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.

Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012.

PROBLEM/CONDITION: Autism spectrum disorder (ASD). PERIOD COVERED: 2012. DESCRIPTION OF SYSTEM: The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system that provides estimates of the prevalence and characteristics of ASD among children aged 8 years whose parents or guardians reside in 11 ADDM Network sites in the United States (Arkansas, Arizona, Colorado, Georgia, Maryland, Missouri, New Jersey, North Carolina, South Carolina, Utah, and Wisconsin). Surveillance to determine ASD case status is conducted in two phases. The first phase consists of screening and abstracting comprehensive evaluations performed by professional service providers in the community. Data sources identified for record review are categorized as either 1) education source type, including developmental evaluations to determine eligibility for special education services or 2) health care source type, including diagnostic and developmental evaluations. The second phase involves the review of all abstracted evaluations by trained clinicians to determine ASD surveillance case status. A child meets the surveillance case definition for ASD if one or more comprehensive evaluations of that child completed by a qualified professional describes behaviors that are consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision diagnostic criteria for any of the following conditions: autistic disorder, pervasive developmental disorder-not otherwise specified (including atypical autism), or Asperger disorder. This report provides ASD prevalence estimates for children aged 8 years living in catchment areas of the ADDM Network sites in 2012, overall and stratified by sex, race/ethnicity, and the type of source records (education and health records versus health records only). In addition, this report describes the proportion of children with ASD with a score consistent with intellectual disability on a standardized intellectual ability test, the age at which the earliest known comprehensive evaluation was performed, the proportion of children with a previous ASD diagnosis, the specific type of ASD diagnosis, and any special education eligibility classification. RESULTS: For 2012, the combined estimated prevalence of ASD among the 11 ADDM Network sites was 14.5 per 1,000 (one in 69) children aged 8 years. Estimated prevalence was significantly higher among boys aged 8 years (23.4 per 1,000) than among girls aged 8 years (5.2 per 1,000). Estimated ASD prevalence was significantly higher among non-Hispanic white children aged 8 years (15.3 per 1,000) compared with non-Hispanic black children (13.1 per 1,000), and Hispanic (10.2 per 1,000) children aged 8 years. Estimated prevalence varied widely among the 11 ADDM Network sites, ranging from 8.2 per 1,000 children aged 8 years (in the area of the Maryland site where only health care records were reviewed) to 24.6 per 1,000 children aged 8 years (in New Jersey, where both education and health care records were reviewed). Estimated prevalence was higher in surveillance sites where education records and health records were reviewed compared with sites where health records only were reviewed (17.1 per 1,000 and 10.4 per 1,000 children aged 8 years, respectively; p<0.05). Among children identified with ASD by the ADDM Network, 82% had a previous ASD diagnosis or educational classification; this did not vary by sex or between non-Hispanic white and non-Hispanic black children. A lower percentage of Hispanic children (78%) had a previous ASD diagnosis or classification compared with non-Hispanic white children (82%) and with non-Hispanic black children (84%). The median age at earliest known comprehensive evaluation was 40 months, and 43% of children had received an earliest known comprehensive evaluation by age 36 months. The percentage of children with an earliest known comprehensive evaluation by age 36 months was similar for boys and girls, but was higher for non-Hispanic white children (45%) compared with non-Hispanic black children (40%) and Hispanic children (39%). INTERPRETATION: Overall estimated ASD prevalence was 14.5 per 1,000 children aged 8 years in the ADDM Network sites in 2012. The higher estimated prevalence among sites that reviewed both education and health records suggests the role of special education systems in providing comprehensive evaluations and services to children with developmental disabilities. Disparities by race/ethnicity in estimated ASD prevalence, particularly for Hispanic children, as well as disparities in the age of earliest comprehensive evaluation and presence of a previous ASD diagnosis or classification, suggest that access to treatment and services might be lacking or delayed for some children. PUBLIC HEALTH ACTION: The ADDM Network will continue to monitor the prevalence and characteristics of ASD among children aged 8 years living in selected sites across the United States. Recommendations from the ADDM Network include enhancing strategies to 1) lower the age of first evaluation of ASD by community providers in accordance with the Healthy People 2020 goal that children with ASD are evaluated by age 36 months and begin receiving community-based support and services by age 48 months; 2) reduce disparities by race/ethnicity in identified ASD prevalence, the age of first comprehensive evaluation, and presence of a previous ASD diagnosis or classification; and 3) assess the effect on ASD prevalence of the revised ASD diagnostic criteria published in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

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.

