Search

Background: Cancer patients are more exposed to opportunistic infections, such as COVID-19, due to their poor health status. The aim of this study was to identify the clinical characteristics of cancer and non-cancer patients with COVID-19 that may lead to death, intubation, and ICU admission. Materials and Methods: A Multicenter Cross-Sectional study was conducted on confirmed COVID-19 adult patients with and without a history of cancer from March 2019 to March 2021. Demographic and clinical features, ICU admission, intubation, and discharge status have been extracted from patients' medical records. Chi-square, odds ratio, Mann-Whitney test, and logistic regression were used to analyze the data. Results: The death rate in 1332 cancer patients was 28% compared to the 91464 noncancer patients which was 9% with an odds ratio of 3.94 and p<0.001. ICU admission rates among the cancer group were 43%, while in the noncancer group, it was 17.9% (p<0.001). Moreover, intubation was done for 20.9% of cancer patients and 7.4% of non-cancer patients (p<0.001). However, no significant difference was observed between the two groups in terms of length of stay in the hospital. Multivariable logistic regression analysis showed that age, level of consciousness, SPO2, and autoimmune disorders were associated with mortality in cancer patients with COVID-19. Conclusion: This study showed that older age, loss of consciousness, low oxygen saturation, and suffering from autoimmune disorders were the predictors of death in cancer patients with COVID-19. These results can have important implications for the management and care of cancer patients with COVID-19. [ABSTRACT FROM AUTHOR]

Background: Depression and anxiety are among the most critically recognized psychological complication of the COVID‐19 pandemic. Aim: This study aimed to examine the two predictors among COVID‐19 survivors in Ahvaz, Iran, in 2022. Methodology: Using a descriptive‐analytical design, 347 COVID‐19 survivors referred to hospitals in Ahvaz were meticulously examined. The database of the COVID‐19 registration system contained primary information about the samples. For data collection, questionnaires, including the Beck Anxiety and Depression Inventory and a demographic questionnaire, were utilized. Results: The results revealed a significant relationship between anxiety score and family size (p = 0.019), education level (p < 0.001), occupation (p = 0.015), household income status (p = 0.017), disease history (p = 0.017), ethnicity (p < 0.001), disease severity (p < 0.001), and quarantine period (p < 0.001). Furthermore, a significant correlation was observed between the average depression score and gender (p = 0.023), number of family households (p = 0.009), level of education (p < 0.001), occupation (p < 0.001), household income (p < 0.001), disease severity (p < 0.001), disease history (p < 0.001), and quarantine period (p < 0.001). Moreover, linear regression analysis indicated only a significant relationship between depression scores and the quarantine period variable (p < 0.001), among others. The simultaneous examination of all variables on depression disorder highlighted a meaningful relationship between depression score and disease history (p = 0.013), occupation (p = 0.002), household income status (p = 0.002), and family size (p = 0.039). Conclusions: This study revealed a significant relationship between certain demographic variables, such as quarantine period, disease history, employment status, household income status, and the number of family households, and an increase in the average depression and anxiety score. [ABSTRACT FROM AUTHOR]

Background and Aims: Spinal canal stenosis is one of the most common vertebral column diseases, which can lead to disability. Developing a registry system can help in research on the prevention and effective treatment of it. This study designs a minimum data set (MDS) as the first step in creating a registry system for spinal canal stenosis. Method: The present research is of applied‐descriptive type, performed in 2022. First, the applicable data elements about the disease were selected from a vast range of English and Farsi references, including peer reviewed articles, academic books, credible websites, and medical records of hospitalized patients. Through the extracted data, the primary MDS plan was designed as a questionnaire. The validity of the questionnaire was conducted via asking the opinion of experts (neurosurgeons, physiotherapists, epidemiologists, and health information management specialists). Also, its reliability was calculated via Cronbach ⍺ coefficient, which was 86%. Finally, the MDS of the spinal canal stenosis national registry system (for Iran) was confirmed through a two stage Delphi technique. Data analysis was applied through descriptive statistics via SPSS21 software. Results: The proposed MDS is offered in two general sets of data: administrative and clinical. For the administrative data set, 40 data elements had been proposed, as five classes. Twenty‐six of them were confirmed. In the clinical section, 95 data elements had been proposed in 14 classes; 94 of which were finally confirmed. Conclusion: Since there is no spinal canal stenosis MDS available, this study can be a turning point in the standardization of the data on this disease. Moreover, these precise, coherent, and standard data elements can be contributed to improving disease management and enhancing the public healthcare quality. Also, the MDS proposed in this study can help researchers and experts, design a spinal canal stenosis registry system in other countries. [ABSTRACT FROM AUTHOR]

