Anxiety during the COVID-19 pandemic in hospital staff: systematic review plus meta-analysis.

BACKGROUND: This study was conducted to systematically review the existing literature examining the prevalence of anxiety among hospital staff and identifying the contributing factors to address the complications of this disorder and develop effective programmes for reducing the complications of this mental health problem. METHODS: We searched the electronic databases including PubMed, EMBASE, Scopus, Web of Science and Google Scholar from January 2020 to February 2021. To perform meta-analysis, the random effects model was used. To assess the statistical heterogeneity of the included studies, the I2 index was used, and 95% CI was estimated. Data analysis was performed by R software. RESULTS: In the final analysis, 46 articles with the total sample size of 61 551 hospital staff members were included. Accordingly, anxiety prevalence among healthcare workers (HCWs) was 26.1% (95% CI 19% to 34.6%). The prevalence rates of anxiety in health technicians and medical students were 39% (95% CI 13% to 73%) and 36% (95% CI 15% to 65%), respectively, indicating a much higher prevalence than other hospital staff members. Furthermore, a positive significant relationship between prevalence of anxiety among HCWs and their age was approved (p<0.001). The prevalence rate of anxiety was higher among women 37.7% (95% CI 25.4% to 51.8%) than men 27.2% (95% CI 18.2% to 38.6%). CONCLUSION: The findings show a moderately high prevalence rate of anxiety in hospital staff. Due to the high prevalence of this mental health problem in health technicians, medical students and frontline health workers, it is highly suggested that healthcare institutions offer mental health programmes for these working groups in order to appropriately manage anxiety during the COVID-19 pandemic.

Developing a Personal Protective Equipment Calculator during the COVID-19 Pandemic.

These days, because of the Covid-19 pandemic, we have faced a number of challenges and scarcities in Iran. Lack of Personal Protective Equipment (PPE) is one the most remarkable problems which can have damaging consequences on the health system. In this letter we introduce software which can help hospitals to manage their PPEs in terms of purchasing, distributing and predicting the future needs in different time intervals. The software has several distinctive features such as superior speed, cost management, managerial dashboard, a wide range of applicability, comprehensiveness, supply chain management, and quality appraisal. We hope that our findings can assist health authorities in planning and optimizing the use of PPEs for the response to coronavirus disease, where the shortage of resources may occur due to supply chain issues.

The need for attention to mental health of nursing staff during COVID-19 pandemic as their basic human right

Purpose This purpose of this study was to investigate the role of nurses' resilience as an indicator of their mental health on sick leave absenteeism during the COVID-19 pandemic. Design/methodology/approach This descriptive-analytical study was conducted in 2020 to identify the predictors of absenteeism among 260 nurses working in two training hospitals delivering specialized services in the treatment of COVID-19 patients. Data was collected through the use of standard questionnaires including demographic information, nurses' resilience, intention for job turnover and absenteeism from the workplace. To predict sick leave absenteeism, regression analyses were implemented. Findings Study results revealed that the most influencing features for predicting the probability of taking sick leave among nurses were marital status, tenacity, age, work experience and optimism. Logistic regression also depicted that nurses who had less faith in God or less self-control were more likely to take sick leave. Practical implications The resilience of nurses working in the COVID-19 pandemic was relatively low, which needs careful consideration to apply for organizational support. Main challenge that most of the health systems face include an inadequate supply of nurses which consequently lead to reduced efficiency, poor quality of care and decreased job performance. Thus, hospital managers need to put appropriate managerial interventions into practice, such as building a pleasant and healthy work environment, to improve nurses' resilience in response to heavy workloads and stressful conditions. Originality/value To the best of the authors' knowledge, this is the first study to examine such a relationship, thus contributing findings will provide a clear contribution to nursing management and decision-making processes. Resilience is an important factor for nurses who constantly face challenging situations in a multifaceted health-care system.

Corrigendum to "The global Prevalence of Depression among Health Workers during the COVID-19 pandemic: a Systematic review and Meta-analysis". [Journal of Affective Disorders Reports Volume 8, April 2022, 100326].

