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Community Predictors of COVID-19 Cases and Deaths in Massachusetts: Evaluating Changes Over Time Using Geospatially Refined Data
  • +9
  • Keith Spangler,
  • Prasad Patil,
  • Xiaojing Peng,
  • Jonathan Levy,
  • Kevin Lane,
  • Koen Tieskens,
  • Fei Carnes,
  • R. Monina Klevens,
  • Elizabeth Erdman,
  • T. Troppy,
  • M. Fabian,
  • Jessica Leibler
Keith Spangler
Boston University School of Public Health
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Prasad Patil
Boston University School of Public Health
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Xiaojing Peng
Boston University School of Public Health
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Jonathan Levy
Boston University School of Public Health
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Kevin Lane
Boston University School of Public Health
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Koen Tieskens
Boston University School of Public Health
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Fei Carnes
Boston University School of Public Health
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R. Monina Klevens
Massachusetts Department of Public Health
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Elizabeth Erdman
Massachusetts Department of Public Health
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T. Troppy
Massachusetts Department of Public Health
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M. Fabian
Boston University School of Public Health
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Jessica Leibler
Boston University School of Public Health
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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.
17 Sep 2021Submitted to Influenza and other respiratory viruses
20 Sep 2021Assigned to Editor
20 Sep 2021Submission Checks Completed
23 Sep 2021Editorial Decision: Accept
Mar 2022Published in Influenza and Other Respiratory Viruses volume 16 issue 2 on pages 213-221. 10.1111/irv.12926