Evidence map›Paper›PMID 21592398›Full record

ArticleBMC public health2011

Syndromic surveillance for influenza in two hospital emergency departments. Relationships between ICD-10 codes and notified cases, before and during a pandemic.

Karen Moore, James Black, Stacey Rowe, Lucinda Franklin

Abstract read
In one paragraph

Article in BMC public health, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 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

20 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Observational
  7. Article
  8. Article
  9. Article
  10. Barriers to hospital electronic public health reporting and implications for the COVID-19 pandemic.Journal of the American Medical Informatics Association : JAMIA · 2020
    Article
  11. Article
  12. Article
  13. Observational
  14. [Infectious diseases and their ICD coding : What could be improved by the introduction of ICD-11?]Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2018
    Review
  15. Article
  16. Article
  17. Surveillance for emerging respiratory viruses.The Lancet. Infectious diseases · 2014
    Review
  18. Article
  19. Article
  20. Impact of seasonal and pandemic influenza on emergency department visits, 2003-2010, Ontario, Canada.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2013
    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

4 authors.

Karen MooreMonash University, Melbourne, Victoria, Australia. karen.moore@monash.edu
James Black
Stacey Rowe
Lucinda Franklin

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInterest in the use of emergency department (ED) data by syndromic surveillance systems to detect influenza outbreaks has been growing. Evaluations of these systems generally focus on events during influenza seasons. The aims of this study were to identify which emergency department disease codes best correlated with confirmed influenza cases and to determine if these same codes would be useful in the non-influenza season. The 2009 influenza pandemic in Victoria, Australia, provided further opportunity to examine the performance of the syndromic surveillance system during this event.

methodsWe undertook a retrospective analysis of data from the Victorian Department of Health's pilot syndromic surveillance programme, 'SynSurv'. SynSurv automatically captures patient information as it is entered by ED staff. This information includes patient demographics, their presenting symptoms and a preliminary diagnosis using ICD-10 coding. To determine which codes were best correlated with influenza notifications, weekly counts for each of the ICD-10 diagnosis codes ever used in the dataset were calculated and compared with the corresponding weekly count of confirmed influenza cases. Correlations between these codes and confirmed influenza cases in the non-influenza season were then undertaken. The data covered the period from July 2001 until August 2009 and included the 2009 influenza pandemic.

resultsThere was a marked increase in weekly counts of both laboratory-confirmed influenza cases and relevant ICD-10 codes during the influenza pandemic period. The increase in laboratory confirmed cases was more than four times greater than the previous highest number reported, in 2007, even though the influenza-like-illness activity in the community was considered comparable to 2003 and 2007. We found five ICD-10 codes to be moderately and significantly correlated with influenza cases. None of these codes was correlated with laboratory confirmed influenza notifications outside the influenza season, at least in part because of the small number of influenza cases notified during that period.

conclusionsThis study suggests that the choice of codes made by ED staff to record a case of influenza-like illness is influenced by their perceptions of how much influenza is circulating at the time. The ability of syndromic surveillance to detect outbreaks early may be impeded because case diagnosis is influenced by what ED staff believes to be occurring in the community.

Indexed as

Disease NotificationEmergency Service, HospitalInternational Classification of DiseasesHumansInfluenza A Virus, H1N1 SubtypeInfluenza, HumanPandemicsPopulation SurveillanceRetrospective StudiesVictoria

Identifiers

PMID21592398
PMCPMC3111580

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Registered trials

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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.