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Community predictors of COVID-19 cases and deaths in Massachusetts: Evaluating changes over time using geospatially refined data.
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.
  • Spangler KR; Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA.
  • Patil P; Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.
  • Peng X; Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.
  • Levy JI; Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA.
  • Lane KJ; Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA.
  • Tieskens KF; Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA.
  • Carnes F; Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA.
  • Klevens RM; Bureau of Infectious Disease and Laboratory Sciences, Massachusetts Department of Public Health, Boston, Massachusetts, USA.
  • Erdman EA; Office of Population Health, Massachusetts Department of Public Health, Boston, Massachusetts, USA.
  • Troppy TS; Bureau of Infectious Disease and Laboratory Sciences, Massachusetts Department of Public Health, Boston, Massachusetts, USA.
  • Fabian MP; Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA.
  • Leibler JH; Department of Environmental Health, Boston University School of Public Health, Boston, Massachusetts, USA.
Influenza Other Respir Viruses ; 16(2): 213-221, 2022 03.
Article in English | MEDLINE | ID: covidwho-1511324
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 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: Influenza Other Respir Viruses Journal subject: Virology Year: 2022 Document Type: Article Affiliation country: Irv.12926

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: Influenza Other Respir Viruses Journal subject: Virology Year: 2022 Document Type: Article Affiliation country: Irv.12926