Computerized general practice based networks yield comparable performance with sentinel data in monitoring epidemiological time-course of influenza-like illness and acute respiratory illness.
Identifieur interne : 000073 ( Main/Exploration ); précédent : 000072; suivant : 000074Computerized general practice based networks yield comparable performance with sentinel data in monitoring epidemiological time-course of influenza-like illness and acute respiratory illness.
Auteurs : Carla Truyers [Belgique] ; Emmanuel Lesaffre ; Stefaan Bartholomeeusen ; Bert Aertgeerts ; René Snacken ; Bernard Brochier ; Fernande Yane ; Frank BuntinxSource :
- BMC family practice [ 1471-2296 ] ; 2010.
Descripteurs français
- KwdFr :
- Belgique (épidémiologie), Courrier électronique, Enquêtes de santé, Europe (épidémiologie), Flambées de maladies (), Grippe humaine (diagnostic), Grippe humaine (épidémiologie), Humains, Infections de l'appareil respiratoire (épidémiologie), Informatique en santé publique (), Maladies virales (épidémiologie), Médecine de famille (), Médecine de famille (instrumentation), Réseaux de communication entre ordinateurs (organisation et administration), Saisons, Sous-type H1N1 du virus de la grippe A (isolement et purification), Surveillance de l'environnement (), Surveillance de la population (), Surveillance sentinelle, Épidémiosurveillance, Évaluation des risques.
- MESH :
- diagnostic : Grippe humaine.
- isolement et purification : Sous-type H1N1 du virus de la grippe A.
- organisation et administration : Réseaux de communication entre ordinateurs.
- épidémiologie : Belgique, Europe, Grippe humaine, Infections de l'appareil respiratoire, Maladies virales, Médecine de famille.
- Courrier électronique, Enquêtes de santé, Flambées de maladies, Humains, Informatique en santé publique, Médecine de famille, Saisons, Surveillance de l'environnement, Surveillance de la population, Surveillance sentinelle, Épidémiosurveillance, Évaluation des risques.
- Wicri :
- geographic : Belgique.
English descriptors
- KwdEn :
- Belgium (epidemiology), Computer Communication Networks (organization & administration), Disease Outbreaks (statistics & numerical data), Electronic Mail, Environmental Monitoring (methods), Epidemiological Monitoring, Europe (epidemiology), Family Practice (instrumentation), Family Practice (methods), Family Practice (statistics & numerical data), Health Surveys, Humans, Influenza A Virus, H1N1 Subtype (isolation & purification), Influenza, Human (diagnosis), Influenza, Human (epidemiology), Population Surveillance (methods), Public Health Informatics (methods), Respiratory Tract Infections (epidemiology), Risk Assessment, Seasons, Sentinel Surveillance, Virus Diseases (epidemiology).
- MESH :
- geographic , epidemiology : Belgium, Europe.
- diagnosis : Influenza, Human.
- epidemiology : Influenza, Human, Respiratory Tract Infections, Virus Diseases.
- instrumentation : Family Practice.
- isolation & purification : Influenza A Virus, H1N1 Subtype.
- methods : Environmental Monitoring, Family Practice, Population Surveillance, Public Health Informatics.
- organization & administration : Computer Communication Networks.
- statistics & numerical data : Disease Outbreaks, Family Practice.
- Electronic Mail, Epidemiological Monitoring, Health Surveys, Humans, Risk Assessment, Seasons, Sentinel Surveillance.
Abstract
BACKGROUND
Computerized morbidity registration networks might serve as early warning systems in a time where natural epidemics such as the H1N1 flu can easily spread from one region to another.
METHODS
In this contribution we examine whether general practice based broad-spectrum computerized morbidity registration networks have the potential to act as a valid surveillance instrument of frequently occurring diseases. We compare general practice based computerized data assessing the frequency of influenza-like illness (ILI) and acute respiratory infections (ARI) with data from a well established case-specific sentinel network, the European Influenza Surveillance Scheme (EISS). The overall frequency and trends of weekly ILI and ARI data are compared using both networks.
RESULTS
Detection of influenza-like illness and acute respiratory illness occurs equally fast in EISS and the computerized network. The overall frequency data for ARI are the same for both networks, the overall trends are similar, but the increases and decreases in frequency do not occur in exactly the same weeks. For ILI, the overall rate was slightly higher for the computerized network population, especially before the increase of ILI, the overall trend was almost identical and the increases and decreases occur in the same weeks for both networks.
CONCLUSIONS
Computerized morbidity registration networks are a valid tool for monitoring frequent occurring respiratory diseases and the detection of sudden outbreaks.
