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Serology assays used in SARS-CoV-2 seroprevalence surveys worldwide: a systematic review and meta-analysis of assay features, testing algorithms, and performance
Xiaomeng Ma; Zihan Li; Mairead G. Whelan; Dayoung Kim; Christian Cao; Mercede Yanes-Lane; Tingting Yan; Thomas Jaenisch; May C. Chu; David A. Clifton; Lorenzo Subissi; Niklas Bobrovitz; Rahul K. Arora.
Affiliation
  • Xiaomeng Ma; Institute of Health Policy Management & Evaluation, University of Toronto
  • Zihan Li; Wyss Institute for Biologically Inspired Engineering, University of California Berkeley
  • Mairead G. Whelan; Cumming School of Medicine, University of Calgary
  • Dayoung Kim; Cumming School of Medicine, University of Calgary
  • Christian Cao; Temerty Faculty of Medicine, University of Toronto
  • Mercede Yanes-Lane; COVID-19 Immunity Task Force, McGill University
  • Tingting Yan; Temerty Faculty of Medicine, University of Toronto
  • Thomas Jaenisch; Department of Epidemiology & Center for Global Health, Colorado School of Public Health
  • May C. Chu; Department of Epidemiology & Center for Global Health, Colorado School of Public Health
  • David A. Clifton; COVID-19 Immunity Task Force, McGill University
  • Lorenzo Subissi; World Health Organization
  • Niklas Bobrovitz; Temerty Faculty of Medicine, University of Toronto
  • Rahul K. Arora; Institute of Biomedical Engineering, University of Oxford
Preprint in En | PREPRINT-MEDRXIV | ID: ppmedrxiv-22280957
ABSTRACT
BackgroundMany serological assays to detect SARS-CoV-2 antibodies were developed during the COVID-19 pandemic. Differences in the detection mechanism of SARS-CoV-2 serological assays limited the comparability of seroprevalence estimates for populations being tested. MethodsWe conducted a systematic review and meta-analysis of serological assays used in SARS-CoV-2 population seroprevalence surveys, searching for published articles, preprints, institutional sources, and grey literature between January 1, 2020, and November 19, 2021. We described features of all identified assays and mapped performance metrics by the manufacturers, third-party head-to-head, and independent group evaluations. We compared the reported assay performance by evaluation source with a mixed-effect beta regression model. A simulation was run to quantify how biased assay performance affects population seroprevalence estimates with test adjustment. ResultsAmong 1807 included serosurveys, 192 distinctive commercial assays and 380 self-developed assays were identified. According to manufacturers, 28.6% of all commercial assays met WHO criteria for emergency use (sensitivity [Sn.] >= 90.0%, specificity [Sp.] >= 97.0%). However, manufacturers overstated the absolute values of Sn. of commercial assays by 1.0% [0.1, 1.4%] and 3.3% [2.7, 3.4%], and Sp. by 0.9% [0.9, 0.9%] and 0.2% [-0.1, 0.4%] compared to third-party and independent evaluations, respectively. Reported performance data was not sufficient to support a similar analysis for self-developed assays. Simulations indicate that inaccurate Sn. and Sp. can bias seroprevalence estimates adjusted for assay performance; the error level changes with the background seroprevalence. ConclusionsThe Sn. and Sp. of the serological assay are not fixed properties, but varying features depending on the testing population. To achieve precise population estimates and to ensure the comparability of seroprevalence, serosurveys should select assays with high performance validated not only by their manufacturers and adjust seroprevalence estimates based on assured performance data. More investigation should be directed to consolidating the performance of self-developed assays.
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Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Diagnostic_studies / Experimental_studies / Observational_studies / Prognostic_studies / Review / Systematic_reviews Language: En Year: 2022 Document type: Preprint
Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Diagnostic_studies / Experimental_studies / Observational_studies / Prognostic_studies / Review / Systematic_reviews Language: En Year: 2022 Document type: Preprint