Comparison of the healthcare-associated infections in intensive care units in Turkey before and during COVID-19.

Background: Secondary bacterial infections are an important cause of mortality in patients with coronavirus disease 2019 (COVID-19). All healthcare providers acted with utmost care with the reflex of protecting themselves during the COVID-19 period. We aimed to compare the rates of ventilator-associated pneumonia (VAP) and bloodstream infections (BSIs) in our intensive care units (ICUs) before and during the COVID-19 outbreak surges. Methods: This multicenter, retrospective, cross-sectional study was performed in six centers in Turkey. We collected the patient demographic characteristics, comorbidities, reasons for ICU admission, mortality and morbidity scores at ICU admission, and laboratory test data. Results: A total of 558 patients who required intensive care from six centers were included in the study. Four hundred twenty-two of these patients (males (62%), whose mean age was 70 [IQR, 58-79] years) were followed up in the COVID period, and 136 (males (57%), whose mean age was 73 [IQR, 61-82] years) were followed up in the pre-COVID period. BSI and VAP rates were 20.7 (19 events in 916 patient days) and 17 (74 events in 4361 patient days) with a -3.8 difference (P = 0.463), and 33.7 (31 events in 919 patient days) and 34.6 (93 events in 2685 patient days) with a 0.9 difference (P = 0.897), respectively. The mortality rates were 71 (52%) in pre-COVID and 291 (69%) in COVID periods. Conclusion: Protective measures that prioritize healthcare workers rather than patients and exceed standard measures made no difference in terms of reducing mortality.

Anxiety and Depression among People Living with HIV During the COVID-19 Pandemic: A Face-to-Face Survey from Turkey.

BACKGROUND: During the COVID-19 pandemic, there have been some difficulties in the routine care of people living with HIV (PLWH). SUBJECTS AND METHODS: We aimed to evaluate the impact of COVID-19 on mental health of PLWH and their use of health services. This study was conducted using the face-to-face interview method in the outpatient clinic of a university hospital, between 01.09.2020 and 30.11.2020. Hospital Anxiety and Depression Scale and survey instrument designed by the researchers investigating socio-demographic data and access to health services were used. RESULTS: The study included 217 patients, 91.7% (n=199) of whom were male. All of the patients were postponed their hospital appointments, 60.8% were concerned about not being able to contact their physician and 53% had concerned about being stigmatized if they went to the hospital. Of the participants, 27.6% had depression, 12.9% had anxiety and 8.3% had both depression and anxiety. Low income, job loss, and fear of being stigmatized were associated with depression and anxiety. Lower level of education, discontinuation of medications and lack of opportunity to work remotely were associated with depression, while history of psychiatric illness, worry about not being able to contact their physician and cessation of antiretroviral therapy were associated with higher anxiety levels. CONCLUSION: It is important to develop strategies ensuring the continuity of care for PWLH and identify and support those with a higher mental health impact.

Assessment of the dissimilarities of totally 186 countries and regions according to COVID-19 indicators at the end of March 2020.

Background: This study is aimed at evaluating the relationship between the number of days elapsed since a country's first case(s) of coronavirus disease 2019 (COVID-19), the total number of tests conducted, and outbreak indicators such as the total numbers of cases, deaths, and patients who recovered. The study compares COVID-19 indicators among countries and clusters them according to similarities in the indicators. Methods: Descriptive statistics of the indicators were computed and the results were presented in figures and tables. A fuzzy c-means clustering algorithm was used to cluster/group the countries according to the similarities in the total numbers of patients who recovered, deaths, and active cases. Results: The highest numbers of COVID-19 cases were found in Gibraltar, Spain, Switzerland, Liechtenstein and Italy were also of that order with about 1500 cases per million population. Spain and Italy had the highest total number of deaths, which were about 140 and 165 per million population, respectively. In Japan, where exposure to the causative virus was longer than in most other countries, the total number of deaths per million population was less than 0.5. According to cluster analysis, the total numbers of deaths, patients who recovered, and active cases were higher in Western countries, especially in central and southern European countries, which had the highest numbers when compared with other countries. Conclusion: There may be various reasons for the differences between the clusters obtained by fuzzy c-means clustering. These include quarantine measures, climatic conditions, economic levels, health policies, and the duration of the fight against the outbreak.

Comparison of Initial CT Findings and CO-RADS Stage in COVID-19 Patients with PCR, Inflammation and Coagulation Parameters in Diagnostic and Prognostic Perspectives.

