Show Summary Details
Page of

Epidemiology of Pervasive Developmental Disorders 

Epidemiology of Pervasive Developmental Disorders

Chapter:
Epidemiology of Pervasive Developmental Disorders
Author(s):

Eric Fombonne

, Sara Quirke

, and Arlene Hagen

DOI:
10.1093/med/9780195371826.003.0007
Page of

PRINTED FROM OXFORD MEDICINE ONLINE (www.oxfordmedicine.com). © Oxford University Press, 2015. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a title in Oxford Medicine Online for personal use (for details see Privacy Policy).

Subscriber: null; date: 16 August 2017

Points of Interest

  • The best estimate of prevalence for all PDDs combined is 0.7% (1 in 143 children is affected), and several recent surveys even point at figures closer to 1%.

  • The proportion of children with cognitive functioning within the normal range is 30% for autistic disorder and 55% for all PDDs.

  • There is a consistent male overrepresentation for autistic disorder, childhood disintegrative disorder, and all PDDs.

  • Regression, or loss of skills in the developmental course, occurs in about 1 in 4 children with PDD.

  • Upward trends in rates of prevalence cannot be directly attributed to an increase in the incidence of the disorder as changes in referral patterns and availability of services, heightened public awareness, decreasing age at diagnosis, and changes over time in diagnostic concepts and practices confound the interpretation of data.

  • Diagnostic substitution (the reclassification as PDD of a child who had previously received a different diagnosis) has been shown to contribute to the increase of prevalence in several studies.

The aims of this chapter are to provide an up-to-date review of the methodological features and substantive results of published epidemiological surveys of the prevalence of pervasive developmental disorders (PDD). This chapter updates previous reviews (Fombonne, 2003a; Fombonne, 2005) with the inclusion of new studies made available since then. The specific questions addressed in this chapter are: (1) what is the range of prevalence estimates for autism and related pervasive developmental disorders, and what are the correlates of PDD in epidemiological surveys?; and (2) what interpretation can be given to time trends observed in prevalence rates of PDDs?

Selection of Studies

The studies were identified through systematic searches from the major scientific literature databases (MEDLINE, PSYCINFO, EMBASE) and from prior reviews (Fombonne, 2003a, 2003b; Fombonne, 2005; Williams et al., 2006). Only studies published in the English language were included. Surveys that relied only on a questionnaire-based approach to define caseness (for example, Ghanizadeh, 2008) were also excluded, as the validity of the diagnosis is uncertain in these studies. Overall, 61 studies published between 1966 and 2009 were selected that surveyed PDDs in clearly demarcated, nonoverlapping samples. Of these, 48 studies provided information on rates of autistic disorder, 13 studies on Asperger’s disorder (later referred to as Asperger’s syndrome; AS), and 12 studies on childhood disintegrative disorder (CDD). A total of 27 studies provided estimates on all PDDs combined, of which 14 provided rates for specific PDD subtypes as well, and 13 only for the combined category of PDD.

Surveys were conducted in 18 countries, including 16 studies from the UK, 13 from the United States, and 7 from Japan. The results of over half of the studies (N = 33) have been published since 2001. The age range of the population included in the surveys is spread from birth to early adult life, but most studies have relied on school-age samples. Similarly, there was huge variation in the size of the population surveyed (range: 826 to 4.9 million; mean: 267,000; median: 44,900), with some recent studies conducted by the U.S. Centers for Disease Control (CDC, 2007b; 2009) relying on very large samples of several hundreds of thousands of individuals.

Study Designs

In designing a prevalence study, two major features are critical for the planning and logistics of the study, as well as for the interpretation of its results: case definition, and case ascertainment (or case identification methods) (Fombonne, 2007).

Case Definition

Over time, the definitions of autism have changed as illustrated by the numerous diagnostic criteria that were used in both epidemiological and clinical settings (see Figure 6-1). Starting with the narrowly defined Kanner’s autism (1943), definitions progressively broadened in the criteria proposed by Rutter (1970), and then ICD-9 (1977), DSM-III (1980) and DSM-III-R (1987), and more recently in the 2 major nosographies used worldwide, ICD-10 (1992) and DSM-IV (American Psychiatric Association [APA], 1994). The early diagnostic criteria reflected the more qualitatively severe forms of the phenotype of autism, usually associated with severe delays in language and cognitive skills. It is only in the 1980s that less severe forms of autism were recognized, either as a qualifier for autism occurring without mental retardation (so-called “high-functioning” autism), or as separate diagnostic categories (Pervasive Developmental Disorders Not-Otherwise-Specified–PDD NOS) within a broader class of autism spectrum disorders (ASD) denominated “pervasive developmental disorders” (PDD, an equivalent to ASD) in current nosographies. While it had been described in the literature as early as 1944 (Asperger, 1944), one type of PDD, Asperger’s disorder, appeared in official nosographies only in the 1990s, and then with unclear validity, especially with respect to its differentiation from “high-functioning” autism. Subtypes of PDD that existed in DSM-III subsequently disappeared (i.e., Autism–residual state). While there is generally high interrater reliability on the diagnosis of PDDs and commonality of concepts across experts, some differences persist between nomenclatures about the terminology and precise operationalized criteria of PDDs. For example, DSM-IV (1994) has a broad category of PDD NOS, sometimes referred to loosely as “atypical autism,” whereas ICD-10 (1992) has several corresponding diagnoses for clinical presentations that fall short of autistic disorder, that include Atypical autism (F84.1, a diagnostic category that existed already in ICD-9), Other PDD (F84.8), and PDD-, unspecified (F84.9). As a result, studies that refer to “atypical autism” must be carefully interpreted, and no direct equivalence with the DSM-IV concept of PDD NOS should be assumed. In recent years, the definitions of syndromes falling on the autism spectrum have been expanded further with reference to the broader autism phenotype, and, with an increasing reliance on a dimensionalization of the autism phenotype. As no impairment or diagnostic criteria are available for these milder forms, the resulting boundaries with the spectrum of PDDs are left uncertain. Whether or not this plays a role in more recent epidemiological studies is difficult to know, but the possibility should be considered in assessing results for the new generation of surveys.

Figure 6–1. Historical changes in diagnostic concepts and criteria.

Figure 6–1.
Historical changes in diagnostic concepts and criteria.

Broader autism phenotype: a pattern of mild developmental deficits similar to, but less severe than, symptoms of autism seen in relatives of subjects affected with a pervasive development disorder.

Case Identification

When an area or population have been identified for a survey, different strategies have been employed to find subjects matching the case definition retained for the study. Some studies have relied solely on existing service providers databases (Croen et al., 2002); on special educational databases (Gurney et al., 2003; Fombonne et al., 2006; Lazoff et al., 2010); or on national registers (Madsen et al., 2002) for case identification. These studies have the limitation in common of relying on a population group that happened to access the service provider or agencies rather than sampling from the population at large. As a result, subjects with the disorder who are not in contact with these services are yet unidentified and not included as cases, leading to a underestimation of the prevalence proportion.

Other investigations have relied on a multistage approach to identify cases in underlying populations. The aim of the first screening stage of these studies is to cast a wide net in order to identify subjects possibly affected with a PDD, with the final diagnostic status being determined at a next phase. The process utilized by the researchers often consists of sending letters or brief screening scales requesting school and health professionals and/or other data sources to identify possible cases of autism. Few of these investigations relied on systematic sampling techniques that would ensure a near complete coverage of the target population. Moreover, each investigation differed in several key aspects of this screening stage. First, the thoroughness of the coverage of all relevant data sources varied enormously from one study to another. In addition, the surveyed areas were not comparable in terms of service development, reflecting the specific educational or health care systems of each country and of the period of investigation. Second, the type of information sent out to professionals invited to identify children varied from simple letters, including a few clinical descriptors of autism-related symptoms or diagnostic checklists rephrased in nontechnical terms, to more systematic screening strategy based on questionnaires or rating scales of known reliability and validity. Third, variable participation rates in the first screening stages provide another source of variation in the screening efficiency of surveys, although refusal rates tended, on average, to be very low.

Few studies provided an estimate of the reliability of the screening procedure. The sensitivity of the screening methodology is also difficult to gauge in autism surveys and the proportion of children truly affected with the disorder but not identified in the screening stage (the “false negatives”) remains generally unmeasured. The usual approach, which consists of sampling at random screened negative subjects in order to estimate the proportion of false negatives and adjusting the estimate accordingly, has not been used in these surveys for the obvious reason that, due to the low frequency of the disorder, it would be both imprecise and very costly to undertake such estimations. As a consequence, prevalence estimates must be understood as underestimates of “true” prevalence rates. The magnitude of this underestimation is unknown in each survey.

When the screening phase is completed, subjects identified as positive screens go through the next step involving a more in-depth diagnostic evaluation to confirm their case status. Similar considerations about the methodological variability across studies apply to these more intensive assessment phases. In the studies reviewed, participation rates in second-stage assessments were generally high. The source of information used to determine caseness usually involved a combination of data coming from different informants (parents, teachers, pediatricians, other health professionals, etc.) and data sources (medical records, educational sources), with an in-person assessment of the person with autism being offered in some but not all studies. Obviously, surveys of very large populations as those conducted in the United States by the CDC (2007a, 2007b, 2009) or in national registers (Madsen et al., 2002) did not include a direct diagnostic assessment of all subjects by the research team. However, these investigators could generally confirm the accuracy of their final caseness determination by undertaking, on a randomly selected subsample, a more complete diagnostic workup. The CDC surveys have established a methodology for surveys of large populations that relies on screening of population using multiple data sources, a systematic review and scoring system for the data gathered in the screening phase combined with, in the less obvious cases, input from experienced clinicians with known reliability and validity. This methodology is adequate for large samples, and is likely to be used in the future for surveillance efforts.

When subjects were directly examined, the assessments were conducted with various diagnostic instruments, ranging from a typical unstructured examination by a clinical expert (but without demonstrated psychometric properties), to the use of batteries of standardized measures by trained research staff. The Autism Diagnostic Interview (Le Couteur et al., 1989) and/or the Autism Diagnostic Observational Schedule (Lord et al., 2000) have been increasingly used in the most recent surveys.

Prevalence Estimations

We now present the results of the 61 studies in several tables that summarize results by type of PDD or for PDD as a spectrum of disorders. Characteristics of samples surveyed in these studies are briefly summarized.

