Evidence map›Paper›PMID 26097505›Full record

ArticleEpidemics2015

On the relative role of different age groups in influenza epidemics.

Colin J Worby, Sandra S Chaves, Jacco Wallinga, Marc Lipsitch, Lyn Finelli, Edward Goldstein

Abstract read
In one paragraph

Article in Epidemics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
106citing 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

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

  1. Pooled it
  2. Trial
  3. Trial
  4. Article
  5. Article
  6. Influenza.Nature reviews. Disease primers · 2026
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Homophily within and across groups.Nature communications · 2025
    Article
  17. Article
  18. Article
  19. Observational
  20. Article

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

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.

Colin J WorbyDepartment of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston MA, 02115 USA.
Sandra S ChavesInfluenza Division, National Center for Immunization and Respiratory Disease, Centers for Disease Control and Prevention, Atlanta GA, 30333 USA.
Jacco WallingaNational Institute of Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The Netherlands.
Marc LipsitchDepartment of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston MA, 02115 USA ; Department of Immunology and Infectious Disease, Harvard School of Public Health, Boston MA, 02115 USA.
Lyn FinelliInfluenza Division, National Center for Immunization and Respiratory Disease, Centers for Disease Control and Prevention, Atlanta GA, 30333 USA.
Edward GoldsteinDepartment of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston MA, 02115 USA.

Funding

Understanding and controlling antibiotic resistanceU54GM088558 · NIGMS · HARVARD SCHOOL OF PUBLIC HEALTH · PI COHEN, THEODORE H · 2009 to 2018
$26.4M
Epidemiology and modeling of the population dynamics of influenza and antimicrobiK01AI101010 · NIAID · HARVARD SCHOOL OF PUBLIC HEALTH · PI GOLDSTEIN, EDWARD · 2012 to 2014
$379k
NIAID NIH HHS K01 AI101010NIGMS NIH HHS U54 GM088558
6 · The paper itself

Abstract

The identification of key "driver" groups in influenza epidemics is of much interest for the implementation of effective public health response strategies, including vaccination programs. However, the relative importance of different age groups in propagating epidemics is uncertain. During a communicable disease outbreak, some groups may be disproportionately represented during the outbreak's ascent due to increased susceptibility and/or contact rates. Such groups or subpopulations can be identified by considering the proportion of cases within the subpopulation occurring before (Bp) and after the epidemic peak (Ap) to calculate the subpopulation's relative risk, RR=Bp/Ap. We estimated RR for several subpopulations (age groups) using data on laboratory-confirmed US influenza hospitalizations during epidemics between 2009-2014. Additionally, we simulated various influenza outbreaks in an age-stratified population, relating the RR to the impact of vaccination in each subpopulation on the epidemic's initial effective reproductive number R_e(0). We found that children aged 5-17 had the highest estimates of RR during the five largest influenza A outbreaks, though the relative magnitude of RR in this age group compared to other age groups varied, being highest for the 2009 A/H1N1 pandemic. For the 2010-2011 and 2012-2013 influenza B epidemics, adults aged 18-49, and 0-4 year-olds had the highest estimates of RR respectively. For 83% of simulated epidemics, the group with the highest RR was also the group for which initial distribution of a given quantity of vaccine would result in the largest reduction of R_e(0). In the largest 40% of simulated outbreaks, the group with the highest RR and the largest vaccination impact was children 5-17. While the relative importance of different age groups in propagating influenza outbreaks varies, children aged 5-17 play the leading role during the largest influenza A epidemics. Extra vaccination efforts for this group may contribute to reducing the epidemic's impact in the whole community.

Identifiers

PMID26097505
PMCPMC4469206

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.