TY - JOUR
T1 - Distribution of influenza virus types by age using case-based global surveillance data from twenty-nine countries, 1999-2014
AU - Global Influenza B Study group
AU - Caini, Saverio
AU - Spreeuwenberg, Peter
AU - Kusznierz, Gabriela F.
AU - Rudi, Juan Manuel
AU - Owen, Rhonda
AU - Pennington, Kate
AU - Wangchuk, Sonam
AU - Gyeltshen, Sonam
AU - Ferreira de Almeida, Walquiria Aparecida
AU - Pessanha Henriques, Cláudio Maierovitch
AU - Njouom, Richard
AU - Vernet, Marie Astrid
AU - Fasce, Rodrigo A.
AU - Andrade, Winston
AU - Yu, Hongjie
AU - Feng, Luzhao
AU - Yang, Juan
AU - Peng, Zhibin
AU - Lara, Jenny
AU - Bruno, Alfredo
AU - de Mora, Doménica
AU - de Lozano, Celina
AU - Zambon, Maria
AU - Pebody, Richard
AU - Castillo, Leticia
AU - Clara, Alexey W.
AU - Matute, Maria Luisa
AU - Kosasih, Herman
AU - Puzelli, Simona
AU - Rizzo, Caterina
AU - Kadjo, Herve A.
AU - Daouda, Coulibaly
AU - Kiyanbekova, Lyazzat
AU - Ospanova, Akerke
AU - Mott, Joshua A.
AU - Emukule, Gideon O.
AU - Heraud, Jean Michel
AU - Razanajatovo, Norosoa Harline
AU - Barakat, Amal
AU - el Falaki, Fatima
AU - Huang, Sue Q.
AU - Lopez, Liza
AU - Balmaseda, Angel
AU - Moreno, Brechla
AU - Rodrigues, Ana Paula
AU - Guiomar, Raquel
AU - Ang, Li Wei
AU - Lee, Vernon Jian Ming
AU - Venter, Marietjie
AU - Ellis, Joanna
N1 - Publisher Copyright:
© 2018 The Author(s).
PY - 2018/6/8
Y1 - 2018/6/8
N2 - 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.
AB - 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.
KW - Age distribution
KW - H1N1 subtype
KW - H3N2 subtype
KW - Influenza
KW - Influenza A virus
KW - Influenza A virus
KW - Influenza B virus
KW - Meta-analysis
UR - http://www.scopus.com/inward/record.url?scp=85048291114&partnerID=8YFLogxK
U2 - 10.1186/s12879-018-3181-y
DO - 10.1186/s12879-018-3181-y
M3 - Article
C2 - 29884140
AN - SCOPUS:85048291114
SN - 1471-2334
VL - 18
JO - BMC Infectious Diseases
JF - BMC Infectious Diseases
IS - 1
M1 - 269
ER -