Chapter 12: Epidemiology of Autism Spectrum Disorders Alison Presmanes Hill, Katharine Zuckerman, and Eric Fombonne Abstract In this chapter, we review existing prevalence estimates for ASDs since 2000 and discuss methodological factors impacting the estimation of prevalence and the interpretation of changes in prevalence estimates over time. Possible explanations for an increase in the prevalence of ASD within and across populations are considered. Increases in ASD diagnostic rates cannot currently be attributed to a true increase in the incidence of ASD due to multiple confounding factors. It remains to be seen how changes to diagnostic criteria introduced in the DSM-5 will impact estimates of ASD prevalence going forward. 1. Introduction Epidemiological surveys of autism were first initiated in the mid-1960s in England (Lotter, 1966; 1967) and have since been conducted in over 20 countries. In this chapter, we Alison Presmanes Hill, Research Assistant Professor, Center for Spoken Language Understanding, Institute for Development & Disability, Department of Pediatrics, Oregon Health & Science University, Portland, OR Katharine Zuckerman, Assistant Professor, Division of General Pediatrics, Oregon Health & Science University, Portland, OR Eric Fombonne, Professor of Psychiatry, Department of Psychiatry, Director of Autism Research, Institute for Development & Disability, Oregon Health & Science University, Portland, OR Corresponding Author: Eric Fombonne, Professor of Psychiatry, Department of Psychiatry, Director of Autism Research, Institute for Development & Disability, Gaines Hall 158, Oregon Health & Science University, Portland, OR; email: [email protected] provide a comprehensive review of the findings and methodological features of published epidemiological surveys about the prevalence of autism spectrum disorders (ASDs1). This chapter builds upon previous reviews (Elsabbagh et al., 2012; Fombonne, 2003a; 2005; Fombonne, Quirke, & Hagen, 2011; French, Bertone, Hyde, & Fombonne, 2013; Hill, Zuckerman, & Fombonne, 2014; J. G. Williams, Higgins, & Brayne, 2006) and includes the results of pertinent studies since published. The specific questions addressed are: (1) What is the range of prevalence estimates for ASDs?; and (2) How should the time trends observed in the current prevalence rates of ASDs be interpreted? 1.1. Study Design and Methodological Issues Epidemiologists use several measures of disease occurrence including incidence, cumulative incidence, and prevalence. Prevalence is a measure used in cross-sectional surveys (in which there is no passage of time) and reflects the proportion of subjects in a given population who suffer from the disease at that point in time. Most epidemiological studies of ASDs have assessed prevalence (point prevalence or period prevalence) as a cross-sectional approach is more appropriate for disorders where timing of diagnosis lags behind onset of symptoms and is likely to be influenced by a range of factors unrelated to risk. In designing a prevalence study, three elements are critical: case definition, case identification (or case ascertainment), and case evaluation methods (Fombonne, 2007). 1.1.1. Case Definition The definition and diagnostic criteria of autism has changed over time. Starting with Kanner’s definition of autism (1943), case definitions have progressively broadened to include 1 Autism spectrum disorder (ASD) is the modern term that replaces the former pervasive developmental delay (PDD). 2 criteria proposed by Rutter (1970), and subsequently the International Classification of Diseases, ninth revision (ICD-9; World Health Organization, 1977); the Diagnostic and Statistical Manual of Mental Disorders, third edition (DSM-III; American Psychiatric Association [APA], 1980), until two recent nosographies were adopted worldwide; ICD-10 (World Health Organization, 1992) and the Diagnostic and Statistical Manual of Mental Disorders, fourth edition and text revision (DSM-IV and DSM-IV-TR, respectively; APA, 1994; 2000). Early diagnostic criteria reflected the more qualitatively severe behavioral phenotypes, usually associated with severe delays in language and cognitive skills. In the 1980s less severe forms of autism were recognized, either as a qualifier for autism occurring without intellectual disability (i.e., high-functioning autism), or as separate diagnostic categories (e.g. Pervasive Developmental Disorders Not Otherwise Specified [PDD-NOS] or Autism Spectrum Disorders [ASD]). Asperger’s disorder appeared in the 1990s, with unclear validity, particularly with respect to its differentiation from high-functioning autism. Some ASD subtypes that were described in DSM-III subsequently disappeared (e.g., Autism-Residual State); however, other nomenclatures have since added new diagnostic categories, such as “atypical autism” and “PDD unspecified” (ICD-10). The changes now occurring with the introduction of Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5; APA, 2013), may impact prevalence estimates in the future. DSM-5 proposes a single new category of Autism Spectrum Disorders, conceptually equivalent to the previous diagnostic class of PDDs. However, fewer diagnostic criteria have been retained that