Evidence map›Paper›PMID 27724915›Full record

ArticleBMC infectious diseases2016

Reducing disease burden in an influenza pandemic by targeted delivery of neuraminidase inhibitors: mathematical models in the Australian context.

Robert Moss, James M McCaw, Allen C Cheng, Aeron C Hurt, Jodie McVernon

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.4field-weighted citation impact, top 17% 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

10 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Antiviral Therapy for the Next Influenza Pandemic.Tropical medicine and infectious disease · 2019
    Article
  10. 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

5 authors at 3 institutions in 1 country.

Robert MossModelling and Simulation Unit Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie St, Melbourne, 3010, Victoria, Australia. rgmoss@unimelb.edu.au.ORCID http://orcid.org/0000-0002-4568-2012
James M McCawModelling and Simulation Unit Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie St, Melbourne, 3010, Victoria, Australia.
Allen C ChengInfectious Disease Epidemiology Unit, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia.
Aeron C HurtWHO Collaborating Centre for Reference and Research on Influenza, Peter Doherty Institute, Melbourne, Australia.
Jodie McVernonModelling and Simulation Unit Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health, The University of Melbourne, Level 3, 207 Bouverie St, Melbourne, 3010, Victoria, Australia.
The University of Melbourne · AUAlfred Health · AUPeter Doherty Institute · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany nations maintain stockpiles of neuraminidase inhibitor (NAI) antiviral agents for use in influenza pandemics to reduce transmission and mitigate the course of clinical infection. Pandemic preparedness plans include the use of these stockpiles to deliver proportionate responses, informed by emerging evidence of clinical impact. Recent uncertainty about the effectiveness of NAIs has prompted these nations to reconsider the role of NAIs in pandemic response, with implications for pandemic planning and for NAI stockpile size.

methodsWe combined a dynamic model of influenza epidemiology with a model of the clinical care pathways in the Australian health care system to identify effective NAI strategies for reducing morbidity and mortality in pandemic events, and the stockpile requirements for these strategies. The models were informed by a 2015 assessment of NAI effectiveness against susceptibility, pathogenicity, and transmission of influenza.

resultsLiberal distribution of NAIs for early treatment in outpatient settings yielded the greatest benefits in all of the considered scenarios. Restriction of community-based treatment to risk groups was effective in those groups, but failed to prevent the large proportion of cases arising from lower risk individuals who comprise the majority of the population.

conclusionsThese targeted strategies are only effective if they can be deployed within the constraints of existing health care infrastructure. This finding highlights the critical importance of identifying optimal models of care delivery for effective emergency health care response.

Indexed as

Cost of IllnessAntiviral AgentsAustraliaEnzyme InhibitorsHumansInfluenza, HumanModels, TheoreticalNeuraminidasePandemicsRisk AssessmentAntiviral AgentsEnzyme InhibitorsNeuraminidaseEmergency responseInfluenzaNeuraminidase inhibitorPandemic preparedness

Identifiers

PMID27724915
PMCPMC5057455
OpenAlexW2531229133

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.