Number Of COVID-19 Cases Research Articles

The role of safety leadership in nursing management during the pandemic

The role of safety leadership in nursing management during the pandemic

COVID-19 and Severe Acute Respiratory Infections: Monitoring Trends in 421 German Hospitals During the First Four Pandemic Waves.

Reliable surveillance systems to monitor trends of COVID-19 case numbers and the associated healthcare burden play a central role in efficient pandemic management. In Germany, the federal government agency Robert-Koch-Institute uses an ICD-code-based inpatient surveillance system, ICOSARI, to assess temporal trends of severe acute respiratory infection (SARI) and COVID-19 hospitalization numbers. In a similar approach, we present a large-scale analysis covering four pandemic waves derived from the Initiative of Quality Medicine (IQM), a German-wide network of acute care hospitals. Routine data from 421 hospitals for the years 2019-2021 with a "pre-pandemic" period (01-01-2019 to 03-03-2020) and a "pandemic" period (04-03-2020 to 31-12-2021) was analysed. SARI cases were defined by ICD-codes J09-J22 and COVID-19 by ICD-codes U07.1 and U07.2. The following outcomes were analysed: intensive care treatment, mechanical ventilation, in-hospital mortality. Over 1.1 million cases of SARI and COVID-19 were identified. Patients with COVID-19 and additional codes for SARI were at higher risk for adverse outcomes when compared to non-COVID SARI and COVID-19 without any coding for SARI. During the pandemic period, non-COVID SARI cases were associated with 28%, 23% and 27% higher odds for intensive care treatment, mechanical ventilation and in-hospital mortality, respectively, compared to pre-pandemic SARI. The nationwide IQM network could serve as an excellent data source to enhance COVID-19 and SARI surveillance in view of the ongoing pandemic. Future developments of COVID-19/SARI case numbers and associated outcomes should be closely monitored to identify specific trends, especially considering novel virus variants.

Pembentukan GELAS KACA (Gerakan Lansia Kelola, Adaptasi, Cegah dan Tangguh) Selama Pandemi Covid-19

The increase in the number of COVID-19 cases took place quite quickly, and spread to various districts in Indonesia in a short time including Poso Regency. The elderly and those with comorbidities are at risk of contracting and experiencing worse complications due to COVID-19. Therefore, it is very important to prevent transmission through promotive and preventive efforts to the elderly, both at the family, community and health facilities levels. To achieve this, one of them is through the establishment of GELAS KACA (Gerakan Elderly Manage, Adapt, Prevent and Resilient), which aims to increase the knowledge of the elderly in preventing the transmission of COVID-19. The problems that the elderly do not know how to manage illness during the pandemic, the elderly feel stressed because they have to stay at home, only health services are carried out if it is really important, the observation results show that there are also some elderly people who are afraid to go to health services for fear of being diagnosed with COVID -19. This is the basis for the implementation of the service which aims to establish GELAS KACA (The Elderly Movement for Managing, Adapting, Preventing and Resilient). The service is carried out by providing education on preventing COVID-19 transmission, self-management, adapting new habits and stress management, then evaluating the knowledge of the elderly after being given education. The results of the service showed that knowledge increased to 95%, psychosocial problems obtained were neurosis symptoms as much as 100%.

The Study of the Potential of Green Infrastructure Using Geodesign Approach in the Context of Covid-19 Pandemic in Bandung

Green open space (RTH) - the main element of green infrastructures - is an essential component that determines the quality of our urban environment. However, the current urban planning practices focus more on achieving a minimum standard or a standard need per capita rather than on the importance of green open space in supporting public health. The COVID-19 pandemic has affected urban life widely and informed us of the central position of public health issues and the provision of green open space. This study aims to examine the integration between factors that control the state of public health and urban landscape planning and design by adequately providing urban green open spaces. It explores the use of geographic information technology (GIS) using the Geodesign method. It develops spatial data content, including the area's physical and non-physical variables, including the number of COVID-19 cases. This study uses a computer-assisted GIS data overlay analysis technique with a multi-objective analysis method. The city of Bandung is chosen as a case study as it is one of the big cities in Indonesia that still needs to meet the minimum green open space proportion required by Regulation Number 26 of 2007 concerning Spatial Planning. This study proposes 3 (three) urban green open space development scenarios, each with different variables following the current (status quo) spatial planning approach, a balanced approach, and a responsive approach. Based on these three scenarios, ten urban villages of Bandung are prioritized to have an additional green open space to achieve the minimum percentage of 20%.Keywords: green infrastructure; green open space planning; urban landscape; pandemic, Bandung city

