文章基本信息

标题：Modeling County-Level Spatio-Temporal Mortality Rates Using Dynamic Linear Models
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作者：Zoe Gibbs ; Chris Groendyke ; Brian Hartman 等
期刊名称：Risks
印刷版ISSN：2227-9091
出版年度：2020
卷号：8
期号：117
页码：117
DOI：10.3390/risks8040117
语种：English
出版社：MDPI, Open Access Journal
摘要：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.