City Research Online

Abstract

The starting point of our research is the inadequacy of assuming, in the construction of a model of mortality, that frailty is constant for the individuals comprising a demographic population. This assumption is implicitly made by standard life table techniques. The substantial differences in the individual susceptibility to specific causes of death lead to heterogeneity in frailty, and this can have a material effect on mortality models and projections – specifically a bias due to the underestimation of longevity improvements. Given these considerations, in order to overcome the misrepresentation of the future mortality evolution, we develop a stochastic model based on a stratification weighting mechanism, which takes into account heterogeneity in frailty. Furthermore, the stratified stochastic model has been adapted also to capture Covid-19 frailty heterogeneity, that is a frailty worsening due to the Covid-19 virus. Based on different frailty levels characterising a population, which affect mortality differentials, the analysis allows for forecasting the temporary excess of deaths by the stratification schemes in a stochastic environment.

Publication Type:	Article
Additional Information:	This article is to be published in a revised form in Journal of Demographic Economics, http://journals.cambridge.org/action/displayJournal?jid=DEM. This version is free to view and download for private research and study only. Not for re-distribution or re-use. © copyright holder.
Publisher Keywords:	Frailty, Mortality Modelling, Post-stratification
Subjects:	H Social Sciences > HC Economic History and Conditions H Social Sciences > HN Social history and conditions. Social problems. Social reform R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Departments:	Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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