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Estimates from SARS-CoV-2 serological surveys could be biased due to convenience sampling and non-response. This study aims to estimate the seroprevalence of SARS-CoV-2 infection in Saint Petersburg, Russia accounting for non-response bias. We recruited a sample of adults residing in St. Petersburg with random digit dialling. Telephone interview was followed by an invitation for an anti-SARS-CoV-2 antibodies tests - CMIA and ELISA. The seroprevalence estimates were corrected for non-response with the aid of bivariate probit model that jointly estimated individual propensity to agree to participate in the survey and seropositivity. 66,250 individuals were contacted, 6,440 adults agreed to be interviewed and blood samples were obtained from 1,038 participants between May 27, 2020 and June 26, 2020. Naive seroprevalence corrected for test characteristics was 9.0% (7.2-10.8) by CMIA and 10.5% (8.6-12.4) by ELISA. Correction for non-response decreased seroprevalence estimates to 7.4% (5.7-9.2) and 9.1% (7.2-10.9) for CMIA and ELISA, respectively. The most pronounced decrease in non-response bias-corrected seroprevalence was attributed to the history of any illnesses in the past 3 months and COVID-19 testing. Seroconversion was negatively associated with smoking status, self-reported history of allergies and changes in hand-washing habits. These results suggest that even low estimates of seroprevalence in Europe's fourth-largest city can be an overestimation in the presence of non-response. Serosurvey design should attempt to identify characteristics that are associated both with participation and seropositivity. Further population-based studies are required to explain the lower seroprevalence in smokers and participant reporting allergies.

Geographical variation in severe acute respiratory syndrome coronavirus 2 (SARS­‐CoV­‐2) spread requires seroprevalence studies based on local tests, but robust validation is needed. We summarize an evaluation of antibody tests used in a serological study of SARS‐­CoV­‐2 in Saint Petersburg, Russia. We validated three different antibody assays: chemiluminescent microparticle immunoassay (CMIA) Abbott Architect SARS‐­CoV­‐2 immunoglobulin G (IgG), enzyme­ linked immunosorbent assay (ELISA) CoronaPass total antibodies test, and ELISA SARS­‐CoV‐­2‐­IgG‐­EIA‐­BEST. Clinical sensitivity was estimated with the SARS­‐CoV­‐2 polymerase chain reaction (PCR) test as the gold standard using manufacturer recommended cutoff. Specificity was estimated using pre­pandemic sera samples. The median time between positive PCR test results and antibody tests was 21 weeks. Measures of concordance were calculated against the microneutralization test (MNA).Sensitivity was equal to 91.1% (95% confidence intervbal [CI]: 78.8–97.5), 90% (95% CI: 76.4–96.4), and 63.1% (95% CI [50.2–74.7]) for ELISA Coronapass, ELISA Vector­Best, and CMIA Abbott, respectively. Specificity was equal to 100% for all the tests. Comparison of receiver operating characteristics has shown lower AUC for CMIA Abbott. The cut­off SC/O ratio of 0.28 for CMIA Abbott resulted in a sensitivity of 80% at the same level of specificity. Less than 33% of the participants with positive antibody test results had neutralizing antibodies in titers 1:80 and above. Antibody assays results and MNA correlated moderately. This study encourages the use of local antibody tests and sets the reference for seroprevalence correction. Available tests' sensitivity allows detecting antibodies within the majority of PCR­ positive individuals. The Abbott assay sensitivity can be improved by incorporating a new cut­off. Manufacturers' test characteristics may introduce bias into the study results.

Properly conducted serological survey can help determine infection disease true spread. This study aims to estimate the seroprevalence of SARS-CoV-2 antibodies in Saint Petersburg, Russia accounting for non-response bias. A sample of adults was recruited with random digit dialling, interviewed and invited for anti-SARS-CoV-2 antibodies. The seroprevalence was corrected with the aid of the bivariate probit model that jointly estimated individual propensity to agree to participate in the survey and seropositivity. 66,250 individuals were contacted, 6,440 adults agreed to be interviewed and blood samples were obtained from 1,038 participants between May 27 and June 26, 2020. Naïve seroprevalence corrected for test characteristics was 9.0% (7.2–10.8) by CMIA and 10.5% (8.6–12.4) by ELISA. Correction for non-response decreased estimates to 7.4% (5.7–9.2) and 9.1% (7.2–10.9) for CMIA and ELISA, respectively. The most pronounced decrease in bias-corrected seroprevalence was attributed to the history of any illnesses in the past 3 months and COVID-19 testing. Seroconversion was negatively associated with smoking status, self-reported history of allergies and changes in hand-washing habits. These results suggest that even low estimates of seroprevalence can be an overestimation. Serosurvey design should attempt to identify characteristics that are associated both with participation and seropositivity.

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