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

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:
10706489
负责人:
Brian Neelon
金额:
$18.55万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-09-19 至 2025-05-31

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中文摘要
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英文摘要
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.
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Multivariate spatiotemporal models to quantify disparities in COVID-19 health outcomes
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