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Joint models for cause-of-death mortality in multiple populations

Published online by Cambridge University Press:  18 May 2023

Nhan Huynh  and
Mike Ludkovski Open the ORCID record for Mike Ludkovski [Opens in a new window]
Nhan Huynh
Affiliation:
Department of Statistics and Applied Probability, University of California at Santa Barbara, Santa Barbara, CA 93106, USA
Mike Ludkovski*
Affiliation:
Department of Statistics and Applied Probability, University of California at Santa Barbara, Santa Barbara, CA 93106, USA
*
Corresponding author: Mike Ludkovski; Email: ludkovski@pstat.ucsb.edu
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Abstract

We investigate jointly modelling age–year-specific rates of various causes of death in a multinational setting. We apply multi-output Gaussian processes (MOGPs), a spatial machine learning method, to smooth and extrapolate multiple cause-of-death mortality rates across several countries and both genders. To maintain flexibility and scalability, we investigate MOGPs with Kronecker-structured kernels and latent factors. In particular, we develop a custom multi-level MOGP that leverages the gridded structure of mortality tables to efficiently capture heterogeneity and dependence across different factor inputs. Results are illustrated with datasets from the Human Cause-of-Death Database (HCD). We discuss a case study involving cancer variations in three European nations and a US-based study that considers eight top-level causes and includes comparison to all-cause analysis. Our models provide insights into the commonality of cause-specific mortality trends and demonstrate the opportunities for respective data fusion.

Keywords

Cause-of-death modellingGaussian process modelsMultiple populationsMultiple causes of death
Type
Original Research Paper
Information
Annals of Actuarial Science , Volume 18 , Issue 1 , March 2024 , pp. 51 - 77
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Institute and Faculty of Actuaries

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