Spatial and temporal trends in social vulnerability and COVID-19 incidence and death rates in the United States

• Published in: PloS one Vol. 16; no. 3; p. e0248702
• Main Authors: Neelon, Brian, Mutiso, Fedelis, Mueller, Noel T, Pearce, John L, Benjamin-Neelon, Sara E
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.03.2021; Public Library of Science (PLoS)
• Subjects: Bayes Theorem; Biology and Life Sciences; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - mortality; COVID-19 - psychology; Databases, Factual; Death; Demographic aspects; Disease transmission; Fatalities; Female; Forecasts and trends; Health aspects; Health risks; Health Status Disparities; Housing; Humans; Incidence; Longitudinal Studies; Male; Medicine and Health Sciences; Mobile homes; Mortality; Pandemics - statistics & numerical data; People and Places; Research and Analysis Methods; SARS-CoV-2 - isolation & purification; Socially handicapped; Socioeconomic Factors; Trends; United States - epidemiology; Vulnerable Populations - psychology; Vulnerable Populations - statistics & numerical data; United States; United States > US; Maryland; South Carolina; Baltimore Maryland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0248702

Socially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time. We conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19. At the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties. The impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.