Predictive performance of telenursing complaints in influenza surveillance: a prospective cohort study in Sweden.
Identifieur interne : 000735 ( PubMed/Curation ); précédent : 000734; suivant : 000736Predictive performance of telenursing complaints in influenza surveillance: a prospective cohort study in Sweden.
Auteurs : T. Timpka [Suède] ; A. Spreco ; O. Eriksson ; Ö Dahlström ; E A Gursky ; M. Strömgren ; E. Holm ; J. Ekberg ; J. Hinkula ; J M Nyce ; H. ErikssonSource :
- Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin [ 1560-7917 ] ; 2014.
Descripteurs français
- KwdFr :
- Adulte, Aire sous la courbe, Algorithmes, Enfant, Fièvre (étiologie), Flambées de maladies, Grippe humaine (diagnostic), Grippe humaine (épidémiologie), Humains, Incidence, Pandémies, Saisons, Sous-type H1N1 du virus de la grippe A (isolement et purification), Sous-type H3N2 du virus de la grippe A (isolement et purification), Surveillance de la population (), Suède (épidémiologie), Systèmes d'information sur la santé, Télénursing, Valeur prédictive des tests, Études prospectives, Études rétrospectives.
- MESH :
- diagnostic : Grippe humaine.
- isolement et purification : Sous-type H1N1 du virus de la grippe A, Sous-type H3N2 du virus de la grippe A.
- épidémiologie : Grippe humaine, Suède.
- étiologie : Fièvre.
- Adulte, Aire sous la courbe, Algorithmes, Enfant, Flambées de maladies, Humains, Incidence, Pandémies, Saisons, Surveillance de la population, Systèmes d'information sur la santé, Télénursing, Valeur prédictive des tests, Études prospectives, Études rétrospectives.
- Wicri :
- geographic : Suède.
English descriptors
- KwdEn :
- Adult, Algorithms, Area Under Curve, Child, Disease Outbreaks, Fever (etiology), Health Information Systems, Humans, Incidence, Influenza A Virus, H1N1 Subtype (isolation & purification), Influenza A Virus, H3N2 Subtype (isolation & purification), Influenza, Human (diagnosis), Influenza, Human (epidemiology), Pandemics, Population Surveillance (methods), Predictive Value of Tests, Prospective Studies, Retrospective Studies, Seasons, Sweden (epidemiology), Telenursing.
- MESH :
- geographic , epidemiology : Sweden.
- diagnosis : Influenza, Human.
- epidemiology : Influenza, Human.
- etiology : Fever.
- isolation & purification : Influenza A Virus, H1N1 Subtype, Influenza A Virus, H3N2 Subtype.
- methods : Population Surveillance.
- Adult, Algorithms, Area Under Curve, Child, Disease Outbreaks, Health Information Systems, Humans, Incidence, Pandemics, Predictive Value of Tests, Prospective Studies, Retrospective Studies, Seasons, Telenursing.
Abstract
Syndromic data sources have been sought to improve the timely detection of increased influenza transmission. This study set out to examine the prospective performance of telenursing chief complaints in predicting influenza activity. Data from two influenza seasons (2007/08 and 2008/09) were collected in a Swedish county (population 427,000) to retrospectively determine which grouping of telenursing chief complaints had the largest correlation with influenza case rates. This grouping was prospectively evaluated in the three subsequent seasons. The best performing telenursing complaint grouping in the retrospective algorithm calibration was fever (child, adult) and syncope (r=0.66; p<0.001). In the prospective evaluation, the performance of 14-day predictions was acceptable for the part of the evaluation period including the 2009 influenza pandemic (area under the curve (AUC)=0.84; positive predictive value (PPV)=0.58), while it was strong (AUC=0.89; PPV=0.93) for the remaining evaluation period including only influenza winter seasons. We recommend the use of telenursing complaints for predicting winter influenza seasons. The method requires adjustments when used during pandemics.
