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Distribution of influenza virus types by age using case-based global surveillance data from twenty-nine countries, 1999-2014

Identifieur interne : 000060 ( Hal/Corpus ); précédent : 000059; suivant : 000061

Distribution of influenza virus types by age using case-based global surveillance data from twenty-nine countries, 1999-2014

Auteurs : Saverio Caini ; Peter Spreeuwenberg ; Gabriela Kusznierz ; Juan Rudi ; Rhonda Owen ; Kate Pennington ; Sonam Wangchuk ; Sonam Gyeltshen ; Walquiria Aparecida Ferreira De Almeida ; Cláudio Maierovitch Pessanha Henriques ; Richard Njouom ; Marie-Astrid Vernet ; Rodrigo Fasce ; Winston Andrade ; Hongjie Yu ; Luzhao Feng ; Juan Yang ; Zhibin Peng ; Jenny Lara ; Alfredo Bruno ; Domenica De Mora ; Celina De Lozano ; Maria Zambon ; Richard Pebody ; Leticia Le ; Alexey Clara ; Maria Luisa Matute ; Herman Kosasih ; N Nurhayati ; Simona Puzelli ; Caterina Rizzo ; Herve Kadjo ; Coulibaly Daouda ; Lyazzat Kiyanbekova ; Akerke Ospanova ; Joshua Mott ; Gideon Emukule ; Jean-Michel Heraud ; Norosoa Harline Razanajatovo ; Amal Barakat ; Fatima El Falaki ; Sue Huang ; Liza Lopez ; Angel Balmaseda ; Brechla Moreno ; Ana Paula Rodrigues ; Raquel Guiomar ; Li Wei Ang ; Vernon Jian Ming Le ; Marietjie Venter ; Cheryl Cohen ; Selim Badur ; Meral Ciblak ; Alla Mironenko ; Olha Holubka ; Joseph Bresee ; Lynnette Brammer ; Phuong Vu Mai Hoang ; Mai Thi Quynh Le ; Douglas Fleming ; Clotilde El-Guerche Séblain ; François Schellevis ; John Paget ; Global Influenza B Study Group

Source :

RBID : Hal:pasteur-01925131

English descriptors

Abstract

BACKGROUND:Influenza disease burden varies by age and this has important public health implications. We compared the proportional distribution of different influenza virus types within age strata using surveillance data from twenty-nine countries during 1999-2014 (N=358,796 influenza cases).METHODS:For each virus, we calculated a Relative Illness Ratio (defined as the ratio of the percentage of cases in an age group to the percentage of the country population in the same age group) for young children (0-4 years), older children (5-17 years), young adults (18-39 years), older adults (40-64 years), and the elderly (65+ years). We used random-effects meta-analysis models to obtain summary relative illness ratios (sRIRs), and conducted meta-regression and sub-group analyses to explore causes of between-estimates heterogeneity.RESULTS:The influenza virus with highest sRIR was A(H1N1) for young children, B for older children, A(H1N1)pdm2009 for adults, and (A(H3N2) for the elderly. As expected, considering the diverse nature of the national surveillance datasets included in our analysis, between-estimates heterogeneity was high (I2>90%) for most sRIRs. The variations of countries' geographic, demographic and economic characteristics and the proportion of outpatients among reported influenza cases explained only part of the heterogeneity, suggesting that multiple factors were at play.CONCLUSIONS:These results highlight the importance of presenting burden of disease estimates by age group and virus (sub)type.


