The COVID-19 pandemic has caused severe public health consequences in the United States. The United States began a vaccination campaign at the end of 2020 targeting primarily elderly residents before extending access to younger individuals. With both COVID-19 infection fatality ratios and vaccine uptake being heterogeneous across ages, an important consideration is whether the age contribution to deaths shifted over time towards younger age groups. In this study, we use a Bayesian non-parametric spatial approach to estimate the age-specific contribution to COVID-19 attributable deaths over time. The proposed spatial approach is a low-rank Gaussian Process projected by regularised B-splines. Simulation analyses and benchmark results show that the spatial approach performs better than a standard B-splines approach and equivalently well as a standard Gaussian Process, for considerably lower runtimes. We find that COVID-19 has been especially deadly in the United States. The mortality rates among individuals aged 85+ ranged from 1\% to 5\% across the US states. Since the beginning of the vaccination campaign, the number of weekly deaths reduced in every US state with a faster decrease among individuals aged 75+ than individuals aged 0-74. Simultaneously to this reduction, the contribution of individuals age 75+ to deaths decreased, with important disparities in the timing and rapidity of this decrease across the country.
翻译:美国的COVID-19大流行给美国造成了严重的公共健康后果。美国在2020年底开始在扩大接触年轻个人之前首先针对老年居民开展疫苗接种运动。由于COVID-19传染死亡率和疫苗摄入率各年龄段之间各不相同,一个重要的考虑因素是,随着时间推移,死亡的年龄因素是否转移到了较年轻的年龄组。在这项研究中,我们采用巴耶斯非对称空间方法来估计85岁以上人口对COVID-19长期导致的死亡的具体贡献。拟议的空间方法是一个低级别高斯人进程,通过定期的B线预测。模拟分析和基准结果显示,空间方法的表现好于标准的B线方法,在相当低的运行时间里,相当于标准高斯人进程。我们发现,在美国,COVID-19的死亡率特别致命。美国各州85岁以上人口的死亡率从1 ⁇ 到5 ⁇ 不等。自疫苗接种运动开始以来,美国各州的每周死亡人数都有所下降,75岁以上人口死亡率比75岁以上人口比例下降,这一比率下降至75岁以上人口比例下降。