changes in autism spectrum disorder prevalence in 4 areas of the united states

Disability and Health Journal 3 (2010) 186e201

www.disabilityandhealthjnl.com

Changes in autism spectrum disorder prevalence in 4 areasof the United States

Catherine Rice, Ph.D.a,*, Joyce Nicholas, Ph.D.b, Jon Baio, Ed.S.a, Sydney Pettygrove, Ph.D.c,Li-Ching Lee, Ph.D.d, Kim Van Naarden Braun, Ph.D.a, Nancy Doernberg, B.A.a,

Chris Cunniff, M.D.c, Craig Newschaffer, Ph.D.e, F. John Meaney, Ph.D.c, Jane Charles, M.D.b,Anita Washington, M.P.H.f, Lydia King, Ph.D.b, Maria Kolotos, M.S.d, Kristen Mancilla, B.A.c,

Cynthia A. Mervis, M.P.H.g, Laura Carpenter, Ph.D.b, Marshalyn Yeargin-Allsopp, M.D.aaNational Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA

bCollege of Medicine, Medical University of South Carolina, Charleston South Caralina 29403, USAcCollege of Public Health (SP) and Department of Pediatrics (CC and FJM), University of Arizona, Tucson 85724, USA

dDepartment of Epidemiology, Johns Hopkins University, Baltimore, MD 21205, USAeSchool of Public Health, Drexel University, Philadelphia, PA 19102, USA

fResearch Triangle Institute International, Atlanta, GA 30341, USAgDepartment of Applied Medical Sciences, University of Southern Maine, Portland, ME 04104, USA

Abstract

Background: We sought to describe autism spectrum disorder (ASD) population characteristics and changes in identified prevalenceacross 3 time periods.

Methods: Children with a potential ASD were identified through records abstraction at multiple sources with clinician review based onDiagnostic and Statistical Manual (DSM-IV-TR) criteria. Multisite, population-based data from the Autism and Developmental DisabilitiesMonitoring (ADDM) Network were analyzed from areas of Arizona (AZ), Georgia (GA), Maryland (MD), and South Carolina (SC). Partic-ipants were 8-year-old children (born in 1992, 1994, or 1996) in 2000, 2002, or 2004 (and children born in 1988 residing in metropolitanAtlanta in 1996) who had been evaluated for a variety of developmental concerns at education and/or health sources.

Results: From 2000 to 2004, the identified prevalence of the ASDs per 1,000 8-year-old children showed significant increases of 38% inGA and 72% in MD and a nonsignificant increase of 26% in AZ. ASD prevalence was relatively stable in SC with a nonsignificant decreaseof 17%. Males had a higher identified prevalence of ASD in all years. Increases among racial, ethnic, and cognitive functioning subgroupsvaried by site and surveillance year. More children were classified with an ASD by community professionals over time, except in AZ.

Conclusions: There was a trend toward increase in identified ASD prevalence among 8-year-old children who met the surveillance casedefinition in 3 of the 4 study sites from 2000 to 2004. Some of the observed increases are due to improved ascertainment; however, a trueincrease in ASD symptoms cannot be ruled out. These data confirm that the prevalence of ASDs is undergoing significant change in someareas of the United States and that ASDs continue to be of urgent public health concern. Published by Elsevier Inc.

Keywords: Autism; Autism spectrum Disorders; Pervasive developmental disorders; Prevalence; Epidemiology

Financial disclosure: Dr. Rice conducts a limited number of training

sessions to professionals on the diagnosis of the autism spectrum disor-

ders as an approved outside activity separate from employment with the

Federal government. The other authors report no conflicts of interest.

The authors acknowledge the collaborative work of the Autism and

Developmental Disabilities Monitoring (ADDM) Network dedicated

project staff who contributed to the data collection for this manuscript.

The participation and support from the many educational and clinical

programs and data sources have been invaluable. Catherine Lord, Ph.D.

(University of Michigan), Gail McGee, Ph.D., and Michael Morrier,

Ph.D. (Emory University) provided expertise related to the case definition.

Diana Schendel, Coleen Boyle, Esther Sumartojo, Ed Trevathan, and

Carole Craft reviewed the manuscript and gave editorial assistance.

Sydney Pettygrove, Catherine Rice, Jon Baio, Li-Ching Lee, and Joyce

Nicholas had full access to all of the data in their study site and take

responsibility for the integrity of the data and the accuracy of the data

analysis. These projects were funded by the Centers for Disease Control

and Prevention (CDC). The findings and conclusions in this report are

those of the authors and do not necessarily represent the official position

of the CDC. For additional information on these projects, see http://www.

cdc.gov/autism.

* Corresponding author: 1600 Clifton Road, MS E86. Fax: (404) 498-

0792.

E-mail address: [email protected] (C. Rice).

1936-6574/$ e see front matter Published by Elsevier Inc.

doi:10.1016/j.dhjo.2009.10.008

http://www.cdc.gov/autism

http://www.cdc.gov/autism

mailto:[email protected]

http://www.disabilityandhealthjnl.com

187C. Rice et al. / Disability and Health Journal 3 (2010) 186e201

Autism spectrum disorders (ASDs) are a group of devel-opmental disabilities characterized by unusual developmentin socialization, communication, behavior, and, in manycases, learning, attention, and sensory functioning [1].Throughout this paper, the term ASD refers to autisticdisorder, Asperger disorder, or pervasive developmental dis-orderenot otherwise specified (PDD-NOS). While thenumber of individuals receiving clinical or educationalservices for an ASD has increased markedly since the early1990s [1-4], controversy remains over whether this increasereflects a true increase in risk for ASDs or is the result of otherfactors, such as changes in diagnostic criteria, expansion ofthe autism spectrum, increased provider awareness and avail-ability of specialized services, or a combination thereof[5-11]. ASD prevalence estimates using the current diag-nostic criteria [12,13] are approximately 6 to 7 per 1000children and are about 10 times higher than earlier estimates[8,10,14]. Population-based studies have shown ASD preva-lence of more than 1% in Japan, Sweden, the UnitedKingdom, and the United States [15-18] and up to 2.7% inNorway [19]. Recent studies also indicate changes in thecharacteristics of individuals with ASDs relative to earlierreports. For instance, the male-to-female ratio for maleswithout cognitive impairment has been found to be higherthan the commonly accepted 4:1 ratio overall, and less thanhalf of children with ASDs have cognitive impairment ratherthan 75%, as had been reported in the past [10,14,15].

Most studies evaluating trends in ASD prevalence haverelied on children classified for service provision or registrypurposes, and increases have often been interpreted as anartifact of criteria, diagnostic, and identification changes[4,6,7,9-11,20]. Only a few studies have examined ASDsymptoms, rather than previously classified ASDs (a docu-mented diagnosis or classification of an ASD), in the samegeographic population using the same methods and thecurrent diagnostic criteria over multiple time points [21-24]. Few studies have reported trends in occurrence ofASDs. In Japan, the cumulative incidence (new cases) ofASDs in children up to age 5 increased for birth cohortsfrom 1988 to 1996 with the most dramatic rise starting withthe 1993 birth cohort [17,24]. A U.S. study to examineASD incidence rates found gradual increases for peopleaged 0 to 21 years from 1976 to 1997, until 1988-1991,when incidence increased substantially for children underthe age of 10 years [21]. An English study found ASD prev-alence (new and existing cases in a defined age group) ofapproximately 6 per 1000 children in 2 cohorts born in1992-1995 and 1996-1998 [22]. Measuring ASD incidenceis problematic because the actual onset is often unknownand is not necessarily related to the time of identification,so changes in identification patterns can influence trendsin age-specific incidence rates [25]. For example, a recentstudy in Denmark found that the increasingly earlier ageof identification of ASDs inflated incidence increases overtime [26]. Prevalence estimates are based on new and exist-ing cases up to a defined age. It has been suggested that

looking at prevalence of ASDs in older children (ages 8-12) may help control for some of the challenges relatedto only focusing on incidence [7,27]. In this report, weuse an approach to track the occurrence of reported ASDsymptoms in children in a specific age cohort to determineASD period prevalence with the assumption that by 8 yearsof age, most children in the time periods covered wouldhave been evaluated for some developmental concern.