[This corrects the article DOI: 10.1016/j.jadr.2022.100326.].

Predicting COVID-19 Cases Among Nurses Using Artificial Neural Network Approach.

We designed a forecasting model to determine which frontline health workers are most likely to be infected by COVID-19 among 220 nurses. We used multivariate regression analysis and different classification algorithms to assess the effect of several covariates, including exposure to COVID-19 patients, access to personal protective equipment, proper use of personal protective equipment, adherence to hand hygiene principles, stressfulness, and training on the risk of a nurse being infected. Access to personal protective equipment and training were associated with a 0.19- and 1.66-point lower score in being infected by COVID-19. Exposure to COVID-19 cases and being stressed of COVID-19 infection were associated with a 0.016- and 9.3-point higher probability of being infected by COVID-19. Furthermore, an artificial neural network with 75.8% (95% confidence interval, 72.1-78.9) validation accuracy and 76.6% (95% confidence interval, 73.1-78.6) overall accuracy could classify normal and infected nurses. The neural network can help managers and policymakers determine which frontline health workers are most likely to be infected by COVID-19.

Risk Factors Associated with In-Hospital Mortality in Iranian Patients with COVID-19: Application of Machine Learning

Introduction: Predicting the mortality risk of COVID-19 patients based on patient’s physiological conditions and demographic characteristics can help optimize resource consumption along with the provision of effective medical services for patients. In the current study, we aimed to develop several machine learning models to forecast the mortality risk in COVID-19 patients, evaluate their performance, and select the model with the highest predictive power.Material and methods: We conducted a retrospective analysis of the records belonging to COVID-19 patients admitted to one of the main hospitals of Qazvin located in the northwest of Iran over 12 months period. We selected 29 variables for developing machine learning models incorporating demographic factors, physical symptoms, comorbidities, and laboratory test results. The outcome variable was mortality as a binary variable. Logistic regression analysis was conducted to identify risk factors of in-hospital death.Results: In prediction of mortality, Ensemble demonstrated the maximum values of accuracy (0.8071, 95%CI: 0.7787, 0.8356), F1-score (0.8121 95%CI: 0.7900, 0.8341), and AUROC (0.8079, 95%CI: 0.7800, 0.8358). Including fourteen top-scored features identified by maximum relevance minimum redundancy algorithm into the subset of predictors of ensemble classifier such as BUN level, shortness of breath, seizure, disease history, fever, gender, body pain, WBC, diarrhea, sore throat, blood oxygen level, muscular pain, lack of taste and history of drug (medication) use are sufficient for this classifier to reach to its best predictive power for prediction of mortality risk of COVID-19 patients.Conclusions: Study findings revealed that old age, lower oxygen saturation level, underlying medical conditions, shortness of breath, seizure, fever, sore throat, and body pain, besides serum BUN, WBC, and CRP levels, were significantly associated with increased mortality risk of COVID-19 patients. Machine learning algorithms can help healthcare systems by predicting and reduction of the mortality risk of COVID-19 patients.

A COVID-19 forecasting system for hospital needs using ANFIS and LSTM models: A graphical user interface unit.

Background: Centers for Disease Control and Prevention data showed that about 40% of coronavirus disease 2019 (COVID-19) patients had been suffering from at least one underlying medical condition were hospitalized; in which nearly 33% of them needed to be admitted to the intensive care unit (ICU) to receive specialized medical services. Our study aimed to find a proper machine learning algorithm that can predict confirmed COVID-19 hospital admissions with high accuracy. Methods: We obtained data on daily COVID-19 cases in regular medical inpatient units, emergency department, and ICU in the time window between 21 July 2020 and 21 November 2021. Data for the first 183 days (training data set) were used for long short-term memory (LSTM) network, adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and decision tree model training, whilst the remaining data for the last 60 days (test data set) were used for model validation. To predict the number of ICU and non-ICU patients, we used these models. Finally, a user-friendly graphical user interface unit was designed to load any time series data (here the trend of population of COVID-19 patients) and train LSTM, ANFIS, SVR or tree models for the prediction of COVID-19 cases for one week ahead. Results: All models predicted the dynamics of COVID-19 cases in ICU and non- wards. The values of root-mean-square error and R 2 as model assessment metrics showed that ANFIS model had better predictive power among all models. Conclusion: Artificial intelligence-based forecasting models such as ANFIS system or deep learning approach based on LSTM or regression models including SVR or tree regression play a key role in forecasting the required number of beds or other types of medical facilities during the coronavirus pandemic. Thus, the designed graphical user interface of the present study can be used for optimum management of resources by health care systems amid COVID-19 pandemic.