Autistic Disorder

Prevalence estimates for autistic disorder are summarized in Table 6-1. There were 48 studies (including 13 in the UK, 6 in the United States, and 6 in Japan), half of them published since 1999. The sample size varied from 826 to 4.95 millions, with a median of 35,300 (mean: 212,500) subjects in the surveyed populations. The age ranged from 3 to 15 years, with a median age of 8.5 years. The number of subjects identified with autistic disorder ranged from 6 to 5,032 (median: 51). Males consistently outnumbered females in 40 studies where gender differences were reported, with a male/female ratio ranging from 1.33:1 to 16.0:1 in 39 studies (1 small study had no girls at all), leading to an average male/female ratio of 4.4:1. Prevalence rates varied from 0.7/10,000 to 72.6/10,000 with a median value of 12.9/10,000. Prevalence rates were negatively correlated with sample size (Spearman’s r: -0.71; p < .001), with small-scale studies reporting higher prevalence rates. The correlation between prevalence rate and year of publication was significant (Spearman’s r: 0.69; p < .001), indicative of higher rates in more recent surveys. Therefore, a current estimate for the prevalence of autistic disorder must be derived from more recent surveys with an adequate sample size. In 23 studies published since 2000, the median rate was 21.6/10,000 (mean rate: 21.9/10,000). After exclusion of the 2 studies with the smallest and largest sample sizes, the results were very similar (mean rate: 22.4/10,000). Thus, the best current estimate for autistic disorder is 22/10,000. In 25 studies where the proportion of subjects with IQ within the normal range was reported, the median value was 30% (interquartile range: 17.5%–50%). In these surveys, there was a significant correlation between a higher proportion of normal IQ subjects and a higher male/female ratio (Spearman’s r: 0.53; p = .007), a result consistent with the known association between gender and IQ in autism. Over time, there were minor associations between the year of publication of the survey and the sample male/female ratio (Spearman’s r: 0.36; p = 0.024) and the proportion of subjects without mental retardation (Spearman’s r: 0.32; p = 0.11). Taken in conjunction with the much stronger increase over time in prevalence rates, these results suggest that the increase in prevalence rates is not entirely accounted for by the inclusion of milder forms (i.e., less cognitively impaired) of autistic disorder, albeit this might have contributed to it to some degree.

Table 6–1. Prevalence surveys of autistic disorder

Year of Publication

Authors

Country

Area

Size of Target Population

Age

Number of Subjects with Autism

Diagnostic Criteria

% with normal |IQ

Gender ratio

(M:F)

Prevalence

Rate/10,000

95% CI

1966

Lotter

UK

Middlesex

78,000

8–10

32

Rating scale

15.6

2.6

(23/9)

4.1

2.7; 5.5

1970

Brask

Denmark

Aarhus County

46,500

2–14

20

Clinical

1.4

(12/7)

4.3

2.4; 6.2

1970

Treffert

USA

Wisconsin

899,750

3–12

69

Kanner

3.06

(52/17)

0.7

0.6; 0.9

1976

Wing et al.

UK

Camberwell

25,000

5–14

171

24 items rating scale of Lotter

30

16

(16/1)

4.82

2.1; 7.5

1982

Hoshino et al.

Japan

Fukushima-Ken

609,848

0–18

142

Kanner’s criteria

9.9

(129/13)

2.33

1.9; 2.7

1983

Bohman et al.

Sweden

County of Västerbotten

69,000

0–20

39

Rutter criteria

20.5

1.6

(24/15)

5.6

3.9; 7.4

1984

McCarthy et al.

Ireland

East

65,000

8–10

28

Kanner

1.33

(16/12)

4.3

2.7; 5.9

1986

Steinhausen et al.

Germany

West Berlin

279,616

0–14

52

Rutter

55.8

2.25

(36/16)

1.9

1.4; 2.4

1987

Burd et al.

USA

North Dakota

180,986

2–18

59

DSM-III

2.7

(43/16)

3.26

2.4; 4.1

1987

Matsuishi et al.

Japan

Kurume City

32,834

4–12

51

DSM-III

4.7

(42/9)

15.5

11.3; 19.8

1988

Tanoue et al.

Japan

Southern Ibaraki

95,394

7

132

DSM-III

4.07

(106/26)

13.8

11.5; 16.2

1988

Bryson et al.

Canada

Part of Nova-Scotia

20,800

6–14

21

New RDC

23.8

2.5

(15/6)

10.1

5.8; 14.4

1989

Sugiyama &

Abe

Japan

Nagoya

12,263

3

16

DSM-III

13.0

6.7; 19.4

1989

Cialdella & Mamelle

France

1 département (Rhône)

135,180

3–9

61

DSM-III like

2.3

4.5

3.4; 5.6

1989

Ritvo et al.

USA

Utah

769,620

3–27

241

DSM-III

34

3.73

(190/51)

2.47

2.1; 2.8

19914

Gillberg et al.

Sweden

South-West Gothenburg + Bohuslän County

78,106

4–13

74

DSM-III-R

18

2.7

(54/20)

9.5

7.3; 11.6

1992

Fombonne & du Mazaubrun

France

4 régions

14 départements

274,816

9 & 13

154

Clinical-ICD-10 like

13.3

2.1

(105/49)

4.9

4.1; 5.7

1992

Wignyosumarto et al.

Indonesia

Yogyakarita (SE of Jakarta)

5,120

4–7

6

CARS

0

2.0

(4/2)

11.7

2.3; 21.1

1996

Honda et al.

Japan

Yokohama

8,537

5

18

ICD-10

50.0

2.6

(13.5)

21.08

11.4; 30.8

1997

Fombonne et al.

France

3 départements

325,347

8–16

174

Clinical ICD-10-like

12.1

1.81

(112/62)

5.35

4.6; 6.1

1997

Webb et al.

UK

South Glamorgan, Wales

73,301

3–15

53

DSM-III-R

6.57

(46/7)

7.2

5.3; 9.3

1997

Arvidsson et al.

Sweden

(West coast)

Mölnlycke

1,941

3–6

9

ICD-10

22.2

3.5

(7/2)

46.4

16.1; 76.6

1998

Sponheim & Skjeldal

Norway

Akershus County

65,688

3–14

34

ICD-10

47.13

2.09

(23/11)

5.2

3.4; 6.9

1999

Taylor et al.

UK

North Thames

490,000

0–16

427

ICD-10

8.7

7.9; 9.5

1999

Kadesjö et al.

Sweden (Central)

Karlstad

826

6.7–7.7

6

DSM-III-R/ICD-10 Gillberg’s criteria (Asperger’s syndrome)

50.0

5.0

(5/1)

72.6

14.7; 130.6

2000

Baird et al.

UK

South-East Thames

16,235

7

50

ICD-10

60

15.7

(47/3)

30.8

22.9; 40.6

2000

Powell et al.

UK

West Midlands

25,377

1–5

62

Clinical/

ICD10/

DSM-IV

7.8

5.8; 10.5

2000

Kielinen et al.

Finland

North (Oulu et Lapland)

27,572

5–7

57

DSM-IV

49.87

4.127

(156/50)

20.7

15.3; 26.0

2001

Bertrand et al.

USA

Brick Township, New Jersey

8,896

3–10

36

DSM-IV

36.7

2.2

(25/11)

40.5

28.0; 56.0

2001

Fombonne et al.

UK

Angleterre et Pays de Galles

10,438

5–15

27

DSM-IV/

ICD-10

55.5

8.0

(24/3)

26.1

16.2; 36.0

2001

Magnússon & Saemundsen

Iceland

Whole Island

43,153

5–14

57

Mostly ICD-10

15.8

4.2

(46/11)

13.2

9.8; 16.6

2001

Chakrabarti & Fombonne

UK (Midlands)

Staffordshire

15,500

2.5–6.5

26

ICD10/

DSM-IV

29.2

3.3

(20/6)

16.8

10.3; 23.2

2001

Davidovitch et al.

Israel

Haiffa

26,160

7–11

26

DSM-III-R/

DSM-IV

4.2

(21/5)

10.0

6.6;14.4

2002

Croen et al.

USA

California DDS

4,950,333

5–12

5,038

CDER “Full syndrome”

62.85

4.47

(4116/921)

11.0

10.7;11.3

2002

Madsen et al.

Denmark

National Register

63,859

8

46

ICD-10

7.2

5.0–10.0

2004

Tebruegge et al.

UK

Kent

2,536

8–9

6

ICD-10

0.0

(6/0)

23.7

9.6; 49.1

2005

Chakrabarti & Fombonne

UK (Midlands)

Staffordshire

10,903

4–7

24

ICD-10/DSM-IV

33.3

3.8

(19/5)

22.0

14.4; 32.2

2005

Barbaresi et al.

USA, Minnesota

Olmstead County

37,726

0–21

112

DSM-IV

29.7

24.0; 36.0

2005

Honda et al.6

Japan

Yokohama

32,791

5

123

ICD-10

25.3

2.5

(70/27)

37.5

31.0; 45.0

2006

Fombonne et al.

Canada (Quebec)

Montreal Island

27,749

5–17

60

DSM-IV

5.7

(51/9)

21.6

16.5; 27.8

2006

Gillberg et al.

Sweden

Göteborg

32,568

7–12

115

Gillberg’s criteria

3.6

(90/25)

35.3

29.2; 42.2

2006

Baird et al.

UK

South Thames, London

56,946

9–10

81

ICD-10

47

8.3 (≈ 72/9)

38.9

29.9; 47.8

2007

Ellefsen et al.

Denmark

Faroe Islands

7,689

8–17

12

ICD-10

Gillberg criteria for AS

3.0 (9/3)

16.0

7.0; 25.0

2007

Oliveira et al.

Portugal

Mainland and Azores

67,795

6–9

115

DSM-IV

17

2.9

16.7

14.0; 20.0

2007

Latif & Williams

UK

Wales

39,220

0–17

50

Kanner

12.7

9.0;17.0

2008

Williams et al.

UK

South West (Avon)

14,062

11

30

ICD-10

86.7

5.0

(25/5)

21.6

13.9; 29.3

2009

van Balkom et al.

Netherlands

Aruba (Caribbean)

13,109

0–13

25

DSM-IV

36.0

7.3 (22/3)

19.1

12.3; 28.1

2010

Lazoff et al.

Canada

Montreal

23,635

5–17

60

DSM-IV

5.0 (50/10)

25.4

19.0; 31.8

1 This number corresponds to the sample described in Wing & Gould (1979).

2 This rate corresponds to the first published paper on this survey and is based on 12 subjects among children aged 5 to 14 years.

3 In this study, mild mental retardation was combined with normal IQ, whereas moderate and severe mental retardation were grouped together.

4 For the Goteborg surveys by Gillberg et al. (Gillberg, 1984; Steffenburg & Gillberg, 1986; Gillberg et al., 1991) a detailed examination showed that there was overlap between the samples included in the 3 surveys; consequently only the last survey has been included in this table.

5 This proportion is likely to be overestimated and to reflect an underreporting of mental retardation in the CDER evaluations.

6 This figure was calculated by the author and refers to prevalence data (not cumulative incidence) presented in the paper (the M:F ratio is based on a subsample).

7 These figures apply to the whole study sample of 206 subjects with an ASD.

Asperger’s Syndrome

Epidemiological studies of Asperger’s syndrome (AS) are sparse, due to the fact that it was acknowledged as a separate diagnostic category only in the early 1990s, in both ICD-10 and DSM-IV. Two epidemiological surveys have been conducted which specifically investigated AS prevalence (Kadesjö et al., 1999; Ehlers & Gillberg, 1993). However, only a handful (N < 5) of cases were identified in these surveys, with the resulting estimates being unacceptably imprecise. In addition, since there was no separate report for children meeting criteria for autistic disorder, it remains unclear if these subjects would have also met criteria for autistic disorder and how prevalence rates would be affected if hierarchical rules were followed to diagnose both disorders. A recent survey of high-functioning PDDs in Welsh mainstream primary schools has yielded a relatively high (uncorrected) prevalence estimate of 14.5/10,000, but no separate rate was available for AS, specifically (Webb et al., 2003).