To better understand the prevalence, population charac-teristics, and public health impact of ASDs in the UnitedStates, the Centers for Disease Control and Prevention(CDC) formed the Autism and Developmental DisabilitiesMonitoring (ADDM) Network to conduct ongoing popula-tion-based surveillance and examine trends in the identifiedprevalence of ASDs over time [28]. The ADDM Networkuses systematic review of developmental evaluation recordsof individual children at multiple health and service provi-sion sources for descriptions of behaviors associated withASDs [14,15]. This method has been used by other CDCautism prevalence studies [1,29]. This paper describes theprevalence and characteristics of 8-year-old childrenmeeting the surveillance case definition for an ASD over3 time periods (2000, 2002, and 2004) in 4 of the ADDMNetwork sites with multiple data points and from an addi-tional year (1996) in the Georgia site [1]. These data allowus to begin to document and evaluate changes in ASD prev-alence and demographic characteristics among the popula-tions studied.

Methods

Children with an ASD were identified as part of theADDM Network active surveillance system for monitoringASDs and other developmental disabilities [14,15,28]. Ofthe ADDM Network sites, 4 sites (specific areas of AZ,GA, MD, and SC) have ASD prevalence data for 3 surveil-lance years, permitting an initial assessment of change inprevalence over time (Table 1). Two of the sites (the spec-ified areas of AZ and MD indicated in Table 1) varied in thegeographic catchment area covered; the data reported hererepresent the common area that was included in all 3surveillance years. Results are presented for each of thesites due to the cross-site variability in the population sizeand results.

Phase 1dascertainment of potential children meetingASD case status

A child meeting the ASD case criteria was defined as an8-year-old whose parent(s) or legal guardian(s) resided inthe surveillance area during the surveillance year andwho displayed behaviors documented from birth throughage 8 consistent with the Diagnostic and Statistical Manualof Mental Disorders, 4th edition, text revision (DSM-IV-TR)[12] criteria for autistic disorder, PDD-NOS, or Asperger

Table 1

Population Characteristics of 8-Year-Old Children in 4 Areas of the United States* by Sex and Race/Ethnicity in Surveillance Years 2000, 2002, and 2004y

Arizona* Georgia* Maryland* South Carolina*

2000 2002 2004 2000 to 2004 2000 2002 2004 2000 to 2004 2000 2002 2004 2000 to 2004 2000 2002 2004 2000 to 2004

N % N % N % % Change N % N % N % % Change N % N % N % % Change N % N % N % % Change

Total 8-year-olds 13,467 13,392 13,575 0.8% 43,840 44,751 44,701 2.0% 11,726 11,337 11,223 �4.3% 24,396 23,198 22,386 �8.2%

White non-Hispanic 7,414 6,879 6,581 �11.2% 18,664 18,245 17,975 �3.7% 9,677 9,255 9,001 �7.0% 11,841 11,629 11,828 �0.1%

55% 51% 48% 43% 41% 40% 83% 82% 80% 49% 50% 53%

Black non-Hispanic 696 730 680 �2.3% 19,712 20,023 19,208 �2.6% 1,295 1,269 1,282 �1.0% 11,682 10,568 9,463 �19.0%

5% 5% 5% 45% 45% 43% 11% 11% 11% 48% 46% 42%

Hispanic 4,641 5,027 5,579 20.2% 3,500 4,371 5,051 44.3% 315 318 370 17.5% 576 684 746 29.5%

34% 38% 41% 8% 10% 11% 3% 3% 3% 2% 3% 3%

Asian non-Hispanic 346 406 412 19.1% 1,857 2,016 2,380 28.2% 415 472 546 31.6% 201 226 249 23.9%

3% 3% 3% 4% 5% 5% 4% 4% 5% 1% 1% 1%

Male 6,906 6,861 6,877 �0.4% 22,130 22,824 22,668 2.4% 6,046 5,849 5,818 �3.8% 12,322 11,835 11,394 �7.5%

White non-Hispanic 3,834 3,507 3,339 �12.9% 9,423 9,345 9,104 �3.4% 4,993 4,760 4,678 �6.3% 5,971 5,944 6,082 1.9%

56% 51% 49% 43% 41% 40% 83% 81% 80% 48% 50% 53%

Black non-Hispanic 350 384 340 �2.9% 9,894 10,140 9,750 �1.5% 671 658 667 �0.6% 5,922 5,404 4,734 �20.1%

5% 6% 5% 45% 44% 43% 11% 11% 11% 48% 46% 42%

Hispanic 2,361 2,578 2,822 19.5% 1,819 2,233 2,581 41.9% 155 173 190 22.6% 286 337 382 33.6%

34% 38% 41% 8% 10% 11% 3% 3% 3% 2% 3% 3%

Asian non-Hispanic 187 219 206 10.2% 944 1,055 1,183 25.3% 213 245 269 26.3% 93 109 145 55.9%

3% 3% 3% 4% 5% 5% 4% 4% 5% 1% 1% 1%

Female 6,561 6,531 6,698 2.1% 21,710 21,927 22,033 1.5% 5,680 5,488 5,405 �4.8% 12,074 11,363 10,992 �9.0%

White non-Hispanic 3,580 3,372 3,242 �9.4% 9,241 8,900 8,871 �4.0% 4,684 4,495 4,323 �7.7% 5,870 5,685 5,746 �2.1%

55% 52% 48% 43% 41% 40% 82% 82% 80% 49% 50% 52%

Black non-Hispanic 346 346 340 �1.7% 9,818 9,883 9,458 �3.7% 624 611 615 �1.4% 5,760 5,164 4,729 �17.9%

5% 5% 5% 45% 45% 43% 11% 11% 11% 48% 45% 43%

Hispanic 2,280 2,449 2,757 20.9% 1,681 2,138 2,470 46.9% 160 145 180 12.5% 290 347 364 25.5%

35% 37% 41% 8% 10% 11% 3% 3% 3% 2% 3% 3%

Asian non-Hispanic 159 187 206 29.6% 913 961 1197 31.1% 202 227 277 37.1% 108 117 104 �3.7%

2% 3% 3% 4% 4% 5% 4% 4% 5% 1% 1% 1%

* The areas of the United States include portions of Maricopa County including metropolitan Phoenix, Arizona (AZ); 5 counties of metropolitan Atlanta, Georgia (GA); 4 counties in Maryland surrounding

metropolitan Baltimore (MD); and 23 counties in the Coastal and PeeDee Regions of South Carolina (SC).y Population data from the National Center on Health Statistics bridged-race postcensal vintage estimates for 2005.

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disorder on evaluations by a qualified professional. Chil-dren from the 2000, 2002, and 2004 surveillance years wereborn in 1992, 1994, and 1996, respectively. Children fromthe additional surveillance year of 1996 in the GA site wereborn in 1988. This project was approved by the institutionalreview boards (IRBs) at each site and data source, asrequired.

In Phase 1, children suspected of having an ASD wereidentified through screening and abstraction of source filesat public schools and multiple health sources, such ashospitals, clinics, diagnostic centers, state-funded serviceproviders, and other clinical providers for children withdevelopmental disabilities. Triggers for abstractionincluded a previous ASD diagnosis, suspicion of ASD,ASD test, or a social behavior related to ASDs noted inan evaluation. All 4 sites had access to both educationand health data [30]. Data sources were stable over thistime period with the only exception being some minor addi-tions and subtractions in Georgia with negligible impact oncases identified. Information collected included demo-graphic and school service data, verbatim descriptions ofbehaviors associated with ASDs, psychometric test results,developmental history, diagnostic summaries, and informa-tion about co-existing developmental disabilities. ASDcases were linked to birth certificates to provide additionaldemographic information.

Phase 2dClinician review

In Phase 2, abstracted data were systematically scoredby clinician reviewers according to a coding scheme thatoperationalized the DSM-IV-TR criteria for pervasive devel-opmental disorders [1,29]. Cognitive functioning was deter-mined from the child’s most recent intelligence quotient(IQ) or developmental test results and was collapsed analyt-ically into 3 levels: (1) average or above average: IQ O85;(2) borderline: IQ 5 71-85; and (3) cognitive impairment:IQ <70. The only variation in review methods was a morelimited review conducted on children who had a clearlydocumented ASD classification in their records in 2002and 2004 for 3 sites (AZ, GA, and MD). This has beenreferred to as ‘‘streamlining’’ elsewhere [14]. However,a previous ASD classification did not result in an automaticinclusion as an ASD case, and a complete review wasundertaken when any doubt was present from the earlydelay and summary statements about the presence of anASD. For each year, initial and ongoing interrater reli-ability was established among clinician reviewers to stan-dards of agreement on individually scored items and onfinal case status in a blinded, random 10% sample ofabstracted records scored independently by 2 reviewersand by monthly cross-site reliability checks. Personnel con-ducting clinician review were consistent across surveillanceyears with only one new reviewer, who met all reliabilitystandards, starting for the 2002 year in GA. No clinical

reexaminations of children were performed by projectpersonnel.

