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

用于量化 COVID-19 健康结果差异的多元时空模型

• 批准号: 10527208
• 负责人: Brian Neelon
• 金额: $ 24.27万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-19 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10527208
• 关键词: 2019-nCoV; Accounting; Address; Affect; Area; Binomial Model; COVID-19; COVID-19 disparity; COVID-19 pandemic; Cessation of life; Code; Color; Communities; Computer software; County; Data; Dependence; Development; Disease; Disease Outbreaks; Economically Deprived Population; Equilibrium; Exhibits; Exposure to; Geographic Locations; Goals; Health; Health Disparities Research; Health system; Healthcare; Healthcare Systems; Hospitalization; Incidence; Individual; Infection; Institutes; Joints; Methods; Modeling; Outcome; Play; Public Health; Research; Resources; Role; SARS-CoV-2 infection; Sampling; Statistical Methods; Testing; Time; Time trend; Update; Vaccination; Vaccines; Vulnerable Populations; Work; flexibility; health equity promotion; health inequalities; high dimensionality; infection rate; long-standing disparities; low socioeconomic status; minority health; minority health disparity; novel; pandemic disease; people of color; response; social; social vulnerability; spatiotemporal; tool; trend; user-friendly; vulnerable community

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.

项目概要 严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2)，2019 年冠状病毒病的病因 （COVID-19）自 2019 年底爆发以来已造成全球公共卫生危机。 影响到所有社区，最近的研究表明，社会弱势群体不成比例地受到影响 受疾病影响。越来越多的证据表明，这一流行病对以下人群的影响尤为严重： 肤色、老年人和社会经济地位较低的人。但迄今为止，还没有出现 社会脆弱性与 COVID-19 结果之间关系的综合时空分析 在全国范围内并在较长一段时间内进行统计，部分原因是这种统计所需的统计工具 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 COVID-19 感染的差异， 社会弱势群体的死亡、检测、住院和疫苗接种。目标 1 提出 贝叶斯多元时空模型可量化 COVID-19 感染、死亡、检测、 美国各县随时间推移的住院率和疫苗接种率。社会脆弱性暴露是 通过新颖的多元核机以非线性和交互的方式纳入模型 回归。目标 2 通过开发贝叶斯多元零膨胀将该方法扩展到零膨胀设置 膨胀的负二项式模型来量化随着时间的推移和各县之间的 COVID-19 趋势差异。目的 3 开发了计算可扩展的贝叶斯软件来实施这些方法。疫情已 对医疗保健系统造成持久破坏，将对弱势群体造成不成比例的影响 未来几年。这里开发的统计方法将在促进健康公平和 缓解因疫情大流行而加剧的长期存在的差距。