Association of hospitalizations for asthma with seasonal and pandemic influenza
Identifieur interne : 000222 ( Istex/Corpus ); précédent : 000221; suivant : 000223Association of hospitalizations for asthma with seasonal and pandemic influenza
Auteurs : Alicia K. Gerke ; Ming Yang ; Fan Tang ; Eric D. Foster ; Joseph E. Cavanaugh ; Philip M. PolgreenSource :
- Respirology [ 1323-7799 ] ; 2014-01.
Abstract
Background and objective: Although influenza has been associated with asthma exacerbations, it is not clear the extent to which this association affects health care use in the United States. The first goal of this project was to determine whether, and to what extent, the incidence of asthma hospitalizations is associated with seasonal variation in influenza. Second, we used influenza trends (2000–2008) to help predict asthma admissions during the 2009 H1N1 influenza pandemic. Methods: We identified all hospitalizations between 1998 and 2008 in the Nationwide Inpatient Sample from the Healthcare Cost and Utilization Project during which a primary diagnosis of asthma was recorded. Separately, we identified all hospitalizations during which a diagnosis of influenza was recorded. We performed time series regression analyses to investigate the association of monthly asthma admissions with influenza incidence. Finally, we applied these time series regression models using 1998–2008 data, to forecast monthly asthma admissions during the 2009 influenza pandemic. Results: Based on time series regression models, a strong, significant association exists between concurrent influenza activity and incidence of asthma hospitalizations (P‐value < 0.0001). Use of influenza data to predict asthma admissions during the 2009 H1N1 pandemic improved the mean squared prediction error by 60.2%. Conclusions: Influenza activity in the population is significantly associated with asthma hospitalizations in the United States, and this association can be exploited to more accurately forecast asthma admissions. Our results suggest that improvements in influenza surveillance, prevention and treatment may decrease hospitalizations of asthma patients.
On a population basis, influenza activity is associated with asthma hospitalizations in the United States, and this association can be exploited to more accurately forecast asthma admissions. Our results suggest that improvements in influenza surveillance, prevention and treatment may help predict and decrease hospitalizations of asthma patients.
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DOI: 10.1111/resp.12165
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ISTEX:7885612C7C442078BCBDD9BD5CAB10FB0059201DLe document en format XML
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Gerke</name><affiliation><mods:affiliation>Department of Internal Medicine, University of Iowa, Iowa, Iowa City, USA</mods:affiliation></affiliation></author><author><name sortKey="Yang, Ming" sort="Yang, Ming" uniqKey="Yang M" first="Ming" last="Yang">Ming Yang</name><affiliation><mods:affiliation>Harvard School of Public Health, Massachusetts, Boston, USA</mods:affiliation></affiliation></author><author><name sortKey="Tang, Fan" sort="Tang, Fan" uniqKey="Tang F" first="Fan" last="Tang">Fan Tang</name><affiliation><mods:affiliation>Department of Biostatistics, University of Iowa, Iowa, Iowa City, USA</mods:affiliation></affiliation></author><author><name sortKey="Foster, Eric D" sort="Foster, Eric D" uniqKey="Foster E" first="Eric D." last="Foster">Eric D. Foster</name><affiliation><mods:affiliation>Department of Biostatistics, University of Iowa, Iowa, Iowa City, USA</mods:affiliation></affiliation></author><author><name sortKey="Cavanaugh, Joseph E" sort="Cavanaugh, Joseph E" uniqKey="Cavanaugh J" first="Joseph E." last="Cavanaugh">Joseph E. Cavanaugh</name><affiliation><mods:affiliation>Department of Biostatistics, University of Iowa, Iowa, Iowa City, USA</mods:affiliation></affiliation></author><author><name sortKey="Polgreen, Philip M" sort="Polgreen, Philip M" uniqKey="Polgreen P" first="Philip M." last="Polgreen">Philip M. 