Analytic methods

The primary outcome measure was period prevalence ofASD cases identified per 1,000 8-year-old children withmore detailed analyses by sex, race/ethnicity, level ofcognitive functioning, and previously documented ASDclassification. Confidence intervals (95%) were estimatedusing a Poisson distribution [31]. MD’s case estimate forthe year 2000 was adjusted based on a sensitivity analysisto account for having screened records from a single schoolyear while the other 3 sites screened records from 2 schoolyears [14]. This sensitivity analysis applied the case identi-fication numbers from the single year of school datascreening to estimate the additional cases that would havebeen identified from screening a second year of school data.An evaluation of the change in the use of streamlinedabstraction and review in 2002 and 2004 compared to2000 was done by applying an assessment of the maximumnumber of cases that would have been called cases in 2002and 2004 but may not have been in 2000. Denominator datawere obtained from the National Center on Health Statistics(NCHS) 2005 Vintage Postcensal Population Estimates for2000, 2002, and 2004 [32]. In AZ, denominators were esti-mated using the NCHS postcensal estimates adjusted by theNational Center for Education Statistics enrollmentnumbers because of a lack of overlap between censusblocks and school districts in the surveillance area [33].Racial and ethnic subgroups included were white non-Hispanic, black non-Hispanic, Hispanic, and Asian/PacificIslander non-Hispanic. Changes in prevalence were exam-ined within sites over time rather than pooling data acrosssites because the sites varied in population size, character-istics, and prevalence, and they were not selected to bea nationally representative sample. Statistical significancefor prevalence changes among ASD cases overall andamong subgroups for each site were evaluated using anExtended Mantel-Haenszel c2 test of trend [34], which isused to evaluate a linear increase in the slope of the logprevalence odds across the years. The p-value is presentedto indicate a significant change among the years. Analysisassuming a binomial distribution did not change any find-ings. The significance of the prevalence rate ratios wasevaluated using a c2 test.

Results

Descriptive characteristics of surveillance population

The number of 8-year-old children in the surveillanceareas ranged from 11,726 to 43,840 for 2000, 11,337 to44,751 in 2002, and 11,223 to 44,701 in 2004. MD hadthe smallest geographic area, and GA, the largest (Table 1).From 2000 to 2004, there were small increases in the total

Table 2

Prevalence of ASD Among 8-Year-Old Children by Sex and Racial/Ethnic Characteristics in 4 Areas of the United States* in Surveillance Years 2000, 2002, and 2004y,{

Arizona* Georgia* Maryland* South Carolina*

2000 2002 2004 2000

to 2004

2000 2002 2004 2000

to 2004

2000 2002 2004 2000

to 2004

2000 2002 2004 2000

to 2004

Prev

95% CIyPrev

95% CIyPrev

95% CIyPrev

%

Prev

95% CIyPrev

95% CIyPrev

95% CIyPrev

%

Prev

95% CIyPrev

95% CIyPrev

95% CIyPrev

%

Prev

95% CIyPrev

95% CIyPrev

95% CIyPrev

%

Total N ASD cases 105 88 133 _ 285 337 401 _ 59 75 97 _ 155 140 118 _

Total ASD Prev 7.8 6.6 9.8 2.0 6.5 7.5 9.0 2.5{ 5.0z 6.6 8.6 3.6{ 6.4 6.0 5.3 �1.1

6.4-9.4 5.3-8.1 8.3-11.6 26% 5.8-7.3 6.8-8.4 8.1-9.9 38% 3.9-6.5 5.3-8.3 7.1-10.6 73% 5.4-7.4 5.1-7.1 4.4-6.3 �17%

White non-Hispanic 10.3 9.3 12.6 2.3 7.7 8.8 9.9 2.2{ 5.1 6.5 8.0 2.9{ 6.4 6.0 5.5 �0.9

8.2-12.8 7.3-11.9 10.2-15.6 22% 6.5-9.0 7.6-10.3 8.6-11.5 29% 3.8-6.7 5.0-8.4 6.4-10.1 57% 5.1-8.0 4.8-7.6 4.3-7.0 �14%

Black non-Hispanic 8.6 1.4 5.9 �2.7 5.2 6.7 7.9 2.7{ 4.6 7.1 11.7 7.1{ 5.7 5.5 4.0 �1.7

3.9-19.2 0.2-9.7 2.2-15.7 �31% 4.3-6.3 5.7-7.9 6.8-9.3 52% 2.1-10.3 3.7-13.6 7.1-19.4 154% 4.5-7.3 4.2-7.1 2.9-5.5 �30%

Hispanic 2.4 4.0 7.0 4.6{ 3.4 4.6 6.5 3.1{ 3.2 _x 2.7 �0.5 _x 4.4 2.7 _x

1.3-4.3 2.6-6.2 5.1-9.6 192% 2.0-6.0 3.0-7.1 4.6-9.2 91% 0.5-22.5 0.4-19.2 �16% 1.4-13.6 0.7-10.7

Asian non-Hispanic 5.8 2.5 9.7 3.9 4.9 5.0 8.4 3.5 _x 4.2 16.5 _x _x 4.4 _x _x

1.5-23.1 0.4-17.5 3.6-25.9 67% 2.5-9.3 2.7-9.2 5.4-13.0 71% 1.1-16.9 8.6-31.7 0.6-31.4

Male 11.6 10.1 15.9 4.3{ 10.9 12.3 14.2 3.3{ 7.8 9.9 13.8 6.0{ 9.3 9.2 9.0 �0.3

9.3-14.4 7.9-12.7 13.1-19.1 37% 9.6-12.4 10.9-13.8 12.7-15.8 30% 5.8-10.4 7.7-12.8 11.0-17.1 77% 7.8-11.2 7.6-11.1 7.4-10.9 �3%

White non-Hispanic 16.2 16.0 21.0 4.8 13.0 14.0 15.9 2.9 7.8 9.5 12.8 5.0{ 9.0 9.3 9.4 0.4

12.6-20.7 12.3-20.8 16.6-26.5 30% 10.8-15.5 11.8-16.6 13.5-18.7 22% 5.7-10.7 7.1-12.7 10.0-16.5 64% 6.9-11.8 7.1-12.1 7.2-12.2 4%

Black non-Hispanic 14.3 2.6 8.8 �5.5 8.5 11.2 12.5 4.0{ 7.5 12.2 16.5 9.0 8.4 8.3 7.2 �1.2

6.0-34.3 0.4-18.5 2.9-27.4 -38% 6.9-10.5 9.4-13.5 10.5-14.9 47% 3.1-17.9 6.1-24.3 9.1-29.8 120% 6.4-11.1 6.2-11.2 5.1-10.1 �14%

Hispanic 2.5 4.3 10.3 7.8{ 5.0 6.7 10.5 5.5{ 6.5 _x 5.3 �1.2 _x 8.9 5.2 _x

1.1-5.7 2.4-7.7 7.1-14.8 312% 2.6-9.5 4.1-11.1 7.2-15.3 110% 0.9-45.8 0.7-37.4 �18% 2.9-27.6 1.3-20.9

Asian non-Hispanic 10.7 _x 19.4 8.7{ 8.5 8.5 11.8 3.3 _x 8.2 29.7 _x _x _x _x _x

2.7-42.8 7.3-51.7 81% 4.2-17.0 4.4-16.4 7.0-20.0 39% 2.0-32.6 14.9-59.5

Female 3.8 2.9 3.6 �0.2 2.0 2.6 3.6 1.6{ 2.1 3.1 3.2 1.1 3.3 2.7 1.5 �1.8{

2.6-5.6 1.9-4.6 2.4-5.4 -5% 1.5-2.7 2.0-3.4 2.9-4.5 80% 1.2-3.7 1.9-5.0 2.0-5.1 52% 2.4-4.5 1.9-3.9 0.9-2.4 �55%

White non-Hispanic 3.9 2.4 4.0 0.1 2.3 3.4 3.7 1.4 2.1 3.3 2.8 0.7 3.8 2.6 1.4 �2.4{

2.3-6.6 1.2-4.7 2.3-6.9 3% 1.5-3.5 2.4-4.8 2.6-5.2 61% 1.2-4.0 2.0-5.5 1.6-4.9 33% 2.5-5.7 1.6-4.4 0.7-2.8 �63%

(Continued)

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population of 8-year-old children in AZ and GA anddecreases in MD and SC. Population change varied moreby race/ethnicity with the greatest growth among Hispanicand Asian/Pacific Islander non-Hispanic children. Popula-tion changes for boys and girls were relatively similar inall sites.

