Jersey (19.7, 22.1, and 28.4 per 1,000, for the same years, respectively). Aggregated prevalence estimates were higher for sites that reviewed education and health care records than for sites that reviewed only health care records. Among all participating sites and years, ASD prevalence among children aged 4 years was consistently higher among boys than girls; prevalence ratios ranged from 2.6 (Arizona and Wisconsin in 2010) to 5.2 boys per one girl (Colorado in 2014). In 2010, ASD prevalence was higher among non-Hispanic white children than among Hispanic children in Arizona and non-Hispanic black children in Missouri; no other differences were observed by race/ethnicity. Among four sites with ≥60% data on cognitive test scores (Arizona, New Jersey, North Carolina, and Utah), the frequency of co-occurring intellectual disabilities was significantly higher among children aged 4 years than among those aged 8 years for each site in each surveillance year except Arizona in 2010. The percentage of children with ASD who had a first evaluation by age 36 months ranged from 48.8% in Missouri in 2012 to 88.9% in Wisconsin in 2014. The percentage of children with a previous ASD diagnosis from a community provider varied by site, ranging from 43.0% for Arizona in 2012 to 86.5% for Missouri in 2012. The median age at earliest known ASD diagnosis varied from 28 months in North Carolina in 2014 to 39.0 months in Missouri and Wisconsin in 2012. In 2014, the ASD prevalence based on the DSM-IV-TR case definition was 20% higher than the prevalence based on the DSM-5 (17.0 versus 14.1 per 1,000, respectively).

Trends in ASD prevalence and characteristics among children aged 4 years during the study period were assessed for the three sites with data for all 3 years and consistent data sources (Arizona, Missouri, and New Jersey) using the DSM-IV-TR case definition; prevalence was higher in 2014 than in 2010 among children aged 4 years in New Jersey and was stable in Arizona and Missouri. In Missouri, ASD prevalence was higher among children aged 8 years than among children aged 4 years. The percentage of children with ASD who had a comprehensive evaluation by age 36 months was stable in Arizona and Missouri and decreased in New Jersey. In the three sites, no change occurred in the age at earliest known ASD diagnosis during 2010–2014.

Interpretation

The findings suggest that ASD prevalence among children aged 4 years was higher in 2014 than in 2010 in one site and remained stable in others. Among children with ASD, the frequency of cognitive impairment was higher among children aged 4 years than among those aged 8 years and suggests that surveillance at age 4 years might more often include children with more severe symptoms or those with co-occurring conditions such as intellectual disability. In the sites with data for all years and consistent data sources, no change in the age at earliest known ASD diagnosis was found, and children received their first developmental evaluation at the same or a later age in 2014 compared with 2010. Delays in the initiation of a first developmental evaluation might adversely affect children by delaying access to treatment and special services that can improve outcomes for children with ASD.

Public Health Action

Efforts to increase awareness of ASD and improve the identification of ASD by community providers can facilitate early diagnosis of children with ASD. Heterogeneity of results across sites suggests that community-level differences in evaluation and diagnostic services as well as access to data sources might affect estimates of ASD prevalence and age of identification. Continuing improvements in providing developmental evaluations to children as soon as developmental concerns are identified might result in earlier ASD diagnoses and earlier receipt of services, which might improve developmental outcomes.

A population-based examination of maltreatment referrals and substantiation for children with autism spectrum disorder

Children with disabilities experience elevated rates of maltreatment, but little is known about the interaction of children with autism spectrum disorder with child protection systems. A population-based dataset of 24,306 children born in 2008 in Tennessee, which included 387 children with autism spectrum disorder identified through the Autism and Developmental Disabilities Monitoring network, was linked with state child protection records. Rates of maltreatment referrals, screening for further action, and substantiated maltreatment were examined for children with versus without autism spectrum disorder. Significantly more children with autism spectrum disorder (17.3%) than without (7.4%) were referred to the Child Abuse Hotline. Children with autism spectrum disorder were less likely than children without autism spectrum disorder to have referrals screened in for further action (62% vs 91.6%, respectively), but substantiated maltreatment rates were similar across groups (3.9% vs 3.4%, respectively). Girls versus boys with autism spectrum disorder were more likely to have substantiated maltreatment (13.6% vs 1.9%, respectively). The high percentage of children with autism spectrum disorder referred for allegations of maltreatment, the differential pattern of screening referrals in for further action, and the high levels of substantiated maltreatment of girls with autism spectrum disorder highlights the need for enhanced training and knowledge of the complex issues faced by children with autism spectrum disorder, their families, and state welfare agencies.

