Author: Karaye, Ibraheem M.; Horney, Jennifer A.
Title: The Impact of Social Vulnerability on COVID-19 in the U.S.: An Analysis of Spatially Varying Relationships Cord-id: kx0u9955 Document date: 2020_6_26
ID: kx0u9955
Snippet: Introduction Because of their inability to access adequate medical care, transportation, and nutrition, socially vulnerable populations are at increased risk of health challenges during disasters. This study estimates the association between case counts of coronavirus disease 2019 (COVID-19) infection and social vulnerability in the U.S., identifying counties at increased vulnerability to the pandemic. Methods Using Social Vulnerability Index and COVID-19 case count data, an ordinary least squar
Document: Introduction Because of their inability to access adequate medical care, transportation, and nutrition, socially vulnerable populations are at increased risk of health challenges during disasters. This study estimates the association between case counts of coronavirus disease 2019 (COVID-19) infection and social vulnerability in the U.S., identifying counties at increased vulnerability to the pandemic. Methods Using Social Vulnerability Index and COVID-19 case count data, an ordinary least squares regression model was fitted to assess the “global†relationship between COVID-19 case counts and social vulnerability. Local relationships were assessed using a geographically weighted regression model, which is effective in exploring spatial non-stationarity. Results As of May 12, 2020, a total of 1,320,909 people had been diagnosed with COVID-19 in the U.S. Of the counties included in this study (91.5%, 2,844/3,108), the highest case count was recorded in Trousdale, Tennessee (16,525.22 per 100,000) and the lowest in Tehama, California (1.54 per 100,000). At the “global†level, overall Social Vulnerability Index (e β=1.65, p=0.03) and minority status and language (e β=6.69, p<0.001) were associated with increased COVID-19 case counts. However, based on the “local†geographically weighted model, the association between social vulnerability and COVID-19 varied among counties. Overall, minority status and language, household composition and transportation, and housing and disability predicted COVID-19 infection. Conclusions Large-scale disasters differentially affect the health of marginalized communities. In this study, minority status and language, household composition and transportation, and housing and disability predicted COVID-19 case counts in the U.S. Addressing the social factors that create poor health is essential to reducing inequities in the health impacts of disasters.
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