Age-period modeling of mortality gaps: the cases of cancer and circulatory diseases

Giacomo Lanfiuti Baldi; Andrea Nigri; Han Lin Shang

doi:10.1177/0282423X251350772

Age-period modeling of mortality gaps: the cases of cancer and circulatory diseases

Giacomo Lanfiuti Baldi^*, Andrea Nigri, Han Lin Shang

^*Corresponding author for this work

Department of Actuarial Studies and Business Analytics

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

1 Downloads (Pure)

Abstract

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.

Original language	English
Pages (from-to)	1154-1180
Number of pages	27
Journal	Journal of Official Statistics
Volume	41
Issue number	4
Early online date	28 Jul 2025
DOIs	https://doi.org/10.1177/0282423X251350772
Publication status	Published - Dec 2025

Bibliographical note

Copyright The Author(s) 2025. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

Italian age-period data
Mortality modeling
Causes-of-deaths
Skellam regression

Access to Document

10.1177/0282423X251350772Licence: CC BY-NC

Publisher version (open access)Final published version, 4.19 MBLicence: CC BY-NC

Cite this

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title = "Age-period modeling of mortality gaps: the cases of cancer and circulatory diseases",

abstract = "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.",

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T1 - Age-period modeling of mortality gaps

T2 - the cases of cancer and circulatory diseases

AU - Lanfiuti Baldi, Giacomo

AU - Nigri, Andrea

AU - Shang, Han Lin

N1 - Copyright The Author(s) 2025. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2025/12

Y1 - 2025/12

N2 - 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.

AB - 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.

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ER -

Age-period modeling of mortality gaps: the cases of cancer and circulatory diseases

Abstract

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Keywords

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