Autism spectrum disorders: Toward a gendered embodiment model

Social Science & Medicine 74 (2012) 1667e1674

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Social Science & Medicine journal homepage: www.elsevier.com/locate/socscimed

Autism spectrum disorders: Toward a gendered embodiment model Keely Cheslack-Postava a, *, Rebecca M. Jordan-Young b a b

Paul F. Lazarsfeld Center for the Social Sciences, Columbia University, 420 W, 118th Street, Mail Code 3355, New York, NY 10027, United States Department of Women’s, Gender, and Sexuality Studies, Barnard College, Columbia University, New York, NY, United States

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 12 July 2011

One of the most consistent observations in the epidemiology of autism spectrum disorders (ASD) is the preponderance of male cases. A few hypotheses have been put forth which attempt to explain this divergence in terms of sex-linked biology, with limited success. Feminist epidemiologists suggest the importance of investigating specific mechanisms for male-female differences in health outcomes, which may include sex-linked biology and/or gender relations, as well as complex biosocial interactions. Neither domain has been systematically investigated for autism, and the possible role of gender has been particularly neglected. In this article, we posit hypotheses about how social processes based on perception of persons as male or female, particularly patterns of social and physical interaction in early development, may affect the observed occurrence and diagnosis of ASD. We gesture toward an embodiment model, incorporating hypotheses about initial biological vulnerabilities to autism e which may or may not be differentially distributed in relation to sex biology e and their interactions with gender relations, which are demonstrably different for male and female infants. Toward building such a model, we first review the epidemiology of ASD with an eye toward male-female differences, then present a theory of gender as a “pervasive developmental environment” with relevance for the excess burden of autism among males. Finally, we suggest research strategies to further investigate this issue. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Autism spectrum disorders Gender Sex Biosocial Gender socialization Diagnostic bias Developmental context

Introduction Autistic disorder (which we also refer to as autism) is characterized by impaired development in social interaction and communication, accompanied by patterns of repetitive behavior or restricted interests, appearing before age 3. The broader category “autism spectrum disorders” (ASD) encompasses autistic disorder, Asperger syndromeewhere language develops typically, despite other impairmentseand Pervasive Developmental Disorder Not Otherwise Specified (PDD-NOS), diagnosed where some, but not all criteria for an autism diagnosis are present. A complex set of genetic factors is believed to play an important role in the etiology of these conditions; however, given that autism is a complex trait with great variability in specific manifestations, and because of the diversity of genetic associations that have been suggested thus far, a role for environmental factors, broadly construed, is also virtually certain (Balaban, 2006). Autism is diagnosed behaviorally based on a variety of symptoms and signs that vary by age. For example, an infant with autism

* Corresponding author. E-mail address: [email protected] (K. Cheslack-Postava). 0277-9536/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2011.06.013

may not respond to his/her name, while a toddler may have delayed speech, fail to engage in “pretend” play, or line up toys obsessively. An older child or adult may have difficulty “getting” jokes, engaging in conversation, and forming friendships. Cognitive impairment is present in a substantial proportion of cases. The ‘gold standard’ instruments for diagnosis are a standardized clinical interview, the Autism Diagnostic Interview-Revised (ADI-R) and an observation of set activities, the Autism Diagnostic Observation Schedule (ADOS); a variety of other scales and instruments are used for clinical, epidemiological, or screening purposes. A key characteristic of all such instruments is that they are to some extent subjective, relying on parental, caregiver, or self-report and/or clinician observation and assessment. One of the most consistent observations in the epidemiology of ASDs is the preponderance of male cases. A comprehensive review of 43 studies published between 1966 and 2008 found male:female (M:F) ratios ranging from 1.33:1 to 16:1, with a mean of 4.2:1; among studies reporting on ASDs combined, the median ratio was 4.0:1 (Fombonne, 2009). This disparity is even greater among Asperger syndrome and “high functioning” autism cases where it can approach 10:1 (Fombonne, 1999). Several hypotheses have been advanced to explain the malefemale (MeF) disparity in ASD diagnoses. Among the most

