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01.12.2018 | Research article | Ausgabe 1/2018 Open Access

BMC Infectious Diseases 1/2018

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

Zeitschrift:
BMC Infectious Diseases > Ausgabe 1/2018
Autoren:
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
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12879-018-3181-y) contains supplementary material, which is available to authorized users.

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.
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