tetano
Editor, Senior Moderator
Vaccine
. 2024 Nov 7;43(Pt 2):126492.
doi: 10.1016/j.vaccine.2024.126492. Online ahead of print. Assessment and mitigation of bias in influenza and COVID-19 vaccine effectiveness analyses - IVY Network, September 1, 2022-March 30, 2023
Nathaniel M Lewis[SUP] 1 [/SUP], Elizabeth J Harker[SUP] 2 [/SUP], Aleda Leis[SUP] 3 [/SUP], Yuwei Zhu[SUP] 4 [/SUP], H Keipp Talbot[SUP] 4 [/SUP], Carlos G Grijalva[SUP] 4 [/SUP], Natasha Halasa[SUP] 4 [/SUP], James D Chappell[SUP] 4 [/SUP], Cassandra A Johnson[SUP] 4 [/SUP], Todd W Rice[SUP] 4 [/SUP], Jonathan D Casey[SUP] 4 [/SUP], Adam S Lauring[SUP] 3 [/SUP], Manjusha Gaglani[SUP] 5 [/SUP], Shekhar Ghamande[SUP] 6 [/SUP], Cristie Columbus[SUP] 7 [/SUP], Jay S Steingrub[SUP] 8 [/SUP], Nathan I Shapiro[SUP] 9 [/SUP], Abhijit Duggal[SUP] 10 [/SUP], Jamie Felzer[SUP] 11 [/SUP], Matthew E Prekker[SUP] 12 [/SUP], Ithan D Peltan[SUP] 13 [/SUP], Samuel M Brown[SUP] 13 [/SUP], David N Hager[SUP] 14 [/SUP], Michelle N Gong[SUP] 15 [/SUP], Amira Mohamed[SUP] 15 [/SUP], Matthew C Exline[SUP] 16 [/SUP], Akram Khan[SUP] 17 [/SUP], Jennifer G Wilson[SUP] 18 [/SUP], Jarrod Mosier[SUP] 19 [/SUP], Nida Qadir[SUP] 20 [/SUP], Steven Y Chang[SUP] 20 [/SUP], Adit A Ginde[SUP] 21 [/SUP], Nicholas M Mohr[SUP] 22 [/SUP], Christopher Mallow[SUP] 23 [/SUP], Estelle S Harris[SUP] 24 [/SUP], Nicholas J Johnson[SUP] 25 [/SUP], Vasisht Srinivasan[SUP] 25 [/SUP], Kevin W Gibbs[SUP] 26 [/SUP], Jennie H Kwon[SUP] 27 [/SUP], Ivana A Vaughn[SUP] 28 [/SUP], Mayur Ramesh[SUP] 28 [/SUP], Basmah Safdar[SUP] 29 [/SUP], Jennifer DeCuir[SUP] 2 [/SUP], Diya Surie[SUP] 30 [/SUP], Fatimah S Dawood[SUP] 30 [/SUP], Sascha Ellington[SUP] 2 [/SUP], Wesley H Self[SUP] 4 [/SUP], Emily T Martin[SUP] 3 [/SUP]
Affiliations
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.
Keywords: Bias; COVID-19; Estimation; Influenza; RSV; Vaccine effectiveness.
. 2024 Nov 7;43(Pt 2):126492.
doi: 10.1016/j.vaccine.2024.126492. Online ahead of print. Assessment and mitigation of bias in influenza and COVID-19 vaccine effectiveness analyses - IVY Network, September 1, 2022-March 30, 2023
Nathaniel M Lewis[SUP] 1 [/SUP], Elizabeth J Harker[SUP] 2 [/SUP], Aleda Leis[SUP] 3 [/SUP], Yuwei Zhu[SUP] 4 [/SUP], H Keipp Talbot[SUP] 4 [/SUP], Carlos G Grijalva[SUP] 4 [/SUP], Natasha Halasa[SUP] 4 [/SUP], James D Chappell[SUP] 4 [/SUP], Cassandra A Johnson[SUP] 4 [/SUP], Todd W Rice[SUP] 4 [/SUP], Jonathan D Casey[SUP] 4 [/SUP], Adam S Lauring[SUP] 3 [/SUP], Manjusha Gaglani[SUP] 5 [/SUP], Shekhar Ghamande[SUP] 6 [/SUP], Cristie Columbus[SUP] 7 [/SUP], Jay S Steingrub[SUP] 8 [/SUP], Nathan I Shapiro[SUP] 9 [/SUP], Abhijit Duggal[SUP] 10 [/SUP], Jamie Felzer[SUP] 11 [/SUP], Matthew E Prekker[SUP] 12 [/SUP], Ithan D Peltan[SUP] 13 [/SUP], Samuel M Brown[SUP] 13 [/SUP], David N Hager[SUP] 14 [/SUP], Michelle N Gong[SUP] 15 [/SUP], Amira Mohamed[SUP] 15 [/SUP], Matthew C Exline[SUP] 16 [/SUP], Akram Khan[SUP] 17 [/SUP], Jennifer G Wilson[SUP] 18 [/SUP], Jarrod Mosier[SUP] 19 [/SUP], Nida Qadir[SUP] 20 [/SUP], Steven Y Chang[SUP] 20 [/SUP], Adit A Ginde[SUP] 21 [/SUP], Nicholas M Mohr[SUP] 22 [/SUP], Christopher Mallow[SUP] 23 [/SUP], Estelle S Harris[SUP] 24 [/SUP], Nicholas J Johnson[SUP] 25 [/SUP], Vasisht Srinivasan[SUP] 25 [/SUP], Kevin W Gibbs[SUP] 26 [/SUP], Jennie H Kwon[SUP] 27 [/SUP], Ivana A Vaughn[SUP] 28 [/SUP], Mayur Ramesh[SUP] 28 [/SUP], Basmah Safdar[SUP] 29 [/SUP], Jennifer DeCuir[SUP] 2 [/SUP], Diya Surie[SUP] 30 [/SUP], Fatimah S Dawood[SUP] 30 [/SUP], Sascha Ellington[SUP] 2 [/SUP], Wesley H Self[SUP] 4 [/SUP], Emily T Martin[SUP] 3 [/SUP]
Affiliations
- PMID: 39515195
- DOI: 10.1016/j.vaccine.2024.126492
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.
Keywords: Bias; COVID-19; Estimation; Influenza; RSV; Vaccine effectiveness.