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widely cited is Baron-Cohen’s et al. (2005) “extreme male brain” hypothesis. Baron-Cohen holds that there is an “essential difference” between male and female cognitive and affective styles, with males oriented toward understanding and building law-like systems (systemizing), and females oriented toward understanding and predicting humans (empathizing). ASDs, in this model, reflect deficits in the cognitive processes of “empathizing”, and strengths in “systemizing” (Baron-Cohen, 2009). Baron-Cohen further suggests that the “male brain” pattern reflects the organizing action of testosterone on the developing fetal brain, and has reported some correlations between steroids in amniotic fluid and sex-typed behavior in infants and young children (Knickmeyer et al., 2006; Knickmeyer, Baron-Cohen, Raggatt, & Taylor, 2005; Knickmeyer, Baron-Cohen, Raggatt, Taylor, & Hackett, 2006). Several serious weaknesses with this line of thinking must be noted, however, beginning with the observation that the concepts of “systemizing” and “empathizing” are not well-specified (Grossi & Fine, in press). Further, Baron-Cohen’s studies have not been independently replicated, and have been faulted on extensive methodological grounds (Grossi & Fine, in press; Jordan-Young, 2010). Finally, a systematic analysis of studies linking sex-typed cognitive traits in humans with pre-natal steroid hormone exposures suggests that pre-natal steroids probably do not make a meaningful contribution to MeF cognitive differences (JordanYoung, 2010). Early hormone exposures could alternatively alter expression of specific disease related genes, either increasing or decreasing risk (Kaminsky, Wang, & Petronis, 2006). Specific mechanisms are as yet undetermined, but could include sensory perception, neuroendocrine reward processes involved in social-emotional interaction and learning, or factors related to ease of building or pruning neural connections, to name a few (see the risk model developed by Dawson (2008) for elaboration). Genetic sex could also affect risk for ASD through X-linked traits. Having two X chromosomes protects females from some X-linked disordersdknown or hypothesized mechanisms include genes outside the pseudoautosomal region that escape X-inactivation; genes expressed only from the paternally inherited allele through imprinting, a parent-specific process of gene silencing; and production of some level of functional gene product. For example, Fragile X syndrome, involving mutations in the FMR1 gene on the long arm of the X chromosome resulting in decreased levels of its protein product, affects females more rarely and less severely than males, and as many as 30% of those with Fragile X are also diagnosed with ASDs (Marco & Skuse, 2006). However, only a small percentage of ASD cases overall have been associated with X-linked disorders. More perfunctory theories include disruptions in the vasopressin system or a protective role of oxytocin (Carter, 2007) and sex differences in neurodevelopmental susceptibility to environmental insults (Dean & McCarthy, 2008). Varying degrees of evidence support each of these theories, but to date, none has provided a strong account for MeF differences in autism. While it is beyond the scope of this article to evaluate this evidence, what these theories hold in common is that they place the mechanisms responsible for these observed discrepancies firmly in the realm of sex-linked biology (“sex”), which is conceptualized as distinct from the social realm of MeF difference (“gender”). However, considering how social as well as biological forces might be acting to cause observed disparity may provide new insights into its etiology. Though sex and gender may be conceptually distinct, they are in practice often inseparable (see Springer, Stellman, and JordanYoung, 2011). Even if an investigator posits a mechanism involving sex chromosomes, the identities of which are laid down prior to the possibility of any socialization, gene expression is influenced by experience and learning, as has been demonstrated

in multiple animal (Majdan & Shatz, 2006; Mello, 2002; Sng & Meaney, 2009) and human (Karmiloff-Smith, 2007) studies. Moreover, most studies of genetics and health involve behavioral or other phenotypic outcomes that are measured at the level of the whole organism (i.e., the individual). At this level, it is not possible to separate the individual’s gendered biography from their biology e in fact, from the moment of birth, gendered processes literally become biological (see (Fausto-Sterling, 2005; Jordan-Young, 2010). For other purposes, we might follow Springer et al. (2011) and use the composite term “sex/gender” to highlight the fundamental entanglements of these domains while generally avoiding the stand-alone term “sex.” Below, though, we develop a conceptual model that is meant to reflect the underlying mechanisms through which sex and gender become sex/gender e that is, the moment(s) of their entanglement. Thus, we do use the term “sex,” though we caution that its use in the abstract model should not distract from the principle that it will generally not be possible to actually assess sex apart from gender. Moreover, when we use the stand-alone term “gender,” we refer not to properties of individuals (as in gender identity or gender roles), but rather to higher-order processes of interaction and norms, specifically patterns of early interaction with adults that systematically differ by the perceived sex/gender of the child. Before proceeding further, we find it important to clarify the relationship between the argument we advance here, and prior models of autism that invoked the social environment, specifically parentechild relations. As Siller and Sigman (2002, p. 78) have noted, " The possible contributions of parents to their [autistic] children’s development have been neglected, probably because of a sensitivity of investigators, clinicians, and parents to previous, fallacious, psychogenic theories of autism." In contrast to prior "psychogenic" theories that have blamed parenting styles, especially of mothers, for autism, we wish to be clear that our model does not concern deficits in parenting in any sense. Instead, it engages the idea that “normal" and normative parenting, like all other family and community relations, is gendered, meaning that boy and girl children, on average, encounter different experiences, interactions, and environments. Moreover, we assert that those aspects of the social environment that differ, on average, for boys and girls are not enough in and of themselves to generate ASDs: there must be some sort of underlying vulnerability. Epidemiology of ASD and maleefemale comparisons ASDs are characterized by marked heterogeneity in symptoms, developmental course, and associated co-morbidities. By way of example, subsets of cases experience developmental regression; differences in head circumference, serotonin levels, and immunological parameters; epilepsy, gastrointestinal complaints, and sleep disturbances (Levy, Mandell, & Schultz, 2009; Newschaffer et al., 2007). Some heterogeneity likely stems from the exponential increase in recognized cases from 1943 when Kanner published the first case series of eleven patients with “autistic disturbances” (Kanner, 1943), to a 1966 survey of autism in the UK reporting a prevalence of 0.04% (Fombonne, 2003), to a 2009 CDC publication estimating ASD prevalence at 1% of U.S. 8-year olds (Autism and Developmental Disabilities Monitoring Network, 2009). The extent to which changes in diagnostic criteria, awareness, destigmatization, and diagnostic substitution can account for the trend is not certain. Etiology also appears heterogeneous. Approximately 10% of cases are linked to recognized genetic syndromes, and associations with genes or loci on nearly every chromosome have been reported (Abrahams & Geschwind, 2008). With technological advances, rare copy number variations (CNVs) e insertions or deletions of