Background: Perception of the threatening disease leads to coping behaviors that can affect the treatment process. Social support can be one of the factors influencing the perception of the disease and coping strategies. Our study aimed to determine the perception of the disease, its relationship with coping strategies and social support in COVID-19 patients in Iran. Methods: This cross-sectional study was conducted on 1014 patients who were hospitalized during October 2020 to May 2021 through multi-stage sampling method. The data-gathering instruments consisted a demographic information checklist, and standard questionnaires including disease perception, social support, and coping strategies. Correlation coefficient, multiple linear regression model, and simple linear regression model were used for data analysis. Results: The mean age of the participants was 40.87 ± 12.42 and the majority of them were female (67.2%(, married (60.1%), and had relatives who had COVID-19 (82.6%). There was a significant inverse relationship between variables (identity, outcomes, emotional expressions etc.) and social support (> 0.01). Also there was a significant direct relationship between variables (self-control, therapeutic susceptibility etc.) and the coping behavior (p < 0.05). There was an inverse relationship between the variables (outcomes, self-blaming, sex, etc.) (P = 0.0001) and a direct one between the variables (education, disease phase, etc.) and perceived social support (P = 0.004). Conclusions: These results show the importance of promoting positive coping strategies and social support in the face of large-scale health crises. The knowledge of nurses about the results of this study, who are responsible for the care and education of the patient, can be effective in the length of hospitalization and reducing costs. [ABSTRACT FROM AUTHOR]

Copyright of Acta Facultatis Medicae Naissensis is the property of Nis University, Faculty of Medicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The importance of successful implementation of e-learning, especially since the emergence of the Covid-19 pandemic, has become increasingly apparent to universities. Thus, identifying the effective factors in adopting e-learning in the Covid-19 pandemic is crucial. This study was conducted to identify determining factors in adopting E-learning in healthcare. Method: This was a descriptive-analytical study in which 143 faculty members from Iran were randomly selected. The faculty members' intentions, concerning the adoption of e-learning, were assessed by the conceptual path model of integration of unified theory of acceptance and use of technology (UTAUT) and The Task-Technology Fit (TTF). Results: The results showed that the combination of the two classical theories, UTAUT and TTF, was an appropriate model to explain faculty members' intention in adopting e-learning. Moreover, the findings showed that technology and task characteristics, task- technology fit, social influences, effort expectancy, performance expectancy and facilitating conditions had direct and significant effect on e-learning adoption. Conclusion: By presenting a conceptual path model to elucidate users' behavior in adopting e-learning, this study investigated and identified the key determining factors in adopting e-learning. The findings of the present study can contribute to the design and implementation of e-learning by practitioners, policy makers, and curriculum designers. [ABSTRACT FROM AUTHOR]

In recent years, COVD-19 has made millions of death worldwide. When reviewing the death rate, we encountered a very notable difference in death rate of Iran and Iraq, which are two neighboring countries. Investigating the COVID-19 risk factors, parameters, such as ethnicity and vaccination, do not appear not to be affecting our observation. We also could not find important differences in mortality rate being under-reported in the two countries. In this letter, we tried to discuss the possible effect of Iraq pre-COVID-19 mass gatherings on the death rate. The authors would like to highlight the effect of immune system on COVID-19. [ABSTRACT FROM AUTHOR]

Background and Aim: Recently, coronavirus has become a major cause of death and hospital admission worldwide. This study was aimed to assess the factors associated with the presentation via ambulance and time to in-hospital death or discharge from the hospital using a multilevel joint modeling approach. Materials and Methods: In this historical cohort study, hospitalized patients with COVID-19 were included from 34 medical centers in Khuzestan province, Iran, from February 18th, 2020, to January 5th, 2021. Joint model analysis was used to assess the impact of demographic and clinical characteristics on the mode of hospital presentation and time to death/discharge from hospitals in Khuzestan province, Iran. Results: Among 22,356 patients, 14.2% presented to the hospital via ambulance, and 11.2% died in the hospital. The odds of ambulance use was higher in patients with older age, male sex, comorbidities including respiratory disease, diabetes, cancer, and drug abuse, and symptoms such as respiratory distress and loss of consciousness. Older age, male sex, a higher burden of comorbidities, symptoms of chest pain, respiratory distress, and loss of consciousness, and admission to intensive care unit were predictors of in-hospital mortality. The median survival time was longer for patients with COVID-19 who selfpresented to the hospital compared to those who presented with ambulance (31 vs 20 days; log-rank P<0.001). Conclusion: Several demographic and clinical factors were found to predict the EMS utilization and in-hospital mortality in patients hospitalized with COVID-19 and can be used for risk-stratification. Controlling for the predictors of ambulance use in COVID-19 infection may help improve patient outcomes. [ABSTRACT FROM AUTHOR]

