How can a cause-of-death reduction be compensated for by the population heterogeneity? A dynamic approach

Abstract

A growing number of studies indicate a widening of socioeconomic inequalities in mortality over the past decades. It has therefore become crucially important to understand the impact of heterogeneity and its evolution on the future mortality of heterogeneous populations. In particular, recent developments in multi-population mortality have raised a number of questions, among which is the issue of evaluating cause-of-death reduction targets set by national and international institutions in the presence of heterogeneity. The aim of this paper is to show how the study of the population data and the population dynamics framework contribute to addressing these issues, by providing a new viewpoint on the evolution of aggregate mortality indicators in the presence of heterogeneity. Our findings rely on two unique datasets on the English population and cause-specific number of deaths by socioeconomic circumstances, over the period 1981-2015. The analysis of the data first highlights the complexity of recent demographic developments, characterized by significant composition changes in the population, with considerable variations according to the age class or cohort, along with a widening of socioeconomic inequalities. We then introduce a dynamic framework for studying the impact of composition changes on the mortality of the global population. In particular, we are interested in quantifying the impacts of cause-of-death mortality reduction in comparison with changes of composition in a heterogeneous population. We show how a cause of death reduction could be compensated for in the presence of heterogeneity, which could lead to misinterpretations when assessing public policies impacts and/or for the forecasting of future trends.

Simulating long-term impacts of mortality shocks: learning from the cholera pandemic

“…Our goal is to study, in a simplified model, the impact of a mortality shock in the population, for instance due to an epidemic outbreak, when the shock not only impact individuals' longevity, but also the population composition through swap events ( [Kaakai et al, 2019]). Swap events can provide a way to model compositional changes induced by public measures or variations in the general quality of life.…”

“…However, this is not consistent with the demographic rates. Indeed, there should naturally be more individual in the less deprived Subgroup 1 in older age classes, due to mortality differences between the subgroup, which is reflected by the subgroups' age pyramids (see [Kaakai et al, 2019] for more details).…”

Continuous time model for notional defined contribution pension schemes: Liquidity and solvency

“…We develop a continuous deterministic framework. It is common in the population dynamics literature to study deterministic models as they can be viewed as limit models when the size of the population becomes very large (Kaakai et al, 2018;Attias et al, 2016). 5 Furthermore, continuous frameworks also improve traceability of the results (Bommier and Lee, 2003).…”

Effect of the COVID-19 frailty heterogeneity on the future evolution of mortality by stratified weighting

“…Systematic mortality improvement trends vary with the risk characteristics of individuals in a population (including by age and gender) and this variation determines the degree of mortality heterogeneity within a population [Vaupel et al (1979), Meyricke and Sherris (2013)]. Further, some authors have shown that population heterogeneity can impact population dynamics and aggregate mortality trends [Kaakai et al (2019)] Our paper relies on the assumption that mortality improvement trends differ due to different health status across individuals with the same age and gender, based on evidence that health status (especially chronic illness) is significantly correlated with disability [Sherris and Wei (2021)] and mortality [Brown and Warshawsky (2013), Koijen et al (2016), Yogo (2016)].…”