Influenza-related mortality in Spain, 1999-2005.
Identifieur interne : 000283 ( Main/Exploration ); précédent : 000282; suivant : 000284Influenza-related mortality in Spain, 1999-2005.
Auteurs : Teresa L Pez-Cuadrado [Espagne] ; Salvador De Mateo ; Silvia Jiménez-Jorge ; Camelia Savulescu ; Amparo LarrauriSource :
- Gaceta sanitaria [ 1578-1283 ]
Descripteurs français
- KwdFr :
- MESH :
- mortalité : Grippe humaine.
- épidémiologie : Espagne.
- Adulte d'âge moyen, Cause de décès, Facteurs temps, Humains.
- Wicri :
- geographic : Espagne.
English descriptors
- KwdEn :
- MESH :
- geographic , epidemiology : Spain.
- mortality : Influenza, Human.
- Cause of Death, Humans, Middle Aged, Time Factors.
Abstract
OBJECTIVE
To estimate the excess deaths attributed to influenza in Spain, using age-specific generalized linear models (GLM) and the Serfling model for the period 1999-2005.
METHOD
We reviewed mortality from influenza and pneumonia and all-cause deaths. We used an additive GLM procedure, including the numbers of weekly deaths as a response variable and the number of influenza virus and respiratory syncytial virus weekly isolates, the population and two variables to adjust for annual fluctuations as covariates. Using the Serfling model, we removed the trend and applied a temporal regression model, excluding data from December to April to account for the expected baseline mortality in the absence of influenza activity.
RESULTS
Globally, the excess mortality attributable to influenza was 1.1 deaths per 100,000 for influenza and pneumonia and 11 all-cause deaths per 100,000 using the GLM model. The highest mortality rates were obtained with the Serfling model in adults older than 64 years, with an excess mortality attributable to influenza of 57 and 164 deaths per 100,000 for influenza and pneumonia and all-cause, respectively.
CONCLUSIONS
The GLM model, which takes viral activity into account, yields systematically lower estimates of excess mortality than the Serfling model. The GLM model provides independent estimates associated with the activity of different viruses and even with other factors, which is a significant advantage when trying to understand the impact of viral respiratory infections on mortality in the Spanish population.
DOI: 10.1016/j.gaceta.2011.09.033
PubMed: 22284214
Affiliations:
Links toward previous steps (curation, corpus...)
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first="Silvia" last="Jiménez-Jorge">Silvia Jiménez-Jorge</name></author><author><name sortKey="Savulescu, Camelia" sort="Savulescu, Camelia" uniqKey="Savulescu C" first="Camelia" last="Savulescu">Camelia Savulescu</name></author><author><name sortKey="Larrauri, Amparo" sort="Larrauri, Amparo" uniqKey="Larrauri A" first="Amparo" last="Larrauri">Amparo Larrauri</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012 Jul-Aug</date><idno type="RBID">pubmed:22284214</idno><idno type="pmid">22284214</idno><idno type="doi">10.1016/j.gaceta.2011.09.033</idno><idno type="wicri:Area/Main/Corpus">00324</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">00324</idno><idno type="wicri:Area/Main/Curation">000324</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000324</idno><idno 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sort="Jimenez Jorge, Silvia" uniqKey="Jimenez Jorge S" first="Silvia" last="Jiménez-Jorge">Silvia Jiménez-Jorge</name></author><author><name sortKey="Savulescu, Camelia" sort="Savulescu, Camelia" uniqKey="Savulescu C" first="Camelia" last="Savulescu">Camelia Savulescu</name></author><author><name sortKey="Larrauri, Amparo" sort="Larrauri, Amparo" uniqKey="Larrauri A" first="Amparo" last="Larrauri">Amparo Larrauri</name></author></analytic><series><title level="j">Gaceta sanitaria</title><idno type="eISSN">1578-1283</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Cause of Death (MeSH)</term><term>Humans (MeSH)</term><term>Influenza, Human (mortality)</term><term>Middle Aged (MeSH)</term><term>Spain (epidemiology)</term><term>Time Factors (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte d'âge moyen (MeSH)</term><term>Cause de décès (MeSH)</term><term>Espagne (épidémiologie)</term><term>Facteurs temps (MeSH)</term><term>Grippe humaine (mortalité)</term><term>Humains (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Spain</term></keywords><keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="mortalité" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Espagne</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Cause of Death</term><term>Humans</term><term>Middle Aged</term><term>Time Factors</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte d'âge moyen</term><term>Cause de décès</term><term>Facteurs temps</term><term>Humains</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>OBJECTIVE</b></p><p>To estimate the excess deaths attributed to influenza in Spain, using age-specific generalized linear models (GLM) and the Serfling model for the period 1999-2005.</p></div><div type="abstract" xml:lang="en"><p><b>METHOD</b></p><p>We reviewed mortality from influenza and pneumonia and all-cause deaths. We used an additive GLM procedure, including the numbers of weekly deaths as a response variable and the number of influenza virus and respiratory syncytial virus weekly isolates, the population and two variables to adjust for annual fluctuations as covariates. Using the Serfling model, we removed the trend and applied a temporal regression model, excluding data from December to April to account for the expected baseline mortality in the absence of influenza activity.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Globally, the excess mortality attributable to influenza was 1.1 deaths per 100,000 for influenza and pneumonia and 11 all-cause deaths per 100,000 using the GLM model. The highest mortality rates were obtained with the Serfling model in adults older than 64 years, with an excess mortality attributable to influenza of 57 and 164 deaths per 100,000 for influenza and pneumonia and all-cause, respectively.