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Gerke</name><affiliation><mods:affiliation>Department of Internal Medicine, University of Iowa, Iowa, Iowa City, USA</mods:affiliation></affiliation></author><author><name sortKey="Yang, Ming" sort="Yang, Ming" uniqKey="Yang M" first="Ming" last="Yang">Ming Yang</name><affiliation><mods:affiliation>Harvard School of Public Health, Massachusetts, Boston, USA</mods:affiliation></affiliation></author><author><name sortKey="Tang, Fan" sort="Tang, Fan" uniqKey="Tang F" first="Fan" last="Tang">Fan Tang</name><affiliation><mods:affiliation>Department of Biostatistics, University of Iowa, Iowa, Iowa City, USA</mods:affiliation></affiliation></author><author><name sortKey="Foster, Eric D" sort="Foster, Eric D" uniqKey="Foster E" first="Eric D." last="Foster">Eric D. Foster</name><affiliation><mods:affiliation>Department of Biostatistics, University of Iowa, Iowa, Iowa City, USA</mods:affiliation></affiliation></author><author><name sortKey="Cavanaugh, Joseph E" sort="Cavanaugh, Joseph E" uniqKey="Cavanaugh J" first="Joseph E." last="Cavanaugh">Joseph E. Cavanaugh</name><affiliation><mods:affiliation>Department of Biostatistics, University of Iowa, Iowa, Iowa City, USA</mods:affiliation></affiliation></author><author><name sortKey="Polgreen, Philip M" sort="Polgreen, Philip M" uniqKey="Polgreen P" first="Philip M." last="Polgreen">Philip M. Polgreen</name><affiliation><mods:affiliation>Department of Internal Medicine, University of Iowa, Iowa, Iowa City, USA</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: philip-polgreen@uiowa.edu</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Respirology</title><title level="j" type="alt">RESPIROLOGY</title><idno type="ISSN">1323-7799</idno><idno type="eISSN">1440-1843</idno><imprint><biblScope unit="vol">19</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="116">116</biblScope><biblScope unit="page" to="121">121</biblScope><biblScope unit="page-count">6</biblScope><date type="published" when="2014-01">2014-01</date></imprint><idno type="ISSN">1323-7799</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1323-7799</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract">Background and objective: Although influenza has been associated with asthma exacerbations, it is not clear the extent to which this association affects health care use in the United States. The first goal of this project was to determine whether, and to what extent, the incidence of asthma hospitalizations is associated with seasonal variation in influenza. Second, we used influenza trends (2000–2008) to help predict asthma admissions during the 2009 H1N1 influenza pandemic. Methods: We identified all hospitalizations between 1998 and 2008 in the Nationwide Inpatient Sample from the Healthcare Cost and Utilization Project during which a primary diagnosis of asthma was recorded. Separately, we identified all hospitalizations during which a diagnosis of influenza was recorded. We performed time series regression analyses to investigate the association of monthly asthma admissions with influenza incidence. Finally, we applied these time series regression models using 1998–2008 data, to forecast monthly asthma admissions during the 2009 influenza pandemic. Results: Based on time series regression models, a strong, significant association exists between concurrent influenza activity and incidence of asthma hospitalizations (P‐value < 0.0001). Use of influenza data to predict asthma admissions during the 2009 H1N1 pandemic improved the mean squared prediction error by 60.2%. Conclusions: Influenza activity in the population is significantly associated with asthma hospitalizations in the United States, and this association can be exploited to more accurately forecast asthma admissions. Our results suggest that improvements in influenza surveillance, prevention and treatment may decrease hospitalizations of asthma patients.</div><div type="abstract">On a population basis, influenza activity is associated with asthma hospitalizations in the United States, and this association can be exploited to more accurately forecast asthma admissions. Our results suggest that improvements in influenza surveillance, prevention and treatment may help predict and decrease hospitalizations of asthma patients.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Alicia K. Gerke</name><affiliations><json:string>Department of Internal Medicine, University of Iowa, Iowa, Iowa City, USA</json:string></affiliations></json:item><json:item><name>Ming Yang</name><affiliations><json:string>Harvard School of Public Health, Massachusetts, Boston, USA</json:string></affiliations></json:item><json:item><name>Fan Tang</name><affiliations><json:string>Department of Biostatistics, University of Iowa, Iowa, Iowa City, USA</json:string></affiliations></json:item><json:item><name>Eric D. Foster</name><affiliations><json:string>Department of Biostatistics, University of Iowa, Iowa, Iowa City, USA</json:string></affiliations></json:item><json:item><name>Joseph E. Cavanaugh</name><affiliations><json:string>Department of Biostatistics, University of Iowa, Iowa, Iowa City, USA</json:string></affiliations></json:item><json:item><name>Philip M. Polgreen</name><affiliations><json:string>Department of Internal Medicine, University of Iowa, Iowa, Iowa City, USA</json:string><json:string>E-mail: philip-polgreen@uiowa.edu</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>asthma</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>forecasting</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>health‐care utilization</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>influenza</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>resource allocation</value></json:item></subject><articleId><json:string>RESP12165</json:string></articleId><arkIstex>ark:/67375/WNG-N8VWS195-K</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>Background and objective: Although influenza has been associated with asthma exacerbations, it is not clear the extent to which this association affects health care use in the United States. The first goal of this project was to determine whether, and to what extent, the incidence of asthma hospitalizations is associated with seasonal variation in influenza. Second, we used influenza trends (2000–2008) to help predict asthma admissions during the 2009 H1N1 influenza pandemic. Methods: We identified all hospitalizations between 1998 and 2008 in the Nationwide Inpatient Sample from the Healthcare Cost and Utilization Project during which a primary diagnosis of asthma was recorded. Separately, we identified all hospitalizations during which a diagnosis of influenza was recorded. We performed time series regression analyses to investigate the association of monthly asthma admissions with influenza incidence. Finally, we applied these time series regression models using 1998–2008 data, to forecast monthly asthma admissions during the 2009 influenza pandemic. Results: Based on time series regression models, a strong, significant association exists between concurrent influenza activity and incidence of asthma hospitalizations (P‐value > 0.0001). Use of influenza data to predict asthma admissions during the 2009 H1N1 pandemic improved the mean squared prediction error by 60.2%. Conclusions: Influenza activity in the population is significantly associated with asthma hospitalizations in the United States, and this association can be exploited to more accurately forecast asthma admissions. 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<ref target="http://www.tei-c.org/">We use TEI</ref></desc></application></appInfo></encodingDesc><profileDesc><abstract style="main"><head>Abstract</head><head>Background and objective</head><p>Although influenza has been associated with asthma exacerbations, it is not clear the extent to which this association affects health care use in the <hi rend="fc">U</hi>nited <hi rend="fc">S</hi>tates. The first goal of this project was to determine whether, and to what extent, the incidence of asthma hospitalizations is associated with seasonal variation in influenza. Second, we used influenza trends (2000–2008) to help predict asthma admissions during the 2009 <hi rend="fc">H1N1</hi> influenza pandemic.</p><head>Methods</head><p>We identified all hospitalizations between 1998 and 2008 in the <hi rend="fc">N</hi>ationwide <hi rend="fc">I</hi>npatient <hi rend="fc">S</hi>ample from the <hi rend="fc">H</hi>ealthcare <hi rend="fc">C</hi>ost and <hi rend="fc">U</hi>tilization <hi rend="fc">P</hi>roject during which a primary diagnosis of asthma was recorded. Separately, we identified all hospitalizations during which a diagnosis of influenza was recorded. We performed time series regression analyses to investigate the association of monthly asthma admissions with influenza incidence. Finally, we applied these time series regression models using 1998–2008 data, to forecast monthly asthma admissions during the 2009 influenza pandemic.</p><head>Results</head><p>Based on time series regression models, a strong, significant association exists between concurrent influenza activity and incidence of asthma hospitalizations (<hi rend="italic"><hi rend="fc">P</hi></hi>‐value < 0.0001). Use of influenza data to predict asthma admissions during the 2009 <hi rend="fc">H1N1</hi> pandemic improved the mean squared prediction error by 60.2%.</p><head>Conclusions</head><p>Influenza activity in the population is significantly associated with asthma hospitalizations in the <hi rend="fc">U</hi>nited <hi rend="fc">S</hi>tates, and this association can be exploited to more accurately forecast asthma admissions. Our results suggest that improvements in influenza surveillance, prevention and treatment may decrease hospitalizations of asthma patients.</p></abstract><abstract style="short"><p>On a population basis, influenza activity is associated with asthma hospitalizations in the United States, and this association can be exploited to more accurately forecast asthma admissions. Our results suggest that improvements in influenza surveillance, prevention and treatment may help predict and decrease hospitalizations of asthma patients.