Other recent surveys have examined samples with respect to the presence of both autistic disorder and Asperger’s syndrome. Thirteen studies (already listed in Table 6-1), published since 1998, provided usable data (Table 6-2). The median population size was 16,200, and the median age 8.0 years. Numbers of children with AS varied from 6 to 427, with a median sample size of 32. There was a 160-fold variation in estimated rates of AS (range: 0.3 to 48.4/10,000) that demonstrates the lack of reliability of these estimates. The median value was 10.5/10,000. With the exception of one study (Latif & Williams, 2007), the number of children with autistic disorder was consistently higher than that of children with AS. The prevalence ratio (Table 6-2, right-hand column) exceeded 1, with a median value of 2.1, indicating that the rate of AS was consistently lower than that for autism (Table 6-2). The unusually high rate of AS relative to autistic disorder obtained in Latif and Williams’s study (2007) appeared to be inflated due to the inclusion of high-functioning autism in the AS definition. The epidemiological data on AS are therefore of dubious quality, reflecting the difficult nosological issues that have surrounded the inclusion of AS in recent nosographies as well as the lack of proper measurement strategies that ensure a reliable difference between AS and autistic disorder.

Table 6–2. Asperger’s syndrome (AS) in recent autism surveys

Assessment

Autism

Asperger Syndrome

Size of Population

Age Group

Informants

Instruments

Diagnostic Criteria

N

Rate/10,000

N

Rate/10,000

Autism/AS Ratio

Sponheim & Skjeldal, 1998

65,688

3–14

Parent

Child

Parental Interview + direct observation, CARS, ABC

ICD-10

32

4.9

2

0.3

16.0

Taylor et al., 1999

490,000

0–16

Record

Rating of all data available in child record

ICD-10

427

8.7

71

1.4

6.0

Kadesjö et al., 1999

826

6.7–7.7

Child

Parent

Professional

ADI-R, Griffiths Scale or WISC, Asperger Syndrome Screening Questionnaire

DSM-III-R/ICD-10 Gillberg’s criteria (Asperger syndrome)

6

72.6

4

48.4

1.5

Powell et al., 2000

25,377

1–4.9

Records

ADI-R

Available data

DSM-III-R

DSM-IV

ICD-10

54

16

3.4

Baird et al., 2000

16,235

7

Parents

Child

Other data

ADI-R

Psychometry

ICD-10

DSM-IV

45

27.7

5

3.1

9.0

Chakrabarti & Fombonne, 2001

15,500

2.5–6.5

Child

Parent

Professional

ADI-R, 2 wks multidisciplinary assessment, Merrill-Palmer, WPPSI

ICD-10

DSM-IV

26

16.8

13

8.4

2.0

Chakrabarti & Fombonne, 2005

10,903

2.5–6.5

Child

Parent

Professional

ADI-R, 2 wks multidisciplinary assessment, Merrill-Palmer, WPPSI

ICD-10

DSM-IV

24

22.0

12

11.0

2.0

Fombonne et al., 2006

27,749

5–17

School registry

Clinical

DSM-IV

60

21.6

28

10.1

2.1

Ellefsen et al., 2007

7,689

8–17

Parent

Child

Professional

DISCO, WISC-R,

ASSQ

ICD-10

Gillberg AS criteria

21

28.0

20

26.0

1.1

Latif & Williams, 2007

39,220

0–17

?

Clinical

Kanner,

Gillberg AS criteria

50

12.7

139

35.4

0.36

William et al., 2008

14,062

11

Medical records and educational registry

Clinical

ICD-10

30

21.6

23

16.6

1.3

van Balkom et al., 2009

13,109

0–13

Clinic series

Review of medical records

DSM-IV

25

19.1

2

1.5

12.5

Lazoff et al., 2010

23,635

5–17

School registry

Review of educational records

DSM-IV

60

25.4

23

9.7

2.6

Childhood Disintegrative Disorder

Twelve surveys provided data on childhood disintegrative disorder (CDD) (Table 6-3). In 5 of these, only 1 case was reported; no case of CDD was identified in 4 other studies. Prevalence estimates ranged from 0 to 9.2/100,000, with a median rate of 1.8/100,000. The pooled estimate, based on 11 identified cases and a surveyed population of about 560,000 children, was 1.9/100,000. Gender was reported in 10 of the 11 studies, and males appear to be overrepresented with a male/female ratio of 9:1. The upperbound limit of confidence interval associated to the pooled prevalence estimate (3.4/100,000) indicates that CDD is a quite rare condition, with about 1 case occurring for every 112 cases of autistic disorder.

Table 6–3. Surveys of childhood disintegrative disorder (CDD)

Study

Country (Region/State)

Size of Target Population

Age Group

Assessment

N

M/F

Prevalence Estimate (/100,000)

95% CI (/100,000)

Burd et al., 1987

USA

(North Dakota)

180,986

2–18

Structured parental interview and review of all data available–DSM-III criteria

2

2/–

1.11

0.13–3.4

Sponheim & Skjeldal, 1998

Norway

(Akershus County)

65,688

3–14

Parental interview and direct observation (CARS, ABC)

1

?

1.52

0.04–8.5

Magnusson et Saemundsen, 2001

Iceland

(whole island)

85,556

5–14

ADI-R, CARS and psychological tests–mostly ICD-10

2

2/–

2.34

0.3–8.4

Chakrabarti & Fombonne, 2001

UK

(Staffordshire, Midlands)

15,500

2.5–6.5

ADI-R, two weeks multidisciplinary assessment, Merrill-Palmer, WPPSI–ICD-10/DSM-IV

1

1/–

6.45

0.16–35.9

Chakrabarti & Fombonne, 2005

UK

(Staffordshire, Midlands)

10,903

2.5–6.5

ADI-R, two weeks multidisciplinary assessment, Merrill-Palmer, WPPSI–ICD-10/DSM-IV

1

1/–

9.17

0–58.6

Fombonne et al., 2006

Montreal, Canada

27,749

5–17

DSM-IV, special needs school survey

1

1/–

3.60

0–20.0

Gillberg et al., 2006

Sweden, Götenborg

102,485

7–24

DSM-IV, review of medical records of local diagnostic center

2

1/1

2.0

0.2–7.1

Ellefsen et al., 2007

Faroe Islands, Denmark

7,689

8–17

DISCO, Vineland, WISC-R, ICD-10/DSM-IV

0

0

Kawamura et al., 2008

Japan, Toyota

12,589

5–8

DSM-IV, population based screening at 18 and 36 mths

0

0

Williams et al., 2008

UK, Avon

14,062

11

ICD-10, educational and medical record review

0

0

van Balkom et al., 2009

Netherlands, Aruba

13,109

0–13

Clinic medical record review

0

0

Lazoff et al., 2010

Canada,

Montreal

23,635

5–17

DSM-IV, special needs school survey

1

1/0

4.23

0.0–24.0

Pooled Estimates

559,951

11

9/1

1.96

1.1–3.4

Prevalence for Combined PDDs

A new objective of more recent epidemiological surveys was to estimate the prevalence of all disorders falling onto the autism spectrum, thereby prompting important changes in the conceptualization and design of surveys. However, before reviewing the findings of these studies mostly conducted since 2000, we examine to which extent findings of the first generation of epidemiological surveys of a narrow definition of autism also informed our understanding of the modern concept of autism spectrum disorders.

Unspecified Autism Spectrum Disorders in Earlier Surveys

In previous reviews, we documented that several studies performed in the 1960s and 1970s had provided useful information on rates of syndromes similar to autism but not meeting of the strict diagnostic criteria for autistic disorder then in use (Fombonne, 2003a, 2003b; Fombonne, 2005). At the time, different labels were used by authors to characterize these clinical pictures, such as the triad of impairments involving deficits in reciprocal social interaction, communication, and imagination (Wing & Gould 1979), autistic mental retardation (Hoshino et al., 1982), borderline childhood psychoses (Brask, 1970) or “autistic-like” syndromes (Burd et al., 1987). These syndromes would be falling within our currently defined autistic spectrum, probably with diagnostic labels such as atypical autism and/or PDD NOS. In 8 of 12 surveys providing separate estimates of the prevalence of these developmental disorders, higher rates for the atypical forms were actually found compared to those for more narrowly defined autistic disorder (see Fombonne, 2003a, Table 3, p. 172). However, this group received little attention in previous epidemiological studies, and these subjects were not defined as “cases” and therefore not included in the numerators of prevalence calculations, thereby underestimating systematically the prevalence of what would be defined today as the spectrum of autistic disorders. For example, in the first survey by Lotter (1966), the prevalence would rise from 4.1 to 7.8/10,000 if these atypical forms had been included in the case definition. Similarly, in Wing et al.’s study (1976), the prevalence was 4.9/10,000 for autistic disorder, but, adding the figure of 16.3/10,000 (Wing & Gould, 1979) corresponding to the triad of impairments, the prevalence for the whole PDD spectrum was in fact 21.1/10,000. For the purpose of historical comparison, it is important to be attentive to this earlier figure, bearing in mind that the study was conducted in the early 1970s for the field work and that autism occurring in subjects with an IQ within the normal range was not yet being investigated. Progressive recognition of the importance and relevance to autism of these less typical clinical presentations has led to changes in the design of more recent epidemiological surveys (see below), that are now using case definitions that incorporate upfront these milder phenotypes.

Newer Surveys of PDDs

The results of surveys that estimated the prevalence of the whole spectrum of PDDs are summarized in Table 6-4. Of the 27 studies listed, 14 also provided separate estimates for autistic disorder and other PDD subtypes; the other 13 studies provided only an estimate for the combined PDD rate. All these surveys were published since 2000, and the majority since 2006; the studies were performed in 8 countries (including 10 in the UK and 8 in the United States). Sample sizes ranged from 2,536 to 4,247,206 (median: 32,568; mean: 243,156). The median age of samples ranged from 5.0 to 12.5, with 8.0 years being both the modal and median age. The diagnostic criteria used in the 25 studies where they were specified reflect reliance on modern diagnostic schemes by all authors (10 studies used ICD-10, 17 the DSM-IV or DSM-IV-TR, both schemes being used simultaneously in 2 studies). In 14 studies where IQ data were reported, the proportion of subjects within the normal IQ range varied from 30% to 85.3% (median: 57.1%; mean: 56.1%), a proportion that is higher than that for autistic disorder and reflects the lesser degree of association, or lack thereof, between intellectual impairment and milder forms of PDDs. Overrepresentation of males was the rule, with male/female ratio ranging from 2.7:1 to 15.7:1 (mean: 5.5; median: 4.9). There was a 6-fold variation in prevalence proportions that ranged from a low 30.0/10,000 to a high of 181.1/10,000. However, some degree of consistency is found in the center of this distribution, with a median rate of 62.0/10,000 and a mean rate of 69.2/10,000 (interquartile range: 53.3–80.4/10,000). This mean rate coincides with the rate reported recently for PDDs in 14 sites (CDC, 2007b); the CDC value represents, however, an average, and that study conducted at 14 different sites utilizing the same methodology found a 3-fold variation of rate by state. Alabama had the lowest rate of 3.3/1,000 whereas New Jersey had the highest value with 10.6/1,000 (CDC, 2007b). As expected, a new CDC report on 307,000 U.S. children aged 8 and born 4 years later than children from the previous survey reported an average prevalence of 89.6/10,000 (CDC, 2009). Again, substantial variation across states was reported as prevalence ranged from 4.2/1,000 in Florida to 12.1/1,000 in Arizona and Missouri. One factor associated with the prevalence increase in the CDC monitoring survey was improved quality and quantity of information available through records, indicative of greater awareness about ASD among community professionals. As surveillance efforts continue, it is likely that awareness and services will develop in states that were lagging behind, resulting in a predictable increase in the average rate for the United States as time elapses. These CDC findings apply to other countries as well, and prevalence estimates from any study should always be regarded in the context of the imperfect sensitivity of case ascertainment that results in downward biases in prevalence proportions in most surveys.

