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

Show simple item record Groendyke, Chris Gibbs, Zoe Hartman, Brian Richardson, Robert 2021-02-09T18:49:08Z 2021-02-09T18:49:08Z 2020-10-08
dc.identifier.citation Groendyke, C. (2020) Modeling County-Level Spatio-Temporal Mortality Rates Using Dynamic Linear Models, Risks. Retrieved from en_US
dc.description.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. en_US
dc.language.iso en_US en_US
dc.publisher Risks en_US
dc.subject mortality improvement en_US
dc.subject Bayesian modeling en_US
dc.subject spatial generalized linear model en_US
dc.subject conditional auto-regressive priors en_US
dc.title Modeling County-Level Spatio-Temporal Mortality Rates Using Dynamic Linear Models en_US
dc.type Article en_US

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