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Potential effect of virus interference on influenza vaccine effectiveness estimates in test-negative designs.

ABSTRACT: A hypothetical influenza infection-induced non-specific immunity may reduce the risk of subsequent non-influenza respiratory virus (NIRV) infection and bias the influenza vaccine effectiveness (VE) estimates in test-negative designs (TNDs). We conducted a simulation study using a simple TND model and explored the degree of bias in the VE estimates. The bias was marginal during the usual seasons and most of the time during pandemics; the bias only became large when the influenza infection attack rate increased to pandemic levels (>50%), the true VE was low to moderate, and the non-specific immunity almost completely protected from NIRV infections and lasted at least half the influenza season.

SUBMITTER: Suzuki M

PROVIDER: S-EPMC9151296 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Potential effect of virus interference on influenza vaccine effectiveness estimates in test-negative designs.

Suzuki M M Camacho A A Ariyoshi K K

Epidemiology and infection 20141201 12

A hypothetical influenza infection-induced non-specific immunity may reduce the risk of subsequent non-influenza respiratory virus (NIRV) infection and bias the influenza vaccine effectiveness (VE) estimates in test-negative designs (TNDs). We conducted a simulation study using a simple TND model and explored the degree of bias in the VE estimates. The bias was marginal during the usual seasons and most of the time during pandemics; the bias only became large when the influenza infection attack ...[more]

PMID: 25372226

Similar Datasets

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

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| S-EPMC5535302 | biostudies-literature

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| S-EPMC6449847 | biostudies-literature

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| S-EPMC5712239 | biostudies-literature

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| S-EPMC4277796 | biostudies-literature

Effects of Confounding Bias in COVID-19 and Influenza Vaccine Effectiveness Test-Negative Designs Due to Correlated Influenza and COVID-19 Vaccination Behaviors.

Project description:The test-negative design is commonly used to estimate influenza and COVID-19 vaccine effectiveness (VE). In these studies, correlated COVID-19 and influenza vaccine behaviors may introduce a confounding bias where controls are included with the other vaccine-preventable acute respiratory illness (ARI). We quantified the impact of this bias on VE estimates in studies where this bias is not addressed. We simulated study populations under varying vaccination probabilities, COVID-19 VE, influenza VE, and proportions of controls included with the other vaccine-preventable ARI. Mean bias was calculated as the difference between estimated and true VE. Absolute mean bias in VE estimates was classified as low (<10%), moderate (10% to <20%), and high (≥20%). Where vaccination probabilities are positively correlated, COVID-19 and influenza VE test-negative studies with influenza and SARS-CoV-2 ARI controls, respectively, underestimate VE. For COVID-19 VE studies, mean bias was low for all scenarios where influenza represented ≤25% of controls. For influenza VE studies, mean bias was low for all scenarios where SARS-CoV-2 represented ≤10% of controls. Although bias was driven by the conditional probability of vaccination, low VE of the vaccine of interest and high VE of the confounding vaccine increase its magnitude. Where a low percentage of controls are included with the other vaccine-preventable ARI, bias in COVID-19 and influenza VE estimates is low. However, influenza VE estimates are likely more susceptible to bias. Researchers should consider potential bias and its implications in their respective study settings to make informed methodological decisions in test-negative VE studies.

| S-EPMC9129127 | biostudies-literature

Regression approaches in the test-negative study design for assessment of influenza vaccine effectiveness.

Project description:Influenza vaccination is the most practical means available for preventing influenza virus infection and is widely used in many countries. Because vaccine components and circulating strains frequently change, it is important to continually monitor vaccine effectiveness (VE). The test-negative design is frequently used to estimate VE. In this design, patients meeting the same clinical case definition are recruited and tested for influenza; those who test positive are the cases and those who test negative form the comparison group. When determining VE in these studies, the typical approach has been to use logistic regression, adjusting for potential confounders. Because vaccine coverage and influenza incidence change throughout the season, time is included among these confounders. While most studies use unconditional logistic regression, adjusting for time, an alternative approach is to use conditional logistic regression, matching on time. Here, we used simulation data to examine the potential for both regression approaches to permit accurate and robust estimates of VE. In situations where vaccine coverage changed during the influenza season, the conditional model and unconditional models adjusting for categorical week and using a spline function for week provided more accurate estimates. We illustrated the two approaches on data from a test-negative study of influenza VE against hospitalization in children in Hong Kong which resulted in the conditional logistic regression model providing the best fit to the data.

| S-EPMC5545127 | biostudies-other

Pooled influenza vaccine effectiveness estimates for Australia, 2012-2014.

Project description:Data were pooled from three Australian sentinel general practice influenza surveillance networks to estimate Australia-wide influenza vaccine coverage and effectiveness against community presentations for laboratory-confirmed influenza for the 2012, 2013 and 2014 seasons. Patients presenting with influenza-like illness at participating GP practices were swabbed and tested for influenza. The vaccination odds of patients testing positive were compared with patients testing negative to estimate influenza vaccine effectiveness (VE) by logistic regression, adjusting for age group, week of presentation and network. Pooling of data across Australia increased the sample size for estimation from a minimum of 684 to 3,683 in 2012, from 314 to 2,042 in 2013 and from 497 to 3,074 in 2014. Overall VE was 38% [95% confidence interval (CI) 24-49] in 2012, 60% (95% CI 45-70) in 2013 and 44% (95% CI 31-55) in 2014. For A(H1N1)pdm09 VE was 54% (95% CI-28 to 83) in 2012, 59% (95% CI 33-74) in 2013 and 55% (95% CI 39-67) in 2014. For A(H3N2), VE was 30% (95% CI 14-44) in 2012, 67% (95% CI 39-82) in 2013 and 26% (95% CI 1-45) in 2014. For influenza B, VE was stable across years at 56% (95% CI 37-70) in 2012, 57% (95% CI 30-73) in 2013 and 54% (95% CI 21-73) in 2014. Overall VE against influenza was low in 2012 and 2014 when A(H3N2) was the dominant strain and the vaccine was poorly matched. In contrast, overall VE was higher in 2013 when A(H1N1)pdm09 dominated and the vaccine was a better match. Pooling data can increase the sample available and enable more precise subtype- and age group-specific estimates, but limitations remain.

