ArticlePopulation health metrics2016
A new parametric model to assess delay and compression of mortality.
Article in Population health metrics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- An interpretable piecewise model for human mortality: the Christopher-Samba model.Scientific reports · 2026Article
- What Can We Learn from the Functional Clustering of Mortality Data? An Application to the Human Mortality Database.European journal of population = Revue europeenne de demographie · 2021Article
- Impact of COVID-19 on life expectancy at birth in India: a decomposition analysis.BMC public health · 2021Article
- Focus on disability-free life expectancy: implications for health-related quality of life.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2021Article
- Smoothing, Decomposing and Forecasting Mortality Rates.European journal of population = Revue europeenne de demographie · 2021Article
- A Mixture-Function Mortality Model: Illustration of the Evolution of Premature Mortality.European journal of population = Revue europeenne de demographie · 2021Article
- Location-Scale Models in Demography: A Useful Re-parameterization of Mortality Models.European journal of population = Revue europeenne de demographie · 2019Article
- Advances in mortality forecasting: introduction.Genus · 2018Article
- Maximum human lifespan may increase to 125 years.Nature · 2017Article
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2 authors.
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Abstract
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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