Background The COVID-19 (SARS-CoV-2) is an emerging epidemic caused by the new Coronavirus. It has affected more than 200 countries, infected 5,939,234 people, and killed 367,255 in the world until 1 June 2020. While the disease epidemic could affect population mental health, this study aimed to investigate stress, anxiety, and depression during the Corona pandemic in Iran. Methods An online survey was designed using the depression, anxiety, and stress scale (DASS-21) questionnaire. The questionnaire was available for all Iranian population from 18 to 28 April 2020. Finally, 1498 participants filled the questionnaire using snowball sampling. Data were analyzed using multivariate regression models. Results Findings showed that most participants had experienced a normal level of stress (36.6%), anxiety (57.9%) and depression (47.9%). About 2.5% of respondents report an extremely severe level of stress. This amount of anxiety and depression was 6.3 and 7.9%, respectively. Regression model showed being female (CI: − 1.299; − 0.248), living with a high risk family member (CI: 0.325; 1.400), health status (CI: − 0.857; − 0.595), economic status (CI: − 0.396; − 0.141), social capital (CI: − 0.475; − 0.244), risk of disease (CI: 0.081; 0.729), and following COVID-19 news (CI: 0.111; 0.551) have a relation with stress level. Education level (CI: − 0.252; − 0.017), living with a high risk family member (CI: 0.0301; 1.160), health status (CI: − 0.682; − 0.471), social capital (CI: − 0.236; − 0.048), risk of disease (CI: 0.154; 0.674), and following COVID-19 news (CI: 0.046; 0.401) have a relation with anxiety score. Depression score was in relation with education level (CI: − 0.263; − 0.022), having a high-risk family member (CI: 0.292; 1.155), health status (CI: − 0.687; − 0.476), social capital (CI: − 0.235; − 0.048), risk of disease (CI: 0.144; 0.667), and following Covid-19 news (CI: 0.053; 0.408). Conclusions Most of the factors related to depression, anxiety, and stress are related to COVID-19, such as having a vulnerable person in the family, risk of disease, and following COVID-19 news. The findings suggest the factors that should be taken into consideration for improving population mental health during pandemics.
: Today, a new disease, called coronavirus disease 2019 (COVID-19), has affected the entire world. This disease has severely disrupted the global economy. Therefore, in this study, we aimed to investigate how people’s income was affected by the early phase of the COVID-19 pandemic in Iran. We used an online questionnaire in the Persian language, which was available from April 18 to April 28, 2020. The survey asked the participants about their job and income during the COVID-19 pandemic as compared to the pre-COVID period. The results showed that most participants (41%) experienced no change in their income during the COVID-19 outbreak. Therefore, the global economy should be supportive of people during epidemics.
Introduction: Diabetes is a public health problem which is originating an increment in the demand for health services. There is an obvious gap exists between actual clinical practice and optimal patient care, Clinical decision support systems (CDSSs) have been promoted as a promising approach that targets safe and effective diabetes management. The purpose of this article is reviewing diabetes decision support systems based on system design metrics, type and purpose of decision support systems. Materials and Methods: The literature search was performed in peer reviewed journals indexed in PubMed by keywords such as medical decision making, clinical decision support systems, Reminder systems, diabetes, interface, interaction, information to 2019. This article review the diabetes decision support systems based on system design metrics (interface, interaction, and information), type and purpose of decision support system. Results: 32 of the 35 articles were decision support systems that provided specific warnings, reminders, a set of physician guidelines, or other recommendations for direct action. The most important decisions of the systems were support for blood glucose control and insulin dose adjustment, as well as 13 warning and reminder articles. Of the 35 articles, there were 21 user interface items (such as simplicity, readability, font sizes and ect), 23 interaction items (such as Fit, use selection tools, facilitate ease of use and ect. ) and 31 item information items (such as Content guidance, diagnostic support and concise and ect ).Discussion: This study identified important aspects of designing decision support system, It can be applied not only to diabetic patients but also to other decision support systems.Conclusion: Most decision support systems take into account a number of design criteria; system designers can look at design aspects to improve the efficiency of these systems. Decision support system evaluation models can also be added to the factors under consideration.
Background: Health information systems have major impacts on improving the decision-making process during pandemics. Objectives: The qualitative study explored health information systems (HISs) challenges based on HIS’s manager viewpoint during the COVID-19 outbreak. Methods: In the study, data were collected in two phases: First, in response to an official letter, all participants reported their challenges with HISs. Second, semi-structured interviews were conducted with the same participants. Results: The semantic units were extracted, collected, and coded. Eighteen HIS managers from 18 hospitals reported the challenges of HISs. The 46 HIS challenges and 24 solutions were classified into three categories: Technology, organization, and human. The technology category included three sub-categories: Multiple non-interoperable HISs, inconsistencies between reports of HISs, and lack of suitable hardware equipment. The organizational category encompassed three sub-categories: Data quality, environment and equipment contamination by COVID-19 virus, and lack of sufficient preparedness to respond to the pandemic condition in hospitals. The human category had four subsets: multiple resources of stress, instability in business conditions, and shortage of human resources, and increased workload. Conclusions: It seems that reconstruction of health information systems, revision of medical record documentation processes, holding training courses, and planning to deal with pandemics, human resource support programs are very necessary.
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