Estimates of US influenza‐associated deaths made using four different methods

W. Thompson, Eric Weintraub, Praveen Dhankhar, Po‐Yung Cheng, Lynnette Brammer, Martin I. Meltzer, Joseph Bresee, David K. Shay

Influenza and Other Respiratory Viruses · 2009 · 398 citations · 33 references

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Abstract

BACKGROUND: 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. OBJECTIVE: Compare estimates of US influenza-associated deaths made by using four models and summarize strengths and weaknesses of each model. METHODS: 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. RESULTS: 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)]. CONCLUSIONS: 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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