Multivariate spatiotemporal models to quantify disparities in COVID-19 health outcomes

项目总结 严重急性呼吸综合征冠状病毒2(SARS-CoV-2)，冠状病毒疾病的原因2019年 新冠肺炎(Alipay)，自2019年末爆发以来，引发了一场全球公共卫生危机。尽管大流行已经 影响到所有社区，最近的研究表明，社会弱势群体已经不成比例地 受到疾病的影响。越来越多的证据发现，这种大流行不成比例地影响着 肤色、年龄较大的人和社会经济地位较低的人。然而，到目前为止，还没有 新冠肺炎成果与社会脆弱性关系的时空综合分析 在全国范围内和在较长的一段时间内，部分原因是这样的统计工具 缺乏分析。该提案的目标是开发多变量模型来识别时空 相关计数结果的趋势，并使用这些模型来量化新冠肺炎感染的差异， 整个社会弱势社区的死亡、检测、住院和疫苗接种。目标1提出了一个 贝叶斯多变量时空模型量化新冠肺炎感染、死亡、检测 住院率和美国各县随时间推移的疫苗接种率。社会漏洞暴露是 通过一种新型的多元核机器以非线性和交互的方式融入到模型中 回归。Aim 2通过发展贝叶斯多元零点模型将该方法扩展到零点膨胀设置。 膨胀的负二项模型，以量化新冠肺炎趋势随时间和跨县的差异。目标 3开发了可计算扩展的贝叶斯软件，用于实现这些方法。这场大流行已经 对医疗保健系统造成持久的破坏，这将对弱势群体造成不成比例的影响 在未来的几年里。这里开发的统计方法将在促进卫生公平和 缓解因大流行而加剧的长期差距。

PROJECT SUMMARY Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has created a global public health crisis since its onset in late 2019. Although the pandemic has affected all communities, recent work suggests that socially vulnerable populations have been disproportionately impacted by the disease. Mounting evidence has found that the pandemic disproportionately affects people of color, older individuals, and those of lower socioeconomic status. To date, however, there has been no comprehensive spatiotemporal analysis of the relationship between social vulnerability and COVID-19 outcomes at a national scale and over an extended period of time, in part because the statistical tools needed for such an analysis are lacking. The objective of the proposal is to develop multivariate models to identify spatiotemporal trends in correlated count outcomes, and to use these models to quantify disparities in COVID-19 infection, death, testing, hospitalizations, and vaccinations across socially vulnerable communities. Aim 1 proposes a Bayesian multivariate spatiotemporal model to quantify disparities in COVID-19 infection, death, testing, hospitalization, and vaccination rates over time across US counties. Social vulnerability exposures are incorporated into the model in a nonlinear and interactive manner through a novel multivariate kernel machine regression. Aim 2 extends the method to the zero inflated setting by developing a Bayesian multivariate zero- inflated negative binomial model to quantify disparities in COVID-19 trends over time and across counties. Aim 3 develops computationally scalable Bayesian software for implementation of the methods. The pandemic has caused enduring disruptions to the health care system that will disproportionately impact vulnerable populations for years to come. The statistical methods developed here will play a critical role in promoting health equity and mitigating long-standing disparities exacerbated by the pandemic.