Table 6–4. Newer epidemiological surveys of pervasive developmental disorders

References

Country

Area

Size

Age

N

Diagnostic Criteria

% With Normal IQ

Gender Ratio (M:F)

Prevalence/ 10,000

95% CI

Baird et al., 2000

UK

South East Thames

16,235

7

94

ICD-10

60%

15.7

(83: 11)

57.9

46.8–70.9

Bertrand et al., 2001

USA

New Jersey

8,896

3–10

60

DSM-IV

51%

2.7

(44: 16)

67.4

51.5–86.7*

Chakrabarti & Fombonne, 2001

UK

Stafford

15,500

4–7

96

ICD-10

74.2%

3.8

(77: 20)

61.9

50.2–75.6

Madsen et al., 2002

Denmark

National register

8

738

ICD-10

30.0

Scott et al., 2002

UK

Cambridge

33,598

5–11

196

ICD-10

4.0

(—)

58.3*

50

67*

Yeargin-Allsopp et al., 2003

USA

Atlanta

289,456

3-10

987

DSM-IV

31.8%

4.0

(787: 197)

34.0

32–36

Gurney et al., 2003

USA

Minnesota

8–10

52.0**

66.0

Icasiano et al., 2004

Australia

Barwon

≈ 54,000

2–17

177

DSM-IV

53.4%

8.3

(158: 19)

39.2

Tebruegge et al., 2004

UK

Kent

2,536

8–9

21

ICD-10

6.0

(18:3)

82.8

51.3–126.3

Chakrabarti & Fombonne, 2005

UK

Stafford

10,903

4–6

64

ICD-10

70.2%

6.1

(55: 9)

58.7

45.2–74.9

Baird et al., 2006

UK

South Thames

56,946

9–10

158

ICD-10

45%

3.3

(121: 37)

116.1

90.4–141.8

Fombonne et al., 2006

Canada

Montreal

27,749

5–17

180

DSM-IV

4.8

(149: 31)

64.9

55.8–75.0

Harrison et al., 2006

UK

Scotland

134,661

0–15

443

ICD-10, DSMIV

7.0

(369: 53)

44.2

39.5–48.9

Gillberg et al., 2006

Sweden

Göteborg

32,568

7 12

262

DSM-IV

3.6

(205:57)

80.4

71.3–90.3

CDC, 2007a

USA

6 states

187,761

8

1,252

DSM-IV-TR

38% to 60%§

2.8 to 5.5

67.0

§

CDC, 2007b

USA

14 states

407,578

8

2,685

DSM-IV-TR

55.4%

3.4 to 6.5

66.0

63–68

Ellefsen et al., 2007

Denmark

Faroe Islands

7,689

8–17

41

DSM-IV,

Gillberg’s criteria

68.3%

5.8

(35: 6)

53.3

36–70

Latif & Williams, 2007

UK

South Wales

39,220

0–17

240

ICD-10, DSM-IV, Kanner’s & Gillberg’s criteria

6.8

61.2

54–69*

Wong et al., 2008

China

Hong Kong

4,247,206

0–14

682

DSM-IV

30%

6.6

(592: 90)

16.1 (1986–2005)

30.0 (2005)

Nicholas et al., 2008

USA

South Carolina††

47,726

8

295

DSM-IV-TR

39.6%

3.1

(224: 71)

62.0

56–70

Kawamura et al., 2008

Japan

Toyota

12,589

5–8

228

DSM-IV

66.4%

2.8

(168: 60)

181.1

158.5–205.9*

Williams et al., 2008

UK

Avon

14,062

11

86

ICD-10

85.3%

6.8

(75:11)

61.9

48.8–

74.9

Baron–Cohen et al., 2009

UK

Cambridgeshire

8,824

5–9

83

ICD-10

94‡‡

75 - 116

Kogan et al., 2009

USA

nationwide

77,911

3–17

913

4.5 (746:167)

110

94 - 128

Van Balkom et al., 2009

Netherlands

Aruba

13,109

0–13

69

DSM-IV

58.8%

6.7 (60:9)

52.6

41.0-66.6

CDC, 2009

USA

11 states

307,790

8

2,757

DSM-IV

59%

4.5 (–)

89.6

86 - 93

Lazoff et al., 2010

Canada

Montreal

23,635

5–17

187

DSM-IV

5.4

(158:29)

79.1

67.8–90.4

* calculated by the author.

§ specific values for % with normal IQ and confidence intervals are available for each state prevalence.

† average across 7 states.

‡ estimated using a capture-recapture analysis, the number of cases used to calculate prevalence was estimated to be 596.

** these are the highest prevalences reported in this study of time trends. The prevalence in 10-year-olds is for the 1991 birth cohort, and that for 8-year-olds for the 1993 birth cohort. Both prevalences were calculated in the 2001–2002   school year.

†† children aged 8, born either in 2000 and 2002, and included in the two CDC multisite reports.

‡‡ rate based on Special Education Needs register. A figure of 99/10,000 is provided from a parental and diagnostic survey. Other estimates in this study vary from 47 to 165/10,000 deriving from various assumptions made by the authors.

As an illustration, the 4 surveys in Table 6-4 with the lowest rates probably underestimated the true population rates. In the Danish investigation (Madsen et al., 2002), case finding depended on notification to a National Registry, a method which is usually associated with lower sensitivity for case finding. The Hong Kong (Wong et al., 2008) and Australian (Icasiano et al., 2004) surveys have relied on less systematic ascertainment techniques. The Atlanta survey by the CDC (Yeargin-Allsopp et al., 2003) was based on a very large population and included younger age groups than subsequent CDC surveys, and age specific rates were in fact in the 40–45/10,000 range in some birth cohorts (Fombonne, 2003a, 2003b). Case finding techniques employed in the other surveys were more proactive, relying on multiple and repeated screening phases, involving both different informants at each phase and surveying the same cohorts at different ages, which certainly enhanced the sensitivity of case identification (Chakrabarti & Fombonne, 2005; Baird et al., 2006). Assessments were often performed with standardized diagnostic measures (i.e., ADI-R and ADOS) which match well the more dimensional approach retained for case definition.

Overall, results of recent surveys agree that an average figure of 70/10,000 can be used as the current estimate for the spectrum of PDDs.

Regression: a loss of skills including language (the child stops using 5 to 30 words that he had gained) often associated with contemporaneous changes in social and play skills.

Regressive Autism in Population Surveys

The studies of regression or loss of skills in the developmental course of PDDs has gained much attention in recent years. As there is evidence that regression is associated with younger age at identification (Shattuck et al., 2009), and with slight increase in severity of autistic symptoms and cognitive deficits (Meilleur & Fombonne, 2009; Fombonne & Chakrabarti, 2001; Parr et al., submitted), we examined how often regression was reported and how it was measured in population based studies, and, when available, the age at regression and the outcome (Table 6-5). Ten studies reported the frequency of regression in the survey sample. Interestingly, Lotter (1966) documented in the first epidemiological survey of autism that a “set-back” had occurred in almost one third of the children. There has been no standardized way to evaluate regression, and the methods have relied on record abstraction or parents’ retrospective recall. Regression has often focused on the loss of language skills, with or without the loss of other (social, play) skills. Regression ranged from 12.5% (Sugiyama & Abe, 1989) to 38.6% (Baird et al., 2008), with a median rate of 23%. In the majority of studies, the age at regression is the 6 months preceding the 2nd birthday or close to age 2, which differentiates this pattern of loss from that seen in CDD. Symptoms of CDD are typically observed at the end of the 3rd year of life and are characterized by marked developmental and behavioral changes and deterioration that result in profound, lasting, autistic and cognitive impairments. Two studies provided separate figures for a strict definition of autism compared to atypical or milder forms. In one study (Taylor et al., 2002), regression occurs at comparable frequency in typical and atypical autism, whereas in the other (Baird et al., 2008) there is a marked difference between the 38.6% figure of regression (including both definite and lower level) in autism as opposed to a much lower corresponding figure of 10.6% in the broadly defined ASD group. Finally, in 3 studies where some kind of outcome data were reported, all studies pointed at higher symptom severity in the regressive group. Overall, these results do not differ from studies of regression based on clinical samples and they confirm that a loss of skills in the development of PDD children is common, affecting about 1 in 4 children with ASD.

Table 6–5. “Regressive” autism in population-based studies

Authors

Measurement of Regression

Proportion of Study Sample with Regression/Loss

Age at Regression

Skills Lost

Diagnosis

Outcome

Lotter, 1966

“set-back” in development

31.2%

18–27 months (N = 3)

27–36 months (N = 4)

3 to 4.5 years (N = 3)2

Loss of some ability (i.e., speech) or failure to progress after a satisfactory beginning

Kanner’s autism

All but 2 of the children with a set-back (80%) had a low (< 55) IQ, compared to 63.4% in the group without set-back

Sugiyama & Abe, 1989

Developmental checkups from 18 months to age 3

12.5%

2 years

Loss of a few words or of one-word sentences

DSM-III autism

Bertrand et al., 2001

Question to parents during clinical evaluation1

24%

12–18 months

Single words and social smiling

All had autistic disorder

Fombonne & Chakrabarti, 2001

ADI-R items on regression before age 5

15.6%

19.8 months

Any language, social, play, or motor skill

Trend (p = .08) for children with regression to have lower IQ scores (IQ < 70) than nonregressive children (21.5%)

Taylor et al., 2002

Abstraction of medical records

25%

Deterioration in any aspect of a child’s development or reported loss of skills

Typical autism (23%); atypical autism (27%)

Icasiano et al., 2004

Parental interview

27.1%3

5.6%3

Previously acquired language skills

Previously acquired motor skills

Autism spectrum disorders

CDC, 2007a

Abstraction of all records by an ASD clinician reviewer

19.0%4

24 months (median age across 6 sites)

Loss of previously acquired skills in social, communication, play, or motor areas

Autism spectrum disorders

CDC, 2007b

Abstraction of all records by an ASD clinician reviewer

19.7%5

24 months (median age across 14 sites)

Loss of previously acquired skills in social, communication, play, or motor areas

Autism spectrum disorders

Baird et al., 2008

ADI-R items (11–15, and 20) on regression

30.2% (8%)7

8.4% (2.6%)7

25 months

25 months

Definite language regression6

Lower level regression6

Narrow autism (broad ASD)

Increase in severity of autistic symptoms in both regression groups

CDC, 2009

Abstraction of all records by an ASD clinician reviewer

21.9%8

19 months (median age across 11 sites)

Loss of previously acquired skills in social, communication, play, or motor areas

Autism spectrum disorders

1 “Did your child experience any loss of previously acquired skills?”

