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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

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Abstract and Figures

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.
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Surveillance Summaries / Vol. 68 / No. 2 April 12, 2019
U.S. Department of Health and Human Services
Centers for Disease Control and Prevention
Morbidity and Mortality Weekly Report
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
Surveillance Summaries
The MMWR series of publications is published by the Center for Surveillance, Epidemiology, and Laboratory Services, Centers for Disease Control and Prevention (CDC),
U.S. Department of Health and Human Services, Atlanta, GA 30329-4027.
Suggested citation: [Author names; first three, then et al., if more than six.] [Title]. MMWR Surveill Summ 2019;68(No. SS-#):[inclusive page numbers].
Centers for Disease Control and Prevention
Robert R. Redfield, MD, Director
Anne Schuchat, MD, Principal Deputy Director
Chesley L. Richards, MD, MPH, Deputy Director for Public Health Science and Surveillance
Rebecca Bunnell, PhD, MEd, Director, Office of Science
Barbara Ellis, PhD, MS, Acting Director, Office of Science Quality, Office of Science
Michael F. Iademarco, MD, MPH, Director, Center for Surveillance, Epidemiology, and Laboratory Services
MMWR Editorial and Production Staff (Serials)
Charlotte K. Kent, PhD, MPH, Editor in Chief
Christine G. Casey, MD, Editor
Mary Dott, MD, MPH, Online Editor
Teresa F. Rutledge, Managing Editor
David C. Johnson, Lead Technical Writer-Editor
Catherine B. Lansdowne, MS, Project Editor
Martha F. Boyd, Lead Visual Information Specialist
Maureen A. Leahy, Julia C. Martinroe,
Stephen R. Spriggs, Tong Yang,
Visual Information Specialists
Quang M. Doan, MBA, Phyllis H. King,
Terraye M. Starr, Moua Yang,
Information Technolog y Specialists
MMWR Editorial Board
Timothy F. Jones, MD, Chairman
Matthew L. Boulton, MD, MPH
Virginia A. Caine, MD
Katherine Lyon Daniel, PhD
Jonathan E. Fielding, MD, MPH, MBA
David W. Fleming, MD
William E. Halperin, MD, DrPH, MPH
Robin Ikeda, MD, MPH
Phyllis Meadows, PhD, MSN, RN
Jewel Mullen, MD, MPH, MPA
Jeff Niederdeppe, PhD
Patricia Quinlisk, MD, MPH
Stephen C. Redd, MD
Patrick L. Remington, MD, MPH
Carlos Roig, MS, MA
William Schaffner, MD
Morgan Bobb Swanson, BS
CONTENTS
Introduction ............................................................................................................ 2
Methods .................................................................................................................... 3
Results ....................................................................................................................... 8
Discussion ............................................................................................................. 10
Limitations ............................................................................................................ 12
Conclusion ............................................................................................................ 13
References ............................................................................................................. 13
Appendix ............................................................................................................... 15
Surveillance Summaries
MMWR / April 12, 2019 / Vol. 68 / No. 2 1
US Department of Health and Human Services/Centers for Disease Control and Prevention
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
Deborah L. Christensen, PhD1; Matthew J. Maenner, PhD1; Deborah Bilder, MD2; John N. Constantino, MD3; Julie Daniels, PhD4;
Maureen S. Durkin, PhD5; Robert T. Fitzgerald, PhD3; Margaret Kurzius-Spencer, PhD6; Sydney D. Pettygrove, PhD6; Cordelia Robinson, PhD7;
Josephine Shenouda, MS8; Tiffany White, PhD9; Walter Zahorodny, PhD8; Karen Pazol, PhD1; Patricia Dietz, DrPH1
1Division of Congenital and Developmental Disorders, National Center on Birth Defects and Developmental Disabilities, CDC
2University of Utah, Salt Lake City
3Washington University in St. Louis, Missouri
4University of North Carolina, Chapel Hill
5University of Wisconsin, Madison
6University of Arizona, Tucson
7University of Colorado School of Medicine, Aurora
8Rutgers New Jersey Medical School, Newark
9Colorado Department of Public Health and Environment, Denver
Abstract
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 childrens 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).
Surveillance Summaries
2 MMWR / April 12, 2019 / Vol. 68 / No. 2 US Department of Health and Human Services/Centers for Disease Control and Prevention
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.
Introduction
Autism spectrum disorder (ASD) is a developmental
disability marked by social and communication impairments,
as well as restricted interests and repetitive behaviors (1). ASD
prevalence has been measured by special education and other
administrative records (24), national surveys (59), and active
public health surveillance conducted through the Metropolitan
Atlanta Developmental Disabilities Surveillance Program
(MADDSP) and its extended surveillance network, the
Autism and Developmental Disabilities Monitoring (ADDM)
Network (1017). ASD prevalence was first measured by CDC
among children aged 3–10 years children by MADDSP in
1996 (16). In that analysis, the peak prevalence of ASD was
determined to be at age 8 years. Therefore, subsequent to that
report, CDC has reported ASD prevalence among children
aged 8 years based on data collected every 2 years from 2000
through 2014. Surveillance was conducted by MADDSP and
other sites across the United States that participated in the
ADDM Network. The most recent ASD prevalence estimate
from the ADDM Network was 16.8 per 1,000 children aged
8 years in 2014 (13), compared with 14.5 per 1,000 in 2012
(14) and 14.7 per 1,000 in 2010 (15).
Measuring ASD prevalence and age at diagnosis in elementary
school–aged children is expected to yield the most complete
information on ASD prevalence and characteristics (1315);
Surveillance Summaries
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US Department of Health and Human Services/Centers for Disease Control and Prevention
however, measuring ASD prevalence in preschool-aged children
provides more timely assessment of efforts to increase awareness
and early detection of ASD. Evidence linking early treatment
for ASD with improved outcomes (1821) implies that an
absence or delay in ASD identification could adversely affect
children by delaying interventions and initiation of special
services. The American Academy of Pediatrics supports early
identification in their recommendation that all children receive
ASD screening at ages 18 and 24 months (22). Each state has
programs to identify children with disabilities and provide
special services from birth through age 2 years; children at
risk for or with disabilities are eligible for early intervention
services through part C of the Individuals with Disabilities
Education Act (IDEA) (http://idea.ed.gov). Children aged
≥3 years with disabilities are eligible for evaluation and special
education services through part B of IDEA, and these services
are provided by public school systems (http://idea.ed.gov).
This report describes ASD prevalence estimates and
characteristics among children aged 4 years in the Early
ADDM Network for 2010, 2012, and 2014. Selected trend
analyses also are presented. The findings in this report can
be used by pediatric health care providers, early intervention
service providers, therapists, school psychologists, educators,
researchers, policymakers, and program administrators seeking
to understand and provide for the needs of persons with ASD
and their families. These data can be used to help plan for
service needs and initiate and implement policies that promote
early identification of children with ASD.
Methods
To estimate the prevalence of ASD in a younger age group,
seven of the 13 ADDM sites that conducted ASD surveillance
among children aged 8 years during 2010, 2012, 2014 (or all
these years) also collected ASD surveillance data for children
aged 4 years. These sites are collectively known as the Early
ADDM Network. The data for children aged 4 years were
collected in subsets of the ADDM geographic areas for children
aged 8 years.
Study Sites
The ADDM Network uses a multisite, multiple-source,
records-based surveillance method based on a model developed
by CDC’s MADDSP (16,23). In 2010, 2012, and 2014, a total
of 13 sites contributed data to the ADDM Network of ASD
surveillance among children aged 8 years for at least 1 year
(Alabama, Arizona, Arkansas, Colorado, Georgia, Maryland,
Minnesota, Missouri, New Jersey, North Carolina, Tennessee,
Utah, and Wisconsin). As part of the Early ADDM Network,
seven of these sites also conducted ASD surveillance and
reported data for children aged 4 years for at least 1 year. The
Early ADDM Network included areas of Arizona, Colorado,
Missouri, New Jersey, North Carolina, Utah, and Wisconsin
(Figure 1). Five Early ADDM sites participated in 2010 and
2012, and six sites participated in 2014. Three Early ADDM
sites (Arizona, Missouri, and New Jersey) contributed data and
had consistent data sources in all 3 surveillance years.
Because of resource constraints, Early ADDM surveillance
was not conducted for the total geographic area covered by
each study site’s ADDM surveillance for children aged 8 years;
rather, each Early ADDM Network surveillance area was a
subset of the site’s total ADDM surveillance area. Each Early
ADDM surveillance area included at least 8,000 children
aged 4 years and a similar number of children aged 8 years. In
comparison, the total ADDM surveillance areas for children
aged 8 years for each site included 9,767–51,161 children. The
Early ADDM surveillance areas were not random subsets of the
total surveillance areas for the respective sites but were selected
to form areas of full counties or school districts, within the total
ADDM surveillance area that met or exceeded the minimum
population size of 8,000 children aged 4 years. Therefore,
prevalence estimates for children aged 4 years generated by
the Early ADDM Network should not be interpreted as being
representative of the prevalence among children aged 4 years
for the total ADDM study area at a given site.
Children included in this analysis were born in 2006, 2008,
or 2010 for the surveillance years 2010, 2012, and 2014,
respectively, and had a parent or guardian who lived in the Early
ADDM Network surveillance area during all or part of the
specific surveillance year. Participating Early ADDM sites were
selected through a competitive review process and were not
selected to be nationally representative. A diverse population
was preferred during the review process. Each ADDM site
functioned as a public health authority under HIPAA (the
Health Insurance Portability and Accountability Act of 1996)
and met applicable local Institutional Review Board, privacy,
and confidentiality requirements (24).
Case Ascertainment
ADDM is an active surveillance system that does not
depend on family or professional reporting of an existing ASD
diagnosis or classification to determine ASD case status. Case
determination is a two-phase process. The first phase involves
review and abstraction of records at multiple data sources in
the community. In the second phase, all abstracted evaluations
are compiled and reviewed by trained study personnel to
determine ASD case status. Data sources are categorized as
either 1) education source type, including evaluations to
Surveillance Summaries
4 MMWR / April 12, 2019 / Vol. 68 / No. 2 US Department of Health and Human Services/Centers for Disease Control and Prevention
FIGURE 1. Early Autism and Developmental Disabilities Monitoring Network surveillance areas — seven sites, United States, 2010, 2012, and 2014
determine eligibility for special education services or 2) health
care source type, including diagnostic and developmental
evaluations. Evaluations must have been performed by a
qualified professional, such as a psychologist, physician,
physical therapist, occupational therapist, speech or language
pathologist, or educator. Children’s records are screened from
multiple data sources to determine eligibility for inclusion as a
potential case. Developmental assessments completed by a wide
range of health care and education providers are reviewed. All
Early ADDM Network sites had agreements in place to access
records at health care sources. Special education records (for
children aged ≥3 years) were reviewed in Arizona, Colorado,
New Jersey, North Carolina, and Utah, and early intervention
records (for children aged 0 to <3 years) were reviewed in New
Jersey, North Carolina, Utah, and Wisconsin; in Wisconsin,
early intervention records were reviewed for 2014 only. The
ADDM Network review only includes existing records, not
clinical examinations of children.
