Prevalence of SARS-Cov-2 antibodies and living conditions: the French national random population-based EPICOV cohort

Abstract

Background We aimed to estimate the seroprevalence of SARS-CoV-2 infection in France and to identify the populations most exposed during the first epidemic wave. Methods Random selection of individuals aged 15 years or over, from the national tax register (96% coverage). Socio-economic data, migration history, and living conditions were collected via self-computer-assisted-web or computer-assisted-telephone interviews. Home self-sampling was performed for a random subsample, to detect IgG antibodies against spike protein (Euroimmun), and neutralizing antibodies with in-house assays, in dried blood spots (DBS). Results The questionnaire was completed by 134,391 participants from May 2nd to June 2st, 2020, including 17,441 eligible for DBS 12,114 of whom were tested. ELISA-S seroprevalence was 4.5% [95% CI 3.9–5.0] overall, reaching up to 10% in the two most affected areas. High-density residences, larger household size, having reported a suspected COVID-19 case in the household, working in healthcare, being of intermediate age and non-daily tobacco smoking were independently associated with seropositivity, whereas living with children or adolescents did not remain associated after adjustment for household size. Adjustment for both residential density and household size accounted for much of the higher seroprevalence in immigrants born outside Europe, twice that in French natives in univariate analysis. Conclusion The EPICOV cohort is one of the largest national representative population-based seroprevalence surveys for COVID-19. It shows the major role of contextual living conditions in the initial spread of COVID-19 in France, during which the availability of masks and virological tests was limited.

SARS-CoV-2 seroprevalence in healthcare workers and risk factors

“…We compared our results to that of other studies that evaluated HCW SARS-CoV-2 seroprevalence, keeping in mind that results may be impacted by time-point and window for sampling. On the one hand, the seroprevalence of SARS-CoV-2 in HCW reached 7% In a study including 230,398 HCWs from 24 countries located all over the world (Europe, USA, China, Singapore, Mexico, India, South Korea), and 11% in a French national study ( 2 ), higher than in our hospital. On the other hand, lower seroprevalences were observed in Danish HCWs (4.04%) ( 3 ) and in a children's hospital in Southern Italy (1.3%)( 4 ).…”

“…The EpiCoV survey conducted in May 2020 revealed that seroprevalence was highest in Paris (9%), and the Haut-Rhin department (10.8%), located in the Northeast of France, was the epicenter of the start of the epidemic in France. Seroprevalence in the whole Northeast of France was from 6.7% in May 2020 ( 2 ).…”

When mistrust in the government and scientists reinforce social inequalities in vaccination against Covid-19

“…Six multinomial regressions were then performed, each one adjusted for one combining variable at a time. Final calibrated weights were calculated to correct for non-response, as detailed elsewhere [25] for the first, second and third waves of the EpiCov survey. Response homogeneity groups were derived from the sampling weight divided by the probability of response estimated with logit models adjusted for auxiliary variables potentially linked to both the response mechanism and the main variables of interest in the EpiCov survey (age, gender, French départment, educational level, and region).…”

Labor market participation and depression during the COVID-19 epidemic among young adults (18 to 30 years): a nationally representative study in France

“…While future studies should examine prospective changes in mental health among cohorts of young people followed since prior to the COVID-19 pandemic, our results do not appear to be due to pre-existing severe mental health difficulties. Second, the EPICOV cohort suffered attrition, with participants belonging to socioeconomically disadvantaged groups least likely to participate over the long term (17). This lead us to conduct repeated measures analyses, which make it possible to maximize the use of information provided by study participants and limit possible biases due to attrition.…”