The spatial distribution of known predictors of autism spectrum disorders impacts geographic variability in prevalence in central North Carolina
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.
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.
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).
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.
Full-textDOI: · Available from: Kate Hoffman, Feb 12, 2015
- SourceAvailable from: Marco Baldini
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- "Changes in the model-selected optimal span of analysis with and without adjustment were observed and investigated, because a smaller optimal span size is selected when data presents more peaks. A change in optimal size after the inclusion of a covariate can indicate spatial confounding (Hoffman et al., 2012). "
ABSTRACT: Introduction The study investigated the geographic variation of mortality risk for hematological malignancies (HMs) in order to identify potential high-risk areas near an Italian petrochemical refinery. Material and methods A population-based case-control study was conducted and residential histories for 171 cases and 338 sex- and age-matched controls were collected. Confounding factors were obtained from interviews with consenting relatives for 109 HM deaths and 267 controls. To produce risk mortality maps, two different approaches were applied. We mapped (1) adptive kernel density relative risk estimation (KDE) for case-control studies which estimates a spatial relative risk function using the ratio between cases and controls’ densities, and (2) estimated odds ratios for case-control study data using generalized additive models (GAMs) to smooth the effect of location, a proxy for exposure, while adjusting for confounding variables. Results No high-risk areas for HM mortality were identified among all subjects (men and women combined), by applying both approaches. Using the adaptive KDE approach, we found a significant increase in death risk only among women in a large area 2–6 km southeast of the refinery and the application of GAMs also identified a similarly-located significant high-risk area among women only (global p-value<0.025). Potential confounding risk factors we considered in the GAM did not alter the results. Conclusion Both approaches identified a high-risk area close to the refinery among women only. Those spatial methods are useful tools for public policy management to determine priority areas for intervention. Our findings suggest several directions for further research in order to identify other potential environmental exposures that may be assessed in forthcoming studies based on detailed exposure modeling. Keywords: Hematological malignancies, Disease mapping, Generalized Additive Models (GAMs), Kernel density estimation
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ABSTRACT: Autism spectrum disorders (ASD) are disorders of the central nervous system characterized by impairments in communication and social reciprocity. Despite thousands of studies on this topic, the etiopathogenesis of these disorders remains unclear, apart from a general belief that they derive from an interaction between several genes and the environment. Given the mystery surrounding the etiopathogenesis of ASD it is impossible to plan effective preventive and treatment measures. This is of particular concern due to the progressive increase in the prevalence of ASD, which has reached a figure as high as 1:88 children in the USA. Here we present data corroborating a novel unifying hypothesis of the etiopathogenesis of ASD. We suggest that ASD are disorders of the immune system that occur in a very early phase of embryonic development. In a background of genetic predisposition and environmental predisposition (probably vitamin D deficiency), an infection (notably a viral infection) could trigger a deranged immune response which, in turn, results in damage to specific areas of the central nervous system. If proven, this hypothesis would have dramatic consequences for strategies aimed at preventing and treating ASD. To confirm or refute this hypothesis, we need a novel research approach, which unlike former approaches in this field, examine the major factors implicated in ASD (genetic, infections, vitamin D deficiency, immune system deregulation) not separately, but collectively and simultaneously.Medical Hypotheses 04/2013; 81(1). DOI:10.1016/j.mehy.2013.04.002 · 1.07 Impact Factor
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ABSTRACT: We investigated differences in the geographic distribution of autism spectrum disorders (ASD) over time in central North Carolina with data from the Autism and Developmental Disabilities Monitoring Network. Using generalized additive models and geographic information systems we produced maps of ASD risk in 2002-2004 and 2006-2008. Overall the risk of ASD increased 52.9 % from 2002-2004 to 2006-2008. However, the magnitude of change in risk was not uniform across the study area; while some areas experienced dramatic increases in ASD risk (>400 %), others experienced slight decreases. Generally, areas with the lowest risk in 2002-2004 experienced the greatest increases over time. Education and outreach efforts in North Carolina expanded during this period, possibly contributing to the observed leveling of risk over time.Journal of Autism and Developmental Disorders 08/2013; DOI:10.1007/s10803-013-1907-7 · 3.06 Impact Factor