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Seroprevalence of antibodies to SARS-CoV-2 and predictors of seropositivity among employees of a teaching hospital in New Delhi, India.
- Source :
- Osong Public Health & Research Perspectives; Apr2021, Vol. 12 Issue 2, p88-95, 8p
- Publication Year :
- 2021
-
Abstract
- Objectives: Healthcare workers (HCWs) are at a high risk of contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) due to the increased likelihood of clinical exposure during patient management. The study objective was to determine the seroprevalence of antibodies to SARS-CoV-2 and its predictors among hospital employees. Methods: The cross-sectional study was conducted at a teaching hospital from August 2020 to September 2020 among 1,401 employees, including 1,217 HCWs, in New Delhi, India. The serum samples were examined for immunoglobulin G (IgG) antibodies to SARS-CoV-2 using the COVID Kavach-Anti-SARS-CoV-2 IgG Antibody Detection enzyme-linked immunosorbent assay kit. Data were collected electronically using the EpiCollect mobile platform. A p<0.05 was considered to indicate statistical significance. Results: A total of 169 participants (12.1%) had detectable IgG antibodies to SARS-CoV-2. The highest seropositivity rate was observed in the administrative staff (20.1%), while it was lowest among medical doctors (5.5%, p<0.001). Male sex and ever having lived in a containment zone were independently associated with past infection with SARS-CoV-2. Conclusion: The seroprevalence of SARS-CoV-2 infection in health workers may be lower than in the general population in New Delhi. However, nonpharmaceutical interventions were not associated with a reduction in the risk of acquisition of SARS-CoV-2. [ABSTRACT FROM AUTHOR]
- Subjects :
- RESEARCH
SEROPREVALENCE
STATISTICS
ACADEMIC medical centers
COVID-19
IMMUNOGLOBULINS
CROSS-sectional method
EXECUTIVES
OCCUPATIONAL exposure
SEX distribution
ENZYME-linked immunosorbent assay
DESCRIPTIVE statistics
CHI-squared test
VIRAL antibodies
COVID-19 testing
PHYSICIANS
INDUSTRIAL hygiene
DATA analysis software
LOGISTIC regression analysis
BODY mass index
Subjects
Details
- Language :
- English
- ISSN :
- 22109099
- Volume :
- 12
- Issue :
- 2
- Database :
- Supplemental Index
- Journal :
- Osong Public Health & Research Perspectives
- Publication Type :
- Academic Journal
- Accession number :
- 150629201
- Full Text :
- https://doi.org/10.24171/j.phrp.2021.12.2.06