Autism is a developmental disorder characterized by impairments in social interaction and communication, often accompanied by stereotypical or repetitive behaviors. As there are no definitive biological markers for the vast majority of cases, diagnosis relies on the recognition of a range of behavioral symptoms that vary greatly from case to case, that are increasingly heterogeneous, that are more difficult to isolate because age of diagnosis has declined, and that overlap with those of other childhood neuropsychiatric disorders. Despite hundreds of studies, we still do not know why autism incidence increased rapidly during the 1990s (Croen et al. 2002
) or why increased incidence is associated with marked spatial clustering (Mazumdar et al. 2010
). In this article, we identify a central mechanism that yields the increased prevalence and spatial clustering of measured autism and the decreasing age of diagnosis.
Substantial resources flow to families for the treatment of children diagnosed with autism—significantly more than for other childhood neuropsychiatric and developmental disorders.2
But in order to secure these resources for their children, parents have to recognize the behavioral symptoms of autism, identify and reach a physician capable of identifying autism, and learn how to navigate the complex world of state developmental service departments, school systems, and other service vendors. Obviously, this knowledge is achieved, not ascribed. Anecdotal evidence indicates that one of the ways in which parents learn how to navigate the world of autism is from other parents (Grinker 2007
). In other areas of health and medicine, the influence of community-based interpersonal social networks on individuals’ recognition and response to the onset of symptoms has been well documented (see Pescosolido and Levy 
and Rogers 
for reviews). It follows that parental awareness of autism and knowledge about strategies to secure resources for their children is likely to depend strongly on social interactions within the local social networks in which families are embedded (Pescosolido 1992
). These social networks arise out of focal points for interaction that are centered in neighborhoods—from close neighbors to neighborhood parks, stores, and preschools (Feld 1981
; Feld and Grofman 2009
In this article, we show that children living in very close proximity to a child previously diagnosed with autism are significantly more likely to be diagnosed with autism than are comparable children who lack such exposure.3
This fact could arise from four main sources: from shared toxicants, through the diffusion of a virus, as a by-product of neighborhood selection, or through the diffusion of information about autism through social networks.4
First, an environmental toxicant could be associated with increased risk of autism, and so a shared toxicological environment could create similar risks to children living near one another. This would be an environmental effect, if present. Second, the effect could be caused by the diffusion of a virus that was associated with autism either directly or indirectly by altering maternal immune response during pregnancy (Ashwood and Van de Water 2004
), the period most important to neurological development (Grandjean and Landrigan 2006
). Independent of the specific mechanism, this would be a viral effect, if present. A third mechanism could be compositional—specifically, known risk factors for autism may cluster within neighborhoods through selection dynamics. If parents at greater risk for a child with autism selected a particular kind of neighborhood to reside in, selection would account for the microlevel spatial clustering of autism. In fact, residential selection and sorting mechanisms do induce clustering on many risk factors for autism—this is the case for socioeconomic status, parental age, and access to health-care resources—all of which structure residential choices and are associated with autism (Massey and Denton 2001
; Morenoff 2003
; King and Bearman 2009b
Finally, meeting children with autism and having discussions with parents of children with autism could lead parents (of children not diagnosed with autism) to observe behavioral symptoms consistent with autism, to learn how to effectively identify and reach a physician, and to learn how to access and subsequently navigate services and service agencies. If these dynamics were at play, this would be a social influence effect.
There is widespread recognition in everyday circles that the measured prevalence of autism has increased dramatically over the past two decades. Nationwide, the prevalence of autism has increased from 4 cases per 10,000 people in 1989 to 67 cases per 10,000 people in 2000 (Ritvo et al. 1989
; CDC 2007
). In California—where autism prevalence is lower than the national average—the number of autism cases handled by the California Department of Developmental Services increased 634% between 1987 and 2003 (California Department of Developmental Services 2003
). Whether these increases signify an “epidemic” or an “epidemic of discovery” (Grinker 2007
) can be debated, but what is not really debatable is that the increase in autism incidence is very large, people now think about autism as part of the developmental landscape, and information about autism treatments and causes is now widespread.6
It is perhaps simple enough to say that beyond the stratospheric discussions of academics, autism increasingly appears in the everyday life of American families. Every hour in the United States, three children are diagnosed with autism.
