The modal age at death and the shifting mortality hypothesis

Canudas-Romo, Vladimir

doi:10.4054/demres.2008.19.30

Cited by 136 publications

(165 citation statements)

References 31 publications

(39 reference statements)

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“…In recent studies (Canudas-Romo 2008;Cheung, Robine, and Caselli 2008;Cheung and Robine 2007;Thatcher et al 2010), authors have argued that the general scenario of compression of mortality, where increases in typical length of life are paralleled with decreases in variability of age at death, no longer describes appropriately recent adult mortality changes recorded in some low mortality countries. Instead, the shifting mortality regime (Bongaarts 2005;Kannisto 1996), where the log-force of mortality among adults is assumed to shift downward and reach lower levels while keeping an intact shape, provides a better description.…”

Section: Discussionmentioning

confidence: 99%

“…The traditional way of answering this question is in terms of life expectancy at birth. However, the late life modal age at death found in old-age is another strong candidate that has received much recognition lately (Canudas-Romo 2008, 2010Cheung et al 2005;Cheung, Robine, and Caselli 2008;Cheung et al 2009;Cheung and Robine 2007;Kannisto 2000Kannisto , 2001Kannisto , 2007Paccaud et al 1998;Robine 2001;Thatcher et al 2010). Dating back to the nineteenth century, pioneer work by Lexis (1877Lexis ( , 1878 on the concept of normal life durations identified this modal age at death as the most central and natural characteristic of human longevity.…”

Section: Background 21 Typical Length Of Lifementioning

confidence: 99%

“…Using empirical death rates at ages 70 and 90 only to fit this simple logistic model, the authors found that the resulting modal age at death estimates were reasonably close to those computed with Kannisto's (2001) quadratic method described above. Further theoretical considerations regarding the modal age at death and its related measures in several other mathematical models of mortality are provided by Canudas-Romo (2008) and Robine et al (2006).…”

Section: Background 21 Typical Length Of Lifementioning

confidence: 99%

“…In order to find the value at which the maximum of a given frequency distribution is reached, Pearson (1902) recommended interpolating a curve through the top of ordinates and using the maximum of this interpolated curve to identify the modal value. Many followed this advice and fitted a quadratic polynomial model to life table deaths around the age M * where the highest number of deaths occurs in the life table (Canudas-Romo 2008, 2010Cheung, Robine, and Caselli 2008;Kannisto 2001Kannisto , 2007Thatcher et al 2010). All except Cheung, Robine, and Caselli (2008) applied Kannisto's (2001) quadratic procedure, which consists of fitting a quadratic model using life table deaths at ages M * , M * −1, and M * + 1.…”

Section: Background 21 Typical Length Of Lifementioning

confidence: 99%

“…either stopped or slowed down substantially, even at older ages (Canudas-Romo 2008;Cheung, Robine, and Caselli 2008;Cheung and Robine 2007;Thatcher et al 2010). In contrast, adult mortality progress in the USA has been very slow in recent decades and the level of lifespan inequality is high compared to other low mortality countries (CanudasRomo 2008;Wilmoth and Horiuchi 1999).…”

Section: Introductionmentioning

confidence: 99%

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Changes in the age-at-death distribution in four low mortality countries: A nonparametric approach

