A polygon-based locally-weighted-average method for smoothing disease rates of small units

Disease rates for geographic areas with small populations may be unstable. Therefore, accurate nonparametric methods for smoothing or stabilizing rates are needed.

We propose an innovative locally-weighted-average method as an easy tool for disease surveillance. Our approach has several important advantages over existing locally-weighted-average methods. One advantage is that the buffer zone is created based on a polygon rather than centroid. Second, the buffer distance is determined by a user-specified population threshold. Third, a weighting factor that accounts for variability in the rate is used in the smoothing process. We further propose a variance-driven procedure to reduce arbitrariness in selecting the population threshold, and a binary search technique to quickly and precisely find the buffer distance according to the specified population threshold. Lastly, we develop a software tool using ArcObjects (ESRI, Redland, CA) to implement this method.

Our method was applied to town-level lung cancer incidence rates for New Hampshire. A comparison with a traditional point-based method indicated that our method produced less under- and over-smoothing.

Our method and the software tool are suitable for researchers and public health workers who want to apply geographic information systems to map smoothed disease rates for exploratory purposes.