Multivariate spatiotemporal models to quantify disparities in COVID-19 health outcomes
- Funded by National Institutes of Health (NIH)
- Total publications:0 publications
Grant number: 5R21MD016947-02
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Key facts
Disease
COVID-19Start & end year
20222025Known Financial Commitments (USD)
$185,516Funder
National Institutes of Health (NIH)Principal Investigator
ASSOCIATE PROFESSOR Brian NeelonResearch Location
United States of AmericaLead Research Institution
MEDICAL UNIVERSITY OF SOUTH CAROLINAResearch Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
Disease susceptibility
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Vulnerable populations unspecified
Occupations of Interest
Unspecified
Abstract
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.