2 In the 3 children with an onset after age 3, the setback was “severe and fairly rapid” (Lotter, 1966, p. 130) (these cases would probably meet criteria for CDD).

3 Potential overlap between these 2 groups was not reported.

4 Weighted average computed by the authors (238 out of 1252; range: 12.5%–26.9%).

5 Weighted average computed by the authors (530 out of 2685; range: 13.8%–31.6%).

6 Definite regression: at least 5 words used before regression, and strict language regression with or without regression of other skills; Lower level regression: loss of fewer words than five words, or of babble, or regression of other skills than language. The rates for definite and lower level regression can be summed up to derive a frequency of “any” regression.

7 The first rate is for narrow autism, the figure within brackets is for broad ASD.

8 Weighted average computed by the authors (603 out of 2757; range: 13.3%–29.6%).

In conclusion, conducted in different regions and countries by different teams, the convergence of estimates around 70 per 10,000 for all PDDs combined is striking especially when derived from studies with improved methodology. This estimate is now the best estimate for the prevalence of PDDs currently available. However, this represents an average and conservative figure, and it is important to recognize the substantial variability that exists between studies, and within studies, across sites or areas. The prevalence figure of 70/10,000 (equivalent to 7/1,000 or 0.7%) translates into 1 child out of 143 suffering from a PDD. It should be noted, however, that some studies have reported rates that are even two to three times higher (Baird et al., 2006; Kawamura et al., 2008), and that the most recent and reliable estimates point at a 0.9% to 1% prevalence.

Time Trends in Prevalence and Their Interpretation

The debate on the hypothesis of a secular increase in rates of autism has been obscured by a lack of clarity in the measures of disease occurrence used by investigators, or rather in the interpretation of their meaning. In particular, it is crucial to differentiate prevalence from incidence. Prevalence is useful to estimate needs and plan services; however, only incidence rates can be used for causal research. Both prevalence and incidence estimates will increase when case definition is broadened and case ascertainment is improved. Time trends in rates can therefore only be gauged in investigations that hold these parameters under strict control over time. These methodological requirements must be borne in mind while reviewing the evidence for a secular increase in rates of PDDs, or testing for the “epidemic” hypothesis. The “epidemic” hypothesis emerged in the 1990s when, in most countries, increasing numbers were diagnosed with PDDs leading to an upward trend in children registered in service providers databases that was paralleled by higher prevalence rates in epidemiological surveys. These trends were interpreted by some observers as evidence that the actual population incidence of PDDs was going up (what the term “epidemic” means); however, alternative explanations to explain the rise in numbers of children diagnosed with PDDs had to be ruled out first before attaining this conclusion.

Prevalence: the proportion of individuals in a population who suffer from a defined disorder at any point in time.
Incidence: the number of new cases occurring in a population over a period of time.

Several approaches to assessing this question have been used in the literature and these fall into 5 broad categories.

1. Use of Inappropriate Referral Statistics

Increasing numbers of children referred to specialist services or known to special education registers have been taken as evidence for an increased incidence of autism-spectrum disorders. Upward trends in national registries, medical, and educational databases have been seen in many different countries (Taylor et al., 1999; Madsen et al., 2002; Shattuck, 2006; Gurney et al., 2003), all occurring in the late 1980s and early 1990s. However, trends over time in referred samples are confounded by many factors such as referral patterns, availability of services, heightened public awareness, decreasing age at diagnosis, and changes over time in diagnostic concepts and practices, to name only a few. Failure to control for these confounding factors was obvious in some recent reports (Fombonne, 2001), such as the widely quoted reports from California Developmental Database Services (CDDS, 1999; CDDS, 2003). First, these reports applied to numbers rather than rates, and failure to relate these numbers to meaningful denominators left the interpretation of an upward trend vulnerable to changes in the composition of the underlying population. For example, the population of California was 19,971,000 in 1970 and rose to 35,116,000 as of July 1, 2002, a change of +75.8%. Second, the focus on the year-to-year changes in absolute numbers of subjects known to California state-funded services detracts from more meaningful comparisons. For example, as of December 2007, the total number of subjects with a PDD diagnosis was 31,332 in the 3–21 age group (including all CDER autism codes) (California Department of Developmental Services, December 2007). The population of 3–21 year olds of California was 9,976,768 on July 1, 2007 (Census Bureau for the US, 2009). If one applies the 2007 average U.S. rate of 67/10,000 deriving from the CDC (2007b), one would expect to have 66,844 subjects with a PDD, within this age group, living in California. The expected number is twice as high as the number of subjects recorded in the public service at the same time. The discrepancy would be more pronounced if the latest CDC figures of 9/1,000 (Table 6-4; CDC, 2009) were used to estimate the expected number of Californian residents with a PDD. Certainly, these calculations do not support the “epidemic” interpretation of the California DDS data, and confirm the selective nature of the referred sample. Unfortunately, these data have been misused in many ways to infer population trends and causes for autism in California, for which they are simply not suited. The upward trends in the DDS database simply suggest that children identified in the California DDS database were only a subset of the population prevalence pool and that the increasing numbers reflect merely an increasing proportion of children receiving services. Third, with one exception (see below), no attempt was made to adjust the trends for changes in diagnostic concepts and definitions. However, major nosographical modifications were introduced during the corresponding years with a general tendency in most classifications to broaden the concept of autism (as embodied in the terms “autism spectrum” or “pervasive developmental disorder”). Fourth, age characteristics of the subjects recorded in official statistics were portrayed in a misleading manner where the preponderance of young subjects was presented as evidence of increasing rates in successive birth cohorts (Fombonne, 2001). The problems associated with disentangling age from period and cohort effects in such observational data are well known in the epidemiological literature and deserve better statistical handling. Fifth, the decreasing age at diagnosis leads in itself to increasing numbers of young children being identified in official statistics (Wazana et al., 2007) or referred to specialist medical and educational services. Earlier identification of children from the prevalence pool may therefore result in increased service activity that may lead to a misperception by professionals of an “epidemic”; however, an increase in referrals does not necessarily mean increased incidence. A more refined analysis of the effect of a younger age at diagnosis using cumulative incidence data by age 5 years showed that 12% of the increase in incidence from the 1990 to the 1996 birth cohort could be explained by this factor, and up to 24% with an extrapolation to the 2002 cohort (Hertz-Picciotto & Delwiche, 2009). Although younger age at diagnosis can explain only a small proportion of the increase in diagnoses in this analysis, it does play a role in several published reports though its effect would attenuate as the cohort becomes older. Hertz-Picciotto and Delwiche’s analysis (2009) of the California DDS data is also limited by their reliance on the DDS database that reflected changes in regional referral patterns, especially during that period.

Another study of this dataset was subsequently launched to demonstrate the validity of the “epidemic” hypothesis (MIND, 2002). The authors relied on DDS data and aimed at ruling out changes in diagnostic practices and immigration into California as factors explaining the increased numbers. While immigration was reasonably ruled out, the study comparing diagnoses of autism and mental retardation over time was impossible to interpret in light of the extremely low (< 20%) response rates. Furthermore, a study only based on cases registered for services cannot rule out that the proportion of cases within the general population who registered with services has changed over time. For example, assuming a constant incidence and prevalence at 2 different time points (i.e., hypothesizing no epidemic), the number of cases known to a public agency delivering services could well increase by 200% if the proportion of cases from the community referred to services rises from 25% to 75% in the same interval. In order to eliminate this plausible (see above) explanation, data over time are needed both on referred subjects and on nonreferred (or referred to other services) subjects. Failure to address this phenomenon precludes any inference to be drawn from a study of the California DDS database population to the California population (Fombonne, 2003a). The conclusions of this report were therefore simply unfounded.

2. The Role of Diagnostic Substitution

One possible explanation for increased numbers of a diagnostic category is that children presenting with the same developmental disability may receive one particular diagnosis at one time, and another diagnosis later. Such diagnostic substitution (or switching) may occur when diagnostic categories become increasingly familiar to health professionals and/or when access to better services is ensured by using a new diagnostic category. The strongest evidence of “diagnostic switching” contributing to the prevalence increase was produced in all U.S. states in a complex analysis of Department of Education Data in 50 U.S. states (Shattuck, 2006), indicating that a relatively high proportion of children previously diagnosed as having mental retardation were subsequently identified as having a PDD diagnosis. Shattuck showed that the odds of being classified in autism category increased by 1.21 during 1994–2003. In the meantime, the odds decreased significantly of being classified in the learning disability (LD) (odds ratio: OR = 0.98) and the mental retardation (MR) categories (OR = 0.97). He further demonstrated that the growing prevalence of autism was directly associated with decreasing prevalence of LD and MR within states, and that a significant downward deflection in the historical trajectories of LD and MR occurred when autism became reported in the United States as an independent category in 1993–94. Finally, this author showed that, from 1994 to 2003, the mean increase for the combined category of Autism + Other Health Impairments +Trauma Brain Injury + Developmental Delay was 12/1000, whereas the mean decrease for MR and LD was 11/1000 during the same period. One exception to that was California, for which previous authors had debated the presence of diagnostic substitution between MR and autism (Croen et al., 2002; Eagle, 2004). The previous investigations have largely relied on ecological, aggregated data that have known limitations. Using individual level data, a new study has reexamined the hypothesis of diagnostic substitution in the California DDS dataset (King & Bearman, 2009) and has shown that 24% of the increase in caseload was attributable to such diagnostic substitution (from the mental retardation to the autism category). It is important to keep in mind that other types of diagnostic substitution are likely to have occurred as well for milder forms of the PDD phenotype, from various psychiatric disorders (including childhood schizoid “personality” disorders; Wolff & Barlow, 1979) that have not been studied yet (Fombonne, 2009). For example, children currently diagnosed with Asperger’s disorder were previously diagnosed with other psychiatric diagnoses (i.e., obsessive-compulsive disorder, school “phobia,” social anxiety, etc.) in clinical settings before the developmental nature of their condition was fully recognized.

Diagnostic substitution occurs when an individual presenting with a diagnosis at one point in time receives another diagnosis later and is “re-classified”.

Evidence of diagnostic substitution within the class of developmental disorders has also been provided in UK studies. Using the General Practitioner Research Database, Jick and Kaye (2003) have shown that the incidence of specific developmental disorders (including language disorders) decreased by about the same amount that the incidence of diagnoses of autism increased in boys born from 1990 to 1997. A more recent UK study (Bishop et al., 2008) has shown that up to 66% of adults previously diagnosed as children with developmental language disorders would meet diagnostic criteria for a broad definition of PDD. This change was observed for children initially diagnosed with specific language impairments, but even more so for those with a pragmatic language impairment.

