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Abstract #158124

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James L. Wilson, PhD¹, Omur Cinar Elci, MD, PhD², and Denise A. Kirk, MS¹. (1) Center for Health Services Research and Development, East Carolina University, Physicians Quadrangle Building N, Greenville, NC 27834, 252-744-2953, wilsonja@ecu.edu, (2) Division of Community Health and Preventive Medicine, Brody School of Medicine at East Carolina University, 1709 West Sixth Street, Greenville, NC 27834

Purpose: This presentation shows the results from experimentation of different smoothing techniques used in the detection of mapped clusters of mortality from rare causes. It also describes spatial autocorrelation analyses that can be used to evaluate clusters.

Data and Methods: The data are derived from the Compressed Mortality Files and the 2000 US Census for populations 15 years and greater. Geographic units are the county and Health Service Area (HSA) and the study period is for the years 1994 to 2003. The mortality variables studied include laryngeal cancer, silicosis, alcohol related liver disease, and cancer of the trachea, bronchus and lung. Occupational variables are also included. Two publicly available software programs, Head-Bang and GeoDA, produce the smoothed and borrowed strength values that are later mapped. Using global and local spatial autocorrelation analytical techniques from both GeoDA and ESRI's ArcGIS, spatial clusters are evaluated for potential as regions of statistically significant excess mortality.

Results: The Head-Bang method (non-parametric and can use population weights) produces smoothed maps from spatially heterogeneous and unstable county rates. The techniques in GeoDA also produce smoothed map distributions, but are based on spatial context and contiguity (i.e., spatial weighting). Preliminary results suggest that the clusters and regions constructed by these techniques are meaningful and can provide risk “surfaces” for less common causes of mortality.

Conclusion: Regionalizing can be useful in understanding geographical associations and distributions of mortality. However, process scales, spatial structure are also important considerations.

Learning Objectives:

• Identify smoothing and borrowed strength methods used in exploring spatially referenced data.
• Discuss global and local spatial autocorrelation techniques and their role in cluster delineation.
• Recognize the role of dependence, scale, and spatial context in regionalizing mortality.

Keywords: Geographic Information Systems, Statistics

Presenting author's disclosure statement:

Any relevant financial relationships? No
Any institutionally-contracted trials related to this submission?

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.

• Slides (pdf) or Handout