Prevalence estimates and demographics

Identified period prevalence among 8-year-old childrenshowed a statistically significant increase in GA ( p !.001) and MD ( p ! .001), a nonstatistically significantincrease in AZ ( p 5 .067), and a nonsignificant decreasein SC ( p 5 .129) from 2000 to 2004 (Table 2, Figure 1).The prevalence (ASD cases per 1,000 8-year-old children)during 2004 was 9.0 in GA, 8.6 in MD, 9.8 in AZ, and5.3 in SC.

We found a predominance of boys in each of the 3 timeperiods for all sites ( p ! .001) for all M:F ratios in all sitesfor all 3 years (Table 3). The male-to-female prevalence ratiodecreased in GA from 2000 to 2004 but increased in the other3 sites (Table 3). There was an increase in identified ASDprevalence among boys in AZ ( p ! .05), GA ( p ! .001),and MD ( p ! .001) with no significant change in SC( p 5 .760) (Table 2). However, male prevalence changesvaried by race/ethnicity across the sites with significantincreases among white non-Hispanic boys in MD ( p !.01), black non-Hispanic boys in GA ( p ! .01), Hispanicboys in AZ ( p ! .001) and GA ( p ! .05), and Asian/PacificIslander non-Hispanic boys in AZ ( p ! .01). For girls,a significant increase in ASD prevalence was found only inGA ( p ! .01); this increase was not accounted for by anyracial or ethnic group. However, in SC, prevalence decreasedsignificantly ( p ! .01) for both white non-Hispanic ( p ! .01)and black non-Hispanic ( p ! .05) girls.

Identified ASD prevalence was similar by race/ethnicity,except in AZ and GA, where Hispanic children were signif-icantly less likely to be identified with an ASD in all 3 years.However, the prevalence rate ratio decreased from 2000 to2004, indicating increasing identification of Hispanic chil-dren with ASDs relative to white, non-Hispanic children(Table 3). Also in GA, black non-Hispanic children were lesslikely than white non-Hispanic children to be identified withan ASD in all 3 years (Table 3).

For the GA site, inclusion of an earlier surveillance yearindicated the period prevalence among 8-year-old childrenin metropolitan Atlanta (n 5 36,749) was 4.2 (95% confi-dence interval [CI], 3.6-5.0) per 1,000 in 1996. There wasa steady increase in ASD prevalence from 1996 to 2004,representing an approximate doubling of ASD prevalenceto 9.0 (8.1-9.9) in the 8-year period ( p ! .001) (Figure 1).Increases were found among most subgroups (sex, race/ethnicity, and level of cognitive functioning), except amongHispanic girls and girls with cognitive impairment.Increases were gradual for most subgroups, although somevaried across the years (data not shown).

192 C. Rice et al. / Disability and Health Journal 3 (2010) 186e201

Role of cognitive functioning

Cognitive functioning data were available for themajority of ASD cases in AZ (88%-93% for all 3 years),GA (93%-94%), and SC (86%-93%). Data were limitedin MD, so that site was excluded from these analyses.The proportion of ASD cases with cognitive impairmentdecreased from 2000 to 2004 in AZ (58%-38%) and SC(62%-45%), but it was relatively stable in GA (40%-41%) (Figure 2).

Changes in identified ASD prevalence varied by IQ levelacross the 3 time periods (Table 4). For children with ASDsand average to above-average IQ scores, only prevalenceamong white non-Hispanic boys in AZ increased signifi-cantly ( p ! .05). There was a nonsignificant tendencytoward an increase among this group overall in AZ (60%,p 5 .062), GA (25%, p 5 .064), and SC (44%, p 5 .219),but SC prevalence remained lower than AZ and GA. For chil-dren with a borderline IQ, prevalence was stable in SC( p 5 .881), but it increased in AZ overall ( p ! .001). InAZ, these increases were among both boys ( p ! .01),primarily Hispanic boys ( p ! .05), and girls ( p ! .05),primarily non-Hispanic white girls ( p ! .01). In GA, ASDprevalence increased in the borderline IQ group overall( p ! .01) and among girls (p ! .001), particularly whitenon-Hispanic girls ( p ! .05) in that group. The prevalenceof ASD among children with cognitive impairment wasstable in AZ (p 5 0.488), increased in GA ( p ! .01), anddecreased in SC ( p ! .01). The increases in GA wereprimarily among black non-Hispanic boys ( p ! .05), andthe decreases in SC were primarily among black non-Hispanic girls ( p ! .01). AZ also showed increases amongHispanic boys with cognitive impairment ( p ! .05).

Previously documented ASD classifications

The mean age in months of earliest ASD diagnosis did notchange in GA (58 and 59) and SC (56 to 54) from 2000 to2004, but actually increased in AZ (48 and 66) by 18 months( p ! .001) and in MD (52 and 65) by 13 months ( p ! .01).The percentage of children who met the ASD ADDM surveil-lance case definition and had a previously documented ASDclassification (an ASD diagnosis or autism special education[SpED] eligibility on record provided by a communityprofessional) increased from 2000 to 2004 in GA (70%-86%, p ! .01), MD (73%-89%, p ! .05), and SC (50%-64%, p ! .05). No such increase was observed in AZ (54%in 2000 and 53% in 2004, p 5 .74). In 2004, among childrenwith an ASD diagnosis documented by a community healthprofessional (excludes classifications for SpED services),34% in GA, 41% in MD, 47% in AZ, and 69% in SC wereclassified as having autism (autistic disorder) only. AnotherASD classification only (PDD-NOS, Asperger disorder, orPDD/ASD unspecified) was documented among 19% inSC, 38% in AZ, 44% in GA, and 51% in MD. Multiplesubtypes (autism and ASD diagnosed during separate

evaluations of the same child) were diagnosed among 9%of previously documented cases in MD, 12% in SC, 16% inAZ, and 22% in GA. Between 2000 and 2004, these patternsremained similar with a slight decrease in the proportion ofautism diagnoses in AZ and a slight increase in MD and SC.

Most children who met the ASD case definition werereceiving SpED services in 2004. Children who receivedSpED services under an autism eligibility classificationincreased significantly from 2000 to 2004 in GA (from60% to 67%), MD (from 50% to 67%), and SC (from 28%to 53%) but not in AZ (from 42% to 44%). Some childrenwho met the ADDM ASD case definition were receivingSpED services under other eligibility categories. To examinethe changes in the use of eligibility categories for SpEDamong children identified with ASDs for surveillance, wecalculated the prevalence of SpED eligibilities and examinedthe net prevalence changes from 2000 to 2004 (Figure 3). Theprevalence of children with an SpED autism eligibilityincreased over time in all sites. In SC, this increase was offsetby decreases in other eligibility categories. In the other 3sites, the net prevalence decrease across all other eligibilitycategories did not completely account for the increase in chil-dren with autism eligibility (Figure 3). These data are aggre-gate estimates of different cohorts but do reflect the shifts inthe use of SpED eligibilities among cohorts of 8-year-oldswith documented symptoms of ASD. These findings indicatethat children meeting the ASD case definition were morelikely to have an autism SpED classification in all 4 sites,but that the use of other SpED eligibility classifications alsoincreased, except in SC.

