Decomposing changes in life expectancy: Compression versus shifting mortality

Bergeron-Boucher, Marie-Pier; Ebeling, Marcus; Canudas-Romo, Vladimir

doi:10.4054/demres.2015.33.14

Cited by 44 publications

(32 citation statements)

References 48 publications

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“…None of the models reproduces the observed 'accident bump' of excess mortality of young adult ages. The findings in this paragraph confirm prior observations by others about the fit between human mortality data and the Gompertz, Makeham, and Siler models (e.g., Bongaarts 2005;Canudas-Romo 2008;Horiuchi et al 2013;Bergeron-Boucher, Ebeling, and Canudas-Romo 2015).…”

Section: Availability Of Data and Supplementary Informationsupporting

confidence: 81%

“…We used mathematical expressions of the models of Gompertz, Makeham, and Siler that are based on the (old age) modal age at death (Horiuchi et al 2013;Missov et al 2015;Bergeron-Boucher, Ebeling, and Canudas-Romo 2015). The modal age at death is the age (beyond infancy and childhood) at which the probability density of life table deaths has a maximum.…”

Section: Three Theoretical Models Of Mortalitymentioning

confidence: 99%

See 1 more Smart Citation

Gompertz, Makeham, and Siler models explain Taylor's law in human mortality data

Cohen¹,

Bohk

Rau

2018

DemRes

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Section: Availability Of Data and Supplementary Informationsupporting

confidence: 81%

Section: Three Theoretical Models Of Mortalitymentioning

confidence: 99%

Gompertz, Makeham, and Siler models explain Taylor's law in human mortality data

Cohen¹,

Bohk

Rau

2018

DemRes

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“…More recent models that specifically included mortality delay focused on adult ages alone [12, 19]. Also, the few previous attempts to assess delay versus compression did not distinguish mortality compression below and above the modal age at death [20, 21]. …”

Section: Introductionmentioning

confidence: 99%

A new parametric model to assess delay and compression of mortality

Beer

Janssen

2016

Popul Health Metrics

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BackgroundA decrease in mortality across all ages causes a shift of the age pattern of mortality, or mortality delay, while differences in the rate of decrease across ages cause a change in the shape of the age-at-death distribution, mortality compression or expansion. Evidence exists for both compression and delay of mortality. Existing parametric models to describe the full age pattern of mortality are not able to capture mortality delay versus mortality compression. More recent models that assess delay versus compression mostly focused on the adult or old ages alone and did not distinguish mortality compression below and above the modal age at death, although they represent different mechanisms.MethodsThis paper presents a new parametric model that describes the full age pattern of mortality and assesses compression – at different stages of life – and delay of mortality: the CoDe model. The model includes 10 parameters, of which five are constant over time. The five time-varying parameters reflect delay of mortality and compression of mortality in infancy, adolescence, young adulthood, late adulthood, and old age. The model describes infant and background mortality by two simple functions, uses a mixed logistic model with different slopes in adult, middle, and old age, and includes the modal age at death as a parameter to account for the delay in mortality.ResultsApplying the CoDe model to age-specific probabilities of death for Japanese, French, American, and Danish men and women between 1950 and 2010 showed a very good fit of the full age pattern of mortality. Delay of mortality explained about two-thirds of the increase in life expectancy at birth, whereas compression of mortality due to mortality declines in young age explained about one-third. No strong compression of mortality in late adulthood age was observed. Mortality compression in old age has had a small negative impact on life expectancy.ConclusionsThe CoDe model proved a valid instrument for describing the full age pattern of mortality and for disentangling the effects of mortality delay and compression – at different stages of life – on the increase in life expectancy.

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“…The succinct notation of a dot on top of a variable, used here, indicates the derivative with respect to time, which is shown to simplify equations and aid in the development of new methodology (Vaupel and Canudas-Romo 2003;Bergeron-Boucher, Ebeling, and Canudas-Romo 2015). When the change in scale factor (…”

Section: Decomposition Methodsmentioning

confidence: 99%

“…This decomposition is inspired by research that separates transitions in life expectancy into change due to compression and shifting effects (Bergeron-Boucher, Ebeling, and Canudas-Romo 2015). Figure 2 illustrates four different age patterns of first marriage distributions for Swedish females.…”

Section: Decomposition Methodsmentioning

confidence: 99%