Modeling gradually changing seasonal variation in count data using state space models: a cohort study of hospitalization rates of stroke in atrial fibrillation patients in Denmark from 1977 to 2011

Abstract

Abstract Background Seasonal 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. Methods Using 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. Results The 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. Conclusion The 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.