Fig. 2.1
Assuming a true ASD prevalence of 150/10,000 and a sensitivity of 100% for the screening process and total accuracy in the diagnostic confirmation, weighting back phase 2 data results in an unbiased prevalence estimate when caseness is unrelated to participation in screening (Scenario A), but when participation in screening is more likely for ASD cases than for non-cases (Scenario B), prevalence will be overestimated (see discussion in text)
It is also possible that individuals with ASD participate less than non-cases, which would result in underestimates of prevalence. For example, Posserud and colleagues (2010) reported the ASD prevalence of 72/10,000 in their identified sample and estimated a prevalence of 128/10,000 in no responders (based on teacher ratings during the screening phase), indicating increased refusal rates among those with more ASD symptoms. Unfortunately, few studies have been able to estimate the extent to which willingness or refusal to participate is associated with final caseness, so it is not known what effect differential participation rates at different phases in population surveys may have on prevalence estimates.
The sensitivity of the screening methodology is difficult to gauge in autism surveys, as the proportion of children truly affected with the disorder but not identified in the screening stage (false negatives) remains generally unmeasured. Few studies provided an estimate of the reliability of the screening procedure. The usual approach, which consists of randomly sampling screen-negative subjects to adjust estimates, has not been generally used, mainly due to the relatively low frequency of ASD, which makes such a strategy both imprecise and costly.
As an example, the surveys conducted by US CDC (2007a, 2007b, 2009, 2012, 2014) rely, for case ascertainment, on scrutinizing educational and medical records. Children not accessing such services cannot be identified. Although some recent surveys that systematically screen the normal school population might detect a large pool of unidentified cases (Kim et al. 2011) , it remains to be seen if this applies to most populations and requires change in sampling approaches for surveying autism. Of note, the CDC methodology identifies ASD cases without prior official ASD diagnosis (21 % of identified cases in 2008; Centers for Disease Control and Prevention 2012), suggesting that underidentification is a widespread phenomenon.
Since more recent prevalence studies suggest that autism can no longer be regarded as rare, screening for false negatives may become a more common strategy. Currently, however, prevalence estimates must be understood as underestimates of “true” prevalence rates, with the magnitude of this underestimation unknown in each survey.
2.1.1.3 Case Evaluation
When the screening phase is completed, subjects identified as positive go through a more in-depth diagnostic evaluation to confirm case status. Similar considerations about methodological variability across studies apply in more intensive assessment phases. The information used to determine diagnosis usually involves a combination of data from informants (parents, teachers, pediatricians, other health professionals, etc.) and data sources (medical records, educational sources), with a direct assessment of the person with autism being offered in some but not all studies. When subjects are directly examined, assessments typically use 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-Revised (ADI-R; Lord et al. 1994) and/or the Autism Diagnostic Observation Schedule (ADOS; Lord et al. 2000) have been increasingly used in the most recent surveys (Table 2.1) .
Table 2.1
Prevalence surveys of ASDs since 2000.
