Spectrum of COVID-19 cases in Arkhangelsk, Northwest Russia: Findings from a population-based study linking serosurvey, registry data, and self-reports of symptoms

Abstract

Introduction The spectrum of COVID-19 manifestations makes it challenging to estimate the exact proportion of people who had the infection in a population, with the proportion of asymptomatic cases likely being underestimated. We aimed to assess and describe the spectrum of COVID-19 cases in a sample of adult population aged 40–74 years in Arkhangelsk, Northwest Russia, a year after the start of the pandemic. Materials and methods A population-based survey conducted between February 24, 2021 and June 30, 2021 with an unvaccinated sample aged 40–74 years (N = 1089) combined a serological survey data, national COVID-19 case registry, and self-reported data on COVID-19 experience and symptoms. Based on the agreement between these sources, we classified the study participants as non-infected and previously infected (asymptomatic, non-hospitalized and hospitalized symptomatic) cases, and compared these groups regarding demographics, lifestyle and health characteristics. Results After a year of the pandemic in Arkhangelsk, 59.7% 95% confidence intervals (CI) (56.7; 62.6) of the surveyed population had had COVID-19. Among those who had been infected, symptomatic cases comprised 47.1% 95% CI (43.2; 51.0), with 8.6% 95% CI (6.6; 11.1) of them having been hospitalized. Of the asymptomatic cases, 96.2% were not captured by the healthcare system. Older age was positively associated, while smoking showed a negative association with symptomatic COVID-19. Individuals older than 65 years, and those with poor self-rated health were more likely to be hospitalized. Conclusion More than half of the infected individuals were not captured by the healthcare-based registry, mainly those with asymptomatic infections. COVID-19 severity was positively associated with older age and poor self-rated health, and inversely associated with smoking. Combining different sources of surveillance data could reduce the number of unidentified asymptomatic cases and enhance surveillance for emerging infections.