Modeling County-Level Spatio-Temporal Mortality Rates Using Dynamic Linear Models

Zoe Gibbs, Chris Groendyke, Brian Hartman and Robert Richardson
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Zoe Gibbs: Department of Statistics, Brigham Young University, Provo, UT 84602, USA
Chris Groendyke: Department of Mathematics, Robert Morris University, Moon Township, PA 15108, USA
Brian Hartman: Department of Statistics, Brigham Young University, Provo, UT 84602, USA
Robert Richardson: Department of Statistics, Brigham Young University, Provo, UT 84602, USA

Risks, 2020, vol. 8, issue 4, 1-15

Abstract: The lifestyles and backgrounds of individuals across the United States differ widely. Some of these differences are easily measurable (ethnicity, age, income, etc.) while others are not (stress levels, empathy, diet, exercise, etc.). Though every person is unique, individuals living closer together likely have more similar lifestyles than individuals living hundreds of miles apart. Because lifestyle and environmental factors contribute to mortality, spatial correlation may be an important feature in mortality modeling. However, many of the current mortality models fail to account for spatial relationships. This paper introduces spatio-temporal trends into traditional mortality modeling using Bayesian hierarchical models with conditional auto-regressive (CAR) priors. We show that these priors, commonly used for areal data, are appropriate for modeling county-level spatial trends in mortality data covering the contiguous United States. We find that mortality rates of neighboring counties are highly correlated. Additionally, we find that mortality improvement or deterioration trends between neighboring counties are also highly correlated.

Keywords: mortality improvement; Bayesian modeling; spatial generalized linear model; conditional auto-regressive priors (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2020
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