Discussion

We report changes in identified ASD prevalence overtime points covering several cohorts of 8-year-old childrenborn from 1988 to 1996. The 4 ADDM Network sites re-ported in this paper showed a range in ASD prevalence(5.0-9.8 per 1000) similar to that of other studies usingcurrent criteria [8]. Two sites (GA and MD) experiencedstatistically significant increases in ASD period prevalencefrom 2000 to 2004, and a third site (AZ) experienced a trendtoward an increase. On the other hand, one site (SC)observed a trend toward a decrease in identified ASD prev-alence but was relatively stable. Although these results donot confirm an across-the-board increase in ASD preva-lence in these sites, the significant increase in 2 sites andtendency toward increase in a third site confirms concernsabout changes in ASD prevalence. Significant debate existsabout whether changes in ASD prevalence reflect method-ological, awareness, and/or etiologic factors, and no singlestudy is likely to fully address these issues. While thisreport represents the first effort to document changes inASD among some of the ADDM Network sites, wedid not find clear consistent patterns that could whollyaccount for variation in prevalence among the sites. Using

1

3

5

7

9

11

13

AZ 2000

AZ 2002

AZ 2004

GA 1996

GA 2000

GA 2002

GA 2004

MD 2000

MD 2002

MD 2004

SC 2000

SC 2002

SC 2004

Site and Year

-A

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ADDM 1996 ASD PrevalenceADDM 2000 ASD PrevalenceADDM 2002 ASD PrevalenceADDM 2004 ASD Prevalence

Figure 1. ASD prevalence in 4 areas of the United States.

0%10%20%30%40%50%60%70%80%90%

100%

AZ 200

0

AZ 200

2

AZ 200

4

GA 2000

GA 2002

GA 2004

SC 2000

SC 2002

SC 2004

Average-Above (IQ>85)

Borderline (IQ=71-85)

Cognitive Impairment(IQ≤70)

Figure 2. Level of cognitive functioning of children with ASD in 3 sites

from 2000 to 2004.


a record-review methodology, the primary way in whichprevalence changes can be accounted for by methodologicor awareness changes is if changes in access to records orthe amount and quality of information documented in therecords allows for an increasing number of children to beclassified over time as having an ASD. Below, we considersome of the community awareness and identificationpatterns as well as methodological issues by particularsubgroups and within sites in light of the identified preva-lence findings reported here.

Data sources and SpED classification

Overall, there was consistency in data sources for allsites, so variation in source access was not a factor in ex-plaining prevalence changes. However, increases in theclassification of children as having autism in the commu-nity may lead to better documentation of symptoms usedto confirm ASD case status. Each of these ADDM siteshad consistent access to education records [14], and althougheducation sources were only one of multiple data sources,

Table 3

Prevalence Rate Ratio of ASD Among 8-Year-Old Children by Sex and Racial/E

2000, 2002, and 2004

2000 2002

Prevalence rate ratio p* Prevalen

AZ

Male:female 3.1 !.001 3.5

White:black 1.2 .683 6.6

White:Hispanic 4.3 !.001 2.3

GA



White:Hispanic 2.6 .005 1.9

MD



White:Hispanic 1.6 .644 _

SC



White:Hispanic _ _ 1.4

* Bold and italic values indicate statistically significant at least p ! .01.

public education records were particularly crucial inproviding the behavioral descriptors needed to confirm cases.The proportion of children with ASD receiving SpEDservices was consistently high across surveillance years forAZ, GA, and SC and showed an increase in MD. Childrenwith ASDs were increasingly more likely to receive SpEDservices under an autism eligibility in all sites, except AZ,with the most dramatic increase in SC. Although there wasan increase in autism classification in SC and no change inAZ, ADDM ASD prevalence was relatively stable in SCand increased in AZ indicating no clear relationshipbetween ASD prevalence changes and changes in SpEDclassification in these sites. Had we used a SpED classifica-tion of autism alone to examine ASD trends instead ofADDM ASD prevalence data, we would have underesti-mated the absolute prevalence, but overestimated the magni-tude of the prevalence increases in 3 of the 4 sites.

We also analyzed the changes in identified prevalence bySpED classification over time and found that while morecase children in the entire group were receiving SpEDunder an autism classification, decreases in the use of othereligibility categories such as intellectual disabilities or

thnic characteristics in 4 Areas of the United States* in Surveillance Years

2004

ce rate ratio p* Prevalence rate ratio p*

!.001 4.4 !.001.057 2.1 .136

!.001 1.8 .002

!.001 4.0 !.001

.018 1.3 .042

.006 1.5 .028

!.001 4.4 !.001

.802 0.7 .180

_ 3.0 .281

!.001 6.2 !.001

.603 1.4 .125

.591 2.1 .317

Table 4

Prevalence of ASD Among 8-Year-old Children by Level of Cognitive Functioningz in 3 Areas of the United States* in Surveillance Years 2000, 2002, and 2004y,{

Arizona* Georgia* South Carolina*

2000 2002 2004 2000 to 2004 2000 2002 2004 2000 to 2004 2000 2002 2004 2000 to 2004

Proportion of ASD Cases

with IQ data

87.6% 88.6% 93.2% _ 94.0% 93.2% 93.5% _ 89.0% 92.9% 86.4% _

Prev

95% CIyPrev

95% CIyPrev

95% CIyPrev

%

Prev

95% CIyPrev

95% CIyPrev

95% CIyPrev

%

Prev

95% CIyPrev

95% CIyPrev

95% CIyPrev

%

Average or Above Average (IQ O 85)

Total 2.0 2.7 3.2 1.2 2.4 2.3 3.0 0.6 0.9 1.7 1.3 0.4

1.4-2.9 1.9-3.7 2.4-4.3 60% 1.9-2.9 1.9-2.8 2.5-3.5 25% 0.6-1.4 1.3-2.4 0.9-1.9 44%

Male 3.3 4.7 5.5 2.2{ 4.3 3.9 5.2 0.9 1.4 3.0 2.3 0.9

2.2-5.0 3.3-6.6 4.0-7.6 67% 3.5-5.3 3.2-4.8 4.3-6.2 21% 0.9-2.2 2.1-4.1 1.6-3.4 64%

White non-Hispanic 5.7 7.7 10.2 4.5{ 6.8 6.4 8.2 1.4 1.8 3.9 3.3 1.5

3.8-8.7 5.3-11.2 7.3-14.3 79% 5.3-8.7 5.0-8.3 6.6-10.3 21% 1.0-3.3 2.6-5.8 2.1-5.1 83%

Black non-Hispanic _x _x _x _x 1.8 1.1 2.3 0.5 0.7 1.9 1.3 0.6

1.2-2.9 0.6-2.0 1.5-3.4 28% 0.3-1.8 1.0-3.4 0.6-2.8 86%

Hispanic _x 1.9 1.1 1.1 2.2 2.7 3.9 1.7 _x 3.0 _x _x

0.8-4.7 0.3-3.3 0.6-5.6 1.1-5.8 1.9-7.1 77% 0.1-16.5

Female 0.6 0.6 0.8 0.2 0.4 0.6 0.7 0.3 0.5 0.4 0.4 �0.1

0.2-1.6 0.2-1.6 0.3-1.8 33% 0.2-0.7 0.4-1.1 0.4-1.2 75% 0.2-1.1 0.2-1.1 0.1-1.0 �20%

White non-Hispanic 0.8 _x 0.9 0.1 0.7 1.2 1.2 0.5 0.7 0.4 0.4 �0.3

0.3-2.6 0.3-2.9 13% 0.3-1.5 0.7-2.2 0.7-2.2 71% 0.3-1.8 0.1-1.4 0.1-1.4 �43%

Black non-Hispanic _x _x _x _x 0.2 0.2 0.4 0.2 0.4 0.6 _x _x

0.1-0.8 0.1-0.8 0.2-1.1 100% 0.1-1.4 0.2-1.8

Hispanic _x 1.2 0.7 _x _x 4.7 _x _x _x _x _x _x

0.4-3.8 0.2-2.9 0.1-26.2

Borderline (IQ 5 71-85)Total 0.9 1.3 2.5 1.6{ 1.3 1.3 2.0 0.7{ 1.2 0.6 1.2 0.0

0.5-1.6 0.8-2.0 1.8-3.5 178% 1.0-1.7 1.0-1.7 1.6-2.4 54% 0.8-1.7 0.4-1.0 0.8-1.7 0%

Male 1.6 2.0 3.8 2.2{ 2.2 2.2 2.8 0.6 2.0 1.0 1.9 �0.1

0.9 5 2.9 1.2-3.5 2.6-5.6 138% 1.7-2.9 1.6-2.8 2.2-3.6 27% 1.3-2.9 0.6-1.8 1.3-2.9 �5%

White non-Hispanic 2.4 3.1 4.5 2.1 2.4 2.4 2.8 0.4 1.7 0.8 2.0 0.3

1.2-4.5 1.7-5.7 2.7-7.5 88% 1.6-3.7 1.6-3.6 1.9-4.1 17% 0.9-3.1 0.4-2.0 1.1-3.5 18%