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relatively large segments of DNA e have emerged as possibly important. Yet, because CNVs have been found in a wide variety of locations in the genome, and in unaffected subjects, they may have limited explanatory power (Abrahams & Geschwind, 2008). Several pre-natal exposures have also been implicated, including thalidomide, anti-convulsant drugs, and immune factors (Newschaffer et al., 2007); however these encompass a very small subset of total cases. The most consistently identified risk factors for ASD are having an affected sibling, older parental ages, and male sex/ gender, with the former two hypothesized to arise from genetic factors. Meanwhile, in trying to determine the mechanisms behind the MeF disparity, it makes sense to ask whether there is systematic variation in M:F ratios for ASD which may provide insight about the role of sex/gender in etiology and diagnosis, and would suggest that mechanisms associating sex/gender with autism risk are subject to modification. There are in fact several instances of such variation in M:F ratios. A number of examples suggest that in the presence of additional developmental insults or delays, M:F ratios for autism are lower (though there are exceptions; see for example Schendel, Autry, Wines, and Moore (2009) regarding major birth defects). Intellectual disability (ID, also referred to as mental retardation or cognitive impairment, typically IQ below 70 or equivalent developmental test performance) is one of the most prevalent co-morbidities to ASD. M:F ratios decline with increasingly severe ID (Fombonne, 1999; Nicholas et al., 2008; Volkmar, Szatmari, & Sparrow, 1993; Yeargin-Allsopp et al., 2003). For example, M:F ratios in ASD among 3e10 year olds in Atlanta were 6.7:1 among children without ID, 4.4:1 in those with IQ 50e70, and 1.3:1 among those with IQ < 20 (Yeargin-Allsopp et al., 2003). The correlation between epilepsy and ID likely explains a reported M:F ratio of 2:1 for autism with epilepsy versus 3.5:1 without (Amiet et al., 2008). Lower M:F ratios have also been reported among cases with dysmorphic features or microcephaly (Miles et al., 2005), and various studies have found associations between autism and obstetric and perinatal complications (reviewed in: (Bilder, Pinborough-Zimmerman, Miller, & McMahon, 2009; Kolevzon, Gross, & Reichenberg, 2007)). In a cohort of over 400,000 births in metropolitan Atlanta, Schendel and Bhasin (2008) found that M:F ratios for autism decreased with decreasing birth weight or gestational age, among all children with autism and by presence of ID or other developmental disability. These observations collectively suggest that while there is something protective about being female with regard to ASD, that protective element is less functional when accompanied by other impairments or vulnerabilities. If there are subtle protective elements of female gender socialization, then infants affected by ID, or prematurity, for example, may be less able to benefit from such experiences. Neonatal interventions common to both male and female infants with early pre-term birth or other complications could also plausibly reduce MeF differences in early social environment. Differences in M:F ratios for ASD across other social categories such as race, socioeconomic status (SES), or immigrant status could reflect variation in the social structuring of gender, and a corresponding impact on developmental or diagnostic processes. A range of studies have explored variation in ASD diagnoses across single dimensions of social difference. Several have reported associations of autism with measures of higher SES, though not all studies find such an association (see Durkin et al., 2010 for summary and references). Researchers tend to conclude that such associations reflect ascertainment bias, however Durkin et al. reported a positive gradient in ASD prevalence by SES even among children who had not previously received a diagnosis (Durkin et al., 2010). Higher prevalence has also been reported in some immigrant minority groups (Keen, Reid, & Arnone, 2010), which is