Opportunities and Challenges for Dietary Arsenic Intervention.

The diet is emerging as the dominant source of arsenic exposure for most of the U.S. population. Despite this, limited regulatory efforts have been aimed at mitigating exposure, and the role of diet in arsenic exposure and disease processes remains understudied. In this brief, we discuss the evidence linking dietary arsenic intake to human disease and discuss challenges associated with exposure characterization and efforts to quantify risks. In light of these challenges, and in recognition of the potential longer-term process of establishing regulation, we introduce a framework for shorter-term interventions that employs a field-to-plate food supply chain model to identify monitoring, intervention, and communication opportunities as part of a multisector, multiagency, science-informed, public health systems approach to mitigation of dietary arsenic exposure. Such an approach is dependent on coordination across commodity producers, the food industry, nongovernmental organizations, health professionals, researchers, and the regulatory community. https://doi.org/10.1289/EHP3997.

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.

Characterizing Health Disparities in the Age of Autism Diagnosis in a Study of 8-Year-Old Children.

The diagnosis of autism spectrum disorder (ASD) is often delayed from the time of noted concerns to the actual diagnosis. The current study used child- and family-level factors to identify homogeneous classes in a surveillance-based sample (n = 2303) of 8-year-old children with ASD. Using latent class analysis, a 5-class model emerged and the class memberships were examined in relation to the child's median age at ASD diagnosis. Class 3, with known language delays and a high advantage socioeconomically had the lowest age of ASD diagnosis (46.74 months) in comparison to Classes 1 (64.99 months), 4 (58.14 months), and 5 (69.78 months) in this sample. Findings demonstrate sociodemographic and developmental disparities related to the age at ASD diagnosis.

Optimizing Corpus Creation for Training Word Embedding in Low Resource Domains: A Case Study in Autism Spectrum Disorder (ASD).

Automating the extraction of behavioral criteria indicative of Autism Spectrum Disorder (ASD) in electronic health records (EHRs) can contribute significantly to the effort to monitor the condition. Word embedding algorithms such as Word2Vec can encode semantic meanings of words in vectors and assist in automated vocabulary discovery from EHRs. However, text available for training word embeddings for ASD is miniscule compared to the billions of tokens typically used. We evaluate the importance of corpus specificity versus size and hypothesize that for specific domains small corpora can generate excellent word embeddings. We custom-built 6 ASD-themed corpora (N=4482), using ASD EHRs and abstracts from PubMed (N=39K) and PsychInfo (N=69K) and evaluated them. We were able to generate the most useful 200-dimension embeddings based on the small ASD EHR data. Due to diversity in its vocabulary, the abstract-based embeddings generated fewer related terms and saw minimal improvement when the size of the corpus increased.

Nutrients in one-carbon metabolism and urinary arsenic methylation in the National Health and Nutrition Examination Survey (NHANES) 2003-2004.

Exposure to inorganic arsenic (inAs), a potent toxicant, occurs primarily through ingestion of food and water. The efficiency with which it is methylated to mono and dimethyl arsenicals (MMA and DMA) affects toxicity. Folate, vitamins B12 and B6 are required for 1C metabolism, and studies have found that higher levels of these nutrients increase methylation capacity and are associated with protection against adverse health effects from inAs, especially in undernourished populations. Our aim was to determine whether 1C-related nutrients are associated with greater inAs methylation capacity in a general population sample with overall adequate nutrition and low levels of As exposure. Univariate and multivariable regression models were used to evaluate the relationship of dietary and blood nutrients to urinary As methylation in the National Health and Nutrition Examination Survey (NHANES) 2003-2004. Outcome variables were the percent of the sum of inAs and methylated As species (inAs+MMA+DMA) excreted as inAs, MMA, and DMA, and the ratio of MMA:DMA. In univariate models, dietary folate, vitamin B6 and protein intake were associated with lower urinary inAs% and greater DMA% in adults (≥18years), with similar trends in children (6-18). In adjusted models, vitamin B6 intake (p=0.011) and RBC folate (p=0.036) were associated with lower inAs%, while dietary vitamin B12 was associated with higher inAs% (p=0.002) and lower DMA% (p=0.030). Total plasma homocysteine was associated with higher MMA% (p=0.004) and lower DMA% (p=0.003), but not with inAs%; other blood nutrients showed no association with urinary As. Although effect size is small, these findings suggest that 1C nutrients can influence inAs methylation and potentially play an indirect role in reducing toxicity in a general population sample.