Consequently, disentangling these competing accounts—whether the increase in measured prevalence, the spatial clustering of autism cases, and the shifting distribution of case severity arise from social influence, toxicological change, or viral transmission—is more than a simple intellectual exercise. In this article, we design a data structure and a series of critical tests to disentangle these explanations. To anticipate the main finding, our observations are consistent with a social influence process and inconsistent with other explanations.
Diagnostic Ambiguity, Increased Attention, and the Difficulty of Getting a Diagnosis
In the absence of unambiguous markers for autism, diagnosis is based primarily on the recognition and interpretation of behavioral symptoms. As autism incidence has increased, the behavioral symptoms associated with an autism diagnosis have become increasingly heterogeneous (Eyal et al., in press
). As Eyal and others have noted, the increasing heterogeneity of autism is encoded in a series of diagnostic changes built into the very idea of a spectrum. In the present context, symptoms vary widely across the autism spectrum and overlap markedly with other neurodevelopmental disorders.7
Making sense of the role that increasing heterogeneity—and diagnostic expansion—of autism has played in increasing prevalence has become a cottage industry. King and Bearman (2009a
) show that roughly 25% of the increased caseload in California arises from diagnostic change on the mental retardation (MR) pathway—specifically, the accretion of autism diagnoses yielding autism-MR comorbidity. More difficult is the estimation of the role that diagnostic expansion has played on the less severe side of the spectrum.8
A disproportionate share of the increased autism caseload in California (the setting from which our data are drawn) arises from the tails of this distribution, most markedly on the high-functioning tail.
If two decades ago the boundary between MR and autism was the most blurred, this is now the case for the highest-functioning autism, which diagnostically overlaps most with other disorders on the autism spectrum—PDD-NOS and Asperger’s syndrome—as well as learning disorders more generally. Even where considerable scientific care is exercised across multiple assessment tools, diagnostic ambiguity on the high end is striking. Charman et al. (2009
, p. 1236), for example, report that “within our own study where all design and methodological factors are invariant, our prevalence estimates varied by up to 4.5 times from the strictest to the least demanding set of diagnostic criteria.” Thus, for both those on the MR-autism border and those in the highest-functioning range, finding the right diagnostician and accessing the right service providers are likely strongly related to the probability of acquiring an autism diagnosis.
Children diagnosed with autism who have severe disability are more similar to those whose primary diagnosis is (etiologically unknown) MR. In our data, the symptom-expression overlap can be observed by noting that the assessment scores for autism symptoms at first diagnosis for individuals with only an MR diagnosis completely envelop those for individuals with a full-syndrome autism diagnosis. As with the high-functioning end of the distribution, differential diagnosis between autism and MR can be difficult, particularly in young children. Overlapping severity scores (from observation of behavioral symptoms) at first diagnosis between autism and MR confirm the shady boundary for those children whose MR—should it be diagnosed—is of unknown etiology. The fact that diagnosis on the border can be difficult is compounded by differential attention dynamics associated with MR and autism and differential benefits arising for children with autism relative to those with MR (Goffman 1963
; Link and Phelan 2001
). While autism was highly stigmatized before the 1970s, with increasing attention this situation appears to have changed radically.
We know very little about the process by which autism diagnoses are obtained, other than from a scattering of autobiographical accounts and selective parent surveys. All of these, however, suggest that diagnoses can be excruciatingly difficult to obtain.9
For instance, one decade-old survey found that upon first consultation, parents of children with autism were three times more likely to be told that there was “no problem [with their child]” than to be given a diagnosis (Howlin and Moorf 1997
). In the 10 years since that survey was administered, the situation has improved for older children whose presentation at first diagnosis is most severe, which suggests that the community and school-based ascertainment regime is able to identify and secure treatment for children most affected by autism (Howlin and Asgharian 1999
; Eyal et al., in press
). These developments notwithstanding, parents of younger children whose presentation is less clear rely on information from other parents as their most effective guides. For example, one mother of young twins residing in California reported, “My neighbor gave me the name of the only doctor in [town name deleted] who would give my son an autism diagnosis” (personal correspondence, February 10, 2008). This sentiment was echoed in numerous parent-based autism support groups.