are combined in two clusters of social communication deficits and restricted patterns of behaviors and interests. The removal of the loosely defined PDD-NOS that was in DSM-IV-TR (APA, 2000) will likely increase the specificity of the ASD diagnostic category, and 3 the removal of Asperger Disorder as a separate category is consistent with research that has generally failed to provide evidence for the discriminant validity of this diagnostic concept vis-à- vis forms of autistic disorder that are not associated with severe language impairments or intellectual deficits. The impact of DSM-5 changes remains to be fully assessed in the context of epidemiological surveys. Two recent population-based surveys have addressed this issue. Maenner and colleagues (2014) retrospectively applied the new diagnostic criteria to a previously obtained population-based sample from the Centers for Disease Control and Prevention (CDC) 2006 and 2008 surveillance years. They found that 81.2% of children classified as having ASD according to DSM-IV-TR (APA, 2000) also met DSM-5 criteria (APA, 2013), resulting in a DSM-5 based prevalence of 100/10,000 – an estimate lower than the 2006 and 2008 estimates. In addition, 304 children met DSM-5 but not DSM-IV-TR criteria. In a similar study, Kim and colleagues (2014) reported that 92% of children with ASD according to DSM-IV-TR also met DSM-5 criteria. However, when DSM-5 ASD and Social Communication Disorder (SCD; a new diagnostic category in DSM-5) were considered together, there was no significant change in the prevalence estimate (Kim et al., 2014). It is important to note that new diagnostic information required in DSM-5 (e.g., emphasis on sensory processing deficits) is generally not available in prior studies, leading to potentially biased estimates. Additionally, previous studies are often constrained in sampling children with a DSM-IV PDD diagnosis and cannot therefore accurately estimate the proportion of children who did not meet criteria for DSM-IV yet would have met those for DSM-5. While there is currently high interrater reliability overall regarding diagnosis of ASDs and commonality of concepts across experts, differences still persist between nomenclatures 4 about the terminology and operationalized criteria of ASDs. It is unclear to what extent the changing nomenclature of ASDs plays a role in prevalence estimates described in epidemiological studies. More studies are on their way that will provide further examination of the impact on prevalence estimates of narrowing the ASD definition in DSM-5. 1.1.2. Case Identification/Ascertainment When a population is identified for a survey, different strategies are employed to find individuals matching the study’s case definition. Some studies rely solely on service provider databases (Chien, Lin, Chou, & Chou, 2011; Croen, Grether, Hoogstrate, & Selvin, 2002b; Davidovitch, Hemo, Manning-Courtney, & Fombonne, 2013), special education databases (Fombonne, Zakarian, Bennett, Meng, & McLean-Heywood, 2006; Gurney et al., 2003; Lazoff, Zhong, Piperni, & Fombonne, 2010; Maenner & Durkin, 2010), or national registers (Al-Farsi et al., 2011; Parner et al., 2012; Samadi, Mahmoodizadeh, & McConkey, 2011) for case identification. These studies have the common limitation of relying on a population group that was readily accessible, rather than sampling from the population at large. As a result, individuals with the disorder who are not in contact with services are not included as cases, leading to an underestimation of prevalence. This limitation is particularly problematic in communities with recognized limitations in available services. Other investigations have relied on a multistage approach to identify cases in underlying populations (e.g., CDC, 2014; Idring et al., 2012; Kim et al., 2011). In these studies’ first screening stage, a wide net is cast to identify subjects possibly affected with ASD, with the final diagnostic status being determined at subsequent stages. This process often consists of sending letters or screeners to school and health professionals, searching for possible cases of autism. Few such investigations rely on systematic sampling techniques that would ensure a near 5 complete coverage of the target population, and screening often varies substantially in ascertainment of all relevant data sources. Additionally, surveyed areas often differ in terms of specific educational or health care systems available, and inclusion information sent often varies in reliability and validity. Finally, uneven participation rates in the screening stage can lead to variation in the screening efficiency of surveys. To illustrate how differential participation in the screening stage affect prevalence estimates, two hypothetical scenarios are illustrated in Figure 1, both of which are based on a true ASD prevalence of 150/10,000 and a sensitivity of 100% for the screening process and total accuracy in the diagnostic confirmation. In Scenario A, we assume 60% participation for ASD and non-ASD cases in the first screening stage, resulting in 90 participating ASD cases that screen positive. With 70% participation for both ASD and non-ASD cases in the diagnostic stage, we would identify and confirm 