Identifying Patterns of Abridged Life Table Elements

Abstract

The CDC Wide-ranging ONline Data for Epidemiologic Research (CDC WONDER) makes many health-related datasets available to the public health community through web applications. One such available dataset is The Multiple Cause of Death data which displays county-level national mortality and population data. One of the main issues with this particular dataset is that the death counts within the age groups can be very small or equal to zero for various counties which can cause the conditional probability of death to be small or even zero. This issue causes the estimates for life expectancy within the abridged life table to be unreliable. This research utilizes the data provided by CDC WONDER, distance measures (Euclidean and discrete Hellinger distances), Metric Multidimensional Scaling, and Partitioning Around Medoids to identify patterns of life table elements among the "stable" counties within the dataset. The identification of these patterns is then used to classify the patterns which the "unstable" counties fall into. Future work will aim at borrowing from the "stable" counties, geographic and demographic information, which the "unstable" counties most closely resemble in order to better predict their life table elements, particularly life expectancies.