ABSTRACT
Background: The COVID-19 pandemic has highlighted the need for
targeted local interventions given substantial heterogeneity within
cities and counties. Publicly available case data are typically
aggregated to the city or county level to protect patient privacy, but
more granular data are necessary to identify and act upon
community-level risk factors that can change over time.
Methods : Individual COVID-19 case and mortality data from
Massachusetts were geocoded to residential addresses and aggregated into
two time periods: “Phase 1” (March–June 2020) and “Phase 2”
(September 2020–February 2021). Institutional cases associated with
long-term care facilities, prisons, or homeless shelters were identified
using address data and modeled separately. Census tract sociodemographic
and occupational predictors were drawn from the 2015-2019 American
Community Survey. We used mixed-effects negative binomial regression to
estimate incidence rate ratios (IRRs), accounting for town-level spatial
autocorrelation.
Results : Case incidence was elevated in census tracts with
higher proportions of Black and Latinx residents, with larger
associations in Phase 1 than Phase 2. Case incidence associated with
proportion of essential workers was similarly elevated in both Phases.
Mortality IRRs had differing patterns from case IRRs, decreasing less
substantially between Phases for Black and Latinx populations and
increasing between Phases for proportion of essential workers. Mortality
models excluding institutional cases yielded stronger associations for
age, race/ethnicity, and essential worker status.
Conclusions: Geocoded home address data can allow for nuanced
analyses of community disease patterns, identification of high-risk
subgroups, and exclusion of institutional cases to comprehensively
reflect community risk.