Local implementation of a syndromic influenza surveillance system using emergency department data in Santander, Spain.
Identifieur interne : 000252 ( Main/Exploration ); précédent : 000251; suivant : 000253Local implementation of a syndromic influenza surveillance system using emergency department data in Santander, Spain.
Auteurs : S. Schrell [Pays-Bas] ; A. Ziemann ; L. Garcia-Castrillo Riesgo ; N. Rosenkötter ; J. Llorca ; D. Popa ; T. KrafftSource :
- Journal of public health (Oxford, England) [ 1741-3850 ] ; 2013.
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Courbe ROC (MeSH), Espagne (épidémiologie), Facteurs temps (MeSH), Grippe humaine (épidémiologie), Humains (MeSH), Mise au point de programmes (MeSH), Sensibilité et spécificité (MeSH), Service hospitalier d'urgences (statistiques et données numériques), Surveillance de la population (méthodes), Surveillance sentinelle (MeSH), Épidémies de maladies (statistiques et données numériques).
- MESH :
- méthodes : Surveillance de la population.
- statistiques et données numériques : Service hospitalier d'urgences, Épidémies de maladies.
- épidémiologie : Espagne, Grippe humaine.
- Algorithmes, Courbe ROC, Facteurs temps, Humains, Mise au point de programmes, Sensibilité et spécificité, Surveillance sentinelle.
- Wicri :
- geographic : Espagne.
English descriptors
- KwdEn :
- Algorithms (MeSH), Disease Outbreaks (statistics & numerical data), Emergency Service, Hospital (statistics & numerical data), Humans (MeSH), Influenza, Human (epidemiology), Population Surveillance (methods), Program Development (MeSH), ROC Curve (MeSH), Sensitivity and Specificity (MeSH), Sentinel Surveillance (MeSH), Spain (epidemiology), Time Factors (MeSH).
- MESH :
- geographic , epidemiology : Spain.
- epidemiology : Influenza, Human.
- methods : Population Surveillance.
- statistics & numerical data : Disease Outbreaks, Emergency Service, Hospital.
- Algorithms, Humans, Program Development, ROC Curve, Sensitivity and Specificity, Sentinel Surveillance, Time Factors.
Abstract
BACKGROUND
We assessed the local implementation of syndromic surveillance (SyS) as part of the European project 'System for Information on, Detection and Analysis of Risks and Threats to Health' in Santander, Spain.
METHODS
We applied a cumulative sum algorithm on emergency department (ED) chief complaints for influenza-like illness in the seasons 2010-11 and 2011-12. We fine tuned the algorithm using a receiver operating characteristic analysis to identify the optimal trade-off of sensitivity and specificity and defined alert criteria. We assessed the timeliness of the SyS system to detect the onset of the influenza season.
RESULTS
The ED data correlated with the sentinel data. With the best algorithm settings we achieved 70/63% sensitivity and 89/95% specificity for 2010-11/2011-12. At least 2 consecutive days of signals defined an alert. In 2010-11 the SyS system alerted 1 week before the sentinel system and in 2011-12 in the same week. The data from the ED is available on a daily basis providing an advantage in timeliness compared with the weekly sentinel data.
CONCLUSIONS
ED-based SyS in Santander complements sentinel influenza surveillance by providing timely information. Local fine tuning and definition of alert criteria are recommended to enhance validity.
