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. 2012 Nov;23(6):829-38.
doi: 10.1097/EDE.0b013e31826c2dda.

Improving the estimation of influenza-related mortality over a seasonal baseline

Edward Goldstein  1 , Cecile Viboud, Vivek Charu, Marc Lipsitch
Affiliations

Improving the estimation of influenza-related mortality over a seasonal baseline

Edward Goldstein et al. Epidemiology. 2012 Nov.

Abstract

Background: Existing methods for estimation of mortality attributable to influenza are limited by methodological and data uncertainty. We have used proxies for disease incidence of the three influenza cocirculating subtypes (A/H3N2, A/H1N1, and B) that combine data on influenza-like illness consultations and respiratory specimen testing to estimate influenza-associated mortality in the United States between 1997 and 2007.

Methods: Weekly mortality rate for several mortality causes potentially affected by influenza was regressed linearly against subtype-specific influenza incidence proxies, adjusting for temporal trend and seasonal baseline, modeled by periodic cubic splines.

Results: Average annual influenza-associated mortality rates per 100,000 individuals were estimated for the following underlying causes of death: for pneumonia and influenza, 1.73 (95% confidence interval = 1.53-1.93); for chronic lower respiratory disease, 1.70 (1.48-1.93); for all respiratory causes, 3.58 (3.04-4.14); for myocardial infarctions, 1.02 (0.85-1.2); for ischemic heart disease, 2.7 (2.23-3.16); for heart disease, 3.82 (3.21-4.4); for cerebrovascular deaths, 0.65 (0.51-0.78); for all circulatory causes, 4.6 (3.79-5.39); for cancer, 0.87 (0.68-1.05); for diabetes, 0.33 (0.26-0.39); for renal disease, 0.19 (0.14-0.24); for Alzheimer disease, 0.41 (0.3-0.52); and for all causes, 11.92 (10.17-13.67). For several underlying causes of death, baseline mortality rates changed after the introduction of the pneumococcal conjugate vaccine.

Conclusions: The proposed methodology establishes a linear relation between influenza incidence proxies and excess mortality, rendering temporally consistent model fits, and allowing for the assessment of related epidemiologic phenomena such as changes in mortality baselines.

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Conflict of interest statement

Conflicts of Interest

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health.

Marc Lipsitch has received consulting fees or honoraria from Pfizer, Novartis, AIR Worldwide, and the Avian/Pandemic Flu Registry (Outcome Sciences and Roche). Other authors declare no conflicts on interest.

Figures

Figure 1
Figure 1
All-cause mortality (black) and the model fit (red).
Figure 2
Figure 2
Annual all-cause mortality baselines (black indicates Base1; red, Base2). Base1: seasons 1997–1998 through 2000–2001; Base2: seasons 2001–2002 through 2006–2007.
Figure 3
Figure 3
Recorded (black) and fitted (red) mortality for various underlying causes.
Figure 3
Figure 3
Recorded (black) and fitted (red) mortality for various underlying causes.
Figure 4
Figure 4
Mortality baselines Base1 (black) and Base2 (red) for various underlying causes.
Figure 4
Figure 4
Mortality baselines Base1 (black) and Base2 (red) for various underlying causes.
Figure 5
Figure 5
Correlation between annual influenza-associated mortality and excess mortality during various time periods (with a linear regression line): (A) Summer, (B) Winter, (C) Annual.
Figure 5
Figure 5
Correlation between annual influenza-associated mortality and excess mortality during various time periods (with a linear regression line): (A) Summer, (B) Winter, (C) Annual.
Figure 5
Figure 5
Correlation between annual influenza-associated mortality and excess mortality during various time periods (with a linear regression line): (A) Summer, (B) Winter, (C) Annual.
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Comment in

References

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