Abstract
BackgroundAssociations between community-level risk factors and COVID-19 incidence have been used to identify vulnerable subpopulations and target interventions, but the variability of these associations over time remains largely unknown. We evaluated variability in the associations between community-level predictors and COVID-19 case incidence in 351 cities and towns in Massachusetts from March to October 2020.MethodsUsing publicly available sociodemographic, occupational, environmental, and mobility datasets, we developed mixed-effect, adjusted Poisson regression models to depict associations between these variables and town-level COVID-19 case incidence data across five distinct time periods from March to October 2020. We examined town-level demographic variables, including population proportions by race, ethnicity, and age, as well as factors related to occupation, housing density, economic vulnerability, air pollution (PM2.5), and institutional facilities. We calculated incidence rate ratios (IRR) associated with these predictors and compared these values across the multiple time periods to assess variability in the observed associations over time.ResultsAssociations between key predictor variables and town-level incidence varied across the five time periods. We observed reductions over time in the association with percentage of Black residents (IRR = 1.12 [95%CI: 1.12–1.13]) in early spring, IRR = 1.01 [95%CI: 1.00–1.01] in early fall) and COVID-19 incidence. The association with number of long-term care facility beds per capita also decreased over time (IRR = 1.28 [95%CI: 1.26–1.31] in spring, IRR = 1.07 [95%CI: 1.05–1.09] in fall). Controlling for other factors, towns with higher percentages of essential workers experienced elevated incidences of COVID-19 throughout the pandemic (e.g., IRR = 1.30 [95%CI: 1.27–1.33] in spring, IRR = 1.20 [95%CI: 1.17–1.22] in fall). Towns with higher proportions of Latinx residents also had sustained elevated incidence over time (IRR = 1.19 [95%CI: 1.18–1.21] in spring, IRR = 1.14 [95%CI: 1.13–1.15] in fall).ConclusionsTown-level COVID-19 risk factors varied with time in this study. In Massachusetts, racial (but not ethnic) disparities in COVID-19 incidence may have decreased across the first 8 months of the pandemic, perhaps indicating greater success in risk mitigation in selected communities. Our approach can be used to evaluate effectiveness of public health interventions and target specific mitigation efforts on the community level.
Highlights
As of May 2021, the United States had the highest number of Coronavirus Disease 2019 (COVID-19) cases and deaths in the world [1]
Town-level COVID-19 risk factors varied with time in this study
Statistical analysis We developed a series of mixed-effect Poisson regression models to predict COVID-19 case incidence by town for each of the five distinct time periods of the pandemic
Summary
As of May 2021, the United States had the highest number of Coronavirus Disease 2019 (COVID-19) cases and deaths in the world [1]. In Massachusetts, during the two-week period from January 10–23, 2021, COVID-19 average daily incidence exceeded 100 confirmed cases per 100,000 persons in multiple urban communities (including Chelsea, Lawrence and New Bedford), with a low of zero in a number of more-rural communities [6]. Multiple community-level factors were associated with higher COVID-19 incidence, with disproportionate burdens among communities with more racial and ethnic diversity and workers in essential services [7,8,9,10]. Higher COVID-19 case incidence is associated with greater percentage of immigrants and lower education at the community level, likely due to occupational, medical, and housing risk factors that elevate risk of disease transmission and severity [16]. We evaluated variability in the associations between community-level predictors and COVID-19 case incidence in 351 cities and towns in Massachusetts from March to October 2020
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.