14 research outputs found
Respiratory viral pathogens among Singapore military servicemen 2009 - 2012: Epidemiology and clinical characteristics
10.1186/1471-2334-14-204BMC Infectious Diseases141-BIDM
Identification of viral and bacterial pathogens from hospitalized children with severe acute respiratory illness in Lusaka, Zambia, 2011–2012: a cross-sectional study
Pan-human coronavirus and human bocavirus SYBR Green and TaqMan PCR assays; use in studying influenza A viruses co-infection and risk of hospitalization
Longitudinal study of age-specific pattern of coronavirus infection in Lyle’s flying fox (Pteropus lylei) in Thailand
Epidemiology and Molecular Characterization of Human Respiratory Syncytial Virus in Senegal after Four Consecutive Years of Surveillance, 2012–2015
Coronavirus surveillance in wildlife from two Congo basin countries detects RNA of multiple species circulating in bats and rodents
Distribution of influenza virus types by age using case-based global surveillance data from twenty-nine countries, 1999-2014
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