In the first phase of surveillance, ADDM Network sites
identify source records to review according to a child’s year of
birth and either 1) eligibility classifications in special education
or early intervention, or 2) International Classification of
Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)
or International Classification of Diseases, Tenth Revision
(ICD-10) billing codes for select childhood disabilities or
conditions. Childrens records are screened to confirm year
of birth and residency in the surveillance area at some time
during the surveillance year. For children meeting age and
residency requirements, the source files are screened for certain
behavioral or diagnostic descriptions defined by ADDM as
triggers for abstraction (e.g., child does not initiate interactions
with others, prefers to play alone or engage in solitary
activities, or has received a documented ASD diagnosis). If
abstraction triggers are found, evaluation information from
birth through the current surveillance year is abstracted into a
single composite record for each child. The composite record
includes comprehensive evaluations by qualified professionals
from birth through the end of the year when the child reaches
either age 4 or 8 years.
In the second phase of surveillance, the abstracted
comprehensive evaluations are deidentified and reviewed
systematically by clinicians who have undergone standardized
training to determine ASD case status using a coding scheme
based on the Diagnostic and Statistical Manual of Mental
Disorders, 4th Edition, Text Revision (DSM-IV-TR) (25)
criteria for ASD. These clinicians review each comprehensive
evaluation and code the behavioral descriptors according to
the DSM-IV-TR criteria represented by the descriptor.
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US Department of Health and Human Services/Centers for Disease Control and Prevention
Surveillance Case Definition
Children included in this analysis were born in 2006, 2008, or
2010 for the surveillance years 2010, 2012, and 2014, respectively,
and had a parent or guardian who lived in the Early ADDM
Network surveillance area during all or part of the specific
surveillance year. A child aged 4 or 8 years met the surveillance
case definition for ASD if behaviors described within one or more
comprehensive evaluations were consistent with the 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
(Box 1). An ASD diagnosis alone was not sufficient to meet the
DSM-IV-TR surveillance case definition but was considered
during the clinician review process, along with behavioral
criteria. Most records were reviewed by a single person,
although clinicians were able to request a second review if they
were uncertain about whether the behaviors were consistent
with the DSM-IV-TR diagnostic criteria. Children could have
been disqualified from meeting the case definition if their
behaviors met the surveillance case definition but one or more
clinician reviewers judged that sufficient information existed to
rule out ASD, information to support an ASD diagnosis was
conflicting or insufficient, or that one or more other diagnosed
conditions better accounted for their symptoms.
Updated behavioral criteria for an ASD diagnosis were
published in 2013 in the Diagnostic and Statistical Manual
of Mental Disorders, 5th Edition (DSM-5) (1). To determine
the effect of the updated DSM-5 behavioral criteria on ASD
prevalence, a revised surveillance case definition (Box 2) also
was used to classify cases for the 2014 surveillance year. A child
aged 4 or 8 years met the DSM-5 surveillance case definition if
behaviors described within one or more comprehensive evaluations
were consistent with the DSM-5 diagnostic criteria or if an
ASD diagnosis had been documented, regardless of whether the
behavioral criteria had been met. Most records were reviewed by
a single person, although clinicians were able to request a second
review if they were uncertain about whether the behaviors were
consistent with the DSM-5 diagnostic criteria. Children could
have been disqualified from meeting the case definition if their
behaviors met the surveillance case definition but one or more
clinician reviewers judged that sufficient information existed to
rule out ASD, information to support an ASD diagnosis was
conflicting or insufficient, or that one or more other diagnosed
conditions better accounted for their symptoms.
In this report, most results are based on the DSM-IV-TR
surveillance case definition for consistency and comparison
across surveillance years. Results comparing ASD prevalence
using both DSM-IV-TR and DSM-5 surveillance case
definitions are included for 2014.
Descriptive Characteristics
Demographic information, including sex and race/ethnicity,
was abstracted. Data on sex were available for all children.
Data on race/ethnicity were missing for <5% of children
across all years, age groups, and surveillance sites. Children
with missing race/ethnicity data were not included in analyses
stratified by race/ethnicity but were included in analyses of all
children combined. Each site obtained vital records data for the
relevant birth year, which were linked to surveillance data to
obtain supplemental information on race/ethnicity and other
demographic characteristics.
Diagnostic summaries from each evaluation were abstracted
for each child, including notation of any ASD diagnosis by
subtype. Children were considered to have an ASD diagnosis
from a community provider if they received a diagnosis of
autistic disorder, Asperger disorder, PDD-NOS, or ASD that
was documented in an abstracted evaluation at any time from
birth through the year when they reached age 4 or 8 years. The
age at each documented ASD diagnosis from a community
provider was abstracted, as well as the age at each comprehensive
developmental evaluation. These data were used to determine
the age at the earliest known ASD diagnosis, if any, and the
age at the first comprehensive developmental evaluation. Data
on age at first evaluation were restricted to children who were
born in the state where the ADDM Network site was located
to avoid bias from the inability to locate early evaluations for
children who moved into the study area. In-state birth was
determined through a successful match to a birth certificate
from that state. If no birth certificate was found, the child
was presumed to have been born outside the state where the
surveillance site was located. Because all children had at least
one evaluation, the age at the first evaluation was available for
all children and is reported as the median age (in months),
along with the percentage of children with a first evaluation by
age 36 months. This age was chosen to align with the Healthy
People 2020 (http://www.healthypeople.gov/2020/default.
aspx) goal of increasing the percentage of children with ASD
who receive their first developmental evaluation by the age of
36 months. Not all children had a documented ASD diagnosis
from a community provider; a total of 272 (34.7%), 318
(35.1%), and 508 (42.1%) children had no ASD diagnosis for
2010, 2012, and 2014, respectively. The age at earliest known
ASD diagnosis could be described only for those children with
a documented diagnosis and is reported as the median age in
months. Ages of <6 months at earliest known ASD diagnosis
were excluded for implausibility (n = 2).
Data were collected on results of standardized tests of
intellectual ability found in childrens records, and children
were considered to have an intellectual disability if they had
Surveillance Summaries
6 MMWR / April 12, 2019 / Vol. 68 / No. 2 US Department of Health and Human Services/Centers for Disease Control and Prevention
BOX 1. Surveillance case definition based on behavioral criteria for diagnosis of autism spectrum disorder: Diagnostic and Statistical Manual
of Mental Disorders, 4th Edition, Text Revision
DSM-IV-TR behavioral criteria
Social 1a. Marked impairment in the use of multiple nonverbal behaviors, such as eye-to-eye gaze, facial expression, body postures,
and gestures to regulate social interaction
1b. Failure to develop peer relationships appropriate to developmental level
1c. A lack of spontaneous seeking to share enjoyment, interests, or achievements with other people (e.g., by a lack of showing,
bringing, or pointing out objects of interest)
1d. Lack of social or emotional reciprocity
Communication 2a. Delay in, or total lack of, the development of spoken language (not accompanied by an attempt to compensate through
alternative modes of communication, such as gesture or mime)
2b. In individuals with adequate speech, marked impairment in the ability to initiate or sustain a conversation with others
2c. Stereotyped and repetitive use of language or idiosyncratic language
2d. Lack of varied, spontaneous make-believe play or social imitative play appropriate to developmental level
Restricted behavior/Interest 3a. Encompassing preoccupation with one or more stereotyped and restricted patterns of interest that is abnormal either in
intensity or focus
3b. Apparently inflexible adherence to specific, nonfunctional routines, or rituals
3c. Stereotyped and repetitive motor mannerisms (e.g., hand or finger flapping or twisting, or complex whole body movements)
3d. Persistent preoccupation with parts of objects
Developmental history Child had identified delays or any concern with development in the following areas at or before the age of 3 years:
Social, Communication, Behavior, Play, Motor, Attention, Adaptive, or Cognitive
Autism discriminators Oblivious to children
Oblivious to adults or others
Rarely responds to familiar social approach
Language primarily echolalia or jargon
Regression/loss of social, language, or play skills
Previous ASD diagnosis, whether based on DSM-IV-TR or DSM-5 diagnostic criteria
Lack of showing, bringing, etc.
Little or no interest in others
Uses others as tools
Repeats extensive dialog
Absent or impaired imaginative play
Markedly restricted interests
Unusual preoccupation
Insists on sameness
Nonfunctional routines
Excessive focus on parts
Visual inspection
Movement preoccupation
Sensory preoccupation
DSM-IV-TR surveillance case definition
At least six behaviors coded with a minimum of two Social, one Communication, and one Restricted Behavior/Interest; AND evidence of developmental delay
or concern at or before the age of 3 years
OR
At least two behaviors coded with a minimum of one Social and either one Communication and/or one Restricted Behavior/Interest; AND at least one autism
discriminator coded
Note: A child might be disqualified from meeting the DSM-IV-TR surveillance case definition for ASD if, based on the clinical judgment of one or more reviewers,
there is insufficient or conflicting information in support of ASD, sufficient information to rule out ASD, or if one or more other diagnosed conditions better
account for the child’s symptoms.
Abbreviations: ASD = autism spectrum disorder; DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision; DSM-5= Diagnostic
and Statistical Manual of Mental Disorders, Fifth Edition.
a score of ≤70 on their most recent test. Data on intellectual
ability were included for sites for which ≥60% of children
meeting the ASD surveillance case definition had an intellectual
ability test score. Among those sites, children without a test
score were categorized as having unknown intellectual ability
(n = 114 [18.8%], n = 114 [21.5%], and n = 225 [25.7%] for
2010, 2012, and 2014, respectively). Uncertainty surrounding
the reliability of measurement of intellectual ability in early
childhood prevents further subclassification of intellectual
ability (26,27).
