Bias in Vaccine Effectiveness Estimation Under Changing Testing Probabilities When Using the Test-Negative Design Open Access

Shi, Evelyn (Spring 2023)

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Background: In recent years, the test negative design (TND) has emerged as a popular study design in estimating vaccine effectiveness (VE) for COVID-19 vaccines. While a useful and important tool, this design has major considerations that must be made before using it to estimate VE. It is vital to understand how simple changes in test reporting or differences in added protection in disease severity levels can lead to a change in VE estimates.

Objectives: We would like to characterize how sensitive VE estimates from a test-negative design are to changes in test reporting, and how it varies by disease severity and vaccine protection at different levels of disease severity.

Methods: We used a simulation to generate COVID-19 test results and demographic data and study this relationship. In particular, we looked at different patterns of testing probabilities and vaccine protection and how they would bias VE estimates in five different settings.

Results: Vaccine effectiveness for protection against infection has been found to be biased by as much as 8.64% (an estimated VES of 78.6% and a true value of 70%) in settings where testing probabilities were differential by disease severity and there was added vaccine protection against severe cases like hospitalization.

Conclusions: VES is often biased and overestimated when testing probabilities are differential by disease severity, and this is exacerbated by added vaccine protection against severe outcomes. These overestimations were due to a much greater fraction of hospitalized or severe cases represented in the test results compared to those who were mildly symptomatic or asymptomatic.

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