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Assessment of Virus Interference in a Test-negative Study of Influenza Vaccine Effectiveness.

ABSTRACT: The observational test-negative study design is used to estimate vaccine effectiveness against influenza virus infection. An important assumption of the test-negative design is that vaccination does not affect the risk of infection with another virus. If such virus interference occurred, detection of other respiratory viruses would be more common among influenza vaccine recipients and vaccine effectiveness estimates could differ. We evaluated the potential for virus interference using data from the Influenza Incidence Surveillance Project.From 2010 to 2013, outpatients presenting to clinics in 13 US jurisdictions with acute respiratory infections were tested for influenza and other respiratory viruses. We investigated whether virus interference might affect vaccine effectiveness estimates by first evaluating the sensitivity of estimates using alternative control groups that include or exclude patients with other respiratory virus detections by age group and early/middle/late stage of influenza seasons. Second, we evaluated the association between influenza vaccination receipt and other respiratory virus detection among influenza test-negative patients.Influenza was detected in 3,743/10,650 patients (35%), and overall vaccine effectiveness was 47% (95% CI: 42%, 52%). Estimates using each control group were consistent overall or when stratified by age groups, and there were no differences among early, middle, or late phase during influenza season. We found no associations between detection of other respiratory viruses and receipt of influenza vaccination.In this 3-year test-negative design study in an outpatient setting in the United States, we found no evidence of virus interference or impact on influenza vaccine effectiveness estimation.

SUBMITTER: Feng S

PROVIDER: S-EPMC5535302 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Assessment of Virus Interference in a Test-negative Study of Influenza Vaccine Effectiveness.

Feng Shuo S Fowlkes Ashley L AL Steffens Andrea A Finelli Lyn L Cowling Benjamin J BJ

Epidemiology (Cambridge, Mass.) 20170701 4

<h4>Background</h4>The observational test-negative study design is used to estimate vaccine effectiveness against influenza virus infection. An important assumption of the test-negative design is that vaccination does not affect the risk of infection with another virus. If such virus interference occurred, detection of other respiratory viruses would be more common among influenza vaccine recipients and vaccine effectiveness estimates could differ. We evaluated the potential for virus interferen ...[more]

PMID: 28362642

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Project description:BackgroundRoutine influenza vaccine effectiveness (VE) surveillance networks use frequentist methods to estimate VE. With data from more than a decade of VE surveillance from diverse global populations now available, using Bayesian methods to explicitly account for this knowledge may be beneficial. This study explores differences between Bayesian vs. frequentist inference in multiple seasons with varying VE.MethodsWe used data from the United States Influenza Vaccine Effectiveness (US Flu VE) Network. Ambulatory care patients with acute respiratory illness were enrolled during seasons of varying observed VE based on traditional frequentist methods. We estimated VE against A(H1N1)pdm in 2015/16, dominated by A(H1N1)pdm; against A(H3N2) in 2017/18, dominated by A(H3N2); and compared VE for live attenuated influenza vaccine (LAIV) vs. inactivated influenza vaccine (IIV) among children aged 2-17 years in 2013/14, also dominated by A(H1N1)pdm. VE was estimated using both frequentist and Bayesian methods using the test-negative design. For the Bayesian estimates, prior VE distributions were based on data from all published test-negative studies of the same influenza type/subtype available prior to the season of interest.ResultsAcross the three seasons, 16,342 subjects were included in the analyses. For 2015/16, frequentist and Bayesian VE estimates were essentially identical (41% each). For 2017/18, frequentist and Bayesian estimates of VE against A(H3N2) viruses were also nearly identical (26% vs. 23%, respectively), even though the presence of apparent antigenic match could potentially have pulled Bayesian estimates upward. Precision of estimates was similar between methods in both seasons. Frequentist and Bayesian estimates diverged for children in 2013/14. Under the frequentist approach, LAIV effectiveness was 62 percentage points lower than IIV, while LAIV was only 27 percentage points lower than IIV under the Bayesian approach.ConclusionBayesian estimates of influenza VE can differ from frequentist estimates to a clinically meaningful degree when VE diverges substantially from previous seasons.

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Assessment of Virus Interference in a Test-negative Study of Influenza Vaccine Effectiveness.

Publications

Assessment of Virus Interference in a Test-negative Study of Influenza Vaccine Effectiveness.

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