3. Comparison of Cross-Sectional Epidemiological Surveys

As shown earlier, epidemiological surveys of autism each possess unique design features that could account almost entirely for between-studies variation in rates; therefore, time trends in rates of autism are difficult to gauge from published prevalence rates. The significant correlation previously mentioned between prevalence rate and year of publication for autistic disorder could merely reflect increased efficiency over time in case identification methods used in surveys as well as changes in diagnostic concepts and practices (Kielinen et al., 2000; Webb et al., 1997; Magnusson et al., 2001; Shattuck, 2006; Bishop et al., 2008). In studies using capture-recapture methods, it is apparent that up to a third of prevalent cases may be missed by an ascertainment source, even in recently conducted studies (Harrison et al., 2006). Evidence that method factors could account for most of the variability in published prevalence estimates comes from a direct comparison of 8 recent surveys conducted in the UK and the United States (Fombonne, 2005). In each country, 4 surveys were conducted around the same year and with similar age groups. As there is no reason to expect huge between-area differences in rates, prevalence estimates should therefore be comparable within each country. However, there was a 6-fold variation in rates for UK surveys, and a 14-fold variation in U.S. rates. In each set of studies, high rates derived from surveys where intensive population-based screening techniques were employed whereas lower rates were obtained from studies relying on passive administrative methods for case finding. Since no passage of time was involved, the magnitude of these gradients in rates can only be attributed to differences in case identification methods across surveys. Even more convincing evidence comes from the large survey by the CDC on 408,000 U.S. children aged 8 and born in 1994 (CDC, 2007b) where an average prevalence of 66/10,000 was reported for 14 U.S. states. One striking finding of this report is that there was more than a 3-fold variation in state specific rates that ranged from a low 33/10,000 for Alabama to a high of 106/10,000 in New Jersey. It would be surprising if there were truly this much variance in the number of children with autism in different states in the United States. These substantial differences most certainly reflected ascertainment variability across sites in a study that was otherwise performed with the same methods and at the same time. In the more recent CDC 11 states study (CDC, 2009), the same variability is reported again. Prevalence was significantly lower (7.5/1,000) in states that had access to health sources only compared to that (10.2/1,000) of states where educational data was also available. The authors also reported that the quality and quantity of information available in abtracted records (the main method for case ascertainment) had increased between the 2002 and 2006. Together with a reported average decrease of 5 months for the age at diagnosis and a larger increase in the non–mentally retarded population, these factors suggest that improved sensitivity in case ascertainment in the CDC monitoring network has contributed substantially to the increase in prevalence. Thus, no inference on trends in the incidence of PDDs can be derived from a simple comparison of prevalence rates over time, since studies conducted at different periods are likely to differ even more with respect to their methodologies.

4. Repeat Surveys in Defined Geographical Areas

Repeated surveys, using the same methodology and conducted in the same geographical area at different points in time, can potentially yield useful information on time trends provided that methods are kept relatively constant. The Göteborg studies (Gillberg et al., 1991; Gillberg, 1984) provided three prevalence estimates that increased over a short period of time from 4.0 (1980) to 6.6 (1984) and 9.5/10,000 (1988), the gradient being even steeper if rates for the urban area alone are considered (4.0, 7.5, and 11.6/10,000, respectively) (Gillberg et al., 1991). However, comparison of these rates is not straightforward, as different age groups were included in each survey. Secondly, the increased prevalence in the second survey was explained by improved detection among the mentally retarded, and that of the third survey by cases born to immigrant parents. That the majority of the latter group was born abroad suggests that migration into the area could be a key explanation. Taken in conjunction with a change in local services and a progressive broadening of the definition of autism over time that was acknowledged by the authors (Gillberg et al., 1991), these findings do not provide evidence for an increased incidence in the rate of autism. Similarly, studies conducted in Japan at different points in time in Toyota (Kawamura et al., 2008) and Yokohama (Honda et al., 1996 and 2005) showed rises in prevalence rates that their authors interpreted as reflecting the effect of both improved population screening of preschoolers and of a broadening of diagnostic concepts and criteria.

Two separate surveys of children born 1992–1995 and 1996–1998 in Staffordshire in the UK (Chakrabarti & Fombonne, 2001, 2005) were performed with rigorously identical methods for case definition and case identification. The prevalence for combined PDDs was comparable and not statistically different in the 2 surveys (Chakrabarti & Fombonne, 2005), suggesting no upward trend in overall rates of PDDs, at least during the short time interval between studies. In two recent CDC surveys (2007a, 2007b), the prevalence at six sites included in the 2000 and 2002 surveys remained constant at 4 sites, and increased in 2 states (Georgia and West Virginia), most likely due to improved quality of survey methods at these sites. In the 2009 CDC report, an average increase of 57% in prevalence was reported in 10 sites with 2002 and 2006 data, with a smaller increase in Colorado. Increases of different magnitude and directions were reported in all subgroups, making it difficult to detect a particular explanation. The CDC researchers identified a number of factors associated with the change in prevalence but could not conclude on the hypothesis of a real change in the risk of ASD in the population.

5. Successive Birth Cohorts

In large surveys encompassing a wide age range, increasing prevalence rates among most recent birth cohorts could be interpreted as indicating a secular increase in the incidence of the disorder, provided that alternative explanations can confidently be eliminated. This analysis was used in two large French surveys (Fombonne & du Mazaubrun, 1992; Fombonne et al., 1997). The surveys included birth cohorts from 1972 to 1985 (735,000 children, 389 of whom had autism), and, pooling the data of both surveys, age-specific rates showed no upward trend (Fombonne et al., 1997).

An analysis of special educational disability from Minnesota showed a 16-fold increase in the number of children identified with a PDD from 1991–1992 to 2001–2002 (Gurney et al., 2003). The increase was not specific to autism since, during the same period, an increase of 50% was observed for all disability categories (except severe mental handicap), especially for the category including ADHD. The large sample size allowed the authors to assess age, period, and cohort effects. Prevalence increased regularly in successive birth cohorts; for example, among 7-year-olds, the prevalence rose from 18/10,000 in those born in 1989, to 29/10,000 in those born in 1991 and to 55/10,000 in those born in 1993, suggestive of birth cohort effects. Within the same birth cohorts, age effects were also apparent since for children born in 1989 the prevalence rose with age from 13/10,000 at age 6, to 21/10,000 at age 9, and 33/10,000 at age 11. As argued by the authors, this pattern is not consistent with what one would expect from a chronic nonfatal condition diagnosed in the first years of life. Their analysis also showed a marked period effect that identified the early 1990s as the period where rates started to increase in all ages and birth cohorts. Gurney et al. (2003) further argued that this phenomenon coincided closely with the inclusion of PDDs in the federal Individual with Disabilities Educational Act (IDEA) funding and reporting mechanism in the United States. A similar interpretation of upward trends had been put forward by Croen et al. (2002) in their analysis of the California DDS data, and by Shattuck (2006) in his well-executed analysis of trends in the Department of Education data in all U.S. states.

Conclusion on Time Trends

As it stands now, the recent upward trend in rates of prevalence cannot be directly attributed to an increase in the incidence of the disorder, or to an “epidemic” of autism. There is good evidence that changes in diagnostic criteria, diagnostic substitution, changes in the policies for special education, and the increasing availability of services are responsible for the higher prevalence figures. It is also noteworthy that the rise in number of children diagnosed occurred at the same time in many countries (in the early 1990s), when radical shifts occurred in the ideas, diagnostic approaches, and services for children with PDDs. Alternatively, this might, of course, reflect the effect of environmental influences operating simultaneously in different parts of the world. However, there has been no proposed and legitimate risk mechanism to account for this worldwide effect. Most of the existing epidemiological data are inadequate to properly test hypotheses on changes in the incidence of autism in human populations. Moreover, due to the relatively low frequency of autism and PDDs, power is seriously limited in most investigations, and variations of small magnitude in the incidence of the disorder are very likely to go undetected. Equally, the possibility that a true increase in the incidence of PDDs has also partially contributed to the upward trend in prevalence rates cannot, and should not, be eliminated based on available data.

Conclusion

Epidemiological surveys of autism and PDDs have now been conducted in many countries. Methodological differences in case definition and case finding procedures make between survey comparisons difficult to perform. However, from recent studies, a best estimate of 70/10,000 (equivalences = 7/1,000; or 0.7%; or 1 child in about 143 children) can be confidently derived for the prevalence of autism spectrum disorders. Current evidence does not strongly support the hypothesis of a secular increase in the incidence of autism, but power to detect time trends is seriously limited in existing datasets. While it is clear that prevalence estimates have increased over time, this increase most likely represents changes in the concepts, definitions, service availability, and awareness of autistic-spectrum disorders in both the lay and professional public. To assess whether or not the incidence has increased, methodological factors that account for an important proportion of the variability in rates must be tightly controlled. New survey methods have been developed to be used in multinational comparisons; ongoing surveillance programs are currently under way and will soon provide more meaningful data to evaluate this hypothesis. The possibility that a true change in the underlying incidence has contributed to higher prevalence figures remains to be adequately tested. Meanwhile, the available prevalence figures carry straightforward implications for current and future needs in services and early educational intervention programs.

Challenges and Future Directions

  • The boundaries of the spectrum of PDDs with both severe developmental and neurogenetic disorders and mild forms of atypical development remain uncertain and unreliable. Measures of impairment will need to be added to symptom and developmental assessments in order to refine case definitions for epidemiological studies and other research endeavors.

  • Future epidemiological surveys should estimate the proportion of “false negatives” in order to estimate the sensitivity of case ascertainment methods and obtain more accurate rates. Current prevalence rates underestimate the “true” prevalence rates as they are not adjusted to compensate for missed cases.

  • Monitoring trends in prevalence and incidence is needed. It will require methods that allow meaningful comparisons over time of cases defined and ascertained with stable approaches.

Suggested Readings

Centers for Disease Control. Prevalence of autism spectrum disorders—Autism and developmental disabilities monitoring network, United States, 2006. Morbidity and Mortality Weekly Report Surveillance Summary 2009, 58, 1–14.

Chakrabarti, S., & Fombonne, E. (2001). Pervasive developmental disorders in preschool children. Journal of the American Medical Association, 285(24), 3093–3099.

Fombonne, E. (2007) Epidemiology. In A. Martin & F. Volkmar (Eds.), Lewis’s child and adolescent psychiatry: A comprehensive textbook (4th ed., pp. 150–171). Lippincott, Williams, and Wilkins.

Shattuck, P. T. (2006). The contribution of diagnostic substitution to the growing administrative prevalence of autism in US special education. Pediatrics, 117(4), 1028–1037.