Trends in documented co-occurring conditions in children with autism spectrum disorder, 2002-2010

BACKGROUND

Autism spectrumdisorder (ASD) commonly presents with co-occurring medical conditions (CoCs). Little is known about patterns in CoCs in a time of rising ASD prevalence.

AIMS

To describe trends in number and type of documented CoCs in 8-year-old children with ASD.

METHODS

We used Autism and Developmental Disabilities Monitoring Network (ADDM) data, a multi-source active surveillance system monitoring ASD prevalence among 8-year-old children across the US. Data from surveillance years 2002, 2006, 2008, and 2010 were used to describe trends in count, categories, and individual CoCs.

RESULTS

Mean number of CoCs increased from 0.94 CoCs in 2002 to 1.06 CoCs in 2010 (p < 0.001). The percentage of children with ASD with any CoC increased from 44.5% to 56.4% (p < 0.001). CoCs with the greatest increases were in general developmental disability (10.4% to 14.5%), language disorder (18.9% to 23.6%), and motor developmental disability (10.5% to 15.6%). Sex modified the relationship between developmental (P = 0.02) and psychiatric (P < 0.001) CoCs and surveillance year. Race/ethnicity modified the relationship between neurological conditions (P = 0.04) and surveillance year.

CONCLUSIONS

The increase in the percentage of children with ASD and CoCs may suggest the ASD phenotype has changed over time or clinicians are more likely to diagnose CoCs.

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.

Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application

Background

Electronic health records (EHRs) bring many opportunities for information utilization. One such use is the surveillance conducted by the Centers for Disease Control and Prevention to track cases of autism spectrum disorder (ASD). This process currently comprises manual collection and review of EHRs of 4- and 8-year old children in 11 US states for the presence of ASD criteria. The work is time-consuming and expensive.

Objective

Our objective was to automatically extract from EHRs the description of behaviors noted by the clinicians in evidence of the diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM). Previously, we reported on the classification of entire EHRs as ASD or not. In this work, we focus on the extraction of individual expressions of the different ASD criteria in the text. We intend to facilitate large-scale surveillance efforts for ASD and support analysis of changes over time as well as enable integration with other relevant data.

Methods

We developed a natural language processing (NLP) parser to extract expressions of 12 DSM criteria using 104 patterns and 92 lexicons (1787 terms). The parser is rule-based to enable precise extraction of the entities from the text. The entities themselves are encompassed in the EHRs as very diverse expressions of the diagnostic criteria written by different people at different times (clinicians, speech pathologists, among others). Due to the sparsity of the data, a rule-based approach is best suited until larger datasets can be generated for machine learning algorithms.

Results

We evaluated our rule-based parser and compared it with a machine learning baseline (decision tree). Using a test set of 6636 sentences (50 EHRs), we found that our parser achieved 76% precision, 43% recall (ie, sensitivity), and >99% specificity for criterion extraction. The performance was better for the rule-based approach than for the machine learning baseline (60% precision and 30% recall). For some individual criteria, precision was as high as 97% and recall 57%. Since precision was very high, we were assured that criteria were rarely assigned incorrectly, and our numbers presented a lower bound of their presence in EHRs. We then conducted a case study and parsed 4480 new EHRs covering 10 years of surveillance records from the Arizona Developmental Disabilities Surveillance Program. The social criteria (A1 criteria) showed the biggest change over the years. The communication criteria (A2 criteria) did not distinguish the ASD from the non-ASD records. Among behaviors and interests criteria (A3 criteria), 1 (A3b) was present with much greater frequency in the ASD than in the non-ASD EHRs.

Conclusions

Our results demonstrate that NLP can support large-scale analysis useful for ASD surveillance and research. In the future, we intend to facilitate detailed analysis and integration of national datasets.

Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014

PROBLEM/CONDITION

Autism spectrum disorder (ASD).

PERIOD COVERED

2014.