| S-EPMC9150515 | biostudies-literature

Effectiveness of Trivalent Inactivated Influenza Vaccine in Children Estimated by a Test-Negative Case-Control Design Study Based on Influenza Rapid Diagnostic Test Results.

Project description:We assessed vaccine effectiveness (VE) against medically attended, laboratory-confirmed influenza in children 6 months to 15 years of age in 22 hospitals in Japan during the 2013-14 season. Our study was conducted according to a test-negative case-control design based on influenza rapid diagnostic test (IRDT) results. Outpatients who came to our clinics with a fever of 38 °C or over and had undergone an IRDT were enrolled in this study. Patients with positive IRDT results were recorded as cases, and patients with negative results were recorded as controls. Between November 2013 and March 2014, a total of 4727 pediatric patients (6 months to 15 years of age) were enrolled: 876 were positive for influenza A, 66 for A(H1N1)pdm09 and in the other 810 the subtype was unknown; 1405 were positive for influenza B; and 2445 were negative for influenza. Overall VE was 46% (95% confidence interval [CI], 39-52). Adjusted VE against influenza A, influenza A(H1N1)pdm09, and influenza B was 63% (95% CI, 56-69), 77% (95% CI, 59-87), and 26% (95% CI, 14-36), respectively. Influenza vaccine was not effective against either influenza A or influenza B in infants 6 to 11 months of age. Two doses of influenza vaccine provided better protection against influenza A infection than a single dose did. VE against hospitalization influenza A infection was 76%. Influenza vaccine was effective against influenza A, especially against influenza A(H1N1)pdm09, but was much less effective against influenza B.

| S-EPMC4552891 | biostudies-other

Modeling the Impacts of Clinical Influenza Testing on Influenza Vaccine Effectiveness Estimates.

Project description:BackgroundTest-negative design studies for evaluating influenza vaccine effectiveness (VE) enroll patients with acute respiratory infection. Enrollment typically occurs before influenza status is determined, resulting in over-enrollment of influenza-negative patients. With availability of rapid and accurate molecular clinical testing, influenza status could be ascertained before enrollment, thus improving study efficiency. We estimate potential biases in VE when using clinical testing.MethodsWe simulate data assuming 60% vaccinated, 25% of those vaccinated are influenza positive, and VE of 50%. We show the effect on VE in 5 scenarios.ResultsVaccine effectiveness is affected only when clinical testing preferentially targets patients based on both vaccination and influenza status. Vaccine effectiveness is overestimated by 10% if nontesting occurs in 39% of vaccinated influenza-positive patients and 24% of others. VE is also overestimated by 10% if nontesting occurs in 8% of unvaccinated influenza-positive patients and 27% of others. Vaccine effectiveness is underestimated by 10% if nontesting occurs in 32% of unvaccinated influenza-negative patients and 18% of others.ConclusionsAlthough differential clinical testing by vaccine receipt and influenza positivity may produce errors in estimated VE, bias in testing would have to be substantial and overall proportion of patients tested would have to be small to result in a meaningful difference in VE.

| S-EPMC8967445 | biostudies-literature

Detailed Report on 2014/15 Influenza Virus Characteristics, and Estimates on Influenza Virus Vaccine Effectiveness from Austria's Sentinel Physician Surveillance Network.

Project description:BACKGROUND:Influenza vaccine effectiveness (VE) is influenced by the antigenic similarity between vaccine- and circulating strains. MATERIAL AND METHODS:This paper presents data obtained by the Austrian sentinel surveillance system on the evolution of influenza viruses during the season 2014/15 and its impact on influenza vaccine effectiveness in primary care in Austria as estimated by a test-negative case control design. VE estimates were performed for each influenza virus type/subtype, stratified by underlying diseases and adjusted for age, sex and calendar week of infection. RESULTS:Detailed genetic and antigenic analyses showed that circulating A(H3N2) viruses were genetically distinct from the 2014/15 A(H3N2) vaccine component indicating a profound vaccine mismatch. The Influenza A(H1N1)pdm09 viruses were antigenically conserved and matched the respective vaccine component. Influenza B viruses were lineage-matched B/Yamagata viruses with a clade-level variation. Consistent with substantial vaccine mismatch for the A(H3N2) viruses a crude overall VE of only 47% was estimated, whereas the VE estimates for A(H1N1)pdm09 were 84% and for influenza B viruses 70%. Increased VE estimates were obtained after stratification by underlying diseases and adjustment for the covariates sex and age, whereby the adjustment for the calendar week of infection was the covariate exerting the highest influence on adjusted VE estimates. CONCLUSION:In summary, VE data obtained in this study underscore the importance to perform VE estimates in the context of detailed characterization of the contributing viruses and also demonstrate that the calendar week of influenza virus infection is the most important confounder of VE estimates.

| S-EPMC4790898 | biostudies-literature

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