Persistence of racial/ethnic and socioeconomic status disparities among non-institutionalized patients hospitalized with COVID-19 in Connecticut, July to December 2020

Background COVID-19 hospitalizations of non-institutionalized persons during the first COVID-19 wave in Connecticut disproportionately affected the elderly, communities of color, and individuals of low socioeconomic status (SES). Whether the magnitude of these disparities changed after the initial lockdown and before vaccine rollout is not well documented. Methods All first-time hospitalizations with laboratory-confirmed COVID-19 during July to December 2020, including patients' geocoded residential addresses, were obtained from the Connecticut Department of Public Health. Those living in congregate settings, including nursing homes, were excluded. Community-dwelling patients were assigned census tract-level poverty and crowding measures from the 2014-2018 American Community Survey by linking their geocoded addresses to census tracts. Age-adjusted incidence and relative rates were calculated across demographic and SES measures and compared with those from a similar analysis of hospitalized cases during the initial wave. Results During July to December 2020, there were 5652 COVID-19 hospitalizations in community residents in Connecticut. Incidence was highest among those >85 years, non-Hispanic Blacks and Hispanic/Latinx compared with non-Hispanic Whites {relative rate (RR) 3.1 (95% confidence interval [CI] 2.83-3.32) and 5.9 (95% CI 5.58-6.28)}, and persons living in high poverty and high crowding census tracts. Although racial/ethnic and SES disparities during the study period were substantial, they were significantly decreased compared with the first wave of COVID-19. Conclusions The finding of persistent, if reduced, large racial/ethnic disparities in COVID-19 hospitalizations 2-7 months after the initial lockdown was relaxed and before vaccination was widely available is of concern. These disparities cause a challenge to achieving health equity and are relevant for future pandemic planning.