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Mapping Disease At An Approximated Individual Level Using Aggregate Data: A Case Study Of Mapping New Hampshire Birth Defects, Xun Shi, Stephanie Miller, Kevin Mwenda, Akikazu Onda

Dartmouth Scholarship

Limited by data availability, most disease maps in the literature are for relatively large and subjectively-defined areal units, which are subject to problems associated with polygon maps. High resolution maps based on objective spatial units are needed to more precisely detect associations between disease and environmental factors. Method: We propose to use a Restricted and Controlled Monte Carlo (RCMC) process to disaggregate polygon-level location data to achieve mapping aggregate data at an approximated individual level. RCMC assigns a random point location to a polygon-level location, in which the randomization is restricted by the polygon and controlled by the background (e.g., …