Background: From the beginning of the COVID-19 pandemic, the development of infrastructures to record, collect and report COVID-19‏ ‏data has become a fundamental necessity in the world. The disease registry system can help build an infrastructure to collect data systematically. The study aimed to design a minimum data set for the COVID-19 registry system.Methods: A qualitative study to design an MDS for the COVID-19 registry system was performed in five phases at Ahvaz University of Medical Sciences in Khuzestan Province in southwestern Iran, 2020-2021. In the first phase, assessing the information requirements was performed for the COVID-19 registry system. Data elements were identified in the second phase. In the third phase, the MDS was selected, and in the four phases, the COVID-19 registry system was implemented as a pilot study to test the MDS. Finally, based on the experiences gained from the COVID-19 registry system implementation, the MDS were evaluated, and corrections were made.Results: MDS of the COVID-19 registry system contains eight top groups including administrative (34 data elements), disease exposure (61 data elements), medical history and physical examination (138 data elements), findings of clinical diagnostic tests (101 data elements), disease progress and outcome of treatment (55 data elements), medical diagnosis and cause of death (12 data elements), follow-up (14 data elements), and COVID-19 vaccination (19 data elements) data, respectively.Conclusion: Creating a standard and comprehensive MDS can help to design any national data dictionary for COVID-19 and improve the quality of COVID-19 data. [ABSTRACT FROM AUTHOR]

Natural gas is a green fossil fuel with the highest demand growth. Natural gas industry is composed of different sectors that form a complex and large supply chain. These sectors make their own decisions individually, resulting in implementation of non-optimal decisions. The aim of this study is to design and optimize an integrated natural gas supply chain (NGSC) formulated as a mixed integer linear programming (MILP) model. The model minimizes total cost and optimizes gas flow between supply chain nodes through pipelines, location-allocation of facilities and pipeline routes along with their capacities, number of pipelines, capacity expansion, extraction, production, storage, exports and imports. The proposed model is applied to a real world case study based on information derived from Iran's NGSC. Finally, the change effect of the most important parameters on the optimal solution is investigated and three actual scenarios are analyzed. The results indicate that the parameters of operating costs and demand volume have a significant effect on the optimal solution and the largest share of supply chain costs is related to operating expenditures. Moreover, the expansion of joint fields and zero import capacity will lead to a remarkable increase in total supply chain costs. • A novel optimization framework for natural gas supply chain design is proposed.. • The integrated strategic and tactical planning of gas supply chain is performed. • Location-allocation and capacity expansion of facilities and pipeline routes are considered. • Gas storage process in the supply chain is carried out using underground reservoirs. [ABSTRACT FROM AUTHOR]

Background: Providing immediate assistance to people in medical emergencies and natural disasters are one of the biggest rescue challenges. The widespread use of smartphones and their capabilities has created the opportunity to use their capabilities to assist in the rescue. Many countries have developed apps to help rescue. Objective: This study aimed to identify Android-based rescue Mobile applications in Iran, also study their structure and content. Material and Methods: This was a cross-sectional study. The population of the study consisted of all Android apps related to the rescue in Iran. First, by searching Google, Google Play (Google App Store), Bazaar (Persian app store) and others Persian apps stores all related rescue applications were identified and the applications downloaded, installed and evaluated. The application evaluation tool was a researcher-made checklist. The collected data were analyzed using descriptive statistics in Microsoft Excel 2019 software. Results: In total, 65 Iranian rescue apps for Android were identified. The apps fell into three general categories, including first aid training apps, disaster response training such as earthquakes, and emergency rescue requests. The automatic location detection of the person and the possibility of asking for help in emergencies was available in only three apps. The use of most rescue apps was completely free (84%). Very few of these apps were developed by rescue organizations such as the Red Crescent or the fire department (17%). Also, a review of app stores showed that most apps had less than 500 active downloads (86%). Conclusion: The ability to detect the geographical location of the injured person, automatic calls to emergency centers or family, notification of the latest news in natural disasters, etc. are the most important features of a rescue app that did not exist in most rescue Mobile applications in Iran. Therefore, it is recommended that the Iranian Crisis Management Organization, in cooperation with other organizations involved in rescue, design a national rescue app. [ABSTRACT FROM AUTHOR]

• A hybrid game theory and system dynamics model of electricity supply is proposed. • Optimal electricity generation mix is determined in different games. • The share of green energy generation will reach about 28% by 2040 in Nash model. • Water and gas use in Stackelberg-cooperation model decrease by 18.4 and 16.5%. One criteria for the success in achieving sustainable development is to supply electricity by considering the diversity of generation technology. Also, the consumption of fossil fuels is considered as a major challenge for sustainable development. Relying heavily on fossil resources for electricity generation and the growing trend in emissions is one of the serious problems of developing countries. In this research, the sustainable growth of Iran's electricity industry in the short and long term time horizons is investigated using three game theory models in a system dynamic model. The competition, cooperation and profits of electricity suppliers as well as the government's role are modeled. Carbon dioxide emissions, water and natural gas consumption are also calculated. The results show the amount of carbon dioxide emissions, water and gas consumption in the Stackelberg and cooperation model decrease by 19.3%, 18.4% and 16.5%, respectively, compared to the continuation of the current trend. [ABSTRACT FROM AUTHOR]

