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混合函数死亡率模型:过早死亡率演变的例证

A Mixture-Function Mortality Model: Illustration of the Evolution of Premature Mortality.

作者信息

Zanotto Lucia, Canudas-Romo Vladimir, Mazzuco Stefano

机构信息

Department of Economics, Ca' Foscari University of Venice, Venice, Italy.

School of Demography, Australian National University, Canberra, Australia.

出版信息

Eur J Popul. 2020 Mar 20;37(1):1-27. doi: 10.1007/s10680-019-09552-x. eCollection 2021 Mar.

Abstract

Premature mortality is often a neglected component of overall deaths, and the most difficult to identify. However, it is important to estimate its prevalence. Following Pearson's theory about mortality components, a definition of premature deaths and a parametric model to study its transformations are introduced. The model is a mixture of three distributions: a Half Normal for the first part of the death curve and two Skew Normals to fit the remaining pieces. One advantage of the model is the possibility of obtaining an explicit equation to compute life expectancy at birth and to break it down into mortality components. We estimated the mixture model for Sweden, France, East Germany and Czech Republic. In addition, to the well-known reduction in infant deaths, and compression and shifting trend of adult mortality, we were able to study the trend of the central part of the distribution of deaths in detail. In general, a right shift of the modal age at death for young adults is observed; in some cases, it is also accompanied by an increase in the number of deaths at these ages: in particular for France, in the last twenty years, premature mortality increases.

摘要

过早死亡往往是总死亡人数中一个被忽视的组成部分,也是最难识别的部分。然而,估计其患病率很重要。根据皮尔逊关于死亡构成部分的理论,引入了过早死亡的定义和一个用于研究其变化的参数模型。该模型是三种分布的混合:死亡曲线第一部分为半正态分布,其余部分为两个偏态正态分布。该模型的一个优点是有可能获得一个明确的方程来计算出生时的预期寿命,并将其分解为死亡构成部分。我们对瑞典、法国、东德和捷克共和国的混合模型进行了估计。此外,除了众所周知的婴儿死亡减少以及成人死亡率的压缩和转移趋势外,我们还能够详细研究死亡分布中心部分的趋势。一般来说,观察到青年成年人的死亡模态年龄向右移动;在某些情况下,这些年龄段的死亡人数也会增加:特别是法国,在过去二十年中,过早死亡率有所上升。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7009/7865056/f1d777538990/10680_2019_9552_Fig1_HTML.jpg

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