DOI: 10.1093/pubmed/fdt043
PubMed: 23620543
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Ziemann</name></author><author><name sortKey="Garcia Castrillo Riesgo, L" sort="Garcia Castrillo Riesgo, L" uniqKey="Garcia Castrillo Riesgo L" first="L" last="Garcia-Castrillo Riesgo">L. Garcia-Castrillo Riesgo</name></author><author><name sortKey="Rosenkotter, N" sort="Rosenkotter, N" uniqKey="Rosenkotter N" first="N" last="Rosenkötter">N. Rosenkötter</name></author><author><name sortKey="Llorca, J" sort="Llorca, J" uniqKey="Llorca J" first="J" last="Llorca">J. Llorca</name></author><author><name sortKey="Popa, D" sort="Popa, D" uniqKey="Popa D" first="D" last="Popa">D. Popa</name></author><author><name sortKey="Krafft, T" sort="Krafft, T" uniqKey="Krafft T" first="T" last="Krafft">T. 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Schrell</name><affiliation wicri:level="1"><nlm:affiliation>Department of International Health, CAPHRI School of Public Health and Primary Care, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, MD 6200, The Netherlands.</nlm:affiliation><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Department of International Health, CAPHRI School of Public Health and Primary Care, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, MD 6200</wicri:regionArea><wicri:noRegion>MD 6200</wicri:noRegion></affiliation></author><author><name sortKey="Ziemann, A" sort="Ziemann, A" uniqKey="Ziemann A" first="A" last="Ziemann">A. Ziemann</name></author><author><name sortKey="Garcia Castrillo Riesgo, L" sort="Garcia Castrillo Riesgo, L" uniqKey="Garcia Castrillo Riesgo L" first="L" last="Garcia-Castrillo Riesgo">L. Garcia-Castrillo Riesgo</name></author><author><name sortKey="Rosenkotter, N" sort="Rosenkotter, N" uniqKey="Rosenkotter N" first="N" last="Rosenkötter">N. Rosenkötter</name></author><author><name sortKey="Llorca, J" sort="Llorca, J" uniqKey="Llorca J" first="J" last="Llorca">J. Llorca</name></author><author><name sortKey="Popa, D" sort="Popa, D" uniqKey="Popa D" first="D" last="Popa">D. Popa</name></author><author><name sortKey="Krafft, T" sort="Krafft, T" uniqKey="Krafft T" first="T" last="Krafft">T. Krafft</name></author></analytic><series><title level="j">Journal of public health (Oxford, England)</title><idno type="eISSN">1741-3850</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Disease Outbreaks (statistics & numerical data)</term><term>Emergency Service, Hospital (statistics & numerical data)</term><term>Humans (MeSH)</term><term>Influenza, Human (epidemiology)</term><term>Population Surveillance (methods)</term><term>Program Development (MeSH)</term><term>ROC Curve (MeSH)</term><term>Sensitivity and Specificity (MeSH)</term><term>Sentinel Surveillance (MeSH)</term><term>Spain (epidemiology)</term><term>Time Factors (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Courbe ROC (MeSH)</term><term>Espagne (épidémiologie)</term><term>Facteurs temps (MeSH)</term><term>Grippe humaine (épidémiologie)</term><term>Humains (MeSH)</term><term>Mise au point de programmes (MeSH)</term><term>Sensibilité et spécificité (MeSH)</term><term>Service hospitalier d'urgences (statistiques et données numériques)</term><term>Surveillance de la population (méthodes)</term><term>Surveillance sentinelle (MeSH)</term><term>Épidémies de maladies (statistiques et données numériques)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Spain</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Population Surveillance</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Surveillance de la population</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Disease Outbreaks</term><term>Emergency Service, Hospital</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Service hospitalier d'urgences</term><term>Épidémies de maladies</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Espagne</term><term>Grippe humaine</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Humans</term><term>Program Development</term><term>ROC Curve</term><term>Sensitivity and Specificity</term><term>Sentinel Surveillance</term><term>Time Factors</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Courbe ROC</term><term>Facteurs temps</term><term>Humains</term><term>Mise au point de programmes</term><term>Sensibilité et spécificité</term><term>Surveillance sentinelle</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Espagne</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>We assessed the local implementation of syndromic surveillance (SyS) as part of the European project 'System for Information on, Detection and Analysis of Risks and Threats to Health' in Santander, Spain.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>We applied a cumulative sum algorithm on emergency department (ED) chief complaints for influenza-like illness in the seasons 2010-11 and 2011-12. We fine tuned the algorithm using a receiver operating characteristic analysis to identify the optimal trade-off of sensitivity and specificity and defined alert criteria. We assessed the timeliness of the SyS system to detect the onset of the influenza season.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>The ED data correlated with the sentinel data. With the best algorithm settings we achieved 70/63% sensitivity and 89/95% specificity for 2010-11/2011-12. At least 2 consecutive days of signals defined an alert. In 2010-11 the SyS system alerted 1 week before the sentinel system and in 2011-12 in the same week. The data from the ED is available on a daily basis providing an advantage in timeliness compared with the weekly sentinel data.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>ED-based SyS in Santander complements sentinel influenza surveillance by providing timely information. Local fine tuning and definition of alert criteria are recommended to enhance validity.