ArticleBMC infectious diseases2017
A comparison of the test-negative and the traditional case-control study designs for estimation of influenza vaccine effectiveness under nonrandom vaccination.
Article in BMC infectious diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- The Use of Test-negative Controls to Monitor Vaccine Effectiveness: A Systematic Review of Methodology.Epidemiology (Cambridge, Mass.) · 2020Pooled it
- A comparison of the test-negative and the matched case-control study designs for estimation of EV71 vaccine immunological surrogate endpoints from a randomized controlled trial.Human vaccines & immunotherapeutics · 2022Trial
- Effect of seasonal influenza vaccination on influenza symptom severity among children in Hutterite communities: Follow-up study of a randomized trial.Influenza and other respiratory viruses · 2020Trial
- Validity of influenza vaccine effectiveness calculations using a test-negative design in Thailand.American journal of epidemiology · 2026Article
- Evaluating the Test-Negative Design for COVID-19 Vaccine Effectiveness Using Randomized Trial Data: A Secondary Cross-Protocol Analysis of 5 Randomized Clinical Trials.JAMA network open · 2025Article
- Review
- Correction of vaccine effectiveness derived from test-negative case-control studies.BMC medical research methodology · 2023Article
- Article
- COVID-19 vaccine effectiveness studies in Nigeria: Quo vadis?Journal of global health · 2022Article
- Article
- Influenza vaccination: protecting the most vulnerable.European respiratory review : an official journal of the European Respiratory Society · 2021Review
- Influenza and Respiratory Virus Surveillance, Vaccine Uptake, and Effectiveness at a Time of Cocirculating COVID-19: Protocol for the English Primary Care Sentinel System for 2020-2021.JMIR public health and surveillance · 2021Article
- Assessment of the benefits of seasonal influenza vaccination: Elements of a framework to interpret estimates of vaccine effectiveness and support robust decision-making and communication.Influenza and other respiratory viruses · 2021Review
- Influenza Vaccine Effectiveness in the Inpatient Setting: Evaluation of Potential Bias in the Test-Negative Design by Use of Alternate Control Groups.American journal of epidemiology · 2020Article
- Article
- Challenges in estimating influenza vaccine effectiveness.Expert review of vaccines · 2019Review
- Article
- Article
- Bias of influenza vaccine effectiveness estimates from test-negative studies conducted during an influenza pandemic.Vaccine · 2019Article
- A Dynamic Model for Evaluation of the Bias of Influenza Vaccine Effectiveness Estimates From Observational Studies.American journal of epidemiology · 2019Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAs annual influenza vaccination is recommended for all U.S. persons aged 6 months or older, it is unethical to conduct randomized clinical trials to estimate influenza vaccine effectiveness (VE). Observational studies are being increasingly used to estimate VE. We developed a probability model for comparing the bias and the precision of VE estimates from two case-control designs: the traditional case-control (TCC) design and the test-negative (TN) design. In both study designs, acute respiratory illness (ARI) patients seeking medical care testing positive for influenza infection are considered cases. In the TN design, ARI patients seeking medical care who test negative serve as controls, while in the TCC design, controls are randomly selected individuals from the community who did not contract an ARI.
methodsOur model assigns each study participant a covariate corresponding to the person's health status. The probabilities of vaccination and of contracting influenza and non-influenza ARI depend on health status. Hence, our model allows non-random vaccination and confounding. In addition, the probability of seeking care for ARI may depend on vaccination and health status. We consider two outcomes of interest: symptomatic influenza (SI) and medically-attended influenza (MAI).
resultsIf vaccination does not affect the probability of non-influenza ARI, then VE estimates from TN studies usually have smaller bias than estimates from TCC studies. We also found that if vaccinated influenza ARI patients are less likely to seek medical care than unvaccinated patients because the vaccine reduces symptoms' severity, then estimates of VE from both types of studies may be severely biased when the outcome of interest is SI. The bias is not present when the outcome of interest is MAI.
conclusionsThe TN design produces valid estimates of VE if (a) vaccination does not affect the probabilities of non-influenza ARI and of seeking care against influenza ARI, and (b) the confounding effects resulting from non-random vaccination are similar for influenza and non-influenza ARI. Since the bias of VE estimates depends on the outcome against which the vaccine is supposed to protect, it is important to specify the outcome of interest when evaluating the bias.
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