A Multi-population Approach to Forecasting All-Cause Mortality Using Cause-of-Death Mortality Data

Abstract

All-cause mortality is driven by various types of cause-specific mortality. Projecting all-cause mortality based on cause-ofdeath mortality allows one to understand the drivers of the recent changes in all-cause mortality. However, the existing literature has argued that all-cause mortality projections based on cause-specific mortality experience have a number of serious drawbacks, including the inferior cause-of-death mortality data and the complex dependence structure between causes of death. In this article, we use the recent World Health Organization causes-of-death data to address this issue in a multipopulation context. We construct a new model in the spirit of N. Li and Lee (2005) but in terms of cause-specific mortality. A new two-step beta convergence test is used to capture the cause-specific mortality dynamics between different countries and between different causes. We show that the all-cause mortality estimations produced by the new model perform in the sample similarly to the estimations by the Lee-Carter and Li-Lee all-cause mortality models. However, in contrast to results from earlier studies, we find that the all-cause mortality projections of the new model have better out-of-sample performance in a long forecast horizon. Moreover, for the case of The Netherlands, an approximately 1-year higher remaining life expectancy projection for a 67-year-old Dutch male in a 30-year forecast horizon is obtained by this new model, compared to the all-cause Li-Lee mortality model.

Breast Cancer Mortality Trends and Predictions to 2030 and Its Attributable Risk Factors in East and South Asian Countries

“…In this analysis, death rates (per 100,000) of female breast cancer, corresponding to age groups 20-84 years with 5 E x,t . The data to be used are annual age-specific death rates (22).…”

“…The LC-based modeling frameworks are viewed in the current literature as among the most efficient and transparent methods of modeling and projecting mortality improvements. Previously, scholars have used this approach to forecast all-cause and causespecific mortality in different countries (22)(23)(24)(25)(26). The general expression of the model is expressed as: m x,t = e a x +b x +k t +ε x,t , where, m x,t is the central mortality rate at age x and year t, and a x is the average (over time) log-mortality at age x, and b x measures the response at age x to changes in the overall level of mortality over time, and k t represents the overall level of mortality in year t, and ε x,t is the residual term.…”

“…The data includes the number of deaths D x, t and exposure to death E x, t for a person of age x last birthday during year t. The central mortality rate m x, t is calculated using the formula . The data to be used are annual age-specific death rates ( 22 ).…”

“…Additionally, to test the sensitivity of the method, we evaluated the performance of the LC model in comparison with an alternative stochastic mortality model called Booth–Maindonald–Smith (BMS) model ( 33 ) and predicted the breast cancer mortality trends by using both the models. A detailed description of about BMS method has been provided elsewhere ( 22 , 33 ).…”

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;…”