Assessment of medical staff adherence to infection control measures during the COVID-19 pandemic

The organization of an effective infection control system is one of the main tasks of medical organizations to prevent the occurrence and spread of infections associated with the provision of medical care (ISMP). In connection with the pandemic of the new coronavirus infection (COVID-19) that has engulfed all countries of the world, the problem of ISMP and infection control (IC) in medical organizations has become particularly relevant. The aim of the study was to assess the adherence of medical workers to the principles of IC in the departments for patients with COVID-19 and to determine the occupational risk factors of infection of personnel during the pre-vaccination period of the pandemic. The study included medical workers of «covid» hospital departments (n = 216; age 25–54 years), of which 105 doctors (41.7 %), average medical workers (nurses and nurses) — 111 people (58.3 %). All employees were trained in biological safety and conducting sanitary and anti-epidemic measures with COVID-19 before starting work in the hospital. In most of the staff (114 people, 52.8 ± 8.7 %), the compliance of the IC was assessed as high in the absence of detection of violations of the requirements of infectious safety. The average degree of adherence to IR measures was found in 102 observed medical workers (47.2 ± 9.9 %). In the cohort of medical workers with a high degree of adherence to IR measures, 2 people suffered a new coronavirus infection, which is 1.8 ± 0.5 %, while among the staff with a moderate degree of adherence (n = 102) 10 people (9.8 ± 2.2 %). The difference between the indicators is statistically significant at p < 0.001 and indicates a smaller (5.4 times) number of COVID-19 cases in the cohort of health workers with a high degree of compliance with IR measures. There was no dependence of the degree of adherence of medical personnel to the principles of IC on the age, gender, medical experience and functionality of medical workers. Nosocomial cases of COVID-19 that occurred during monitoring of compliance with the requirements of the IC were characterized by polymorphism of clinical manifestations with leading respiratory syndrome, the presence of mild and moderate forms and the predominance of average medical staff among the sick. Thus, IC in the conditions of global biological threats requires constant, complete and adequate training of medical personnel in various forms, systematic professional development of all personnel on issues of hospital hygiene and infectious occupational safety, including regular annual refresher courses.

Evaluation of the Canadian government policies on controlling the COVID-19 outbreaks

In this paper, we investigate the COVID-19 pandemic in Canada and evaluate the Canadian government policies on controlling COVID-19 outbreaks. The first case of COVID-19 was reported in Ontario on 25 January 2020. Since then, there have been over million cases by now. During this time period, the federal, provincial and local governments have implemented regulations and policies in order to control the pandemic. To evaluate these government policies, which may be done by analysing the infection rate, infection period and reproductive number of COVID-19, we approach the problem by introducing an extended susceptible-exposed-infectious-removed (SEIR) model and conduct the model inference by using the iterated filter ensemble adjustment Kalman filter (IF-EAKF) algorithm. We first divide the time period into phases according to the policy intensities in each province by segmenting the time period from 4 March 2020 to 31 October 2020 into three time phases: the exploding phase, the strict policy implementation phase, and the provincial reopening phase. We then use IF-EAKF algorithm to obtain the estimates of the model parameters. We show that the infection rate in the second phase is lower than that in both first and third phases. We also discuss the number of new COVID-19 cases under different policy intensities and different policy durations in the third wave of the pandemic.