Url:
DOI: 10.1186/s12879-018-3181-y

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Hal:pasteur-01925131

Le document en format XML

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<name sortKey="Seblain, Clotilde El Guerche" sort="Seblain, Clotilde El Guerche" uniqKey="Seblain C" first="Clotilde El-Guerche" last="Séblain">Clotilde El-Guerche Séblain</name>
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<orgName>Netherlands Institute for Health Services Research</orgName>
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<name sortKey="Paget, John" sort="Paget, John" uniqKey="Paget J" first="John" last="Paget">John Paget</name>
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<addrLine>Utrecht</addrLine>
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<author>
<name sortKey="Influenza B Study Group, Global" sort="Influenza B Study Group, Global" uniqKey="Influenza B Study Group G" first="Global" last="Influenza B Study Group">Global Influenza B Study Group</name>
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<idno type="DOI">10.1186/s12879-018-3181-y</idno>
<series>
<title level="j">BMC Infectious Diseases</title>
<idno type="ISSN">1471-2334</idno>
<imprint>
<date type="datePub">2018-06-08</date>
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<keywords scheme="mix" xml:lang="en">
<term>Age distribution</term>
<term>H1N1 subtype</term>
<term>H3N2 subtype</term>
<term>Influenza</term>
<term>Influenza A virus</term>
<term>Influenza B virus</term>
<term>Meta-analysis</term>
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<front>
<div type="abstract" xml:lang="en">
<p>BACKGROUND:Influenza disease burden varies by age and this has important public health implications. We compared the proportional distribution of different influenza virus types within age strata using surveillance data from twenty-nine countries during 1999-2014 (N=358,796 influenza cases).METHODS:For each virus, we calculated a Relative Illness Ratio (defined as the ratio of the percentage of cases in an age group to the percentage of the country population in the same age group) for young children (0-4 years), older children (5-17 years), young adults (18-39 years), older adults (40-64 years), and the elderly (65+ years). We used random-effects meta-analysis models to obtain summary relative illness ratios (sRIRs), and conducted meta-regression and sub-group analyses to explore causes of between-estimates heterogeneity.RESULTS:The influenza virus with highest sRIR was A(H1N1) for young children, B for older children, A(H1N1)pdm2009 for adults, and (A(H3N2) for the elderly. As expected, considering the diverse nature of the national surveillance datasets included in our analysis, between-estimates heterogeneity was high (I2>90%) for most sRIRs. The variations of countries' geographic, demographic and economic characteristics and the proportion of outpatients among reported influenza cases explained only part of the heterogeneity, suggesting that multiple factors were at play.CONCLUSIONS:These results highlight the importance of presenting burden of disease estimates by age group and virus (sub)type.</p>
</div>
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<title xml:lang="en">Distribution of influenza virus types by age using case-based global surveillance data from twenty-nine countries, 1999-2014</title>
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<persName>
<forename type="first">Saverio</forename>
<surname>Caini</surname>
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<email type="md5">f0fdf7d4231a7a22e3dd2ce1c483593a</email>
<email type="domain">nivel.nl</email>
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<forename type="first">Peter</forename>
<surname>Spreeuwenberg</surname>
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<persName>
<forename type="first">Gabriela</forename>
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<forename type="first">Juan</forename>
<forename type="middle">Manuel</forename>
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<forename type="first">Rhonda</forename>
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<forename type="first">Kate</forename>
