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At the heart of spatial epidemiology is the need to describe and understand variation in population health. In this review and introduction to the themed issue on “Spatial Analysis and GIS in Epidemiology,” we present theoretical foundations and methodological developments in spatial epidemiology, discuss spatial analytical techniques and their public health applications, and identify novel data sources and applications with the potential to make epidemiology more consequential. Challenges with using georeferenced data are also explored, including dealing with small sample sizes, missingness, generalizability, and geographic scale. Given the increasing availability of spatial data and visualization tools, we have an opportunity to overcome traditionally siloed fields and practice settings to advance knowledge and more appropriately respond to emerging public health crises.

The field of spatial epidemiology has evolved rapidly in the past 2 decades. This study serves as a brief introduction to spatial epidemiology and the use of geographic information systems in applied research in epidemiology. We highlight technical developments and highlight opportunities to apply spatial analytic methods in epidemiologic research, focusing on methodologies involving geocoding, distance estimation, residential mobility, record linkage and data integration, spatial and spatio-temporal clustering, small area estimation, and Bayesian applications to disease mapping. The articles included in this issue incorporate many of these methods into their study designs and analytical frameworks. It is our hope that these studies will spur further development and utilization of spatial analysis and geographic information systems in epidemiologic research.

Purpose Many types of cancer have an underlying spatial incidence distribution. Spatial model selection methods can be useful when determining the linear predictor that best describes incidence outcomes. Methods In this article, we examine the applications and benefits of using two different types of spatial model selection techniques, Bayesian model selection and Bayesian model averaging, in relation to colon cancer incidence in the state of Georgia, United States. Results Both methods produce useful results that lead to the determination that median household income and percent African American population are important predictors of colon cancer incidence in the Northern counties of the state, whereas percent persons below poverty level and percent African American population are important in the Southern counties. Conclusions Of the two presented methods, Bayesian model selection appears to provide more succinct results, but applying the two in combination offers even more useful information into the spatial preferences of the alternative linear predictors.

Purpose Numerous studies establish associations between adverse perinatal outcomes/complications and autism spectrum disorder (ASD). There has been little assessment of population attributable fractions (PAFs). Methods We estimated average ASD PAFs for preterm birth (PTB), small for gestational age (SGA), and Cesarean delivery (CD) in a U.S. population. Average PAF methodology accounts for risk factor co-occurrence. ASD cases were singleton non-Hispanic white, non-Hispanic black, and Hispanic children born in 1994 (n = 703) or 2000 (n = 1339) who resided in 48 U.S. counties included within eight Autism and Developmental Disabilities Monitoring Network sites. Cases were matched on birth year, sex, and maternal county of residence, race-ethnicity, age, and education to 20 controls from U.S. natality files. Results For the 1994 cohort, average PAFs were 4.2%, 0.9%, and 7.9% for PTB, SGA, and CD, respectively. The summary PAF was 13.0% (1.7%-19.5%). For the 2000 cohort, average PAFs were 2.0%, 3.1%, and 6.7% for PTB, SGA, and CD, respectively, with a summary PAF of 11.8% (7.5%-15.9%). Conclusions Three perinatal risk factors notably contribute to ASD risk in a U.S. population. Because each factor represents multiple etiologic pathways, PAF estimates are best interpreted as the proportion of ASD attributable to having a suboptimal perinatal environment resulting in PTB, SGA, and/or CD.

Purpose: To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion. Methods: The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero inflation, and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion, and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature. Results: The results indicate that hurdle models with a random effects term accounting for extra variance in the Bernoulli zero -inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary. Conclusions: Models taking into account zero inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this. (C) 2016 Elsevier Inc. All rights reserved. Support from the National Institutes of Health is acknowledged (award number R01CA172805). Support from the IAP Research Network, P7/06, of the Belgian State (Belgian Science Policy) is gratefully acknowledged.