Evidence map›Paper›PMID 25878298›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2015

Estimating the potential effects of a vaccine program against an emerging influenza pandemic--United States.

Matthew Biggerstaff, Carrie Reed, David L Swerdlow, Manoj Gambhir, Samuel Graitcer, Lyn Finelli, Rebekah H Borse, Sonja A Rasmussen, Martin I Meltzer, Carolyn B Bridges

Abstract read
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Multi-period vaccine allocation model in a pandemic: A case study of COVID-19 in Australia.Transportation research. Part E, Logistics and transportation review · 2022
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  15. Standardizing scenarios to assess the need to respond to an influenza pandemic.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2015
    Review
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

10 authors.

Matthew BiggerstaffEpidemiology and Prevention Branch, Influenza Division.
Carrie ReedEpidemiology and Prevention Branch, Influenza Division.
David L SwerdlowModeling Unit and Office of the Director, and.
Manoj GambhirModeling Unit, National Center for Immunization and Respiratory Diseases (NCIRD), Centers for Disease Control and Prevention (CDC), Atlanta, Georgia Epidemiological Modelling Unit, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia.
Samuel GraitcerImmunization Services Division, NCIRD.
Lyn FinelliEpidemiology and Prevention Branch, Influenza Division.
Rebekah H BorseScientific and Program Services Branch, Division of Preparedness and Emerging Infections, National Center for Emerging and Zoonotic Infectious Diseases.
Sonja A RasmussenInfluenza Coordination Unit, Office of Infectious Diseases, CDC, Atlanta, Georgia.
Martin I MeltzerScientific and Program Services Branch, Division of Preparedness and Emerging Infections, National Center for Emerging and Zoonotic Infectious Diseases.
Carolyn B BridgesImmunization Services Division, NCIRD.

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

backgroundHuman illness from influenza A(H7N9) was identified in March 2013, and candidate vaccine viruses were soon developed. To understand factors that may impact influenza vaccination programs, we developed a model to evaluate hospitalizations and deaths averted considering various scenarios.

methodsWe utilized a model incorporating epidemic curves with clinical attack rates of 20% or 30% in a single wave of illness, case hospitalization ratios of 0.5% or 4.2%, and case fatality ratios of 0.08% or 0.53%. We considered scenarios that achieved 80% vaccination coverage, various starts of vaccination programs (16 or 8 weeks before, the same week of, or 8 or 16 weeks after start of pandemic), an administration rate of 10 or 30 million doses per week (the latter rate is an untested assumption), and 2 levels of vaccine effectiveness (2 doses of vaccine required; either 62% or 80% effective for persons aged <60 years, and either 43% or 60% effective for persons aged ≥ 60 years).

resultsThe start date of vaccination campaigns most influenced impact; 141,000-2,200,000 hospitalizations and 11,000-281,000 deaths were averted when campaigns started before a pandemic, and <100-1 300,000 hospitalizations and 0-165,000 deaths were averted for programs beginning the same time as or after the introduction of the pandemic virus. The rate of vaccine administration and vaccine effectiveness did not influence campaign impact as much as timing of the start of campaign.

conclusionsOur findings suggest that efforts to improve the timeliness of vaccine production will provide the greatest impacts for future pandemic vaccination programs.

Indexed as

Communicable Diseases, EmergingDisaster PlanningEpidemiological MonitoringHumansImmunization ProgramsInfluenza A Virus, H7N9 SubtypeInfluenza, HumanInfluenza VaccinesModels, TheoreticalPandemicsUnited StatesInfluenza Vaccinesinfluenzainfluenza A(H7N9)influenza vaccinemathematical modelingpandemic

Identifiers

PMID25878298
PMCPMC4610126

What OpenQuestion holds

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Registered trials

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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.