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somewhat at odds with the pattern of higher prevalence among socially advantaged groups. The intersection of sex/gender with race, SES, or immigrant status, though on occasion presented descriptively (see for instance (Bhasin & Schendel, 2007)), has not been systematically explored as a potential window on the joint roles of multiple social processes in ASD risk and diagnosis. Gender as a “Pervasive Developmental Environment”: potential roles of gendered social processes in producing M:F patterns in autism In the following sections, we introduce a simplified model of developmental trajectories for an infant with underlying susceptibility for ASD that may or may not eventually lead to an ASD diagnosis (Fig. 1). Given the etiologic heterogeneity of ASDs, and the large and uncertain array of candidates for the specific disruptions underlying ASDs, we cannot be certain about specific elements in the diagram. For example, we expect that an accurate model would require multiple different forms of initial vulnerability, any of which might be differentially distributed by (genetic) sex, or not. However, we can identify points where gendered social processes, functioning through adult perception of, response to and interaction with an infant may interact with biology producing probabilistic ‘switches’ whereby males end up both more likely than females to express ASD symptoms, and more likely to be diagnosed, given expression of symptoms. Our aim, then, is not to create a singular model of development in autism but instead to show the structure of developmental cascades that always involve both biological and environmental factors. Social interactions have material effects on the developing brain, which in turn affect the biological substrate at the next level of development. Indeed, brain development requires interactions. As Karmiloff-Smith has noted, "the neonate cortex is neither localized nor very specialized at birth. This allows interaction with the environment to play a crucial role in gene expression and in the

Fig. 1. Hypothesized model for effects of gender environment on developmental and diagnositic trajectories for an infant with initial underlying susceptibility for ASD, which may or may not be differentially distributed by genetic sex. Adult perception of, response to, and interaction with the infant is shaped by perceived gender, so that certain types of interaction are more common with male than female infants, and vice versa. Some element(s) of this differential social environment that is more frequently/ intensely experienced by female infants acts as a protective factor, muting the emergence or expression of ASD-related symptoms. Among children who express symptoms of ASD compatible with diagnosis, recognition and identification is more likely among males. The model is simplified to focus on gendered environment in that it excludes other environmental or contextual inputs which may affect probability of symptom expression or diagnosis. Additionally, it is expected, given etiologic heterogeneity of ASDs, that hypothesized gender-linked mechanisms may be operant under some forms of initial underlying vulnerability but not others.

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ultimate cognitive phenotype.. epigenesis is probabilistic, and gene expression is activity-dependent" (Karmiloff-Smith, 2007, p. 84). As we explore below, empirical evidence on gender and social interactions demonstrates that experiences potentially impinging on the ultimate cognitive phenotype are profoundly shaped by gender. Hypothesizing developmental mechanisms: sex and gender in interaction Many developmental scenarios could explain a "true" difference in rates of autism between males and females. Here, we suggest three plausible scenarios involving the presence of initial biologically-based vulnerabilities, related for example to sensory and/or information processing abilities. Such vulnerabilities may or may not be sex-linked. That is, initial "vulnerable" phenotypes may be randomly distributed among male and female infants, but MeF differences might emerge via interaction of vulnerabilities with gendered aspects of the developmental context. The first scenario thus begins with vulnerabilities that are randomly distributed among males and females, then expressed differentially as ASD symptoms because of gendered socialization, resulting in increased M:F ratios. A second scenario would involve vulnerabilities that are more prevalent in males, but are amplified through gendered socialization. A third scenario would involve vulnerabilities that are more prevalent in males but unaffected by socialization. In the first two scenarios, biological vulnerabilities play a role in disparities without constituting an "innate" sex difference in autism. This is more obvious in the first scenario, where initial vulnerabilities are randomly distributed, but it is also the case when the initial vulnerabilities are not randomly distributed, so long as the final MeF difference in autism emerges only in the context of gender-differentiated socialization. That is, the model treats the developmental context as a crucial component in the development of autism, not a lesser "modifier" of a biological substrate conceived as prior to and separate from developmental context. Both hypotheses explain disparities in autism by taking seriously extensive data demonstrating pervasive gender differences in children’s early socialization. Currently, however, only the third scenario is represented in the literature. By omitting gendered socialization from the array of factors considered in producing MeF differences, researchers make the implicit assumption that it has no effect. Gender socialization occurs through interactions with other people, as well as through exposure to different physical environments, even from very young ages. As an example of the latter, studies have found pronounced differences in the toys and physical environments of girl versus boy infants (Nash & Krawczyk, 1994; Pomerleau, Bolduc, Malcuit, & Cossette, 1990). In this discussion we focus on social interactions, encompassing both verbal and physical exchanges. These are shaped by a wide range of factors that include characteristics of the infant, as well as the intentions, implicit beliefs, and sensitivity (i.e., skill in perceiving and decoding infant cues) of the infant’s social partner. There is evidence that the latter three factors are all influenced by the perceived gender of the infant. Scores of studies have documented parents’ differential socialization of boys and girls (Fine, 2010; Harris, 2009), though the degree and precise domains of differential treatment are somewhat controversial. One meta-analysis suggests that while there is evidence of parents’ sex-typed perception and encouragement of sex-typed activity, they may treat boys and girls more or less similarly in most other broad domains, including overall amount of interaction; achievement encouragement; restrictiveness versus encouragement of independence; warmth, nurturance, and responsiveness; disciplinary strictness; and clarity of

