Abstract
BackgroundThe presence of considerable spatial variability in incidence intensity suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern. As most human common diseases that challenge investigators are complex traits and as more factors associated with increased risk are discovered, statistical spatial models are needed that investigate geographical variability in the association between disease incidence and confounding variables and evaluate spatially varying effects on disease risk related to known or suspected risk factors. Information on geography that we focus on is geographical disease clusters of peak incidence and paucity of incidence.MethodsWe proposed and illustrated a statistical spatial model that incorporates information on known or hypothesized risk factors, previously detected geographical disease clusters of peak incidence and paucity of incidence, and their interactions as covariates into the framework of interaction regression models. The spatial scan statistic and the generalized map-based pattern recognition procedure that we recently developed were both considered for geographical disease cluster detection. The Freeman-Tukey transformation was applied to improve normality of distribution and approximately stabilize the variance in the model. We exemplified the proposed method by analyzing data on the spatial occurrence of sudden infant death syndrome (SIDS) with confounding variables of race and gender in North Carolina.ResultsThe analysis revealed the presence of spatial variability in the association between SIDS incidence and race. We differentiated spatial effects of race on SIDS incidence among previously detected geographical disease clusters of peak incidence and incidence paucity and areas outside the geographical disease clusters, determined by the spatial scan statistic and the generalized map-based pattern recognition procedure. Our analysis showed the absence of spatial association between SIDS incidence and gender.ConclusionThe application to the SIDS incidence data demonstrates the ability of our proposed model to estimate spatially varying associations between disease incidence and confounding variables and distinguish spatially related risk factors from spatially constant ones, providing valuable inference for targeted environmental and epidemiological surveillance and management, risk stratification, and thorough etiologic studies of disease.
Highlights
The presence of considerable spatial variability with respect to incidence intensity of disease suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern
Results we first present the analysis of detecting geographical disease clusters of peak incidence and incidence paucity performed by the generalized map-based pattern recognition procedure and the spatial scan statistic, respectively, based on data on the spatial occurrence of sudden infant death syndrome (SIDS) incidence in North Carolina counties
Information on geography that we focus on is geographical disease clusters of peak incidence and paucity of incidence identified by the spatial scan statistic and the generalized map-based pattern recognition procedure
Summary
The presence of considerable spatial variability with respect to incidence intensity of disease suggests that risk factors are unevenly distributed in space and influence the geographical disease incidence distribution and pattern. The vast majority of human common diseases that continue to challenge investigators are complex traits, such as cardiovascular disease, cancers, psychiatric disorders, and auto-immune disorders They are caused by several or many genetic, environmental, or lifestyle factors and possibly interaction between risk factors combined with small effect each [5]. The presence of considerable spatial variability in incidence intensity suggests that risk factors are une‐ venly distributed in space and influence the geographical disease incidence distribution and pattern. As most human common diseases that challenge investigators are complex traits and as more factors associated with increased risk are discovered, statistical spatial models are needed that investigate geographical variability in the association between disease incidence and confounding variables and evaluate spatially varying effects on disease risk related to known or suspected risk factors. Information on geography that we focus on is geographical disease clusters of peak incidence and paucity of incidence
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