Abstract
BackgroundFrom a public health perspective, a healthier community environment correlates with fewer occurrences of chronic or infectious diseases. Our premise is that community health is a non-linear function of environmental and socioeconomic effects that are not normally distributed among communities. The objective was to integrate multivariate data sets representing social, economic, and physical environmental factors to evaluate the hypothesis that communities with similar environmental characteristics exhibit similar distributions of disease.ResultsThe SOM algorithm used the intrinsic distributions of 92 environmental variables to classify 511 communities into five clusters. SOM determined clusters were reprojected to geographic space and compared with the distributions of several health outcomes. ANOVA results indicated that the variability between community clusters was significant with respect to the spatial distribution of disease occurrence.ConclusionOur study demonstrated a positive relationship between environmental conditions and health outcomes in communities using the SOM-GIS method to overcome data and methodological challenges traditionally encountered in public health research. Results demonstrated that community health can be classified using environmental variables and that the SOM-GIS method may be applied to multivariate environmental health studies.
Highlights
From a public health perspective, a healthier community environment correlates with fewer occurrences of chronic or infectious diseases
We present the results of our work and discuss the challenges of implementing self-organizing map algorithm (SOM)-geographic information systems (GIS) for public health research
Cluster 1 included communities characterized by small to mid sized cities distributed throughout Erie, Westchester, and Steuben counties
Summary
From a public health perspective, a healthier community environment correlates with fewer occurrences of chronic or infectious diseases. [1,2] The underlying patterns of exposure that influence health status are the non-random result of interactions between the social, economic, and environmental networks people live within. [3] understanding the macro-level effects of environmental determinants of health has become increasingly important. [6] Such patterned regularity between groups and communities over time, despite the movement of people in and out of groups, demonstrates a dynamic at the environmental level that accounts for the observed differences in disease rates across spatial and temporal dimensions. [11] challenges for conducting studies rooted in complexity arise when standard statistical modelling methods are applied to nonlinear and skewed data sets with interactive variables, hierarchical levels of analysis, and feedback mechanisms. The challenge is to understand the environment as it influences health outcomes by using analytical systems that are neither to simplified nor too complex. [12]
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