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This study analyzed the cumulative number of confirmed COVID-19 deaths over a fixed time period using pre-existing cross-sectional social vulnerability data in counties in the United States. First, to analyze the association between social vulnerability and COVID-19 deaths, the number of confirmed COVID-19 deaths in counties in the United States was obtained from USA Facts [2]. At the time of this study, USA Facts released data on the cumulative number of COVID-19 deaths every day. For this study, the cumulative number of confirmed COVID-19 deaths up to November 14, 2020, was analyzed. The dependent variable (cumulative number of confirmed COVID-19 deaths) was changed to a per capita figure (deaths per 100,000 residents) to minimize the effect of the number of residents in a county on the dependent variable. The cumulative number of COVID-19 deaths (per 100,000) was mapped (Figure 1). The dependent variable was log-transformed to reduce skewness.

Mainland United States map of cumulative number of confirmed deaths from coronavirus disease 2019 (per 100,000 population) by county level through November 14, 2020.

Second, variables related to social vulnerability were obtained from the Centers for Disease Control and Prevention (CDC) Social Vulnerability Index [28] and the 2018 5-Year American Community Survey at the county level [29]. Fifteen high social vulnerability variables (flags) were obtained from the CDC Social Vulnerability Index. Flags are interpreted as follows: “tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability whereas tracts below the 90th percentile are given a value of 0” [28]. Next, the percentage of people without health insurance and 2 control variables (population and the percentage of male) were obtained at the county level from the 2018 5-Year American Community Survey.

This study did not require approval from the Institutional Review Board because the datasets were secondary data that did not include personal information.

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