Literature DB >> 21757104

The perils of using annual all-cause mortality data to estimate pandemic influenza burden.

Viggo Andreasen1, Lone Simonsen.   

Abstract

Measuring the burden of historic pandemics is not straightforward and often must be based on suboptimal mortality data. For example, the critical 1918 pandemic global burden estimate was based on excess in annual all-cause mortality--calculated as the difference between deaths during 1918-1920 and the surrounding 3-year periods. One intriguing result was a ∼ 40-fold between-country variation in pandemic mortality burden: ∼ 0.2% of Danes died, compared to ∼ 8% of populations in some Indian provinces (Murray et al., 2006 [16]). Using the same methodology and data source we explore the robustness of this methodology for different age-groups. For infants the country estimates varied 100-fold, from 15 to 1500 excess deaths/10,000 population, while for adults ≥ 45 years estimates ranged from -70 to 170/10,000 population. In contrast, estimates for children, 1-14 years, and adults aged 15-44 years, were far more stable. We next used detailed mortality data from Copenhagen to compare such estimates to the more precise estimates obtained from monthly mortality time series data and respiratory deaths. We found that the all-cause annual method substantially underestimated due to an unexplained depression in all-cause mortality in Denmark in 1918 and deaths caused by other epidemic diseases during the baseline periods. We conclude that country estimates for infants and older adults were highly variable by the Murray method due to substantial variability in annual all-cause mortality. A more precise 1918 pandemic burden estimate would be gotten from either focusing analysis on persons age 1-44 who suffered 95% of all pandemic deaths and had a substantial rise over their baseline mortality level, or if possible focus analysis on annual respiratory deaths. For less severe pandemics, including the ongoing 2009 H1N1 pandemic, the use of all-cause mortality data requires careful consideration of excess deaths in defined pandemic periods and a focus on age groups known to be at risk.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 21757104     DOI: 10.1016/j.vaccine.2011.03.061

Source DB:  PubMed          Journal:  Vaccine        ISSN: 0264-410X            Impact factor:   3.641


  7 in total

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2.  Immune boosting explains regime-shifts in prevaccine-era pertussis dynamics.

Authors:  Jennie S Lavine; Aaron A King; Viggo Andreasen; Ottar N Bjørnstad
Journal:  PLoS One       Date:  2013-08-26       Impact factor: 3.240

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Authors:  Cees C van den Wijngaard; Liselotte van Asten; Marion P G Koopmans; Wilfrid van Pelt; Nico J D Nagelkerke; Cornelia C H Wielders; Alies van Lier; Wim van der Hoek; Adam Meijer; Gé A Donker; Frederika Dijkstra; Carel Harmsen; Marianne A B van der Sande; Mirjam Kretzschmar
Journal:  PLoS One       Date:  2012-02-03       Impact factor: 3.240

4.  Death patterns during the 1918 influenza pandemic in Chile.

Authors:  Gerardo Chowell; Lone Simonsen; Jose Flores; Mark A Miller; Cécile Viboud
Journal:  Emerg Infect Dis       Date:  2014-11       Impact factor: 6.883

5.  Global Mortality Impact of the 1957-1959 Influenza Pandemic.

Authors:  Cécile Viboud; Lone Simonsen; Rodrigo Fuentes; Jose Flores; Mark A Miller; Gerardo Chowell
Journal:  J Infect Dis       Date:  2016-03-01       Impact factor: 5.226

6.  Severe mortality impact of the 1957 influenza pandemic in Chile.

Authors:  Gerardo Chowell; Lone Simonsen; Rodrigo Fuentes; Jose Flores; Mark A Miller; Cécile Viboud
Journal:  Influenza Other Respir Viruses       Date:  2017-03-31       Impact factor: 4.380

7.  The 1918 influenza pandemic in Montevideo: The southernmost capital city in the Americas.

Authors:  Juan Cristina; Raquel Pollero; Adela Pellegrino
Journal:  Influenza Other Respir Viruses       Date:  2019-02-14       Impact factor: 4.380

  7 in total

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