Community Health Assessment Using Self-Organizing Maps and GIS
Author Information
Author(s): Heather G Basara, May Yuan
Primary Institution: University of Oklahoma
Hypothesis
Communities with similar environmental characteristics exhibit similar distributions of disease.
Conclusion
The study demonstrated a positive relationship between environmental conditions and health outcomes in communities using the SOM-GIS method.
Supporting Evidence
- The SOM algorithm classified 511 communities into five clusters based on 92 environmental variables.
- ANOVA results indicated significant differences in disease occurrence between community clusters.
- The study utilized a novel approach combining SOM and GIS to analyze complex environmental data.
Takeaway
This study shows that the environment can affect health, and we can group communities based on their environmental features to understand this better.
Methodology
The study used self-organizing maps (SOM) and geographic information systems (GIS) to analyze environmental data and classify communities.
Potential Biases
Potential bias due to reliance on secondary data sources and the inability to account for patient migration.
Limitations
The study faced challenges in obtaining comprehensive data on environmental conditions and health outcomes.
Participant Demographics
Communities from five counties in New York State were analyzed.
Statistical Information
P-Value
p<0.01
Statistical Significance
p<0.01
Digital Object Identifier (DOI)
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