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

Abstract

Background: Emerging evidence suggests that socially vulnerable communities are at higher risk for coronavirus disease 2019 (COVID-19) outbreaks in the United States. However, no prior studies have examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. The purpose of this study was to examine temporal trends among counties with high and low social vulnerability and to quantify disparities in these trends over time. We hypothesized that highly vulnerable counties would have higher incidence and death rates compared to less vulnerable counties and that this disparity would widen as the pandemic progressed. Methods: We conducted a retrospective longitudinal analysis examining COVID-19 incidence and death rates from March 1 to August 31, 2020 for each county in the US. We obtained daily COVID-19 incident case and death data from USAFacts and the Johns Hopkins Center for Systems Science and Engineering. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention in which higher scores represent 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. We adjusted for percentage of the county designated as rural, percentage in poor or fair health, percentage of adult smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, and the proportion tested for COVID-19 in the state. Results: In unadjusted analyses, we found that for most of March 2020, counties in the upper SVI quartile had significantly fewer cases per 100,000 than lower SVI quartile counties. However, on March 30, we observed a crossover effect in which the RR became significantly greater than 1.00 (RR = 1.10, 95% PI: 1.03, 1.18), indicating that the most vulnerable counties had, on average, higher COVID-19 incidence rates compared to least vulnerable counties. Upper SVI quartile counties had higher death rates on average starting on March 30 (RR = 1.17, 95% PI: 1.01,1.36). The death rate RR achieved a maximum value on July 29 (RR = 3.22, 95% PI: 2.91, 3.58), indicating that most vulnerable counties had, on average, 3.22 times more deaths per million than the least vulnerable counties. However, by late August, the lower quartile started to catch up to the upper quartile. In adjusted models, the RRs were attenuated for both incidence cases and deaths, indicating that the adjustment variables partially explained the associations. We also found positive associations between COVID-19 cases and deaths and percentage of the county designated as rural, percentage of resident in fair or poor health, and average daily PM2.5. Conclusions: Results indicate that the impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties over time. This highlights the importance of protecting vulnerable populations as the pandemic unfolds.

Spatiotemporal Associations Between Social Vulnerability, Environmental Measurements, and COVID‐19 in the Conterminous United States

“…However, the relationship between the overall SVI score and COVID-19 incidence was not statistically significant. In a study by Neelon et al (2020) utilizing COVID-19 cases and deaths within a Bayesian hierarchical negative binomial model between March 1 and August 31, 2020, counties were classified based on SVI composite percentiles (Neelon et al, 2020). Cases and deaths were examined daily for all U.S. counties after adjusting for percentage rural, percentage poor or in fair health, percentage of adult smokers, county average daily PM2.5 and primary care physicians per 100,000.…”

“…The past decade has witnessed an increasing trend in research activity focusing on social and environmental vulnerability as it relates to geophysical and man‐made hazards. More recently, there has been vigorous interest in social vulnerability as it relates to the ongoing COVID‐19 pandemic (Bilal et al., 2020 ; Coelho et al., 2020 ; Dasgupta et al., 2020 ; Gaynor & Wilson, 2020 ; Khazanchi et al., 2020 ; Kim & Bostwick, 2020 ; Lancet, 2020 ; Mishra et al., 2020 ; Mohanty, 2020 ; Neelon et al., 2020 ; Snyder & Parks, 2020 ). Additionally, researchers have attempted to construct COVID‐19‐specific vulnerability indices, examine spatial relationships, or integrate both social and environmental determinants into a complete model, illustrating areas more prone to adverse impacts (Karaye & Horney, 2020 ; Khazanchi et al., 2020 ; Snyder & Parks, 2020 ).…”

The Association Between Neighborhood Social Vulnerability and COVID-19 Testing, Positivity, and Incidence in Alabama and Louisiana

“…Several recent studies have reported associations between SVI scores and COVID-19 case rates [ 20 – 22 ]. In addition, an earlier analysis indicated that the socioeconomic status and minority status themes had the strongest correlation with case rates [ 23 ].…”

Dynamics of Covid-19 mortality and social determinants of health: a spatiotemporal analysis of exceedance probabilities

“…While researchers studied the spatial associations of SDoH with Covid-19 mortality [94] , there is a lack of understanding about the spatiotemporal dynamics of Covid-19 and how it relates to social factors, which is important for state and county-level resource allocations and safe operations of schools and businesses. There are limited research [19] , [20] , [21] , [22] on the spatiotemporal cluster analysis of Covid-19. A recently published study [20] considered Social Vulnerability Indices (SVI) but did not consider differential effects of rurality on SVI.…”

“…There are limited research [19] , [20] , [21] , [22] on the spatiotemporal cluster analysis of Covid-19. A recently published study [20] considered Social Vulnerability Indices (SVI) but did not consider differential effects of rurality on SVI. In the U.S., rural/urban residence status creates major healthcare access and health outcomes disparities [23] .…”