Objectives: This study aims to determine whether COVID-19 patients with different initial reverse transcriptase-polymerase chain reaction (RT-PCR), computed tomography (CT) and laboratory findings have different clinical outcomes. Materials and Methods: In this multi-center retrospective cohort study, 895 hospitalized patients with the diagnosis of COVID-19 were included. According to the RT-PCR positivity and presence of CT findings, the patients were divided into four groups. These groups were compared in terms of mortality and need for intensive care unit (ICU). According to the COVID-19 Reporting and Data System (CO-RADS), all patients' CT images were staged. Multivariate binary logistic regression analysis was used to examine the relationship between CO-RADS and predictive inflammation and coagulation parameters. Results: RT-PCR test positivity was 51.5%, the CT finding was 70.7%, and 49.7% of the patients were in the CO-RADS 5 stage. The need for ICU and mortality rates was higher in the group with only CT findings compared to the group with only RT-PCR positivity, (14.9% vs. 4.0%, p < 0.001; 9.3% vs. 3.3%, p > 0.05; respectively). Mortality was 3.27 times higher in patients with CO-RADS 4 compared to those with CO-RADS 1-2. Being in the CO-RADS 4 stage and LDH were discovered to be the most efficient parameters in determining mortality risk. Conclusion: Performing only the RT-PCR test in the initial evaluation of patients in SARS-CoV-2 infection may lead to overlooking groups that are more at risk for severe disease. The use of a chest CT to perform CO-RADS staging would be beneficial in terms of providing both diagnostic and prognostic information.

Yoğun Bakımdaki COVID-19 Hastalarında Konvalesan Plazma: Ne Zaman Daha Etkili?

Amaç: Konvalesan plazma (CP) pandemilerde aşı ya da etkin ilaç tedavisi bulunana kadar faydalı olabileceği gösterilmiş bir immün tedavi yöntemidir. Özellikle COVID-19’da CP tedavisinin uygulanma zamanının sonuçlara etkisi tartışılmaktadır.Yoğun bakımda (YB) takip edilen COVID19’da erken ve geç dönem uygulanan CP tedavisinin, klinik ve laboratuvar sonuçlara etkilerini karşılaştırmak amaçlanmıştır. Gereç ve Yöntem: Bu retrospektif çalışmada etik kurul iznini takiben Mart-Aralık 2020 tarihlerinde yoğun bakımda CP alan PCR testi pozitif 152 hasta;erken tedavi (semptomlarının başlangıcı ile CP tedavisi arasında geçen süre ≤7 gün) ve geç tedavi (>7 gün) gruplarına ayrıldı. Hastaların laboratuvar değerleri ve klinik parametreleri dosyaları ve takip formları incelenerek kaydedildi. Çalışma grupları demografik ve klinik veriler, laboratuvar değerleri ve sağkalım açısından karşılaştırıldı (Tablo 1). Bulgular: Erken tedavi (n=82) ve geç tedavi (n=70) grupları arasında demografik özellikler, komorbiditeler, uygulanan solunum desteği, ilaç tedavileri, şok, organ yetmezliği gelişimi, yatış süresi, ventilatördeki gün sayısı ve mortalite açısından fark yoktu (Tablo 1). Laboratuvar tetkiklerinde, geç tedavi grubunda, tedavi öncesi ve sonrası 1. günde bakılan ferritin değerleri ve tedavi sonrası 3. günde bakılan CRP düzeyi, erken tedavi grubuna göre daha düşüktü (p<0,05). Sonuç: CP tedavisinin mortaliteyi azalttığına dair çalışmalar bulunmaktadır. Enfeksiyonun erken döneminde uygulanması ile tedavinin etkinliğinin arttığına dair sonuçlar bulunmakla birlikte daha geç uygulanması sonrası da olumlu sonuçların elde edildiği çalışmalar görülmektedir. Araştırmamızda tedavi zamanının klinik ve sağkalım açısından farklılık yaratmadığı gözlendi. Kullanılan plazmalardaki antikor titresinin bilinememesinin tedavi etkinliğini değerlendirmede belirsizlik yaratabileceği düşüncesindeyiz. Ayrıca geç tedavi grubundaki ferritin ve CRP düşüklüğünü bu gruptaki hastaların enflamasyon düzeylerinin daha hafif olmasına bağladık. CP tedavisinin erken veya geç dönemde uygulanması klinik ve sağkalım açısından farklılık yaratmamaktadır, COVID-19’un aşısı bulunmuş olmakla birlikte aşısız ve ağır komorbiditeli COVID-19 hastaları açısından CP tedavilerinin ileri çalışmalarla incelenmeye devam edilmesi gerektiği kanısındayız. [ FROM AUTHOR] Copyright of Turkish Journal of Intensive Care is the property of Galenos Yayinevi Tic. LTD. STI 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 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 . (Copyright applies to all s.)

Global evaluation of coronavirus disease 2019 cases and clustering of similar countries.

