Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?

<h4>Background</h4>Although syndromic surveillance is increasingly used to detect unusual illness, there is a debate whether it is useful for detecting local outbreaks. We evaluated whether syndromic surveillance detects local outbreaks of lower-respiratory infections (LRIs) without swam...

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Autores principales:	Cees C van den Wijngaard, Liselotte van Asten, Wilfrid van Pelt, Gerda Doornbos, Nico J D Nagelkerke, Gé A Donker, Wim van der Hoek, Marion P G Koopmans
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/40a195761a5f444eb27a691cb749dd33
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spelling	oai:doaj.org-article:40a195761a5f444eb27a691cb749dd332021-12-02T20:22:02ZSyndromic surveillance for local outbreaks of lower-respiratory infections: would it work?1932-620310.1371/journal.pone.0010406https://doaj.org/article/40a195761a5f444eb27a691cb749dd332010-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20454449/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Although syndromic surveillance is increasingly used to detect unusual illness, there is a debate whether it is useful for detecting local outbreaks. We evaluated whether syndromic surveillance detects local outbreaks of lower-respiratory infections (LRIs) without swamping true signals by false alarms.<h4>Methods and findings</h4>Using retrospective hospitalization data, we simulated prospective surveillance for LRI-elevations. Between 1999-2006, a total of 290762 LRIs were included by date of hospitalization and patients place of residence (>80% coverage, 16 million population). Two large outbreaks of Legionnaires disease in the Netherlands were used as positive controls to test whether these outbreaks could have been detected as local LRI elevations. We used a space-time permutation scan statistic to detect LRI clusters. We evaluated how many LRI-clusters were detected in 1999-2006 and assessed likely causes for the cluster-signals by looking for significantly higher proportions of specific hospital discharge diagnoses (e.g. Legionnaires disease) and overlap with regional influenza elevations. We also evaluated whether the number of space-time signals can be reduced by restricting the scan statistic in space or time. In 1999-2006 the scan-statistic detected 35 local LRI clusters, representing on average 5 clusters per year. The known Legionnaires' disease outbreaks in 1999 and 2006 were detected as LRI-clusters, since cluster-signals were generated with an increased proportion of Legionnaires disease patients (p:<0.0001). 21 other clusters coincided with local influenza and/or respiratory syncytial virus activity, and 1 cluster appeared to be a data artifact. For 11 clusters no likely cause was defined, some possibly representing as yet undetected LRI-outbreaks. With restrictions on time and spatial windows the scan statistic still detected the Legionnaires' disease outbreaks, without loss of timeliness and with less signals generated in time (up to 42% decline).<h4>Conclusions</h4>To our knowledge this is the first study that systematically evaluates the performance of space-time syndromic surveillance with nationwide high coverage data over a longer period. The results show that syndromic surveillance can detect local LRI-outbreaks in a timely manner, independent of laboratory-based outbreak detection. Furthermore, since comparatively few new clusters per year were observed that would prompt investigation, syndromic hospital-surveillance could be a valuable tool for detection of local LRI-outbreaks.Cees C van den WijngaardLiselotte van AstenWilfrid van PeltGerda DoornbosNico J D NagelkerkeGé A DonkerWim van der HoekMarion P G KoopmansPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 4, p e10406 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Cees C van den Wijngaard Liselotte van Asten Wilfrid van Pelt Gerda Doornbos Nico J D Nagelkerke Gé A Donker Wim van der Hoek Marion P G Koopmans Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?
description	<h4>Background</h4>Although syndromic surveillance is increasingly used to detect unusual illness, there is a debate whether it is useful for detecting local outbreaks. We evaluated whether syndromic surveillance detects local outbreaks of lower-respiratory infections (LRIs) without swamping true signals by false alarms.<h4>Methods and findings</h4>Using retrospective hospitalization data, we simulated prospective surveillance for LRI-elevations. Between 1999-2006, a total of 290762 LRIs were included by date of hospitalization and patients place of residence (>80% coverage, 16 million population). Two large outbreaks of Legionnaires disease in the Netherlands were used as positive controls to test whether these outbreaks could have been detected as local LRI elevations. We used a space-time permutation scan statistic to detect LRI clusters. We evaluated how many LRI-clusters were detected in 1999-2006 and assessed likely causes for the cluster-signals by looking for significantly higher proportions of specific hospital discharge diagnoses (e.g. Legionnaires disease) and overlap with regional influenza elevations. We also evaluated whether the number of space-time signals can be reduced by restricting the scan statistic in space or time. In 1999-2006 the scan-statistic detected 35 local LRI clusters, representing on average 5 clusters per year. The known Legionnaires' disease outbreaks in 1999 and 2006 were detected as LRI-clusters, since cluster-signals were generated with an increased proportion of Legionnaires disease patients (p:<0.0001). 21 other clusters coincided with local influenza and/or respiratory syncytial virus activity, and 1 cluster appeared to be a data artifact. For 11 clusters no likely cause was defined, some possibly representing as yet undetected LRI-outbreaks. With restrictions on time and spatial windows the scan statistic still detected the Legionnaires' disease outbreaks, without loss of timeliness and with less signals generated in time (up to 42% decline).<h4>Conclusions</h4>To our knowledge this is the first study that systematically evaluates the performance of space-time syndromic surveillance with nationwide high coverage data over a longer period. The results show that syndromic surveillance can detect local LRI-outbreaks in a timely manner, independent of laboratory-based outbreak detection. Furthermore, since comparatively few new clusters per year were observed that would prompt investigation, syndromic hospital-surveillance could be a valuable tool for detection of local LRI-outbreaks.
format	article
author	Cees C van den Wijngaard Liselotte van Asten Wilfrid van Pelt Gerda Doornbos Nico J D Nagelkerke Gé A Donker Wim van der Hoek Marion P G Koopmans
author_facet	Cees C van den Wijngaard Liselotte van Asten Wilfrid van Pelt Gerda Doornbos Nico J D Nagelkerke Gé A Donker Wim van der Hoek Marion P G Koopmans
author_sort	Cees C van den Wijngaard
title	Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?
title_short	Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?
title_full	Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?
title_fullStr	Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?
title_full_unstemmed	Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?
title_sort	syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/40a195761a5f444eb27a691cb749dd33
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Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?

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