OBJECTIVE: To estimate the excess deaths attributed to influenza in Spain, using age-specific generalized linear models (GLM) and the Serfling model for the period 1999-2005. METHOD: We reviewed mortality from influenza and pneumonia and all-cause deaths. We used an additive GLM procedure, including the numbers of weekly deaths as a response variable and the number of influenza virus and respiratory syncytial virus weekly isolates, the population and two variables to adjust for annual fluctuations as covariates. Using the Serfling model, we removed the trend and applied a temporal regression model, excluding data from December to April to account for the expected baseline mortality in the absence of influenza activity. RESULTS: Globally, the excess mortality attributable to influenza was 1.1 deaths per 100,000 for influenza and pneumonia and 11 all-cause deaths per 100,000 using the GLM model. The highest mortality rates were obtained with the Serfling model in adults older than 64 years, with an excess mortality attributable to influenza of 57 and 164 deaths per 100,000 for influenza and pneumonia and all-cause, respectively. CONCLUSIONS: The GLM model, which takes viral activity into account, yields systematically lower estimates of excess mortality than the Serfling model. The GLM model provides independent estimates associated with the activity of different viruses and even with other factors, which is a significant advantage when trying to understand the impact of viral respiratory infections on mortality in the Spanish population.
OBJECTIVE: To estimate the excess deaths attributed to influenza in Spain, using age-specific generalized linear models (GLM) and the Serfling model for the period 1999-2005. METHOD: We reviewed mortality from influenza and pneumonia and all-cause deaths. We used an additive GLM procedure, including the numbers of weekly deaths as a response variable and the number of influenza virus and respiratory syncytial virus weekly isolates, the population and two variables to adjust for annual fluctuations as covariates. Using the Serfling model, we removed the trend and applied a temporal regression model, excluding data from December to April to account for the expected baseline mortality in the absence of influenza activity. RESULTS: Globally, the excess mortality attributable to influenza was 1.1 deaths per 100,000 for influenza and pneumonia and 11 all-cause deaths per 100,000 using the GLM model. The highest mortality rates were obtained with the Serfling model in adults older than 64 years, with an excess mortality attributable to influenza of 57 and 164 deaths per 100,000 for influenza and pneumonia and all-cause, respectively. CONCLUSIONS: The GLM model, which takes viral activity into account, yields systematically lower estimates of excess mortality than the Serfling model. The GLM model provides independent estimates associated with the activity of different viruses and even with other factors, which is a significant advantage when trying to understand the impact of viral respiratory infections on mortality in the Spanish population.
Authors: Li Li; Jessica Y Wong; Peng Wu; Helen S Bond; Eric H Y Lau; Sheena G Sullivan; Benjamin J Cowling Journal: Am J Epidemiol Date: 2018-02-01 Impact factor: 4.897
Authors: A Danielle Iuliano; Katherine M Roguski; Howard H Chang; David J Muscatello; Rakhee Palekar; Stefano Tempia; Cheryl Cohen; Jon Michael Gran; Dena Schanzer; Benjamin J Cowling; Peng Wu; Jan Kyncl; Li Wei Ang; Minah Park; Monika Redlberger-Fritz; Hongjie Yu; Laura Espenhain; Anand Krishnan; Gideon Emukule; Liselotte van Asten; Susana Pereira da Silva; Suchunya Aungkulanon; Udo Buchholz; Marc-Alain Widdowson; Joseph S Bresee Journal: Lancet Date: 2017-12-14 Impact factor: 79.321
Authors: D Gomez-Barroso; M A Martinez-Beneito; V Flores; R Amorós; C Delgado; P Botella; O Zurriaga; A Larrauri Journal: Epidemiol Infect Date: 2014-01-27 Impact factor: 4.434
Authors: Gonçalo Matias; Robert J Taylor; François Haguinet; Cynthia Schuck-Paim; Roger L Lustig; Douglas M Fleming Journal: BMC Public Health Date: 2016-06-08 Impact factor: 3.295