Abstract
BackgroundSeasonal variation in the occurrence of cardiovascular diseases has been recognized for decades. In particular, incidence rates of hospitalization with atrial fibrillation (AF) and stroke have shown to exhibit a seasonal variation. Stroke in AF patients is common and often severe. Obtaining a description of a possible seasonal variation in the occurrence of stroke in AF patients is crucial in clarifying risk factors for developing stroke and initiating prophylaxis treatment.MethodsUsing a dynamic generalized linear model we were able to model gradually changing seasonal variation in hospitalization rates of stroke in AF patients from 1977 to 2011. The study population consisted of all Danes registered with a diagnosis of AF comprising 270,017 subjects. During follow-up, 39,632 subjects were hospitalized with stroke. Incidence rates of stroke in AF patients were analyzed assuming the seasonal variation being a sum of two sinusoids and a local linear trend.ResultsThe results showed that the peak-to-trough ratio decreased from 1.25 to 1.16 during the study period, and that the times of year for peak and trough changed slightly.ConclusionThe present study indicates that using dynamic generalized linear models provides a flexible modeling approach for studying changes in seasonal variation of stroke in AF patients and yields plausible results.
Highlights
Seasonal variation in the occurrence of cardiovascular diseases has been recognized for decades
Underlying risk factors may change over time, affecting the seasonal variation, and it is desirable to be able to model gradual changes in covariates over time in time series [11,12]
This paper demonstrates an alternative statistical model to investigate seasonal variation of incidence rates of stroke in atrial fibrillation (AF) patients in Denmark, in terms of state space modeling, including a procedure to estimate hyperparameters, and propose a specific structure in specifying the linear predictor and covariance matrix between coefficients internally and over time
Summary
Seasonal variation in the occurrence of cardiovascular diseases has been recognized for decades. A variety of statistical methods to describe seasonal variations have been employed. These methods range from χ 2 testing of difference in frequencies to linear regression. The model derived by Edwards in 1961 [1] has been employed in epidemiological studies of seasonal variation in frequencies of the occurrence of diseases [2,3,4,5]. Underlying risk factors may change over time, affecting the seasonal variation, and it is desirable to be able to model gradual changes in covariates over time in time series [11,12]
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