Assessment and mitigation of bias in influenza and COVID-19 vaccine effectiveness analyses - IVY Network, September 1, 2022-March 30, 2023.

Background: In test-negative studies of vaccine effectiveness (VE), including patients with co-circulating, vaccine-preventable, respiratory pathogens in the control group for the pathogen of interest can introduce a downward bias on VE estimates.
Methods: A multicenter sentinel surveillance network in the US prospectively enrolled adults hospitalized with acute respiratory illness from September 1, 2022-March 31, 2023. We evaluated bias in estimates of VE against influenza-associated and COVID-19-associated hospitalization based on: inclusion vs exclusion of patients with a co-circulating virus among VE controls; observance of VE against the co-circulating virus (rather than the virus of interest), unadjusted and adjusted for vaccination against the virus of interest; and observance of influenza or COVID-19 against a sham outcome of respiratory syncytial virus (RSV).
Results: Overall VE against influenza-associated hospitalizations was 6 percentage points lower when patients with COVID-19 were included in the control group, and overall VE against COVID-19-associated hospitalizations was 2 percentage points lower when patients with influenza were included in the control group. Analyses of VE against the co-circulating virus and against the sham outcome of RSV showed that downward bias was largely attributable the correlation of vaccination status across pathogens, but also potentially attributable to other sources of residual confounding in VE models.
Conclusion: Excluding cases of confounding respiratory pathogens from the control group in VE analysis for a pathogen of interest can reduce downward bias. This real-world analysis demonstrates that such exclusion is a helpful bias mitigation strategy, especially for measuring influenza VE, which included a high proportion of COVID-19 cases among controls.
Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: All authors have completed and submitted the International Committee of Medical Journal Editors form for disclosure of potential conflicts of interest. Michelle Ng Gong reports grants from NHLBI as a HART co-investigator and APS steering committee co-chair, support for travel to the ATS meeting, participating on an advisory board from Novartis, Philips Healthcase, Regeneron, and Radiomater, and participating on the DSMB for Best clinical trials, outside the submitted work. Carlos G. Grijalva reports receiving compensation for participation in an advisory board for Merck, and receiving research support from CDC, NIH, FDA, AHRQ and SyneosHealth, outside the submitted work. Natasha Halasa receives current support from Merck (investigator-initiated grant), past support from Sanofi and Quidel, and served on an advisory board for Seqirus, outside the submitted work. Akram Khan reports receiving research funding from Dompe pharmaceuticals, 4D Medical, Eli Lilly, and Direct Biologics for patient enrollment in studies, outside the submitted work. Adam S. Lauring reports consulting fees and research support from Roche, and research funding from NIAID, NSF, CDC, and Burroughs Wellcome Fund, outside the submitted work. Ithan Peltan reports funding from NIH (R35GM151147), funding from NHLBI and Janssen, and payments to his institution from Regeneron, Bluejay Diagnostics, and Novartis, outside the submitted work. Mayur Ramesh reports participating on an advisory board for AstraZeneca, Moderna, and Pfizer, outside the submitted work. Ivana A Vaughn reports funding from CDC via University of Michigan for US Flu VE Network, funding from eMaxHealth through her institution, and funding from Lilly USA through her institution, outside the submitted work. No other potential conflicts of interest were disclosed.
(Published by Elsevier Ltd.)