Geographical variations in stroke incidence and case-fatality have been documented worldwide. This study examines whether there are spatio-temporal variations in stroke incidence and case-fatality in Hong Kong and attempts to determine to what extent socioeconomic status (SES) and healthcare provision account for these variations. Residence-based hospital discharge data from the Hospital Authority (HA) in Hong Kong were geo-referenced and used to examine incidence rates and case-fatality rates by stroke subtype among the population aged 35 years and above in 1999-2007. Multilevel models were used to examine the spatio-temporal variations. Ischemic stroke incidence was found to decrease among those aged above 55 years, while hemorrhagic stroke incidence increased. Ischemic stroke case-fatality was found to decrease but hemorrhagic stroke case-fatality remained stable. For both subtypes, there were significant variations in stroke incidence and case-fatality across the districts of residence, but insignificant variations across HA service clusters. Only variations in ischemic stroke incidence and hemorrhagic stroke case-fatality at the district level could be partly explained by district-level SES. Identification of districts with higher risk for stroke incidence and case-fatality would help to formulate enhanced preventive measures. Future studies are needed to identify factors that contribute to the geographical variations.
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http://dx.doi.org/10.4081/gh.2011.153 | DOI Listing |
Sci Rep
December 2024
Department of Neurology, The First Affiliated Hospital of Zhengzhou University, JianShe Road 1#, Zhengzhou, 450000, China.
Previous observational studies have suggested at a potential link between migraine, particularly migraine with aura, and the susceptibility to early-onset ischemic stroke. We aimed to investigate the causal effects of genetically determined migraine and its subtypes on the risk of early-onset ischemic stroke using the two-sample Mendelian randomization method. Genetic instrumental variables associated with migraine and its subtypes were acquired from two sources with the largest sample sizes available.
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December 2024
Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, USA.
This study investigated the incidence of new-onset cardiovascular disorders up to 3.5 years post SARS-CoV-2 infection for 56,400 individuals with COVID-19 and 1,093,904 contemporary controls without COVID-19 in the Montefiore Health System (03/11/2020 to 07/01/2023). Outcomes were new incidence of major adverse cardiovascular event (MACE), arrhythmias, inflammatory heart disease, thrombosis, cerebrovascular disorders, ischemic heart disease and other cardiac disorders between 30 days and (up to) 3.
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December 2024
Bao Feng Key Laboratory of Genetics and Metabolism, Beijing, China.
Many lipid biomarkers of stroke have been identified, but the lipid metabolism in elderly patients with leukoaraiosis remains poorly understood. This study aims to explore lipid metabolic processes in stroke among leukoaraiosis patients, which could provide valuable insights for guiding future antithrombotic therapy. In a cohort of 215 individuals undergoing MRI, 13 stroke patients were matched with controls, and 48 stroke patients with leukoaraiosis were matched with 40 leukoaraiosis patients.
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December 2024
Neurology Department of Affiliated Hospital of Jiangsu University, No. 438 Jiefang Road, Zhenjiang, Jiangsu, China.
This study aims to compare the incidences of neurological deterioration (ND) and poor outcome (a modified Ranking scale > 2 points at discharge) among patients with different atherosclerotic stroke patterns. A total of 688 participants were categorized into 4 groups according to atherosclerotic stroke pattern: multiple small infarcts (MSI), single subcortical infarction (SSI), borderzone infarct (BZI) and large infarct groups. Among the 4 groups, MSI group had the lowest incidences of ND and poor outcome (13.
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December 2024
Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran.
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.
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