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Cause of death decomposition of cohort survival comparisons
Full text linkLife expectancy is most commonly measured for a period (corresponding to mortality within a given year) or for a specific birth cohort. Although widely used, period and cohort life expectancy have limitations as their time-trends often show disparities and can mask the historical mortality experience of all cohorts present at a given time. The truncated cross-sectional average length of life, or TCAL, is a period measure including all available cohort mortality information, irrespective of whether all cohort members have died. It is particularly useful for comparing cohort mortality between populations. This study extends TCAL by disentangling causes of death contributions. The strength of the approach is that it allows identification of mortality differences in cohorts with members still alive, as well as identification of which ages and causes of death contribute to mortality differentials between populations. Application of the method to Japan shows that over the period 1950-2014 a major contributor to TCAL differences with other high-longevity countries was its lower cardiovascular disease mortality
A mixture-function mortality model: illustration of the evolution of Premature Mortality
Full text linkPremature 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
Measures and Models of Mortality ☆ ☆This document is a collaborative effort
No full textMortality analysis and modeling remains one of the core jobs of demographers, epidemiologists, actuaries, biostatisticians, and other population-studies experts. In this chapter we make a detailed overview of the latest developments in demographic measures and mortality modeling. We emphasize the work on measures of longevity which are of particular interest for aging populations, but we also highlight models that work with mortality at earlier ages. Trends over time, patterns over age and convergence/divergence between measures are all covered in the text. A particular important contribution of this chapter is including estimation tools for some of the most important mortality models
Lifespan variation among people with a given disease or condition
Full text linkIn addition to fundamental mortality metrics such as mortality rates and mortality rate ratios, life expectancy is also commonly used to investigate excess mortality among a group of individuals diagnosed with specific diseases or conditions. However, as an average measure, life expectancy ignores the heterogeneity in lifespan. Interestingly, the variation in lifespan-a measure commonly used in the field of demography-has not been estimated for people with a specific condition. Based on recent advances in methodology in research within epidemiology and demography, we discuss two metrics, namely, the average life disparity and average lifetable entropy after diagnosis, which estimate the variation in lifespan for time-varying conditions in both absolute and relative aspects. These metrics are further decomposed into early and late components, separated by their threshold ages. We use mortality data for women with mental disorders from Danish registers to design a population-based study and measure such metrics. Compared with women from the general population, women with a mental disorder had a shorter average remaining life expectancy after diagnosis (37.6 years vs. 44.9 years). In addition, women with mental disorders also experienced a larger average lifespan variation, illustrated by larger average life disparity (9.5 years vs 9.1 years) and larger average lifetable entropy (0.33 vs 0.27). More specifically, we found that women with a mental disorder had a larger early average life disparity but a smaller late average life disparity. Unlike the average life disparity, both early and late average lifetable entropy were higher for women with mental disorders compared to the general population. In conclusion, the metric proposed in our study complements the current research focusing merely on life expectancy and further provides a new perspective into the assessment of people's health associated with time-varying conditions
No consistent effects of prenatal or neonatal exposure to Spanish flu on late-life mortality in 24 developed countries
Full text linkWe test the effects of early life exposure to disease on later health by looking for differences in late-life mortality in cohorts born around the 1918-1919 flu pandemic using data from the Human Mortality Database for 24 countries. After controlling for age, period, and sex effects, residual mortality rates did not differ systematically for flu cohorts relative to surrounding cohorts. We calculate at most a 20-day reduction in life expectancy for flu cohorts; likely values are much smaller. Estimates of influenza incidence during the pandemic suggest that exposure was high enough for this to be a robust negative result
Revisiting life expectancy rankings in countries that have experienced fast mortality decline
No full textIn this chapter, we propose a simple procedure for making international comparisons of life expectancy. This procedure builds on the theoretical advantages of using actual cohorts (as opposed to synthetic cohorts) for building life tables, but uses all the available mortality information up to the present. Specifically, for each non-extinct cohort present in the population at time t, we calculate the cohort’s truncated life expectancy at birth, with the truncation age being the age reached by the cohort at time t. We calculate truncated cohort life expectancies for 17 countries using data from the Human Mortality Database, and compare them with their period, synthetic-cohort equivalent. We find that a number of countries, including Italy and Spain, rank consistently lower in terms of cohorts vs. periods. The US, however, ranks more favorably in terms of cohorts as opposed to periods. We argue that the examination of truncated cohort life expectancies offers a simple solution for summarizing complex time- and age-specific mortality trajectories in a way that is meaningful for real cohorts of individuals, and thus enriches international mortality comparisons. This approach is particularly relevant for countries that have experienced fast mortality change. We also introduce the concept of “momentum of mortality disadvantage,” which states that some countries currently ranking high in terms of period life expectancy have accumulated such cohort mortality disadvantages up to the present that these disadvantages are not likely to be reversed, even if currently-observed mortality advantages persist in the future
Location-Scale Models in Demography: A Useful Re‑parameterization of Mortality Models
Full text linkSeveral parametric mortality models have been proposed to describe the age pattern of mortality since Gompertz introduced his “law of mortality” almost two centuries ago. However, very few attempts have been made to reconcile most of these models within a single framework. In this article, we show that many mortality models used in the demographic and actuarial literature can be re-parameterized in terms of a general and flexible family of models, the family of location–scale (LS) models. These models are characterized by two parameters that have a direct demographic interpretation: the location and scale parameters, which capture the shifting and compression dynamics of mortality changes, respectively. Re-parameterizing a model in terms of the LS family has several advantages over its classic formulation. In addition to aiding parameter interpretability and comparability, the statistical estimation of the LS parameters is facilitated due to their significantly lower correlation. The latter, in turn, further improves parameter interpretability and reduces estimation bias. We show the advantages of the LS family over the typical parameterization of mortality models with two illustrations using the Human Mortality Database.UB was supported by an INED-iPOPs doctoral contract, the University of Southern Denmarkand the Max Planck International Research Network on Aging
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