176508 US county maps showing premature mortality from selected causes based on life expectancy at age of death

Tuesday, October 28, 2008

James L. Wilson, PhD , Department of Geography, Northern Illinois University, DeKalb, IL
Denise A. Kirk, MS , Center for Health Services Research and Development, East Carolina University, Greenville, NC
Purpose. In the study of premature mortality, an arbitrary age like 75 years truncates the future experience of those surviving the gauntlet of various causes of death. Increasing the upward limit of age-at-death to expected years remaining based on life tables incorporates the population experience of mortality and provides more information about the effects of different causes of death in older age groups. We expand on earlier efforts of the National Cancer Institute with mapping and other graphical illustrations from a larger array of selected causes of death.

Materials and Methods. Data for calculating premature mortality were obtained from NCHS's Compressed Mortality Files 1999-2004. Rates were calculated for a 5-year period (2000-2004) and categorized into 5 categories for mapping. Life table values were for 2004, obtained from a recent National Statistics Vital Report. Calculations were done in SAS and mapping was done with ESRI's ArcMap.

Results. The use of life table values instead of age 75-years did not present any special challenges. The resulting calculations, although larger, correlated nearly perfectly with those calculated for 75 years. The preliminary map for all deaths did not show any major differences in the distribution of premature mortality.

Discussion. The life table approach to premature mortality study and mapping is more suitable than the arbitrary approach because it incorporates a population's mortality experience. Life expectancy is increasing and using a measure that accommodates this dynamic is more useful for understanding changing patterns of premature mortality.

Learning Objectives:
1. Describe the geographic disparities in premature mortality within the US using years of life lost before the expected age of death. 2. Evaluate the effect of a measure based on population experience versus a measure based on an arbitrary limit (e.g., 75 years)on the interpretation of mortality patterns. 3. Recognize how years of life lost measures are important for understanding characteristic mortality patterns for age groups by selected causes of death (e.g., female breast cancer and prostate cancer).

Keywords: Mortality, Population

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I will be developing the maps, statistical graphs, and text that will appear on the poster. I am primarily responsible for the project idea.
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.