Seasonal Variation in Neurologic Hospitalizations in the United States

Objective: Certain neurologic diseases have been noted to vary by season, and this is important for understanding disease mechanisms and risk factors, but seasonality has not been systematically examined across the spectrum of neurologic disease, and methodologic guidance is also lacking. Methods: Using nationally representative data from the National Inpatient Sample, a stratified 20% sample of all non-federal acute care hospitalizations in the United States, we calculated the monthly rate of hospitalization for 14 neurologic diseases from 2016 to 2018. For each disease, we assessed seasonality of hospitalization using chi-squared, Edward, and Walter-Elwood tests and seasonal time series regression models. Statistical tests were adjusted for multiple hypothesis testing using Bonferroni correction. Results: Meningitis, encephalitis, ischemic stroke, intracerebral hemorrhage, Guillain-Barre syndrome, and multiple sclerosis had statistically significant seasonality according to multiple methods of testing. Subarachnoid hemorrhage, status epilepticus, myasthenia gravis, and epilepsy had significant seasonality according to Edwards and Walter-Elwood tests but not chi-square tests. Seasonal time series regression illustrated seasonal variation in all 14 diseases of interest, but statistical testing for seasonality within these models using the Kruskal-Wallis test only achieved statistical significance for meningitis. Interpretation: Seasonal variation is present across the spectrum of acute neurologic disease, including some conditions for which seasonality has not previously been described, and can be examined using multiple different methods.

Automated summary

A study using data from the National Inpatient Sample in the United States found that certain neurologic diseases exhibit seasonal variation in hospitalization rates. Six diseases showed statistically significant seasonality according to multiple testing methods, while four diseases showed seasonality according to some testing methods. This study highlights the importance of considering seasonality in understanding neurologic diseases.