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BOX 2. Surveillance case definition based on behavioral criteria for diagnosis of autism spectrum disorder*: Diagnostic and Statistical Manual
of Mental Disorders, 5th Edition
DSM-5 behavioral criteria
A. Persistent deficits in social
communication and social
interaction
A1: Deficits in social emotional reciprocity
A2. Deficits in nonverbal communicative behaviors
A3. Deficits in developing, maintaining, and understanding relationships
B. Restricted, repetitive patterns
of behavior, interests, or
activities, currently or by
history
B1: Stereotyped or repetitive motor movements, use of objects or speech
B2. Insistence on sameness, inflexible adherence to routines, or ritualized patterns of verbal or nonverbal behavior
B3. Highly restricted interests that are abnormal in intensity or focus
B4. Hyperreactivity or hyporeactivity to sensory input or unusual interest in sensory aspects of the environment
Historical pervasive developmental
disorder diagnosis
Any ASD diagnosis documented in a comprehensive evaluation, including a DSM-IV diagnosis of autistic disorder, Asperger
disorder, or pervasive developmental disorder–not otherwise specified
DSM-5 surveillance case definition
All three behavioral criteria coded under part A, and at least two behavioral criteria coded under part B
OR
Any ASD diagnosis documented in a comprehensive evaluation, whether based on DSM-IV-TR or DSM-5 diagnostic criteria
Note: A child might be disqualified from meeting the DSM-5 surveillance case definition for ASD if, based on the clinical judgment of one or more reviewers,
there is insufficient or conflicting information in support of ASD, sufficient information to rule out ASD, or if one or more other diagnosed conditions better
account for the child’s symptoms.
Abbreviations: ASD = autism spectrum disorder; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition; DSM-IV-TR = Diagnostic and
Statistical Manual of Mental Disorders, Fourth Edition, Text Revision; DSM-V = Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.
* DSM-5 also includes a previous DSM-IV diagnosis of ASD as a sole criterion for a clinical diagnosis.
Quality Assurance
All Early ADDM sites follow the same quality assurance
conventions established by the ADDM Network. For the
first phase of ADDM, screening and abstraction of source
records are checked periodically for accuracy. For the second
phase, interrater reliability receives ongoing monitoring, with
a blinded, random 10% sample of abstracted records that are
scored independently by two reviewers. Across surveillance
years, the final average interrater agreements for determining
ASD surveillance case status in the Early ADDM study sites
ranged from 87.3% (κ=0.74) to 91.1% (κ =0.81) among
children aged 4 years and from 89.2% (κ =0.77) to 91.0%
(κ =0.80) among those aged 8 years.
Analytic Methods
The objectives of this report are to describe ASD prevalence
and characteristics among children aged 4 years in the Early
ADDM Network for 2010, 2012, and 2014, including
1) overall prevalence and prevalence by sex and race/ethnicity;
2) characteristics of children aged 4 years with ASD, including
age at first developmental evaluation, age at ASD diagnosis,
and cognitive function; and 3) trends in ASD prevalence
and characteristics in the three Early ADDM sites with data
and consistent data sources for all 3 surveillance years (2010,
2012, and 2014), including comparisons with children aged
8 years living in the same geographic areas. Data for 2010
were previously published (28) but are included in the results
to provide a comprehensive representation of ASD prevalence
and characteristics for all the years of Early ADDM Network
surveillance, as well as a comparison among children from the
sites with data from all 3 surveillance years.
The prevalence estimate of ASD among children aged
4 years was calculated as the number of children aged 4 years
who met the ASD surveillance case definition in the Early
ADDM Network sites in 2010, 2012, and 2014 divided by
the number of children aged 4 years living in the surveillance
areas according to the 2010 decennial bridged-race population
estimates (29), the vintage 2014 postcensal bridged-race
population estimates for 2012 (http://www.cdc.gov/nchs), and
the vintage 2016 postcensal bridged-race population estimates
for 2014 (http://www.cdc.gov/nchs). In Arizona and Utah,
the surveillance area included some but not all of the school
districts in two counties (Maricopa and Salt Lake counties,
respectively). Therefore, investigators developed a method
using census and school district data to estimate the numbers
of children aged 4 and 8 years living in these surveillance areas.
Detailed methods are provided (Appendix). Overall prevalence
estimates included all children identified with ASD regardless
of sex, race/ethnicity, or intellectual ability and therefore were
unaffected by the availability of these data elements.
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Statistical tests and 95% confidence interval (CI) estimates
were derived under the assumption that the observed counts
of ASD surveillance cases were sampled from an underlying
Poisson distribution. Because previous ADDM Network
reports presented CIs based on an underlying Poisson
distribution with an asymptotic approximation to the
normal, slight differences might exist between those and the
exact Poisson confidence intervals presented in this report.
Generalized linear models with a Poisson distribution were
used to calculate prevalence ratios (PRs) and CIs. Pearson
chi-square tests were used to examine frequency differences in
the characteristics of children with ASD by surveillance area,
sex, race/ethnicity, and intellectual ability; ASD prevalence
was estimated both for children aged 4 years and 8 years
living in the Early ADDM surveillance areas. Because the
data for children aged 8 years are restricted to this smaller
area, the estimates for those aged 8 years do not match those
previously published from the ADDM Network reports on
ASD prevalence and characteristics (1315). Trend analyses
for ASD prevalence were restricted to the three sites (Arizona,
Missouri, and New Jersey) with data and consistent data
sources for all 3 years; trends in the proportion of children
with ASD who had co-occurring intellectual disabilities were
restricted to the two sites with data for all 3 years (Arizona
and New Jersey). Cochran-Armitage trend tests were used to
estimate the significance of changes in ASD characteristics
over the 2010–2014 period. The nonparametric median
test was used to determine differences in median age at first
developmental evaluation and earliest known ASD diagnosis
from 2010 to 2014 and by sex and race/ethnicity within
surveillance years. PRs with CIs that did not include 1.00
were used to assess whether ASD prevalence was higher in
one population than another. For results from chi-square,
Cochran-Armitage, and median tests, a p value of <0.05 was
considered significant. Analyses were performed using SAS
(version 9.4; SAS Institute).
Results
Population Distribution
The overall Early ADDM Network geographic surveillance
area includes the seven sites that participated in at least one
surveillance year (Figure 1). The Early ADDM Network
comprised a population from 58,467 (2010) to 70,887 (2014)
children aged 4 years and 56,727 (2010) to 71,928 (2014)
children aged 8 years (Supplemental Table 1, https://stacks.
cdc.gov/view/cdc/76016). The distribution of children by
race/ethnicity varied across the sites. Among children aged
4 years, the percentage of white children ranged from 29.4%
(New Jersey in 2014) to 70.9% (Wisconsin in 2014), and
the percentage of black children ranged from 3.5% (Arizona
in 2012 and 2014) to 33.1% (New Jersey in 2014). The
percentage of Hispanic children ranged from 4.5% (Missouri
in 2014) to 47.3% (Colorado in 2014). American Indian/
Alaska Native children comprised 0.2%–3.1% of the total
population, and Asian/Pacific Islander children comprised
2.7%–6.5%. The population distribution by race/ethnicity
across sites was similar for children aged 8 years. Aggregating
data across sites for each surveillance year, the total percentages
by race/ethnicity among children aged 4 years ranged from
46.8% to 51.9% for white (in 2014 and 2010, respectively),
19.1% to 22.7% for black (in 2010 and 2014, respectively),
23.2% to 25.1% for Hispanic (in 2010 and 2014, respectively),
4.7% to 5.0% for Asian/Pacific Islander (in 2014 and 2012,
respectively), and 0.7% to 0.9% for American Indian/Alaska
Native (in 2014 and 2010–2012, respectively), with similar
percentages among children aged 8 years.
Overall ASD Prevalence
Among Children Aged 4 Years
Aggregating data across participating surveillance sites for
each year, the estimated prevalence of ASD among children
aged 4 years was 13.4 per 1,000 in 2010, 15.3 in 2012, and
17.0 in 2014 (Table 1). Prevalence ranged from 8.1 per 1,000
children aged 4 years in Missouri (2012) to 28.4 in New Jersey
(2014). For each year, aggregated ASD prevalence was higher
for study sites that reviewed education and health care records
rather than health care records alone (Table 1); PRs for sites that
reviewed both types compared with only health care records
were 1.8 (95% CI: 1.6–2.2) in 2010, 1.6 (95% CI: 1.4–1.8)
in 2012, and 1.7 (95% CI: 1.5–2.0) in 2014 (data not shown).
ASD Prevalence
Among Children Aged 4 Years
by Sex and Race/Ethnicity
Across all sites and years, ASD prevalence per 1,000 boys
aged 4 years ranged from 12.2 in Missouri (2010) to 44.0
in New Jersey (2014) (Table 2). Prevalence per 1,000 girls
aged 4 years ranged from 3.2 in Missouri (2012) to 12.1
in New Jersey (2014). Male-to-female PRs indicated ASD
prevalence was higher among boys than girls in all sites and
years, ranging from 2.6 (Arizona and Wisconsin in 2010) to
5.2 boys per one girl (Colorado in 2014).
Across all study sites and years for children aged 4 years,
prevalence among white children ranged from 7.7 per 1,000
in Missouri (2014) to 29.3 in New Jersey (2014) (Table 3).
Prevalence among black children ranged from 3.8 per 1,000
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US Department of Health and Human Services/Centers for Disease Control and Prevention
in Missouri (2010) to 24.7 in New Jersey (2014). Prevalence
among Hispanic children ranged from 9.1 per 1,000 (in
Arizona (2010) to 28.2 in New Jersey (2014). In 2010, white
children had a higher ASD prevalence than Hispanic children in
Arizona (PR=1.7) and black children in Missouri (PR=2.5);
no other differences were observed by race/ethnicity.
Frequency of Co-Occurring
Intellectual Disabilities
Among Children Aged 4 and 8 Years
Scores on intellectual ability tests were available for at least
60% of children in four sites for at least one surveillance year
(Arizona, New Jersey, North Carolina, and Utah). These sites
all reviewed education and health care records. In the two
sites (Arizona and New Jersey) with data for all surveillance
years, the percentage of children aged 4 years with ASD who
had co-occurring intellectual disabilities was stable over time
at 47.0%, 43.6%, and 46.0% in 2010, 2012, and 2014,
respectively (test for trend p value =0.84) and also was stable
over time among both boys and girls (Table 4). The proportion
of children with ASD who had co-occurring intellectual
disabilities was significantly higher among children aged 4 years
than among those aged 8 years across all sites and surveillance
years, with the exception of Arizona (2010) (Supplemental
Table 2, https://stacks.cdc.gov/view/cdc/76016).