Black non-Hispanic _x _x 2.9 _x 1.8 2.1 2.5 0.7 2.0 1.1 1.5 �0.5

0.4-20.1 1.2-2.9 1.4-3.2 1.7-3.7 39% 1.2-3.6 0.5-2.5 0.7-3.1 �25%

Hispanic 0.4 0.8 2.8 2.4{ 5.5 1.3 2.7 �2.8 _x _x _x _x

0.1-3 0.2-3.1 1.4-5.7 600% 0.1-30.6 0.3-3.9 1.1-5.6 �51%

Female 0.2 0.5 1.2 1.0{ 0.4 0.4 1.1 0.7{ 0.4 0.2 0.4 0.0

0.0-1.1 0.2-1.4 0.6-2.4 500% 0.2-0.8 0.2-0.7 0.7-1.6 175% 0.2-1.0 0.0-0.7 0.1-1.0 0%

White non-Hispanic _x 0.3 1.9 1.9{ 0.4 0.7 1.5 1.1{ 0.7 0.4 0.4 �0.3

0.0-2.1 0.8-4.1 � 0.2-1.2 0.3-1.5 0.9-2.5 275% 0.3-1.8 0.1-1.4 0.1-1.4 �43%

Black non-Hispanic _x _x _x _x 0.3 0.2 0.5 0.2 0.2 _x 0.4 0.2

0.1-1.0 0.1-0.8 0.2-1.3 67% 0.0-1.2 0.1-1.7 100%

Hispanic _x 0.8 0.7 _x 1.2 _x 2.0 0.8 _x _x _x _x

0.2-3.3 0.2-2.9 0.1-4.3 0.7-4.7 67%

Cognitive Impairment (IQ < 70)Total 3.9 1.9 3.5 �0.4 2.4 3.4 3.5 1.1{ 3.5 3.3 2.1 �1.4{

(Continued)

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Table 4 Continued

Arizona* Georgia* South Carolina*

2000 2002 2004 2000 to 2004 2000 2002 2004 2000 to 2004 2000 2002 2004 2000 to 2004

3.0-5.2 1.3-2.8 2.6-4.6 �10% 2.0-3.0 2.9-4.0 2.9-4.0 46% 2.9-4.4 2.6-4.1 1.6-2.7 �40%

Male 5.1 2.3 5.4 0.3 3.7 5.4 5.2 1.5{ 5.0 4.5 3.5 �1.5

3.6-7.1 1.4-3.8 3.9-7.4 6% 3.0-4.6 4.6-6.5 4.3-6.2 41% 3.9-6.4 3.4-5.9 2.6-4.8 �30%

White non-Hispanic 5.5 3.1 5.4 �0.1 3.1 4.0 3.7 0.6 4.0 4.2 2.8 �1.2

3.6-8.4 1.7-5.7 3.4-8.6 �2% 2.1-4.4 2.9-5.5 2.7-5.2 19% 2.7-6.0 2.8-6.2 1.7-4.5 �30%

Black non-Hispanic 14.3 2.6 5.9 �8.4 4.5 7.5 7.2 2.7{ 5.2 4.4 4.0 �1.2

6.0-34.3 0.4-18.5 1.5-23.5 �59% 3.3-6.0 6.0-9.4 5.7-9.1 60% 3.7-7.4 3.0-6.6 2.6-6.3 �23%

Hispanic 2.1 1.6 5.0 2.9{ 2.2 2.7 2.7 0.5 _x 5.9 5.2 _x

0.9-5.1 0.6-4.1 2.9-8.4 138% 0.6-5.6 1.0-5.8 1.1-5.6 23% 0.7-21.4 0.6-18.9

Female 2.7 1.4 1.5 �1.2 1.2 1.4 1.7 0.5 2.3 2.0 0.6 �1.7{

1.7-4.4 0.7-2.7 0.8-2.8 �44% 0.8-1.7 1.0-2.0 1.2-2.3 42% 1.3-3.9 1.4-3.1 0.3-1.2 �74%

White non-Hispanic 2.8 1.8 1.2 �1.6 1.0 1.2 1.0 0.0 1.9 1.8 0.7 �1.2

1.5-5.2 0.8-4.0 0.5-3.3 �57% 0.5-1.9 0.7-2.2 0.5-2.0 0% 1.0-3.4 1.0-3.3 0.3-1.9 �63%

Black non-Hispanic 2.9 _x 2.9 0.0 1.3 1.4 2.1 0.8 2.3 1.9 0.2 �2.1{

0.4-20.5 0.4-20.9 0% 0.8-2.3 0.8-2.4 1.4-3.3 62% 1.3-3.9 1.0-3.6 0.0-1.5 �91%

Hispanic 1.8 0.8 1.8 0.0 5.9 1.9 _x _x _x _x _x _x

0.7-4.7 0.2-3.3 0.8-4.4 0% 0.1-33.1 0.5-4.8

* The areas of the United States include: Portions of Maricopa County including metropolitan Phoenix, Arizona (AZ); 5 Counties of metropolitan Atlanta (Georgia); and 23 Counties in the Coastal

Regions. MD was excluded from these analyses due to limited availability of cognitive functioning information.y Prevalence values are reported as ASD cases per 1000 8-year-old children based on data from the National Center on Health Statistics bridged-race postcensal vintage estimates for 2005. Confidence

intervals based on a poisson distribution.x No ASD cases were identified in this racial/ethnic group.{ Bolded prevalence values indicate a significant change at the 95% confidence interval over 2000, 2002, and 2004 using an Extended Mantel-Haenszel Test of Trend.z Cognitive functioning defined based on IQ or developmental test scores from records.

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-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

AZ GA MD SC

Site

Net P

revalen

ce C

han

ge b

y S

ite

Other Special EducationEligibilitiesAutism Eligibility

Figure 3. Net prevalence change of special education eligibility classifica-

tion of ASD cases from 2000 to 2004. Includes intellectual disabilities,

emotional or behavioral disorders, specific learning disabilities, speech

or language impairment, other health impairments, significant develop-

mental delay, multiple disabilities, orthopedic impairment, hearing loss,

and visual impairment.


emotional behavioral disorders were not equal in magni-tude to the increases in autism eligibilities in 3 of the 4sites. However, in SC, where overall ASD prevalence wasrelatively stable, increases in the use of autism eligibilityfor SpED were met with concomitant decreases in the clas-sification of children with other classifications, suggestingthe possibility of diagnostic substitution in this site.Approaches that have looked at cohorts based on classifica-tion labels, rather than symptoms, have found inconsistentresults indicating that diagnostic substitution of SpEDservice classifications both did [35] and did not [4] accountfor increases in administrative prevalence of ASDs. In thisreport, we show that children with documented ASD symp-toms were increasingly more likely to have an autism SpEDclassification and other SpED classifications, rather thana decrease in those classifications from 2000 to 2004 in 3of the 4 sites.

ASD prevalence among subgroups of children

Changes in identified ASD prevalence were observedwithin specific subgroups of children with ASD (e.g., sex,cognitive functioning, race/ethnicity) that are not fullycaptured by the more global prevalence estimates. Thepreponderance of boys with an ASD is among the mostconsistent findings across ASD prevalence studies [8,10].In our study, the male:female ratio ranged from 4.0:1.0 to6.2:1.0 in 2004. Although this ratio varied both withinand among sites, 3 of 4 sites experienced increases in themale:female ratio from 2000 to 2004; only GA decreasedduring the same period. The increase in the male:femaleratio in AZ and MD was primarily the result of a significantincrease in the period prevalence among males, coupledwith a relatively constant prevalence in females, while theincrease in the male:female ratio in SC is the result of a rela-tively stable period prevalence for males and a significantdecrease among females. In GA, the period prevalenceamong both males and females increased significantly,

and this increase among females resulted in a net decreasein the male:female prevalence ratio.