communication (Lytton & Romney, 1991). Notably, that review found larger sex-typing effects for younger children, and for studies where parents are actually observed, rather than queried (Lytton & Romney, 1991). Suggestive evidence that gender socialization may be relevant for traits related to autism is provided by studies of subtle differences in how adults communicate with infants of different genders. A recent study of 6377 infant-mother dyads from the (U.S.) nationally-representative Early Childhood Longitudinal Study Birth Cohort found that verbal stimulation has a strong positive effect on infants’ social-emotional and cognitive development, and also found a small but significant bias toward mothers offering more verbal stimulation to girls than boys (Page, Wilhelm, Gamble, & Card, 2010). Likewise, a recent observational study of 36 infantmother dyads found that "Mothers of daughters made more interpretations and engaged in more conversation with their daughters, whereas mothers of sons made more comments and attentionals, which were typified by instructions rather than conversation" (Clearfield & Nelson, 2006, p. 127). Mothers also spent more time engaged in activity with daughters versus watching sons (Clearfield & Nelson, 2006). In this study, investigators did not find maleefemale differences in infant contact/ proximity to mothers or looking behavior (Clearfield & Nelson, 2006), however individual children were not observed over time. In a longitudinal study in which infants were observed within days of birth by an adult interactor unaware of child’s sex, male and female infants did not exhibit any significant differences in mutual eye gaze with the adult; however, by four months of age differences in eye contact behavior appeared, these being accounted for by an increase in eye contact made by girls (Leeb & Rejskind, 2004). While this study did not directly measure adult behavior in relation to child gender, it is compelling in demonstrating infant differences by gender emerging only after inception of the earliest social interactions, in a behavior with direct relevance to ASD, namely eye contact. Another body of research has documented that attributions of gender profoundly shape the way observers perceive emotions and behavior in children. Often referred to as “baby X studies,” such research involves creatively manipulating the perceived sex of identical infants or very small children, and analyzing how observers describe or react to the children when labeled as female versus male. One well-known study involved a short film of a baby in an infant seat presented with four different stimuli ea teddy bear, a jack-in-the-box, a doll, and a buzzer e each presented several times in succession. More than 200 observers were randomly assigned the information that the child was a boy versus a girl, without making an obvious point of this. There were remarkable differences in the way that observers saw the same infant when they thought they were watching “David” versus “Dana,” especially when presented with the child’s somewhat ambiguous response to the jack-in-the-box. Those observers who thought they were watching “David” were much more likely to describe the child as “angry,” while those who thought they were watching “Dana” described the child as “afraid” (Condry & Condry, 1976). Subsequent studies have extended these observations, including by controlling for actual variations in infant characteristics (e.g., (Culp, Cook, & Housley, 1983; Delk, Madden, Livingston, & Ryan, 1986; Donovan, Taylor, & Leavitt, 2007; Lyons & Serbin, 1986; Seavey, Katz, & Zalk, 1975; Sidorowicz & Lunney, 1980)). On the other hand, one review of infant gender labeling studies concluded that overall effects, though present, were not strong or consistent (Stern & Karraker, 1989) belying the limitations of this experimental method for understanding real-world phenomena. Nonetheless, a clue about how differences in perception of infant expressions by perceived gender may translate to differences in response is given

