County-Level Association of Social Vulnerability with COVID-19 Cases and Deaths in the USA
- PDF / 1,231,407 Bytes
- 4 Pages / 595.276 x 790.866 pts Page_size
- 78 Downloads / 167 Views
J Gen Intern Med DOI: 10.1007/s11606-020-05882-3 © Society of General Internal Medicine 2020
INTRODUCTION
In past pandemics, vulnerable populations faced greater disease burden and decreased testing and treatment access.1 As coronavirus disease 2019 (COVID-19) spreads in the USA, concern is growing that even the early stages of this pandemic have disproportionately impacted vulnerable communities.2–4 However, the relationship between social vulnerability and COVID-19 diagnosis and mortality in rural and urban communities remains unknown.
METHODS
We performed a county-level, cross-sectional analysis using COVID-19 case and death rates compiled by The New York Times from health agency reports as of April 19, 2020. We stratified counties into quartiles using the U.S. Centers for Disease Control’s Social Vulnerability Index (SVI), a validated measure of community resilience during natural disasters and disease outbreaks across four domains: socioeconomic status, household composition and disability, minority status and language, and housing and transportation.5 We defined urbanicity using the U.S. Department of Agriculture Economic Research Service’s 2013 Urban Influence Codes.6 We merged data sources using Federal Information Processing Standard (FIPS) codes, including counties with a linkable FIPS code and at least one COVID-19 case. Our primary outcomes were positive tests per capita and COVID-19 deaths per capita. We built populationweighted, quasi-Poisson regression models to compare outcomes between the first and fourth quartiles of counties by SVI and each SVI domain. In secondary analyses, we stratified counties by rural and urban classification. We included state fixed effects to account for Rohan Khazanchi and Evan R. Beiter contributed equally to this work. Received April 23, 2020 Accepted April 28, 2020
heterogeneity in policies and disease spread. We analyzed data with R Statistical Software, version 3.6.3, and considered P < 0.002 significant after the Bonferroni correction. This study was approved by Partners Healthcare Institutional Review Board.
RESULTS
As of April 19, there were 612,404 confirmed cases and 25,978 COVID-19 deaths across the 2754 (of 3143 total) counties analyzed (mean cases 102.2 per 100,000 [SE 3.8], deaths 4.0 per 100,000 [0.2]). Compared with those in the least vulnerable counties, people in the most vulnerable counties had 1.63-fold greater risk of COVID-19 diagnosis and 1.73-fold greater risk of death (Table 1). When considering only the minority status and language domain, people in the most vulnerable counties had 4.94-fold and 4.74-fold greater risks of COVID-19 diagnosis and death, respectively. Mapping case burden in the most and least vulnerable counties by minority status revealed regional trends of this differential risk (Fig. 1). Similarly, people in the most vulnerable counties by socioeconomic status (relative risks [RR] of 1.42 and 1.71) and housing and transportation (RR 1.52 and 1.32) domains had greater risk of COVID-19 diagnosis and death. Vulnerability by t
Data Loading...