Age at First Comprehensive
Developmental Evaluation
Among Children Aged 4 Years
Across all participating sites and surveillance years and
among children born in the state where the ADDM Network
site was located, the percentage of children who received
their first comprehensive developmental evaluation by age
36 months ranged from 48.8% (Missouri in 2012) to 88.9%
(Wisconsin in 2014) (Table 5). Among the three sites with
data and consistent data sources for all 3 years, patterns in the
age at the first developmental evaluation varied by site. No
trend was observed in Arizona or Missouri. In New Jersey,
from 2010 to 2014, the percentage of children who received a
first evaluation by age 36 months decreased significantly (from
76.5% to 66.7%). In Wisconsin, the percentage of children
who received a first developmental evaluation by age 36 months
was higher in 2014 (88.9%), when early intervention records
were reviewed, than in 2010 and 2012 (69.0% and 73.4%,
respectively). Percentages stratified by sex and race/ethnicity
by site are provided (Supplemental Table 3, https://stacks.cdc.
gov/view/cdc/76016).
ASD Diagnosis from a Community
Provider Among Children Aged 4 Years
The percentage of children with a documented ASD
diagnosis from a community provider ranged from 43.0%
in Arizona (2012) to 86.5% in Missouri (2012) but did not
vary by sex (Table 6). The median age at first known ASD
diagnosis ranged from 28 months in North Carolina (2014)
to 39.0 months in Missouri and Wisconsin (2012). Among the
three sites with data for all 3 surveillance years and consistent
data sources, no significant trends were found in the proportion
of children with an ASD diagnosis, overall or by sex
Trends in ASD Prevalence
Among Children Aged 4 and 8 Years
Four Early ADDM Network sites (Arizona, Missouri, New
Jersey, and Wisconsin) participated in all 3 surveillance years;
however, Wisconsin reviewed early intervention records in
2014 but not earlier years, whereas data sources for other
sites were consistent across years. Among children aged
4 years, ASD prevalence was higher in 2014 than in 2010 in
New Jersey (PR: 1.4) but not in Arizona or Missouri (Figure 2;
Supplemental Table 4, https://stacks.cdc.gov/view/cdc/76016).
In Wisconsin, ASD prevalence was higher in 2012 and 2014
than in 2010. Among children aged 8 years living in the Early
ADDM Network geographical areas, ASD prevalence was
higher in 2014 than in 2010 in New Jersey (PR: 1.3) but not
in the other sites.
In Missouri and Wisconsin, ASD prevalence was higher
among children aged 8 years than among those aged 4 years in
all 3 years (Supplemental Table 4, https://stacks.cdc.gov/view/
cdc/76016). In Arizona, ASD prevalence was higher among
children aged 8 years than among those aged 4 years in 2012
only, and in New Jersey, no differences by age were found.
ASD Prevalence Using DSM-IV-TR and
DSM-5 Case Definitions
A revised ADDM Network ASD surveillance case definition
was developed for the 2014 surveillance year to provide ASD
prevalence estimates based on the updated DSM-5 diagnostic
criteria published in 2013. All sites reviewed childrens
records in the Early ADDM Network by both surveillance
case definitions to evaluate the effect on estimated prevalence
because of the change to DSM-5 diagnostic criteria. Among
children aged 4 years in the Early ADDM Network in 2014,
the prevalence of ASD using the DSM-5 surveillance case
definition was 14.1 compared with 17.0 for DSM-IV-TR
(DSM-IV-TR-to-DSM-5PR: 1.2) (Table 7). Among
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1,237 children who met the surveillance case definition for
either DSM-IV-TR or DSM-5, 974 (78.7%) met both case
definitions, 234 (18.9%) met the DSM-IV-TR but not the
DSM-5 case definition, and 29 (2.3%) met the DSM-5 but
not the DSM-IV-TR case definition.
Discussion
This report provides data on ASD prevalence among children
aged 4 years using ADDM surveillance methods across several
sites participating in the Early ADDM Network during 2010,
2012, and 2014. Among these children aged 4 years, overall
estimated ASD prevalence was 13.4 per 1,000 in 2010, 15.3 in
2012, and 17.0 in 2014. ASD prevalence was higher among boys
than girls. Across all sites and surveillance years, few differences
in ASD prevalence were found by race/ethnicity among children
aged 4 years, and those that were identified occurred in 2010
but not in later years. In the four sites that participated in Early
ADDM Network surveillance in all 3 years, ASD prevalence
among children aged 4 years was approximately 40% higher
in New Jersey in 2014 than in 2010 and similar across the
years in Arizona and Missouri. In Wisconsin, ASD prevalence
was significantly higher in 2014 than in 2010. However, the
availability of early intervention records in 2014 but not in
earlier years might have influenced the prevalence estimates for
that year, even though prevalence was similar in 2012 when early
intervention records were not reviewed.
The overall prevalence estimate using a DSM-IV-TR case
definition was approximately 20% higher than the prevalence
estimate based on DSM-5 criteria. Meeting the DSM-5
surveillance case definition required either documentation of
the more extensive behavioral criteria required for a DSM-5
diagnosis or an ASD diagnosis by a community provider, and
preschool-aged children might have had fewer comprehensive
evaluations containing behavioral information and been less
likely to have a diagnosis. For the 2016 surveillance year, all
ADDM Network surveillance sites will use the DSM-5 case
definition, and trends in the prevalence of ASD among children
aged 4 years and 8 years will be monitored according to this
surveillance case definition.
The estimated ASD prevalence in sites that reviewed both
education and health care records was 60%–80% higher than
the estimated ASD prevalence among sites that reviewed
only health care records. Although ASD prevalence varied
even among sites that reviewed education records, the total
prevalence among these sites (15.9, 17.4, and 19.3 per
1,000 children aged 4 years, respectively, for 2010–2014) is
likely a more sensitive estimate of ASD prevalence among
children aged 4 years, suggesting that the overall estimated
ASD prevalence in the Early ADDM Network would have
been higher had all sites had access to education records.
Early intervention records also are an important source of
information, particularly for tracking the age at earliest
evaluation. For example, the percentage of children evaluated
* In Arizona in 2012, the prevalence among children aged 4 years and children aged 8 years was significantly different (p<0.05 for chi-square test). In Missouri, the
prevalence was significantly different in all 3 years. (In New Jersey, no differences were significant in any years.)
FIGURE 2. Trends in autism spectrum disorder prevalence* among children aged 4 years and 8 years — Early Autism and Developmental
Disabilities Monitoring Network, three sites, United States, 2010, 2012, and 2014
New JerseyArizona
2010 2012 2014 2010 2012 2014 2010 2012 2014
Missouri
YearYear Year
0
5
10
15
20
25
30
Prevalence per 1,000 children aged 4 or 8 years
4 years
8 years
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US Department of Health and Human Services/Centers for Disease Control and Prevention
by age 36 months in Wisconsin was higher when early
intervention records were included for 2014 but not for earlier
years. Together, these findings suggest that early intervention
and public education systems are a critical community resource
for the evaluation of preschool-aged children who exhibit
social, communication, and behavioral impairments. Lack of
access to early intervention and education records, combined
with indications from earlier reports (1015) that many
children with ASD are not evaluated until after age 4 years,
suggests that the estimate of ASD prevalence among children
aged 4 years might be an underestimate of the actual ASD
prevalence in this birth cohort.
Other Studies of ASD Prevalence
Population-based data on the prevalence of ASD in
preschool-aged children are limited, and various case
ascertainment methods have been used; nevertheless, studies
indicate that the prevalence of ASD in this age group has been
higher in recent years. In 1996, estimated ASD prevalence
among children aged 4 years in MADDSP was 3.1 per 1,000
(95% CI: 2.6–3.7), and the estimated prevalence per 1,000
children aged 8 years was 4.7 (95% CI: 4.0–5.5) (16). A
study using similar methods conducted in Brick Township,
New Jersey, reported an estimated ASD prevalence of 7.8
per 1,000 children aged 3–5 years (95% CI: 5.1–11.3) in
1998 (30). A study from South Carolina in 2006 using
MADDSP methods found an ASD prevalence of 8.0 per 1,000
children aged 4 years (31). A population-based study in the
United Kingdom during 1998–1999 that used a multistage
screening and diagnosis methodology to identify children
with PDD reported a prevalence estimate of 6.3 per 1,000
children aged 3.5–6.5 years (32). Another study using the same
methods that was conducted several years later in a subsequent
birth cohort reported a prevalence estimate of 5.9 per 1,000
children aged 4–6 years (33). Approximately 10 years later, a
report from the 2007 National Survey of Children’s Health
(NSCH) described estimated ASD prevalence by parent or
caregiver report to be 8.5 per 1,000 children aged 3–5 years
(95% CI: 6.0–12.0), compared with 13.2 per 1,000 children
aged 6–8 years (95% CI: 9.6–18.3) (6,34). Most recently, the
2016 NSCH reported ASD prevalence estimates of 19.7 per
1,000 children aged 3–5 years, 26.1 per 1,000 children aged
6–11 years, and 26.5 per 1,000 children aged 12–17 years
(9). The most recent data from the National Health Interview
Survey showed a prevalence estimate (based on parent or
caregiver report) of 22.3 per 1,000 children aged 3–7 years in
2016, which was lower than the prevalence estimate among
children aged 8–12 years (28.8) (34).
In addition to the findings among preschool-aged children,
studies using different surveillance methods also have identified
higher ASD prevalence among children in recent years
(515,35). Several studies highlight changes in community
practice for recognizing and diagnosing ASD in children with
developmental concerns, as well as expansion of the diagnostic
criteria for ASD during 1987–2013 to include children with
fewer or more mild symptoms, as factors contributing to the
higher prevalence (3639). Although assessing whether ASD
prevalence trends are, in part, associated with changes in
etiologic risk is not possible with ADDM Network data, the
heterogeneity of Early ADDM Network prevalence estimates
across study sites, even among sites that reviewed both
education and health care records, supports the hypothesis
that differences in evaluation, diagnostic, and service practices
affect measured prevalence. Previous data from the ADDM
Network indicate a lower proportion of children with ASD
with co-occurring intellectual disabilities (1015) over time,
consistent with improvements in the identification of children
who have milder ASD. In addition, changes in the availability
of services for children with ASD through insurance mandates
(40), willingness of parents and providers to consider an ASD
diagnosis, and greater awareness of and concern regarding ASD
might contribute to the higher prevalence.