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<forename type="first">Sonam</forename>
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<forename type="first">Sonam</forename>
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<forename type="first">Walquiria Aparecida</forename>
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<funder>The Global Influenza B Study is funded by an unrestricted research grant from Sanofi Pasteur.</funder>
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<date type="whenSubmitted">2018-11-16 14:46:15</date>
<date type="whenModified">2019-04-10 11:58:03</date>
<date type="whenReleased">2018-12-11 15:19:25</date>
<date type="whenProduced">2018-06-08</date>
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<idno type="halRefHtml">BMC Infectious Diseases, BioMed Central, 2018, 18 (1), pp.269. ⟨10.1186/s12879-018-3181-y⟩</idno>
<idno type="halRef">BMC Infectious Diseases, BioMed Central, 2018, 18 (1), pp.269. ⟨10.1186/s12879-018-3181-y⟩</idno>
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<idno type="stamp" n="RIIP_MADAGASCAR" corresp="RIIP">Institut Pasteur de Madagascar</idno>
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<notesStmt>
<note type="commentary">The database of the Global Influenza B Study was created by collecting surveillance datasets from each of the participating countries. These national datasets are owned by the participating countries, and thus cannot be shared publicly. Researchers interested in obtaining the country-specific datasets may contact the individuals listed below for further details regarding data access.Argentina (Santa Fe Province): Gabriela Kusznierz (labconi@yahoo.com.ar)Australia: Kate Pennington (kate.pennington@health.gov.au)Bhutan: Sonam Wangchuk (swangchuk@health.gov.bt)Brazil: Cláudio Maierovitch Pessanha Henriques (claudio.henriques@saude.gov.br)Cameroon: Guy Vernet (vernet@pasteur-yaounde.org)Chile: Rodrigo Fasce (rfasce@ispch.cl)China: Feng Luzhao (fenglz@chinacdc.cn)Costa Rica: Alexey W. Clara (wclara@cdc.gov)Ecuador: Alfredo Bruno (alfredobruno@yahoo.es)El Salvador: Alexey W. Clara (wclara@cdc.gov)England: Maria Zambon (maria.zambon@phe.gov.uk)Guatemala: Alexey W. Clara (wclara@cdc.gov)Honduras: Alexey W. Clara (wclara@cdc.gov)Indonesia: Herman Kosasih (hermaninarespond@gmail.com)Italy: Caterina Rizzo (caterina.rizzo@iss.it)Ivory Coast: Herve A. Kadjo (hervekadjo@pasteur.ci)Kazakhstan: Gulzhan Muratbayeva (hnv2@cdc.gov)Kenya: Joshua Mott (zud9@cdc.gov)Madagascar: Jean-Michel Heraud (jmheraud@pasteur.mg)Morocco: Amal Barakat (amal.barakat@yahoo.fr)New Zealand: Sue Huang (sue.huang@esr.cri.nz)Nicaragua: Alexey W. Clara (wclara@cdc.gov)Panama: Alexey W. Clara (wclara@cdc.gov)Portugal: Ana Paula Rodrigues (ana.rodrigues@insa.min-saude.pt)Singapore: Vernon Lee (vernonljm@hotmail.com)South Africa: Cheryl Cohen (cherylc@nicd.ac.za)Turkey: Meral Akcay Ciblak (ciblakm@yahoo.com)Ukraine: Alla Mironenko (miralla@ukr.net)Viet Nam: Le Thi Quinh Mai (lom9@hotmail.com)</note>
<note type="audience" n="2">International</note>
<note type="popular" n="0">No</note>
<note type="peer" n="1">Yes</note>
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<title xml:lang="en">Distribution of influenza virus types by age using case-based global surveillance data from twenty-nine countries, 1999-2014</title>
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<forename type="first">Global</forename>
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<idno type="issn">1471-2334</idno>
<idno type="eissn">1471-2334</idno>
<title level="j">BMC Infectious Diseases</title>
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<publisher>BioMed Central</publisher>
<biblScope unit="volume">18</biblScope>
<biblScope unit="issue">1</biblScope>
<biblScope unit="pp">269</biblScope>
<date type="datePub">2018-06-08</date>
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<idno type="doi">10.1186/s12879-018-3181-y</idno>
<idno type="pubmed">29884140</idno>
<idno type="pubmedcentral">PMC5994061</idno>
<ref type="seeAlso" target="https://static-content.springer.com/esm/art%3A10.1186%2Fs12879-018-3181-y/MediaObjects/12879_2018_3181_MOESM1_ESM.docx">https://static-content.springer.com/esm/art%3A10.1186%2Fs12879-018-3181-y/MediaObjects/12879_2018_3181_MOESM1_ESM.docx</ref>
