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Abstract

The statistical modeling of the causes of death of the oldest old (persons aged 80 and over) in the U.S. in 2001 was conducted in this article. Data were analyzed using a multinomial logistic regression model (MNLM) because multiple causes of death are coded on death certificates and the codes are nominal. The percentage distribution of the 10 major causes of death among the oldest old was first examined; we next estimated a multinomial logistic regression equation to predict the likelihood of elders dying of one of the causes of death compared to dying of an “other cause.” The independent variables used in the equation were age, sex, race, Hispanic origin, marital status, education, and metropolitan/non-metropolitan residence. Our analysis provides insights into the cause of death structure and dynamics of the oldest old in the U.S., demonstrates that MNLM is an appropriate statistical model when the dependent variable has nominal outcomes, and shows the statistical interpretation for complex results provided by MNLM.

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