</p></abstract><textClass><keywords><term xml:id="resp12165-kwd-0001">asthma</term><term xml:id="resp12165-kwd-0002">forecasting</term><term xml:id="resp12165-kwd-0003">health‐care utilization</term><term xml:id="resp12165-kwd-0004">influenza</term><term xml:id="resp12165-kwd-0005">resource allocation</term></keywords><keywords rend="tocHeading1"><term>ORIGINAL ARTICLES</term></keywords><keywords rend="articleCategory"><term>ORIGINAL ARTICLE</term></keywords></textClass><langUsage><language ident="en"></language></langUsage></profileDesc><revisionDesc><change when="2019-12-20" who="#istex" xml:id="pub2tei">formatting</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/ark:/67375/WNG-N8VWS195-K/fulltext.txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component type="serialArticle" version="2.0" xml:id="resp12165" xml:lang="en"><header><publicationMeta level="product"><doi origin="wiley">10.1111/(ISSN)1440-1843</doi><issn type="print">1323-7799</issn><issn type="electronic">1440-1843</issn><idGroup><id type="product" value="RESP"></id></idGroup><titleGroup><title sort="RESPIROLOGY" type="main">Respirology</title><title type="short">Respirology</title></titleGroup></publicationMeta><publicationMeta level="part" position="01101"><doi>10.1111/resp.2014.19.issue-1</doi><copyright ownership="thirdParty">Respirology © 2014 Asian Pacific Society of Respirology</copyright><numberingGroup><numbering number="19" type="journalVolume">19</numbering><numbering type="journalIssue">1</numbering></numberingGroup><coverDate startDate="2014-01">January 2014</coverDate></publicationMeta><publicationMeta level="unit" position="200" status="forIssue" type="article"><doi>10.1111/resp.12165</doi><idGroup><id type="unit" value="RESP12165"></id></idGroup><countGroup><count number="6" type="pageTotal"></count></countGroup><titleGroup><title type="tocHeading1">ORIGINAL ARTICLES</title><title type="articleCategory">ORIGINAL ARTICLE</title></titleGroup><copyright ownership="thirdParty">© 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology</copyright><eventGroup><event agent="bestset" date="2013-08-16" type="xmlCreated"></event><event date="2013-04-09" type="manuscriptReceived"></event><event date="2013-06-04" type="manuscriptRevised"></event><event date="2013-07-11" type="manuscriptRevised"></event><event date="2013-07-17" type="manuscriptAccepted"></event><event type="publishedOnlineAccepted" date="2013-08-12"></event><event type="publishedOnlineFinalForm" date="2013-12-23"></event><event type="firstOnline" date="2013-12-23"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.6.4 mode:FullText" date="2015-10-07"></event></eventGroup><numberingGroup><numbering type="pageFirst">116</numbering><numbering type="pageLast">121</numbering></numberingGroup><correspondenceTo>Correspondence: Philip M. Polgreen, Department of Internal Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA. Email: <email>philip-polgreen@uiowa.edu</email></correspondenceTo><linkGroup><link type="toTypesetVersion" href="file://RESP.RESP12165.pdf"></link></linkGroup></publicationMeta><contentMeta><titleGroup><title type="short">Flu predicts asthma hospitalizations</title><title type="shortAuthors">AK Gerke <i>et al</i>.</title><title type="main">Association of hospitalizations for asthma with seasonal and pandemic influenza</title></titleGroup><creators><creator affiliationRef="#resp12165-aff-0001" creatorRole="author" xml:id="resp12165-cr-0001"><personName><givenNames>Alicia K.</givenNames><familyName>Gerke</familyName></personName></creator><creator affiliationRef="#resp12165-aff-0003" creatorRole="author" xml:id="resp12165-cr-0002"><personName><givenNames>Ming</givenNames><familyName>Yang</familyName></personName></creator><creator affiliationRef="#resp12165-aff-0002" creatorRole="author" xml:id="resp12165-cr-0003"><personName><givenNames>Fan</givenNames><familyName>Tang</familyName></personName></creator><creator affiliationRef="#resp12165-aff-0002" creatorRole="author" xml:id="resp12165-cr-0004"><personName><givenNames>Eric D.</givenNames><familyName>Foster</familyName></personName></creator><creator affiliationRef="#resp12165-aff-0002" creatorRole="author" xml:id="resp12165-cr-0005"><personName><givenNames>Joseph E.</givenNames><familyName>Cavanaugh</familyName></personName></creator><creator affiliationRef="#resp12165-aff-0001" corresponding="yes" creatorRole="author" xml:id="resp12165-cr-0006"><personName><givenNames>Philip M.