Age-Period Modeling of Mortality Gaps: The Cases of Cancer and Circulatory Diseases

Understanding and modeling mortality patterns, especially differences in mortality rates between populations, is vital for demographic analysis and public health planning. We compare three statistical models within the age-period framework to examine differences in death counts. The models are based on the double Poisson, bivariate Poisson, and Skellam distributions, each of which provides unique strengths in capturing underlying mortality trends. Focusing on mortality data from 1960 to 2015, we analyze the two leading causes of death in Italy, which exhibit significant temporal and age-related variations. Our results reveal that the Skellam distribution offers superior accuracy and simplicity in capturing mortality differentials. These findings highlight the potential of the Skellam distribution for analyzing mortality gaps effectively.