References

American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author.Find this resource:

    American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., rev.). Washington, DC: Author.Find this resource:

      American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders. (4th ed.). Washington, DC: Author.Find this resource:

        American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.; DSM-IV-TR). Washington, DC: Author.Find this resource:

          Arvidsson, T., Danielsson, B., Forsberg, P., Gillberg, C., Johansson, M., & Kjellgren, G. (1997). Autism in 3–6 year-olds in a suburb of Goteborg, Sweden. Autism, 2, 163–173.Find this resource:

          Baird, G., Charman, T., Baron-Cohen, S., Cox, A., Swettenham, J., Wheelwright, S., et al. (2000). A screening instrument for autism at 18 months of age: A 6-year follow-up study. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 694–702.Find this resource:

          Baird, G., Charman, T., Pickles, A., Chandler, S., Loucas, T., Meldrum, D., et al. (2008). Regression, developmental trajectory, and associated problems in disorders in the autism spectrum: The SNAP study. Journal of Autism and Developmental Disorders, 38(10), 1827–1836.Find this resource:

          Baird, G., Simonoff, E., Pickles, A., Chandler, S., Loucas, T., Meldrum, D., et al. (2006). Prevalence of disorders of the autism spectrum in a population cohort of children in South Thames: The special needs and autism project (SNAP). Lancet, 368(9531), 210–215.Find this resource:

          Barbaresi, W. J., Katusic, S. K., Colligan, R. C., Weaver, A. L., & Jacobsen, S. J. (2005). The incidence of autism in Olmsted County, Minnesota, 1976–1997: Results from a population-based study. Archives of Pediatrics and Adolescent Medicine, 159(1), 37–44.Find this resource:

          Baron-Cohen, S., Scott, F. J., Allison, C., Williams, J., Bolton, P., Matthews, F. E., et al. Prevalence of autism-spectrum conditions: UK school-based population study. British Journal of Psychiatry, 194(6), 500–509.Find this resource:

          Bertrand, J., Mars, A., Boyle, C., Bove, F., Yeargin-Allsopp, M., & Decoufle, P. (2001). Prevalence of autism in a United States population: The Brick Township, New Jersey, investigation. Pediatrics, 108(5), 1155–1161.Find this resource:

          Bishop, D. V., Whitehouse, A. J., Watt, H. J., & Line, E. A. (2008). Autism and diagnostic substitution: Evidence from a study of adults with a history of developmental language disorder. Developmental Medicine and Child Neurology, 50(5), 341–345.Find this resource:

          Bohman, M., Bohman, I., Bjorck, P., & Sjoholm, E. (1983). Childhood psychosis in a northern Swedish county: Some preliminary findings from an epidemiological survey. In M. Schmidt & H. Remschmidt (Eds.), Epidemiological approaches in child psychiatry (pp. 164–173). Stuttgart: Georg Thieme Verlag.Find this resource:

            Brask, B. (1970). A prevalence investigation of childhood psychoses. Paper presented at the Nordic Symposium on the Care of Psychotic Children, Oslo.Find this resource:

              Bryson, S. E., Clark, B. S., & Smith, I. M. (1988). First report of a Canadian epidemiological study of autistic syndromes. Journal of Child Psychology and Psychiatry and Allied Disciplines, 29(4), 433–445.Find this resource:

              Burd, L., Fisher, W., & Kerbeshan, J. (1987). A prevalance study of pervasive developmental disorders in North Dakota. Journal of the American Academy of Child and Adolescent Psychiatry, 26, 700–703.Find this resource:

              California Department of Developmental Services. (2003, April). Autism spectrum disorders: Changes in the California caseload–An update 1999 through 2002. Available at http://www.dds.ca.gov/Autism/pdf/AutismReport2003.pdf

              California Department of Developmental Services. (1999, March 1). Changes in the population of persons with autism and pervasive developmental disorders in California’s Developmental Services System: 1987 through 1998. Report to the Legislature March 1, 1999, 19 pages. Available at http://www.dds.ca.gov

              California Department of Developmental Services (2007, December). Table 34. Retrieved January 28, 2009 from http://www.dds.ca.gov/FactsStats/docs/Dec07_QRTTBLS.pdf.

              Census Bureau for the US. Accessed January 28, 2009. http://www.census.gov/popest/states/asrh/SC-EST2007-01.html.

              Centers for Disease Control. (2007a). Prevalence of autism spectrum disorders—Autism and developmental disabilities monitoring network, six sites, United States, 2000. Morbidity and Mortality Weekly Report Surveillance Summary, 56(1), 1–11.Find this resource:

                Centers for Disease Control. (2007b) Prevalence of autism spectrum disorders—Autism and developmental disabilities monitoring network, 14 sites, United States, 2002. Morbidity and Mortality Weekly Report Surveillance Summary, 56(1), 12–28.Find this resource:

                  Centers for Disease Control. (2009). Prevalence of autism spectrum disorders—Autism and developmental disabilities monitoring network, United States, 2006. Morbidity and Mortality Weekly Report Surveillance Summary 2009, 58, 1–14.Find this resource:

                    Chakrabarti, S., & Fombonne, E. (2001). Pervasive developmental disorders in preschool children. Journal of the American Medical Association, 285(24), 3093–3099.Find this resource:

                    Chakrabarti, S., & Fombonne, E. (2005). Pervasive developmental disorders in preschool children: Confirmation of high prevalence. American Journal of Psychiatry, 162(6), 1133–1141.Find this resource:

                    Cialdella, P., & Mamelle, N. (1989). An epidemiological study of infantile autism in a French department (Rhone): A research note. Journal of Child Psychology and Psychiatry and Allied Disciplines, 30(1), 165–175.Find this resource:

                    Croen, L. A., Grether, J. K., Hoogstrate, J., & Selvin, S. (2002). The changing prevalence of autism in California. Journal of Autism and Developmental Disorders, 32(3), 207–215.Find this resource:

                    Davidovitch, M., Holtzman, G., & Tirosh, E. (2001, March). Autism in the Haifa area: An epidemiological perspective. Israeli Medical Association Journal, 3, 188–189.Find this resource:

                    Eagle, R. S. (2004). Commentary: Further commentary on the debate regarding increase in autism in California. Journal of Autism and Developmental Disorders, 34(1), 87–88.Find this resource:

                    Ehlers, S., & Gillberg, C. (1993). The epidemiology of Asperger syndrome: A total population study. Journal of Child Psychology and Psychiatry and Allied Disciplines, 34(8), 1327–1350.Find this resource:

                    Ellefsen, A., Kampmann, H., Billstedt, E., Gillberg, I. C., & Gillberg, C. (2007). Autism in the Faroe islands: An epidemiological study. Journal of Autism and Developmental Disorders, 37(3), 437–444.Find this resource:

                    Fombonne, E. (2001). Is there an epidemic of autism? Pediatrics, 107, 411–413.Find this resource:

                    Fombonne, E. (2003a). Epidemiological surveys of autism and other pervasive developmental disorders: An update. Journal of Autism and Developmental Disorders, 33(4), 365–382.Find this resource:

                    Fombonne, E. (2003b). The prevalence of autism. Journal of the American Medical Association, 289(1), 1–3.Find this resource:

                    Fombonne, E. (2005). Epidemiology of autistic disorder and other pervasive developmental disorders. Journal of Clinical Psychiatry, 66 (Suppl. 10), 3–8.Find this resource:

                    Fombonne, E. (2007) Epidemiology. In A. Martin & F. Volkmar (Eds.), Lewis’s child and adolescent psychiatry: A comprehensive textbook (4th ed., pp. 150–171). Lippincott, Williams, and Wilkins.Find this resource:

                      Fombonne, E. (2009). Commentary: On King and Bearman. International Journal of Epidemiology, 38(5), 1241–1242.Find this resource:

                      Fombonne, E., & Chakrabarti, S. (2001). No evidence for a new variant of measles-mumps-rubella-induced autism. Pediatrics, 108(4), E58.Find this resource:

                      Fombonne, E., & du Mazaubrun, C. (1992). Prevalence of infantile autism in four French regions. Social Psychiatry and Psychiatric Epidemiology, 27(4), 203–210.Find this resource:

                      Fombonne, E., du Mazaubrun, C., Cans, C., & Grandjean, H. (1997). Autism and associated medical disorders in a French epidemiological survey. Journal of the American Academy of Child and Adolescent Psychiatry, 36(11), 1561–1569.Find this resource:

                      Fombonne, E., Simmons, H., Ford, T., Meltzer, H., & Goodman, R. (2001). Prevalence of pervasive developmental disorders in the British nationwide survey of child mental health. Journal of the American Academy of Child and Adolescent Psychiatry, 40(7), 820–827.Find this resource:

                      Fombonne, E., Zakarian, R., Bennett, A., Meng, L., & McLean-Heywood, D. (2006). Pervasive developmental disorders in Montreal, Quebec, Canada: Prevalence and links with immunizations. Pediatrics, 118(1), e139–e150.Find this resource:

                      Ghanizadeh, A. (2008). A preliminary study on screening prevalence of pervasive developmental disorder in school children in Iran. Journal of Autism and Developmental Disorders, 38(4), 759–763.Find this resource:

                      Gillberg, C. (1984). Infantile autism and other childhood psychoses in a Swedish urban region: Epidemiological aspects. Journal of Child Psychology and Psychiatry and Allied Disciplines, 25(1), 35–43.Find this resource:

                      Gillberg, C., Steffenburg, S., & Schaumann, H. (1991). Is autism more common now than ten years ago? British Journal of Psychiatry, 158, 403–409.Find this resource:

                      Gillberg, C., Cederlund, M., Lamberg, K., & Zeijlon, L. (2006). Brief report: “The autism epidemic.” The registered prevalence of autism in a Swedish urban area. Journal of Autism and Developmental Disorders, 36(3), 429–435.Find this resource:

                      Gurney, J. G., Fritz, M. S., Ness, K. K., Sievers, P., Newschaffer, C. J., & Shapiro, E. G. (2003). Analysis of prevalence trends of autism spectrum disorder in Minnesota. Archives of Pediatrics and Adolescent Medicine, 157(7), 622–627.Find this resource:

                      Harrison, M. J., O’Hare, A. E., Campbell, H., Adamson, A., & McNeillage, J. (2006). Prevalence of autistic spectrum disorders in Lothian, Scotland: An estimate using the “capture-recapture” technique. Archives of Disease in Childhood, 91(1), 16–19.Find this resource:

                      Hertz-Picciotto I., & Delwiche L. (2009). The rise in autism and the role of age at diagnosis. Epidemiology 38(5), 84–90.Find this resource:

                      Honda, H., Shimizu, Y., Misumi, K., Niimi, M., & Ohashi, Y. (1996). Cumulative incidence and prevalence of childhood autism in children in Japan. British Journal of Psychiatry, 169, 228–235.Find this resource:

                      Honda, H., Shimizu, Y., & Rutter, M. (2005). No effect of MMR withdrawal on the incidence of autism: A total population study. Journal of Child Psychology and Psychiatry and Allied Disciplines, 46(6), 572–579.Find this resource:

                      Hoshino, Y., Kumashiro, H., Yashima, Y., Tachibana, R., & Watanabe, M. (1982). The epidemiological study of autism in Fukushima-Ken. Folia Psychiatrica et Neurologica Japonica, 36, 115–124.Find this resource:

                      Icasiano, F., Hewson, P., Machet, P., Cooper, C., & Marshall, A. (2004). Childhood autism spectrum disorder in the Barwon region: A community based study. Journal of Paediatrics and Child Health, 40(12), 696–701.Find this resource:

                      ICD-9. (1977). The ICD-9 classification of mental and behavioural disorders: Clinical descriptions and diagnostic guidelines. Geneva, World Health Organization.Find this resource:

                        ICD-10. (1992). The ICD-10 classification of mental and behavioural disorders: Clinical descriptions and diagnostic guidelines. Geneva, World Health Organization.Find this resource:

