Towards optimization of community vulnerability indices for COVID-19 prevalence - Lung-Chang Chien; L.-W. Antony Chen; Chad L. Cross; Edom Gelaw; Cheryl Collins; Lei Zhang; Cassius Lockett (BMC Public Health)
Abstract Background The Centers for Disease Control and Prevention (CDC)’s social vulnerability index (SVI) for exploring social and health disparities in the United States may not be suitable for assessing COVID-19 risk in specific communities and subpopulations. This study aims to develop the community vulnerability index (CVI) optimized for demographic-specific COVID-19 prevalence at the census tract level and apply it to Clark County, Nevada, which includes the vibrant Las Vegas metropolitan area. Methods We constructed the CVI using fifteen social condition variables from the CDC’s SVI along with eight additional community variables measuring inactive commuting, park deprivation, retail density, low-income homeowner or renter severe housing cost burden, housing inadequacy, segregation, and population density. Deploying weighted quantile sum (WQS) regression through a bootstrapping technique, the CVI was optimized by linking the 23 community variables to cumulative confirmed cases of COVID-19 from January 2020 to November 2021, excluding reinfections. This study resulted in whole-population and 13 demographic-specific CVIs representative of various age (0–4, 5–17, 18–24, 25–49, 50–64, and 65 +), race (White, Black, Hispanic, Asian/Pacific Islander, and others), and sex (male and female) groups. Results All WQS regressions revealed significant associations between the CVIs and corresponding COVID-19 prevalence. The most influential variables to the whole-population CVI included minority status, park deprivation, aged 17 and younger, inactive commuting, and housing inadequacy, which also contributed significantly to several CVIs corresponding to COVID-19 prevalence in subpopulations. Other influential community variables to the CVIs in general varied by subpopulation. The distributions of the subpopulation CVIs showed different levels of spatial disparities, with the largest disparities observed in female, White, and age 50–64 groups. Conclusions This study established a practical approach to optimize CVI for assessing COVID-19 risk. The incorporation of additional variables, specificity for subpopulations, and adaptability through the WQS regression collectively contribute to its value in informing evidence-based policy decisions and guiding targeted interventions to mitigate the impact of COVID-19 on vulnerable communities.