Evaluation of age-specific causes of death in the context of the Italian longevity transition

Abstract

In many low-mortality countries, life expectancy at birth increased steadily over the last century. In particular, both Italian females and males benefited from faster improvements in mortality compared to other high-income countries, especially from the 1960s, leading to an exceptional increase in life expectancy. However, Italy has not become the leader in longevity. Here, we investigate life expectancy trends in Italy during the period 1960–2015 for both sexes. Additionally, we contribute to the existing literature by complementing life expectancy with an indicator of dispersion in ages at death, also known as lifespan inequality. Lifespan inequality underlies heterogeneity over age in populating health improvements and is a marker of uncertainty in the timing of death. We further quantify the contributions of different age groups and causes of death to recent trends in life expectancy and lifespan inequality. Our findings highlight the contributions of cardiovascular diseases and neoplasms to the recent increase in life expectancy but not necessarily to the decrease in lifespan inequality. Our results also uncover a more recent challenge across Italy: worsening mortality from infectious diseases and mortality at older age.

Disaggregating Death Rates of Age-Groups Using Deep Learning Algorithms

“…Therefore, it is important to investigate the age-specific differences between the real and the estimated mortality data according to specific phases of longevity evolution namely regularities, improvements, and stagnation, that can be represented by Japan, Italy, and the USA respectively. Specifically, Italian life expectancy exhibits a long-run transition with considerably upward shifts, converging to longevity records (Nigri et al 2021a, 2022b, 2022c). This result has been achieved by a mix of a relatively healthy lifestyle and an efficient health system.…”

“…The literature devoted to the longevity model for indirect estimation is based on the deep learning model. Indeed, more recently, Nigri et al (2022d) formalized a deep neural networks approach to indirectly derive age-specific mortality from observed or predicted life expectancy by leveraging deep learning algorithms akin to demography’s indirect estimation techniques.…”

Multistate analysis and decomposition of disability-free life expectancy trends in Italy 2004-2019

“…Despite several studies reporting the evolution of mortality and life expectancy in Italy (among the most recent, Nigri et al, 2022), there are only a few studies that explore trends in DFLE for the country. Frova et al (2010) found that, between 1994 and 2005, both life expectancy and DFLE increased, and that gender differences in DFLE gradually reduced.…”