Lockdowns during the COVID-19 pandemic among small island developing states and Guam

PurposeDuring the COVID-19 pandemic, lockdowns were implemented to achieve two goals: (1) to reduce the number of COVID-19 cases and (2) to reduce the number of COVID-19 deaths. In this paper, the authors aim to look at empirical evidence on how effectively lockdowns achieved these goals among small island developing states (SIDS) and for one specific SIDS economy, Guam.Design/methodology/approachThe authors reviewed existing studies to form two hypotheses: that lockdowns reduced cases, and that lockdowns reduced deaths. Defining a lockdown as a positive value for Oxford COVID-19 government response tracker, OxCGRT's stringency index, the authors tested the above hypotheses on 185 countries, 27 SIDS economies and Guam using correlation and regression analyses, and using different measures of the strictness, duration and timing of the lockdown.FindingsThe authors found no evidence to support the hypothesis that lockdowns reduced the number of cases based on data for all 185 countries and 27 SIDS economies. While the authors found evidence to support the hypothesis in the case of Guam, the result required an unrealistically and implausibly long time lag of 365 days. As to the second hypothesis that lockdowns reduced the number of deaths, the authors found no evidence to support it for 185 countries, 27 SIDS economies as well as Guam.Originality/valueFrom the review of the existing literature, the authors are the first to conduct this type of study among SIDS economies as a group and on Guam.

Demographic and clinical characteristics of COVID-19 patients in the least developed province Balochistan of Pakistan – an observational descriptive study

This study was designed to determine the epidemiological and clinical attributes of COVID-19 patients in the least developed province of Balochistan, Pakistan. The information was obtained from the daily situation report by the Health Department, Government of Balochistan, Pakistan. We investigated the reports of 4177 patients confirmed by RT-PCR tests. Demographic, epidemiological and risk factors data along with comorbidities and clinical signs were recorded. Out of 4500 suspected cases, 4177 cases were directed for the confirmation of COVID-19. A sum of 2177 patients was confirmed to have COVID-19 and 2000 individuals tested negative for the illness. Out of 4177 patients, 2000 patients recovered but 177 patients died because of COVID-19. In current statistics, most males were affected by COVID-19 as 3243 (77.69%) were males and 934 (22.36%) were females. A total of 90.81% of individuals had fever, 88.97% had a cough, 81% had body throbs, and 89.66% had a sore throat. Shortness of breath was observed in 97.06% and 44.09 % had comorbidity. Multiple logistic regression analysis showed that the outcome of patients was associated with gender and symptoms. The district Quetta had the maximum number of COVID-19 cases and deaths. COVID-19 cases and case casualty proportion are low in Balochistan. Whether this is because of failure to do more tests is still to be discovered. Males and individuals of older age are more impacted, and fatalities were higher in cases with co-morbid conditions. Balochistan has a feeble medical care framework and many asymptomatic cases, and needs more rigid screening activities.

Improved healthcare disaster decision-making utilizing information extraction from complementary social media data during the COVID-19 pandemic

Managing an extreme event like a healthcare disaster requires accurate information about the event's circumstances to comprehend the full consequences of acting. However, information quality is rarely optimal since it takes time to determine the information of relevance. The COVID-19 pandemic showed that even official data sources are far from optimal since they suffer from reporting delays that slow decision-making. To support decision-makers with timely information, we utilize data from online social networks to propose an adaptable information extraction solution to create indices helping to forecast COVID-19 case numbers and hospitalization rates. We show that combining heterogeneous data sources like Twitter and Reddit can leverage these sources' inherent complementarity and yield better predictions than those using a single data source alone. We further show that the predictions run ahead of the official COVID-19 incidences by up to 14 days. Additionally, we highlight the importance of model adjustments whenever new information becomes available or the underlying data changes by observing distinct changes in the presence of specific symptoms on Reddit.

The impact of COVID-19 on psychiatric clinical encounters among low-income racially-diverse children.