63 ASD cases in the second phase. Weighting back phase 2 data, we would obtain an unbiased prevalence estimate of 1.5% (or 150/10,000) in this scenario. In Scenario B, we also assume 60% overall participation, but with a 80% participation rate for ASD cases, reflecting a scenario in which individuals with ASD are more likely to participate in the first screening stage than non-ASD cases. Thus, with the same participation rates in the first screening (60%) and final diagnostic stages (70%), we identify 84 ASD cases and calculate a biased prevalence estimate of 2% (200/10,000), an estimate that is 0.5% higher than true prevalence. The bias arises for two reasons: (1) participation in screening is associated with case status (here, with ASD cases more likely to participate than non-cases); and (2) as investigators typically have no such information, weights used for prevalence estimation were not adjusted correspondingly, resulting in the upward bias. [INSERT FIGURE 1 HERE] 6 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 ASD prevalence of 72/10,000 in their identified sample and estimated a prevalence of 128/10,000 in nonresponders (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 the 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; CDC, 2012), suggesting that underidentification is a widespread phenomenon. 7 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. 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, Rutter, & Couteur, 1994) and/or the Autism Diagnostic Observation Schedule (ADOS; Lord et al., 2000) have been increasingly used in the most recent surveys (Table 1). Obviously, surveys of large populations, such as those conducted in the United States’ CDC ADDM Network (2007a; 2007b; 2009; 2012; 2014) or in national registers (Idring et al., 2012), cannot include direct diagnostic assessment of all subjects by researchers. However, investigators generally improve the accuracy of caseness determinations by undertaking, on a randomly selected subsample, a more complete diagnostic workup (Rice et al., 2007). The CDC surveys have established a methodology for surveys of large populations based on screening of 8 the population using multiple data sources, standardized records abstraction, and systematic review and scoring of the data gathered in the screening phase. In the less obvious cases, this information is combined with 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. 2. Systematic Review of Prevalence Estimates 2.1. Unspecified ASDs in Earlier Surveys A new objective of more recent epidemiological surveys has been to estimate the prevalence of all disorders falling onto the autism spectrum, thereby prompting important changes in the conceptualization and design of surveys. However, in previous reviews, we documented that several studies performed in the 1960s and 1970s provided useful information on rates of syndromes similar to autism but not meeting the strict diagnostic criteria for autistic disorder then in use (Fombonne, 2003a; 2003b; 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, Kumashiro, Yashima, Tachibana, & Watanabe, 1982), borderline childhood psychoses (Brask, 1970), or autistic-like syndromes (Burd, Fisher, & Kerbeshian, 1987). These syndromes would fall 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). However, this atypical group received little attention in previous epidemiological studies; these subjects were not defined as “cases” and were not included in the 9 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 of autism had been included in the case definition. Similarly, in Wing, Yeates, Brierly, & Gould’s study (1976), the prevalence was 4.9/10,000 for autistic disorder, but the prevalence for the whole ASD spectrum was in fact 21.1/10,000 after the figure of 16.3/10,000 (Wing & Gould, 1979), corresponding to the triad of impairments, was added. The progressive recognition of the importance and relevance of these less typical clinical presentations has led to changes in the design of more recent epidemiological surveys that use case definitions that incorporate a priori these milder phenotypes, which we now turn to. 2.2. Search Strategies Keeping in mind the range and limitations of case definition, identification, and evaluation methods employed in epidemiological surveys, we present the results of epidemiological reports conducted since 2000 in Table 1. These reports were identified from previous reviews of epidemiological surveys (Elsabbagh et al., 2012; Fombonne, 2003a; 2003b; 2005; 2009b; Fombonne et al., 2011; French et al., 2013; J. G. Williams et al., 2006) and through systematic searches using major scientific literature databases (Medline, PsycINFO, Embase, PubMed). Where multiple surveys based on the same or overlapping populations were evident, the publication listed is the most detailed and comprehensive account. For example, surveys conducted by the U.S. CDC (2007a; 2007b; 2009; 2012; 2014) as part of the Autism and Developmental Disabilities Monitoring (ADDM) Network are each included in the table, although additional accounts for individual states are available elsewhere (Nicholas et al., 2008; Pinborough-Zimmerman et al., 2012; Rice et al., 2010; Zahorodny et al., 2014). 10