Background: In test-negative design (TND) studies that use rapid tests to estimate influenza vaccine effectiveness (VE), a common concern is case-control misclassification due to imperfect diagnostic accuracy. An imperfect test can also fail to exclude from the control group people infected with other influenza types or other vaccine-preventable respiratory viruses. We investigated these biases while evaluating VE for the 2023/24 seasonal influenza vaccination.

Methods: A TND study was conducted among outpatients aged ≥6 months of age who visited an outpatient clinic in Hong Kong between 15 December 2023 and 13 August 2024. VE was estimated for polymerase chain reaction (PCR)- and rapid-test-confirmed influenza A and B with exclusions of other types of influenza and SARS-CoV-2 based on either PCR or rapid test. Alternatively, for the exclusion of SARS-CoV-2, we adjusted the analysis with COVID-19 vaccination. VE was estimated by using logistic regression adjusted for confounders.

Results: In a study population of 1691 participants, VE against influenza A by PCR was 49% [95% confidence interval (CI) 26%-65%] after the exclusion of PCR-confirmed influenza B and SARS-CoV-2 controls. The corresponding VE against influenza B was 65% (95% CI 35%-81%). VE estimated by adjusting for COVID-19 vaccination status yielded similar estimates to these. When case-control status and exclusions from test-negative controls were determined by using the rapid test, VE was reduced by 5%age-15%age points.

Conclusion: In TND studies using rapid tests, reduced sensitivity affects both case-control classification and control group exclusions, potentially causing bias. New methods for these biases could help to adapt participatory cohorts for the monitoring of VE for influenza, COVID-19, and respiratory syncytial virus.