Drewnowski’s index to measure lifespan variation: Revisiting the Gini coefficient of the life table

IF 1.2 4区 生物学 Q4 ECOLOGY
José Manuel Aburto , Ugofilippo Basellini , Annette Baudisch , Francisco Villavicencio
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引用次数: 4

Abstract

The Gini coefficient of the life table is a concentration index that provides information on lifespan variation. Originally proposed by economists to measure income and wealth inequalities, it has been widely used in population studies to investigate variation in ages at death. We focus on the complement of the Gini coefficient, Drewnowski’s index, which is a measure of equality. We study its mathematical properties and analyze how changes over time relate to changes in life expectancy. Further, we identify the threshold age below which mortality improvements are translated into decreasing lifespan variation and above which these improvements translate into increasing lifespan inequality. We illustrate our theoretical findings simulating scenarios of mortality improvement in the Gompertz model, and showing an example of application to Swedish life table data. Our experiments demonstrate how Drewnowski’s index can serve as an indicator of the shape of mortality patterns. These properties, along with our analytical findings, support studying lifespan variation alongside life expectancy trends in multiple species.

Drewnowski指数衡量寿命变化:重新审视生命表的基尼系数
寿命表的基尼系数是提供寿命变化信息的浓度指数。它最初是由经济学家提出的,用于衡量收入和财富的不平等,现已被广泛用于人口研究,以调查死亡年龄的变化。我们关注的是基尼系数的补充,德鲁诺斯基指数,这是一个衡量平等的指标。我们研究了它的数学性质,并分析了随时间的变化与预期寿命的变化之间的关系。此外,我们确定了阈值年龄,低于该年龄,死亡率的改善转化为寿命变化的减少,高于该年龄,这些改善转化为寿命不平等的增加。我们在Gompertz模型中模拟了死亡率改善的情景,并展示了一个应用于瑞典生命表数据的例子。我们的实验证明了Drewnowski指数可以作为死亡率模式形状的一个指标。这些特性,以及我们的分析发现,支持研究多种物种的寿命变化和预期寿命趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Theoretical Population Biology
Theoretical Population Biology 生物-进化生物学
CiteScore
2.50
自引率
14.30%
发文量
43
审稿时长
6-12 weeks
期刊介绍: An interdisciplinary journal, Theoretical Population Biology presents articles on theoretical aspects of the biology of populations, particularly in the areas of demography, ecology, epidemiology, evolution, and genetics. Emphasis is on the development of mathematical theory and models that enhance the understanding of biological phenomena. Articles highlight the motivation and significance of the work for advancing progress in biology, relying on a substantial mathematical effort to obtain biological insight. The journal also presents empirical results and computational and statistical methods directly impinging on theoretical problems in population biology.
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