If parents now seek information to more efficiently secure a diagnosis of autism, the historical pattern was the obverse. For several decades after its initial identification in 1943, autism was thought to be the by-product of the response of children to the double bind of cold and ineffective parenting, a view initially promoted by Kanner (1949)
and later by Bettelheim (1967)
with the imagery of the “refrigerator mother.” Throughout the 1950s and 1960s, whole generations of American parents of children with autism suffered stigmatization (Bettelheim 1967
). As noted earlier, conversely and simultaneously, MR was losing much of its stigma as a powerful advocacy movement took hold and garnered a wealth of resources for people with MR. In this regard, MR advocates pioneered a resource niche for people with developmental disabilities. Between 1948 and 1966, the number of children with MR in public school classrooms increased by a factor of five because of the political and administrative success of MR advocates in securing special education classes (Trent 1994
). In 1973, the Rehabilitation Act, a precursor to the Individuals with Disabilities Education Act, guaranteed the right to an appropriate education for people with a recognized disability, which included MR but not autism. In addition, Medicaid and Medicare coverage, Supplemental Security Income, and a guarantee of services were secured (Eyal et al., in press
). While MR advocates were making progress in securing rights and benefits for people with MR, virtually no resources were available to individuals with autism. Autism was not excluded by omission—rather, MR advocates worked hard to ensure that autism was disqualified from federal legislation (Sullivan 1979
With hindsight, we can recognize that autism was increasingly destigmatized through the mobilization efforts of Bernard Rimland and the National Society for Autistic Children (NSAC), whose work refuted psychogenic theories of autism and set the stage for the research program that would identify autism as a neurological disorder (Dolnick 1998
). Deploying mounting empirical evidence, NSAC was able to ensure that autism was recognized in the Developmental Disability Act reauthorization. In the decades to follow, two processes already underway were sewn up, allowing the autism and MR stigmas to reverse. First, autism was successfully reclassified from a severe emotional disorder to a developmental disability. Second, resources directed to autism research and treatment and care for individuals with autism expanded radically, fueled both by increasing autism prevalence and an expanding advocacy effort. For example, in 2006, the revenue raised by the largest autism advocacy organization was nearly 10 times the funding reaped by the largest intellectual disability organization, despite the higher prevalence of MR ($33 million vs. $3.4 million; National Center for Charitable Statistics 2008
). In the research world, CDC funding of autism activities increased from $2.1 million in 2000 to about $16.7 million in 2005 (GAO 2006
), and National Institutes of Health funding for autism increased more than fourfold, from $22 million in 1997 to $108 million in 2007 (Nature 2007
). In contrast, total public financial support for MR increased only 16% between 2000 and 2004 (Braddock 2007
The relative destigmatization of autism and the enhanced resources devoted to understanding autism etiology, treatment, and education meant that parents who in the 1960s and 1970s may have deployed their resources to avoid a stigmatized autism diagnosis no longer needed to do so.10
In fact, facing likely strong incentives for an autism diagnosis, parents seeking to provide benefits to their children would have reason to deploy their resources for an autism diagnosis. But having resources and knowing how to deploy them are two different things. Here we provide evidence that knowledge diffuses through local social networks, enabling parents to effectively deploy resources, including but not limited to finding (and collaborating with) physicians whose diagnostic practices are long known to be influenced by patient understandings (cf. Freidson 
for the classic argument).
Spatial Distance and Social Interactions
Social interactions are strongly conditioned by the spatial distance between individuals (Festinger, Schachter, and Back 1950
; Haynes 1974
; Gonzalez, Hidalgo, and Barabasi 2008
). In this context, the relevant interactions are between parents of young children and other parents of young children, where conversation turns to shared interests such as child development, pediatricians, sleep deprivation, and the like. Parents meet other parents at schools, shops, playgrounds, and other focal points, the vast majority of which are rooted in local communities (Fischer 1982
; Huckfeldt 1983
; Campbell and Lee 1992
; Guest and Wierzbicki 1999
; Small 2009
As a consequence, the movement of persons in their everyday life (not counting the commute to work, which can involve significant distance) tends to be highly spatially restricted. As a recent large-scale study reports, human movement is characterized by a predominance of short-distance travel (Gonzalez et al. 2008
). This feature of human activity fundamentally shapes our patterns of social interactions. There are reasons to expect that neighborhood interactions are important to parents with young children, net of the general finding that many interactions occur in delimited spatial context: observational data strongly suggest that parents of small children associate with other parents of small children at greater than expected rates. The typical distance that parents travel with their young children is limited. In the epicenter of our study area—Los Angeles, California—the vast majority (85%) of adults define their neighborhood to be an area that is within a 15-minute walk from home (approximately one kilometer to 0.68 miles), and having children is negatively associated with the size of the neighborhood reported (Sastry, Pebley, and Zonta 2002
