Using geospatial mapping the address the burden of diabetes in durham county, NC
Objective Through a grant from the Bristol-Myers Squibb Foundation, the Durham Diabetes Coalition is working to reduce the burden of diabetes and understand disparities in Durham County, NC. We are using spatial methods to characterize the geographic distribution of diabetes across the county, exploring how diabetes varies across neighborhood characteristics, socioeconomic conditions, and environmental exposures, controlling for relevant information from patient records.
Methods Patient records for 2007-2011 from the Duke University Decision Support Repository were spatially referenced, allowing us to map the residential addresses of 244,317 patients in Durham County who accessed the Duke Health system. Of these patients, 9.7% were diagnosed with diabetes. A risk algorithm was developed using both patient-level and neighborhood level data to classify patients into risk categories according to their likelihood for a significant event (cardiac arrest, stroke, etc). Spatial analysis was used to characterize risk levels across the study area and to identify venues for community-based interventions.
Results The risk algorithm successfully captured those patients at highest risk for a significant event. For example, of the 10% highest risk patients with diabetes defined by the risk algorithm in 2010, 56 % had a significant event in 2011. The risk algorithm is being used as the basis for clinical and community interventions currently taking place in three pilot neighborhoods.
Discussion Our goal is to create a spatially-informed model for diabetes management that can be replicated nationwide. By using spatially-enabled informatics in conjunction with electronic medical records, more at-risk subpopulations can be targeted to more effectively improve diabetes management.
Learning Areas:Chronic disease management and prevention
Communication and informatics
Public health or related research
Identify key aspects to incorporating a spatial data architecture with an electronic patient warehouse Develop understanding of how risk algorithms based on combined patient and environmental data can be employed to good effect. Discuss various methods for targeting disparate sub-populations
Keyword(s): Diabetes, Geographic Information Systems
Qualified on the content I am responsible for because: I am the GIS Analyst primarily responsible for the spatial analysis on this project. In this capacity I have worked extensively with doctors and community health workers to understand the underlying causes of diabetes and how to use novel spatial and analytic approaches to address them.
Any relevant financial relationships? No
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.