DESCRIPTION OF SYSTEM

The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system that provides estimates of the prevalence of autism spectrum disorder (ASD) among children aged 8 years whose parents or guardians reside within 11 ADDM sites in the United States (Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin). ADDM surveillance is conducted in two phases. The first phase involves review and abstraction of comprehensive evaluations that were completed by professional service providers in the community. Staff completing record review and abstraction receive extensive training and supervision and are evaluated according to strict reliability standards to certify effective initial training, identify ongoing training needs, and ensure adherence to the prescribed methodology. Record review and abstraction occurs in a variety of data sources ranging from general pediatric health clinics to specialized programs serving children with developmental disabilities. In addition, most of the ADDM sites also review records for children who have received special education services in public schools. In the second phase of the study, all abstracted information is reviewed systematically by experienced clinicians to determine ASD case status. A child is considered to meet the surveillance case definition for ASD if he or she displays behaviors, as described on one or more comprehensive evaluations completed by community-based professional providers, consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) diagnostic criteria for autistic disorder; pervasive developmental disorder-not otherwise specified (PDD-NOS, including atypical autism); or Asperger disorder. This report provides updated ASD prevalence estimates for children aged 8 years during the 2014 surveillance year, on the basis of DSM-IV-TR criteria, and describes characteristics of the population of children with ASD. In 2013, the American Psychiatric Association published the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), which made considerable changes to ASD diagnostic criteria. The change in ASD diagnostic criteria might influence ADDM ASD prevalence estimates; therefore, most (85%) of the records used to determine prevalence estimates based on DSM-IV-TR criteria underwent additional review under a newly operationalized surveillance case definition for ASD consistent with the DSM-5 diagnostic criteria. Children meeting this new surveillance case definition could qualify on the basis of one or both of the following criteria, as documented in abstracted comprehensive evaluations: 1) behaviors consistent with the DSM-5 diagnostic features; and/or 2) an ASD diagnosis, whether based on DSM-IV-TR or DSM-5 diagnostic criteria. Stratified comparisons of the number of children meeting either of these two case definitions also are reported.

RESULTS

For 2014, the overall prevalence of ASD among the 11 ADDM sites was 16.8 per 1,000 (one in 59) children aged 8 years. Overall ASD prevalence estimates varied among sites, from 13.1-29.3 per 1,000 children aged 8 years. ASD prevalence estimates also varied by sex and race/ethnicity. Males were four times more likely than females to be identified with ASD. Prevalence estimates were higher for non-Hispanic white (henceforth, white) children compared with non-Hispanic black (henceforth, black) children, and both groups were more likely to be identified with ASD compared with Hispanic children. Among the nine sites with sufficient data on intellectual ability, 31% of children with ASD were classified in the range of intellectual disability (intelligence quotient [IQ] <70), 25% were in the borderline range (IQ 71-85), and 44% had IQ scores in the average to above average range (i.e., IQ >85). The distribution of intellectual ability varied by sex and race/ethnicity. Although mention of developmental concerns by age 36 months was documented for 85% of children with ASD, only 42% had a comprehensive evaluation on record by age 36 months. The median age of earliest known ASD diagnosis was 52 months and did not differ significantly by sex or race/ethnicity. For the targeted comparison of DSM-IV-TR and DSM-5 results, the number and characteristics of children meeting the newly operationalized DSM-5 case definition for ASD were similar to those meeting the DSM-IV-TR case definition, with DSM-IV-TR case counts exceeding DSM-5 counts by less than 5% and approximately 86% overlap between the two case definitions (kappa = 0.85).

INTERPRETATION

Findings from the ADDM Network, on the basis of 2014 data reported from 11 sites, provide updated population-based estimates of the prevalence of ASD among children aged 8 years in multiple communities in the United States. The overall ASD prevalence estimate of 16.8 per 1,000 children aged 8 years in 2014 is higher than previously reported estimates from the ADDM Network. Because the ADDM sites do not provide a representative sample of the entire United States, the combined prevalence estimates presented in this report cannot be generalized to all children aged 8 years in the United States. Consistent with reports from previous ADDM surveillance years, findings from 2014 were marked by variation in ASD prevalence when stratified by geographic area, sex, and level of intellectual ability. Differences in prevalence estimates between black and white children have diminished in most sites, but remained notable for Hispanic children. For 2014, results from application of the DSM-IV-TR and DSM-5 case definitions were similar, overall and when stratified by sex, race/ethnicity, DSM-IV-TR diagnostic subtype, or level of intellectual ability.

