Evidence map›Paper›PMID 29216845›Full record

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.

Meng Shi, Qian An, Kylie E C Ainslie, Michael Haber, Walter A Orenstein

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
3.3field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.

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  11. Influenza vaccination: protecting the most vulnerable.European respiratory review : an official journal of the European Respiratory Society · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Meng ShiDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, 30322, USA.
Qian AnDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, 30322, USA.
Kylie E C AinslieDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, 30322, USA.
Michael HaberDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, 30322, USA. mhaber@emory.edu.
Walter A OrensteinDivision of Infectious Diseases, Department of Medicine, School of Medicine, Emory University, Atlanta, 30322, USA.
Emory University · US

Funding

Study Designs for Estimating the Effectiveness of Vaccination Against InfluenzaR01AI110474 · NIAID · EMORY UNIVERSITY · PI HABER, MICHAEL J · 2014 to 2016
$858k
National Institute of Allergy and Infectious Diseases R01AI110474NIAID NIH HHS R01 AI110474
6 · The paper itself

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.

Indexed as

AdultCase-Control StudiesFemaleHumansInfluenza, HumanInfluenza VaccinesMaleMiddle AgedModels, TheoreticalPatient Acceptance of Health CareSeverity of Illness IndexInfluenza VaccinesCase-control studyMedically-attended influenzaProbability modelSymptomatic influenzaTest-negative study

Identifiers

PMID29216845
PMCPMC5721721
OpenAlexW2771870730

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.