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by a study of Donovan et al. (2007) who found that mothers of 6month old infants were more sensitive to changes in facial expressions of a similarly-aged infant when the child was presented as female. It is not clear precisely what effect described differences in adult perception, response or interaction might have on infants with ASD vulnerabilities. For one thing, none of these studies focused on children at risk for ASD, and may not be generalizable. Nonetheless, sex-typing may condition responses to both gender-typical and gender atypical behaviors in vulnerable girls, in ways that constitute early, naturalistic interventions by parents and caregivers. Siller and Sigman (2002), in a longitudinal study, found that among children with autism, levels of synchronization between caregiver interactions and child’s focus of attention during play were associated with gains in joint attention and language up to 16 years later. If findings of increased sensitivity to female infants extend to focus of attention, then female infants may experience on average higher levels of such synchronization. Additionally, a focus of parental participation in early intervention for ASD is to adapt interactive styles to promote early social engagement and communication (Dawson, 2008). Studies above suggest that the social environment of a female infant may provide on average more bids for social engagement, whether through format of speech, play, or eye contact. Another possibility is that certain behaviorsdlike eye contact, or vocal response to questionse may be more heavily ‘coached’ in girls, so that behaviors appear more ‘typical’ and may mask other ASD symptoms. While the studies discussed here have important limitations and the potential relevance of any specific scenario to ASD is unknown, the main point is that development is a "looping process" in which gender is a critical, ongoing factor: it shapes initial social and tactile input that the child receives from human interaction, as well as other people’s responses to the child’s behaviors, in an iterative manner. Gender bias in diagnosis Some of the observed MeF difference in autism may stem from parents’, clinicians’, and others’ different perceptions and expectations for boys versus girls, beginning in early infancy. Lest readers are tempted to speculate that gender labeling is a thing of the past, it is worth noting that recent studies generally indicate labeling effects as strong as those found in the earliest studies, more than 30 years ago. Reliance on cognitive schemas about gender to label behavior not only affects how we perceive specific people and their actions, it loops back to reinforce belief in fundamental, generalized gender differences. Importantly, this process leads people to discount actual evidence of intra-sex variability and inter-sex similarity, and operates even in the face of commitments to similarity or a ‘wish’ for similarity. That is, being egalitarian in orientation doesn’t seem to affect gender labeling very much (Fine, 2010). Infants randomly labeled as male are perceived to be stronger and more masculine than infants labeled as female (Burnham & Harris, 1992), and newborn girls are described as littler, softer, and more finer featured, even in when they do not differ on any objective measures (Rubin, Provenzano, & Luria, 1974). Mothers in one study were asked to estimate how capable their infants would be in crawling up a ramp; mothers exaggerated their infant sons’ physical abilities, while underestimating their girls’ abilities, relative to objective tests of infants’ abilities made immediately following these estimates (Mondschein, Adolph, & Tamis-LeMonda, 2000). In terms of how gendered expectations might affect ASD diagnosis, the simplest and most likely scenario is that ASD, as a syndrome that is commonly and epidemiologically associated

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with males, would be more readily recognized in male versus female children, given equal presentation of symptoms. Welldocumented examples of disease being more readily diagnosed in males due to such labeling effects include heart disease (Goldberg, 2002; Institute of Medicine, 2003) and HIV/AIDS (Goldstein & Manlowe, 1997). The extremely prolific citation, both within and outside psychology, of Baron-Cohen’s theory of autism as representing an “extreme male brain”, lend credence to this scenario. Conversely, we should not discount the possibility that parents would be likely to notice and be concerned about traits if they are "gender atypical." In this case, ASD symptoms (such as disliking touch or difficulty displaying mutuality in interaction) might raise concern at a lower threshold in girls as opposed to boys. This would run counter to the observed trend of a higher M:F ratio. Yet again, given the association of autism with males, parents and clinicians may more readily label an uncommunicative girl as “timid” or “shy” rather than entertaining a diagnosis of ASD. Another possibility is that given the same underlying deficits, “female autism” tends to look different than “male autism” in terms of symptom expression, resulting in female cases that share these deficits but go undiagnosed or receive alternative diagnoses. Nearly two decades ago, Kopp and Gillberg described six girls who met diagnostic criteria for autism, but took several years of clinical encounters to receive autism diagnoses (Kopp & Gillberg, 1992). Likely contributing to the delay were qualitative differences in these girls’ behavior versus the expected behavioral presentation that these authors described. Their social impairment was described as tending “more toward ‘clinging’ to other people, imitating their speech and movements without a deeper understanding., and only brief periods of aloofness” (Kopp & Gillberg, 1992, p. 96). They also showed high frequency of language use, though often echolalic and uncommunicative, as opposed to muteness more common in males. Despite such suggestive evidence, there is still a dearth of research on MeF differences in presentation (Thompson, Caruso, & Ellerbeck, 2003), a clear impediment being that recognized female cases are likely to be those most similar to male cases. Lack of knowledge about the nature and extent of such differences may contribute to the observed M:F disparity. Some evidence of gender bias in identification of ASD exists. The United States Centers for Disease Control and Prevention’s Autism and Developmental Disabilities Monitoring Network consists of sites in several U.S. states that conduct surveillance to identify children with ASDs. Beginning in 2000, school and medical records of 8-year-old children have been periodically examined to determine the prevalence of children meeting a study case definition for ASD (Autism and Developmental Disabilities Monitoring Network, 2009). Because identification is not dependent on previous diagnosis, this method may allow detection of diagnostic biases, although case identification still depends on relevant behaviors having been recorded, and a record existing in the first place. For children born in 1994, the median age of diagnosis was a half-year later in girls than in boys (Shattuck et al., 2009), and boys with ASD were significantly more likely to have been so identified than were girls (Mandell et al., 2009). Although in other studies, reports of later age or longer time to diagnosis in girls versus boys failed to reach statistical significance (Mandell, Listerud, Levy, & PintoMartin, 2002; Mandell, Novak, & Zubritsky, 2005; Ouellette-Kuntz et al., 2009), significance may be affected by low numbers of female cases and study designs dependent on prior diagnosis with ASD. Two recent studies compared rates of ASD diagnosis among siblings of children with ASD (Constantino, Zhang, Frazier, Abbacchi, & Law, 2010) and subjects from the 14,000 member Avon Longitudinal Study of Parents and Children (ALSPAC) (Russell, Steer, & Golding, in press) in males and females who showed