The Effect of Educational Intervention on Anxiety and Quality of Life Among Individuals Referring to Healthcare Centers in the Face of COVID-19

Educational interventions are helpful strategies to empower communities encountering the threat of pandemics like Covid-19. This study was carried out to examine the effect of educational intervention on anxiety control and improvement in public quality of life. A quasi-experimental study. The study was conducted among individuals referred to healthcare centers of Qazvin province, Iran, in 2020. Given that Qazvin consists of nine urban healthcare centers, two centers were selected by a simple random selection method. After considering inclusion and exclusion mentioned criteria, 240 individuals were selected to participate in the research and were randomly assigned into two groups of experimental and control. Following the educational intervention, all study variables, including knowledge score, anxiety level, and quality of life, improved significantly in the experimental group compared to the pre-intervention phase (P<0.05). The most significant change was in knowledge score with a nearly large effect size (0.63), presenting an increase of 40.09% from 11.1 to 18.8 exactly after intervention and 12.2 after passing one month from the date of educational intervention;while the quality of life presented a 3.2% increase with a small effect size (0.28). Our findings have implications for the development and implementation of psychological interventions, particularly educational programs. During the outbreak, such strategies can empower the public and diminish the negative emotional effects of the pandemic, helping people to cope with the current situation, and decrease the risk of suffering future psychological disorders.

The global png health workers during the COVID-19 pandemic: A systematic review and meta-analysis.

BACKGROUND: This study was designed to conduct a systematic review and meta-analysis of existing literature examining the prevalence of depression among hospital staff and the impact of various factors with a view to organize related programs for reducing the complications of this mental disorder. METHOD: A total of 24 studies were extracted from a literature search conducted through electronic databases including PubMed, EMBASE, Scopus, and Web of Science from January 2019 to February 2021. FINDINGS: Following the extraction of data, the total number of hospital staff was reported to be 42,010. Based on the results, depression prevalence among them was 26% (95% Cl, 0.18-0.35). Furthermore correlation coefficients revealed a significant relationship between the rate of depression and variables including type of career, age, and gender (P-value < 0.05). The highest and lowest prevalence of depressive disorder among hospital staff was in Africa 82% (95% Cl, 0.35-0.97) and Asia 19% (95% Cl, 0.11-0.29). CONCLUSION: Our findings affirmed that female workers who aged between 29 and 35 and worked as administrative and support staff in hospitals were among the population being at higher risk of developing mental health problems during the COVID-19 pandemic.

Machine learning-based COVID-19 diagnosis by demographic characteristics and clinical data.