There is a lack of longitudinal data to examine the impact of COVID-19 on all types of clinical encounters among United States, underrepresented BIPOC (Black, Indigenous, and people of color), children. This study aims to examine the changes in all the outpatient clinical encounters during the pandemic compared to the baseline, with particular attention to psychiatric encounters and diagnoses. This study analyzed 3-year (January 2019 to December 2021) longitudinal clinical encounter data from 3,394 children in the Boston Birth Cohort, a US urban, predominantly low-income, Black and Hispanic children. Outcomes of interest were completed outpatient clinical encounters and their modalities (telemedicine vs. in person), including psychiatric care and diagnoses, primary care, emergency department (ED), and developmental and behavioral pediatrics (DBP). The study children's mean (SD) age is 13.9 (4.0) years. Compared to 2019, psychiatric encounters increased by 38% in 2020, most notably for diagnoses of adjustment disorders, depression, and post-traumatic stress disorders (PTSD). In contrast, primary care encounters decreased by 33%, ED encounters decreased by 55%, and DBP care decreased by 16% in 2020. Telemedicine was utilized the most for psychiatric and DBP encounters and the least for primary care encounters in 2020. A remarkable change in 2021 was the return of primary care encounters to the 2019 level, but psychiatric encounters fluctuated with spikes in COVID-19 case numbers. Among this sample of US BIPOC children, compared to the 2019 baseline, psychiatric encounters increased by 38% during 2020, most notably for the new diagnoses of adjustment disorder, depression, and PTSD. The 2021 data showed a full recovery of primary care encounters to the baseline level but psychiatric encounters remained sensitive to the pandemic spikes. The long-term impact of the pandemic on children's mental health warrants further investigation.

Deep learning based an efficient hybrid prediction model for Covid-19 cross-country spread among E7 and G7 countries

The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries.

Associations between the stringency of COVID-19 containment policies and health service disruptions in 10 countries

BackgroundDisruptions in essential health services during the COVID-19 pandemic have been reported in several countries. Yet, patterns in health service disruption according to country responses remain unclear. In this paper, we investigate associations between the stringency of COVID-19 containment policies and disruptions in 31 health services in 10 low- middle- and high-income countries in 2020.MethodsUsing routine health information systems and administrative data from 10 countries (Chile, Ethiopia, Ghana, Haiti, Lao People’s Democratic Republic, Mexico, Nepal, South Africa, South Korea, and Thailand) we estimated health service disruptions for the period of April to December 2020 by dividing monthly service provision at national levels by the average service provision in the 15 months pre-COVID (January 2019-March 2020). We used the Oxford COVID-19 Government Response Tracker (OxCGRT) index and multi-level linear regression analyses to assess associations between the stringency of restrictions and health service disruptions over nine months. We extended the analysis by examining associations between 11 individual containment or closure policies and health service disruptions. Models were adjusted for COVID caseload, health service category and country GDP and included robust standard errors.FindingsChronic disease care was among the most affected services. Regression analyses revealed that a 10% increase in the mean stringency index was associated with a 3.3 percentage-point (95% CI -3.9, -2.7) reduction in relative service volumes. Among individual policies, curfews, and the presence of a state of emergency, had the largest coefficients and were associated with 14.1 (95% CI -19.6, 8.7) and 10.7 (95% CI -12.7, -8.7) percentage-point lower relative service volumes, respectively. In contrast, number of COVID-19 cases in 2020 was not associated with health service disruptions in any model.ConclusionsAlthough containment policies were crucial in reducing COVID-19 mortality in many contexts, it is important to consider the indirect effects of these restrictions. Strategies to improve the resilience of health systems should be designed to ensure that populations can continue accessing essential health care despite the presence of containment policies during future infectious disease outbreaks.

Demographic Features and Laboratory Parameters Among Hospitalized Vaccinated Patients With COVID-19 in Istanbul, Turkey