DOI: 10.2807/1560-7917.es2014.19.46.20966
PubMed: 25425514
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This study set out to examine the prospective performance of telenursing chief complaints in predicting influenza activity. Data from two influenza seasons (2007/08 and 2008/09) were collected in a Swedish county (population 427,000) to retrospectively determine which grouping of telenursing chief complaints had the largest correlation with influenza case rates. This grouping was prospectively evaluated in the three subsequent seasons. The best performing telenursing complaint grouping in the retrospective algorithm calibration was fever (child, adult) and syncope (r=0.66; p<0.001). In the prospective evaluation, the performance of 14-day predictions was acceptable for the part of the evaluation period including the 2009 influenza pandemic (area under the curve (AUC)=0.84; positive predictive value (PPV)=0.58), while it was strong (AUC=0.89; PPV=0.93) for the remaining evaluation period including only influenza winter seasons. We recommend the use of telenursing complaints for predicting winter influenza seasons. The method requires adjustments when used during pandemics.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">25425514</PMID><DateCompleted><Year>2015</Year><Month>04</Month><Day>16</Day></DateCompleted><DateRevised><Year>2019</Year><Month>06</Month><Day>09</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1560-7917</ISSN><JournalIssue CitedMedium="Internet"><Volume>19</Volume><Issue>46</Issue><PubDate><Year>2014</Year><Month>Nov</Month><Day>20</Day></PubDate></JournalIssue><Title>Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin</Title><ISOAbbreviation>Euro Surveill.</ISOAbbreviation></Journal><ArticleTitle>Predictive performance of telenursing complaints in influenza surveillance: a prospective cohort study in Sweden.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">20966</ELocationID><Abstract><AbstractText>Syndromic data sources have been sought to improve the timely detection of increased influenza transmission. This study set out to examine the prospective performance of telenursing chief complaints in predicting influenza activity. Data from two influenza seasons (2007/08 and 2008/09) were collected in a Swedish county (population 427,000) to retrospectively determine which grouping of telenursing chief complaints had the largest correlation with influenza case rates. This grouping was prospectively evaluated in the three subsequent seasons. The best performing telenursing complaint grouping in the retrospective algorithm calibration was fever (child, adult) and syncope (r=0.66; p<0.001). In the prospective evaluation, the performance of 14-day predictions was acceptable for the part of the evaluation period including the 2009 influenza pandemic (area under the curve (AUC)=0.84; positive predictive value (PPV)=0.58), while it was strong (AUC=0.89; PPV=0.93) for the remaining evaluation period including only influenza winter seasons. We recommend the use of telenursing complaints for predicting winter influenza seasons. The method requires adjustments when used during pandemics.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Timpka</LastName><ForeName>T</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>Department of Public Health, ostergotland County Council, Linkoping, Sweden.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Spreco</LastName><ForeName>A</ForeName><Initials>A</Initials></Author><Author ValidYN="Y"><LastName>Eriksson</LastName><ForeName>O</ForeName><Initials>O</Initials></Author><Author ValidYN="Y"><LastName>Dahlström</LastName><ForeName>Ö</ForeName><Initials>Ö</Initials></Author><Author ValidYN="Y"><LastName>Gursky</LastName><ForeName>E A</ForeName><Initials>EA</Initials></Author><Author ValidYN="Y"><LastName>Strömgren</LastName><ForeName>M</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Holm</LastName><ForeName>E</ForeName><Initials>E</Initials></Author><Author ValidYN="Y"><LastName>Ekberg</LastName><ForeName>J</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Hinkula</LastName><ForeName>J</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Nyce</LastName><ForeName>J M</ForeName><Initials>JM</Initials></Author><Author ValidYN="Y"><LastName>Eriksson</LastName><ForeName>H</ForeName><Initials>H</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D064888">Observational Study</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>11</Month><Day>20</Day></ArticleDate></Article><MedlineJournalInfo><Country>Sweden</Country><MedlineTA>Euro Surveill</MedlineTA><NlmUniqueID>100887452</NlmUniqueID><ISSNLinking>1025-496X</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019540" MajorTopicYN="N">Area Under Curve</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004196" MajorTopicYN="N">Disease Outbreaks</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005334" MajorTopicYN="N">Fever</DescriptorName><QualifierName UI="Q000209" MajorTopicYN="N">etiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D063005" MajorTopicYN="Y">Health Information Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015994" MajorTopicYN="N">Incidence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D053118" MajorTopicYN="N">Influenza A Virus, H1N1 Subtype</DescriptorName><QualifierName UI="Q000302" MajorTopicYN="N">isolation & purification</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D053122" MajorTopicYN="N">Influenza A Virus, H3N2 Subtype</DescriptorName><QualifierName UI="Q000302" MajorTopicYN="N">isolation & purification</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011159" MajorTopicYN="N">Population Surveillance</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011237" MajorTopicYN="N">Predictive Value of Tests</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011446" MajorTopicYN="N">Prospective Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012189" MajorTopicYN="N">Retrospective Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012621" MajorTopicYN="N">Seasons</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013548" MajorTopicYN="N" Type="Geographic">Sweden</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D055817" MajorTopicYN="Y">Telenursing</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>11</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>11</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>4</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">25425514</ArticleId><ArticleId IdType="doi">10.2807/1560-7917.es2014.19.46.20966</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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