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.

Characteristics of seasonal influenza A and B in Latin America: Influenza surveillance data from ten countries.

INTRODUCTION: The increased availability of influenza surveillance data in recent years justifies an actual and more complete overview of influenza epidemiology in Latin America. We compared the influenza surveillance systems and assessed the epidemiology of influenza A and B, including the spatio-temporal patterns of influenza epidemics, in ten countries and sub-national regions in Latin America. METHODS: We aggregated the data by year and country and characteristics of eighty-two years were analysed. We calculated the median proportion of laboratory-confirmed influenza cases caused by each virus strain, and compared the timing and amplitude of the primary and secondary peaks between countries. RESULTS: 37,087 influenza cases were reported during 2004-2012. Influenza A and B accounted for a median of 79% and, respectively, 21% of cases in a year. The percentage of influenza A cases that were subtyped was 82.5%; for influenza B, 15.6% of cases were characterized. Influenza A and B were dominant in seventy-five (91%) and seven (9%) years, respectively. In half (51%) of the influenza A years, influenza A(H3N2) was dominant, followed by influenza A(H1N1)pdm2009 (41%) and pre-pandemic A(H1N1) (8%). The primary peak of influenza activity was in June-September in temperate climate countries, with little or no secondary peak. Tropical climate countries had smaller primary peaks taking place in different months and frequently detectable secondary peaks. CONCLUSIONS: We found that good influenza surveillance data exists in Latin America, although improvements can still be made (e.g. a better characterization of influenza B specimens); that influenza B plays a considerable role in the seasonal influenza burden; and that there is substantial heterogeneity of spatio-temporal patterns of influenza epidemics. To improve the effectiveness of influenza control measures in Latin America, tropical climate countries may need to develop innovative prevention strategies specifically tailored to the spatio-temporal patterns of influenza in this region.

[Effect of influenza epidemics on mortality in Santa Fe, Argentina, during 1992-1999]. / Efecto de las epidemias de influenza sobre la mortalidad en Santa Fe, Argentina, en 1992-1999.

OBJECTIVES: To define the effect of influenza epidemics on mortality and to establish the best criterion for predicting mortality so as to provide a method for advance warning of the severity of an influenza epidemic. METHODS: The study was carried out in La Capital, a department in Santa Fe province, Argentina, during 1992-1999. In order to fulfill the first objective, a retrospective analysis was performed with mortality data for pneumonia and influenza in persons over 65 years of age, using the auto-regressive integrated moving averages (ARIMA). The latter were used to determine the excess mortality attributable to influenza epidemics. In order to attain the second objective, a regression analysis was performed so as to study the correlation between weekly morbidity from influenza and monthly mortality from pneumonia or influenza in personas over 65. Morbidity was expressed in terms of three summary measures which were derived from the number of cases of influenza that were reported during the first 35 weeks of the year: the sum total of all cases reported weekly, their standard deviation, and the maximum number of cases in any given week. We included in the analysis the type and subtype of influenza. These four parameters (type and subtype of influenza, along with one of the three summary measures) were compared among themselves in terms of their ability to explain the mortality observed during the first eight months of the year. RESULTS: Epidemics occurred during the winters of 1993, 1995, and 1999 and in the spring of 1997. During those seasons, excess deaths were observed in connection with the circulation of a predominant strain of influenza virus, type A (H3N2). There were no epidemics in the winter months of 1994, 1996, and 1998, despite the circulation of this viral strain. During the winters in which influenza virus strains A (H1N1) and B were in circulation (1992 and 1997, respectively) - both are associated with low mortality figures - no excess deaths were detected. CONCLUSIONS: The number of weekly cases of influenza reported during the peak of the winter season is the best criterion for predicting how much excess mortality can be attributed to the epidemic.