PUBLIC HEALTH ACTION

Beginning with surveillance year 2016, the DSM-5 case definition will serve as the basis for ADDM estimates of ASD prevalence in future surveillance reports. Although the DSM-IV-TR case definition will eventually be phased out, it will be applied in a limited geographic area to offer additional data for comparison. Future analyses will examine trends in the continued use of DSM-IV-TR diagnoses, such as autistic disorder, PDD-NOS, and Asperger disorder in health and education records, documentation of symptoms consistent with DSM-5 terminology, and how these trends might influence estimates of ASD prevalence over time. The latest findings from the ADDM Network provide evidence that the prevalence of ASD is higher than previously reported estimates and continues to vary among certain racial/ethnic groups and communities. With prevalence of ASD ranging from 13.1 to 29.3 per 1,000 children aged 8 years in different communities throughout the United States, the need for behavioral, educational, residential, and occupational services remains high, as does the need for increased research on both genetic and nongenetic risk factors for ASD.

Trends in Special Education Eligibility Among Children With Autism Spectrum Disorder, 2002-2010

OBJECTIVE

Although data on publicly available special education are informative and offer a glimpse of trends in autism spectrum disorder (ASD) and use of educational services, using these data for population-based public health monitoring has drawbacks. Our objective was to evaluate trends in special education eligibility among 8-year-old children with ASD identified in the Autism and Developmental Disabilities Monitoring Network.

METHODS

We used data from 5 Autism and Developmental Disabilities Monitoring Network sites (Arizona, Colorado, Georgia, Maryland, and North Carolina) during 4 surveillance years (2002, 2006, 2008, and 2010) and compared trends in 12 categories of special education eligibility by sex and race/ethnicity. We used multivariable linear risk regressions to evaluate how the proportion of children with a given eligibility changed over time.

RESULTS

Of 6010 children with ASD, more than 36% did not receive an autism eligibility in special education in each surveillance year. From surveillance year 2002 to surveillance year 2010, autism eligibility increased by 3.6 percentage points ( P = .09), and intellectual disability eligibility decreased by 4.6 percentage points ( P < .001). A greater proportion of boys than girls had an autism eligibility in 2002 (56.3% vs 48.8%). Compared with other racial/ethnic groups, Hispanic children had the largest increase in proportion with autism eligibility from 2002 to 2010 (15.4%, P = .005) and the largest decrease in proportion with intellectual disability (-14.3%, P = .004).

CONCLUSION

Although most children with ASD had autism eligibility, many received special education services under other categories, and racial/ethnic disparities persisted. To monitor trends in ASD prevalence, public health officials need access to comprehensive data collected systematically, not just special education eligibility.

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Brief Report: Prevalence of Self-injurious Behaviors among Children with Autism Spectrum Disorder-A Population-Based Study

Self-injurious behaviors (SIB) have been reported in more than 30 % of children with an autism spectrum disorder (ASD) in clinic-based studies. This study estimated the prevalence of SIB in a large population-based sample of children with ASD in the United States. A total of 8065 children who met the surveillance case definition for ASD in the Autism and Developmental Disabilities Monitoring (ADDM) Network during the 2000, 2006, and 2008 surveillance years were included. The presence of SIB was reported from available health and/or educational records by an expert clinician in ADDM Network. SIB prevalence averaged 27.7 % across all sites and surveillance years, with some variation between sites. Clinicians should inquire about SIB during assessments of children with ASD.

Factors Associated with Self-Injurious Behaviors in Children with Autism Spectrum Disorder: Findings from Two Large National Samples

In this study, we explored potential associations among self-injurious behaviors (SIB) and a diverse group of protective and risk factors in children with autism spectrum disorder from two databases: Autism and Developmental Disabilities Monitoring (ADDM) Network and the Autism Speaks-Autism Treatment Network (AS-ATN). The presence of SIB was determined from children's records in ADDM and a parent questionnaire in AS-ATN. We used multiple imputation to account for missing data and a non-linear mixed model with site as a random effect to test for associations. Despite differences between the two databases, similar associations were found; SIB were associated with developmental, behavioral, and somatic factors. Implications of these findings are discussed in relation to possible etiology, future longitudinal studies, and clinical practice.