                          Jick, H., Kaye, J. A., & Black, C. (2003). Epidemiology and possible causes of autism changes in risk of autism in the UK for birth cohorts 1990–1998. Pharmacotherapy, 23(12), 1524–1530.Find this resource:

                          Kadesjo, B., Gillberg, C., & Hagberg, B. (1999). Brief report: Autism and Asperger syndrome in seven-year-old children: A total population study. Journal of Autism and Developmental Disorders, 29(4), 327–331.Find this resource:

                          Kawamura, Y., Takahashi, O., & Ishii, T. (2008). Reevaluating the incidence of pervasive developmental disorders: Impact of elevated rates of detection through implementation of an integrated system of screening in Toyota, Japan. Psychiatry and Clinical Neurosciences, 62(2), 152–159.Find this resource:

                          Kielinen, M., Linna, S.-L., & Moilanen, I. (2000). Autism in northern Finland. European Child and Adolescent Psychiatry, 9, 162–167.Find this resource:

                          King, M., & Bearman, P. (2009). Diagnostic change and the increase in prevalence of autism. International Journal of Epidemiology, 38(5), 1224–1234.Find this resource:

                          Kogan, M. D., Blumberg, S. J., Schieve, L. A., Boyle, C. A., Perrin, J. M., Ghandour, R. M., et al. (2009). Prevalence of parent-reported diagnosis of autism spectrum disorder among children in the US, 2007. Pediatrics, 124 (5), 1395–1403.Find this resource:

                          Latif, A. H., & Williams, W. R. (2007). Diagnostic trends in autistic spectrum disorders in the South Wales valleys. Autism, 11(6), 479–487.Find this resource:

                          Lazoff, T., Zhong L., Piperni, T., & Fombonne, E. (2010). Prevalence rates of PDD among children in a Montreal School Board. Canadian Journal of Child Psychiatry, 55(11), 715–720.Find this resource:

                          Le Couteur, A., Rutter, M., Lord, C., Rios, P., Robertson, S., Holdgrafer, M., et al. (1989). Autism diagnostic interview: A standardized investigator-based instrument. Journal of Autism and Developmental Disorders, 19, 363–387.Find this resource:

                          Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Jr., Leventhal, B. L., DiLavore, P. C., et al. (2000). The Autism Diagnostic Observation Schedule-Generic: A standard measure of social and communication deficits associated with the spectrum of autism. Journal of Autism and Developmental Disorders, 30(3), 205–223.Find this resource:

                          Lotter, V. (1966). Epidemiology of autistic conditions in young children: I. Prevalence. Social Psychiatry, 1, 124–137.Find this resource:

                          Madsen, K. M., Hviid, A., Vestergaard, M., Schendel, D., Wohlfahrt, J., Thorsen, P., et al. (2002). A population-based study of measles, mumps, and rubella vaccination and autism. New England Journal of Medicine, 347(19), 1477–1482.Find this resource:

                          Magnusson, P., & Saemundsen, E. (2001). Prevalence of autism in Iceland. Journal of Autism and Developmental Disorders, 31(2), 153–163.Find this resource:

                          Matsuishi, T., Shiotsuki, M., Yoshimura, K., Shoji, H., Imuta, F., & Yamashita, F. (1987). High prevalence of infantile autism in Kurume city, Japan. Journal of Child Neurology, 2, 268–271.Find this resource:

                          McCarthy, P., Fitzgerald, M., & Smith, M. (1984). Prevalence of childhood autism in Ireland. Irish Medical Journal, 77, 129–130.Find this resource:

                          Meilleur, A. A., & Fombonne, E. (2009). Regression of language and non-language skills in pervasive developmental disorders. Journal of Intellectual Disability Research, 53(2), 115–124.Find this resource:

                          MIND Institute. (2002, October 17). Report to the Legislature on the Principal Findings from the epidemiology of autism in California. A comprehensive pilot study. University of California, Davis.Find this resource:

                            Nicholas, J. S., Charles, J. M., Carpenter, L. A., King, L. B., Jenner, W., & Spratt, E. G. (2008). Prevalence and characteristics of children with autism-spectrum disorders. Annals of Epidemiology, 18(2), 130–136.Find this resource:

                            Oliveira, G., Ataide, A., Marques, C., Miguel, T. S., Coutinho, A. M., Mota-Vieira, L., et al. (2007). Epidemiology of autism spectrum disorder in Portugal: Prevalence, clinical characterization, and medical conditions. Developmental Medicine and Child Neurology, 49(10), 726–733.Find this resource:

                            Parr, J. R., Le Couteur, A., Baird, G., Fombonne, E., Rutter, M., Bailey, A. J., & International Molecular Genetic Study of Autism Consortium (IMGSAC). (submitted). Early developmental regression in autism: Evidence from an International Multiplex Sample.Find this resource:

                              Powell, J., Edwards, A., Edwards, M., Pandit, B., Sungum-Paliwal, S., & Whitehouse, W. (2000). Changes in the incidence of childhood autism and other autistic spectrum disorders in preschool children from two areas of the West Midlands, UK. Developmental Medicine and Child Neurology, 42, 624–628.Find this resource:

                              Ritvo, E., Freeman, B., Pingree, C., Mason-Brothers, A., Jorde, L., Jenson, W., et al. (1989). The UCLA-University of Utah epidemiologic survey of autism: Prevalence. American Journal of Psychiatry, 146, 194–199.Find this resource:

                              Rutter, M. (1970). Autistic children: Infancy to adulthood. Seminars in Psychiatry, 2(4), 435–450.Find this resource:

                              Scott, F. J., Baron-Cohen, S., Bolton, P., & Brayne, C. (2002). Brief report: Prevalence of autism spectrum conditions in children aged 5–11 years in Cambridgeshire, UK. Autism, 6(3), 231–237.Find this resource:

                              Shattuck, P. T. (2006). The contribution of diagnostic substitution to the growing administrative prevalence of autism in US special education. Pediatrics, 117(4), 1028–1037.Find this resource:

                              Shattuck, P. T., Durkin, M., Maenner, M., Newschaffer, C., Mandell, D. S., Wiggins, L., et al. (2009). Timing of identification among children with an autism spectrum disorder: Findings from a population-based surveillance study. Journal of the American Academy of Child and Adolescent Psychiatry, 48(5), 474–483.Find this resource:

                              Sponheim, E., & Skjeldal, O. (1998). Autism and related disorders: Epidemiological findings in a Norwegian study using icd-10 diagnostic criteria. Journal of Autism and Developmental Disorders, 28, 217–227.Find this resource:

                              Steffenburg, S., & Gillberg, C. (1986). Autism and autistic-like conditions in Swedish rural and urban areas: a population study. British Journal of Psychiatry, 149(1), 81–87.Find this resource:

                              Steinhausen, H.-C., Gobel, D., Breinlinger, M., & Wohlloben, B. (1986). A community survey of infantile autism. Journal of the American Academy of Child Psychiatry, 25, 186–189.Find this resource:

                              Sugiyama, T., & Abe, T. (1989). The prevalence of autism in Nagoya, Japan: A total population study. Journal of Autism and Developmental Disorders, 19, 87–96.Find this resource:

                              Tanoue, Y., Oda, S., Asano, F., & Kawashima, K. (1988). Epidemiology of infantile autism in southern Ibaraki, Japan: Differences in prevalence in birth cohorts. Journal of Autism and Developmental Disorders, 18, 155–166.Find this resource:

                              Taylor, B., Miller, E., Farrington, C., Petropoulos, M.-C., Favot-Mayaud, I., Li, J., et al. (1999, June 12). Autism and measles, mumps, and rubella vaccine: No epidemiological evidence for a causal association. Lancet, 353, 2026–2029.Find this resource:

                              Taylor, B., Miller, E., Lingam, R., Andrews, N., Simmons, A., & Stowe, J. (2002). Measles, mumps, and rubella vaccination and bowel problems or developmental regression in children with autism: Population study. British Medical Journal, 324(7334), 393–396.Find this resource:

                              Tebruegge, M., Nandini, V., & Ritchie, J. (2004). Does routine child health surveillance contribute to the early detection of children with pervasive developmental disorders? An epidemiological study in Kent, UK. BMC Pediatrics, 4, 4.Find this resource:

                              Treffert, D. A. (1970). Epidemiology of infantile autism. Archives of General Psychiatry, 22, 431–438.Find this resource:

                              van Balkom, I. D. C., Bresnahan, M., Vogtländer, M. F., van Hoeken, D., Minderaa, R., Susser, E., et al. (2009). Prevalence of treated autism spectrum disorders in Aruba. Journal of Neurodevelopmental Disorders, 1, 197–204.Find this resource:

                              Wazana, A., Bresnahan, M., & Kline, J. (2007). The autism epidemic: Fact or artifact? Journal of the American Academy of Child and Adolescent Psychiatry, 46(6), 721–730.Find this resource:

                              Webb, E., Lobo, S., Hervas, A., Scourfield, J., & Fraser, W. (1997). The changing prevalence of autistic disorder in a Welsh health district. Developmental Medicine and Child Neurology, 39, 150–152.Find this resource:

                              Webb, E., Morey, J., Thompsen, W., Butler, C., Barber, M., & Fraser, W. I. (2003). Prevalence of autistic spectrum disorder in children attending mainstream schools in a Welsh education authority. Developmental Medicine and Child Neurology, 45(6), 377–384.Find this resource:

                              Wignyosumarto, S., Mukhlas, M., & Shirataki, S. (1992). Epidemio-logical and clinical study of autistic children in Yogyakarta, Indonesia. Kobe Journal of Medical Sciences, 38(1), 1–19.Find this resource:

                              Williams, J. G., Brayne, C. E., & Higgins, J. P. (2006). Systematic review of prevalence studies of autism spectrum disorders. Archives of Disease in Childhood, 91(1), 8–15.Find this resource:

                              Williams, E., Thomas, K., Sidebotham, H., & Emond, A. (2008). Prevalence and characteristics of autistic spectrum disorders in the ALSPAC cohort. Developmental Medicine and Child Neurology, 50(9), 672–677.Find this resource:

                              Wing, L., & Gould, J. (1979). Severe impairments of social interaction and associated abnormalities in children: Epidemiology and classification. Journal of Autism and Developmental Disorders, 9, 11–29.Find this resource:

                              Wing, L., Yeates, S., Brierly, L., & Gould, J. (1976). The prevalence of early childhood autism: Comparison of administrative and epidemiological studies. Psychological Medicine, 6, 89–100.Find this resource:

                              Wolff, S., & Barlow, A. (1979). Schizoid personality in childhood: A comparative study of schizoid, autistic and normal children. Journal of Child Psychology and Psychiatry, 20 (1), 29–46.Find this resource:

                              Wong, V. C., & Hui, S. L. (2008). Epidemiological study of autism spectrum disorder in China. Journal of Child Neurology, 23(1), 67–72.Find this resource:

                              Yeargin-Allsopp, M., Rice, C., Karapurkar, T., Doernberg, N., Boyle, C., & Murphy, C. (2003). Prevalence of autism in a US metropolitan area[comment]. Journal of the American Medical Association, 289(1), 49–55.Find this resource: