This study was designed and implemented to analyze and establish documents related to the above cases in the first to third COVID-19 epidemic waves for the use of researchers and doctors during and after the epidemic. The current case series study was conducted on 24,563 thousand hospitalized COVID-19 patients by examining their clinical characteristics within a one-year period from the beginning of the pandemic on 02.22.2020 to 02.14.2021, which included the first to the third waves, based on gender and severity of COVID-19. The mean age of the participants was 56 ± 20.71, and 51.8% were male. Out of a total of 24,563 thousand hospitalized COVID-19 patients until February 2021, there were 2185 mortalities (9.8%) and 2559 cases of severe COVID-19 (13.1%). The median length of hospitalization from the time of admission to discharge or death in the hospital (IQR: 13–41) was estimated to be 21 days. The rate of hospital mortality was higher in severe (37.8%) than in non-severe (4.8%) cases of COVID-19, While the risk of severe cases increased significantly in the third (HR = 1.65, 95% CI: 1.46–1.87, P < 0.001) and early fourth waves (HR = 2.145, 95% CI: 1.7–2.71, P < 0.001). Also, the risk of contracting severe COVID-19 increased significantly in patients aged ≥ 65 years old (HR = 2.1, 95% CI 1.1.93–2.72, P < 0.001). As shown by the results, the rates of hospital mortality (9.3% vs. 8.5%) and severe cases of COVID-19 (13.6% vs. 12.5%) were higher among men than women (P < 0.01). In our study, the mortality rate and severity of COVID-19 were within the scope of global studies. Men experienced higher severity and mortality than women. The was a significantly higher prevalence of old age and underlying diseases in individuals with severe COVID-19. Our data also showed that patients with a previous history of COVID-19 had a more severe experience of COVID-19, while most of these patients were also significantly older and had an underlying disease.
Background: The population attributable risk (PAR) percent has used widely in public health policy. We aimed to calculate the attribute risk of hypertension due to hyperuricemia by Levin's formulas compare to direct PAR calculation method.
Methods: This was a sub-study of Yazd Healthy Heart Cohort (YHHC). Overall, 1256 normotensive individuals were enrolled through multistage randomized cluster sampling and followed up for mean 9.8 years, from 2005-2015. The threshold cutoff point of the hyperuricemia was considered equal and more than 75th percentile that equal to 5.5 mg/dl for men and 4.3mg/dl for women. To calculate the attributable risk of hyperuricemia in developing hypertension, two methods were applied. Levin's formulas and direct PAR estimation by population risk calculation via exposure prevalence weighted formula. Multiple logistic regression was used for estimate of odds ratio (OR) of hyperuricemia in developing hypertension. We calculated Relative Risk (RR) from OR. The data were analyzed using SPSS software version 16. A significant level of 0.05 was considered.
Results: Hypertension developed in 44.7% of individuals with uric acid level ≥ 75th percentile vs. 35.6% of other individuals (P=0.024). Attributable risk (AR) of hyperuricemia in hypertension incidence was 9.1%. PAR of hyperuricemia for hypertension incidence by using two methods mentioned before was 6%, 5.8% respectively.
Conclusion: The results of the study confirmed the noticeable contribution of hyperuricemia as an independent other risk factor for the occurrence of hypertension. PAR of hyperuricemia for hypertension incidence by using two methods almost near was 6%, 5.8% respectively.
Background:Job stress is considered one of the common disorders in societies. There are several factors involved in creating job stress. Shift work can be considered one of these factors which affects sleep quality among the staff. This study was conducted to investigate the effect of job stress on sleep quality of the healthcare personnel of the educational hospitals affiliated with Shahid Sadoughi University of Medical Sciences. Methods:This was a descriptive-analytical study conducted in 2012-2011. All the health care providers working in Shahid Sadoghi University of Medical Sciences were included in the study by Census method. Data collection tools consisted of the demographic information, HSE job stress questionnaires and Pittsburgh sleep Quality Index (PSQI). Descriptive statistics, Chi-square and Kruskal-Wallis tests, Pearson correlation, and SPSS software version 21 were used for data analysis. The significance level of the test was 5%. Results: The results suggested that there was no significant relationship between job stress and shift work (p = 0.08), but there was a positive and significant relationship between the job stress score and sleep quality (r=0.17 p=0.001). Conclusion:The results of this study showed that the employees' sleep quality affect their job stress, reduce their productivity and cause some problems for them; therefore, managers must heed the results of this research and schedule the shifts in such a way as to reduce job stress. They can also hold training sessions and workshops to regulate sleep patterns and manage stress in due course of time.
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