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Although neurocognitive dysfunction and physical performance are known to be impaired in patients with schizophrenia, evidence regarding the relationship between these two domains remains insufficient. Thus, we aimed to investigate the relationship between various physical performance domains and cognitive domains in individuals with schizophrenia, while considering other disorder-related clinical symptoms.
Sixty patients with schizophrenia participated in the study. Cardiorespiratory fitness and functional mobility were evaluated using the step test and supine-to-standing (STS) test, respectively. Executive function and working memory were assessed using the Stroop task and Sternberg working memory (SWM) task, respectively. Clinical symptoms were evaluated using the Brief Psychiatric Rating Scale, Beck Depression Inventory, and State-Trait Anxiety Inventory. Multivariate analyses were performed to adjust for relevant covariates and identify predictive factors associated with neurocognition.
Multiple regression analysis revealed that the step test index was most strongly associated with reaction time in the Stroop task (β = 0.434, p = 0.001) and SWM task (β = 0.331, p = 0.026), while STS test time was most strongly associated with accuracy on the Stoop task (β=−0.418, p = 0.001) and SWM task (β=−0.383, p = 0.007). Total cholesterol levels were positively associated with Stroop task accuracy (β=−0.307, p = 0.018) after controlling for other clinical correlates. However, clinical symptoms were not associated with any variables in Stroop or SWM task.
The present findings demonstrate the relationship between physical performance and neurocognition in patients with schizophrenia. Considering that these factors are modifiable, exercise intervention may help to improve cognitive symptoms in patients with schizophrenia, thereby leading to improvements in function and prognosis.
Major incidents affecting large numbers of people may increase the rate of acute cardiovascular events, even among those who are not directly involved in the incident. It is hypothesized that the MV Sewol ferry disaster (South Korea) would increase the incidence of cardiovascular events nation-wide.
Data on all adult patients (>18 years) who were diagnosed with acute cardiovascular events, including acute myocardial infarction (MI), angina, and cardiac arrhythmias, were extracted from the National Emergency Department Information System (NEDIS) from March 15 through June 17, during the years 2011-2014 (four weeks before to eight weeks after the event date). Poisson regression models were used to calculate the incidence rate ratios (IRRs) comparing the weekly changes in the occurrences of cardiovascular events from the week of the Sewol event (April 16-22, 2014) to eight weeks after the disaster (June 11-17, 2014), using the one-month period before Sewol as a reference period (March 15-April 15), adjusting for calendar years (years 2011-2014) and environmental factors.
During the study periods, cardiovascular events were identified in 73,823 patients. Compared to the reference period, the week of the Sewol disaster and the three weeks after the disaster showed a significant increase in the number of acute cardiovascular events, IRRs of 1.09 (95% CI, 1.03-1.15) and 1.08 (95% CI, 1.02-1.15), respectively (P <.01 for both). In particular, there was 21% increase in incidence of arrhythmia (IRR = 1.21; 95% CI, 1.02-1.44; P = .03) during the week of the Sewol disaster compared with the reference period.
This study showed a significant increase in the incidence of acute cardiovascular events during the week of, and the three weeks after, the Sewol ferry disaster in 2014. These additional cardiac emergencies may be triggered by emotional stressors related to the event, highlighting the public health importance of indirect exposure to a tragic catastrophe.
Kong SY, Song KJ, Shin SD, Ro YS. Cardiovascular events after the Sewol ferry disaster, South Korea. Prehosp Disaster Med. 2019;34(2):142–148
We investigated the extent of delays in the response time of emergency medical services (EMS) as an impact of mass casualty incidences (MCIs) in the same area.
We defined an MCI case as an event that resulted in 6 or more patients being transported by EMS, and prehospital response time as the time from the call to arrival at the scene. We matched patients before and after MCIs by dividing them into categories of 3 hours before, 0-1 hour after, 1-2 hours after, and 2-3 hours after the MCIs. We compared prehospital response times using multiple linear regression.
A total of 33,276 EMS-treated patients were matched. The prehospital response time for the category of 3 hours before the MCIs was 8.8 minutes (SD: 8.2), treated as the reference, whereas that for the category of 0-1 hour after the MCI was 11.3 minutes (P<0.01). The multiple linear regression analysis revealed that prehospital response time increased by 2.5 minutes (95% CI: 2.3-2.8) during the first hour and by 0.3 minutes (95% CI: 0.1-0.6) during the second hour after MCIs.
There were significant delays in the prehospital response time for emergency patients after MCIs, and it lasted for 2 hours as the spillover effect. (Disaster Med Public Health Preparedness. 2018;12:94–100)