Background The number of COVID-19 cases has been decreasing recently, and the restrictions are waived in many countries. The data about vaccine efficacy are essential to be prepared for the future even if the pandemic ends and the disease becomes endemic. Aims The aim of this study was to define demographic and laboratory data for disease severity among vaccinated COVID-19 cases who were hospitalized. Study Design This is a retrospective cohort study. Methods SARS-CoV-2 polymerase chain reaction–positive patients who were fully vaccinated (2 doses of vaccines and 3 doses of vaccines) and had been hospitalized at least 15 days after the last vaccine dose were enrolled in this study. Patients' data including laboratory parameters were retrieved, and descriptive statistics and comparison of variables were calculated. Results Between September 1, 2021, and February 28, 2022, 685 patients (mean age, 67.84 years; 50.8% female) were hospitalized. Inactivated whole-virion SARS-CoV-2 vaccine (CoronaVac) had been administered to 467 of the patients (68.4%), BNT162b2 mRNA vaccine (Pfizer-Pfizer-BioNTech) had been administered to 107 of the patients (15.6%), and to 111 of the patients (16%), a combination of 2 vaccines had been administered. At least 1 comorbidity was present in 160 participants (23%) and more than 4 comorbidities in 46 patients (6.6%). The most common comorbidity was hypertension (349, 50.2%). The presence of comorbidities was significantly higher in patients having 3 doses of vaccine. Increased ferritin levels were determined in 56.4% of the patients, increased D-dimer levels in 69.9% of the patients, increased C-reactive protein levels in 79.7% of the patients, and increased procalcitonin levels in 61.5% of the patients. A total of 36.4% of the patients had decreased lymphocyte counts, 20% had low lymphocyte/monocyte ratio, and 22% had decreased neutrophil/lymphocyte ratio. The only parameter that was significantly higher in patients having 3 vaccine doses was procalcitonin. Mean duration of hospitalization was 9.68 ± 7.29 days. In the CoronaVac and Pfizer-BioNTech groups, 68.5% and 21.4%, respectively, of the patients were older than 65 years. Seventeen (3.6%) patients in the CoronaVac group, 6 (5.4%) patients in the combination group, and 3 (2.8%) patients in Pfizer-BioNTech group had been admitted to the intensive care unit. Mortality rate was 0.3% (2 of 685 patients). Conclusions The incidence of severe COVID-19 disease among fully vaccinated patients is low even in the presence of comorbidities, older age, and presence of abnormal laboratory parameters, regardless of the vaccine type.

ESTIMATING AND FORECASTING COVID-19 CASES IN SULAWESI ISLAND USING GENERALIZED SPACE-TIME AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL

A range of spatio-temporal models has been used to model Covid-19 cases. However, there is only a small amount of literature on the analysis of estimating and forecasting Covid-19 cases using the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model. This model is a development of the GSTARMA model which has non-stationary data. This paper aims to estimate and forecast the daily number of Covid-19 cases in Sulawesi Island using GSTARIMA models. We compared two models namely GSTARI and GSTIMA considering the root mean square error (RMSE). Data on a daily number of Covid-19 cases (from April 10, 2020, to May 07, 2021) were used. The location weight used is the inverse distance weight based on the distance between airports in the capital cities of each province. The appropriate models obtained based on the data are the GSTARIMA (1;0;1;1) model and the GSTARIMA (1;1;1;0) model. The results showed that the forecast for the number of new Covid-19 cases is accurate and reliable only for the short term.

COVID-19 epidemic phases and morbidity in different areas of Chinese mainland, 2020.

In the early stage of COVID-19 epidemic, the Chinese mainland once effectively controlled the epidemic, but COVID-19 eventually spread faster and faster in the world. The purpose of this study is to clarify the differences in the epidemic data of COVID-19 in different areas and phases in Chinese mainland in 2020, and to analyze the possible factors affecting the occurrence and development of the epidemic. We divided the Chinese mainland into areas I, I and III, and divided the epidemic process into phases I to IV: limited cases, accelerated increase, decelerated increase and containment phases. We also combined phases II and III as outbreak phase. The epidemic data included the duration of different phases, the numbers of confirmed cases, asymptomatic infections, and the proportion of imported cases from abroad. In area I, II and III, only area I has a Phase I, and the Phase II and III of area I are longer. In Phase IV, there is a 17-day case clearing period in area I, while that in area II and III are 2 and 0 days, respectively. In phase III or the whole outbreak phase, the average daily increase of confirmed cases in area I was higher than that in areas II and III (P = 0.009 and P = 0.001 in phase III; P = 0.034 and P = 0.002 in the whole outbreak phase), and the average daily in-hospital cases were most in area I and least in area III (P = 0.000, P = 0.000, and P = 0.000 in phase III; P = 0.000, P = 0.000, and P = 0.009 in the whole outbreak phase). The average number of daily in-hospital COVID-19 cases in phase III was more than that in phase II in each area (P = 0.000, P = 0.000, and P = 0.001). In phase IV, from March 18, 2020 to January 1, 2021, the increase of confirmed cases in area III was higher than areas I and II (both P = 0.000), and the imported cases from abroad in Chinese mainland accounted for more than 55-61%. From June 16 to July 2, 2020, the number of new asymptomatic infections in area III was higher than that in area II (P = 0.000), while there was zero in area I. From July 3, 2020 to January 1, 2021, the increased COVID-19 cases in area III were 3534, while only 14 and 0, respectively, in areas I and II. The worst epidemic areas in Chinese mainland before March 18, 2020 and after June 15, 2020 were area I and area III, respectively, and area III had become the main battlefield for Chinese mainland to fight against imported epidemic since March 18, 2020. In Wuhan, human COVID-19 infection might occur before December 8, 2019, while the outbreak might occur before January 16 or even 10, 2020. Insufficient understanding of COVID-19 hindered the implementation of early effective isolation measures, leading to COVID-19 outbreak in Wuhan, and strict isolation measures were effective in controlling the epidemic. The import of foreign COVID-19 cases has made it difficult to control the epidemic of area III. When humans are once again faced with potentially infectious new diseases, it is appropriate to first and foremost take strict quarantine measures as soon as possible, and mutual cooperation between regions should be explored to combat the epidemic.