</givenNames><familyName>Polgreen</familyName></personName></creator></creators><affiliationGroup><affiliation countryCode="US" xml:id="resp12165-aff-0001"><orgDiv>Department of Internal Medicine</orgDiv><orgName>University of Iowa</orgName><address><city>Iowa City</city><countryPart>Iowa</countryPart><country>USA</country></address></affiliation><affiliation countryCode="US" xml:id="resp12165-aff-0002"><orgDiv>Department of Biostatistics</orgDiv><orgName>University of Iowa</orgName><address><city>Iowa City</city><countryPart>Iowa</countryPart><country>USA</country></address></affiliation><affiliation countryCode="US" xml:id="resp12165-aff-0003"><orgName>Harvard School of Public Health</orgName><address><city>Boston</city><countryPart>Massachusetts</countryPart><country>USA</country></address></affiliation></affiliationGroup><keywordGroup type="author"><keyword xml:id="resp12165-kwd-0001">asthma</keyword><keyword xml:id="resp12165-kwd-0002">forecasting</keyword><keyword xml:id="resp12165-kwd-0003">health‐care utilization</keyword><keyword xml:id="resp12165-kwd-0004">influenza</keyword><keyword xml:id="resp12165-kwd-0005">resource allocation</keyword></keywordGroup><fundingInfo><fundingAgency>National Institutes of Health</fundingAgency><fundingNumber>1KL2RR024980</fundingNumber></fundingInfo><fundingInfo><fundingAgency>Institute for Clinical and Translational Science, University of Iowa (AKG)</fundingAgency></fundingInfo><fundingInfo><fundingAgency>National Institutes of Health Career Investigator Award</fundingAgency><fundingNumber>K01 AI75089</fundingNumber></fundingInfo><abstractGroup><abstract type="main"><title type="main">Abstract</title><section xml:id="resp12165-sec-0001"><title type="main">Background and objective</title><p>Although influenza has been associated with asthma exacerbations, it is not clear the extent to which this association affects health care use in the <fc>U</fc>nited <fc>S</fc>tates. The first goal of this project was to determine whether, and to what extent, the incidence of asthma hospitalizations is associated with seasonal variation in influenza. Second, we used influenza trends (2000–2008) to help predict asthma admissions during the 2009 <fc>H1N1</fc> influenza pandemic.</p></section><section xml:id="resp12165-sec-0002"><title type="main">Methods</title><p>We identified all hospitalizations between 1998 and 2008 in the <fc>N</fc>ationwide <fc>I</fc>npatient <fc>S</fc>ample from the <fc>H</fc>ealthcare <fc>C</fc>ost and <fc>U</fc>tilization <fc>P</fc>roject during which a primary diagnosis of asthma was recorded. Separately, we identified all hospitalizations during which a diagnosis of influenza was recorded. We performed time series regression analyses to investigate the association of monthly asthma admissions with influenza incidence. Finally, we applied these time series regression models using 1998–2008 data, to forecast monthly asthma admissions during the 2009 influenza pandemic.</p></section><section xml:id="resp12165-sec-0003"><title type="main">Results</title><p>Based on time series regression models, a strong, significant association exists between concurrent influenza activity and incidence of asthma hospitalizations (<i><fc>P</fc></i>‐value < 0.0001). Use of influenza data to predict asthma admissions during the 2009 <fc>H1N1</fc> pandemic improved the mean squared prediction error by 60.2%.</p></section><section xml:id="resp12165-sec-0004"><title type="main">Conclusions</title><p>Influenza activity in the population is significantly associated with asthma hospitalizations in the <fc>U</fc>nited <fc>S</fc>tates, and this association can be exploited to more accurately forecast asthma admissions. Our results suggest that improvements in influenza surveillance, prevention and treatment may decrease hospitalizations of asthma patients.</p></section></abstract><abstract type="short"><p>On a population basis, influenza activity is associated with asthma hospitalizations in the United States, and this association can be exploited to more accurately forecast asthma admissions. Our results suggest that improvements in influenza surveillance, prevention and treatment may help predict and decrease hospitalizations of asthma patients.</p></abstract></abstractGroup></contentMeta><noteGroup><note numbered="no" xml:id="resp12165-note-0004">(Associate Editor: Bob Hancox).</note></noteGroup></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Association of hospitalizations for asthma with seasonal and pandemic influenza</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>Flu predicts asthma hospitalizations</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Association of hospitalizations for asthma with seasonal and pandemic influenza</title></titleInfo><name type="personal"><namePart type="given">Alicia K.</namePart><namePart type="family">Gerke</namePart><affiliation>Department of Internal Medicine, University of Iowa, Iowa, Iowa City, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Ming</namePart><namePart type="family">Yang</namePart><affiliation>Harvard School of Public Health, Massachusetts, Boston, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Fan</namePart><namePart type="family">Tang</namePart><affiliation>Department of Biostatistics, University of Iowa, Iowa, Iowa City, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Eric D.