). Focal meeting points can typically be found within one to two kilometers in California: the median distance to the nearest playground or local park or preschool is 0.7 kilometers, and the distance to the nearest pediatrician is 1.1 kilometers (see ). Given the spatial structuring of interactions, if information about autism is flowing through interpersonal networks, close proximity to a child with autism should increase the likelihood of information diffusion (Rogers 2003
Spatial Distribution of Focal Points in California
Disentangling Social, Viral, Environmental, or Compositional Causes of Spatial Clustering
The tails of the autism severity distribution have increased the most precipitously. Specifically, holding the 1992 severity distribution constant, we can observe that the proportion of children scoring above the first decile of the 1992 distribution has more than doubled, moving from 10% (a mathematical truism) to 22%. Likewise, the bottom decile of the severity distribution has increased 60% over the same period, covering the birth cohorts of 1992–2003. Consequently, any account of the mechanism by which autism prevalence has increased needs to explain simultaneous growth on both sides. This requirement is challenging. If a single mechanism were at play, it would have to operate disproportionately on the tails of the distribution and not on the middle. While this requirement does not rule out toxicants, viruses, or compositional effects, it raises the stakes for each of the explanations competing with information diffusion and sets up a series of tests—the results of which can point in one or another direction as the most likely.12
A social influence dynamic should drive autism diagnoses for children at both extremes of the severity distribution. For parents with children on the border of an autism-MR diagnosis, we expect that exposure to children (and their parents) with autism—who are perceived as having a greater chance for recovery and have greater access to resources and services—should be associated with a decreased likelihood of a sole MR diagnosis and an increased likelihood of an autism-MR diagnosis. With respect to the less severe tail of the distribution, social influence should play a significant role in diffusing information about symptoms, services, and access. The same information that helps parents understand how to secure the most advantageous opportunities for their children on the low-functioning tail of the distribution should operate to lead parents to obtain an autism diagnosis—rather than a diagnosis of PDD-NOS or the like—on the less severe side of the distribution. In sum, social influence is most likely to be observed for high- and low-functioning individuals, where interpretation of symptoms is most difficult, ascertainment from service agencies without parent agency is most unlikely, and knowledge of service systems and pediatricians is most important. In this regard, a social influence effect should be stronger for younger children (age 3) than for those who are school-age (ages 5–6), both because of the more ambiguous symptom presentation and the absence of institutional ascertainment.
In contrast, the sociological null hypothesis—that microlevel spatial clustering of autism arises as an unintended by-product of residential sorting dynamics—does not make a prediction with respect to the probability of diagnosis and severity. If the proximity finding were driven by selection dynamics, spatial clustering would be uninformative, and individual-level risk factors for autism would be dispositive. We show that controlling for known individual-level and community-level risk factors for autism does not mitigate the increased probability of acquiring an autism diagnosis associated with living very close to a child with autism. Thus, our findings are not consistent with a compositional effect. They suggest, conversely, that the social characteristics seen as associated with autism for which there is no known mechanism are by-products of local diffusion dynamics.13
Falsifying the idea that spatial clustering of autism is the simple byproduct of residential sorting, however, does not help clarify whether the clustering of autism is associated with a localized environmental toxicant or the diffusion of a virus. Both toxicants and viruses could operate at the microlevel of the close neighborhood, and in fact we would expect virus diffusion to have a significant geographical foundation since viral transmission most routinely involves person-to-person contact (Klovdahl, Graviss, and Muser 2002
). Turning to toxicants first, it is possible that a single toxicant could act largely on the tails of the severity distribution, but it is not likely. Environmental toxicity mechanisms do not have clear predictions concerning differential effects by severity; a single toxicant with such differential effects, operating at very local geographic scales, has not been identified for other developmental disorders.14
More problematic for an environmental toxicity account is the fact that the relationship we find between proximity and subsequent transition to autism is observed across a wide variety of neighborhoods—from those primarily agricultural to those primarily suburban, where the mix of toxicants present are quite different.
At first glance, viruses are more challenging, since they are passed from person to person through social contact, the same mechanism by which information is diffused. But a virus is very unlikely to cause a reduction of MR, so the negative impact of residing very close to a person with autism on the probability of a sole MR diagnosis cannot be ascribed to the operation of a virus. And as with an environmental toxicant, there is no reason to think that the effect of a virus would concentrate on the high-functioning tail of the severity distribution. Viruses and toxicants, all things equal, should express themselves across the severity spectrum.
