Browse
Submit Data
Databases
API
Help

Dataset Information

27 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Influenza Vaccine Effectiveness in the Inpatient Setting: Evaluation of Potential Bias in the Test-Negative Design by Use of Alternate Control Groups.

ABSTRACT: The test-negative design is validated in outpatient, but not inpatient, studies of influenza vaccine effectiveness. The prevalence of chronic pulmonary disease among inpatients can lead to nonrepresentative controls. Test-negative design estimates are biased if vaccine administration is associated with incidence of noninfluenza viruses. We evaluated whether control group selection and effects of vaccination on noninfluenza viruses biased vaccine effectiveness in our study. Subjects were enrolled at the University of Michigan and Henry Ford hospitals during the 2014-2015 and 2015-2016 influenza seasons. Patients presenting with acute respiratory infection were enrolled and tested for respiratory viruses. Vaccine effectiveness was estimated using 3 control groups: negative for influenza, positive for other respiratory virus, and pan-negative individuals; it was also estimated for other common respiratory viruses. In 2014-2015, vaccine effectiveness was 41.1% (95% CI: 1.7, 64.7) using influenza-negative controls, 24.5% (95% CI: -42.6, 60.1) using controls positive for other virus, and 45.8% (95% CI: 5.7, 68.9) using pan-negative controls. In 2015-2016, vaccine effectiveness was 68.7% (95% CI: 44.6, 82.5) using influenza-negative controls, 63.1% (95% CI: 25.0, 82.2) using controls positive for other virus, and 71.1% (95% CI: 46.2, 84.8) using pan-negative controls. Vaccination did not alter odds of other respiratory viruses. Results support use of the test-negative design among inpatients.

SUBMITTER: Segaloff HE

PROVIDER: S-EPMC8567846 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Bias of influenza vaccine effectiveness estimates from test-negative studies conducted during an influenza pandemic.

Project description:Test-negative (TN) studies have become the most widely used study design for the estimation of influenza vaccine effectiveness (VE) and are easily incorporated into existing influenza surveillance networks. We seek to determine the bias of TN-based VE estimates during a pandemic using a dynamic probability model. The model is used to evaluate and compare the bias of VE estimates under various sources of bias when vaccination occurs after the beginning of an outbreak, such as during a pandemic. The model includes two covariates (health status and health awareness), which may affect the probabilities of vaccination, developing influenza and non-influenza acute respiratory illness (ARI), and seeking medical care. Specifically, we evaluate the bias of VE estimates when (1) vaccination affects the probability of developing a non-influenza ARI; (2) vaccination affects the probability of seeking medical care; (3) a covariate (e.g. health status) is related to both the probabilities of vaccination and developing an ARI; and (4) a covariate (e.g. health awareness) is related to both the probabilities of vaccination and of seeking medical care. We considered two outcomes against which the vaccine is supposed to protect: symptomatic influenza and medically-attended influenza. When vaccination begins during an outbreak, we found that the effect of delayed onset of vaccination is unpredictable. VE estimates from TN studies were biased regardless of the source of bias present. However, if the core assumption of the TN design is satisfied, that is, if vaccination does not affect the probability of non-influenza ARI, then TN-based VE estimates against medically-attended influenza will only suffer from small (<0.05) to moderate bias (≥0.05 and <0.10). These results suggest that if sources of bias listed above are ruled out, TN studies are a valid study design for the estimation of VE during a pandemic.

| S-EPMC6449847 | biostudies-literature

On the bias of estimates of influenza vaccine effectiveness from test-negative studies.

Project description:Estimates of the effectiveness of influenza vaccines are commonly obtained from a test-negative design (TND) study, where cases and controls are patients seeking care for an acute respiratory illness who test positive and negative, respectively, for influenza infection. Vaccine effectiveness (VE) estimates from TND studies are usually interpreted as vaccine effectiveness against medically-attended influenza (MAI). However, it is also important to estimate VE against any influenza illness (symptomatic influenza (SI)) as individuals with SI are still a public health burden even if they do not seek medical care. We present a numerical method to evaluate the bias of TND-based estimates of influenza VE with respect to MAI and SI. We consider two sources of bias: (a) confounding bias due to a (possibly unobserved) covariate that is associated with both vaccination and the probability of the outcome of interest and (b) bias resulting from the effect of vaccination on the probability of seeking care. Our results indicate that (a) VE estimates may suffer from substantial confounding bias when a confounder has a different effect on the probabilities of influenza and non-influenza ARI, and (b) when vaccination reduces the probability of seeking care against influenza ARI, then estimates of VE against MAI may be unbiased while estimates of VE against SI may be have a substantial positive bias.

| S-EPMC5712239 | biostudies-literature

The case test-negative design for studies of the effectiveness of influenza vaccine in inpatient settings.

