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Timeliness of reporting of SARS-CoV-2 seroprevalence results and their utility for infectious disease surveillance
Claire Donnici; Natasha Ilincic; Christian Cao; Caseng Zhang; Gabriel Deveaux; David A Clifton; David Buckeridge; Niklas Bobrovitz; Rahul K Arora.
Affiliation
  • Claire Donnici; Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
  • Natasha Ilincic; Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
  • Christian Cao; Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
  • Caseng Zhang; Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
  • Gabriel Deveaux; Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
  • David A Clifton; Institute of Biomedical Engineering, University of Oxford, UK
  • David Buckeridge; Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
  • Niklas Bobrovitz; University of Toronto
  • Rahul K Arora; Institute of Biomedical Engineering, University of Oxford, UK; Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
Preprint in English | medRxiv | ID: ppmedrxiv-22271099
ABSTRACT
Seroprevalence studies have been used throughout the COVID-19 pandemic to monitor infection and immunity. These studies are often reported in peer-reviewed journals, but the academic writing and publishing process can delay reporting and thereby public health action. Seroprevalence estimates have been reported faster in preprints and media, but with concerns about data quality. We aimed to (i) describe the timeliness of SARS-CoV-2 serosurveillance reporting by publication venue and study characteristics and (ii) identify relationships between timeliness, data validity, and representativeness to guide recommendations for serosurveillance efforts. We included seroprevalence studies published between January 1, 2020 and December 31, 2021 from the ongoing SeroTracker living systematic review. For each study, we calculated timeliness as the time elapsed between the end of sampling and the first public report. We evaluated data validity based on serological test performance and correction for sampling error, and representativeness based on use of a representative sample frame and adequate sample coverages. We examined how timeliness varied with study characteristics, representativeness, and data validity using univariate and multivariate Cox regression. We analyzed 1,844 studies. Median time to publication was 154 days (IQR 64-255), varying by publication venue (journal articles 212 days, preprints 101 days, institutional reports 18 days, and media 12 days). Multivariate analysis confirmed the relationship between timeliness and publication venue and showed that general population studies were published faster than special population or health care worker studies; there was no relationship between timeliness and study geographic scope, geographic region, representativeness, or serological test performance. Seroprevalence studies in peer-reviewed articles and preprints are published slowly, highlighting the limitations of using the academic literature to report seroprevalence during a health crisis. More timely reporting of seroprevalence estimates can improve their usefulness for surveillance, enabling more effective responses during health emergencies.
License
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Experimental_studies / Observational study / Review / Systematic review Language: English Year: 2022 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Experimental_studies / Observational study / Review / Systematic review Language: English Year: 2022 Document type: Preprint
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