Dataset Information

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

ABSTRACT:

Background

Test-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.

Methods

We 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.

Results

Vaccine 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.

Conclusions

Although 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.

SUBMITTER: Feldstein LR

PROVIDER: S-EPMC8967445 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

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

Feldstein Leora R LR Ferdinands Jill M JM Self Wesley H WH Randolph Adrienne G AG Aboodi Michael M Baughman Adrienne H AH Brown Samuel M SM Exline Matthew C MC Clark Files D D Gibbs Kevin K Ginde Adit A AA Gong Michelle N MN Grijalva Carlos G CG Halasa Natasha N Khan Akram A Lindsell Christopher J CJ Newhams Margaret M Peltan Ithan D ID Prekker Matthew E ME Rice Todd W TW Shapiro Nathan I NI Steingrub Jay J Talbot H Keipp HK Halloran M Elizabeth ME Patel Manish M

The Journal of infectious diseases 20211201 12

<h4>Background</h4>Test-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.< ...[more]

PMID: 34013330

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Project description:BACKGROUND: A multicentre case-control study based on sentinel practitioner surveillance networks from seven European countries was undertaken to estimate the effectiveness of 2009-2010 pandemic and seasonal influenza vaccines against medically attended influenza-like illness (ILI) laboratory-confirmed as pandemic influenza A (H1N1) (pH1N1). METHODS AND FINDINGS: Sentinel practitioners swabbed ILI patients using systematic sampling. We included in the study patients meeting the European ILI case definition with onset of symptoms >14 days after the start of national pandemic vaccination campaigns. We compared pH1N1 cases to influenza laboratory-negative controls. A valid vaccination corresponded to >14 days between receiving a dose of vaccine and symptom onset. We estimated pooled vaccine effectiveness (VE) as 1 minus the odds ratio with the study site as a fixed effect. Using logistic regression, we adjusted VE for potential confounding factors (age group, sex, month of onset, chronic diseases and related hospitalizations, smoking history, seasonal influenza vaccinations, practitioner visits in previous year). We conducted a complete case analysis excluding individuals with missing values and a multiple multivariate imputation to estimate missing values. The multivariate imputation (n?=?2902) adjusted pandemic VE (PIVE) estimates were 71.9% (95% confidence interval [CI] 45.6-85.5) overall; 78.4% (95% CI 54.4-89.8) in patients <65 years; and 72.9% (95% CI 39.8-87.8) in individuals without chronic disease. The complete case (n?=?1,502) adjusted PIVE were 66.0% (95% CI 23.9-84.8), 71.3% (95% CI 29.1-88.4), and 70.2% (95% CI 19.4-89.0), respectively. The adjusted PIVE was 66.0% (95% CI -69.9 to 93.2) if vaccinated 8-14 days before ILI onset. The adjusted 2009-2010 seasonal influenza VE was 9.9% (95% CI -65.2 to 50.9). CONCLUSIONS: Our results suggest good protection of the pandemic monovalent vaccine against medically attended pH1N1 and no effect of the 2009-2010 seasonal influenza vaccine. However, the late availability of the pandemic vaccine and subsequent limited coverage with this vaccine hampered our ability to study vaccine benefits during the outbreak period. Future studies should include estimation of the effectiveness of the new trivalent vaccine in the upcoming 2010-2011 season, when vaccination will occur before the influenza season starts.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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