Causes-of-Death Mortality: What Do We Know on Their Dependence?

Abstract

Over the last century, the assumption usually made was that causes of death are independent, although it is well-known that dependancies exist. Recent developments in econometrics allow, through Vector Error Correction Models (VECMs), to model multivariate dynamic systems including time dependency between economic variables. Common trends that exist between the variables may then be highlighted, the relation between these variables being represented by a long-run equilibrium relationship. In this work, VECMs are developed for causes-of-death mortality. We analyze the five main causes of death across 10 major countries representing a diversity of developed economies. The World Health Organization website provides cause-of-death information for about the last 60 years. Our analysis reveals that long-run equilibrium relationships exist between the five main causes of death, improving our understanding of the nature of dependence between these competing risks over recent years. It also highlights that countries usually had different past experience in regard to cause-of-death mortality trends, and, thus, applying results from one country to another may be misleading. INTRODUCTIONModels for trends in mortality rates for different ages and sexes as well as for different countries are often based on the assumption that past trends in historical data will continue in the future. Past mortality trends and variability reflect many factors, and these include changes in the causes of death. These causes have differing age patterns and have shown different trends over recent years. At the same time, systematic changes in causes of death have often been shown or assumed to be common across the developing economies. Tuljapurkar et al. (2000) show how mortality declines have had common trends in the G7 countries, although evidence of variability is seen in those trends. Booth et al. (2006) also demonstrate common improvement trends based on the Lee-Carter model and variants of the model. Wilmoth (1995) shows how taking into account causes of death can influence projected trends and effectively highlights how cause-of-death trends are hidden in aggregate data.Dependence between competing risks is important in constructing aggregate mortality rates. However, the relations that exist between the causes of death are not well understood. Usually an assumption is made that causes of death are independent. Cause elimination models as well as cause-delay models are two well-

COVID-19 and Excess Mortality: An Actuarial Study

“…However, recent research on cause of death mortality incorporates dependence, as this yields better long-term forecasts of aggregate mortality. Examples of this are Arnold and Sherris (2013) and Arnold and Sherris (2015), who used Vector Error Correlation Models; ; Zheng and Klein (1995) and Zittersteyn and Alonso-García (2021), who used copula theory, and , who utilized clustering methods to group different causes of death. In all cases, dependence between cause-specific death is considered, and better cause removal and aggregate mortality results were obtained.…”

Longevity risk and capital markets: the 2018–19 update

“…Darkiewicz & Hoedemakers, 2004;Li & Lee, 2005;Cairns et al, 2011b;Dowd et al, 2011b;Jarner & Kryger, 2011;Njenga & Sherris, 2011;Börger & Ruß, 2012;Torri & Vaupel, 2012;D'Amato et al, 2014;Raftery et al, 2014;Zhou et al, 2014;Chen et al, 2015;Kleinow, 2015;Biffis et al, 2017;Li et al, 2015cDe Jong et al, 2016;Enchev et al, 2017;Zhu et al, 2017;Hunt & Blake, 2018;Pascariu et al, 2018;Wang et al, 2018;Yang et al, 2020;Chang et al, 2020) • Cause-of-death mortality modelling (e.g. Beard, 1971;McNown & Rogers, 1992;Christensen & Vaupel, 1996;Hanewald, 2011;Murphy & Di Cesare, 2012;Arnold & Sherris, 2015;Gourieroux & Lu, 2015;Alai et al, 2018;Boumezoued et al, 2018;Yue et al, 2018;Li et al, 2019;Lyu et al, 2020) • Longevity risk and financial innovation (improvements in the analysis and design of longevity-linked products) (e.g. Gong & Webb, 2010;Stevens at al., 2010;Richter & Weber, 2011;Cocco & Gomes, 2012;…”

Short- and Long-Term Dynamics of Cause-Specific Mortality Rates Using Cointegration Analysis

“…To the best of our knowledge, cointegration analysis was first applied to the cause-specific mortality rates in Arnold and Sherris (2013, 2015. We want to go further and extend the analysis by applying a wider range of cointegration and VECM tools to the cause-of-death mortality rates.…”