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

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 tha...

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Autores principales: Gerbier-Colomban, Solweig, Potinet-Pagliaroli, Véronique, Metzger, Marie-Hélène
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4227032/
https://www.ncbi.nlm.nih.gov/pubmed/25011679
http://dx.doi.org/10.1186/1471-2334-14-381
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author Gerbier-Colomban, Solweig
Potinet-Pagliaroli, Véronique
Metzger, Marie-Hélène
author_facet Gerbier-Colomban, Solweig
Potinet-Pagliaroli, Véronique
Metzger, Marie-Hélène
author_sort Gerbier-Colomban, Solweig
collection PubMed
description 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.
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spelling pubmed-42270322014-11-12 Can epidemic detection systems at the hospital level complement regional surveillance networks: Case study with the influenza epidemic? Gerbier-Colomban, Solweig Potinet-Pagliaroli, Véronique Metzger, Marie-Hélène BMC Infect Dis Research Article 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. BioMed Central 2014-07-10 /pmc/articles/PMC4227032/ /pubmed/25011679 http://dx.doi.org/10.1186/1471-2334-14-381 Text en Copyright © 2014 Gerbier-Colomban et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Gerbier-Colomban, Solweig
Potinet-Pagliaroli, Véronique
Metzger, Marie-Hélène
Can epidemic detection systems at the hospital level complement regional surveillance networks: Case study with the influenza epidemic?
title Can epidemic detection systems at the hospital level complement regional surveillance networks: Case study with the influenza epidemic?
title_full Can epidemic detection systems at the hospital level complement regional surveillance networks: Case study with the influenza epidemic?
title_fullStr Can epidemic detection systems at the hospital level complement regional surveillance networks: Case study with the influenza epidemic?
title_full_unstemmed Can epidemic detection systems at the hospital level complement regional surveillance networks: Case study with the influenza epidemic?
title_short Can epidemic detection systems at the hospital level complement regional surveillance networks: Case study with the influenza epidemic?
title_sort can epidemic detection systems at the hospital level complement regional surveillance networks: case study with the influenza epidemic?
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4227032/
https://www.ncbi.nlm.nih.gov/pubmed/25011679
http://dx.doi.org/10.1186/1471-2334-14-381
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