Early Identification of and
Intervention for ASD
The American Academy of Pediatrics prioritized the early
identification of ASD through its recommendation for
universal ASD screening during pediatric preventive care visits
at ages 18 and 24 months (22) and by the U.S. Department
of Health and Human Services through the Healthy People
2020 goal to increase the proportion of children with ASD who
receive their first evaluation by age 36 months. Evidence linking
early treatment for ASD with improved outcomes (1821,41)
implies that an absence or a delay in ASD identification could
delay interventions and initiation of special services. Identifying
the need for special services before school entry to minimize
educational disruption and optimize educational outcomes
might be especially important.
In this report, across all sites and surveillance years, the
median age at first known ASD evaluation among children
aged 4 years with ASD ranged from 23 to 37 months, and
48.8% to 88.9% received their first ASD evaluation by age
36 months. The percentage of children with an ASD diagnosis
varied widely by study site, ranging from 43.0% to 86.5%, with
sites that reviewed only health care records generally reporting
a greater percentage of children with an ASD diagnosis. This
is not unexpected because other sites include children based
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wholly or partly on review of education records, which might
not contain a formal ASD diagnosis. Among sites with data
from all surveillance years and consistent data sources, the age
at first evaluation was stable from 2010 to 2014 in Arizona and
Missouri. In New Jersey, the age at first evaluation increased
from 2010 to 2014. The Wisconsin site gained access to records
from early intervention services for children aged <3 years
for the 2014 surveillance year, which likely contributed to
detecting a greater number of children with a first evaluation
by age 36 months. Age at first evaluation might be easier
to lower than age at diagnosis because diagnosing ASD in
young children is challenging, which might be related to the
prodromal nature of autisms phenotypic onset that has recently
become apparent through longitudinal studies of infant siblings
at high risk for autism (42). However, greater awareness of
ASD might result in more children being identified, including
those with symptoms that do not fully manifest until the
child is close to school age, increasing prevalence while also
increasing the age of identification. Prevalence was higher
among children aged 8 years than among those aged 4 years in
some sites, which might reflect the identification of children
with milder symptoms later in development or on school entry;
this is supported by the difference in frequency of co-occurring
intellectual disabilities between children aged 4 and 8 years.
Efforts to identify developmental concerns as early as possible
and decrease the age at first evaluation for all children with ASD
are warranted. As recommended by the American Academy
of Pediatrics, universal screening might identify children
who need a comprehensive evaluation for ASD, even in the
absence of previous developmental concerns or co-occurring
intellectual disabilities, and improved tools for discerning the
signs of ASD among the range of typical childhood behaviors
might aid efforts to identify children earlier. Public health
campaigns such as Learn the Signs. Act Early. (https://www.
cdc.gov/ncbddd/actearly/index.html) provide informational
materials for parents, providers, and community members
aimed at improving awareness of developmental milestones
and increasing early identification of developmental delays so
that children can receive appropriate services and treatments
as early as possible.
No significant trends were found in the percentage of
children with a documented ASD diagnosis or in the age at
earliest known diagnosis. Children with an early evaluation
can begin to receive behavioral and developmental services
and interventions even if a formal ASD diagnosis is not made
at that time. However, a formal diagnosis might be necessary
to receive certain ASD services; therefore, the 35%–40% of
children who met the ASD surveillance case definition but did
not have a documented ASD diagnosis might not be eligible
for services that depend on an ASD diagnosis.
Limitations
This report is subject to several limitations. First, because
these ASD prevalence estimates are based on a record review,
with no clinical examination, Early ADDM Network data
reflect the information available in the source records. The
amount and quality of the data determine the potential for
a child to meet the ASD surveillance case definition and the
extent to which they can be used to describe the characteristics
of the identified population. Some children with ASD might
not have been included because their records were incomplete
or not available or they had not come to the attention of
schools or clinical providers, which might have resulted in an
underestimate of the ASD prevalence. Second, the types of
source records varied across surveillance sites, and the lack of
availability of education or early intervention records at some
sites might have led to an underestimate of ASD prevalence
among children aged 4 years in those sites and consequently for
the Early ADDM Network overall. Third, early diagnoses of
ASD might change if another diagnosis is determined to better
account for a child’s signs and symptoms (6,43,44), potentially
affecting the specificity of records-based surveillance. However,
the ADDM Network clinician review process allows clinicians
to change the ASD surveillance case status, even if the child
has a previous ASD diagnosis, which helps decrease potential
overestimates. Fourth, the availability of early intervention
records in Wisconsin for 2014 but not for earlier years
prevented the interpretation of changes in prevalence as well the
age at earliest developmental evaluation and ASD diagnosis for
that site. Fifth, measurement of intellectual ability in preschool-
aged children is less reliable than measurement among school-
aged children (26,27), preventing more specific classification
of intellectual ability among children with ASD other than
the presence or absence of intellectual disability. Sixth, data on
intellectual ability were not available for all children, and the
distribution of intellectual ability among the children with these
data might not be generalizable to all children with ASD in the
Early ADDM Network if the data on intellectual ability are not
randomly missing. For example, children without a cognitive
test score might not have been tested because their intellectual
ability was clearly in the average to above-average range, thus
overestimating the proportion of children with ASD and
co-occurring intellectual disabilities. Seventh, the surveillance
sites were selected through a competitive process and were not
selected to be representative of children aged 4 years either in
the United States or in the entire state in which surveillance
occurred. Therefore, the estimated prevalence of ASD is limited
to the surveillance areas. Finally, analyses of trends were limited
to three sites with data and consistent data sources for all 3
surveillance years, and within sites, data were sparse for certain
Surveillance Summaries
MMWR / April 12, 2019 / Vol. 68 / No. 2 13
US Department of Health and Human Services/Centers for Disease Control and Prevention
race/ethnicity groups. In addition, patterns of ASD prevalence
and characteristics varied by site; therefore, in some cases, data
could not be combined, limiting the statistical power.
Estimating ASD Prevalence Using
Surveillance Data
Surveillance data from the Early ADDM Network provides
1) population-based ascertainment of ASD using multiple
community data sources, including education and early
intervention records for some sites; 2) inclusion of children
with documentation of behaviors consistent with ASD but
without a documented ASD diagnosis; 3) data on intellectual
disability based on standardized tests of intellectual ability; and
4) collection of information on the age at first comprehensive
evaluation and ASD diagnosis, when present, that provide
information on early identification of children with ASD. The
record review method allows population-based estimates of ASD
prevalence to be generated cost-effectively. Obtaining data from
multiple community sources helps to improve the sensitivity of
the surveillance system; education and early intervention records
provide important information on services and early identification
of children with ASD. The inclusion of children without a
documented ASD diagnosis allows the surveillance system to
identify children who might have less access to the health care
system, such as children who receive evaluation services only in
school where a formal ASD diagnosis might not be provided.
Although the estimates are not representative of the United States
or the state where each site was located, surveillance conducted
in smaller areas close to evaluation and diagnostic centers might
provide a more valid prevalence estimate than for larger areas where
services might be lacking. Finally, the validity of the surveillance
system compared with clinical examination of children has been
assessed among children aged 8 years in a study using MADDSP
data, which concluded that the ADDM method was unlikely
to overestimate ASD prevalence, although some cases might be
missed that would be identified an in-person evaluation using
gold standard diagnostic instruments (45).
Conclusion
ASD surveillance among children aged 4 years provides
information on progress made toward early identification goals
and informs providers, particularly public schools, of upcoming
service needs. ASD prevalence was stable in some sites participating
in the Early ADDM Network and was higher in 2014 than
2010 in one site; the higher prevalence might reflect improved
identification of children with ASD by community providers.
Lack of access to education records in some sites might have
limited the sensitivity of records-based surveillance in those
sites. However, variations in prevalence did not always align
with access to data sources, and differences in evaluation and
diagnostic services among different areas might account for some
differences in findings across surveillance sites. This suggests that
opportunities for improvements in services might exist based on
successful programs implemented in specific areas. 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. No treatment for ASD
is available, although interventions might maximize each child’s
ability to function and participate in the community (1821,41).
Conflicts of Interest
Deborah Bilder reports personal fees from Audentes erapeutics
and personal fees from BioMarin Pharmaceuticals outside the
submitted work. John Constantino receives royalties from Western
Psychological Services for the commercial distribution of the Social
Responsiveness Scale.
References
1. American Psychiatric Association. Diagnostic and statistical manual
of mental disorders. 5th ed. Arlington, VA: American Psychiatric
Association; 2013.
2. Croen LA, Grether JK, Hoogstrate J, Selvin S. The changing prevalence
of autism in California. J Autism Dev Disord 2002;32:207–15. https://
doi.org/10.1023/A:1015453830880
3. Newschaffer CJ, Falb MD, Gurney JG. National autism prevalence trends
from United States special education data. Pediatrics 2005;115:e277–82.
https://doi.org/10.1542/peds.2004-1958
4. California Department of Developmental Services. Autistic spectrum
disorders: changes in the California caseload, an update: June 1987–
June 2007. Sacramento, CA: California Health and Human Services
Agency, Department of Developmental Services; 2007.
5. Blumberg SJ, Bramlett MD, Kogan MD, Schieve LA, Jones JR, Lu MC.
Changes in prevalence of parent-reported autism spectrum disorder in
school-aged U.S. children: 2007 to 2011–2012. Natl Health Stat Rep
2013;65:1–11.
6. Kogan MD, Blumberg SJ, Schieve LA, et al. Prevalence of parent-
reported diagnosis of autism spectrum disorder among children in the
US, 2007. Pediatrics 2009;124:1395–403. https://doi.org/10.1542/
peds.2009-1522
7. Schieve LA, Rice C, Yeargin-Allsopp M, et al. Parent-reported prevalence
of autism spectrum disorders in U.S.-born children: an assessment of
changes within birth cohorts from the 2003 to the 2007 National Survey
of Children’s Health. Matern Child Health J 2012;16(Suppl 1):S151–7.
https://doi.org/10.1007/s10995-012-1004-0
8. Zablotsky B, Black LI, Maenner MJ, Schieve LA, Blumberg SJ. Estimated
prevalence of autism and other developmental disabilities following
questionnaire changes in the 2014 National Health Interview Survey.
Natl Health Stat Report 2015;87:1–20.
9. Kogan MD, Vladutiu CJ, Schieve LA, et al. The prevalence of parent-
reported autism spectrum disorder among U.S. children. Pediatrics
2018;142:e20174161. https://doi.org/10.1542/peds.2017-4161
10. Autism and Developmental Disabilities Monitoring Network
Surveillance Year 2002 Principal Investigators. Prevalence of autism
spectrum disorders—Autism and Developmental Disabilities Monitoring
Network, four sites, United States, 2002. MMWR Surveill Summ
2007;56(No. SS-1).