Alcohol screening during COVID-19 surges in an urban health system the United States

In 2021, over 50% of U.S. adults drank alcohol in the past month with over 25% reporting binge drinking, an increase over previous years. Alcohol use is associated with increased risk of accidents, poor birth outcomes, cancer, chronic diseases, and mortality. During the COVID-19 pandemic alcohol consumption and alcohol-related deaths increased. National organizations recommend screening for unhealthy alcohol use in general health care settings as a prevention strategy.This observational study examined alcohol screening rates in primary care practices in Boston, MA in the context of the COVID-19 pandemic, from July 2019 through May 2022. Screening rates were mapped, by month, to the number of COVID-19 cases. Alcohol screening dropped substantially during the first COVID-19 surge but steadily increased to baseline between the second and third surges. This decline was likely due to competing pandemic-related priorities (e.g., testing, urgent care) and the transition to telemedicine. While some health screening cannot be completed virtually, screening for alcohol is possible. Innovative workflow strategies (e.g., pre-visit screening via patient portals, support staff screening using virtual rooming processes) should be considered to avoid future interruptions of screening for unhealthy alcohol use in primary care.

Formulating interprofessional anesthesiology and operating room clinical management pathway during COVID-19 pandemic using experiential learning theory in a university hospital in Saudi Arabia.

As the number of COVID-19 cases is rapidly increasing internationally, management, recommendations and guidelines of COVID-19 are rapidly evolving and changing. Formulating local clinical management policies among institutions adopting these recommendations is vital to staff as well as the patients' safety. Also, training multidisciplinary teams on these policies is an important, yet challenging, part of the process. The purpose of this paper is to present the process that has been followed to formulate COVID-19-specific response anesthesiology and operating room (OR) policies at King Abdulaziz University Hospital, by applying David A. Kolb's experiential learning theory during simulation-based training. This project had a total of six simulation-based sessions (four simulation scenarios and two clinical drills) designed to test the efficacy and efficiency of the then current practice in the hospital, facing the COVID-19 pandemic. Qualitative data analysis was completed using qualitative thematic data analysis. To apply experiential Kolb's theory, session's checklist (two raters per session), outcomes, and participants' feedback to develop and improve clinical management pathway in the department were used. The 12 reports and participants' feedback highlighted three main areas for improvement. These are Personal Protective Equipment implementation, team dynamics, and airway management. This process then guided in creating a new understanding of the multidisciplinary clinical management pathway, in addition to enhancing viability of the current practice and clinical management guidelines and protocols, which were already established and adapted at the hospital before the COVID-19 pandemic crisis. The alignment with Kolb's experiential theory helped formulate anesthesiology and OR effective clinical management pathway has been demonstrated. Applying experiential learning theory by a clinical institute using interprofessional, multidisciplinary simulations and clinical drills can guide the process of formulating clinical management pathways during pandemic outbreaks.

Evaluation of SARS-CoV-2 RNA Presence in Treated and Untreated Hospital Sewage.