Health information management is a vital and constructive component of the health system, refers to the process of producing and collecting, organising and storing, analysing, disseminating and using information. The aim of this study was to evaluate the strengths and weaknesses of the information management system in epidemic infectious diseases in Iran, specifically focusing on the registration, reporting, quality, confidentiality, and security of infectious disease data. This assessment was conducted from the perspective of policymakers and experts responsible for data registration and reporting. After examining the processes of registering and reporting infectious disease data and interviewing experts, a researcher-designed questionnaire was prepared to evaluate the infectious disease information management system. To assess the content validity of the Content Validity Index and Content Validity Ratio Index, a questionnaire was utilized. The reliability of the questionnaire was confirmed using Cronbach's alpha. By employing purposeful sampling and adhering to the inclusion criteria, 150 participants were included in the study. Questionnaires were distributed via email, WhatsApp, or Telegram to employees at various levels of Iran's health and treatment systems who were responsible for registering and reporting infectious disease data. The study encompassed 100 participants who successfully concluded the research. The results highlight that the key strength of healthcare data registration lies in its ability to "depict the epidemic curve during outbreaks of infectious diseases." Conversely, a notable weakness was the "insufficient collaboration from non-academic sectors (e.g., clinics, private laboratories) in registering and reporting infectious diseases. The present study's findings suggest that the issue lies not in the framework itself, but rather in the execution and functionality of the strategies. We can cultivate a repository of reliable and beneficial data by incorporating initiatives like training programs, enforcing regulations with consequences for inadequate data documentation, offering both material and motivational rewards, and streamlining all data collection and reporting systems., (© 2024. The Author(s).)

Background: Standardized data collection supports disease information management and leads to better quality of care. The Islamic Republic of Iran lacks a standard data set for data collection in hospitals., Aims: The aim of this study was to design a minimum data set for hospital information systems in the Islamic Republic of Iran., Methods: This study was conducted in 2015. Data sets of other countries, hospital records, hospital information systems and electronic health record systems in the Islamic Republic of Iran were reviewed for data elements for the minimum data set. Data elements were collected using a data extraction form and were categorized into similar classes, which were divided into administrative and clinical sections. The list of data elements was reviewed by experts in technical offices of the Iranian Ministry of Health and Medical Education, and a minimum data set was drawn up., Results: There were nine and 18 data classes in the administrative and clinical sections with a total of 166 and 684 data elements respectively. After review by the expert panel, 159 administrative and 621 clinical data elements were retained as the minimum data set for the Iranian hospital information system., Conclusion: Our dataset can be used by the Iranian health ministry, hospital information system companies and health surveillance centres for more efficient management of health data., (Copyright © World Health Organization (WHO) 2020. Open Access. Some rights reserved. This work is available under the CC BY-NC-SA 3.0 IGO license (https://creativecommons.org/licenses/by-nc-sa/3.0/igo).)

Background: Electronic Medical Record (EMR) offers remarkable facilities such as reducing medical errors, decreasing healthcare costs and promoting quality of healthcare services by collecting, storing and displaying information at the point of care., Aims: This study was carried out to identify the determinants of electronic medical record (EMR) adoption by presenting a comprehensive model., Methods: This was a cross-sectional study in which 330 healthcare personnel working in hospitals affiliated to Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran, were selected as the study sample. A proposed conceptual path model of technology, organization and environment (TOE), and technology acceptance model (TAM) was developed to identify the determinants of EMR adoption. The model was authorized by structural equation modeling (SEM) and represented by Analysis of Moment Structures (AMOS)., Results: The results of the study showed that the integrated model of TOE-TAM explained 68 percent (R 2 = 0.68) of the variance of EMR adoption. The findings also evidenced that perceived ease of use, perceived usefulness, technological context, organization context and environmental context have significant effect on EMR adoption., Conclusions: The study findings suggest that the proposed conceptual integrated model of TOE-TAM is a favourable model for identifying the relevant factors of EMR adoption. The present study clearly recognized nine significant determinants that affect end-users' intention when comprehensive implementation of ERMs is considered., (Copyright © World Health Organization (WHO) 2019. Some rights reserved. This work is available under the CC BY-NC-SA 3.0 IGO license (https://creativecommons.org/licenses/by-nc-sa/3.0/igo).)