Human exposure to dietary inorganic arsenic and other arsenic species: State of knowledge, gaps and uncertainties.

Inorganic arsenic (iAs) is ubiquitous in the environment as arsenite (AsIII) and arsenate (AsV) compounds and biotransformation of these toxic chemicals leads to the extraordinary variety of organoarsenic species found in nature. Despite classification as a human carcinogen based on data from populations exposed through contaminated drinking water, only recently has a need for regulatory limits on iAs in food been recognized. The delay was due to the difficulty in risk assessment of dietary iAs, which critically relies on speciation analysis providing occurrence data for iAs in food - and not simply for total arsenic. In the present review the state of knowledge regarding arsenic speciation in food and diet is evaluated with focus on iAs and human exposure assessment through different dietary approaches including duplicate diet studies, market basket surveys, and total diet studies. The analytical requirements for obtaining reliable data for iAs in food are discussed and iAs levels in foods and beverages are summarized, along with information on other (potentially) toxic co-occurring organoarsenic compounds. Quantitative exposure assessment of iAs in food is addressed, focusing on the need of capturing variability and extent of exposure and identifying what dietary items drive very high exposure for certain population groups. Finally, gaps and uncertainties are discussed, including effect of processing and cooking, and iAs bioavailability.

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.

Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years--Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012.

PROBLEM/CONDITION: Autism spectrum disorder (ASD). PERIOD COVERED: 2012. DESCRIPTION OF SYSTEM: The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system that provides estimates of the prevalence and characteristics of ASD among children aged 8 years whose parents or guardians reside in 11 ADDM Network sites in the United States (Arkansas, Arizona, Colorado, Georgia, Maryland, Missouri, New Jersey, North Carolina, South Carolina, Utah, and Wisconsin). Surveillance to determine ASD case status is conducted in two phases. The first phase consists of screening and abstracting comprehensive evaluations performed by professional service providers in the community. Data sources identified for record review are categorized as either 1) education source type, including developmental evaluations to determine eligibility for special education services or 2) health care source type, including diagnostic and developmental evaluations. The second phase involves the review of all abstracted evaluations by trained clinicians to determine ASD surveillance case status. A child meets the surveillance case definition for ASD if one or more comprehensive evaluations of that child completed by a qualified professional describes behaviors that are consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision diagnostic criteria for any of the following conditions: autistic disorder, pervasive developmental disorder-not otherwise specified (including atypical autism), or Asperger disorder. This report provides ASD prevalence estimates for children aged 8 years living in catchment areas of the ADDM Network sites in 2012, overall and stratified by sex, race/ethnicity, and the type of source records (education and health records versus health records only). In addition, this report describes the proportion of children with ASD with a score consistent with intellectual disability on a standardized intellectual ability test, the age at which the earliest known comprehensive evaluation was performed, the proportion of children with a previous ASD diagnosis, the specific type of ASD diagnosis, and any special education eligibility classification. RESULTS: For 2012, the combined estimated prevalence of ASD among the 11 ADDM Network sites was 14.6 per 1,000 (one in 68) children aged 8 years. Estimated prevalence was significantly higher among boys aged 8 years (23.6 per 1,000) than among girls aged 8 years (5.3 per 1,000). Estimated ASD prevalence was significantly higher among non-Hispanic white children aged 8 years (15.5 per 1,000) compared with non-Hispanic black children (13.2 per 1,000), and Hispanic (10.1 per 1,000) children aged 8 years. Estimated prevalence varied widely among the 11 ADDM Network sites, ranging from 8.2 per 1,000 children aged 8 years (in the area of the Maryland site where only health care records were reviewed) to 24.6 per 1,000 children aged 8 years (in New Jersey, where both education and health care records were reviewed). Estimated prevalence was higher in surveillance sites where education records and health records were reviewed compared with sites where health records only were reviewed (17.1 per 1,000 and 10.7 per 1,000 children aged 8 years, respectively; p<0.05). Among children identified with ASD by the ADDM Network, 82% had a previous ASD diagnosis or educational classification; this did not vary by sex or between non-Hispanic white and non-Hispanic black children. A lower percentage of Hispanic children (78%) had a previous ASD diagnosis or classification compared with non-Hispanic white children (82%) and with non-Hispanic black children (84%). The median age at earliest known comprehensive evaluation was 40 months, and 43% of children had received an earliest known comprehensive evaluation by age 36 months. The percentage of children with an earliest known comprehensive evaluation by age 36 months was similar for boys and girls, but was higher for non-Hispanic white children (45%) compared with non-Hispanic black children (40%) and Hispanic children (39%). INTERPRETATION: Overall estimated ASD prevalence was 14.6 per 1,000 children aged 8 years in the ADDM Network sites in 2012. The higher estimated prevalence among sites that reviewed both education and health records suggests the role of special education systems in providing comprehensive evaluations and services to children with developmental disabilities. Disparities by race/ethnicity in estimated ASD prevalence, particularly for Hispanic children, as well as disparities in the age of earliest comprehensive evaluation and presence of a previous ASD diagnosis or classification, suggest that access to treatment and services might be lacking or delayed for some children. PUBLIC HEALTH ACTION: The ADDM Network will continue to monitor the prevalence and characteristics of ASD among children aged 8 years living in selected sites across the United States. Recommendations from the ADDM Network include enhancing strategies to 1) lower the age of first evaluation of ASD by community providers in accordance with the Healthy People 2020 goal that children with ASD are evaluated by age 36 months and begin receiving community-based support and services by age 48 months; 2) reduce disparities by race/ethnicity in identified ASD prevalence, the age of first comprehensive evaluation, and presence of a previous ASD diagnosis or classification; and 3) assess the effect on ASD prevalence of the revised ASD diagnostic criteria published in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