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">23620543</PMID><DateCompleted><Year>2014</Year><Month>06</Month><Day>23</Day></DateCompleted><DateRevised><Year>2013</Year><Month>08</Month><Day>27</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1741-3850</ISSN><JournalIssue CitedMedium="Internet"><Volume>35</Volume><Issue>3</Issue><PubDate><Year>2013</Year><Month>Sep</Month></PubDate></JournalIssue><Title>Journal of public health (Oxford, England)</Title><ISOAbbreviation>J Public Health (Oxf)</ISOAbbreviation></Journal><ArticleTitle>Local implementation of a syndromic influenza surveillance system using emergency department data in Santander, Spain.</ArticleTitle><Pagination><MedlinePgn>397-403</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/pubmed/fdt043</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">We assessed the local implementation of syndromic surveillance (SyS) as part of the European project 'System for Information on, Detection and Analysis of Risks and Threats to Health' in Santander, Spain.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">We applied a cumulative sum algorithm on emergency department (ED) chief complaints for influenza-like illness in the seasons 2010-11 and 2011-12. We fine tuned the algorithm using a receiver operating characteristic analysis to identify the optimal trade-off of sensitivity and specificity and defined alert criteria. We assessed the timeliness of the SyS system to detect the onset of the influenza season.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">The ED data correlated with the sentinel data. With the best algorithm settings we achieved 70/63% sensitivity and 89/95% specificity for 2010-11/2011-12. At least 2 consecutive days of signals defined an alert. In 2010-11 the SyS system alerted 1 week before the sentinel system and in 2011-12 in the same week. The data from the ED is available on a daily basis providing an advantage in timeliness compared with the weekly sentinel data.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">ED-based SyS in Santander complements sentinel influenza surveillance by providing timely information. Local fine tuning and definition of alert criteria are recommended to enhance validity.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Schrell</LastName><ForeName>S</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of International Health, CAPHRI School of Public Health and Primary Care, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, MD 6200, The Netherlands.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ziemann</LastName><ForeName>A</ForeName><Initials>A</Initials></Author><Author ValidYN="Y"><LastName>Garcia-Castrillo Riesgo</LastName><ForeName>L</ForeName><Initials>L</Initials></Author><Author ValidYN="Y"><LastName>Rosenkötter</LastName><ForeName>N</ForeName><Initials>N</Initials></Author><Author ValidYN="Y"><LastName>Llorca</LastName><ForeName>J</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Popa</LastName><ForeName>D</ForeName><Initials>D</Initials></Author><Author ValidYN="Y"><LastName>Krafft</LastName><ForeName>T</ForeName><Initials>T</Initials></Author><Author ValidYN="Y"><CollectiveName>SIDARTHa Project Consortium</CollectiveName></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2013</Year><Month>04</Month><Day>25</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>J Public Health (Oxf)</MedlineTA><NlmUniqueID>101188638</NlmUniqueID><ISSNLinking>1741-3842</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004196" MajorTopicYN="N">Disease Outbreaks</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004636" MajorTopicYN="N">Emergency Service, Hospital</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011159" MajorTopicYN="N">Population Surveillance</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016730" MajorTopicYN="N">Program Development</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012372" MajorTopicYN="N">ROC Curve</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012680" MajorTopicYN="N">Sensitivity and Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018571" MajorTopicYN="Y">Sentinel Surveillance</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013030" MajorTopicYN="N" Type="Geographic">Spain</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013997" MajorTopicYN="N">Time Factors</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">communicable diseases</Keyword><Keyword MajorTopicYN="N">emergency care</Keyword><Keyword MajorTopicYN="N">epidemiology</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2013</Year><Month>4</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2013</Year><Month>4</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>6</Month><Day>24</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">23620543</ArticleId><ArticleId IdType="pii">fdt043</ArticleId><ArticleId IdType="doi">10.1093/pubmed/fdt043</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Pays-Bas</li></country></list><tree><noCountry><name sortKey="Garcia Castrillo Riesgo, L" sort="Garcia Castrillo Riesgo, L" uniqKey="Garcia Castrillo Riesgo L" first="L" last="Garcia-Castrillo Riesgo">L. Garcia-Castrillo Riesgo</name><name sortKey="Krafft, T" sort="Krafft, T" uniqKey="Krafft T" first="T" last="Krafft">T. Krafft</name><name sortKey="Llorca, J" sort="Llorca, J" uniqKey="Llorca J" first="J" last="Llorca">J. Llorca</name><name sortKey="Popa, D" sort="Popa, D" uniqKey="Popa D" first="D" last="Popa">D. Popa</name><name sortKey="Rosenkotter, N" sort="Rosenkotter, N" uniqKey="Rosenkotter N" first="N" last="Rosenkötter">N. Rosenkötter</name><name sortKey="Ziemann, A" sort="Ziemann, A" uniqKey="Ziemann A" first="A" last="Ziemann">A. Ziemann</name></noCountry><country name="Pays-Bas"><noRegion><name sortKey="Schrell, S" sort="Schrell, S" uniqKey="Schrell S" first="S" last="Schrell">S. Schrell</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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