Life expectancy at birth has attracted interest in various felds, as a health indicator that measures the quality of life. Its appeal relies on the ability to enclose and summarize all the factors afecting longevity. However, more granular information, provided by social indicators such as cause-of-death mortality rates, plays a crucial role in defning appropriate policies for governments to achieve well-being and sustainability goals. Unfortunately, their availability is not always guaranteed. Exploiting the relationship between life expectancy at birth and cause-of-death mortality rates, in this paper we propose an indirect model to produce estimates of death rates due to specifc causes using the summary indicator of life expectancy at birth, thus the general levels of the observed mortality. By leveraging on a constrained optimization procedure, we ensure a robust framework where the causespecifc mortality rates are coherent to the aggregate mortality. The main advantage is that indirect estimations allow us to overcome the data availability problem: very often the cause-specifc mortality data are incomplete, whereas data on the aggregate mortality are not. Using data from the Human Cause-of-Death Database, we show a numerical application of our model to two diferent countries, Russia and Spain, which have experienced a diferent evolution of life expectancy and diferent leading causes of death. In Spain, we detected the impact of several public health policies on the lowered levels of cancer deaths and related life expectancy increases. As regards the Russia, our results catch the efects of the anti-alcohol campaign of 1985–1988 on longevity changes.

Causes-of-Death Specific Estimates from Synthetic Health Measure: A Methodological Framework

Gabriella Piscopo
2022

Abstract

Life expectancy at birth has attracted interest in various felds, as a health indicator that measures the quality of life. Its appeal relies on the ability to enclose and summarize all the factors afecting longevity. However, more granular information, provided by social indicators such as cause-of-death mortality rates, plays a crucial role in defning appropriate policies for governments to achieve well-being and sustainability goals. Unfortunately, their availability is not always guaranteed. Exploiting the relationship between life expectancy at birth and cause-of-death mortality rates, in this paper we propose an indirect model to produce estimates of death rates due to specifc causes using the summary indicator of life expectancy at birth, thus the general levels of the observed mortality. By leveraging on a constrained optimization procedure, we ensure a robust framework where the causespecifc mortality rates are coherent to the aggregate mortality. The main advantage is that indirect estimations allow us to overcome the data availability problem: very often the cause-specifc mortality data are incomplete, whereas data on the aggregate mortality are not. Using data from the Human Cause-of-Death Database, we show a numerical application of our model to two diferent countries, Russia and Spain, which have experienced a diferent evolution of life expectancy and diferent leading causes of death. In Spain, we detected the impact of several public health policies on the lowered levels of cancer deaths and related life expectancy increases. As regards the Russia, our results catch the efects of the anti-alcohol campaign of 1985–1988 on longevity changes.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/866680
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