Open AccessCommunity predictors of COVID‐19 cases and deaths in Massachusetts: Evaluating changes over time using geospatially refined data
Author(s) -
Spangler Keith R.,
Patil Prasad,
Peng Xiaojing,
Levy Jonathan I.,
Lane Kevin J.,
Tieskens Koen F.,
Carnes Fei,
Klevens R. Monina,
Erdman Elizabeth A.,
Troppy T. Scott,
Fabian M. Patricia,
Leibler Jessica H.
Publication year - 2022
Publication title -
influenza and other respiratory viruses
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.743
H-Index - 57
eISSN - 1750-2659
pISSN - 1750-2640
DOI - 10.1111/irv.12926
Subject(s) - geocoding , demography , psychological intervention , geography , american community survey , census , incidence (geometry) , ethnic group , pandemic , medicine , environmental health , gerontology , socioeconomics , covid-19 , population , disease , sociology , cartography , infectious disease (medical specialty) , physics , pathology , psychiatry , anthropology , optics
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 to 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.