Community health assessment using self-organizing maps and geographic information systems.
BACKGROUND
From 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.


RESULTS
The 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.


CONCLUSIONS
Our 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.
Basara
Heather G
HG
Center for Applied Social Research, University of Oklahoma, 101 David L, Boren Blvd,, Norman, OK, USA. hbasara@ou.edu
Yuan
May
M
D003153:Community Health Services / Q000706:statistics & numerical data*; D040362:Geographic Information Systems / Q000706:statistics & numerical data*; D015438:Health Behavior; D006304:Health Status; D006801:Humans; D009518:New York / Q000453:epidemiology; D019371:Topography, Medical / Q000379:methods / Q000706:statistics & numerical data*