Potential for Bias in Prevalence Estimates when Not Accounting for Test Sensitivity and Specificity: A Systematic Review of COVID-19 Seroprevalence Studies - Sarah R. Haile; David Kronthaler (International Journal of Public Health, 2025)
ObjectivesThe COVID-19 pandemic has led to many studies of seroprevalence. A number of methods exist in the statistical literature to correctly estimate disease prevalence or seroprevalence in the presence of diagnostic test misclassification, but these methods seem to be not routinely used in the public health literature. We aimed to examine how widespread the problem is in recent publications, and to quantify the magnitude of bias introduced when correct methods are not used.MethodsA systematic review was performed to estimate how often public health researchers accounted for diagnostic test performance in estimates of seroprevalence.ResultsOf the seroprevalence studies sampled, 77% (95% CI 72%–82%) failed to account for sensitivity and specificity. In high impact journals, 72% did not correct for test characteristics, and 34% did not report sensitivity or specificity. The most common type of correction was the Rogen-Gladen formula (57%, 45%–69%), followed by Bayesian approaches (32%, 21%–44%). Rates of correction increased slightly over time, but type of correction did not change.ConclusionResearchers conducting studies of prevalence should report sensitivity and specificity of the diagnostic test and correctly account for these characteristics.