Our findings suggest an overall decrease in the propor-tion of cases with cognitive impairment (IQ <70), whichis similar to other recent studies indicating an increasinglyless cognitively impaired population when all ASDs areincluded [16,17,21-23]. By 2004, fewer than half of thechildren with ASD had cognitive impairment (38%-45%).This shift might reflect an increased awareness in howASDs present in children with average to above-averageintelligence, improved psychometric testing for childrenwith ASDs, increased availability of early interventionservices, a true increase in the prevalence of ASD caseswith an IQ O70, or a combination thereof. If this shift werecaused primarily by increased identification of childrentypically classified as ‘‘higher-functioning’’ or more mildcases of ASD, we would expect to see larger increasesamong cases in the borderline and average to above averagecognitive functioning groups with decreases among chil-dren with cognitive impairment. For the 3 sites with suffi-cient cognitive data, we found increases in ASDs in thehighest functioning group in AZ, in the borderline groupin AZ and GA, and in the group with cognitive impairmentin GA, and a decrease in prevalence in the group withcognitive impairment in SC. Therefore, identification ofmore mildly impaired children may have been a factor inAZ and somewhat in GA, but overall identified ASD prev-alence increases were not explained simply by increasedidentification primarily among these subgroups.

Similar to other U.S. studies, we found mixed resultsregarding differential identified prevalence among black,white, and Hispanic children [1,14,15,36,37]. By 2004,Hispanic children were still significantly less likely to beidentified with an ASD; in GA, black non-Hispanicchildren were less likely to be identified than white non-Hispanic children. When examined by cognitive func-tioning status, changes in prevalence varied by sex andrace/ethnicity. For example, in GA, ASD prevalenceincreased most from 1996 to 2000 for white non-Hispanicmales with average to above-average IQ and from 2002to 2004 for black non-Hispanic males with cognitiveimpairment. Although our findings could indicate true vari-ation in prevalence among individuals of different races orethnicities, they are more likely related to variability amongethnic populations with regard to access to diagnosticservices; availability of trained, culturally competent diag-nosticians; or records that lack documentation of develop-mental concerns. All sites showed large increases in theHispanic and Asian populations over the 4-year period.Sites with the largest population of Hispanic children(AZ and GA) saw significant increases in prevalence in thissubgroup reflecting an identification and documentation‘‘catch up’’ in these groups. It is likely the earlier preva-lence estimates were an underestimate and some of theincrease is due to increased evaluation and documentationof Hispanic children with developmental concerns. As the


population in these subgroups gets larger and more stable,confidence in prevalence estimates will increase.

Individual site features

It is also important to examine site-specific patterns thatmay shed light on ASD prevalence. In the AZ site, identi-fied ASD prevalence decreased and then increased overthe 3 surveillance years. From 2000 to 2004, ASD preva-lence increased by 2 per 1000 children; however, only thechange from 2002 to 2004 was statistically significant.Variation was seen in the proportion of children born andstill residing in the area across the years indicating differen-tial migration in 2002, which reflects the lower prevalencefor that year compared to 2000 and 2004. From 2000 to2004, significant increases were seen primarily amongHispanic and Asian males, probably reflecting improvedidentification and documentation of ASD symptoms amongthese subgroups previously reported to have lower preva-lence of ASD [37]. This hypothesis is supported by the factthat an increasing proportion of children in these subgroupshad evaluation records abstracted during the period ofconcern. In addition, increases in cases identified in these2 subgroups account for about 75% of the increase in prev-alence from 2000 to 2004. No significant relevant statepolicy changes in SpED eligibility or insurance coveragehave been identified during this period. No significantmethodological changes were made in this site during thistime period and the data sources included were relativelystable. The median and range of evaluations available perchild did not change, and a qualitative estimate by the clini-cian reviewers indicates that there were slight improve-ments in the quality of the information documented;however, these changes were not striking over the courseof the surveillance period. Also, no changes were observedin the use of ASD-specific tests during evaluations. Unlikethe other 3 sites, AZ saw no increase in the proportion ofASD cases that had a documented ASD classification bycommunity professionals (54% in 2000 and 53% in 2004)and no change in the use of an autism classification forSpED. However, there was a slight decrease in the use ofthe autistic disorder diagnosis (from 54% to 47%). Themean age of ASD diagnosis actually increased by 18months over this time period. There was a shift towardfewer cases with cognitive impairment (from 58% to38%), primarily among white, non-Hispanic males. Insummary, for AZ, children were not more likely to be clas-sified as having an ASD by community professionals;however, there was a shift toward a less cognitivelyimpaired population with more Hispanic and Asian chil-dren identified. Therefore, changes in ASD prevalenceappear to result primarily from improved ascertainmentamong specific subgroups during this period rather thanetiology-based factors.

The identified ASD prevalence in the GA site showeda consistent increase of approximately 1 per 1,000 children

every 2 years with a doubling of ASD prevalence over thelonger 8-year period investigated. While it may have beenexpected that the 1994 DSM-IV revision would have trans-lated into a more dramatic ASD increase initially, it appearsthat whatever awareness, methodological, or etiologicfactors that resulted in the increases in ASD prevalenceare operating in a steady rather than sudden pattern. Acrossall 4 surveillance years, the proportion of cases born in thesurveillance area have been stable, indicating that differen-tial in-migration is likely not a contributing factor. From1996 to 2000, the greatest increases were seen in whitenon-Hispanic males in the highest cognitive functioninggroup while in 2000 to 2004 the greatest increases wereseen in black non-Hispanic and Hispanic males. GA isthe only site with a significant increase in ASD amongfemales. From 2000 to 2004, increases were primarilyamong children with borderline or impaired range of cogni-tive functioning. Although fewer children in the 2000cohort had cognitive impairment (40%) compared to the1996 cohort (62%), there was no change from 2000 to2004 (41%). The proportion of cases in GA with a docu-mented ASD classification by a community professionalincreased from 70% to 86% from 2000 to 2004. Similarly,the use of the autism classification for SpED also increased(from 59% to 67%), as did the use of ASD-specific tests inevaluations, indicating changes in community identificationpatterns. However, there was no change in the mean age ofearliest documented ASD diagnosis found in the abstractedrecords. There was some increase in the number of evalua-tions available per child, and the clinician reviewersindicated small, but steady increases in the quality of infor-mation contained in the evaluations. Looking at the largercommunity context, changes in the DSM criteria and inclu-sion of ‘‘autism’’ as a SpED category by the U.S. Depart-ment of Education in the early 1990s most likelyaccounts for much of the change from the 1996 to the2000 cohorts. For the 2000-2004 period, no known majorpolicy changes were introduced. Therefore, in summary,the initial increase in ASD prevalence from 1996 to 2000for GA appears to reflect more frequent identification ofchildren without cognitive impairment, but subsequentincreases reflect increased identification of cases amongblack and Hispanic children; however, the improved identi-fication among minority subgroups only accounts for about15% of the increase. Overall, from the 2000 to 2004surveillance years, the increase in ASD prevalence forGA appears to be due in part to steady improvements indocumentation of ASD-related behaviors in administrativerecords but does not rule out the influence of etiology-basedfactors as well.

The MD site had the greatest increase in identified ASDprevalence. This was primarily among white non-Hispanicand black non-Hispanic males. So, in contrast to AZ, theincrease was not reflective of an identification catch-upamong specific minority subgroups. There was a decreasein the proportion of cases that were inborn to the study area


from 2000 to 2002 but not 2002 to 2004, indicating theremay have been differential migration from 2000 to 2002but not from 2002 to 2004, which were the years whenthe largest increase in ASD prevalence occurred (2 per1000). There were no significant changes in the method-ology used by the MD site concerning sources with theexception of only screening a single year of school datain 2000, which was already accounted for in sensitivityadjustments. Another variation included the use of thelimited streamlining review in 2002 and 2004, which wouldhave increased prevalence less than 10% in this site from2000 to 2002 but does not account for the additionalincrease from 2002 to 2004. Due to limited documentationof cognitive testing in the school evaluations, we were notable to evaluate changes in ASD prevalence by cognitivefunctioning level; however, if subtype assigned by commu-nity professionals is used as a proxy for level of func-tioning, there was no increase in the use of the subtypesof Asperger disorder, PDD-NOS, or ASD-unspecifiedcompared to autistic disorder over this time period. Therewere indications of changes in the use of ASD classifica-tions by community professionals from 2000 to 2004 withmore children being classified with ASD (from 73% to89%) and receiving SpED services under an autism eligi-bility (from 37% to 58%), and the use of ASD-specific testsin evaluations increased. The increased identification ofchildren with ASD in the community may have led to morecomplete documentation of ASD symptoms in records re-sulting in improved case ascertainment. However, themedian and range of diagnostic evaluations per child didnot increase, and the clinician review team did not reportany impression of significant improvements in recordquality during this time period. Also, the mean age of iden-tification increased by 13 months. There were no knownchanges in state policies affecting identification during thistime with the exception of a Medicaid waiver for autismintroduced in October 2004, but this would have only hada 3-month period to impact identification of more severelyaffected children during the period studied. In summary, forMD, community awareness and better documentation ofsymptoms may account for some of the increase in identi-fied ASD prevalence, but the increase cannot be explainedsolely by methodological or awareness changes during thisperiod.

