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Analyzing state-level longevity trends with the U.S. mortality database

Michael Ludkovski & Doris Padilla

Annals of Actuarial Science2025https://doi.org/10.1017/s1748499525000089article
AJG 1ABDC A
Weight
0.37

Abstract

We investigate state-level age-specific mortality trends based on the United States Mortality Database (USMDB) published by the Human Mortality Database. In tandem with looking at the longevity experience across all the states, we also consider a collection of socio-demographic, economic, and educational covariates that correlate with mortality trends. To obtain smoothed mortality surfaces for each state, we implement the machine learning framework of Multi-Output Gaussian Process regression (Huynh & Ludkovski, AAS, 2021) on targeted groupings of 3–6 states. Our detailed exploratory analysis shows that the mortality experience is highly inhomogeneous across states in terms of respective Age structures. We moreover document multiple divergent trends between best and worst states, between Females and Males, and between younger and older Ages. The comparisons across the 50+ fitted models offer opportunities for rich insights about drivers of mortality in the U.S. and are visualized through numerous figures and an online interactive dashboard.

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https://doi.org/https://doi.org/10.1017/s1748499525000089

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@article{michael2025,
  title        = {{Analyzing state-level longevity trends with the U.S. mortality database}},
  author       = {Michael Ludkovski & Doris Padilla},
  journal      = {Annals of Actuarial Science},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1017/s1748499525000089},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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