In summary, it is difficult to disentangle local social influence from local environmental causation, but it is not impossible. Here we design a series of tests to identify mechanisms that can disentangle environmental and viral dynamics from social influence. By focusing on the microcontexts in which parents and children interact, we identify the mechanism by which social influence results in new diagnoses. Because knowledge about autism diffuses through local networks and increases the probability of diagnosis, local network dynamics are shown to be associated with the neighborhood clustering of autism (net of selection), the decreasing age of diagnosis, and the increased prevalence of autism.
As noted earlier, alternative explanations to social influence that can generate an association between the proximity to a child with autism and subsequent diagnosis with autism need to be considered. In order to rule out compositional causation, we control for the effects of a range of variables measuring individual sociodemographic status and parental risk factors, autism service and advocacy organizations, pediatricians, and access to health-care resources. In order to eliminate the effect of unobserved variables, we then turn to a fixed-effects model. While environmental toxicity and viral mechanisms may explain the positive relationship between proximity and subsequent autism diagnosis, it is unlikely that they would lead to a change in the probability of a sole MR diagnosis. We thus consider the effects of close proximity to an individual with autism on the transition to sole MR. We show that proximity reduces sole MR diagnoses. The flip side of this argument is that the proximity effect should be particularly strong for a comorbid autism-MR diagnosis. We show that it is the case.
We then consider the severity of autism symptoms. A social diffusion process is more likely to affect nonsevere autism at first evaluation. In contrast, mechanisms based on the self-selection of high-risk parents to neighborhoods, environmental toxicity, and viral spread do not have clear predictions concerning such differential effects by severity. Our analyses show that the proximity effect is strongest for nonsevere autism cases. Using propensity score matching, we further show that when two children displayed the same level of autism symptoms, the one who lived closer to a child with autism was more likely to be subsequently diagnosed with autism, while the other was more likely to be diagnosed with sole MR.
We then consider age of diagnosis. Early diagnosis is believed to lead to better treatment outcomes, but it is also more difficult, given the greater variation in the level of development at a young age (Committee on Children with Disabilities 2001
; Rutter 2006
). Parents who have better access to information are more likely to ensure an early diagnosis than are those without access to information. As for the case of nonsevere autism, a proximity effect should matter most for early diagnoses, while it should be less important at a later developmental stage and when the child has entered the school system. This turns out to be the case.
The social diffusion of information should lead to similar referral sources (Granovetter 1995
; Fernandez, Castilla, and Moore 2000
), and we show that children who were diagnosed with autism were more likely to have the same referral source as their nearest neighbor with autism. There are no reasons why environmental toxicants or viruses should yield homophily of referral sources or should lead equally severely affected children to two different diagnoses depending on how far their nearest neighbor with autism lived.
Finally, we consider two additional tests that assess the robustness of the underlying social influence mechanism we identify. First, we conduct an “edge analysis” that allows us to exploit the fact that for some families, the nearest neighbor with autism resides in a different school district. Controlling for all of the individual and school district factors that are associated with autism ascertainment, we compare these families with families whose nearest neighbor with autism resides within the same school district. Environmental toxicants should be largely insensitive to social boundaries like school districts, but social influence should not be. We find that while parents’ proximity to a child within their school district increases the chances that their child will be diagnosed with autism, equally close proximity across districts has no effect.
Second, we assess the sensitivity of social influence to duration of contact that arises from residential proximity. Movers into (and out of) communities should be less sensitive to social influence than stayers should, all things being equal. We consider how residential mobility affects exposure to children with autism (either because a child with autism moves and becomes a closest neighbor to a new child or because a family moves and is closer to a child with autism). As expected, duration of exposure is positively associated with increased risk of a subsequent autism diagnosis.
While our analyses do not prove that information diffusion yields autism diagnoses, the results are consistent with that hypothesis, hold across a series of robustness checks, and are consistent with a well-documented body of research that identifies a similar mechanism across a wide variety of lay responses to health problems (Pescosolido and Levy 2002
). Information diffusion therefore provides a parsimonious account for three important observations: the spatial clustering, the earlier diagnoses, and the increased prevalence of autism. Finally, anticipating a central question to be considered later, the population attributable fraction associated with the dynamic we identify in this article is 16%. Another way of thinking about this is that if this mechanism were absent, we would observe, at the least, a 16% decline in the autism caseload. We return to this issue in the discussion section, where we consider what piece of the pie social influence plays in the increased prevalence of measured autism.