OBJECTIVE: It was aimed to be obtained descriptive values with respect to the outbreak time course, demographic structure, and symptom distribution by the help of case-based data, and to be compared countries by being grouped according to their similarities of outbreak indicators. METHODS: The data were obtained from open-access database. Univariate tests and cluster analysis were used to analyze the data. RESULTS: After the symptoms onset, the prolonged admission to the hospital significantly increases the risk of death. The average age and percentage of the male gender of the deceased cases were found to be significantly higher. In addition, the symptoms including fever, throat complaints, and dyspnea were determined in 70%. Countries were divided into four clusters according to their similarities in terms of three outbreak indicators. The differences among the clusters with regard to mean age, urban rate, and average of the outbreak indicators were found significant. CONCLUSION: Delaying treatment from the moment the symptoms appear will increase the risk of death and the average time to recovery or death was 2.5 weeks. It can be stated that the most important measure is to focus on methods that can detect the cases before symptoms. The indicators that have a very important role in defining the pandemic are also related to each other. Therefore, multivariate methods, which take these relationships into account, are able to produce more accurate information in determining the similarities of countries.

Sars-Cov-2 virus and vaccination; biological and statistical framework.

INTRODUCTION: The Development of the SARS-CoV-2 virus vaccine and its update on an ongoing pandemic is the first subject of the world health agenda. AREAS COVERED: First, we will scrutinize the biological features of the measles virus (MV), variola virus (smallpox virus), influenza virus, and their vaccines to compare them with the SARS-CoV-2 virus and vaccine. Next, we will discuss the statistical details of measuring the effectiveness of an improved vaccine. EXPERT OPINION: Amidst the pandemic, we ought to acknowledge our prior experiences with respiratory viruses and vaccines. In the planning stage of observational Phase-III vaccine effectiveness studies, the sample size, sampling method, statistical model, and selection of variables are crucial in obtaining high-quality and valid results.

Anxiety among front-line health-care workers supporting patients with COVID-19: A global survey.

OBJECTIVE: We aimed to explore anxiety status across a broad range of HCWs supporting patients with COVID-19 in different global regions. METHOD: This was an international online survey in which participation was on voluntary basis and data were submitted via Google Drive, across a two-week period starting from March 18, 2020. The Beck Anxiety Inventory was used to quantify the level of anxiety. RESULTS: 1416 HCWs (70.8% medical doctors, 26.2% nurses) responded to the survey from 75 countries. The distribution of anxiety levels was: normal/minimal (n = 503, 35.5%), low (n = 390, 27.5%); moderate (n = 287, 20.3%), and severe (n = 236, 16.7%). According to multiple generalized linear model, female gender (p = 0.001), occupation (ie, being a nurse dealing directly with patients with COVID-19 [p = 0.017]), being younger (p = 0.001), reporting inadequate knowledge on COVID-19 (p = 0.005), having insufficient personal protective equipment (p = 0.001) and poor access to hand sanitizers or liquid soaps (p = 0.008), coexisting chronic disorders (p = 0.001) and existing mental health problems (p = 0.001), and higher income of countries where HCWs lived (p = 0.048) were significantly associated with increased anxiety. CONCLUSIONS: Front-line HCWs, regardless of the levels of COVID-19 transmission in their country, are anxious when they do not feel protected. Our findings suggest that anxiety could be mitigated ensuring sufficient levels of protective personal equipment alongside greater education and information.

MODELLING OF COVID-19 OUTBREAK INDICATORS IN CHINA BETWEEN JANUARY AND JUNE

OBJECTIVE: The objective of this study is to compare the various nonlinear and time series models in describing the course of the COVID-19 outbreak in China. To this aim, we focus on two indicators including the number of total cases diagnosed with the disease, and the death toll. METHODS: The data used for this study isbased on the reports of China between January 22 - June 18, 2020. We used nonlinear growth curves and some time series models for prediction of the number of total cases and total deaths. The determination coefficient (R2), mean square error (MSE), and Bayesian Information Criterion (BIC) were used to select the best model. RESULTS: Our results show that while the Sloboda and ARIMA (0,2,1) models are the most convenient models that elucidate the cumulative number of cases; the Lundqvist-Korf model and Holt linear trend exponential smoothing model are the most suitable models for analyzing the cumulative number of deaths.Our time series models forecast that on 19 July, the number of total cases and total deaths will be 85589 and 4639, respectively. CONCLUSION: The results of this study will be of great importance when it comes to modeling outbreak indicators for other countries. This information will enable governments to implement suitable measures for subsequent similar situations.

A Statistical Modeling of the Course of COVID-19 (SARS-CoV-2) Outbreak: A Comparative Analysis.

