Prevalence of IgG antibodies against SARS-CoV-2 among healthcare workers in a tertiary pediatric hospital in Poland.
Beata KasztelewiczKatarzyna JaniszewskaJulia BurzyńskaEmilia SzydłowskaMarek MigdałKatarzyna Dzierżanowska-FangratPublished in: PloS one (2021)
Data on the prevalence of the SARS-CoV-2 antibody in healthcare workers (HCWs) is scarce, especially in pediatric settings. The purpose of this study was to evaluate SARS-CoV-2 IgG-positivity among HCWs of a tertiary pediatric hospital. In addition, follow-up of the serological response in the subgroup of seropositive HCWs was analysed, to gain some insight on the persistence of IgG antibodies to SARS-CoV-2. We performed a retrospective analysis of voluntary SARS-CoV-2 IgG testing, which was made available free of charge to HCWs of the Children's Memorial Health Institute in Warsaw (Poland). Plasma samples were collected between July 1 and August 9, 2020, and tested using the Abbott SARS-CoV-2 IgG assay. Of 2,282 eligible participants, 1,879 (82.3%) HCWs volunteered to undergo testing. Sixteen HCWs tested positive for SARS-CoV-2 IgG, corresponding to a seroprevalence of 0.85%. Among seropositive HCWs, three HCWs had confirmed COVID-19. Nine (56.3%) of the seropositive HCWs reported neither symptoms nor unprotected contact with confirmed SARS-CoV-2 cases in the previous months. A decline in the IgG index was observed at a median time of 86.5 days (range:84‒128 days) after symptom onset or RT-PCR testing. Further studies are necessary to elucidate the duration of persistence of anti-SARS-CoV-2 antibodies, as well as the correlation between seropositivity and protective immunity against reinfection. Regardless of the persistence of antibodies and their protective properties, such low prevalence indicates that this population is vulnerable to a second wave of the COVID-19 pandemic.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- healthcare
- risk factors
- public health
- emergency department
- risk assessment
- mental health
- coronavirus disease
- machine learning
- depressive symptoms
- deep learning
- social media
- big data
- open label
- sleep quality
- human health
- single cell
- drug induced
- artificial intelligence
- childhood cancer