Potential for Bias in Prevalence Estimates when Not Accounting for Test Sensitivity and Specificity: A Systematic Review of COVID-19 Seroprevalence Studies

TL;DR

Many studies on how common antibodies to COVID-19 are ignore test accuracy, leading to biased results and incorrect conclusions.

Contribution

This study systematically reviews how often test sensitivity and specificity are considered in seroprevalence research.

Findings

77% of seroprevalence studies did not account for test sensitivity and specificity.

High-impact journals had 72% of studies ignoring test characteristics.

The Rogen-Gladen formula was the most common correction method used.

Abstract

The 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.

A systematic review was performed to estimate how often public health researchers accounted for diagnostic test performance in estimates of seroprevalence.

Of 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.

Researchers conducting studies of prevalence should report sensitivity and specificity of the diagnostic test and correctly account for these characteristics.

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