INTRODUCTION: To facilitate rapid and effective diagnosis of COVID-19, effective screening can alleviate the challenges facing healthcare systems. We aimed to develop a machine learning-based prediction of COVID-19 diagnosis and design a graphical user interface (GUI) to diagnose COVID-19 cases by recording their symptoms and demographic features. METHODS: We implemented different classification models including support vector machine (SVM), Decision tree (DT), Naïve Bayes (NB) and K-nearest neighbor (KNN) to predict the result of COVID-19 test for individuals. We trained these models by data of 16973 individuals (90% of all individuals included in data gathering) and tested by 1885 individuals (10% of all individuals). Maximum relevance minimum redundancy (MRMR) algorithms used to score features for prediction of result of COVID-19 test. A user-friendly GUI was designed to predict COVID-19 test results in individuals. RESULTS: Study results revealed that coughing had the highest positive correlation with the positive results of COVID-19 test followed by the duration of having COVID-19 signs and symptoms, exposure to infected individuals, age, muscle pain, recent infection by COVID-19 virus, fever, respiratory distress, loss of smell or taste, nausea, anorexia, headache, vertigo, CT symptoms in lung scans, diabetes and hypertension. The values of accuracy, precision, recall, F1-score, specificity and area under receiver operating curve (AUROC) of different classification models computed in different setting of features scored by MRMR algorithm. Finally, our designed GUI by receiving each of the 42 features and symptoms from the users and through selecting one of the SVM, KNN, Naïve Bayes and decision tree models, predict the result of COVID-19 test. The accuracy, AUROC and F1-score of SVM model as the best model for diagnosis of COVID-19 test were 0.7048 (95% CI: 0.6998, 0.7094), 0.7045 (95% CI: 0.7003, 0.7104) and 0.7157 (95% CI: 0.7043, 0.7194), respectively. CONCLUSION: In this study we implemented a machine learning approach to facilitate early clinical decision making during COVID-19 outbreak and provide a predictive model of COVID-19 diagnosis capable of categorizing populations in to infected and non-infected individuals the same as an efficient screening tool.

Trend and prediction of COVID-19 outbreak in Iran: SEIR and ANFIS model

Background: Mathematical and predictive modeling approaches can be used in COVID-19 crisis to forecast the trend of new cases for healthcare management purposes. Given the COVID-19 disease pandemic, the prediction of the epidemic trend of this disease is so important.Methods: We constructed an SEIR (Susceptible-Exposed-Infected-Recovered) model on the COVID-19 outbreak in Iran. We estimated model parameters by the data on notified cases in Iran in the time window 1/22/2020 – 20/7/2021. Global sensitivity analysis is performed to determine the correlation between epidemiological variables and SEIR model parameters and to assess SEIR model robustness against perturbation to parameters. We Combined Adaptive Neuro-Fuzzy Inference System (ANFIS) as a rigorous time series prediction approach with the SEIR model to predict the trend of COVID-19 new cases under two different scenarios including social distance and non-social distance.Results: The SEIR and ANFIS model predicted new cases of COVID-19 for the period February 7, 2021, till August 7, 2021. Model predictions in the non-social distancing scenario indicate that the corona epidemic in Iran may recur as an immortal oscillation and Iran may undergo a recurrence of the third peak.Conclusion: Combining parametrized SEIR model and ANFIS is effective in predicting the trend of COVID-19 new cases in Iran.

A systematic review of the prevalence of anxiety among the general population during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has had an adverse effect on the mental health of population worldwide. This study was conducted to systematically review the existing literature to identify the individuals at higher risk of anxiety with a view to provide targeted mental health services during this outbreak. METHODS: In this study, the studies focusing on anxiety prevalence among the general population during the COVID-19 pandemic were searched in the PubMed, EMBASE, Scopus, Web of Science (WoS) and Google Scholar from the beginning of Covid-19 pandemic to February 2021. RESULTS: 103 studies constituting 140732 people included in the review. The findings showed that anxiety prevalence was 27.3% (95% CI, 23.7%; 31.2%) among general population while the prevalence in COVID-19 patients was 39.6% (95% CI, 30.1%; 50.1%). Anxiety was significantly higher among females and older adults (p≤0.05). In addition Europe revealed the highest prevalence of anxiety 54.6% (95% CI, 42.5%; 66.2%) followed by America 31.5% (95% CI, 19%; 47.5%) and Asia 28.3% (95% CI, 20.3%; 38%). In the general population the highest prevalence of anxiety was in Africa 61.8% (95% CI, 57%-66.4%) followed by America 34.9% (95% CI, 27.7%-42.9%), Europe 30.7% (95% CI, 22.8%-40%) and Asia 24.5% (95% CI, 20.7%-28.9%). CONCLUSION: During the COVID-19 crisis, through identifying those who are more likely to be suffered from mental disorders at different layers of populations, it would be possible to apply appropriate supportive interventions with a view to provide targeted mental health services during the outbreak.