218 Mapping Age-Adjusted Mortality Rates at the Sub-County Level: Best Practices, Challenges, and Utilization in North Carolina

Monday, June 20, 2016: 3:30 PM-4:00 PM
Exhibit Hall Section 1, Dena'ina Convention Center
Allison Young , Orange County Health Department, Hillsborough, NC
Michael Zelek , Chatham County Public Health Department, Pittsboro, NC
Juliet Sheridan , University of North Carolina, Gillings School of Public Health, Chapel Hill, NC
Mark Smith , Guilford County Health Department, Greensboro, NC

BACKGROUND:   Six county health departments in North Carolina—Alamance, Caswell, Chatham, Durham, Guilford, and Orange County Public Health Departments—received a grant from the CDC to build GIS capacity for chronic disease surveillance. The most immediate challenge facing the counties was a lack of sub-county information. This project will build a foundation of data from which further research can be conducted, and is also designed to build regional partnerships by pooling human and technical resources as well as providing impetus for regional, larger-scale projects.

METHODS:   Using a Python tool developed in ArcGIS by the Children’s Environmental Health Initiative research group and mortality data geocoded by the North Carolina State Center for Health Statistics, the authors age-adjusted mortality data from 2009-2013. The age-adjustment was calculated using the US 2000 standard population with 10-year age groups at the census tract level. The final rates were then spatially joined to census tracts and mapped using ArcGIS. A key component of this project was creating regional standards for data management, statistical methodology, confidentiality practices, and data visualization.

RESULTS:   Maps showing age-adjusted mortality rates for all causes of death, as well as mortality rates specific to heart disease, cerebrovascular disease, diabetes mellitus, cancer, and chronic lower respiratory conditions were created at both the regional and county levels. Differences in mortality rates were found among census tracts within counties as well as across the region. By pooling experience working in different localities, best practices for data management, methodology, and presentation were established.

CONCLUSIONS:   Mapping age-adjusted mortality and life expectancy rates at the sub-county level is an effective tool for identifying disparities across geographic areas and planning local health promotion efforts. In the future, the authors will map sub-county life expectancy and compare both results to socioeconomic indicators and policies, systems, and environmental factors in order to identify possible underlying causes of these differences. Furthermore, the regional partnership promotes the sharing of ideas and tools, which can support and improve the Community Health Assessment process. Finally, this approach is particularly useful for smaller, more rural counties which tend to have limited access to sub-county data, and in the future may provide an important mechanism for increasing capacity and access to information across the state. Mortality data is available to all local health departments, and this project illustrates a practical way in which this information can become a valuable resource.