Surveillance Summaries
14 MMWR / April 12, 2019 / Vol. 68 / No. 2 US Department of Health and Human Services/Centers for Disease Control and Prevention
11. Autism and Developmental Disabilities Monitoring Network Surveillance
Year 2006 Principal Investigators. Prevalence of autism spectrum
disorders—Autism and Developmental Disabilities Monitoring Network,
United States, 2006. MMWR Surveill Summ 2009;58(No. SS-10).
12. Autism and Developmental Disabilities Monitoring Network
Surveillance Year 2008 Principal Investigators. Prevalence of autism
spectrum disorders—Autism and Developmental Disabilities Monitoring
Network—four sites, United States, 2008. MMWR Surveill Summ
2012;61(No. SS-3).
13. Baio J, Wiggins L, Christensen DL, et al. Prevalence of autism spectrum
disorder among children aged 8 years—Autism and Developmental
Disabilities Monitoring Network, 11 sites, United States, 2014. MMWR
Surveill Summ 2018;67:1–23. https://doi.org/10.15585/mmwr.ss6706a1
14. Christensen DL, Braun KVN, Baio J, et al. Prevalence of autism spectrum
disorder among children aged 8 years—Autism and Developmental Disabilities
Monitoring Network, 11 sites, United States, 2012. MMWR Surveill
Summ 2018;65(No. SS-13). https://doi.org/10.15585/mmwr.ss6513a1
15. Autism and Developmental Disabilities Monitoring Network Surveillance
Year 2010 Principal Investigators. Prevalence of autism spectrum disorder
among children aged 8 years—Autism and Developmental Disabilities
Monitoring Network, 11 sites, United States, 2010. MMWR Surveill
Summ 2014;63(No. SS-2).
16. Yeargin-Allsopp M, Rice C, Karapurkar T, Doernberg N, Boyle C,
Murphy C. Prevalence of autism in a U.S. metropolitan area. JAMA
2003;289:49–55. https://doi.org/10.1001/jama.289.1.49
17. Autism and Developmental Disabilities Monitoring Network
Surveillance Year 2000 Principal Investigators. Prevalence of autism
spectrum disorders—autism and developmental disabilities monitoring
network, six sites, United States, 2000. MMWR Surveill Summ
2007;56:1–11.
18. Dawson G, Rogers S, Munson J, et al. Randomized, controlled trial of
an intervention for toddlers with autism: the Early Start Denver Model.
Pediatrics 2010;125:e17–23. https://doi.org/10.1542/peds.2009-0958
19. Eapen V, Crnčec R, Walter A. Clinical outcomes of an early intervention
program for preschool children with Autism Spectrum Disorder in
a community group setting. BMC Pediatr 2013;13:3. https://doi.
org/10.1186/1471-2431-13-3
20. Reichow B, Barton EE, Boyd BA, Hume K. Early intensive behavioral
intervention (EIBI) for young children with autism spectrum disorders
(ASD). Cochrane Database Syst Rev 2012;10:CD009260. https://doi.
org/10.1002/14651858.CD009260.pub2
21. Rogers SJ, Estes A, Lord C, et al. Effects of a brief Early Start Denver
model (ESDM)-based parent intervention on toddlers at risk for
autism spectrum disorders: a randomized controlled trial. J Am Acad
Child Adolesc Psychiatry 2012;51:1052–65. https://doi.org/10.1016/j.
jaac.2012.08.003
22. Johnson CP, Myers SM; American Academy of Pediatrics Council on
Children With Disabilities. Identification and evaluation of children with
autism spectrum disorders. Pediatrics 2007;120:1183–215. https://doi.
org/10.1542/peds.2007-2361
23. Rice CE, Baio J, Van Naarden Braun K, Doernberg N, Meaney FJ,
Kirby RS; ADDM Network. A public health collaboration for the
surveillance of autism spectrum disorders. Paediatr Perinat Epidemiol
2007;21:179–90. https://doi.org/10.1111/j.1365-3016.2007.00801.x
24. US Department of Health and Human Services. Code of Federal
Regulations. Title 45. Public Welfare CFR 46. Washington, DC: US
Department of Health and Human Services; 2010.
25. American Psychiatric Association. Diagnostic and statistical manual of
mental disorders. 4th ed, Text Revision. Washington, DC: American
Psychiatric Association; 2000.
26. Lord C, Schopler E. The role of age at assessment, developmental level,
and test in the stability of intelligence scores in young autistic children.
J Autism Dev Disord 1989;19:483–99. https://doi.org/10.1007/
BF02212853
27. Sattler J. Assessment of children’s intelligence and special abilities.
Boston, MA: Allyn & Bacon; 1982.
28. Christensen DL, Bilder DA, Zahorodny W, et al. Prevalence and
characteristics of autism spectrum disorder among 4-year-old children in the
Autism and Developmental Disabilities Monitoring Network. J Dev Behav
Pediatr 2016;37:1–8. https://doi.org/10.1097/DBP.0000000000000235
29. US Census Bureau. Census summary file 1: Tables PCT12H–PCT12O.
Washington, DC: US Census Bureau; 2010.
30. Bertrand J, Mars A, Boyle C, Bove F, Yeargin-Allsopp M, Decoufle P.
Prevalence of autism in a United States population: the Brick Township,
New Jersey, investigation. Pediatrics 2001;108:1155–61. https://doi.
org/10.1542/peds.108.5.1155
31. Nicholas JS, Carpenter LA, King LB, Jenner W, Charles JM.
Autism spectrum disorders in preschool-aged children: prevalence
and comparison to a school-aged population. Ann Epidemiol
2009;19:808–14. https://doi.org/10.1016/j.annepidem.2009.04.005
32. Chakrabarti S, Fombonne E. Pervasive developmental disorders in
preschool children. JAMA 2001;285:3093–9. https://doi.org/10.1001/
jama.285.24.3093
33. Chakrabarti S, Fombonne E. Pervasive developmental disorders in
preschool children: confirmation of high prevalence. Am J Psychiatry
2005;162:1133–41. https://doi.org/10.1176/appi.ajp.162.6.1133
34. Zablotsky B, Black LI, Blumberg SJ. Estimated prevalence of children
with diagnosed developmental disabilities in the United States, 2014–
2016. NCHS Data Brief, No. 291. Hyattsville, MD: CDC, National
Center for Health Statistics; 2017;291:1–8.
35. Boyle CA, Boulet S, Schieve LA, et al. Trends in the prevalence of
developmental disabilities in U.S. children, 1997–2008. Pediatrics
2011;127:1034–42. https://doi.org/10.1542/peds.2010-2989
36. Hansen SN, Schendel DE, Parner ET. Explaining the increase in the
prevalence of autism spectrum disorders: the proportion attributable to
changes in reporting practices. JAMA Pediatr 2015;169:56–62. https://
doi.org/10.1001/jamapediatrics.2014.1893
37. Hertz-Picciotto I, Delwiche L. The rise in autism and the role of age
at diagnosis. Epidemiology 2009;20:84–90. https://doi.org/10.1097/
EDE.0b013e3181902d15
38. Lundström S, Reichenberg A, Anckarsäter H, Lichtenstein P, Gillberg C.
Autism phenotype versus registered diagnosis in Swedish children:
prevalence trends over 10 years in general population samples.
BMJ 2015;350:h1961. https://doi.org/10.1136/bmj.h1961
39. Nassar N, Dixon G, Bourke J, et al. Autism spectrum disorders in
young children: effect of changes in diagnostic practices. Int J Epidemiol
2009;38:1245–54. https://doi.org/10.1093/ije/dyp260
40. Mandell DS, Barry CL, Marcus SC, et al. Effects of autism spectrum
disorder insurance mandates on the treated prevalence of autism
spectrum disorder. JAMA Pediatr 2016;170:887–93. https://doi.
org/10.1001/jamapediatrics.2016.1049
41. Dawson G, Jones EJ, Merkle K, et al. Early behavioral intervention is
associated with normalized brain activity in young children with autism.
J Am Acad Child Adolesc Psychiatry 2012;51:1150–9. https://doi.
org/10.1016/j.jaac.2012.08.018
42. Piven J, Elison JT, Zylka MJ. Toward a conceptual framework for
early brain and behavior development in autism. Mol Psychiatry
2017;22:1385–94. https://doi.org/10.1038/mp.2017.131
43. Pringle B, Colpe LJ, Blumberg SJ, Avila RM, Kogan MD. Diagnostic
history and treatment of school-aged children with autism spectrum
disorder and special health care needs. NCHS Data Brief 2012;97:1–8.
44. Blumberg SJ, Zablotsky B, Avila RM, Colpe LJ, Pringle BA, Kogan MD.
Diagnosis lost: differences between children who had and who currently
have an autism spectrum disorder diagnosis. Autism 2016;20:783–95.
https://doi.org/10.1177/1362361315607724
45. Avchen RN, Wiggins LD, Devine O, et al. Evaluation of a records-
review surveillance system used to determine the prevalence of autism
spectrum disorders. J Autism Dev Disord 2011;41:227–36. https://doi.
org/10.1007/s10803-010-1050-7
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US Department of Health and Human Services/Centers for Disease Control and Prevention
Appendix
Detailed Method for Estimating Surveillance Area Population Size of Partial Counties
For 2010, the number of children aged 4 years by sex and race/ethnicity was obtained for each census tract in the county from
the 2010 decennial census counts. Next, each census tract was matched to the school district or districts to which it was fully or
partially allocated, using the MABLE/Geocorr12: Geographic Correspondence Engine provided by the Missouri Census Data
Center (http://mcdc.missouri.edu). A list of excluded or partially excluded census tracts was compiled, and the number of
children aged 4 years living in these census tracts was subtracted from the overall and sex- and race-specic total numbers of
children in the county. For census tracts that were partially allocated to a school district, weighting was based on the 2010 census
population of the county. Finally, population counts of children aged 4 years in each specic census race/ethnicity category (white,
black, American Indian/Alaska Native, Asian/Pacic Islander, other race/ethnicity, multiracial, and Hispanic) were adjusted to
the distribution of the National Center on Health Statistics (NCHS) bridged-race category counts for the county, thereby
incorporating children categorized in the census counts as multiracial or other race into the bridged-race categories reported by
NCHS (white, black, Hispanic, American Indian/Alaska Native, and Asian/Pacic Islander). e same methods were used to
estimate the prevalence of ASD among children aged 8 years living in the Early ADDM Network surveillance area.
For the nondecennial census years 2012 and 2014, denominators for sites that covered less than a full county were estimated by
using school enrollment counts for the appropriate grades in the covered area and applying the distribution of these counts to the
county-level bridged-race postcensal population estimates from NCHS (http://www.cdc.gov/nchs).