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>The GLM model, which takes viral activity into account, yields systematically lower estimates of excess mortality than the Serfling model. The GLM model provides independent estimates associated with the activity of different viruses and even with other factors, which is a significant advantage when trying to understand the impact of viral respiratory infections on mortality in the Spanish population.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22284214</PMID><DateCompleted><Year>2012</Year><Month>11</Month><Day>26</Day></DateCompleted><DateRevised><Year>2016</Year><Month>10</Month><Day>20</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1578-1283</ISSN><JournalIssue CitedMedium="Internet"><Volume>26</Volume><Issue>4</Issue><PubDate><MedlineDate>2012 Jul-Aug</MedlineDate></PubDate></JournalIssue><Title>Gaceta sanitaria</Title><ISOAbbreviation>Gac Sanit</ISOAbbreviation></Journal><ArticleTitle>Influenza-related mortality in Spain, 1999-2005.</ArticleTitle><Pagination><MedlinePgn>325-9</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.gaceta.2011.09.033</ELocationID><Abstract><AbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE">To estimate the excess deaths attributed to influenza in Spain, using age-specific generalized linear models (GLM) and the Serfling model for the period 1999-2005.</AbstractText><AbstractText Label="METHOD" NlmCategory="METHODS">We reviewed mortality from influenza and pneumonia and all-cause deaths. We used an additive GLM procedure, including the numbers of weekly deaths as a response variable and the number of influenza virus and respiratory syncytial virus weekly isolates, the population and two variables to adjust for annual fluctuations as covariates. Using the Serfling model, we removed the trend and applied a temporal regression model, excluding data from December to April to account for the expected baseline mortality in the absence of influenza activity.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Globally, the excess mortality attributable to influenza was 1.1 deaths per 100,000 for influenza and pneumonia and 11 all-cause deaths per 100,000 using the GLM model. The highest mortality rates were obtained with the Serfling model in adults older than 64 years, with an excess mortality attributable to influenza of 57 and 164 deaths per 100,000 for influenza and pneumonia and all-cause, respectively.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">The GLM model, which takes viral activity into account, yields systematically lower estimates of excess mortality than the Serfling model. The GLM model provides independent estimates associated with the activity of different viruses and even with other factors, which is a significant advantage when trying to understand the impact of viral respiratory infections on mortality in the Spanish population.</AbstractText><CopyrightInformation>Copyright © 2011 SESPAS. Published by Elsevier España, S.L. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>López-Cuadrado</LastName><ForeName>Teresa</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>National Epidemiology Center, Carlos III Institute of Health, Madrid, Spain. teresalc@isciii.es</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>de Mateo</LastName><ForeName>Salvador</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Jiménez-Jorge</LastName><ForeName>Silvia</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Savulescu</LastName><ForeName>Camelia</ForeName><Initials>C</Initials></Author><Author ValidYN="Y"><LastName>Larrauri</LastName><ForeName>Amparo</ForeName><Initials>A</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>2012</Year><Month>01</Month><Day>28</Day></ArticleDate></Article><MedlineJournalInfo><Country>Spain</Country><MedlineTA>Gac Sanit</MedlineTA><NlmUniqueID>8901623</NlmUniqueID><ISSNLinking>0213-9111</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D002423" MajorTopicYN="N">Cause of Death</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</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></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2011</Year><Month>03</Month><Day>10</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2011</Year><Month>08</Month><Day>22</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2011</Year><Month>09</Month><Day>09</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2012</Year><Month>1</Month><Day>31</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2012</Year><Month>1</Month><Day>31</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2012</Year><Month>12</Month><Day>10</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">22284214</ArticleId><ArticleId IdType="pii">S0213-9111(11)00406-7</ArticleId><ArticleId IdType="doi">10.1016/j.gaceta.2011.09.033</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Espagne</li></country><region><li>Communauté de Madrid</li></region><settlement><li>Madrid</li></settlement></list><tree><noCountry><name sortKey="De Mateo, Salvador" sort="De Mateo, Salvador" uniqKey="De Mateo S" first="Salvador" last="De Mateo">Salvador De Mateo</name><name sortKey="Jimenez Jorge, Silvia" sort="Jimenez Jorge, Silvia" uniqKey="Jimenez Jorge S" first="Silvia" last="Jiménez-Jorge">Silvia Jiménez-Jorge</name><name sortKey="Larrauri, Amparo" sort="Larrauri, Amparo" uniqKey="Larrauri A" first="Amparo" last="Larrauri">Amparo Larrauri</name><name sortKey="Savulescu, Camelia" sort="Savulescu, Camelia" uniqKey="Savulescu C" first="Camelia" last="Savulescu">Camelia Savulescu</name></noCountry><country name="Espagne"><region name="Communauté de Madrid"><name sortKey="L Pez Cuadrado, Teresa" sort="L Pez Cuadrado, Teresa" uniqKey="L Pez Cuadrado T" first="Teresa" last="L Pez-Cuadrado">Teresa L Pez-Cuadrado</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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