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. 2012 Oct 1:11:74.
doi: 10.1186/1476-069X-11-74.

Influenza epidemics, seasonality, and the effects of cold weather on cardiac mortality

Stephanie von Klot  1 , Antonella Zanobetti, Joel Schwartz
Affiliations

Influenza epidemics, seasonality, and the effects of cold weather on cardiac mortality

Stephanie von Klot et al. Environ Health. .

Abstract

Background: More people die in the winter from cardiac disease, and there are competing hypotheses to explain this. The authors conducted a study in 48 US cities to determine how much of the seasonal pattern in cardiac deaths could be explained by influenza epidemics, whether that allowed a more parsimonious control for season than traditional spline models, and whether such control changed the short term association with temperature.

Methods: The authors obtained counts of daily cardiac deaths and of emergency hospital admissions of the elderly for influenza during 1992-2000. Quasi-Poisson regression models were conducted estimating the association between daily cardiac mortality, and temperature.

Results: Controlling for influenza admissions provided a more parsimonious model with better Generalized Cross-Validation, lower residual serial correlation, and better captured Winter peaks. The temperature-response function was not greatly affected by adjusting for influenza. The pooled estimated increase in risk for a temperature decrease from 0 to -5°C was 1.6% (95% confidence interval (CI) 1.1-2.1%). Influenza accounted for 2.3% of cardiac deaths over this period.

Conclusions: The results suggest that including epidemic data explained most of the irregular seasonal pattern (about 18% of the total seasonal variation), allowing more parsimonious models than when adjusting for seasonality only with smooth functions of time. The effect of cold temperature is not confounded by epidemics.

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Figures

Figure 1
Figure 1
Daily Temperature in Detroit and Honolulu (upper Panel), and smoothed percent Difference from the mean Mortality Rate in each City (lower Panel).
Figure 2
Figure 2
Locations of 47 US Cities included in the Study (Honolulu not shown). Symbol Size according to Quartiles of Population Size and Symbol Color according to Quintiles of 99th Percentile of daily Minimum Temperature (green-cold to red-warm) [4].
Figure 3
Figure 3
Comparison of exposure response functions, partial autocorrelation functions, residuals versus linear predictor, and observed/predicted versus time for models 1 and 2 and Detroit data.
Figure 4
Figure 4
Box Plots of the 48 City-Specific Paired Differences between Generalized Cross-Validation Scores of Model 1 and Model 2 (Left Panel), and of the Sum of Partial Autocorrelation Function (PACF) of the Residuals (lag 1 to 30) (Right Panel) of Model 1 and Model 2.
See this image and copyright information in PMC

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