DSM Criteria that Best Differentiate Intellectual Disability from Autism Spectrum Disorder

Clinical characteristics of autism spectrum disorder (ASD) and intellectual disability (ID) overlap, creating potential for diagnostic confusion. Diagnostic and statistical manual of mental disorders (DSM) criteria that best differentiate children with ID and some ASD features from those with comorbid ID and ASD were identified. Records-based surveillance of ASD among 8-year-old children across 14 US populations ascertained 2816 children with ID, with or without ASD. Area under the curve (AUC) was conducted to determine discriminatory power of DSM criteria. AUC analyses indicated that restricted interests or repetitive behaviors best differentiated between the two groups. A subset of 6 criteria focused on social interactions and stereotyped behaviors was most effective at differentiating the two groups (AUC of 0.923), while communication-related criteria were least discriminatory. Matching children with appropriate treatments requires differentiation between ID and ASD. Shifting to DSM-5 may improve differentiation with decreased emphasis on language-related behaviors.

GapMap: Enabling Comprehensive Autism Resource Epidemiology

BACKGROUND

For individuals with autism spectrum disorder (ASD), finding resources can be a lengthy and difficult process. The difficulty in obtaining global, fine-grained autism epidemiological data hinders researchers from quickly and efficiently studying large-scale correlations among ASD, environmental factors, and geographical and cultural factors.

OBJECTIVE

The objective of this study was to define resource load and resource availability for families affected by autism and subsequently create a platform to enable a more accurate representation of prevalence rates and resource epidemiology.

METHODS

We created a mobile application, GapMap, to collect locational, diagnostic, and resource use information from individuals with autism to compute accurate prevalence rates and better understand autism resource epidemiology. GapMap is hosted on AWS S3, running on a React and Redux front-end framework. The backend framework is comprised of an AWS API Gateway and Lambda Function setup, with secure and scalable end points for retrieving prevalence and resource data, and for submitting participant data. Measures of autism resource scarcity, including resource load, resource availability, and resource gaps were defined and preliminarily computed using simulated or scraped data.

RESULTS

The average distance from an individual in the United States to the nearest diagnostic center is approximately 182 km (50 miles), with a standard deviation of 235 km (146 miles). The average distance from an individual with ASD to the nearest diagnostic center, however, is only 32 km (20 miles), suggesting that individuals who live closer to diagnostic services are more likely to be diagnosed.

CONCLUSIONS

This study confirmed that individuals closer to diagnostic services are more likely to be diagnosed and proposes GapMap, a means to measure and enable the alleviation of increasingly overburdened diagnostic centers and resource-poor areas where parents are unable to diagnose their children as quickly and easily as needed. GapMap will collect information that will provide more accurate data for computing resource loads and availability, uncovering the impact of resource epidemiology on age and likelihood of diagnosis, and gathering localized autism prevalence rates.

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

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.

Prevalence of Autism Spectrum Disorders Among Children With Intellectual Disability

Autism spectrum disorders (ASD) often co-occur with intellectual disability (ID) and are associated with poorer psychosocial and family-related outcomes than ID alone. The present study examined the prevalence, stability, and characteristics of ASD estimates in 2,208 children with ASD and ID identified through the South Carolina Autism and Developmental Disabilities Network. The prevalence of ASD in ID was 18.04%, relative to ASD rates of 0.60%-1.11% reported in the general South Carolina population. Compared to children with ASD alone, those with comorbid ID exhibited increased symptom severity and distinct DSM-IV-TR profiles. Further work is needed to determine whether current screening, diagnostic, and treatment practices adequately address the unique needs of children and families affected by comorbid ASD and ID diagnoses.

Autism spectrum disorder reporting in lower socioeconomic neighborhoods

Utilizing surveillance data from five sites participating in the Autism and Developmental Disabilities Monitoring Network, we investigated contributions of surveillance subject and census tract population sociodemographic characteristics on variation in autism spectrum disorder ascertainment and prevalence estimates from 2000 to 2008 using ordinal hierarchical models for 2489 tracts. Multivariable analyses showed a significant increase in ascertainment of autism spectrum disorder cases through both school and health sources, the optimal ascertainment scenario, for cases with college-educated mothers (adjusted odds ratio = 1.06, 95% confidence interval = 1.02–1.09). Results from our examination of sociodemographic factors of tract populations from which cases were drawn also showed that after controlling for other covariates, statistical significance remained for associations between optimal ascertainment and percentage of Hispanic residents (adjusted odds ratio = 0.93, 95% confidence interval = 0.88–0.99) and percentage of residents with at least a bachelor’s degree (adjusted odds ratio = 1.06, 95% confidence interval = 1.01–1.11). We identified sociodemographic factors associated with autism spectrum disorder prevalence estimates including race, ethnicity, education, and income. Determining which specific factors influence disparities is complicated; however, it appears that even in the presence of education, racial and ethnic disparities are still apparent. These results suggest disparities in access to autism spectrum disorder assessments and special education for autism spectrum disorder among ethnic groups may impact subsequent surveillance.