The SC site showed the opposite pattern from the other3 sites with a tendency for decreased identified ASD prev-alence, primarily among black females with cognitiveimpairment. However, preliminary results from the 2006surveillance year indicate an increase in ASD prevalencein that year [38]. Overall, similar proportions of childrenwere born in the surveillance areas during each year, sodifferential migration does not seem to be a contributingfactor. Although ASD prevalence did not increase, indica-tions of changes in community identification patterns wereevident by increases in the proportion of cases witha previous ASD classification (from 50% to 64%),

primarily reflected in the increased use of the autism eligi-bility for SpED among cases (from 28% to 53%). Interest-ingly, SC is the only site in which the increased use of theautism eligibility was matched by decreases in other SpEDlabels. Of the 4 sites, SC has the most restrictive use of theautism eligibility for SpED. It applies exclusively to chil-dren meeting the criteria for autistic disorder. No knownpolicy changes were made during this time. There werealso increases in the use of ASD-specific tests during eval-uations. Children were not more likely to have additionalevaluations and the clinician reviewers indicated thatminor, but steady improvements in the quality of informa-tion documented were present. These identificationchanges did not result in an increase in the population prev-alence of ASD. In addition, there was no change in themean age of ASD diagnosis. Like GA and MD, SC showedincreases among community professionals in ASD-specificevaluation patterns. However, unlike GA and MD, identi-fied ASD prevalence did not change in SC. Another factorto consider is that SC was the only site with a substantialrural area included and further evaluation is needed todetermine if prevalence differences indicate differences insymptom documentation or in actual symptoms by ruralor urban status. Information from the surveillance yearscurrently in progress (2006 and 2008) indicate that signif-icant improvements in symptom documentation occurredfrom the 2004 to 2006 time periods. Although SC identi-fied prevalence remained relatively stable, it will be impor-tant to continue to monitor trends in this site to evaluatechanges, particularly in light of incoming results indicatingan increase in ASD for the 2006 surveillance year [38].

Study strengths and limitations

The ADDM Network surveillance methodology hasmultiple strengths, including standardized procedures forcase identification and reliability during abstraction andcase determination [28,30]. Collaboration across sitesenabled the use of the same methodology in 4 areas ofthe United States over multiple time periods. These resultsare similar to a community investigation of identified ASDprevalence based on child examination [16] and to parentreports of autism from 2 national health surveys [37]. Inaddition, identified ASD prevalence was consistent among12 of 14 sites using the same methodology [14]. These 4sites evaluated the ASD symptoms documented for all chil-dren who were evaluated at one of the multiple sources,including children evaluated at no cost through the publiceducation system. This study was not limited to reviewingrecords of children with a documented ASD classificationor diagnosis. Determining prevalence by counting onlychildren with a previously documented educational or diag-nostic ASD classification would have only identified 30%to 70% of the children who were confirmed ASD cases in2000 and 53% to 89% of cases in 2004. The record-reviewmethodology can be applied to large populations while


reducing response biases, and these data represent informa-tion on approximately 2,000 children identified with anASD for surveillance. A challenge to this methodology isquantifying changes in the amount and quality of informa-tion documented in records, which would reflect increasedawareness of the symptoms of ASDs by community profes-sionals. A qualitative assessment of record quality indicatesincremental, but small increases in information qualityduring these years, probably accounting for a portion ofthe increase in identified ASD prevalence in some, butnot all, sites. Another issue is the use of postcensal datato estimate the population denominators. Postcensal datamay become unstable as time from the census yearincreases; therefore it is recommended that prevalence bere-evaluated when the intracensal estimates are releasedafter the 2010 census [39].

Conclusions

Based on the data from 4 sites in ADDM, there wasa tendency toward an increase in identified ASD prevalencein 3 of the 4 sites studied. These data suggest that even whenusing essentially the same surveillance methods, prevalencetrends may be more variable and gradual than has beenappreciated, especially when observed over a relatively shorttime period. We interpret the increase in ASD prevalence tobe due primarily to a greater number of males with an ASDamong specific subgroups defined by race/ethnicity and levelof cognitive functioning. However, ASD prevalence alsoincreased among females in the GA site. Some of the ASDprevalence increases are explained by awareness factorssuch as increased ascertainment among specific subgroups,such as Hispanic children in AZ and GA and childrenwithout cognitive impairment in AZ and SC. Also, improveddocumentation of autism symptoms enabled more completeclassification over time. However, these factors do notexplain all of the changes, particularly in GA and MD.Recently, Hertz-Picciotto and Delwiche [27] accounted for63% to 71% of ASD administrative prevalence increasesby examining assumptions surrounding factors of decreasingage of diagnosis, inclusion of more mild cases, and change incriteria. They could not evaluate changes due to ‘‘aware-ness’’ factors but noted the ASD was not leveling off inyounger children, which raises concerns about etiology-based increases. Unlike their analysis, we did not finda reduction in the age of identification and criteria changesand identification of more mildly affected children appearsto be most relevant for the earlier period in GA, but doesnot explain all of the observed increases. We have seen thatan increase in community identification of children withASD, particularly among specific subgroups, may havecontributed in part to the increased ASD prevalence in 3sites. However, ASD prevalence did not increase in one siteduring this time period and etiology-based increases cannotbe ruled out.

Although we can begin to evaluate trends with the datareported in this report, caution is urged given the likelihoodof some random variation in prevalence of behavior-definedconditions such as ASDs over time. Understanding thereasons for the increase in ASD prevalence continues tobe a challenge, and it is not possible to make causalassumptions about risk factors for ASD based on thesesurveillance data alone. ASDs are likely caused bya complex interaction of multiple factors; large popula-tion-based surveillance data can be a useful starting pointfor generating causal hypotheses. However, it is unlikelythat a single causal path will be identified to explain allcases of ASD and that ASD subtypes with multiple causesare possible. Much work needs to be done to understandhow complex genetic and environmental factors contributesingly and in interaction by subgroups to result in the symp-toms that make up the autism spectrum. Using the samestandard for evaluating the change in ASD symptoms overtime is critical to understanding the current and changingpopulation of children with ASD. As recent research indi-cates, the core social traits of autism are distributed in thepopulation along a continuum [40]; where the line is drawnbetween trait measures regarded as normal variance inbehavior and those labeled as impairment or disability willhave a potentially large impact on ASD prevalence. Asmore studies indicate that ASD prevalence is approaching1% or greater, we will be able to observe over time if theseestimates level out or continue to change over time. Ofparticular interest will be the cohort of children born in thismillennium, as the impact of broadened diagnostic criteriaand awareness of ASDs would be expected to lessen by thistime. Ongoing monitoring of ASD prevalence using thesame methods is also needed to further evaluate changesbased on race/ethnicity, sex, and cognitive functioningand to explore other potential risk factors associated withASDs. For example, variation by urban and rural area, so-ciodemographic status, perinatal complications, andparental age are possible variables in need of further study.

It is clear no single factor explains changes in identifiedASD prevalence over time, and although some of theincreases reported here can be accounted for by improvedidentification, it is also possible that the symptoms associ-ated with ASDs have steadily increased in the population,particularly in males, indicating increased risk over time.These data give us a starting point to evaluate trends inASDs in multiple areas of the United States. Furtherinvestigation is needed to determine what proportion ofthe observed increase can be explained by changes inthe service delivery system, migration patterns, and/orgrowing prevalence of ASD among children. It will alsobe important to better understand trends in other childhoodconditions such as ADHD, asthma, and allergies to deter-mine if changes in identified ASD prevalence are occur-ring in isolation or in concert with other disorders [41-43]. Clearly, more children are receiving services forASDs than ever before, and concerted efforts are needed


to address the many needs of affected individuals and toprovide coordinated support services for their families.These data confirm that ASDs continue to be of urgentpublic health concern. Collaborative efforts to improvethe research and service systems for people with ASDsare essential in addition to continued monitoring in sitesusing similar methods to better understand changes in thispopulation.

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changes in autism spectrum disorder prevalence in 4 areas of the united states

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