Prevalence and Characteristics of Autism Spectrum Disorder Among 4-Year-Old Children in the Autism and Developmental Disabilities Monitoring Network.

OBJECTIVE: Early identification of children with autism spectrum disorder (ASD) facilitates timely access to intervention services. Yet, few population-based data exist on ASD identification among preschool-aged children. The authors aimed to describe ASD prevalence and characteristics among 4-year-old children in 5 of 11 sites participating in the 2010 Autism and Developmental Disabilities Monitoring Network. METHOD: Children with ASD were identified through screening of health and education records for ASD indicators, data abstraction and compilation for each child, and clinician review of records. ASD prevalence estimates, ages at first evaluation and ASD diagnosis, cognitive test scores, and demographics were compared for 4-year-old children and 8-year-old children living in the same areas. RESULTS: Among 58,467 children in these 5 sites, 4-year-old ASD prevalence was 13.4 per 1000, which was 30% lower than 8-year-old ASD prevalence. Prevalence of ASD without cognitive impairment was 40% lower among 4-year-olds compared with 8-year-olds, but prevalence of ASD with cognitive impairment was 20% higher among 4-year-olds compared with 8-year-olds. Among 4-year-olds with ASD, female and non-Hispanic white children were more likely to receive their first comprehensive evaluation by age 36 months compared with male and non-Hispanic black children, respectively. Among children diagnosed with ASD by age 48 months, median age at first comprehensive evaluation was 27 months for 4-year-olds compared with 32 months for 8-year-olds. CONCLUSION: Population-based ASD surveillance among 4-year-old children provides valuable information about the early identification of children with ASD and suggests progression toward lowering the age of first ASD evaluation within participating Autism and Developmental Disabilities Monitoring communities.

Relation of dietary inorganic arsenic to serum matrix metalloproteinase-9 (MMP-9) at different threshold concentrations of tap water arsenic.

Arsenic (As) exposure is associated with cancer, lung and cardiovascular disease, yet the mechanisms involved are not clearly understood. Elevated matrix metalloproteinase-9 (MMP-9) levels are also associated with these diseases, as well as with exposure to water As. Our objective was to evaluate the effects of dietary components of inorganic As (iAs) intake on serum MMP-9 concentration at differing levels of tap water As. In a cross-sectional study of 214 adults, dietary iAs intake was estimated from 24-h dietary recall interviews using published iAs residue data; drinking and cooking water As intake from water samples and consumption data. Aggregate iAs intake (food plus water) was associated with elevated serum MMP-9 in mixed model regression, with and without adjustment for covariates. In models stratified by tap water As, aggregate intake was a significant positive predictor of serum MMP-9 in subjects exposed to water As≤10 µg/l. Inorganic As from food alone was associated with serum MMP-9 in subjects exposed to tap water As≤3 µg/l. Exposure to iAs from food and water combined, in areas where tap water As concentration is ≤10 µg/l, may contribute to As-induced changes in a biomarker associated with toxicity.