Surveillance Summaries
16 MMWR / April 12, 2019 / Vol. 68 / No. 2 US Department of Health and Human Services/Centers for Disease Control and Prevention
TABLE 1. Prevalence* of autism spectrum disorder among children
aged 4 years — Autism and Developmental Disabilities Monitoring
Network, seven sites, United States, 2010, 2012, and 2014
Year, record source,
and site
No. with
ASD
Total
population Prevalence (95% CI)
2010
Health care and education
Arizona123 9,265 13.3 (11.0–15.8)
New Jersey§,¶ 352 17,860 19.7 (17.7–21.9)
Utah¶,** 132 10,944 12.1 (10.1–14.3)
Total 607 38,069 15.9 (14.7–17.3)
Health care only
Missouri†† 103 12,095 8.5 (7.0–10.3)
Wisconsin§§ 73 8,303 8.8 (6.9–11.1)
Total 176 20,398 8.6 (7.4–10.0)
Combined total 783 58,467 13.4 (12.5–14.4)
2012
Health care and education
Arizona 128 9,621 13.3 (11.1–15.8)
New Jersey403 18,223 22.1 (20.0–24.4)
Utah152 11,398 13.3 (11.3–15.6)
Total 683 39,242 17.4 (16.1–18.8)
Health care only
Missouri 96 11,878 8.1 (6.5–9.9)
Wisconsin 128 8,336 15.4 (12.8–18.3)
Total 224 20,214 11.1 (9.7–12.6)
Combined total 907 59,456 15.3 (14.3–16.3)
2014
Health care and education
Arizona 130 9,624 13.5 (11.3–16.0)
Colorado¶¶ 113 8,438 13.4 (11.0–16.1)
New Jersey514 18,112 28.4 (26.0–30.9)
North Carolina¶,*** 231 14,893 15.5 (13.6–17.6)
Total 988 51,067 19.3 (18.2–20.6)
Health care only
Missouri 112 11,613 9.6 (7.9–11.6)
Wisconsin108 8,207 13.2 (10.8–15.9)
Total 220 19,820 11.1 (9.7–12.7)
Combined total 1,208 70,887 17.0 (16.1–18.0)
Abbreviations: ASD=autism spectrum disorder; CI=confidence interval.
* Prevalence per 1,000 children aged 4 years living in the surveillance areas
according to the 2010 decennial bridged-race population estimates
(US Census Bureau. Census summary file 1: Tables PCT12H–PCT12O.
Washington, DC: US Census Bureau; 2010), the vintage 2014 postcensal
bridged-race population estimates for 2012 (http://www.cdc.gov/nchs), and
the vintage 2016 postcensal bridged-race population estimates for 2014
(http://www.cdc.gov/nchs).
Part of one county in metropolitan Phoenix for 2010, 2012, and 2014.
§ Essex and Union counties for 2010, 2012, and 2014.
Site also reviewed records from early intervention sources.
** Tooele County, part of Salt Lake County, for 2010 and 2012 only.
†† One county in metropolitan St. Louis for 2010, 2012, and 2014.
§§ Dane and Rock counties for 2010, 2012, and 2014.
¶¶ One county in metropolitan Denver for 2014 only.
*** Alamance, Chatham, Guilford, Orange, and Forsyth counties for 2014 only.
TABLE 2. Prevalence* of autism spectrum disorder among children
aged 4 years, by sex — Early Autism and Developmental Disabilities
Monitoring Network, seven sites, United States, 2010, 2012, and 2014
Year,
record source,
and site
Sex
Prevalence ratio,
male to female
(95% CI)
Male Female
Prevalence
(95% CI)
Prevalence
(95% CI)
2010
Health care and education
Arizona 18.9 (15.2–23.3) 7.3 (5.0–10.3) 2.6 (1.7–3.9)
New Jersey§31.7 (28.1–35.5) 7.2 (5.5–9.2) 4.4 (3.3–5.8)
Utah§17.9 (14.6–21.7) 5.9 (4.0–8.3) 3.1 (2.0–4.6)
Health care only
Missouri 12.2 (9.6–15.3) 4.6 (3.0–6.7) 2.7 (1.7–4.1)
Wisconsin 12.5 (9.4–16.4) 4.8 (2.9–7.4) 2.6 (1.6–4.4)
2012
Health care and education
Arizona 21.3 (17.5–25.8) 4.6 (2.8–7.0) 4.7 (2.9–7.5)
New Jersey§33.6 (30.0–37.5) 9.9 (8.0–12.2) 3.4 (2.7–4.3)
Utah§20.7 (17.2–24.8) 5.7 (3.9–8.1) 3.6 (2.5–5.4)
Health care only
Missouri 12.9 (10.2–16.2) 3.2 (1.9–5.0) 4.0 (2.4–6.7)
Wisconsin 23.7 (19.4–28.8) 6.4 (4.2–9.4) 3.7 (2.4–5.7)
2014
Health care and education
Arizona 21.3 (17.4–25.8) 5.2 (3.3–7.7) 4.1 (2.7–6.4)
Colorado 22.3 (18.1–27.3) 4.3 (2.5–6.8) 5.2 (3.1–8.6)
New Jersey§44.0 (39.9–48.5) 12.1 (10.0–14.7) 3.6 (2.9–4.5)
North
Carolina§
24.7 (21.3–28.5) 5.8 (4.2–7.8) 4.2 (3.0–5.9)
Health care only
Missouri 14.2 (11.3–17.5) 4.8 (3.2–7.0) 3.0 (1.9–4.6)
Wisconsin§20.8 (16.7–25.6) 4.8 (2.9–7.6) 4.3 (2.6–7.1)
Abbreviations: CI = confidence interval; PR = prevalence ratio.
* Prevalence per 1,000 children age 4 years living in the surveillance areas
according to the 2010 decennial bridged-race population estimates (US Census
Bureau. Census summary file 1: Tables PCT12H–PCT12O. Washington, DC:
US Census Bureau; 2010), the vintage 2014 postcensal bridged-race population
estimates for 2012 (http://www.cdc.gov/nchs), and the vintage 2016 postcensal
bridged-race population estimates for 2014 (http://www.cdc.gov/nchs).
Results for PRs considered statistically significant when the CI excludes the
null value (PR = 1.0).
§ Site also reviewed records from early intervention sources.
Surveillance Summaries
MMWR / April 12, 2019 / Vol. 68 / No. 2 17
US Department of Health and Human Services/Centers for Disease Control and Prevention
TABLE 3. Prevalence* of autism spectrum disorder among children aged 4 years, by race/ethnicity — Early Autism and Developmental Disabilities
Monitoring Network, seven sites, United States, 2010, 2012, and 2014
Year, record source, and site
Prevalence (95% CI) Prevalence ratio (95% CI)
White, non-Hispanic Black, non-Hispanic Hispanic White to black White to Hispanic
2010
Health care and education
Arizona 15.7 (12.4–19.7) 9.1 (6.2–12.9) 1.7 (1.1–2.6)
New Jersey§18.9 (15.5–22.7) 16.7 (13.6–20.4) 22.5 (18.6–27.0) 1.1 (0.9–1.5) 0.8 (0.6–1.1)
Utah§14.0 (11.3–17.2) 9.8 (6.7–13.8) 1.4 (1.0–2.1)
Health care only
Missouri 9.3 (7.2–11.9) 3.8 (2.1–6.4) 14.4 (6.2–28.4) 2.5 (1.4–4.4) 0.6 (0.3–1.4)
Wisconsin 8.2 (6.0–10.9)
2012
Health care and education
Arizona 14.5 (11.4–18.1) 20.7 (8.3–42.7) 9.9 (6.8–13.8) 0.7 (0.3–1.5) 1.5 (1.0–2.2)
New Jersey§24.2 (20.3–28.5) 19.3 (15.9–23.1) 22.3 (18.6–26.6) 1.3 (1.0–1.6) 1.1 (0.8–1.4)
Utah§14.3 (11.5–17.5) 11.3 (8.1–15.4) 1.3 (0.9–1.8)
Health care only
Missouri 8.3 (6.3–10.8) 7.6 (5.1–11.0) 1.1 (0.7–1.7)
Wisconsin 13.9 (11.0–17.2) 7.6 (3.0–15.6) 15.6 (9.1–24.9) 1.8 (0.8–4.0) 0.9 (0.5–1.5)
2014
Health care and education
Arizona 15.2 (12.0–18.8) 14.9 (4.8–34.8) 11.1 (7.8–15.4) 1.0 (0.4–2.5) 1.4 (0.9–2.0)
Colorado 11.7 (8.3–16.2) 18.0 (10.5–28.9) 12.3 (9.1–16.2) 0.7 (0.4–1.2) 1.0 (0.6–1.5)
New Jersey§29.3 (24.8–34.2) 24.7 (20.9–29.0) 28.2 (24.1–32.8) 1.2 (0.9–1.5) 1.0 (0.8–1.3)
North Carolina§14.6 (11.8–17.8) 16.8 (13.2–21.0) 10.9 (7.5–15.3) 0.9 (0.6–1.2) 1.3 (0.9–2.0)
Health care only
Missouri 7.7 (5.8–10.1) 10.4 (7.3–14.3) 0.7 (0.5–1.1)
Wisconsin§13.1 (10.3–16.3) 9.7 (4.2–19.1) 11.5 (5.9–20.0) 1.3 (0.6–2.8) 1.1 (0.6–2.1)
Abbreviations: CI=confidence interval; PR = prevalence ratio.
* Prevalence per 1,000 children aged 4 years living in the surveillance areas according to the 2010 decennial bridged-race population estimates (US Census Bureau.
Census summary file 1: Tables PCT12H–PCT12O. Washington, DC: US Census Bureau; 2010), the vintage 2014 postcensal bridged-race population estimates for 2012
(http://www.cdc.gov/nchs), and the vintage 2016 postcensal bridged-race population estimates for 2014 (http://www.cdc.gov/nchs).
Results for PRs considered statistically significant when the CI excludes the null value (PR = 1.0).
§ Site also reviewed records from early intervention sources.
Estimates suppressed due to small cell sizes (N<5).
TABLE 4. Number and percentage of children with co-occurring intellectual disability* among children aged 4 years with autism spectrum disorder,
by site, sex, and year — Early Autism and Developmental Disabilities Monitoring Network, four sites, United States, 2010, 2012, and 2014
Site and
sex
2010 2012 2014 2010–2014
Children
with cognitive
test scores
Children
with co-occurring
intellectual disability
Children
with cognitive
test scores
Children
with co-occurring
intellectual disability
Children
with cognitive
test scores
Children
with co-occurring
intellectual disability
p value§
No.
