The burden of COVID-19 in the U.S. has not been equitable at either the individual- or neighborhood-level. Yet, a lack of fine-scale, spatially-referenced SARS-CoV-2 infection data is not available for the U.S. as a whole, impeding our ability to further investigate these trends. Such data would allow for the accurate identification of who is at risk for COVID-19, both individuals and communities, and why and how the burden has shifted across time and space. While SARS-Co-V-2 infections have been differentially distributed across neighborhoods, the mechanisms through which the neighborhood social and physical environment shape the burden of COVID-19 remain unknown. Thus, the long-term goal of this research is to identify neighborhoods that will likely have lasting social and economic consequences from the COVID-19 pandemic so that we can then begin to understand the implications for population health trends going forward, and particularly age-related decline and disease trends. Our long-term hypotheses are that there are hidden, fine-scale, spatial inequalities in the distribution of SARS-CoV-2 infections, and those differences a) are related to pre-pandemic neighborhood conditions, b) influence the neighborhood demographic shifts that have occurred as a result of the pandemic, and c) will have lasting consequences for population health trends post-pandemic. Using fine-resolution, spatially-referenced data, we can better determine which aspects of the neighborhood context drive the observed disease trends, something that is not possible using only the state- or county-level data currently available. The rationale for this proposal is that once we have identified the neighborhoods features that characterize high burden SARS-CoV-2 neighborhoods, it will be possible to begin to elucidate the implications of the changing neighborhood context for population health trends, particularly age-related decline and disease. We will test our hypothesis by leveraging preliminary data from the COVID Neighborhood Project, a data collation effort ongoing at the University of Michigan?s Institute for Social Research that brings together fine-scale (e.g., census tract or ZIP code), spatially referenced SARS-CoV-2 infection data from across the U.S. with social and environmental data from the National Neighborhood Data Archive (NaNDA). In this pilot proposal, we will pursue two specific aims: 1) Identify which neighborhoods in the United States are characterized as persistently high burden SARS-CoV-2 neighborhoods over time; 2) Examine the neighborhood socio-demographic features associated with high burden SARS-CoV-2 neighborhoods. The project is innovative because it a) leverages a novel spatially-referenced SARS-CoV-2 infection dataset and b) integrates infection data with social and physical neighborhood environment data allowing us to carry out biosocial investigations of population health. This project will yield critical preliminary data that will be used to develop an R01 proposal aimed at understanding the implications of the changing neighborhood landscape for population health and mortality from 2020 and beyond.
11/01/2021 to 12/31/2022