Environmental arsenic exposure, selenium and sputum alpha-1 antitrypsin.

Exposure to arsenic in drinking water is associated with increased respiratory disease. Alpha-1 antitrypsin (AAT) protects the lung against tissue destruction. The objective of this study was to determine whether arsenic exposure is associated with changes in airway AAT concentration and whether this relationship is modified by selenium. A total of 55 subjects were evaluated in Ajo and Tucson, Arizona. Tap water and first morning void urine were analyzed for arsenic species, induced sputum for AAT and toenails for selenium and arsenic. Household tap-water arsenic, toenail arsenic and urinary inorganic arsenic and metabolites were significantly higher in Ajo (20.6±3.5 µg/l, 0.54±0.77 µg/g and 27.7±21.2 µg/l, respectively) than in Tucson (3.9±2.5 µg/l, 0.16±0.20 µg/g and 13.0±13.8 µg/l, respectively). In multivariable models, urinary monomethylarsonic acid (MMA) was negatively, and toenail selenium positively associated with sputum AAT (P=0.004 and P=0.002, respectively). In analyses stratified by town, these relationships remained significant only in Ajo, with the higher arsenic exposure. Reduction in AAT may be a means by which arsenic induces respiratory disease, and selenium may protect against this adverse effect.

Contribution of diet to aggregate arsenic exposures-an analysis across populations.

The relative contribution of dietary arsenic (As) to aggregate daily exposure has not been well-characterized, especially in relation to the current EPA maximum contaminant level (MCL) of 10 p.p.b. for As in drinking water. Our objectives were to: (1) model exposure to inorganic and total As among non-seafood eaters using subject-specific data, (2) compare the contribution of food, drinking and cooking water to estimated aggregate exposure in households with variable background tap water As levels, and (3) describe the upper distribution of potential dose at different thresholds of tap water As. Dietary As intake was modeled in regional study populations and NHANES 2003-2004 using dietary records in conjunction with published food As residue data. Water As was measured in the regional studies. Among subjects exposed to tap water As >10 p.p.b., aggregate inorganic exposure was 24.5-26.1 µg/day, with approximately 30% of intake from food. Among subjects living in homes with tap water As ≤10, 5 or 3 p.p.b., aggregate inorganic As exposure was 8.6-11.8 µg/day, with 54-85% of intake from food. Median inorganic As potential dose was 0.42-0.50 µg/kg BW/day in subjects exposed to tap water As >10 p.p.b. and less than half that among subjects exposed to tap water As ≤10 p.p.b. The majority of inorganic and total As exposure is attributable to diet in subjects with tap water As

Measured versus modeled dietary arsenic and relation to urinary arsenic excretion and total exposure.

Chronic exposure to arsenic (As) in food and water is a significant public health problem. Person-specific aggregate exposure is difficult to collect and modeling based on limited food As residue databases is of uncertain reliability. Two cross-sectional population exposure studies, the National Human Exposure Assessment Survey-Arizona and Arizona Border Survey, had a combined total of 252 subjects with diet, water, and urinary As data. Total As was measured in 24-h duplicate diet samples and modeled using 24-h diet diaries in conjunction with several published food surveys of As. Two-stage regression was used to assess the effects of dietary As on urinary total As (uAs): (1) generalized linear mixed models of uAs above versus below the limit of detection (LOD); and (2) restricted models limited to those subjects with uAs>LOD, using bootstrap sampling and mixed models adjusted for age, sex, body mass index, ethnicity, current smoking, and As intake from drinking and cooking water. In restricted models, measured and modeled estimates were significant predictors of uAs. Modeled dietary As based on Total Diet Study mean residues greatly underestimated the dietary intake. In households with tap water As ≤10 p.p.b., over 93% of total arsenic exposure was attributable to diet.

Environmental arsenic exposure and serum matrix metalloproteinase-9.

