Examining associations between social vulnerability indices and COVID-19 incidence and mortality with spatial-temporal Bayesian modeling

This study compares two social vulnerability indices, the U.S. CDC SVI and SoVI (the Social Vulnerability Index developed at the Hazards Vulnerability & Resilience Institute at the University of South Carolina), on their ability to predict the risk of COVID-19 cases and deaths. We utilize COVID-19 cases and deaths data for the state of Indiana from the Regenstrief Institute in Indianapolis, Indiana, from March 1, 2020, to March 31, 2021. We then aggregate the COVID-19 data to the census tract level, obtain the input variables, domains (components), and composite measures of both CDC SVI and SoVI data to create a Bayesian spatial-temporal ecological regression model. We compare the resulting spatial-temporal patterns and relative risk (RR) of SARS-CoV-2 infection (COVID-19 cases) and associated death. Results show there are discernable spatial-temporal patterns for SARS-CoV-2 infections and deaths with the largest contiguous hotspot for SARS-CoV-2 infections found in the southwest of the Indianapolis metropolitan area. We also observed one large contiguous hotspot for deaths that stretches across Indiana from the Cincinnati area in the southeast to just east and north of Terre Haute (southeast to west central). The spatial-temporal Bayesian model shows that a 1-percentile increase in CDC SVI was significantly (p ≤ 0.05) associated with an increased risk of SARS-CoV-2 infection by 6 % (RR = 1.06, 95 %CI = 1.04 -1.08). Whereas a 1-percentile increase in SoVI was significantly predicted to increase the risk of COVID-19 death by 45 % (RR = 1.45, 95 %CI =1.38 - 1.53). Domain-specific variables related to socioeconomic status, age, and race/ethnicity were shown to increase the risk of SARS-CoV-2 infections and deaths. There were notable differences in the relative risk estimates for SARS-CoV-2 infections and deaths when each of the two indices were incorporated in the model. Observed differences between the two social vulnerability indices and infection and death are likely due to alternative methodologies of formation and differences in input variables. The findings add to the growing literature on the relationship between social vulnerability and COVID-19 and further the development of COVID-19-specific vulnerability indices by illustrating the utility of local spatial-temporal analysis.

The Relationship Between Social Vulnerability and COVID-19 Incidence Among Louisiana Census Tracts

Objective: To examine the association between the Centers for Disease Control and Prevention (CDC)'s Social Vulnerability Index (SVI) and COVID-19 incidence among Louisiana census tracts. Methods: An ecological study comparing the CDC SVI and census tract-level COVID-19 case counts was conducted. Choropleth maps were used to identify census tracts with high levels of both social vulnerability and COVID-19 incidence. Negative binomial regression with random intercepts was used to compare the relationship between overall CDC SVI percentile and its four sub-themes and COVID-19 incidence, adjusting for population density. Results: In a crude stratified analysis, all four CDC SVI sub-themes were significantly associated with COVID-19 incidence. Census tracts with higher levels of social vulnerability were associated with higher COVID-19 incidence after adjusting for population density (adjusted RR: 1.52, 95% CI: 1.41-1.65). Conclusions: The results of this study indicate that increased social vulnerability is linked with COVID-19 incidence. Additional resources should be allocated to areas of increased social disadvantage to reduce the incidence of COVID-19 in vulnerable populations.