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A multivariate spatiotemporal model for county-level mortality data in the contiguous United States

Michael L. Shull et al.

Annals of Actuarial Science2025https://doi.org/10.1017/s1748499525000016article
AJG 1ABDC A
Weight
0.37

Abstract

We seek to understand the factors that drive mortality in the contiguous United States using data that are indexed by county and year and grouped into 18 different age bins. We propose a model that adds two important contributions to existing mortality studies. First, we treat age as a random effect. This is an improvement over previous models because it allows the model in one age group to borrow information from other age groups. Second, we utilize Gaussian Processes to create nonlinear covariate effects for predictors such as unemployment rate, race, and education level. This allows for a more flexible relationship to be modeled between mortality and these predictors. Understanding that the United States is expansive and diverse, we allow for many of these effects to vary by location. The flexibility in how predictors relate to mortality has not been used in previous mortality studies and will result in a more accurate model and a more complete understanding of the factors that drive mortality. Both the multivariate nature of the model as well as the spatially varying non-linear predictors will advance the study of mortality and will allow us to better examine the relationships between the predictors and mortality.

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https://doi.org/https://doi.org/10.1017/s1748499525000016

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@article{michael2025,
  title        = {{A multivariate spatiotemporal model for county-level mortality data in the contiguous United States}},
  author       = {Michael L. Shull et al.},
  journal      = {Annals of Actuarial Science},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1017/s1748499525000016},
}

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A multivariate spatiotemporal model for county-level mortality data in the contiguous United States

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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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