SeroTracker-ROB: reproducible decision rules for risk of bias assessment of seroprevalence studies

Abstract

ABSTRACTBackgroundConducting risk of bias assessments for seroprevalence studies is a crucial component of infection surveillance but can be a time-consuming and subjective process. We aimed to develop and evaluate decision rules for transparent and reproducible risk of bias assessments of seroprevalence studies.MethodsWe developed the SeroTracker-ROB decision rules, which generate risk of bias assessments for seroprevalence studies from an adapted version of the Joanna Briggs Institute Critical Appraisal Checklist for Prevalence Studies. The decision rules were developed using published guidance on risk of bias assessment for prevalence studies, and the consensus opinions of researchers that have critically appraised thousands of prevalence studies. The decision rules were evaluated against SeroTracker’s living systematic review database of SARS-CoV-2 seroprevalence studies. We determined decision rule coverage by calculating the proportion of database studies for which SeroTracker-ROB yielded a risk of bias assessment, and reliability by calculating intraclass correlations between SeroTracker-RoB assessments and the consensus manual judgements of two independent reviewers.ResultsThe SeroTracker-ROB decision rules for risk of bias assessment classified 100% (n = 2,070) of prevalence studies in SeroTracker’s database and showed good reliability compared to manual review (intraclass correlation 0.77, 95% CI 0.74 to 0.80). We developed a tool that implements these decision rules for use by other researchers.ConclusionsThe SeroTracker-ROB decision rules enabled rapid, transparent, and reproducible risk of bias assessment of seroprevalence studies, and may serve to support infection surveillance.