Year | Authors | Country | Area | Population | Age | Number Affected | Diagnostic Criteria | % with Normal IQ | Gender Ratio (M:F) | Prevalence /10,000 | 95% CI |
|---|---|---|---|---|---|---|---|---|---|---|---|
2000 | Baird et al. | UK | South East Thames | 16,235 | 7 | 94 | ICD-10 | 60 | 15.7 (83:11) | 57.9 | 46.8; 70.9 |
2000 | Powell et al. | UK | West Midlands | 58,654a | 1–5 | 122 | Clinical, ICD-10, DSM-IV | – | – | 20.8 | 17.3; 24.9 |
2001 | Bertrand et al. | USA | New Jersey | 8896 | 3–10 | 60 | DSM-IV | 51 | 2.7 (44:16) | 67.4 | 51.5; 86.7 |
2001 | Chakrabarti and Fombonne | UK | Stafford | 15,500 | 2.5–6.5 | 96 | ICD-10 | 74.2 | 3.8 (77:20) | 61.9 | 50.2; 75.6 |
2001 | Fombonne et al. | UK | England and Wales | 10,438 | 5–15 | 27 | DSM-IV, ICD-10 | 55.5 | 8.0 (24:3) | 26.1 | 16.2; 36.0 |
2002 | Scott et al. | UK | Cambridge | 33,598 | 5–11 | 196 | ICD-10 | – | 4.0 (–) | 58.3a | 50.7; 67.1a |
2003 | Yeargin-Allsopp et al. | USA | Atlanta, GA | 289,456 | 3–10 | 987 | DSM-IV | 31.8 | 4.0 (787:197) | 34.0 | 32; 36 |
2003 | Gurney et al. | USA | Minnesota (2001–2002) | 787,308a | 6–11 | 4094 | Receipt of MN special education services | – | – | 52.0a | 50.4; 53.6a |
2003 | Lingam et al. | UK | North East London | 186,206 | 5–14 | 567 | ICD-10 | – | 4.8 (469 :98) | 30.5a | 27.9; 32.9a |
2004 | Icasiano et al. | Australia | Barwon | 45,153a | 2–17 | 177 | DSM-IV | 53.4 | 8.3 (158:19) | 39.2 | 33.8; 45.4a |
2005 | Chakrabarti and Fombonne | UK | Stafford | 10,903 | 4–6 | 64 | ICD-10 | 70.2 | 6.1 (55:9) | 58.7 | 45.2; 74.9 |
2006 | Baird et al. | UK | South Thames (1990–1991) | 56,946 | 9–10 | 158 | ICD-10 | 45 | 3.3 (121:37) | 116.1 | 90.4; 141.8 |
2006 | Fombonne et al. | Canada | Montreal | 27,749 | 5–17 | 180 | DSM-IV | – | 4.8 (149:31) | 64.9 | 55.8; 75.0 |
2006 | Harrison et al. | UK | Scotland | 134,661 | 0–15 | 443b | ICD-10, DSM-IV | – | 7.0 (369:53) | 44.2 | 39.5; 48.9 |
2006 | Gillberg et al. | Sweden | Göteborg | 32,568 | 7–12 | 262 | DSM-III, DSM-IV, Gillberg’s criteria for AS | – | 3.6 (205:57) | 80.4 | 71.3; 90.3 |
2006 | Ouellette-Kuntz et al. | Canada | Manitoba and Prince Edward Island | 227,526 | 1–14 | 657 | DSM-IV | – | 4.1 (527:130) | 28.9a | 26.8; 31.2a |
2007 | Croen et al. | USA | Northern California (1995–1999) | 132,844 | 5–10 | 593 | ICD-9-CM | – | 5.5 (501:92) | 45 | 41.2; 48.4a |
2007b | CDC | USA | 6 states | 187,761 | 8 | 1252 | DSM-IV-TR | 38 to 60d | 2.8 to 5.5 | 67.0 | 63.1; 70.5a |
2007c | CDC | USA | 14 states | 407,578 | 8 | 2685 | DSM-IV-TR | 55.4e | 3.4 to 6.5 | 66.0 | 63; 68 |
2007 | Latif and Williams | UK | South Wales | 39,220 | 0–17 | 240 | ICD-10, DSM-IV, Kanner’s and Gillberg’s criteria | – | 6.8 (–) | 61.2 | 53.9; 69.4a |
2008 | Wong and Hui | China | Hong Kong Registry | 4,247,206 | 0–14 | 682 | DSM-IV | 30 | 6.6 (592:90) | 16.1 | 14.9; 17.3a |
2008 | Montiel-Nava and Pena | Venezuela | Maracaibo | 254,905 | 3–9 | 430 | DSM-IV-TR | – | 3.3 (329:101) | 17 | 13; 20 |