</namePart><namePart type="family">Foster</namePart><affiliation>Department of Biostatistics, University of Iowa, Iowa, Iowa City, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Joseph E.</namePart><namePart type="family">Cavanaugh</namePart><affiliation>Department of Biostatistics, University of Iowa, Iowa, Iowa City, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Philip M.</namePart><namePart type="family">Polgreen</namePart><affiliation>Department of Internal Medicine, University of Iowa, Iowa, Iowa City, USA</affiliation><affiliation>E-mail: philip-polgreen@uiowa.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-6N5SZHKN-D">article</genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><dateIssued encoding="w3cdtf">2014-01</dateIssued><dateCreated encoding="w3cdtf">2013-08-16</dateCreated><dateCaptured encoding="w3cdtf">2013-04-09</dateCaptured><dateValid encoding="w3cdtf">2013-07-17</dateValid><copyrightDate encoding="w3cdtf">2014</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract>Background and objective: Although influenza has been associated with asthma exacerbations, it is not clear the extent to which this association affects health care use in the United States. The first goal of this project was to determine whether, and to what extent, the incidence of asthma hospitalizations is associated with seasonal variation in influenza. Second, we used influenza trends (2000–2008) to help predict asthma admissions during the 2009 H1N1 influenza pandemic. Methods: We identified all hospitalizations between 1998 and 2008 in the Nationwide Inpatient Sample from the Healthcare Cost and Utilization Project during which a primary diagnosis of asthma was recorded. Separately, we identified all hospitalizations during which a diagnosis of influenza was recorded. We performed time series regression analyses to investigate the association of monthly asthma admissions with influenza incidence. Finally, we applied these time series regression models using 1998–2008 data, to forecast monthly asthma admissions during the 2009 influenza pandemic. Results: Based on time series regression models, a strong, significant association exists between concurrent influenza activity and incidence of asthma hospitalizations (P‐value < 0.0001). Use of influenza data to predict asthma admissions during the 2009 H1N1 pandemic improved the mean squared prediction error by 60.2%. Conclusions: Influenza activity in the population is significantly associated with asthma hospitalizations in the United States, and this association can be exploited to more accurately forecast asthma admissions. Our results suggest that improvements in influenza surveillance, prevention and treatment may decrease hospitalizations of asthma patients.</abstract><abstract type="short">On a population basis, influenza activity is associated with asthma hospitalizations in the United States, and this association can be exploited to more accurately forecast asthma admissions. Our results suggest that improvements in influenza surveillance, prevention and treatment may help predict and decrease hospitalizations of asthma patients.</abstract><note type="funding">National Institutes of Health - No. 1KL2RR024980; </note><note type="funding">Institute for Clinical and Translational Science, University of Iowa (AKG)</note><note type="funding">National Institutes of Health Career Investigator Award - No. K01 AI75089; </note><subject><genre>keywords</genre><topic>asthma</topic><topic>forecasting</topic><topic>health‐care utilization</topic><topic>influenza</topic><topic>resource allocation</topic></subject><relatedItem type="host"><titleInfo><title>Respirology</title></titleInfo><titleInfo type="abbreviated"><title>Respirology</title></titleInfo><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><subject><genre>article-category</genre><topic>ORIGINAL ARTICLES</topic><topic>ORIGINAL ARTICLE</topic></subject><identifier type="ISSN">1323-7799</identifier><identifier type="eISSN">1440-1843</identifier><identifier type="DOI">10.1111/(ISSN)1440-1843</identifier><identifier type="PublisherID">RESP</identifier><part><date>2014</date><detail type="volume"><caption>vol.</caption><number>19</number></detail><detail type="issue"><caption>no.</caption><number>1</number></detail><extent unit="pages"><start>116</start><end>121</end><total>6</total></extent></part></relatedItem><relatedItem type="references" displayLabel="resp12165-cit-0001"><titleInfo><title>CDC. 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