Evidence map›Paper›PMID 41784456›Full record

ArticleInfluenza and other respiratory viruses2026

Emergency Department Syndromic Surveillance Diagnostic Code Selection for Assessing Severity of Seasonal Influenza in New South Wales, Australia.

Nectarios Rose, Adam T Craig, David J Muscatello

Abstract read
In one paragraph

Article in Influenza and other respiratory viruses, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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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

2 citing papers in PubMed.

  1. Article
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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

3 authors.

Nectarios RoseUniversity of New South Wales, Kensington, Australia.
Adam T CraigUniversity of Queensland, Brisbane, Australia.
David J MuscatelloUniversity of New South Wales, Kensington, Australia.

Funding

Australian Government Research Training Program ScholarshipAustralian National Health and Medical Research Council (NHMRC) APP1194109
6 · The paper itself

Abstract

backgroundEmergency department (ED) syndromic surveillance (EDSyS) often relies on preliminary or ED discharge diagnosis codes as indicators of influenza, but few studies provide a justification for their selection. This retrospective analytical study aimed to optimise the selection of diagnostic codes in EDSyS for monitoring influenza activity and severity.

methodsDiagnostic codes potentially relating to a respiratory infection and assigned to people presenting to over 180 EDs in New South Wales (NSW), Australia, were grouped into 16 mutually exclusive 'ED Syndromes'. Time series of the proportion of ED presentations for each ED syndrome by epidemiological week between 2010 and 2019 were compared to a reference series of the percentage influenza positive results from sentinel laboratories, using two similarity and three timeliness statistics. Hospital inpatient admission and laboratory notification data linked to each ED presentation allowed assessment of patient infection status and outcomes.

results'Unspecified Viral' (any non-specific viral illness, without reference to the respiratory system) and ED syndromes based on influenza like Illness (ILI) and influenza had the best combination of similarity and timeliness measures. Linked data identified relatively high rates of hospital admission, laboratory-confirmed influenza and inpatient influenza diagnosis for ED syndromes based on pneumonia and lower respiratory tract infection.

conclusionIn addition to ILI and influenza, ED syndromes based on unspecified viral illnesses can be used for EDSyS to assess influenza timing and transmissibility in NSW, Australia. The approach outlined in our paper can identify diagnostic codes to improve severity assessment of seasonal influenza using EDSyS.

Indexed as

Emergency Service, HospitalInfluenza, HumanAdolescentAdultEmergency Room VisitsFemaleHospitalizationHumansMaleNew South WalesRetrospective StudiesSeasonsSeverity of Illness Indexemergency department syndromic surveillanceICD‐10influenzaPISASNOMED

Identifiers

PMID41784456
PMCPMC12962033

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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.