TY - JOUR
T1 - Cause of death specific cohort effects in U.S. mortality
AU - Redondo Lourés, Cristian
AU - Cairns, Andrew John George
N1 - Funding Information:
This work forms part of the research programme “Modelling, Measurement and Management of Longevity and Morbidity Risk” funded by the Actuarial Research Centre of the Institute and Faculty of Actuaries, UK , the Society of Actuaries, USA and the Canadian Institute of Actuaries . The authors gratefully acknowledge feedback from the respective steering and oversight groups.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/7
Y1 - 2021/7
N2 - We use a stochastic age–period–cohort mortality model to analyse US data for years 1989–2015 and ages 50-75, separated by gender, educational attainment, and cause of death. The paper focuses, in particular, on the fitted cohort effect for each sub-population and cause of death with two key findings. First, causes of death with a strong or distinctively-shaped cohort effect are also causes of death with significant, controllable risk factors, and that the fitted cohort effect gives us insight into the underlying prevalence of specific risk factors (such as smoking prevalence). Second, although each sub-population and cause of death has its own distinctive model fit, there are sufficient similarities between cohort effects to allow us to postulate that there is a relatively small number of underlying controllable risk factors that drive these cohort effects. The analysis then gives us insight into the modelled cohort effect for all-cause mortality.
AB - We use a stochastic age–period–cohort mortality model to analyse US data for years 1989–2015 and ages 50-75, separated by gender, educational attainment, and cause of death. The paper focuses, in particular, on the fitted cohort effect for each sub-population and cause of death with two key findings. First, causes of death with a strong or distinctively-shaped cohort effect are also causes of death with significant, controllable risk factors, and that the fitted cohort effect gives us insight into the underlying prevalence of specific risk factors (such as smoking prevalence). Second, although each sub-population and cause of death has its own distinctive model fit, there are sufficient similarities between cohort effects to allow us to postulate that there is a relatively small number of underlying controllable risk factors that drive these cohort effects. The analysis then gives us insight into the modelled cohort effect for all-cause mortality.
KW - Bayesian methods
KW - Cause of death
KW - Cohort effect
KW - Controllable risk factors
KW - Stochastic mortality modelling
KW - US mortality
UR - http://www.macs.hw.ac.uk/~andrewc/ARCresources/RedondoCairnsFinal.pdf
UR - http://www.scopus.com/inward/record.url?scp=85105295733&partnerID=8YFLogxK
U2 - 10.1016/j.insmatheco.2021.03.026
DO - 10.1016/j.insmatheco.2021.03.026
M3 - Article
SN - 0167-6687
VL - 99
SP - 190
EP - 199
JO - Insurance: Mathematics and Economics
JF - Insurance: Mathematics and Economics
ER -