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Correspondence Analysis between the Location and the Leading Causes of Death in the United States

Rena Sandy H. Baculinao, Roel F. Ceballos


Correspondence Analysis analyzes two-way or even multi-way tables with each row and column becoming a point on a multidimensional graphical map, called biplot. It can extract the essential dimensions allowing simplification of the data matrix. This study aims to measure the association between the location and the leading causes of death in the United States and to determine the location where a particular leading cause of death is associated. The research data consists of two variables (location and leading causes of death in the United States of America) with 510 data points. Results show that there exist a significant association between the location and the leading causes of death in the United States and 61.6% of the variance in the model was explained by Dimensions 1 and 2. Furthermore, Connecticut, Massachusetts, New Hampshire, New Mexico, Ohio, Rhode Island, West Virginia, and Wisconsin is associated with unintentional injuries while Arizona, California, Georgia, Minnesota, North Dakota, South Carolina, South Dakota, Texas, Vermont, and Washington are associated with Alzheimer’s disease. Cancer is highly associated with Delaware and Maryland. CLRD is associated with Alabama, Arkansas, Indiana, Iowa, Kansas, Kentucky, Maine, Nebraska, and Oklahoma. In the District of Columbia, Michigan, New Jersey, New York, and Pennsylvania heart disease is the leading cause of death. Influenza and pneumonia are associated with Hawaii, Mississippi, and Tennessee. Also, kidney disease is associated in Illinois, Louisiana, Missouri, North Carolina, and Virginia. For stroke, it is associated with Florida. Lastly, suicide is associated with Alaska, Idaho, Montana, Nevada, Oregon, Utah, and Wyoming. The United States government and its health units shall pursue further research and create relevant programs and policies to address the leading cause of death in each state.


Mortality, Correspondence Analysis, Public health, US health data

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