Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence: Navigating the absence of a gold standard.
Adult
Aged
Antibodies, Viral
/ immunology
Bayes Theorem
Blood Donors
COVID-19
/ immunology
Canada
Cross-Sectional Studies
Female
Humans
Immunoglobulin G
/ immunology
Male
Middle Aged
Nucleocapsid
/ immunology
Pandemics
/ prevention & control
SARS-CoV-2
/ immunology
Sensitivity and Specificity
Seroepidemiologic Studies
Spike Glycoprotein, Coronavirus
/ immunology
Young Adult
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2021
2021
Historique:
received:
20
05
2021
accepted:
08
09
2021
entrez:
23
9
2021
pubmed:
24
9
2021
medline:
2
10
2021
Statut:
epublish
Résumé
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence studies bridge the gap left from case detection, to estimate the true burden of the COVID-19 pandemic. While multiple anti-SARS-CoV-2 immunoassays are available, no gold standard exists. This serial cross-sectional study was conducted using plasma samples from 8999 healthy blood donors between April-September 2020. Each sample was tested by four assays: Abbott SARS-Cov-2 IgG assay, targeting nucleocapsid (Abbott-NP) and three in-house IgG ELISA assays (targeting spike glycoprotein, receptor binding domain, and nucleocapsid). Seroprevalence rates were compared using multiple composite reference standards and by a series of Bayesian Latent Class Models. We found 13 unique diagnostic phenotypes; only 32 samples (0.4%) were positive by all assays. None of the individual assays resulted in seroprevalence increasing monotonically over time. In contrast, by using the results from all assays, the Bayesian Latent Class Model with informative priors predicted seroprevalence increased from 0.7% (95% credible interval (95% CrI); 0.4, 1.0%) in April/May to 0.7% (95% CrI 0.5, 1.1%) in June/July to 0.9% (95% CrI 0.5, 1.3) in August/September. Assay characteristics varied over time. Overall Spike had the highest sensitivity (93.5% (95% CrI 88.7, 97.3%), while the sensitivity of the Abbott-NP assay waned from 77.3% (95% CrI 58.7, 92.5%) in April/May to 64.4% (95% CrI 45.6, 83.0) by August/September. Our results confirmed very low seroprevalence after the first wave in Canada. Given the dynamic nature of this pandemic, Bayesian Latent Class Models can be used to correct for imperfect test characteristics and waning IgG antibody signals.
Sections du résumé
BACKGROUND
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence studies bridge the gap left from case detection, to estimate the true burden of the COVID-19 pandemic. While multiple anti-SARS-CoV-2 immunoassays are available, no gold standard exists.
METHODS
This serial cross-sectional study was conducted using plasma samples from 8999 healthy blood donors between April-September 2020. Each sample was tested by four assays: Abbott SARS-Cov-2 IgG assay, targeting nucleocapsid (Abbott-NP) and three in-house IgG ELISA assays (targeting spike glycoprotein, receptor binding domain, and nucleocapsid). Seroprevalence rates were compared using multiple composite reference standards and by a series of Bayesian Latent Class Models.
RESULT
We found 13 unique diagnostic phenotypes; only 32 samples (0.4%) were positive by all assays. None of the individual assays resulted in seroprevalence increasing monotonically over time. In contrast, by using the results from all assays, the Bayesian Latent Class Model with informative priors predicted seroprevalence increased from 0.7% (95% credible interval (95% CrI); 0.4, 1.0%) in April/May to 0.7% (95% CrI 0.5, 1.1%) in June/July to 0.9% (95% CrI 0.5, 1.3) in August/September. Assay characteristics varied over time. Overall Spike had the highest sensitivity (93.5% (95% CrI 88.7, 97.3%), while the sensitivity of the Abbott-NP assay waned from 77.3% (95% CrI 58.7, 92.5%) in April/May to 64.4% (95% CrI 45.6, 83.0) by August/September.
DISCUSSION
Our results confirmed very low seroprevalence after the first wave in Canada. Given the dynamic nature of this pandemic, Bayesian Latent Class Models can be used to correct for imperfect test characteristics and waning IgG antibody signals.
Identifiants
pubmed: 34555095
doi: 10.1371/journal.pone.0257743
pii: PONE-D-21-16700
pmc: PMC8459951
doi:
Substances chimiques
Antibodies, Viral
0
Immunoglobulin G
0
Spike Glycoprotein, Coronavirus
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0257743Déclaration de conflit d'intérêts
Steven J Drews has acted as a content expert for respiratory viruses for Johnson & Johnson (Janssen). Anne-Claude Gingras receives funds from a research contract with Providence Therapeutics Holdings, Inc. The remaining authors have no conflicts of interest to disclose. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
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