Demographic factors have been reported to worsen COVID-19 outcomes. However, there is limited evidence about the different effects of sex and age on COVID-19 death in East Jakarta, Indonesia. This study examined the association between sex and age with COVID-19 mortality. Using COVID- 19 surveillance data of East Jakarta from March 2020 to December 2021, we calculated COVID-19 mortality and examined the risk of COVID-19 death by sex and age. The risk of COVID-19 death associated with sex and age was examined by using Multiple Logistic Regression. A total of 202.412 cases were analyzed and 1.9% of them died. The elderly had a 41.88-folds increased risk of COVID-19 mortality than younger patients (<45 years) (aOR 41.88; 95% CI 37.49-46.77; p-value <0.0001). Male had a higher risk of COVID-19 death compared to female (aOR 1.27; 95% CI 1.19-1.35; pvalue <0.0001). Age and sex had a significant association with COVID-19 mortality. Adequate management of COVID-19 cases, particularly in the elderly and male patients, may reduce the severity of COVID-19 or even mortality.
In this study, two CR receptors (Agfa CR-85X and Agfa CR-10X) and two DDR receptors (Brivo DR-F and Essenta) were characterized in terms of detector sensitivity and image quality. An in-house phantom was specially designed to accommodate image quality assessment for quick QC and is tested as a purpose of this study. The reference quantitative aspect that were used to characterize the imaging units are the correlation between Pixel Value and image receptor dose, whereas evaluation of image quality based on the modules on the in-house phantom uses new proposed metrics (coefficient of linearity, CL, and coefficient of variance, CV). Each system’s unique Pixel Value-dose characteristics are also expressed in the results of the in-house phantom’s metrics (i.e. CL and CV). In addition, the constancy of measured modulation transfer function suggests potential use for system quality judgment. As all characterizations (Pixel Value-dose characteristics, MTF, and new metrics) indicated on unique result for each systems, the study implies that the designed in-house phantom as well as its proposed metric can be promising to be used to characterize digital radiography systems during quick QC.
The IMRT planning technique applies the concept of irradiation, which is controlled automatically by a computer. An IMRT plan is aligned with a trial-and-error approach and still involves non-intuitive, iterative steps based on the planner’s subjective decision. The Neural Network method is used in radiotherapy planning in determining IMRT plans in lung cancer cases. This method is used to predict dose distribution based on previous planning data. The purpose of using this neural network method is to predict the dose distribution in the PTV volume with validation in the previous plan, also predicting the dose distribution for doses that cover 95% of the target volume. So, this can make it easier for a planner to make decisions objectively. The obtained results show that the quality of planning produced based on neural network modelling has a homogeneity index (HI) of 0,09 ± 0,02, and the conformity index (CI) of 1,2 ± 0,27 with an average dose 1,02 ± 0,01 was the mean received at the target organ. The maximum dose to the at-risk right lung organ is 0,82 ± 0,22 Gy, the left lung is 0,75 ± 0,29 Gy, the heart is 0,77 ± 0,14 Gy, and the spinal cord is 0,50 ± 0,14 Gy.
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