Back

Deciphering changes in the incidence of hemorrhagic stroke and cerebral venous sinus thrombosis during the coronavirus disease 2019 pandemic: a nationwide time-series correlation study

Cho, S.; Kim, D. K.; Song, M. C.; Lee, E.; Park, S.; Chung, D.; Ha, J.

2024-03-26 neurology
10.1101/2024.03.17.24304431 medRxiv
Show abstract

IntroductionHemorrhagic stroke and cerebral venous sinus thrombosis (CVST) are associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and vaccination. We aimed to investigate changes in the incidence of hemorrhagic stroke and CVST in South Korea before and during the coronavirus disease 2019 pandemic and the factors associated with these changes. Materials and MethodsWe conducted a nationwide time-series study using population-based databases between 2007 and 2022. The real-world and forecasted incidences of acute non-traumatic subarachnoid hemorrhage (SAH), intracerebral hemorrhage (ICH), and CVST during the pandemic period (2020-2022) were estimated and compared with the pre-pandemic period (2007-2019). The prevalence of conventional risk factors was measured using time-series data. Finally, a time-series correlation analysis was performed to examine the temporal association between conventional risk factors, SARS-CoV-2 infection, and SARS-CoV-2 vaccination. ResultsThe incidence of hemorrhagic stroke (SAH and ICH) was lower during the pandemic than during the pre-pandemic period. This observed decrease was associated with a reduction in the prevalence of conventional risk factors but not with SARS-CoV-2 infection or vaccination. The incidence of CVST was higher during the pandemic than during the pre-pandemic period, which may be temporally related to SARS-CoV-2 vaccination (Pearson correlation coefficient [r] = 0.349, P = 0.031). ConclusionWe report reassuring evidence of hemorrhagic stroke associated with SARS-CoV-2 infection and vaccination. However, awareness of CVST may be required for future vaccine rollouts and SARS-CoV-2 outbreaks.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.