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symptoms of autism derived from other study measures. 27% of female siblings (versus 47% of males) who scored above cut-offs on quantitative scales of social communication or responsiveness, or showed a history of language delay and autistic speech characteristics had received an ASD diagnosis (Constantino et al., 2010). In ALSPAC, 18% of girls, versus 37% of boys who showed developmental impairments most predictive of ASD diagnosis had actually received such a diagnosis in the community (Russell et al., in press). These observations suggest that ASD-related characteristics can be more readily observed and/or more rapidly translated into clinical diagnoses for boys than for girls; this could occur through differential interpretation of behaviors or levels of awareness and concern on the part of caregivers, educators, or clinicians. Estimating Cumulative impact on observed M:F ratios The lines of thought entertained here raise the question of what proportion of the observed discrepancy in diagnoses may be attributed to social processes related to gender. While such estimates involve considerable speculation, it is helpful to examine the range of possibilities. To do so, we define three M:F ratios. Mo:Fo is the observed ratio of males to females among cases receiving diagnoses in the community. Me:Fe is the ratio of males to females among persons in the population expressing symptoms of autism sufficient for diagnosis, irrespective of actually receiving a diagnosis; and Ms:Fs is the ratio of males to females among persons with

underlying susceptibility, irrespective of whether or not they express symptoms. The ratio Me:Fe can then be determined if we know Mo:Fo and the probabilities of diagnosis for males (dm) and females (df), respectively, who show symptoms of ASD:

Me : Fe ¼

df Mo Fo : ¼ Mo : Fo  dm df dm

We assume Mo:Fo to be 4:1, and plot Me:Fe over a range of values of df/dm in Fig. 2A. Limited data suggest that df/dm may fall between 0.5 (Constantino et al., 2010; Russell et al., in press) and 0.75 (OR ¼ 1.35 for previous diagnosis in males versus females (Mandell et al., 2009)), giving Me:Fe values ranging from about 2:1 to 3:1. Ms:Fs is then a function of Me:Fe and risks in males (rm) and females (rf) of expressing autism symptoms, given underlying susceptibility:

Ms : Fs ¼

Me Fe : ¼ Me : Fe  RRf rm rf

vs;m

In Fig. 2B, we begin with several values for Me:Fe ranging from 2 to 3.2. For each of these values, we plot Ms:Fs for RRf vs.m values ranging from 1 to 0.5, assuming that being female is a protective factor, associated with a relative risk below 1. These equations show that the relationship between observed M:F ratios and M:F ratios for underlying susceptibility to ASD depends on levels of diagnostic

Fig. 2. Calculated range of M:F ratios for A) expression of ASD symptoms compatible with diagnosis; and B) underlying susceptibility to ASD; assuming a 4:1 observed M:F ratio.

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bias and the relative effect of gendered social environment on symptom expression. Assuming reasonable values for these variables results in Ms:Fs ranging from 3:1 to much closer to 1:1, suggesting that underlying biologic susceptibility may well be approximately equally distributed with respect to biologic sex. Questions and strategies for research Clearly, the role of gender in ASD etiology and diagnosis is ripe for further research. With regard to diagnosis, questions remain as to whether, and to what extent, girls are under-diagnosed, which may vary by population, time period, case definition, and methods used for diagnosis or case ascertainment. It is difficult to imagine a scenario through which population-level screening or surveillance could be conducted in a manner truly blinded as to sex/ gender. Therefore, the best approach for population-based estimates probably derives from comparisons of community diagnosis with estimates for “true” ASD status obtained using more general criteria. It is also important to gain a better understanding of whether ASDs manifest differently in a subset of girls, and if so, to develop strategies for identifying this subset. An alternative approach would involve examining whether professional perception of ASD symptoms varies by sex/gender of the subject, e.g., by presenting vignettes that vary only in whether a girl or boy is described, an approach previously used to investigate race and SES in autism and ADHD diagnosis (Cuccaro et al., 1996). The role of perceived sex/gender in clinical assessment could be even more directly explored by presenting video footage of clinical interactions which are digitally manipulated to vary the apparent gender of the subject. Because parental efforts may be key in obtaining an ASD diagnosis, MeF disparity in ASD-specific concern elicited by potentially related symptoms or behaviors among parents is also relevant. The epidemiology of MeF differences in ASD has been reasonably well explored with respect to intellectual disability and related factors. Differences in M:F ratios for ASD across other social categories such as race, SES, or immigrant status have been less explored and could reflect variation in the social performance of gender, and a corresponding impact on developmental or diagnostic processes. It would be useful to position such analyses against a backdrop of information on variation in gendered socialization across these categories. It may also be informative to explore change over time, and across any of these categories attention should be paid to formally testing significance for any evidence of variation. Finally, variation by sex/gender in associated features, biomarkers, co-morbidities and developmental trajectories could be cataloged. Gaining a better understanding overall of heterogeneity in this ratio should provide clues about underlying mechanisms. Testing the hypothesis that female gender socialization acts as a protective factor for ASD will be theoretically and logistically challenging, and will need to include the following steps: 1) Identification of specific socially-based exposures associated with gender, particularly in infancy, that could hypothetically affect probability of ASD manifestation in a vulnerable infant; for example, interpretive versus instructive statements. 2) Qualitative and quantitative measurement of exposure to these factors in infants at risk for ASD, which will include observing interaction with caregivers in as naturalistic a setting as possible. In order to gain sufficient power, given low overall prevalence of ASDs, a prospective approach could focus on a high-risk population, such as “baby sibs” of children with ASD diagnoses. However, because the additional cases from such families will by definition arise from “multiplex” (more than one child affected) families, which may have different etiology than simplex families, results from this

