1. INTRODUCTION
The COVID-19 pandemic has exacerbated existing racial and ethnic health
and socioeconomic disparities in the United States. Notably,
Black/African American, Latinx, and Indigenous populations have suffered
disproportionate morbidity and mortality, as well as financial loss from
subsequent economic disruption. These inequities are substantially due
to existing systemic inequities that affect infectious disease
transmission and recovery, including unequal access to medical care,
suboptimal housing characteristics, and employment in essential services
with minimal physical distancing. While the literature highlights the
significant burden on communities of color in the United States as a
result of the pandemic, there are few analyses to date that evaluate
these disparities at higher resolution than the county level, and even
fewer that disentangle cases originating in institutional congregate
settings. Using stratified, higher-resolution data, we may be able to
identify important community-level conditions that contribute to the
clear and persistent disparities induced by the pandemic.
In particular, it has been widely recognized that older Americans,
especially individuals living in nursing homes or assisted-living
facilities, have faced significantly elevated risk of COVID-19 morbidity
and mortality, especially early in the pandemic. Similarly, high case
burdens have been observed in other institutional residential settings,
such as prisons and homeless shelters. Models using total case and
mortality rates without removing or controlling for these institutional
settings may obfuscate trends or risk factors in community transmission.
Since the racial and ethnic composition of institutional and
non-institutional settings may differ, disparities may be better
characterized using higher-resolution data. In addition, characteristics
and risk factors associated with disease transmission and severity
within institutional settings may not coincide with those driving
COVID-19 transmission within non-institutional community settings.
Efforts to disaggregate institutional and community outcomes would
inform a more comprehensive understanding of health disparities in both
institutional and non-institutional settings, in turn directing testing
efforts and informing mitigation activities.
Analyses using highly resolved geospatial data provide the tools to
identify specific, local factors that contribute to disease outcomes and
hone targeted efforts to intervene and support communities. Such data
are particularly useful for local public health departments, for whom
local data from their community is more valuable than aggregated
larger-scale trends. Local case data coupled with community-level
sociodemographic data at the census-tract level can provide public
health leaders with actionable and highly relevant local information and
support pandemic response.
State public health departments have served a critical role during the
pandemic in collecting and aggregating individual patient information
across municipalities within the state and communicating relevant
information back to local leaders. Due to patient privacy regulations,
data on residential addresses associated with COVID-19 that would
support generating census tract resolution case estimates and
distinguishing institutional from non-institutional cases are not
publicly available. Cross-sectoral partnerships and data sharing
agreements between state public health agencies and academic researchers
can support analyses that integrate protected public health data with
community-level characteristics. This study reflects one such
partnership between the Massachusetts Department of Public Health (MDPH)
and academic researchers at Boston University School of Public Health to
inform state and local interventions to mitigate COVID-19 risk and
associated health disparities.
In this study, we geocoded residential home addresses (street,
city/town, and zip code) of all individual COVID-19 cases confirmed via
nucleic acid amplification tests (NAAT) from the first year of the
COVID-19 pandemic in Massachusetts provided by MDPH. We then analyzed
community-level sociodemographic and occupational predictors of outcomes
at the census-tract level . The goals of this project were to: 1)
estimate associations between community-level risk factors and COVID-19
cases and deaths by census tract in the state; 2) evaluate the
sensitivity of these associations to the exclusion of institutional
cases and deaths, given the relevance of institutional settings to
larger-scale disease patterns; and 3) assess changes over time in these
associations during the initial two Phases of the pandemic in
Massachusetts.