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Journal ArticleDOI

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

27 Apr 2018-Vol. 67, Iss: 6, pp 1-23

TL;DR: 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.
Abstract: 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] 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.
Topics: Modified Checklist for Autism in Toddlers (54%), Autism spectrum disorder (53%), Asperger syndrome (53%), Autism (52%), Population (52%)
Citations
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Journal ArticleDOI
Matthew J. Maenner1, Kelly A Shaw1, Jon Baio1, Anita Washington1  +30 moreInstitutions (12)
27 Mar 2020-
TL;DR: The prevalence of ASD varied considerably across sites and was higher than previous estimates since 2014, highlighting the variability in the evaluation and detection of ASD across communities and between sociodemographic groups.
Abstract: Problem/condition Autism spectrum disorder (ASD). Period covered 2016. Description of system The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance program that provides estimates of the prevalence of ASD among children aged 8 years whose parents or guardians live in 11 ADDM Network sites in the United States (Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin). Surveillance is conducted in two phases. The first phase involves review and abstraction of comprehensive evaluations that were completed by medical and educational service providers in the community. In the second phase, experienced clinicians who systematically review all abstracted information determine ASD case status. The case definition is based on ASD criteria described in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Results For 2016, across all 11 sites, ASD prevalence was 18.5 per 1,000 (one in 54) children aged 8 years, and ASD was 4.3 times as prevalent among boys as among girls. ASD prevalence varied by site, ranging from 13.1 (Colorado) to 31.4 (New Jersey). Prevalence estimates were approximately identical for non-Hispanic white (white), non-Hispanic black (black), and Asian/Pacific Islander children (18.5, 18.3, and 17.9, respectively) but lower for Hispanic children (15.4). Among children with ASD for whom data on intellectual or cognitive functioning were available, 33% were classified as having intellectual disability (intelligence quotient [IQ] ≤70); this percentage was higher among girls than boys (39% versus 32%) and among black and Hispanic than white children (47%, 36%, and 27%, respectively) [corrected]. Black children with ASD were less likely to have a first evaluation by age 36 months than were white children with ASD (40% versus 45%). The overall median age at earliest known ASD diagnosis (51 months) was similar by sex and racial and ethnic groups; however, black children with IQ ≤70 had a later median age at ASD diagnosis than white children with IQ ≤70 (48 months versus 42 months). Interpretation The prevalence of ASD varied considerably across sites and was higher than previous estimates since 2014. Although no overall difference in ASD prevalence between black and white children aged 8 years was observed, the disparities for black children persisted in early evaluation and diagnosis of ASD. Hispanic children also continue to be identified as having ASD less frequently than white or black children. Public health action These findings highlight the variability in the evaluation and detection of ASD across communities and between sociodemographic groups. Continued efforts are needed for early and equitable identification of ASD and timely enrollment in services.

1,729 citations


Cites background or methods from "Prevalence of Autism Spectrum Disor..."

  • ...8 per 1,000 (one in 59) children aged 8 years in 2014 (3)....

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  • ...Overall, the magnitude of prevalence differences by race and ethnicity has declined in recent years (3,17)....

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  • ...8 prevalence estimate the ADDM Network reported in 2014 (3) and approximately 175% higher than (2....

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  • ...The ADDM Network ASD case definition is based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), and the process for scoring the features of the surveillance case definition have been described previously (3,25,26)....

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  • ...The ADDM Network ASD surveillance methodology is a two-phase process that has been described previously (3)....

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Journal ArticleDOI
01 Apr 2016-
TL;DR: ASD prevalence estimates for children aged 8 years living in catchment areas of the ADDM Network sites in 2012 are provided, overall and stratified by sex, race/ethnicity, and the type of source records (education and health records versus health records only).
Abstract: 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.