Autism spectrum disorder prevalence and associations with air concentrations of lead, mercury, and arsenic

Lead, mercury, and arsenic are neurotoxicants with known effects on neurodevelopment. Autism spectrum disorder (ASD) is a neurodevelopmental disorder apparent by early childhood. Using data on 4486 children with ASD residing in 2489 census tracts in five sites of the Centers for Disease Control and Prevention's Autism and Developmental Disabilities Monitoring (ADDM) Network, we used multi-level negative binomial models to investigate if ambient lead, mercury, and arsenic concentrations, as measured by the US Environmental Protection Agency National-Scale Air Toxics Assessment (EPA-NATA), were associated with ASD prevalence. In unadjusted analyses, ambient metal concentrations were negatively associated with ASD prevalence. After adjusting for confounding factors, tracts with air concentrations of lead in the highest quartile had significantly higher ASD prevalence than tracts with lead concentrations in the lowest quartile (prevalence ratio (PR) = 1.36; 95 '% CI: 1.18, 1.57). In addition, tracts with mercury concentrations above the 75th percentile (>1.7 ng/m(3)) and arsenic concentrations below the 75th percentile (≤0.13 ng/m(3)) had a significantly higher ASD prevalence (adjusted RR = 1.20; 95 % CI: 1.03, 1.40) compared to tracts with arsenic, lead, and mercury concentrations below the 75th percentile. Our results suggest a possible association between ambient lead concentrations and ASD prevalence and demonstrate that exposure to multiple metals may have synergistic effects on ASD prevalence.

An Emerging Role of micro-RNA in the Effect of the Endocrine Disruptors

Endocrine-disrupting chemicals (EDCs) are diverse natural and synthetic chemicals that may alter various mechanisms of the endocrine system and produce adverse developmental, reproductive, metabolic, and neurological effects in both humans and wildlife. Research on EDCs has revealed that they use a variety of both nuclear receptor-mediated and non-receptor-mediated mechanisms to modulate different components of the endocrine system. The molecular mechanisms underlying the effects of EDCs are still under investigation. Interestingly, some of the effects of EDCs have been observed to pass on to subsequent unexposed generations, which can be explained by the gametic transmission of deregulated epigenetic marks. Epigenetics is the study of heritable changes in gene expression that occur without a change in the DNA sequence. Epigenetic mechanisms, including histone modifications, DNA methylation, and specific micro-RNAs (miRNAs) expression, have been proposed to mediate transgenerational transmission and can be triggered by environmental factors. MiRNAs are short non-coding RNA molecules that post-transcriptionally repress the expression of genes by binding to 3′-untranslated regions of the target mRNAs. Given that there is mounting evidence that miRNAs are regulated by hormones, then clearly it is important to investigate the potential for environmental EDCs to deregulate miRNA expression and action.

Infant Development in Fragile X Syndrome: Cross-Syndrome Comparisons

This study examined the developmental profile of male infants with fragile X syndrome (FXS) and its divergence from typical development and development of infants at high risk for autism associated with familial recurrence (ASIBs). Participants included 174 boys ranging in age from 5 to 28 months. Cross-sectional profiles on the Mullen Scales of Early Learning indicated infants with FXS could be differentiated from typically developing infants and ASIBs by 6 months of age. Infants with FXS displayed a trend of lower developmental skills with increasing age that was unique from the typically developing and ASIB groups. Findings suggest infants with FXS present with more significant, pervasive and early emerging delays than previously reported with potentially etiologically distinct developmental profiles.

Autism Spectrum Disorder, Intellectual Disability, and Delayed Walking

OBJECTIVE

Delayed onset of independent walking is common in intellectual disability (ID). However, in children with autism spectrum disorders (ASD), delayed walking has not been reported as frequently, despite the high rate of concurrent ID in ASD. This study directly examined the relationship between delayed walking and severity of ID in children with ASD versus other non-ASD diagnoses.

