Evidence map›Paper›PMID 33021983›Full record

ArticlePLoS computational biology2020

Optimising age coverage of seasonal influenza vaccination in England: A mathematical and health economic evaluation.

Edward M Hill, Stavros Petrou, Henry Forster, Simon de Lusignan, Ivelina Yonova, Matt J Keeling

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Disparities in Seasonal Influenza Vaccination in Europe.Immunity, inflammation and disease · 2025
    Review
  3. Article
  4. Article
  5. Influenza vaccination allocation in tropical settings under constrained resources.medRxiv : the preprint server for health sciences · 2024
    Article
  6. Article
  7. Article
  8. 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

6 authors at 2 institutions in 1 country.

Edward M HillThe Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom.ORCID 0000-0002-2992-2004
Stavros PetrouWarwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, United Kingdom.
Henry ForsterGovernment Statistics Service, Department of Health and Social Care, Leeds, LS2 7UE, United Kingdom.
Simon de LusignanNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, United Kingdom.ORCID 0000-0002-8553-2641
Ivelina YonovaNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, United Kingdom.ORCID 0000-0003-4856-3075
Matt J KeelingThe Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, CV4 7AL, United Kingdom.ORCID 0000-0003-4639-4765
University of Warwick · GBUniversity of Oxford · GB

Funding

Department of Health 027/0089Department of Health NF-SI-0616-10103
6 · The paper itself

Abstract

For infectious disease prevention, policy-makers are typically required to base policy decisions in light of operational and monetary restrictions, prohibiting implementation of all candidate interventions. To inform the evidence-base underpinning policy decision making, mathematical and health economic modelling can be a valuable constituent. Applied to England, this study aims to identify the optimal target age groups when extending a seasonal influenza vaccination programme of at-risk individuals to those individuals at low risk of developing complications following infection. To perform this analysis, we utilise an age- and strain-structured transmission model that includes immunity propagation mechanisms which link prior season epidemiological outcomes to immunity at the beginning of the following season. Making use of surveillance data from the past decade in conjunction with our dynamic model, we simulate transmission dynamics of seasonal influenza in England from 2012 to 2018. We infer that modified susceptibility due to natural infection in the previous influenza season is the only immunity propagation mechanism to deliver a non-negligible impact on the transmission dynamics. Further, we discerned case ascertainment to be higher for young infants compared to adults under 65 years old, and uncovered a decrease in case ascertainment as age increased from 65 to 85 years of age. Our health economic appraisal sweeps vaccination age space to determine threshold vaccine dose prices achieving cost-effectiveness under differing paired strategies. In particular, we model offering vaccination to all those low-risk individuals younger than a given age (but no younger than two years old) and all low-risk individuals older than a given age, while maintaining vaccination of at-risk individuals of any age. All posited strategies were deemed cost-effective. In general, the addition of low-risk vaccination programmes whose coverage encompassed children and young adults (aged 20 and below) were highly cost-effective. The inclusion of elder age-groups to the low-risk programme typically lessened the cost-effectiveness. Notably, elderly-centric programmes vaccinating from 65-75 years and above had the least permitted expense per vaccine.

Indexed as

Influenza VaccinesVaccinationAdolescentAdultAgedAged, 80 and overAge FactorsChildChild, PreschoolComputational BiologyCost-Benefit AnalysisEnglandHumansInfantInfant, NewbornInfluenza, HumanInfluenza Vaccines

Identifiers

PMID33021983
PMCPMC7567368
OpenAlexW3092153749

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.