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.
翻译:我对美国COVID-19死亡率提出了三种通用添加模型(GAMS)适用于美国COVID-19死亡率的三种应用,目的是推进各种方法,记录在流行病头三年特定时期社区遭受过过不成比例COVID-19-19死亡率的不平等情况;首先,GAMS可用于描述COVID-19死亡率与县级共同变化关系(人口、经济和政治指标)长期变化的关系;第二,GAMs可用于进行零星的平滑工作,在时间和空间上汇集信息,解决统计不稳定问题,因为人口数量少或随机性造成统计不稳定,造成平稳、动态的潜在风险表层,概述与一段时间内地理位置有关的死亡风险;第三,对COVID-19死亡率协会与县级共同变化关系的估计,条件是平稳的波幅风险表,可以更严格地考虑社会环境环境环境和政策如何影响COVID-19死亡率的问题;每一种方法都提供了宝贵的视角,通过解决哪些人口遭受COVID-19死亡率最重负担的问题来记录COVID-19死亡率的不平等情况,处理哪些人口在时间和空间方面承受了最重的死亡率。