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approach may not be widely generalizable. A retrospective casecontrol design could capitalize on archives of video footage of young children prior to diagnosis, but would require sufficient footage of typical interaction with caregivers and would not allow observation of a standard set of activities. 3) Assess association between exposures and outcome. Because exposures of interest are likely to be experienced by all infants to varying degrees, this will involve determining whether there is a threshold above which certain exposures become protective. The ultimate test of any such identified protective ‘exposures’ would be to incorporate them into the design of randomized intervention studies. If this approach was able to help identify elements of an effective very early intervention that could improve developmental trajectories for some children at risk of ASDs, it would be the ultimate proof of utility for this biosocial approach from a public health perspective. Conclusion The case of autism illustrates how gender theory can be used not just to more precisely illuminate the nature and etiology of malefemale health differentials, but also contribute to fundamental knowledge of specific developmental disorders, and the complex interaction of biology and the social environment. Our argument is fully biosocial, and our main points in advancing it are to articulate a model for autism, specifically for explaining the male-female disparities in prevalence, that does not exclude socialenvironmental variables, and is therefore more biologically satisfying; and to demonstrate concrete mechanisms whereby autism may become more prevalent in males as a result of social structures and processes related to gender. What we have presented suggests a domain of research questions and strategies specific to ASDs that have as of yet been little explored; and a theory of gender as a pervasive developmental environment with potential broader applicability to understanding differential health states for males and females, particularly those arising in early life. Acknowledgments The authors thank the Robert Wood Johnson Foundation Health & Society Scholars program and the Foundation for Worker, Veteran, and Environmental Health for financial support. We thank participants in the Gender and Health Working Group at Columbia University for their many helpful comments during the development of this paper. References Abrahams, B. S., & Geschwind, D. H. (2008). Advances in autism genetics: on the threshold of a new neurobiology. Nature Reviews Genetics, 9(5), 341e355. Amiet, C., Gourfinkel-An, I., Bouzamondo, A., Tordjman, S., Baulac, M., Lechat, P., et al. (2008). Epilepsy in autism is associated with intellectual disability and gender: evidence from a meta-analysis. Biological Psychiatry, 64(7), 577e582. Autism and Developmental Disabilities Monitoring Network, CDC. (2009). Prevalence of autism spectrum disorders - autism and developmental disabilities monitoring network, United States, 2006. MMWR Surveillance Summaries, 58(10), 1e20. Balaban, E. (2006). Cognitive developmental biology: history, process and fortune’s wheel. Cognition, 101(2), 298e332. Baron-Cohen, S. (2009). Autism: The empathizing-systemizing (e-s) theory. Annals of the New York Academy of Sciences, 1156, 68e80. Baron-Cohen, S., Knickmeyer, R. C., & Belmonte, M. K. (2005). Sex differences in the brain: implications for explaining autism. Science, 310(5749), 819e823. Bhasin, T. K., & Schendel, D. (2007). Sociodemographic risk factors for autism in a US metropolitan area. Journal of Autism and Developmental Disorders, 37(4), 667e677. Bilder, D., Pinborough-Zimmerman, J., Miller, J., & McMahon, W. (2009). Prenatal, perinatal, and neonatal factors associated with autism spectrum disorders. Pediatrics, 123(5), 1293e1300. Burnham, D. K., & Harris, M. B. (1992). Effects of real gender and labeled gender on adults’ perceptions of infants. Journal of Genetic Psychology, 153(2), 165e183.

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