The spatial structure of autism in California, 1993–2001

Abstract

This article identifies significant high-risk clusters of autism based on residence at birth in California for children born from 1993 to 2001. These clusters are geographically stable. Children born in a primary cluster are at four times greater risk for autism than children living in other parts of the state. This is comparable to the difference between males and females and twice the risk estimated for maternal age over 40. In every year roughly 3% of the new caseload of autism in California arises from the primary cluster we identify—a small zone 20 km by 50 km. We identify a set of secondary clusters that support the existence of the primary clusters. The identification of robust spatial clusters indicates that autism does not arise from a global treatment and indicates that important drivers of increased autism prevalence are located at the local level.

Introduction

Autism (DSM IV-299.0) (APA, 1994) is a developmental disorder that impairs social interaction, communication and predisposes children to restrictive and repetitive behaviors. Between 1992 and 2006, the autism caseload in California increased by 598% (Newschaffer et al., 2007). Nor is California unique: where comparable data are available, comparable increases are observed (Newschaffer et al., 2007). These striking increases are associated with equally striking dissensus as to cause. While there is evidence of heritability, no marker for autism is associated with more than 10–15% of all cases, and prevalence has increased far too rapidly to be accounted for by fundamental changes in the human genome (Folstein and Rosen-Sheidley, 2001). In the scientific community, diagnostic change and/or expansion, increased exposure to environmental toxins, demographic change, shifting prenatal and obstetric practices including the advent of new reproductive technologies, and complex social and environmental interactions with genetics are thought to play a role in the increased prevalence of autism (Ming et al., 2008; Palmer et al., 2006; Palmer and Wood, 2009; Waldman et al., 2008; Windham et al., 2006; Ozand et al., 2003; Kolevzon et al., 2007; Glasson and Bower, 2004). Although credible scientific studies provide no support, the belief that vaccinations cause autism is widely held in the lay community (Florida Institute of Technology, 2008). This article identifies local spatial clusters of excess risk at birth for autism—thus pointing to the presence of local social and/or environmental factors as driving increased autism prevalence. By implication, it challenges arguments that propose causal agents that are distributed at random with respect to space.

Social and epidemiological research has proposed numerous individual-level risk factors as salient for autism (Glasson and Bower, 2004; Kolevzon et al., 2007), yet the evidence for specific risk factors is often contradictory, in part arising from temporally and spatially heterogeneous study populations (King et al., 2009). Most critically, the majority of risk factors identified in the literature explain little of the overall variance. Two risk factors—being male and having older parents—have been established as definitive (Newschaffer et al., 2007, Schubert, 2008). Evidence for the impact of other factors has been weak and inconclusive.

Historically, the discovery of spatial structures for diseases has been important for identifying mechanisms, and for falsification of competing hypotheses (Jacquez, 2004; Buck, 1975; Wagner, 1980). If risk is spatially random or universal, then—after adjusting for population heterogeneity with respect to known causes—there will be no significant spatial variation in prevalence. Consequently, spatial clusters of increased autism should be absent (Fombonne, 2003).On the other hand, the identification of local spatial clusters after adjustment for known causes provides powerful evidence in support of the existence of spatially non-random or non-universal etiological factors that could cause autism or autism diagnosis. Specifically, the observation of spatial clustering of autism cases at residence at birth indicates that a process that operates at a local scale is associated with amplified prevalence. In contrast, the observation of spatial clustering by residence at diagnosis could indicate selection into neighborhoods for services or the non-random diffusion of diagnostic regimes. Consequently, the spatial analysis of residence at birth is preferable to the spatial analysis of residence at diagnosis for identifying potential causes of autism.

Space, as a proxy for exposure, can also serve as a powerful exploratory tool for generating hypotheses. Once a reliable risk map is constructed, hypotheses can be generated about possible exposures. This is especially the case if clusters of significantly increased risk are observed. It is important that a cluster investigation be carried out with care and scientific rigor. All too commonly cluster investigations are driven by media reports, hearsay and anecdotal evidence. For example, the two “autism clusters” reported previously were identified on the basis of observations made by parents (Baron-Cohen et al., 1999; London and Etzel, 2000). In the US case, the perception of a cluster was the result of diagnostic misclassification and exhaustive case finding methods (Bertrand et al., 2001). In contrast, we use a statistically rigorous method—Kulldorff's Spatial Scan Statistic (Kulldorff, 1997), to demarcate clusters of high risk. Kulldorff's SaTScan identifies a single statistically most likely “primary” cluster, and a list of secondary—significant, but less likely—clusters. We systematically search for clusters of sole autism (autism without other co-morbid conditions) for each year for the birth cohorts 1993 to 2001 in California. We map the clusters and their spatial stability over time.