1,371 citations


Journal ArticleDOI
Rachel Loomes1, Laura Hull1, William Mandy1Institutions (1)
TL;DR: The first systematically calculated estimate of the relative proportion of boys and girls with autism spectrum disorder (ASD) through a meta-analysis of prevalence studies conducted since the introduction of the DSM-IV and the International Classification of Diseases, Tenth Revision is derived.
Abstract: Objective To derive the first systematically calculated estimate of the relative proportion of boys and girls with autism spectrum disorder (ASD) through a meta-analysis of prevalence studies conducted since the introduction of the DSM-IV and the International Classification of Diseases, Tenth Revision . Method Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. The Medline, Embase, and PsycINFO databases were searched, and study quality was rated using a risk-of-bias tool. Random-effects meta-analysis was used. The pooled outcome measurement was the male-to-female odds ratio (MFOR), namely the odds of being male in the group with ASD compared with the non-ASD group. In effect, this is the ASD male-to-female ratio, controlling for the male-to-female ratio among participants without ASD. Results Fifty-four studies were analyzed, with 13,784,284 participants, of whom 53,712 had ASD (43,972 boys and 9,740 girls). The overall pooled MFOR was 4.20 (95% CI 3.84–4.60), but there was very substantial between-study variability (I 2 = 90.9%). High-quality studies had a lower MFOR (3.32; 95% CI 2.88–3.84). Studies that screened the general population to identify participants regardless of whether they already had an ASD diagnosis showed a lower MFOR (3.25; 95% CI 2.93–3.62) than studies that only ascertained participants with a pre-existing ASD diagnosis (MFOR 4.56; 95% CI 4.10–5.07). Conclusion Of children meeting criteria for ASD, the true male-to-female ratio is not 4:1, as is often assumed; rather, it is closer to 3:1. There appears to be a diagnostic gender bias, meaning that girls who meet criteria for ASD are at disproportionate risk of not receiving a clinical diagnosis.

882 citations


Journal ArticleDOI
06 Feb 2020-Cell
TL;DR: The largest exome sequencing study of autism spectrum disorder (ASD) to date, using an enhanced analytical framework to integrate de novo and case-control rare variation, identifies 102 risk genes at a false discovery rate of 0.1 or less, consistent with multiple paths to an excitatory-inhibitory imbalance underlying ASD.
Abstract: We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n = 35,584 total samples, 11,986 with ASD). Using an enhanced analytical framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate of 0.1 or less. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained to have severe neurodevelopmental delay, whereas 53 show higher frequencies in individuals ascertained to have ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In cells from the human cortex, expression of risk genes is enriched in excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory-inhibitory imbalance underlying ASD.

620 citations


Journal ArticleDOI
15 Jan 2017-NeuroImage
TL;DR: There is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders, however, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper.
Abstract: Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.

