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Open Access Research article

Can epidemic detection systems at the hospital level complement regional surveillance networks: Case study with the influenza epidemic?

Solweig Gerbier-Colomban12*, Véronique Potinet-Pagliaroli3 and Marie-Hélène Metzger12

Author Affiliations

1 Hospices Civils de Lyon, Hôpital de la Croix-Rousse, Unité d’hygiène et d’épidémiologie, F-69317 Lyon, France

2 Université de Lyon, F-69000 Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622 Villeurbanne, France

3 Hospices Civils de Lyon, Hôpital de la Croix-Rousse, Service des urgences, F-69317 Lyon, France

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BMC Infectious Diseases 2014, 14:381  doi:10.1186/1471-2334-14-381

Published: 10 July 2014

Abstract

Background

Early knowledge of influenza outbreaks in the community allows local hospital healthcare workers to recognise the clinical signs of influenza in hospitalised patients and to apply effective precautions. The objective was to assess intra-hospital surveillance systems to detect earlier than regional surveillance systems influenza outbreaks in the community.

Methods

Time series obtained from computerized medical data from patients who visited a French hospital emergency department (ED) between June 1st, 2007 and March 31st, 2011 for influenza, or were hospitalised for influenza or a respiratory syndrome after an ED visit, were compared to different regional series. Algorithms using CUSUM method were constructed to determine the epidemic detection threshold with the local data series. Sensitivity, specificity and mean timeliness were calculated to assess their performance to detect community outbreaks of influenza. A sensitivity analysis was conducted, excluding the year 2009, due to the particular epidemiological situation related to pandemic influenza this year.

Results

The local series closely followed the seasonal trends reported by regional surveillance. The algorithms achieved a sensitivity of detection equal to 100% with series of patients hospitalised with respiratory syndrome (specificity ranging from 31.9 and 92.9% and mean timeliness from −58.3 to 20.3 days) and series of patients who consulted the ED for flu (specificity ranging from 84.3 to 93.2% and mean timeliness from −32.3 to 9.8 days). The algorithm with the best balance between specificity (87.7%) and mean timeliness (0.5 day) was obtained with series built by analysis of the ICD-10 codes assigned by physicians after ED consultation. Excluding the year 2009, the same series keeps the best performance with specificity equal to 95.7% and mean timeliness equal to −1.7 day.

Conclusions

The implementation of an automatic surveillance system to detect patients with influenza or respiratory syndrome from computerized ED records could allow outbreak alerts at the intra-hospital level before the publication of regional data and could accelerate the implementation of preventive transmission-based precautions in hospital settings.

Keywords:
Emergency service; Hospital; Syndromic surveillance; Influenza; Human; Infection control; Disease outbreak; Population surveillance