<ref type="seeAlso" target="https://static-content.springer.com/esm/art%3A10.1186%2Fs12879-018-3181-y/MediaObjects/12879_2018_3181_MOESM2_ESM.docx">https://static-content.springer.com/esm/art%3A10.1186%2Fs12879-018-3181-y/MediaObjects/12879_2018_3181_MOESM2_ESM.docx</ref>
<ref type="seeAlso" target="https://static-content.springer.com/esm/art%3A10.1186%2Fs12879-018-3181-y/MediaObjects/12879_2018_3181_MOESM3_ESM.docx">https://static-content.springer.com/esm/art%3A10.1186%2Fs12879-018-3181-y/MediaObjects/12879_2018_3181_MOESM3_ESM.docx</ref>
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<language ident="en">English</language>
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<keywords scheme="author">
<term xml:lang="en">Influenza A virus</term>
<term xml:lang="en">Influenza B virus</term>
<term xml:lang="en">Meta-analysis</term>
<term xml:lang="en">H1N1 subtype</term>
<term xml:lang="en">H3N2 subtype</term>
<term xml:lang="en">Influenza</term>
<term xml:lang="en">Age distribution</term>
</keywords>
<classCode scheme="halDomain" n="sdv.spee">Life Sciences [q-bio]/Santé publique et épidémiologie</classCode>
<classCode scheme="halDomain" n="sdv.mhep.mi">Life Sciences [q-bio]/Human health and pathology/Infectious diseases</classCode>
<classCode scheme="halDomain" n="sdv.mhep.me">Life Sciences [q-bio]/Human health and pathology/Emerging diseases</classCode>
<classCode scheme="halDomain" n="sdv.bbm.bm">Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology</classCode>
<classCode scheme="halDomain" n="sdv.mp.vir">Life Sciences [q-bio]/Microbiology and Parasitology/Virology</classCode>
<classCode scheme="halTypology" n="ART">Journal articles</classCode>
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<abstract xml:lang="en">
<p>BACKGROUND:Influenza disease burden varies by age and this has important public health implications. We compared the proportional distribution of different influenza virus types within age strata using surveillance data from twenty-nine countries during 1999-2014 (N=358,796 influenza cases).METHODS:For each virus, we calculated a Relative Illness Ratio (defined as the ratio of the percentage of cases in an age group to the percentage of the country population in the same age group) for young children (0-4 years), older children (5-17 years), young adults (18-39 years), older adults (40-64 years), and the elderly (65+ years). We used random-effects meta-analysis models to obtain summary relative illness ratios (sRIRs), and conducted meta-regression and sub-group analyses to explore causes of between-estimates heterogeneity.RESULTS:The influenza virus with highest sRIR was A(H1N1) for young children, B for older children, A(H1N1)pdm2009 for adults, and (A(H3N2) for the elderly. As expected, considering the diverse nature of the national surveillance datasets included in our analysis, between-estimates heterogeneity was high (I2>90%) for most sRIRs. The variations of countries' geographic, demographic and economic characteristics and the proportion of outpatients among reported influenza cases explained only part of the heterogeneity, suggesting that multiple factors were at play.CONCLUSIONS:These results highlight the importance of presenting burden of disease estimates by age group and virus (sub)type.</p>
</abstract>
<particDesc>
<org type="consortium">The Global Influenza B Study group also includes the following members: Binay Thapa 4, Sangay Zangmo 4, Guy Vernet 6, Patricia Bustos 7, Patricio Loyola 7, Joanna Ellis 12, Antonino Bella 19, Maria Rita Castrucci 18, Gulzhan Muratbayeva 45, Julia Guillebaud 26, Laurence Randrianasolo 46, Ausenda Machado 47, Pedro Pechirra 32, Jeffery Cutter 34, Raymond Tzer Pin Lin 34. 45 Centers for Disease Control and Prevention, Central Asia Regional Office, Almaty, Kazakhstan 46 Epidemiology Unit, Institut Pasteur of Madagascar, Antananarivo, Madagascar 47 National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal</org>
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   |texte=   Distribution of influenza virus types by age using case-based global surveillance data from twenty-nine countries, 1999-2014
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