American journal of epidemiology
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Historical Article
Age-Specific Excess Mortality Patterns During the 1918-1920 Influenza Pandemic in Madrid, Spain.
Although much progress has been made to uncover age-specific mortality patterns of the 1918 influenza pandemic in populations around the world, more studies in different populations are needed to make sense of the heterogeneous death impact of this pandemic. We assessed the absolute and relative magnitudes of 3 pandemic waves in the city of Madrid, Spain, between 1918 and 1920, on the basis of age-specific all-cause and respiratory excess death rates. Excess death rates were estimated using a Serfling model with a parametric bootstrapping approach to calibrate baseline death levels with quantified uncertainty. ⋯ Waves differed in strength; the peak standardized mortality risk occurred during the herald wave in spring 1918, but the highest excess rates occurred during the fall and winter of 1918/1919. Little evidence was found to support a "W"-shaped, age-specific excess mortality curve. Acquired immunity may have tempered a protracted fall wave, but recrudescent waves following the initial 2 outbreaks heightened the total pandemic mortality impact.
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Historical Article
The 1918-1919 Influenza Pandemic in Portugal: A Regional Analysis of Death Impact.
Although the impact of deaths occurring during the 1918-1919 influenza pandemic has been assessed in many archeo-epidemiologic studies, detailed estimates are not available for Portugal. We applied negative binomial models to monthly data on respiratory-related and all-cause deaths at the national and district levels from Portugal for 1916-1922. Influenza-related excess mortality was computed as the difference between observed and expected deaths. ⋯ This pattern changed during the March 1919 to June 1920 wave, when excess mortality increased with population density and in northern and western directions. Portuguese islands were less and later affected. Given the geographic heterogeneity evidenced in our study, subnational sociodemographic characteristics and connectivity should be integrated in pandemic preparedness plans.