DOI: 10.1186/1471-2296-11-24
PubMed: 20307266
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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maladies</term><term>Humains</term><term>Informatique en santé publique</term><term>Médecine de famille</term><term>Saisons</term><term>Surveillance de l'environnement</term><term>Surveillance de la population</term><term>Surveillance sentinelle</term><term>Épidémiosurveillance</term><term>Évaluation des risques</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Belgique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>Computerized morbidity registration networks might serve as early warning systems in a time where natural epidemics such as the H1N1 flu can easily spread from one region to another.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>In this contribution we examine whether general practice based broad-spectrum computerized morbidity registration networks have the potential to act as a valid surveillance instrument of frequently occurring diseases. We compare general practice based computerized data assessing the frequency of influenza-like illness (ILI) and acute respiratory infections (ARI) with data from a well established case-specific sentinel network, the European Influenza Surveillance Scheme (EISS). The overall frequency and trends of weekly ILI and ARI data are compared using both networks.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Detection of influenza-like illness and acute respiratory illness occurs equally fast in EISS and the computerized network. The overall frequency data for ARI are the same for both networks, the overall trends are similar, but the increases and decreases in frequency do not occur in exactly the same weeks. For ILI, the overall rate was slightly higher for the computerized network population, especially before the increase of ILI, the overall trend was almost identical and the increases and decreases occur in the same weeks for both networks.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>Computerized morbidity registration networks are a valid tool for monitoring frequent occurring respiratory diseases and the detection of sudden outbreaks.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">20307266</PMID><DateCompleted><Year>2010</Year><Month>05</Month><Day>05</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2296</ISSN><JournalIssue CitedMedium="Internet"><Volume>11</Volume><PubDate><Year>2010</Year><Month>Mar</Month><Day>22</Day></PubDate></JournalIssue><Title>BMC family practice</Title><ISOAbbreviation>BMC Fam Pract</ISOAbbreviation></Journal><ArticleTitle>Computerized general practice based networks yield comparable performance with sentinel data in monitoring epidemiological time-course of influenza-like illness and acute respiratory illness.</ArticleTitle><Pagination><MedlinePgn>24</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1471-2296-11-24</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Computerized morbidity registration networks might serve as early warning systems in a time where natural epidemics such as the H1N1 flu can easily spread from one region to another.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">In this contribution we examine whether general practice based broad-spectrum computerized morbidity registration networks have the potential to act as a valid surveillance instrument of frequently occurring diseases. We compare general practice based computerized data assessing the frequency of influenza-like illness (ILI) and acute respiratory infections (ARI) with data from a well established case-specific sentinel network, the European Influenza Surveillance Scheme (EISS). The overall frequency and trends of weekly ILI and ARI data are compared using both networks.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Detection of influenza-like illness and acute respiratory illness occurs equally fast in EISS and the computerized network. The overall frequency data for ARI are the same for both networks, the overall trends are similar, but the increases and decreases in frequency do not occur in exactly the same weeks. For ILI, the overall rate was slightly higher for the computerized network population, especially before the increase of ILI, the overall trend was almost identical and the increases and decreases occur in the same weeks for both networks.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Computerized morbidity registration networks are a valid tool for monitoring frequent occurring respiratory diseases and the detection of sudden outbreaks.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Truyers</LastName><ForeName>Carla</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Department of General Practice, Katholieke Universiteit Leuven, Leuven, Belgium. carla.truyers@med.kuleuven.be</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lesaffre</LastName><ForeName>Emmanuel</ForeName><Initials>E</Initials></Author><Author ValidYN="Y"><LastName>Bartholomeeusen</LastName><ForeName>Stefaan</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Aertgeerts</LastName><ForeName>Bert</ForeName><Initials>B</Initials></Author><Author ValidYN="Y"><LastName>Snacken</LastName><ForeName>René</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Brochier</LastName><ForeName>Bernard</ForeName><Initials>B</Initials></Author><Author ValidYN="Y"><LastName>Yane</LastName><ForeName>Fernande</ForeName><Initials>F</Initials></Author><Author ValidYN="Y"><LastName>Buntinx</LastName><ForeName>Frank</ForeName><Initials>F</Initials></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>2010</Year><Month>03</Month><Day>22</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Fam Pract</MedlineTA><NlmUniqueID>100967792</NlmUniqueID><ISSNLinking>1471-2296</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D001530" MajorTopicYN="N" Type="Geographic">Belgium</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003195" MajorTopicYN="N">Computer Communication Networks</DescriptorName><QualifierName UI="Q000458" MajorTopicYN="Y">organization & administration</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004196" MajorTopicYN="N">Disease Outbreaks</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D034742" MajorTopicYN="N">Electronic Mail</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004784" MajorTopicYN="N">Environmental Monitoring</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D062665" MajorTopicYN="N">Epidemiological Monitoring</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005060" MajorTopicYN="N" Type="Geographic">Europe</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005194" MajorTopicYN="N">Family Practice</DescriptorName><QualifierName UI="Q000295" MajorTopicYN="N">instrumentation</QualifierName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006306" MajorTopicYN="N">Health Surveys</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</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="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011159" MajorTopicYN="N">Population Surveillance</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D040442" MajorTopicYN="N">Public Health Informatics</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012141" MajorTopicYN="N">Respiratory Tract 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Bert" sort="Aertgeerts, Bert" uniqKey="Aertgeerts B" first="Bert" last="Aertgeerts">Bert Aertgeerts</name><name sortKey="Bartholomeeusen, Stefaan" sort="Bartholomeeusen, Stefaan" uniqKey="Bartholomeeusen S" first="Stefaan" last="Bartholomeeusen">Stefaan Bartholomeeusen</name><name sortKey="Brochier, Bernard" sort="Brochier, Bernard" uniqKey="Brochier B" first="Bernard" last="Brochier">Bernard Brochier</name><name sortKey="Buntinx, Frank" sort="Buntinx, Frank" uniqKey="Buntinx F" first="Frank" last="Buntinx">Frank Buntinx</name><name sortKey="Lesaffre, Emmanuel" sort="Lesaffre, Emmanuel" uniqKey="Lesaffre E" first="Emmanuel" last="Lesaffre">Emmanuel Lesaffre</name><name sortKey="Snacken, Rene" sort="Snacken, Rene" uniqKey="Snacken R" first="René" last="Snacken">René Snacken</name><name sortKey="Yane, Fernande" sort="Yane, Fernande" uniqKey="Yane F" first="Fernande" last="Yane">Fernande Yane</name></noCountry><country name="Belgique"><region name="Région flamande"><name sortKey="Truyers, Carla" sort="Truyers, Carla" uniqKey="Truyers C" first="Carla" last="Truyers">Carla Truyers</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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