Seasonal Variation in Ischemic Stroke Hospitalization: Results From a Large Health System in Six Western States of the United States

Abstract

Background: Evidence of seasonal variations in the number of stroke admission is inconsistent with some studies reporting no association and some a significant rise in different months of the year. In addition, less is known about how seasonality impacts the admission according to stroke subtype. Methods: This was a cross-sectional, observational study of data from a hospital-based registry (n = 40 hospitals) affiliated with Providence Health and Services in Alaska, California, Montana, Oregon, Texas, and Washington state. We included all cases with acute ischemic stroke admitted from March 1, 2017, to February 29, 2020. Admission data were categorized according to four meteorological seasons: winter, spring, summer, and fall. Acute ischemic stroke was categorized into two sub-types as large vessel occlusion (LVO) or non-LVO. We calculated the aggregate number of individuals admitted with stroke by season. Using linear regression models with generalized estimating equations (GEEs), we assessed the relationship between meteorological season and daily hospitalization number. We used R version 4.0.4 (2021-02-15) for both the descriptive and inferential analyses and the R gee pack package (version 1.2-1) to perform GEEs. Results: During the study period, we identified 18,886 patients with acute ischemic stroke (median age: 73; 48.7% women). Acute ischemic stroke was more commonly observed during winter compared with other seasons with some variations between the selected regions. Based on a GEE model, stroke hospitalization increased during winter, with an additional 3.3 cases per day in comparison with spring in the whole population (beta: 3.3, 95% confidence interval (CI): (2.4, 4.1), P < 0001). Winter is also associated with a higher number of LVO. Conclusions: The total number of ischemic stroke admissions, including cases of LVO, increased during the winter months. The results are important for human resource allocation for better management of cases with ischemic strokes. J Neurol Res. 2023;13(1):33-42 doi: https://doi.org/10.14740/jnr745