Project description:BackgroundThe test-negative design (TND) to evaluate influenza vaccine effectiveness is based on patients seeking care for acute respiratory infection, with those who test positive for influenza as cases and the test-negatives serving as controls. This design has not been validated for the inpatient setting where selection bias might be different from an outpatient setting.MethodsWe derived mathematical expressions for vaccine effectiveness (VE) against laboratory-confirmed influenza hospitalizations and used numerical simulations to verify theoretical results exploring expected biases under various scenarios. We explored meaningful interpretations of VE estimates from inpatient TND studies.ResultsVE estimates from inpatient TND studies capture the vaccine-mediated protection of the source population against laboratory-confirmed influenza hospitalizations. If vaccination does not modify disease severity, these estimates are equivalent to VE against influenza virus infection. If chronic cardiopulmonary individuals are enrolled because of non-infectious exacerbation, biased VE estimates (too high) will result. If chronic cardiopulmonary disease status is adjusted for accurately, the VE estimates will be unbiased. If chronic cardiopulmonary illness cannot be adequately be characterized, excluding these individuals may provide unbiased VE estimates.ConclusionsThe inpatient TND offers logistic advantages and can provide valid estimates of influenza VE. If highly vaccinated patients with respiratory exacerbation of chronic cardiopulmonary conditions are eligible for study inclusion, biased VE estimates will result unless this group is well characterized and the analysis can adequately adjust for it. Otherwise, such groups of subjects should be excluded from the analysis.

| S-EPMC5025336 | biostudies-literature

Estimating Influenza Vaccine Effectiveness With the Test-Negative Design Using Alternative Control Groups: A Systematic Review and Meta-Analysis.

Project description:One important assumption in case-control studies is that control selection should be independent of exposure. Nevertheless, it has been hypothesized that virus interference might lead to a correlation between receipt of influenza vaccination and increased risk of infection with other respiratory viruses. We investigated whether such a phenomenon might affect a study design commonly used to estimate influenza vaccine effectiveness (VE). We searched publications in MEDLINE, PubMed, and Web of Science. We identified 12 studies using the test-negative design (2011-2017) that reported VE estimates separately derived by 3 alternative control groups: 1) all patients testing negative for influenza (FLU), VEFLU-; 2) patients who tested positive for other/another respiratory virus (ORV), VEORV+; and 3) patients who tested negative for all viruses in the panel (PAN), VEPAN-. These included VE estimates from 7 countries for all age groups from 2003/2004 to 2013/2014. We observed no difference in vaccination coverage between the ORV-positive and PAN-negative control groups. A total of 63 VEFLU- estimates, 62 VEORV+ estimates, and 33 VEPAN- estimates were extracted. Pooled estimates of the difference in VE (ΔVE) were very similar between groups. In meta-regression, no association was found between the selection of control group and VE estimates. In conclusion, we did not find any differences in VE estimates based on the choice of control group.

| S-EPMC5860156 | 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

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

Project description: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.

| S-EPMC5535302 | biostudies-literature

Potential of the test-negative design for measuring influenza vaccine effectiveness: a systematic review.

Project description:The test-negative design is a variant of the case-control study being increasingly used to study influenza vaccine effectiveness (VE). In these studies, patients with influenza-like illness are tested for influenza. Vaccine coverage is compared between those testing positive versus those testing negative to estimate VE.We reviewed features in the design, analysis and reporting of 85 published test-negative studies.Studies were identified from PubMed, reference lists and email updates. Study eligibility: All studies using the test-negative design reporting end-of-season estimates were included.Design features that may affect the validity and comparability of reported estimates were reviewed, including setting, study period, source population, case definition, exposure and outcome ascertainment and statistical model.There was considerable variation in the analytic approach, with 68 unique statistical models identified among the studies.Harmonization of analytic approaches may improve the potential for pooling VE estimates.

| S-EPMC4277796 | biostudies-literature

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

Project description: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.

| S-EPMC9151296 | 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

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data