论文标题
使用广义添加剂模型了解美国县一级的社会，地理和经济不平等现象的复杂模式
Understanding Complex Patterns in Social, Geographic, and Economic Inequities in COVID-19 Mortality at the County Level in the US Using Generalized Additive Models
论文作者
论文摘要
我提出了三种类型的应用，在美国，在美国，在美国的Covid-19死亡率上，将其应用于19日，以记录不平等的方法，而在大流行的前三年，社区在特定时间遭受了不成比例的COVID-19死亡率。首先，随着时间的流逝，GAM可以用来描述Covid-19死亡率与县级协变量（社会人口统计学，经济和政治指标）之间不断变化的关系。其次，可以使用GAM来执行时空平滑，以汇集信息和空间的信息，以解决统计不稳定，这是由于人口数量少或随机性而导致的，导致平稳，动态的潜在风险表面总结了与地理位置随时间相关的死亡率风险。第三，以平稳时空风险表面有条件的有条件的县级死亡率关联的估计，可以更严格地考虑社会环境环境和政策如何影响COVID-199的死亡率。这些方法中的每一种都通过解决了哪些人口在哪些人群中考虑到非线性的空间，时间和社交疾病的疾病，遭受了covid-19死亡率的最大负担，从而为记录Covid-19死亡率的不平等现象提供了有价值的观点。
I present three types of applications of generalized additive models (GAMs) to COVID-19 mortality rates in the US for the purpose of advancing methods to document inequities with respect to which communities suffered disproportionate COVID-19 mortality rates at specific times during the first three years of the pandemic. First, GAMs can be used to describe the changing relationship between COVID-19 mortality and county-level covariates (sociodemographic, economic, and political metrics) over time. Second, GAMs can be used to perform spatiotemporal smoothing that pools information over time and space to address statistical instability due to small population counts or stochasticity resulting in a smooth, dynamic latent risk surface summarizing the mortality risk associated with geographic locations over time. Third, estimation of COVID-19 mortality associations with county-level covariates conditional on a smooth spatiotemporal risk surface allows for more rigorous consideration of how socio-environmental contexts and policies may have impacted COVID-19 mortality. Each of these approaches provides a valuable perspective to documenting inequities in COVID-19 mortality by addressing the question of which populations have suffered the worst burden of COVID-19 mortality taking into account the nonlinear spatial, temporal, and social patterning of disease.