Wastewater-based epidemiology (WBE) is a potential approach for determining the viral prevalence in a community. In the wake of the COVID-19 pandemic, researchers have begun to pay close attention to the presence of SARS-COV-2 RNA in various wastewaters. The potential for detecting SARS-CoV-2 RNA in hospital sewage could make it an invaluable resource for epidemiological studies. In this regard, two specialized hospitals dedicated to COVID-19 patients were chosen for this investigation. Both hospitals utilize the same wastewater treatment systems. The influent and effluents of the two hospitals were sampled in May and June of 2021, and the samples were evaluated for their chemical properties. According to the findings of this study, the wastewater qualities of the two studied hospitals were within the standard ranges. The sewage samples were concentrated using ultrafiltration and PEG precipitation techniques. The E and S genes were studied with RT-qPCR commercial kits. We found E gene of SARS-CoV-2 in 83.3% (5/6) and 66.6% (4/6) of wastewater samples from hospital 1 and hospital 2, respectively, using ultrafiltration concentration method. Wastewater samples taken after chlorine treatment accounted for 16.6% of all positive results. In addition, due to the small sample size, there was no significant correlation (p > 0.05) between the presence of SARS-CoV-2 in wastewater and the number of COVID-19 cases. Hospitals may be a source of SARS-CoV-2 pollution, thus it is important to monitor and enhance wastewater treatment systems to prevent the spread of the virus and safeguard the surrounding environment.

Scholarly Output on Dentistry and COVID-19: A Global Bibliometric Analysis

Background: Bibliometric studies help in quantifying the pattern of research published in a particular area of interest to get the measure of research productivity. In this context, we analyzed the dental and COVID-19 research papers published from December 2019 to January 2021 globally. Method: The articles were retrieved from the PubMed and WHO COVID -19 databases using MeSH terms and keywords. The data downloaded as excel and extracted bibliometric information’s and analyzed using Epi-info (7.2) software. Result: We obtained 800 research articles after removing duplicates and non-relevant articles from 1,823 articles from these two databases. The articles were published by 475 institutions in 62 countries. Among the listed countries, more publications were done by the United States of America (USA) (n=135;16.8%). The majority of the papers were published in June 2020 (n=114). The King's College London had the most number of publications (n=15; 1.9%). The majority of the articles were original articles (n=592; 74%). Conclusion: The study reports a gradual increase in publications over the months and a slowing down at the end of the year. The countries such as the USA, England, Italy, and China published more articles and this correlates with the number of covid-19 cases.

Clinical and treatment factors associated with the mortality of COVID-19 patients admitted to a referral hospital in Indonesia.

Indonesia had the second-highest number of COVID-19 cases and deaths in South-East Asia. We aimed to determine the factors associated with this mortality and the effect of the recommended COVID-19 treatment regimen during the first 10 months of the epidemic. This was a retrospective cohort study using secondary data from medical records. In total, 689 adult COVID-19 inpatients hospitalized between March and December 2020 were enrolled. Clinical characteristics, laboratory parameters, and treatments were analyzed by survival outcome. Kaplan-Meier statistics were used to estimate survival. Of the 689 patients enrolled, 103 (14.9%) died. Disease severity was highly associated with mortality (hazard ratio [HR]: 7.69, p<0.001). Other clinical factors associated with mortality were older age and comorbidities. Based on laboratory parameters, higher procalcitonin and C-reactive protein contents and a neutrophil-to-lymphocyte ratio >3.53 were also linked to mortality. Favipiravir was associated with lower mortality, with adjusted HRs of 0.24 (0.11-0.54) and 0.40 (0.17-0.98) among the mild/moderate and severe cases, respectively. Among patients with severe disease, steroids showed some beneficial effects in the early days of hospitalization. Older age and comorbidities were associated with disease severity and, consequently, higher mortality. Higher mortality after the second week of hospitalization may be related to secondary bacterial infection. Favipiravir showed significant benefit for COVID-19 survival, while steroids showed benefit only in the early days of admission among patients with severe disease. This research did not receive a specific grant from any funding agency in the public, commercial, or not-for-profit sectors.