Ouellette¹,

Bourbeau²

2011

DemRes

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References 618Appendix 623 A1 From B-splines to P-splines 623 A2 The penalized likelihood function for P-splines 624 A3 Smoothing mortality data with P-splines 625 A4 Comparison between HMD life table age-at-death distributions and P-spline smoothed density functions for Japan 626Demographic Research: Volume 25, Article 19 Research ArticleChanges in the age-at-death distribution in four low mortality countries: A nonparametric approach Nadine Ouellette 1 Robert Bourbeau 2 AbstractSince the beginning of the twentieth century, important transformations have occurred in the age-at-death distribution within human populations. We propose a flexible nonparametric smoothing approach based on P-splines to refine the monitoring of these changes. Using data from the Human Mortality Database for four low mortality countries, namely Canada (1921Canada ( -2007, France (1920( ), Japan (1947, and the USA , we find that the general scenario of compression of mortality no longer appropriately describes some of the recent adult mortality trends recorded. Indeed, reductions in the variability of age at death above the mode have stopped since the early 1990s in Japan and since the early 2000s for Canadian, US, and French women, while their respective modal age at death continued to increase. These findings provide additional support to the shifting mortality scenario, using an alternative method free from any assumption on the shape of the age-at-death distribution. Ouellette & Bourbeau: Changes in the age-at-death distribution in four low mortality countries IntroductionOver the course of the last century, we have witnessed major improvements in the level of mortality in regions all across the globe. This remarkable mortality decrease has also been characterized by important changes in the age-pattern of mortality, which inevitably led to substantial modifications in the shape of the distribution of age at death and the survival curve over time. Measuring transformations in the age-at-death distribution or in the survival curve quickly became a topic of great interest among researchers, as their implications on societies are profound. For example, with accurate historical trends on average lifespan and lifespan inequality in hand, governments and policymakers are in a better position to ensure sustainability of social security and health-care systems.Efforts to document such trends have indeed been made for several countries and regions: Canada ( Recently, Cheung et al. (2005) listed and reviewed more than 20 indicators used in these studies, each indicator aimed at quantifying either the central tendency or the dispersion (variability) of age at death across individuals. Since the computation of these indicators often involves the use of parametric statistical modelling techniques (e.g., quadratic model, normal model, or logistic model) that impose a predetermined structure on data, an exploration of nonparametric statistical methods, free from assumptions related to the structure of the data, is worth considering. Indeed, concer...

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Section: Discussionmentioning

confidence: 99%

Section: Background 21 Typical Length Of Lifementioning

confidence: 99%

Section: Background 21 Typical Length Of Lifementioning

confidence: 99%

Section: Background 21 Typical Length Of Lifementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Changes in the age-at-death distribution in four low mortality countries: A nonparametric approach

Ouellette¹,

Bourbeau²

2011

DemRes

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Retrospective Testing of Mortality Forecasting Methods for the Projection of Very Elderly Populations in Australia

Terblanche

2016

Journal of Forecasting

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The purpose of this paper is to present the results of retrospective tests of various extrapolative methods to forecast adult mortality and very elderly populations for Australia. Direct extrapolation methods tested include the Geometric method, Ediev variant, Lee-Carter method, BMS variant and a relational model. Indirect methods include the extrapolation of parameters of models fitted to the age profile of death rates and a new method involving the extrapolation of features of death frequency distributions namely the modal age and concentration. The geometric, Ediev and LeeCarter BMS methods were very successful in projecting death rates and very elderly populations. Differences between these methods were small. The extrapolation of parametric functions proved successful for males but less so for females. Very elderly populations can be viably projected by directly extrapolating death rates by age when rates of decline in death rates show consistent relationships between ages and are stable over time.

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Introduction

Bengtsson

Keilman

Alho

et al. 2019

Demographic Research Monographs

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Globally, the twenty-first century will witness rapid population ageing. Already in 2050, one out of five persons in the world, and one out of three in Europe, is expected to be 60 or over (UN 2015). Moreover, we have entered into a new stage of population ageing in terms of its causes, which have altered its consequences. In the first stage, lasting until the middle of the twentieth century in developed countries, population ageing was entirely due to the decline in fertility, with Sweden being commonly used as an example (Coale 1957; Bengtsson and Scott 2010; Lee and Zhou 2017). During this stage, the increase in life expectancy was primarily driven by declines in infant and child mortality. It worked in the opposite direction to

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The modal age at death and the shifting mortality hypothesis

Cited by 136 publications

References 31 publications

Changes in the age-at-death distribution in four low mortality countries: A nonparametric approach

Changes in the age-at-death distribution in four low mortality countries: A nonparametric approach

Retrospective Testing of Mortality Forecasting Methods for the Projection of Very Elderly Populations in Australia

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