Evidence map›Paper›PMID 27587721›Full record

Observational studyAmerican journal of epidemiology2016

Theoretical Basis of the Test-Negative Study Design for Assessment of Influenza Vaccine Effectiveness.

Sheena G Sullivan, Eric J Tchetgen Tchetgen, Benjamin J Cowling

Open access · bronzeAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in American journal of epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 189 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
189citing papers in PubMed, 9 pooled it
16.9field-weighted citation impact, top 1% 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

189 citing papers in PubMed, 9 syntheses or guidelines pooled it, 339 citations in OpenAlex.

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129 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Sheena G Sullivan
Eric J Tchetgen Tchetgen
Benjamin J Cowling
Peter Doherty Institute · AU

Funding

Understanding and controlling antibiotic resistanceU54GM088558 · NIGMS · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIPSITCH, MARC · 2009 to 2018
$26.4M
Identification and Inference for Longitudinal Causal Mediation Analysis in HIVR01AI104459 · NIAID · HARVARD SCHOOL OF PUBLIC HEALTH · PI SHPITSER, ILYA, VANDERWEELE, TYLER · 2013 to 2017
$2.0M
Semiparametric Methods for Gene-environment InteractionR01ES020337 · NIEHS · HARVARD SCHOOL OF PUBLIC HEALTH · PI TCHETGEN TCHETGEN, ERIC JOEL · 2012 to 2015
$1.4M
NIAID NIH HHS R01 AI104459NIEHS NIH HHS R01 ES020337NIGMS NIH HHS U54 GM088558
6 · The paper itself

Abstract

Influenza viruses undergo frequent antigenic changes. As a result, the viruses circulating change within and between seasons, and the composition of the influenza vaccine is updated annually. Thus, estimation of the vaccine's effectiveness is not constant across seasons. In order to provide annual estimates of the influenza vaccine's effectiveness, health departments have increasingly adopted the "test-negative design," using enhanced data from routine surveillance systems. In this design, patients presenting to participating general practitioners with influenza-like illness are swabbed for laboratory testing; those testing positive for influenza virus are defined as cases, and those testing negative form the comparison group. Data on patients' vaccination histories and confounder profiles are also collected. Vaccine effectiveness is estimated from the odds ratio comparing the odds of testing positive for influenza among vaccinated patients and unvaccinated patients, adjusting for confounders. The test-negative design is purported to reduce bias associated with confounding by health-care-seeking behavior and misclassification of cases. In this paper, we use directed acyclic graphs to characterize potential biases in studies of influenza vaccine effectiveness using the test-negative design. We show how studies using this design can avoid or minimize bias and where bias may be introduced with particular study design variations.

Indexed as

Epidemiologic Research DesignAge FactorsCase-Control StudiesConfounding Factors, EpidemiologicDrug EvaluationHumansInfluenza, HumanInfluenza VaccinesOrthomyxoviridaePatient Acceptance of Health CarePopulation SurveillanceSex FactorsInfluenza Vaccinescausal inferencedirected acyclic graphsepidemiologic methodsinfluenzaobservational studiestest-negative study designvaccine effectiveness

Identifiers

PMID27587721
PMCPMC5013887
OpenAlexW2507826825

What OpenQuestion holds

Textmetadata
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.