Is there a clinically significant seasonal component to hospital admissions for atrial fibrillation?
© Upshur et al; licensee BioMed Central Ltd. 2004
Received: 29 October 2003
Accepted: 19 March 2004
Published: 19 March 2004
Atrial fibrillation is a common cardiac dysrhythmia, particularly in the elderly. Recent studies have indicated a statistically significant seasonal component to atrial fibrillation hospitalizations.
We conducted a retrospective population cohort study using time series analysis to evaluate seasonal patterns of atrial fibrillation hospitalizations for the province of Ontario for the years 1988 to 2001. Five different series methods were used to analyze the data, including spectral analysis, X11, R-Squared, autocorrelation function and monthly aggregation.
This study found evidence of weak seasonality, most apparent at aggregate levels including both ages and sexes. There was dramatic increase in hospitalizations for atrial fibrillation over the years studied and an age dependent increase in rates per 100,000. Overall, the magnitude of seasonal difference between peak and trough months is in the order of 1.4 admissions per 100,000 population. The peaks for hospitalizations were predominantly in April, and the troughs in August.
Our study confirms statistical evidence of seasonality for atrial fibrillation hospitalizations. This effect is small in absolute terms and likely not significant for policy or etiological research purposes.
KeywordsAtrial fibrillation seasons stochastic processes statistics time factors
Atrial fibrillation is the most common cardiac arrhythmia in the elderly population requiring medical treatment. The prevalence of this disease is clearly related to age and can be as high as 15 to 18% after the age of 80 [1, 2]. The seasonality of hospitalizations for atrial fibrillation has been the focus of epidemiological study as seasonality is a potential clue to etiology. Recent studies examining the seasonality of atrial fibrillation using monthly aggregations of emergency reports over a 10-year period in one study, and emergency room visits over a 1-year period in another, both found statistically significant seasonal differences in monthly values, with peaks typically occurring in the winter and troughs in the summer [3, 4]. Frost et al in a study of hospitalizations for atrial fibrillation in Denmark found a winter peak and summer trough, with a small but statistically significant relative risk of 1.20 (95% confidence interval: 1.12, 1.29) for winter events . They also reported an inverse relationship between mean outdoor temperature and atrial fibrillation.
Spectral analysis: Spectral analysis is a useful frequency domain tool for detecting the existence of periodicity in a time series. This can be achieved by plotting the periodogram or spectral density of the series against either period or the frequency. There are 2 statistical tests for testing the periodicity of the series: The Fisher's Kappa test and the Bartlett's Kolmogorov-Smirnov (BKS) test. Fisher's Kappa tests the null hypothesis that the series is Gaussian white noise against the alternative hypothesis that the series contains an added deterministic periodic component of unspecified frequency. The BKS test compares the normalized cumulative periodogram with the cumulative distribution function of the uniform (0,1) to test the null hypothesis that the series is white noise [6, 7].
The X11 procedure: 2 tests using this time domain approach were performed, the stable seasonality test and moving seasonality test [8, 9]. The stable seasonality is a 1-way analysis of variance on the de-trended series with months as the factor. The moving seasonality test is a 2-way analysis of variance with month and year as factors.
Autocorrelation function: this measures the correlation between observations at different time lags .
- 5)which measures the strength of seasonality in a time series. Values of 0 to less than 0.4 represent non-existent to weak seasonality, 0.4 to less than 0.7 represent moderate to strong seasonality, and 0.7 to 1 represent strong to perfect seasonality.
The data was subjected to logarithmic transformation to stabilize the variance and make the seasonal effect additive .
Total number of atrial fibrillation hospitalizations by age and gender between the years 1988 and 2001
Age group (years)
Results of the spectral analysis testing the seasonality of atrial fibrillation hospitalizations by age and gender between the years 1988 and 2001
Results of the X-11 analysis testing the seasonality of atrial fibrillation hospitalizations by age and gender between the years 1988 and 2001
Results of the autocorrelation function (lag 12) for testing the seasonality of atrial fibrillation hospitalizations by age and gender between the years 1988 and 2001
Age group (years)
Results of the R-squared autoregression for testing the strength of seasonality of atrial fibrillation hospitalizations by age and gender between the years 1988 and 2001
Age group (years)
The results of this analysis confirm a weak seasonal effect for atrial fibrillation hospitalizations in the Ontario population. As the time series analyses indicate, this seasonality is most apparent in aggregate, occurs as a peak effect in spring and largely disappears when age groups and sexes are considered separately. The results indicate a conspicuous upward trend for hospitalizations in the late 80s and early 90s that has since stabilized.
The study has several limitations. Firstly, the data base cannot distinguish new onset from chronic atrial fibrillation. However, the purpose of the study was not to calculate seasonal incidence of atrial fibrillation. Secondly, the study also only used atrial fibrillation when it was the most responsible diagnosis. This strategy will miss events where atrial fibrillation is a contributing factor or co-morbid condition. However, as noted in the methods, the most responsible diagnosis is also the most reliably coded in the data base. Finally, no attempt was made to link the seasonality with potential causes such as temperature or ambient air quality.
The magnitude of the observed seasonal effect is small, particularly in comparison to the seasonal effects demonstrated for conditions such as pneumonia , asthma , and falls . The seasonal effect is not likely of policy, etiologic or clinical relevance. The dramatic increase in trend for hospitalizations is unexplained, and reflects overall increases in hospitalizations during this time period . The analysis by Frost et al did not indicate the existence of trends and as no time plots were provided, it is uncertain whether this phenomenon is restricted to Ontario, or has occurred elsewhere as well. Furthermore, they reported their outcomes as relative risk increases which do not give an indication of the magnitude of effect.
In conclusion, this study supports a weak, but likely inconsequential seasonal variation in hospital admissions for atrial fibrillation in Ontario.
This project was funded by an operating grant (#MOP57928) from the Canadian Institutes of Health Research. We thank Shari Gruman for her assistance in the preparation of the manuscript. Dr Upshur is supported by a New Investigator Award from the Canadian Institutes of Health Research and a Research Scholar Award from the Department of Family and Community Medicine, University of Toronto.
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