Back to Search Start Over

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

Authors :
Saverio Caini
Peter Spreeuwenberg
Gabriela F. Kusznierz
Juan Manuel Rudi
Rhonda Owen
Kate Pennington
Sonam Wangchuk
Sonam Gyeltshen
Walquiria Aparecida Ferreira de Almeida
Cláudio Maierovitch Pessanha Henriques
Richard Njouom
Marie-Astrid Vernet
Rodrigo A. Fasce
Winston Andrade
Hongjie Yu
Luzhao Feng
Juan Yang
Zhibin Peng
Jenny Lara
Alfredo Bruno
Doménica de Mora
Celina de Lozano
Maria Zambon
Richard Pebody
Leticia Castillo
Alexey W. Clara
Maria Luisa Matute
Herman Kosasih
Nurhayati
Simona Puzelli
Caterina Rizzo
Herve A. Kadjo
Coulibaly Daouda
Lyazzat Kiyanbekova
Akerke Ospanova
Joshua A. Mott
Gideon O. Emukule
Jean-Michel Heraud
Norosoa Harline Razanajatovo
Amal Barakat
Fatima el Falaki
Sue Q. Huang
Liza Lopez
Angel Balmaseda
Brechla Moreno
Ana Paula Rodrigues
Raquel Guiomar
Li Wei Ang
Vernon Jian Ming Lee
Marietjie Venter
Cheryl Cohen
Selim Badur
Meral A. 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 :
BMC Infectious Diseases, Vol 18, Iss 1, Pp 1-10 (2018)
Publication Year :
2018
Publisher :
BMC, 2018.

Abstract

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.

Details

Language :
English
ISSN :
14712334
Volume :
18
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Infectious Diseases
Publication Type :
Academic Journal
Accession number :
edsdoj.03279f4ba054193b05ce652aa2a1bc9
Document Type :
article
Full Text :
https://doi.org/10.1186/s12879-018-3181-y