An article published last year in the American Journal of Sociology argues that knowing someone in your neighborhood who has an autistic child makes it likelier that your child, too, will be diagnosed with autism.
The authors' explanation for this is not that autism is "catching," or that people with autistic children are likelier to live in certain places (they investigated both of those possibilities, along with the possibility that some environmental contaminant makes some neighborhoods have more autistic children than others); it's that parents talk to each other about their children, and if one family has an autistic child, and has gotten that child diagnosed and enrolled in services, those parents will tell other parents about that process, and thereby make it easier for other parents to do the same thing. (They may also, by describing their autistic children's behavior, make it more likely that other parents will consider the possibility that their child may be autistic).
What these researchers --- sociologists Peter Shawn Bearman, Marissa D. King (who has conducted a lot of research into autism demographics) and Ka-Yuet Liu --- did to derive this explanation was look at data on all children born in California since 1997, with younger siblings also born in California. (They needed to restrict their sample to children with younger siblings to get information on where the firstborn children spent their early years --- if the family's residence is the same for every child born to that family, the researchers could assume that the firstborn lived in the same place during the years between hir own birth and the birth of hir youngest sibling. That's the information they wanted --- where families lived during their children's early years). This gave them a pool of 533,244 children whose families lived at the same address when they were born as when their younger siblings were born. Then, they used data from the California Department of Developmental Services (I've written about this data set before, in a different context --- it's been used in a lot of studies investigating autism prevalence) to determine which of those children got an autism diagnosis, or a diagnosis of intellectual disability*, between the ages of 2 and 6.
They created a statistical model to see if they could accurately predict which children would end up with a diagnosis of Autistic Disorder**. Their model, which is supposed to predict the odds of any given child being diagnosed with autism in a given year, t, uses the physical proximity of a child diagnosed with autism in the previous year, t - 1, as its predictive variable. They divide this continuous measure --- how far away does [other child with autism] live? --- into six discrete groups: 1 - 250 m; 251 - 500 m; 501 m - 1 km; 1001 m - 2 km; 2001 m - 5 km; and all distances over 5 km. (They used a continuous measure of distance, too). They controlled for a slew of other variables --- sex, maternal age, socioeconomic status (measured by mother's educational attainment and whether she received state assistance with pregnancy- and birth-related medical expenses), proximity to autism-related healthcare services, neighborhood population density, and neighborhood median income --- and predicted that the odds of a child being diagnosed with autism would increase the closer they lived to a child already bearing that diagnosis, and that the odds of a child being diagnosed with intellectual disability independent of any other condition would decrease with increasing proximity to a child diagnosed with autism. They also predicted a pattern in what kind of autistic children would show the greatest change in likelihood of diagnosis: they thought that the "high[est]-functioning" children, who scored the lowest on their measure of autism severity***, would show the greatest effect, along with the youngest children (age 3 or younger). Their reasoning is that, since these are the most ambiguous cases, whether or not their parents knew someone else with an autistic child, and thus got it into their heads to consider whether their child might also be autistic, would make the most difference, since there are less likely to be really obvious, textbook signs of autism. (Or, if signs are present, say, in the very young children, knowing another family with an autistic child might predispose the parents to have their child evaluated sooner, rather than waiting to see if, say, the child is just a late talker).
Sure enough, they found the patterns they'd predicted.
(Figure 2A, in Bearman, King and Liu, 2010. The x-axis shows distance, in kilometers, from the nearest home with a child with an autism diagnosis, and the y-axis shows the probability that any given child living at that distance will be diagnosed with autism within the next year).
Here's a different representation of the same data:
(Figure 2B, in Bearman, King and Liu, 2010. The x-axis still shows distance, but the y-axis, instead of showing probability of an autism diagnosis in the next year, now shows the odds ratio between each distance category and the reference category, which is 501 m - 1 km. The five different dots in each distance category represent the five different statistical models that were used. You can see that, compared to the middle distance category, the children in the two shorter-distance categories were more likely to receive autism diagnoses, while the children in the three longer-distance categories were less likely to be diagnosed.)
[W]e also report the effects of proximity as a categorical variable. All odds ratios (ORs) are relative to the reference category of 501 meters - 1 kilometer. ... In figure 2B, we can observe that residing in close proximity to a child diagnosed with autism increases one's chance of being diagnosed with autism in the subsequent year. Compared with children who are 501 meters - 1 kilometer away from their nearest neighbor with autism, those in close proximity (1 - 250 meters) to a child with autism have a 42% higher chance of being diagnosed with autism in the subsequent year. Proximity of 201 - 500 meters increases the chance by 22%. In contrast, being farther away from a child with autism reduces the chance of a diagnosis. Although the last three categories were all significantly associated with the decrease (-21%, -36%, and -49%), there were no statistically significant differences among these three categories. This is consistent with the results in figure 2A, which show that the effect of proximity is strongest within one kilometer, followed by a flat tail.
... and 2) being diagnosed with autism at varying levels of severity:(Figure 3, in Bearman, King and Liu, 2010. The x-axis shows the percent increase in odds of receiving an autism diagnosis associated with proximity to a recently-diagnosed autistic child; the y-axis shows the child's level of disability as assessed by the state's Client Development Evaluation Reports. The highest-scoring, or least impaired, 20% are compared to the bottom, or most seriously impaired, 20%, and to the middle 60%. You can see that the "highest-functioning" group shows a stronger effect of proximity to another autistic child on their chances of being diagnosed than either the middle group or the "lowest-functioning" group, whose chances of being diagnosed with autism were least affected by proximity to another recently-diagnosed autistic child.)
Liu KY, King M, & Bearman PS (2010). Social influence and the autism epidemic. AJS; American journal of sociology, 115 (5), 1387-434 PMID: 20503647
**They only counted children in this category, and not any other pervasive developmental disorder, because only Autistic Disorder is a category eligible for state services. The researchers felt that parents' main motivation in pursuing a diagnosis would be to gain access to services, so they didn't bother tracking the diagnoses that wouldn't be useful toward that end.
***Their severity measure involved nine items from the California DDS's Client Development Evaluation Report (CDER) on each child. (Here is a PDF of the complete CDER form, if you want to go and look at what exactly these items are measuring). The overall measure was an equally-weighted average of scores on three "scales" --- communication (made up of 3 CDER items: item # 58, word usage; item # 61, receptive language; and item # 62, expressive language), social interaction (made up of 5 CDER items: item # 27, peer interaction; item # 28, interaction with nonpeers; item # 29, friendship formation; item # 30, friendship maintenance; and item # 31, participation in social activities), and a single CDER item, # 42, measuring repetitive and stereotyped behaviors. (I find it a bit odd that they didn't include self-injurious behavior as part of their severity measure --- there are two items on the CDER addressing that, and the presence or absence of self-harm seems to me a more important measure of how "severe" someone's problems are than, say, being able to interact "normally" with one's peers.)