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Open Access Research

The spatial distribution of known predictors of autism spectrum disorders impacts geographic variability in prevalence in central North Carolina

Kate Hoffman1*, Amy E Kalkbrenner12, Veronica M Vieira34 and Julie L Daniels1

Author Affiliations

1 University of North Carolina, Gillings School of Global Public Health, CB #7435, Chapel Hill, NC, 27599, USA

2 University of Wisconsin at Milwaukee, Zilber School of Public Health, 3230 E. Kenwood Blvd, Milwaukee, WI, 53211, USA

3 Boston University School of Public Health, 715 Albany St, Boston, MA, 02118, USA

4 School of Ecology, University of California, Irvine, CA, 92617, USA

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Environmental Health 2012, 11:80  doi:10.1186/1476-069X-11-80

Published: 31 October 2012

Abstract

Background

The causes of autism spectrum disorders (ASD) remain largely unknown and widely debated; however, evidence increasingly points to the importance of environmental exposures. A growing number of studies use geographic variability in ASD prevalence or exposure patterns to investigate the association between environmental factors and ASD. However, differences in the geographic distribution of established risk and predictive factors for ASD, such as maternal education or age, can interfere with investigations of ASD etiology. We evaluated geographic variability in the prevalence of ASD in central North Carolina and the impact of spatial confounding by known risk and predictive factors.

Methods

Children meeting a standardized case definition for ASD at 8 years of age were identified through records-based surveillance for 8 counties biennially from 2002 to 2008 (n=532). Vital records were used to identify the underlying cohort (15% random sample of children born in the same years as children with an ASD, n=11,034), and to obtain birth addresses. We used generalized additive models (GAMs) to estimate the prevalence of ASD across the region by smoothing latitude and longitude. GAMs, unlike methods used in previous spatial analyses of ASD, allow for extensive adjustment of individual-level risk factors (e.g. maternal age and education) when evaluating spatial variability of disease prevalence.

Results

Unadjusted maps revealed geographic variation in surveillance-recognized ASD. Children born in certain regions of the study area were up to 1.27 times as likely to be recognized as having ASD compared to children born in the study area as a whole (prevalence ratio (PR) range across the study area 0.57-1.27; global P=0.003). However, geographic gradients of ASD prevalence were attenuated after adjusting for spatial confounders (adjusted PR range 0.72-1.12 across the study area; global P=0.052).

Conclusions

In these data, spatial variation of ASD in central NC can be explained largely by factors impacting diagnosis, such as maternal education, emphasizing the importance of adjusting for differences in the geographic distribution of known individual-level predictors in spatial analyses of ASD. These results underscore the critical importance of accounting for such factors in studies of environmental exposures that vary across regions.

Keywords:
Autism spectrum disorders (ASD); Intellectual disability (ID); Spatial analysis; Disease mapping; Generalized additive models (GAMs); Geographic information systems (GIS)