528 citations


References
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Journal ArticleDOI
Vijay A. Mittal1, Elaine F. Walker2Institutions (2)
TL;DR: An issue concerning the criteria for tic disorders is highlighted, and how this might affect classification of dyskinesias in psychotic spectrum disorders.
Abstract: Given the recent attention to movement abnormalities in psychosis spectrum disorders (e.g., prodromal/high-risk syndromes, schizophrenia) (Mittal et al., 2008; Pappa and Dazzan, 2009), and an ongoing discussion pertaining to revisions of the Diagnostic and Statistical Manuel of Mental Disorders (DSM) for the upcoming 5th edition, we would like to take this opportunity to highlight an issue concerning the criteria for tic disorders, and how this might affect classification of dyskinesias in psychotic spectrum disorders. Rapid, non-rhythmic, abnormal movements can appear in psychosis spectrum disorders, as well as in a host of commonly co-occurring conditions, including Tourette’s Syndrome and Transient Tic Disorder (Kerbeshian et al., 2009). Confusion can arise when it becomes necessary to determine whether an observed movement (e.g., a sudden head jerk) represents a spontaneous dyskinesia (i.e., spontaneous transient chorea, athetosis, dystonia, ballismus involving muscle groups of the arms, legs, trunk, face, and/or neck) or a tic (i.e., stereotypic or patterned movements defined by the relationship to voluntary movement, acute and chronic time course, and sensory urges). Indeed, dyskinetic movements such as dystonia (i.e., sustained muscle contractions, usually producing twisting and repetitive movements or abnormal postures or positions) closely resemble tics in a patterned appearance, and may only be visually discernable by attending to timing differences (Gilbert, 2006). When turning to the current DSM-IV TR for clarification, the description reads: “Tic Disorders must be distinguished from other types of abnormal movements that may accompany general medical conditions (e.g., Huntington’s disease, stroke, Lesch-Nyhan syndrome, Wilson’s disease, Sydenham’s chorea, multiple sclerosis, postviral encephalitis, head injury) and from abnormal movements that are due to the direct effects of a substance (e.g., a neuroleptic medication)”. However, as it is written, it is unclear if psychosis falls under one such exclusionary medical disorder. The “direct effects of a substance” criteria, referencing neuroleptic medications, further contributes to the uncertainty around this issue. As a result, ruling-out or differentiating tics in psychosis spectrum disorders is at best, a murky endeavor. Historically, the advent of antipsychotic medication in the 1950s has contributed to the confusion about movement signs in psychiatric populations. Because neuroleptic medications produce characteristic movement disorder in some patients (i.e. extrapyramidal side effects), drug-induced movement disturbances have been the focus of research attention in psychotic disorders. However, accumulating data have documented that spontaneous dyskinesias, including choreoathetodic movements, can occur in medication naive adults with schizophrenia spectrum disorders (Pappa and Dazzan, 2009), as well as healthy first-degree relatives of chronically ill schizophrenia patients (McCreadie et al., 2003). Taken together, this suggests that movement abnormalities may reflect pathogenic processes underlying some psychotic disorders (Mittal et al., 2008; Pappa and Dazzan, 2009). More specifically, because spontaneous hyperkinetic movements are believed to reflect abnormal striatal dopamine activity (DeLong and Wichmann, 2007), and dysfunction in this same circuit is also proposed to contribute to psychosis, it is possible that spontaneous dyskinesias serve as an outward manifestation of circuit dysfunction underlying some schizophrenia-spectrum symptoms (Walker, 1994). Further, because these movements precede the clinical onset of psychotic symptoms, sometimes occurring in early childhood (Walker, 1994), and may steadily increase during adolescence among populations at high-risk for schizophrenia (Mittal et al., 2008), observable dyskinesias could reflect a susceptibility that later interacts with environmental and neurodevelopmental factors, in the genesis of psychosis. In adolescents who meet criteria for a prodromal syndrome (i.e., the period preceding formal onset of psychotic disorders characterized by subtle attenuated positive symptoms coupled with a decline in functioning), there is sometimes a history of childhood conditions which are also characterized by suppressible tics or tic like movements (Niendam et al., 2009). On the other hand, differentiating between tics and dyskinesias has also complicated research on childhood disorders such as Tourette syndrome (Kompoliti and Goetz, 1998; Gilbert, 2006). We propose consideration of more explicit and operationalized criteria for differentiating tics and dyskinesias, based on empirically derived understanding of neural mechanisms. Further, revisions of the DSM should allow for the possibility that movement abnormalities might reflect neuropathologic processes underlying the etiology of psychosis for a subgroup of patients. Psychotic disorders might also be included among the medical disorders that are considered a rule-out for tics. Related to this, the reliability of movement assessment needs to be improved, and this may require more training for mental health professionals in movement symptoms. Although standardized assessment of movement and neurological abnormalities is common in research settings, it has been proposed that an examination of neuromotor signs should figure in the assessment of any patient, and be as much a part of the patient assessment as the mental state examination (Picchioni and Dazzan, 2009). To this end it is important for researchers and clinicians to be aware of differentiating characteristics for these two classes of abnormal movement. For example, tics tend to be more complex than myoclonic twitches, and less flowing than choreoathetodic movements (Kompoliti and Goetz, 1998). Patients with tics often describe a sensory premonition or urge to perform a tic, and the ability to postpone tics at the cost of rising inner tension (Gilbert, 2006). For example, one study showed that patients with tic disorders could accurately distinguish tics from other movement abnormalities based on the subjective experience of some voluntary control of tics (Lang, 1991). Another differentiating factor derives from the relationship of the movement in question to other voluntary movements. Tics in one body area rarely occur during purposeful and voluntary movements in that same body area whereas dyskinesia are often exacerbated by voluntary movement (Gilbert, 2006). Finally, it is noteworthy that tics wax and wane in frequency and intensity and migrate in location over time, often becoming more complex and peaking between the ages of 9 and 14 years (Gilbert, 2006). In the case of dyskinesias among youth at-risk for psychosis, there is evidence that the movements tend to increase in severity and frequency as the individual approaches the mean age of conversion to schizophrenia spectrum disorders (Mittal et al., 2008). As revisions to the DSM are currently underway in preparation for the new edition (DSM V), we encourage greater attention to the important, though often subtle, distinctions among subtypes of movement abnormalities and their association with psychiatric syndromes.