METHOD

Participants were 1185 individuals (ASD, n = 903; non-ASD, n = 282) who received an assessment at age 4 to 12 years (6.89 ± 2.25) that yielded an estimate of nonverbal IQ (NVIQ) and retrospectively reported age of walking from the Autism Diagnostic Interview-Revised. The relationship between diagnostic group and delayed walking (defined as occurring at ≥16 months) as a function of NVIQ was explored using the Cox proportional hazards model.

RESULTS

Children with ASD were less likely to exhibit delayed walking than those with non-ASD diagnoses, and this difference was larger at lower levels of NVIQ (P = .002). For example, rates of delayed walking for ASD and non-ASD were 13% and 19%, respectively, in those with NVIQ >85 but 31% and 60% in children with NVIQ <70.

CONCLUSIONS

Although lower IQ scores were associated with increased rates of late walking in both ASD and non-ASD groups, children with low IQ were more likely to show delayed walking in the absence of ASD. This raises the possibility of separate etiological pathways to ID in children with and without ASD.

Autism spectrum disorder prevalence and proximity to industrial facilities releasing arsenic, lead or mercury

Prenatal and perinatal exposures to air pollutants have been shown to adversely affect birth outcomes in offspring and may contribute to prevalence of autism spectrum disorder (ASD). For this ecologic study, we evaluated the association between ASD prevalence, at the census tract level, and proximity of tract centroids to the closest industrial facilities releasing arsenic, lead or mercury during the 1990s. We used 2000 to 2008 surveillance data from five sites of the Autism and Developmental Disabilities Monitoring (ADDM) network and 2000 census data to estimate prevalence. Multi-level negative binomial regression models were used to test associations between ASD prevalence and proximity to industrial facilities in existence from 1991 to 1999 according to the US Environmental Protection Agency Toxics Release Inventory (USEPA-TRI). Data for 2489 census tracts showed that after adjustment for demographic and socio-economic area-based characteristics, ASD prevalence was higher in census tracts located in the closest 10th percentile compared of distance to those in the furthest 50th percentile (adjusted RR=1.27, 95% CI: (1.00, 1.61), P=0.049). The findings observed in this study are suggestive of the association between urban residential proximity to industrial facilities emitting air pollutants and higher ASD prevalence.

Dental care protocol based on visual supports for children with autism spectrum disorders

Background

Subjects with Autism Spectrum Disorders (ASDs) have often difficulties to accept dental treatments. The aim of this study is to propose a dental care protocol based on visual supports to facilitate children with ASDs to undergo to oral examination and treatments.

Material and Methods

83 children (age range 6-12 years) with a signed consent form were enrolled; intellectual level, verbal fluency and cooperation grade were evaluated. Children were introduced into a four stages path in order to undergo: an oral examination (stage 1), a professional oral hygiene session (stage 2), sealants (stage 3), and, if necessary, a restorative treatment (stage 4). Each stage came after a visual training, performed by a psychologist (stage 1) and by parents at home (stages 2, 3 and 4). Association between acceptance rates at each stage and gender, intellectual level, verbal fluency and cooperation grade was tested with chi-square test if appropriate.

Results

Seventy-seven (92.8%) subjects overcame both stage 1 and 2. Six (7.2%) refused stage 3 and among the 44 subjects who need restorative treatments, only three refused it. The acceptance rate at each stage was statistically significant associated to the verbal fluency (p=0.02; p=0.04; p=0.01, respectively for stage 1, 3 and 4). In stage 2 all subjects accepted to move to the next stage. The verbal/intellectual/cooperation dummy variable was statistically associated to the acceptance rate (p<0.01).

Conclusions

The use of visual supports has shown to be able to facilitate children with ASDs to undergo dental treatments even in non-verbal children with a low intellectual level, underlining that behavioural approach should be used as the first strategy to treat patients with ASDs in dental setting.

Key words:Autism spectrum disorders, behaviour management, paediatric dentistry, visual learning methods.

