Evidence map›Paper›PMID 32386323›Full record

ArticleThe Journal of infectious diseases2020

What If the Influenza Vaccine Did Not Offer Such Variable Protection?

Sarah M Bartsch, Elizabeth A Mitgang, Gail Geller, Sarah N Cox, Kelly J O'Shea, Angie Boyce, Sheryl S Siegmund, Jeffrey Kahn, Bruce Y Lee

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

Article in The Journal of infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 41% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

9 authors at 2 institutions in 1 country.

Sarah M BartschPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health & Health Policy, New York City, New York, USA.
Elizabeth A MitgangPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health & Health Policy, New York City, New York, USA.
Gail GellerJohns Hopkins Berman Institute of Bioethics, Baltimore, Maryland, USA.
Sarah N CoxPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health & Health Policy, New York City, New York, USA.
Kelly J O'SheaPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health & Health Policy, New York City, New York, USA.
Angie BoyceJohns Hopkins Berman Institute of Bioethics, Baltimore, Maryland, USA.
Sheryl S SiegmundPublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health & Health Policy, New York City, New York, USA.
Jeffrey KahnJohns Hopkins Berman Institute of Bioethics, Baltimore, Maryland, USA.
Bruce Y LeePublic Health Informatics, Computational, and Operations Research, CUNY Graduate School of Public Health & Health Policy, New York City, New York, USA.
The Graduate Center, CUNY · USJohns Hopkins Berman Institute of Bioethics · US

Funding

MULTILEVEL SYSTEMS-ORIENTED CHILDHOOD OBESITY STUDY IN CHINAU54HD070725 · NICHD · JOHNS HOPKINS UNIVERSITY · PI CURRIERO, FRANK · 2011 to 2016
$15.9M
MIDAS Informatics Services Group (ISG)U24GM110707 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI ESPINO, JESSI U, WAGNER, MICHAEL MATTHEW · 2014 to 2018
$11.6M
Ethical, Legal and Social Issues for Precision Medicine and Infectious DiseaseRM1HG009038 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI GELLER, GAIL, KAHN, JEFFREY P. · 2016 to 2020
$4.8M
Regional Healthcare Ecosystem Analyst for Healthcare Associated InfectionsR01HS023317 · AHRQ · JOHNS HOPKINS UNIVERSITY · PI LEE, BRUCE Y · 2014 to 2014
$2.4M
AHRQ HHS R01 HS023317NHGRI NIH HHS RM1 HG009038NICHD NIH HHS U54 HD070725NIGMS NIH HHS U24 GM110707
6 · The paper itself

Abstract

backgroundThe protection that an influenza vaccine offers can vary significantly from person to person due to differences in immune systems, body types, and other factors. The question, then, is what is the value of efforts to reduce this variability such as making vaccines more personalized and tailored to individuals.

methodsWe developed a compartment model of the United States to simulate different influenza seasons and the impact of reducing the variability in responses to the influenza vaccine across the population.

resultsGoing from a vaccine that varied in efficacy (0-30%) to one that had a uniform 30% efficacy for everyone averted 16.0-31.2 million cases, $1.9-$3.6 billion in direct medical costs, and $16.1-$42.7 billion in productivity losses. Going from 0-50% in efficacy to just 50% for everyone averted 27.7-38.6 million cases, $3.3-$4.6 billion in direct medical costs, and $28.8-$57.4 billion in productivity losses. Going from 0-70% to 70% averted 33.6-54.1 million cases, $4.0-$6.5 billion in direct medical costs, and $44.8-$64.7 billion in productivity losses.

conclusionsThis study quantifies for policy makers, funders, and vaccine developers and manufacturers the potential impact of efforts to reduce variability in the protection that influenza vaccines offer (eg, developing vaccines that are more personalized to different individual factors).

Indexed as

EpidemicsAdolescentAdultAgedAged, 80 and overChildChild, PreschoolCost-Benefit AnalysisDisease Transmission, InfectiousFemaleHumansInfantInfant, NewbornInfluenza, HumanInfluenza VaccinesMaleInfluenza Vaccinesefficacyinfluenzamodeltailoringvaccination

Identifiers

PMID32386323
PMCPMC7459138
OpenAlexW3022479730

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.