Evidence map›Paper›PMID 31307874›Full record

SynthesisVaccine2019

Models to predict the public health impact of vaccine resistance: A systematic review.

Molly C Reid, Kathryn Peebles, Sarah E Stansfield, Steven M Goodreau, Neil Abernethy, Geoffrey S Gottlieb, John E Mittler, Joshua T Herbeck

Open access · greenAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Vaccine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.9field-weighted citation impact, top 24% 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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
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

8 authors at 4 institutions in 1 country.

Molly C ReidDepartment of Epidemiology, 1959 NE Pacific Street, Magnuson Health Sciences Center, Room F-262, Seattle, WA 98195, United States; International Clinical Research Center, Department of Global Health, 908 Jefferson St., Seattle, WA 98104, United States. Electronic address: mollyr6@uw.edu.
Kathryn PeeblesDepartment of Epidemiology, 1959 NE Pacific Street, Magnuson Health Sciences Center, Room F-262, Seattle, WA 98195, United States; International Clinical Research Center, Department of Global Health, 908 Jefferson St., Seattle, WA 98104, United States. Electronic address: kpeebles@uw.edu.
Sarah E StansfieldDepartment of Epidemiology, 1959 NE Pacific Street, Magnuson Health Sciences Center, Room F-262, Seattle, WA 98195, United States; Department of Anthropologym Denny Hall, University of Washington, Seattle, WA 98195, United States. Electronic address: sestans@uw.edu.
Steven M GoodreauDepartment of Epidemiology, 1959 NE Pacific Street, Magnuson Health Sciences Center, Room F-262, Seattle, WA 98195, United States; Department of Anthropologym Denny Hall, University of Washington, Seattle, WA 98195, United States. Electronic address: goodreau@uw.edu.
Neil AbernethyDepartment of Biomedical Informatics and Medical Education, University of Washington, Box 358047, Seattle, WA 98195, United States; Department of Health Services, 1959 NE Pacific St, Magnuson Health Sciences Center, Room H-680, Seattle, WA 98195-7660, United States. Electronic address: neila@uw.edu.
Geoffrey S GottliebDivision of Allergy and Infectious Diseases & Center for Emerging & Re-Emerging Infectious Diseases, Department of Medicine & Department of Global Health, 750 Republican St., Building E, Seattle, WA 98109, United States. Electronic address: gottlieb@uw.edu.
John E MittlerDepartment of Microbiology, 750 Republican St., Building F, Seattle, WA 98109, United States. Electronic address: jmittler@uw.edu.
Joshua T HerbeckInternational Clinical Research Center, Department of Global Health, 908 Jefferson St., Seattle, WA 98104, United States. Electronic address: herbeck@uw.edu.
Health Alliance International · USSeattle Pacific University · USUniversity of Washington · USNational Center for Emerging and Zoonotic Infectious Diseases · US

Funding

Scientific/Technical CoreP2CHD042828 · NICHD · UNIVERSITY OF WASHINGTON · PI SARA R. CURRAN · 2017 to 2026
$6.3M
Integrated bio-social models for HIV epidemiologyR01AI108490 · NIAID · UNIVERSITY OF WASHINGTON · PI GOODREAU, STEVEN M., HERBECK, JOSHUA T · 2014 to 2018
$3.4M
Modeling the Evolutionary and Public Health Impacts of HIV Adaptation in Response to VaccinationR01GM125440 · NIGMS · UNIVERSITY OF WASHINGTON · PI HERBECK, JOSHUA T · 2017 to 2018
$841k
NIAID NIH HHS R01 AI108490NICHD NIH HHS P2C HD042828NIGMS NIH HHS R01 GM125440
6 · The paper itself

Abstract

Pathogen evolution is a potential threat to the long-term benefits provided by public health vaccination campaigns. Mathematical modeling can be a powerful tool to examine the forces responsible for the development of vaccine resistance and to predict its public health implications. We conducted a systematic review of existing literature to understand the construction and application of vaccine resistance models. We identified 26 studies that modeled the public health impact of vaccine resistance for 12 different pathogens. Most models predicted that vaccines would reduce overall disease burden in spite of evolution of vaccine resistance. Relatively few pathogens and populations for which vaccine resistance may be problematic were covered in the reviewed studies, with low- and middle-income countries particularly under-represented. We discuss the key components of model design, as well as patterns of model predictions.

Indexed as

Global HealthModels, TheoreticalHumansPublic HealthVaccinationVaccination RefusalMathematical modelingVaccine resistance

Identifiers

PMID31307874
PMCPMC7094884
OpenAlexW2959707388

What OpenQuestion holds

Textmetadata
LicenceTDM
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.