Evidence map›Paper›PMID 24146973›Full record

ArticlePloS one2013

Exploring a proposed WHO method to determine thresholds for seasonal influenza surveillance.

Ee Laine Tay, Kristina Grant, Martyn Kirk, Anthony Mounts, Heath Kelly

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

31 citing papers in PubMed, 47 citations in OpenAlex.

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  7. Hospital and Population-Based Evidence for COVID-19 Early Circulation in the East of France.International journal of environmental research and public health · 2020
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  10. Transmissibility of influenza during the 21st-century epidemics, Spain, influenza seasons 2001/02 to 2017/18.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2020
    Article
  11. Article
  12. Article
  13. Inter-seasonality of influenza in Australia.Influenza and other respiratory viruses · 2019
    Article
  14. Influenza surveillance: determining the epidemic threshold for influenza by using the Moving Epidemic Method (MEM), Montenegro, 2010/11 to 2017/18 influenza seasons.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2019
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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 2 countries.

Ee Laine TayVictoria Infectious Diseases Reference Laboratory, Melbourne, Australia ; National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australia.
Kristina Grant
Martyn Kirk
Anthony Mounts
Heath Kelly
Australian National University · AUVictorian Infectious Diseases Reference Laboratory · AUWorld Health Organization · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionHealth authorities find thresholds useful to gauge the start and severity of influenza seasons. We explored a method for deriving thresholds proposed in an influenza surveillance manual published by the World Health Organization (WHO).

methodsFor 2002-2011, we analysed two routine influenza-like-illness (ILI) datasets, general practice sentinel surveillance and a locum medical service sentinel surveillance, plus laboratory data and hospital admissions for influenza. For each sentinel dataset, we created two composite variables from the product of weekly ILI data and the relevant laboratory data, indicating the proportion of tested specimens that were positive. For all datasets, including the composite datasets, we aligned data on the median week of peak influenza or ILI activity and assigned three threshold levels: seasonal threshold, determined by inspection; and two intensity thresholds termed average and alert thresholds, determined by calculations of means, medians, confidence intervals (CI) and percentiles. From the thresholds, we compared the seasonal onset, end and intensity across all datasets from 2002-2011. Correlation between datasets was assessed using the mean correlation coefficient.

resultsThe median week of peak activity was week 34 for all datasets, except hospital data (week 35). Means and medians were comparable and the 90% upper CIs were similar to the 95(th) percentiles. Comparison of thresholds revealed variations in defining the start of a season but good agreement in describing the end and intensity of influenza seasons, except in hospital admissions data after the pandemic year of 2009. The composite variables improved the agreements between the ILI and other datasets. Datasets were well correlated, with mean correlation coefficients of >0.75 for a range of combinations.

conclusionsThresholds for influenza surveillance are easily derived from historical surveillance and laboratory data using the approach proposed by WHO. Use of composite variables is helpful for describing influenza season characteristics.

Indexed as

SeasonsAustraliaHumansInfluenza, HumanPopulation SurveillanceSentinel SurveillanceWorld Health Organization

Identifiers

PMID24146973
PMCPMC3795663
OpenAlexW2089354920

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.