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Influenza Other Respir Viruses. 2009 Jan;3(1):37-49. doi: 10.1111/j.1750-2659.2009.00073.x.

Estimates of US influenza-associated deaths made using four different methods.

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Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.



A wide range of methods have been used for estimating influenza-associated deaths in temperate countries. Direct comparisons of estimates produced by using different models with US mortality data have not been published.


Compare estimates of US influenza-associated deaths made by using four models and summarize strengths and weaknesses of each model.


US mortality data from the 1972-1973 through 2002-2003 respiratory seasons and World Health Organization influenza surveillance data were used to estimate influenza-associated respiratory and circulatory deaths. Four models were used: (i) rate-difference (using peri-season or summer-season baselines), (ii) Serfling least squares cyclical regression, (iii) Serfling-Poisson regression, (iv) and autoregressive integrated moving average models.


Annual estimates of influenza-associated deaths made using each model were similar and positively correlated, except for estimates from the summer-season rate-difference model, which were consistently higher. From the 1976/1977 through the 2002/2003 seasons the, the Poisson regression models estimated that an annual average of 25,470 [95% confidence interval (CI) 19,781-31,159] influenza-associated respiratory and circulatory deaths [9.9 deaths per 100,000 (95% CI 7.9-11.9)], while peri-season rate-difference models using a 15% threshold estimated an annual average of 22,454 (95% CI 16,189-28,719) deaths [8.6 deaths per 100,000 (95% CI 6.4-10.9)].


Estimates of influenza-associated mortality were of similar magnitude. Poisson regression models permit the estimation of deaths associated with influenza A and B, but require robust viral surveillance data. By contrast, simple peri-season rate-difference models may prove useful for estimating mortality in countries with sparse viral surveillance data or complex influenza seasonality.

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