52,117 citations



Journal ArticleDOI
Mayada Elsabbagh1, Gauri Divan, Yun Joo Koh, Young Shin Kim2  +9 moreInstitutions (9)
01 Jun 2012-Autism Research
Abstract: We provide a systematic review of epidemiological surveys of autistic disorder and pervasive developmental disorders (PDDs) worldwide. A secondary aim was to consider the possible impact of geographic, cultural/ethnic, and socioeconomic factors on prevalence estimates and on clinical presentation of PDD. Based on the evidence reviewed, the median of prevalence estimates of autism spectrum disorders was 62/10 000. While existing estimates are variable, the evidence reviewed does not support differences in PDD prevalence by geographic region nor of a strong impact of ethnic/cultural or socioeconomic factors. However, power to detect such effects is seriously limited in existing data sets, particularly in low-income countries. While it is clear that prevalence estimates have increased over time and these vary in different neighboring and distant regions, these findings most likely represent broadening of the diagnostic concets, diagnostic switching from other developmental disabilities to PDD, service availability, and awareness of autistic spectrum disorders in both the lay and professional public. The lack of evidence from the majority of the world's population suggests a critical need for further research and capacity building in low- and middle-income countries. Autism Res 2012, 5: 160–179. © 2012 International Society for Autism Research, Wiley Periodicals, Inc.

1,698 citations


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01 Nov 2007-Pediatrics
TL;DR: This report addresses background information, including definition, history, epidemiology, diagnostic criteria, early signs, neuropathologic aspects, and etiologic possibilities in autism spectrum disorders, and provides an algorithm to help the pediatrician develop a strategy for early identification of children with autism Spectrum disorders.
Abstract: Autism spectrum disorders are not rare; many primary care pediatricians care for several children with autism spectrum disorders. Pediatricians play an important role in early recognition of autism spectrum disorders, because they usually are the first point of contact for parents. Parents are now much more aware of the early signs of autism spectrum disorders because of frequent coverage in the media; if their child demonstrates any of the published signs, they will most likely raise their concerns to their child's pediatrician. It is important that pediatricians be able to recognize the signs and symptoms of autism spectrum disorders and have a strategy for assessing them systematically. Pediatricians also must be aware of local resources that can assist in making a definitive diagnosis of, and in managing, autism spectrum disorders. The pediatrician must be familiar with developmental, educational, and community resources as well as medical subspecialty clinics. This clinical report is 1 of 2 documents that replace the original American Academy of Pediatrics policy statement and technical report published in 2001. This report addresses background information, including definition, history, epidemiology, diagnostic criteria, early signs, neuropathologic aspects, and etiologic possibilities in autism spectrum disorders. In addition, this report provides an algorithm to help the pediatrician develop a strategy for early identification of children with autism spectrum disorders. The accompanying clinical report addresses the management of children with autism spectrum disorders and follows this report on page 1162 [available at www.pediatrics.org/cgi/content/full/120/5/1162]. Both clinical reports are complemented by the toolkit titled "Autism: Caring for Children With Autism Spectrum Disorders: A Resource Toolkit for Clinicians," which contains screening and surveillance tools, practical forms, tables, and parent handouts to assist the pediatrician in the identification, evaluation, and management of autism spectrum disorders in children.

1,600 citations


30 Mar 2012-
TL;DR: This report provides updated ASD prevalence estimates from the 2008 surveillance year, representing 14 ADDM areas in the United States and characteristics of the population of children with ASDs are described, as well as detailed comparisons of the 2008 findings with those for the 2002 and 2006 surveillance years.
Abstract: According to this report, autism spectrum disorders (ASDs) are characterised by impairments in social interaction and communication and by restricted, repetitive, and stereotyped patterns of behaviour and symptoms are typically apparent before the age of three. The complex nature of these disorders, coupled with a lack of biologic markers for diagnosis and changes in clinical definitions over time, creates challenges in monitoring the prevalence of ASDs. Accurate reporting of data is essential to understand the prevalence of ASDs in the population and can help direct research. The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system that estimates the prevalence of ASDs and describes other characteristics among children aged 8 years whose parents or guardians reside within 14 ADDM sites in the United States. This report focuses on the prevalence of ASDs in 2008.

1,531 citations