The objective of this study was to evaluate the relationship between environmental arsenic exposure and serum matrix metalloproteinase (MMP)-9, a biomarker associated with cardiovascular disease and cancer. In a cross-sectional study of residents of Arizona, USA (n=215) and Sonora, Mexico (n=163), drinking water was assayed for total arsenic, and daily drinking water arsenic intake was estimated. Urine was speciated for arsenic, and concentrations were adjusted for specific gravity. Serum was analyzed for MMP-9 using ELISA. Mixed model linear regression was used to assess the relation among drinking water arsenic concentration, drinking water arsenic intake, urinary arsenic sum of species (the sum of arsenite, arsenate, monomethylarsonic acid and dimethylarsinic acid), and MMP-9, controlling for autocorrelation within households. Drinking water arsenic concentration and intake were positively associated with MMP-9, both in crude analysis and after adjustment for gender, country/ethnicity, age, body mass index, current smoking, and diabetes. Urinary arsenic sum of species was positively associated with MMP-9 in multivariable analysis only. Using Akaike's Information Criterion, arsenic concentration in drinking water provided a better fitting model of MMP-9 than either urinary arsenic or drinking water arsenic intake. In conclusion, arsenic exposure evaluated using all three exposure metrics was positively associated with MMP-9.

Acute cardiovascular effects of firefighting and active cooling during rehabilitation.

OBJECTIVES: To determine the cardiovascular and hemostatic effects of fire suppression and postexposure active cooling. METHODS: Forty-four firefighters were evaluated before and after a 12-minute live-fire drill. Next, 50 firefighters performing the same drill were randomized to undergo postfire forearm immersion in 10 °C water or standard rehabilitation. RESULTS: In the first study, heart rate and core body temperature increased and serum C-reactive protein decreased but there were no significant changes in fibrinogen, sE-selectin, or sL-selectin. The second study demonstrated an increase in blood coagulability, leukocyte count, factors VIII and X, cortisol, and glucose, and a decrease in plasminogen and sP-selectin. Active cooling reduced mean core temperature, heart rate, and leukocyte count. CONCLUSIONS: Live-fire exposure increased core temperature, heart rate, coagulability, and leukocyte count; all except coagulability were reduced by active cooling.

Risk factors for subclinical atherosclerosis in firefighters.

OBJECTIVE: Heart disease is the leading cause of firefighter line-of-duty deaths. The study objectives were to identify early atherosclerotic disease through ultrasound measurement of carotid intima-media thickness (CIMT) and risk factors predicting increased CIMT and carotid plaque. METHODS: Following ultrasound evaluation of 597 Phoenix and Tucson firefighters, logistic regression was used to identify risk factors for mean CIMT greater than 75th percentile and for carotid plaque. RESULTS: Age, low-density lipoprotein cholesterol (LDL-C) of 100 mg/dL or more, and high-density lipoprotein cholesterol were significant independent predictors of increased CIMT. Age, hypertension, LDL-C, and plasma soluble P-selectin were significant predictors of carotid plaque. CONCLUSIONS: This study supports an emphasis on traditional risk factors for atherosclerotic disease in firefighters, in particular maintaining LDL-C less than 100 mg/dL. Plasma soluble P-selectin may help identify firefighters at increased risk for carotid plaque.

Familial aggregation of allergen-specific sensitization and asthma.

BACKGROUND: Familial aggregation of specific response to allergens and asthma adjusted for age and sensitization to multiple allergens was assessed in two large population cohorts. METHODS: Allergen skin prick tests (SPTs) were administered to 1151 families in the Tucson Children's Respiratory Study (CRS) and 435 families in the Tucson Epidemiological Study of Airway Obstructive Disease (TESAOD). Sensitization was defined by wheal size ≥3 mm; physician-diagnosed asthma at age ≥8 yr was based on questionnaires. Using S.A.G.E. 6.1 software ASSOC and FCOR, familial correlations of crude and adjusted phenotypes were evaluated. RESULTS: Crude estimates of parent-offspring (P-O) and sibling correlations were statistically significant for most allergens, ranging from 0.03 to 0.29. After adjusting for age of assessment and 'other atopy' (SPT-positive response to additional allergens), correlations were reduced by 14-71%. Sibling correlations for specific response to allergens were consistently higher than P-O correlations, but this difference was significant only for dust mite and weed mix in the TESAOD population. Familial correlation for atopic status (any positive SPTs vs. none) tended to be higher than for specific allergens. Asthma, with and without adjustment, showed greater familial correlation than either specific or general SPT response and significantly higher sibling correlation in TESAOD than in CRS, probably due to the older age of the siblings and the longer period of ascertainment. CONCLUSIONS: Significant familial aggregation of specific response to allergen after adjustment for other atopy appears to reflect a genetic propensity toward atopy, dependent on shared familial exposures. Results also suggest that inheritance of asthma is independent of atopic sensitization.