(% of children
with ASD) No. (%)
No.
(% of children
with ASD) No. (%)
No.
(% of children
with ASD) No. (%)
Site
Arizona 105 (85.4) 43 (41.0) 80 (62.5) 33 (41.3) 90 (69.2) 45 (50.0) 0.21
New Jersey 291 (82.7) 143 (49.1) 337 (83.6) 149 (44.2) 418 (81.3) 189 (45.2) 0.34
North
Carolina
142 (61.5) 64 (45.1)
Utah 97 (73.5) 40 (41.2)
Sex**
Male 312 (82.3) 152 (48.7) 334 (79.1) 146 (43.7) 409 (79.9) 191 (46.7) 0.65
Female 84 (87.5) 34 (40.5) 83 (76.1) 36 (43.4) 99 (75.0) 43 (43.4) 0.69
Total** 396 (83.4) 186 (47.0) 417 (78.5) 182 (43.6) 508 (78.9) 234 (46.1) 0.84
Abbreviation: ASD=autism spectrum disorder.
* Defined as a score of ≤70 on the most recent standardized cognitive ability test.
Including sites for which at least 60% of children with ASD had cognitive ability test score data for at least 1 surveillance year.
§ Cochran-Armitage trend test for percentage with intellectual disability; p<0.05 indicates statistical significance.
No or insufficient data for site and surveillance year.
** Data restricted to sites with information for all 3 years (Arizona and New Jersey).
Surveillance Summaries
18 MMWR / April 12, 2019 / Vol. 68 / No. 2 US Department of Health and Human Services/Centers for Disease Control and Prevention
TABLE 5. Median age at earliest known comprehensive evaluation and percentage of children evaluated by age 36 months among children
aged 4 years with autism spectrum disorder — Early Autism and Developmental Disabilities Monitoring Network, seven sites, United States,
2010, 2012, and 2014
Site and
record source
2010 2012 2014
p value*
Median age
(months)
Total no.
with ASD
No. (%) with
evaluation
by 36 months
Median age
(months)
Total no.
with ASD
No. (%) with
evaluation
by 36 months
Median age
(months)
Total no.
with ASD
No. (%) with
evaluation
by 36 months
Health care and education
Arizona 34.0 95 58 (61.1) 32.0 110 74 (67.3) 32.5 110 76 (69.1) 0.23
Colorado —34.0 93 75 (80.6) §
New Jersey 26.0 307 235 (76.5) 29.0 344 271 (78.8) 34.0 403 269 (66.7) 0.002
North Carolina 23.0 198 164 (82.8) §
Utah 32.0 107 75 (70.1) 32.0 115 72 (62.6) §
Health care only
Missouri 30.0 88 61 (69.3) 37.0 80 39 (48.8) 29.0 90 67 (74.4) 0.46
Wisconsin 27.5 58 40 (69.0) 29.0 109 80 (73.4) 24.0 90 80 (88.9)
Abbreviation: ASD = autism spectrum disorder.
* Cochran-Armitage trend test for proportion with evaluation by age 36 months; p<0.05 indicates statistical significance.
No data for site and surveillance year.
§ Trend not estimated for sites with <3 years of data.
Trend not estimated because records were included from early intervention sources for 2014 but not earlier years.
TABLE 6. Number and percentage of children aged 4 years with a previous autism spectrum disorder diagnosis and median age at earliest
known diagnosis — Early Autism and Developmental Disabilities Monitoring Network, seven sites, United States, 2010, 2012, and 2014
Site and
record source
2010 2012 2014 2010–2014
Total no.
with ASD
No. (%)
with any
ASD
diagnosis
Median age
(months)
of earliest
known
ASD
diagnosis
Total no.
with ASD
No. (%)
with any
ASD
diagnosis
Median age
(months)
of earliest
known
ASD
diagnosis
Total no.
with ASD
No. (%)
with any
ASD
diagnosis
Median age
(months)
of earliest
known
ASD
diagnosis p value*
Health care and education
Arizona 123 53 (43.1) 35.0 128 55 (43.0) 36.0 130 56 (43.1) 36.0 1.0
Colorado —113 72 (63.7) 31.0 §
New Jersey 352 207 (58.8) 32.5 403 236 (58.6) 35.0 514 292 (56.8) 33.5 0.54
North Carolina 231 107 (46.3) 28.0 §
Utah 132 106 (80.3) 35.0 152 122 (80.3) 35.0 §
Health care only
Missouri 103 84 (81.6) 34.0 96 83 (86.5) 39.0 112 96 (85.7) 36.0 0.41
Wisconsin 73 61 (83.6) 34.0 128 93 (72.7) 39.0 108 77 (71.3) 33.0
Abbreviation: ASD = autism spectrum disorder.
* Cochran-Armitage trend test for percentage with any ASD diagnosis; p<0.05 indicates statistical significance.
No data for site for surveillance year.
§ Trend not estimated for sites with <3 years of data.
Trend not estimated because records were included from early intervention sources for 2014 but not earlier years.
Surveillance Summaries
MMWR / April 12, 2019 / Vol. 68 / No. 2 19
US Department of Health and Human Services/Centers for Disease Control and Prevention
TABLE 7. Number and prevalence* of children aged 4 years meeting DSM-IV-TR or DSM-5 autism spectrum disorder case definition — Autism
and Developmental Disabilities Monitoring Network, seven sites, United States, 2014
Site and record source
DSM-IV-TR DSM-5 Prevalence ratio (95% CI),
DSM-IV-TR to DSM-5No. Prevalence (95% CI) N o. Prevalence (95% CI)
Health care and education
Arizona 130 13.5 (11.3–16.0) 102 10.6 (8.6–12.9) 1.3 (1.0–1.7)
Colorado 113 13.4 (11.0–16.1) 93 11.0 (8.9–13.5) 1.2 (0.9–1.6)
New Jersey§514 28.4 (26.0–30.9) 406 22.4 (20.3–24.7) 1.3 (1.1–1.4)
North Carolina§231 15.5 (13.6–17.6) 204 13.7 (11.9–15.7) 1.1 (0.9–1.4)
Health care only
Missouri 112 9.6 (7.9–11.6) 105 9.0 (7.4–10.9) 1.1 (0.8–1.4)
Wisconsin§108 13.2 (10.8–15.9) 93 11.3 (9.1–13.9) 1.2 (0.9–1.5)
Total 1,208 17.0 (16.1–18.0) 1,003 14.1 (13.3–15.1) 1.2 (1.1–1.3)
Abbreviations: CI = confidence interval; DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision; DSM-5 = Diagnostic and Statistical
Manual of Mental Disorder, Fifth Edition.
* Prevalence per 1,000 children aged 4 years living in the surveillance areas according to the vintage 2016 postcensal bridged-race population estimates for 2014
(http://www.cdc.gov/nchs).
Results for PRs considered statistically significant when the CI excludes the null value (PR = 1.0).
§ Site also reviewed records from early intervention sources.
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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. 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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.
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Problem/Condition: Autism spectrum disorder (ASD). Period Covered: 2010. Description of System: The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance system in the United States that provides estimates of the prevalence of ASD and other characteristics among children aged 8 years whose parents or guardians live in 11 ADDM sites in the United States. ADDM surveillance is conducted in two phases. The first phase consists of screening and abstracting comprehensive evaluations performed by professional providers in the community. Multiple data sources for these evaluations include general pediatric health clinics and specialized programs for children with developmental disabilities. In addition, most ADDM Network sites also review and abstract records of children receiving special education services in public schools. The second phase involves review of all abstracted evaluations by trained clinicians to determine ASD surveillance case status. A child meets the surveillance case definition for ASD if a comprehensive evaluation of that child completed by a qualified professional describes behaviors consistent with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) 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 updated prevalence estimates for ASD from the 2010 surveillance year. In addition to prevalence estimates, characteristics of the population of children with ASD are described. Results: For 2010, the overall prevalence of ASD among the ADDM sites was 14.7 per 1,000 (one in 68) children aged 8 years. Overall ASD prevalence estimates varied among sites from 5.7 to 21.9 per 1,000 children aged 8 years. ASD prevalence estimates also varied by sex and racial/ethnic group. Approximately one in 42 boys and one in 189 girls living in the ADDM Network communities were identified as having ASD. Non-Hispanic white children were approximately 30% more likely to be identified with ASD than non-Hispanic black children and were almost 50% more likely to be identified with ASD than Hispanic children. Among the seven sites with sufficient data on intellectual ability, 31% of children with ASD were classified as having IQ scores in the range of intellectual disability (IQ <= 70), 23% in the borderline range (IQ = 71-85), and 46% in the average or above average range of intellectual ability (IQ > 85). The proportion of children classified in the range of intellectual disability differed by race/ethnicity. Approximately 48% of non-Hispanic black children with ASD were classified in the range of intellectual disability compared with 38% of Hispanic children and 25% of non-Hispanic white children. The median age of earliest known ASD diagnosis was 53 months and did not differ significantly by sex or race/ethnicity. Interpretation: These findings from CDC's ADDM Network, which are based on 2010 data reported from 11 sites, provide updated population-based estimates of the prevalence of ASD in multiple communities in the United States. Because the ADDM Network 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 population. Consistent with previous reports from the ADDM Network, findings from the 2010 surveillance year were marked by significant variations in ASD prevalence by geographic area, sex, race/ethnicity, and level of intellectual ability. The extent to which this variation might be attributable to diagnostic practices, underrecognition of ASD symptoms in some racial/ethnic groups, socioeconomic disparities in access to services, and regional differences in clinical or school-based practices that might influence the findings in this report is unclear. Public Health Action: ADDM Network investigators will continue to monitor the prevalence of ASD in select communities, with a focus on exploring changes within these communities that might affect both the observed prevalence of ASD and population-based characteristics of children identified with ASD. Although ASD is sometimes diagnosed by 2 years of age, the median age of the first ASD diagnosis remains older than age 4 years in the ADDM Network communities. Recommendations from the ADDM Network include enhancing strategies to address the need for 1) standardized, widely adopted measures to document ASD severity and functional limitations associated with ASD diagnosis; 2) improved recognition and documentation of symptoms of ASD, particularly among both boys and girls, children without intellectual disability, and children in all racial/ethnic groups; and 3) decreasing the age when children receive their first evaluation for and a diagnosis of ASD and are enrolled in community-based support systems.
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