Brief report: independent validation of autism spectrum disorder case status in the Utah Autism and Developmental Disabilities Monitoring (ADDM) Network Site

An independent validation was conducted of the Utah Autism and Developmental Disabilities Monitoring Network's (UT-ADDM) classification of children with autism spectrum disorder (ASD). UT-ADDM final case status (n = 90) was compared with final case status as determined by independent external expert reviewers (EERs). Inter-rater reliability (ICC = 0.84), specificity [0.83 (95 % CI 0.74-0.90)], and sensitivity [0.99 (95 % CI 0.96-1.00)] were high for ASD case versus non-case classification between UT-ADDM and EER. At least one EER disagreed with UT-ADDM on ASD final case status on nine out of 30 records; however, all three EERs disagreed with UT-ADDM for only one record. Findings based on limited data suggest that children with ASD as identified by UT-ADDM are consistently classified as ASD cases by independent autism experts.

Validation of Autism Spectrum Disorder Diagnoses in Large Healthcare Systems with Electronic Medical Records

To identify factors associated with valid Autism Spectrum Disorder (ASD) diagnoses from electronic sources in large healthcare systems. We examined 1,272 charts from ASD diagnosed youth <18 years old. Expert reviewers classified diagnoses as confirmed, probable, possible, ruled out, or not enough information. A total of 845 were classified with 81% as a confirmed, probable, or possible ASD diagnosis. The predictors of valid ASD diagnoses were >2 diagnoses in the medical record (OR 2.94; 95% CI 2.03-4.25; p < 0.001) and being male (OR 1.51; 95% CI 1.05-2.17; p = 0.03). In large integrated healthcare settings, at least two diagnoses can be used to identify ASD patients for population-based research.

Psychiatric comorbidity and medication use in adults with autism spectrum disorder

The purpose of this study was to investigate comorbid psychiatric disorders and psychotropic medication use among adults with autism spectrum disorder (ASD) ascertained as children during a 1980's statewide Utah autism prevalence study (n = 129). Seventy-three individuals (56.6 %) met criteria for a current psychiatric disorder; 89 participants (69.0 %) met lifetime criteria for a psychiatric disorder. Caregivers reported a psychiatric diagnosis in 44 participants (34.1 %). Anxiety disorder had the highest current and lifetime prevalence (39.5 and 52.7 %, respectively). Participants with intellectual disability (n = 94, 72.8 %) were significantly less likely to have community-based diagnoses of anxiety (χ(2) = 5.37, p = 0.02) or depression (χ(2) = 13.18, p < 0.001) reported by caregivers. Seventy-six participants (58.9 %) were taking ≥1 psychotropic medication. Comorbid psychiatric disorders occur frequently in adults with ASD, though identifying these disorders poses a challenge in community settings. A greater understanding of the presentation of these conditions within this population will increase assessment validity and the potential for efficacious intervention.

Aberrant Behaviors and Co-occurring Conditions as Predictors of Psychotropic Polypharmacy among Children with Autism Spectrum Disorders

OBJECTIVES

The purpose of this study was to identify rates and predictors of psychotropic medication polypharmacy among Medicaid-eligible children in South Carolina with autism spectrum disorder (ASD) from 2000 to 2008.

METHODS

Population-based surveillance data were linked with state Medicaid records to obtain a detailed demographic, behavioral, educational, clinical, and diagnostic data set for all Medicaid-eligible 8-year-old children (n=629) who were identified and diagnosed with ASD using standardized criteria. Polypharmacy was defined as having interclass psychotropic medication claims overlapping for ≥30 consecutive days at any time during the 2-year study period. Multivariable logistic regression was used to model predictors of any polypharmacy, and for the three most common combinations.

RESULTS

Overall, 60% (n=377) used any psychotropic medication, and 41% (n=153) of those had interclass polypharmacy. Common combinations were attention-deficit/hyperactivity disorder (ADHD) medications with an antidepressant (A/AD), antipsychotic (A/AP) or a mood stabilizer (A/MS). Black children had lower odds of any polypharmacy, as did those eligible for Medicaid because of income or being foster care versus those eligible because of disability. There were no significant associations between polypharmacy and social deficits in ASD for any combination, although children with communication deficits diagnostic of ASD had lower odds of any polypharmacy and A/AP polypharmacy. Children with argumentative, aggressive, hyperactive/impulsive, or self-injurious aberrant behaviors had higher odds of polypharmacy, as did children with diagnosed co-occurring ADHD, anxiety or mood disorders, or conduct/oppositional defiant disorder (ODD) in Medicaid records.

CONCLUSIONS

Future research is warranted to investigate how child-level factors impact combination psychotropic medication prescribing practices and outcomes in ASD.