2008 | Kawamura et al. | Japan | Toyota | 12,589 | 5–8 | 228 | DSM-IV | 66.4 | 2.8 (168:60) | 181.1 | 159.2; 205.9a |
2008 | Williams et al. | UK | Avon | 14,062 | 11 | 86 | ICD-10 | 85.3 | 6.8 (75:11) | 61.9 | 48.8; 74.9 |
2009 | Baron-Cohen et al. | UK | Cambridgeshire | 8824 | 5-9 | 83 | ICD-10 | – | – | 94f | 75; 116 |
2009 | Nicholas et al. | USA | South Carolina | 8156 | 4 | 65 | DSM-IV-TR | 44.2 | 4.7 | 80 | 61; 99 |
2009 | van Balkom et al. | Netherlands | Aruba | 13,109 | 0–13 | 69 | DSM-IV | 58.8 | 6.7 (60:9) | 52.6 | 41.0; 66.6 |
2009 | CDC | USA | 11 states | 308,038 | 8 | 2,757 | DSM-IV-TR | 59 | 4.5 | 90 | 86; 93 |
2010 | Fernell and Gillberg | Sweden | Stockholm | 24,084 | 6 | 147 | DSM-IV, DSM-IV-TR, ICD-10 | 33 | 5.1 (123:24) | 62 | 52; 72 |
2010 | Lazoff et al. | Canada | Montreal | 23,635 | 5–17 | 187 | DSM-IV | – | 5.4 (158:29) | 79.1 | 67.8; 90.4 |
2010 | Barnevik-Olsson et al. | Sweden | Stockholm | 113,391 | 6–10 | 250 | DSM-IV | 0 | – | 22 | 19.4; 25.0a |
2010 | Maenner and Durkin | USA | Wisconsin | 428,030 | Elementary school–aged | 3831 | DSM-IV like criteria for WI special education services (by school district) | – | – | 90 | 86.7; 92.4a |
2010 | Posserud et al. | Norway | Bergen | 9,430 | 7–9 | 16 | DSM-IV, ICD-10 Included DAWBA and DISCO | – | 7 (14:2) | 87g | – |
2011 | Al-Farsi et al. | Oman | National Register | 528,335 | 0–14 | 113 | DSM-IV-TR | – | 2.9 (84:29) | 1.4 | 1.2; 1.7 |
2011 | Brugha et al. | UK | England | 7333 | 16–98 | 72 | ADOS | 100 | 3.8 | 98.2 | 30; 165 |
2011 | Kim et al. | S. Korea | Goyang City | 55,266 | 7–12 | 201 | DSM-IV | 31.5 | 3.8 | 264 | 191; 337 |
2011 | Mattila et al. | Finland | Northern Ostrobothnia County | 5484 | 8 | 37 | DSM-IV-TR included ADOS-G and ADI-R | 65 | 1.8 | 84 | 61; 115 |
2011 | Parner et al.h | Australia | Western Australia (1994–1999) | 152,060 | 0–10 | 678 | DSM-IV, DSM-IV-TR | – | 4.1 | 51 | 47; 55.3 |
2011 | Samadi et al. | Iran | National Register | 1,320,334 | 5 | 826 | ADI-R | – | 4.3 | 6.4 | 5.84; 6.70 |
2011 | Chien et al. | Taiwan | National Health Research Institute | 229,457a | 0–18 | 659 | ICD-9 | – | 3.7 | 28.7 | 26.6; 31a |
2011 | Windham et al.i | USA | San Francisco Bay [A-Za-z_-‘&;]{3,20} (1994,1996) | 80,249 | 9 | 374 | “Full syndrome autism”—CA Dept. of Developmental Services, receipt of CA special education services, or DSM-IV | – | 6.2 (324:50) | 47 | 42; 52 |
2012 | CDC | USA | 14 states | 337,093 | 8 | 3820 | DSM-IV-TR | 38 | 4.6 | 113 | 110; 117 |
2012 | Idring et al. | Sweden | Stockholm County Register | 444,154 | 0–17 | 5100 | ICD-09, ICD-10, DSM-IV | 57.4 | 2.6 | 115 | 112; 118 |
2012 | Isaksen et al. | Norway | Oppland and Hedmark | 31,015 | 6–12 | 158 | ICD-10 included ADOS-G and ADI-R | – | 4.27 (128:30) | 51 | 43; 59 |
2012 | Kočovská, Biskuptso, et al.j | Denmark | Faroe Islands | 7128 | 15–24 | 67 | ICD-10, DSM-IV, Gillberg’s criteria | – | 2.7a (49:18) | 94 | 73; 119 |
2012 | Nygren et al. | Sweden | Göteborg | 5007 | 2 | 40 | DSM-IV-TR | 63a | 4
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