This study aims to provide both a model by using cumulative cases and cumulative death toll for SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) outbreak in four countries, China, Italy, South Korea, and Turkey, starting from the first diagnosis and to compare associated indicators. The most successful estimation was obtained from the cubic model with natural logarithm for China, Italy, South Korea, and Turkey. The success of the models was around 99%. However, differences began to emerge in China, Italy, and South Korea after the second week. Although the highest number of new cases per 1 million people in China was 9.8 on February 28, 2020; it was 108.4 on March 21, 2020, in Italy; and this was 16.6 on March 5, 2020, in South Korea. On the other hand, the number of new cases was 24.6 per 1 million people on March 27, 2020, in Turkey. The log-cubic model proposed in this study has been set forth to obtain successful results for aforementioned countries, as well as to estimate the course of the COVID-19 outbreak. Other factors such as climacteric factors and genetic differences, which may have an impact on viral spreading and transmission, would also have strengthened the model prediction capacity.

Modeling and Short-Term Forecasts of Indicators for COVID-19 Outbreak in 25 Countries at the end of March

Y Objective: The coronavirus, which originated in Wuhan, causing the disease called COVID-19, spread more than 200 countries and continents end of the March. In this study, it was aimed to model the outbreak with different time series models and also predict the indicators. Materials and Methods: The data was collected from 25 countries which have different process at least 20 days. ARIMA(p,d,q), Simple Exponential Smoothing, Holt's Two Parameter, Brown's Double Exponential Smoothing Models were used. The prediction and forecasting values were obtained for the countries. Trends and seasonal effects were also evaluated. Results and Discussion: China has almost under control according to forecasting. The cumulative death prevalence in Italy and Spain will be the highest, followed by the Netherlands, France, England, China, Denmark, Belgium, Brazil and Sweden respectively as of the first week of April. The highest daily case prevalence was observed in Belgium, America, Canada, Poland, Ireland, Netherlands, France and Israel between 10% and 12%. The lowest rate was observed in China and South Korea. Turkey was one of the leading countries in terms of ranking these criteria. The prevalence of the new case and the recovered were higher in Spain than Italy. Conclusion: More accurate predictions for the future can be obtained using time series models with a wide range of data from different countries by modelling real time and retrospective data.

COVID-19, SARS-CoV2, Enfeksiyonu: Güncel Epidemiyolojik Analiz ve Hastalık Seyrinin Modellemesi ;COVID-19, SARS-CoV2, Infectıon: Current Epıdemıologıcal Analysıs and Modelıng of Dısease

Dünya çapında 19 Mart 2020 itibariyle 170’in üzerinde ülkeyi saran COVID-19 enfeksiyonu neticesinde pozitif vakaların ve ölüm haberlerinin hızla yayıldığı herkes tarafından endişe ile takip edilmektedir. Sosyal medya ve internet ortamında çok ciddi düzeyde bilgi birikimi ortaya çıkmıştır. Bu çalışmanın amacı an itibariyle elde edilen veriler ışığında tüm dünya ülkelerinde enfeksiyon etkileri ve süreci hakkında genel yapıyı özetleyen istatistiki bilgiler sunmak ve enfeksiyon ölçütlerinin günlük değişimini modellemektir. Elde edilen sonuçlar değerlendirildiğinde, birikimli (kümülatif) pozitif vaka sayısı, birikimli ölüm sayısı ve diğer bazı ölçütlerin ülkelere göre seyrinin aynı olmadığı, süreci en iyi kontrol eden ülkelerin başında Almanya ve Güney Kore’nin geldiği, Türkiye’ nin sürecinin ilk 10 günlük süreç itibariyle hızlı yayılım gösteren ülkelere benzediği görüldü. Ayrıca Türkiye için 20 – 29 Mart 2020 arasında ortaya çıkabilecek pozitif vaka sayısı ve birikimli ölüm sayıları tahmin edildiğinde 20 Mart itibariyle pozitif vaka sayısının sayının 550 civarında, 4 olan mevcut ölüm sayısının hesaplamalarla birikimli ölüm sayısının ise 11 olacağı öngörülmüştür. ;As of March 19, 2020, the worldwide spread of positive cases and news of death as a result of COVID-19 infection, which covers more than 170 countries, is followed with concern. There has been a lot of information accumulation in social media and internet. The aim of this study is to present statistical information summarizing the general structure about the effects and process of infection in all countries of the world in the light of the data obtained and to model the daily change of infection criteria. When the obtained results are evaluated, the cumulative (cumulative) number of positive cases, cumulative number of deaths and some other criteria are not the same of course depending on the country, the process is one of the most well-control countries, Germany and South Korea came from, Turkey 'of the process, the first 10-day period as It was observed that it was similar to the countries with fast spread. In addition to Turkey, 20 to 29 March 2020 the number of positive cases that may arise between and when the cumulative number of deaths is estimated the maximum number of positive cases as of March 20 around 550, while the cumulative death toll of 11 would be ,current measurement 4, is provided.