ArticleAmerican journal of epidemiology2019
A Dynamic Model for Evaluation of the Bias of Influenza Vaccine Effectiveness Estimates From Observational Studies.
Article in American journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed.
- Severity-dependent test-seeking behaviors and test-negative designs: impact on estimated vaccine effectiveness and utility of analytic and design choices.American journal of epidemiology · 2024Article
- 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
- Challenges in estimating influenza vaccine effectiveness.Expert review of vaccines · 2019Review
- Bias of influenza vaccine effectiveness estimates from test-negative studies conducted during an influenza pandemic.Vaccine · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Given that influenza vaccination is now widely recommended in the United States, observational studies based on patients with acute respiratory illness (ARI) remain as the only option to estimate influenza vaccine effectiveness (VE). We developed a dynamic probability model to evaluate bias of VE estimates from passive surveillance cohort, test-negative, and traditional case-control studies. The model includes 2 covariates (health status and health awareness) that might affect the probabilities of vaccination, developing ARI, and seeking medical care. Our results suggest that test-negative studies produce unbiased estimates of VE against medically attended influenza when: 1) Vaccination does not affect the probability of noninfluenza ARI; and 2) health status has the same effect on the probability of influenza and noninfluenza ARIs. The same estimate might be severely biased (i.e., estimated VE - true VE ≥ 0.20) for estimating VE against symptomatic influenza if the vaccine affects the probability of seeking care against influenza ARI. VE estimates from test-negative studies might also be severely biased for both outcomes of interest when vaccination affects the probability of noninfluenza ARI, but estimates from passive surveillance cohort studies are unbiased in this case. Finally, VE estimates from traditional case-control studies suffer from bias regardless of the source of bias.
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Registered trials
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