A comprehensive health effects assessment of the use of sanitizers and disinfectants during COVID-19 pandemic: a global survey.

COVID-19 has affected all aspects of human life so far. From the outset of the pandemic, preventing the spread of COVID-19 through the observance of health protocols, especially the use of sanitizers and disinfectants was given more attention. Despite the effectiveness of disinfection chemicals in controlling and preventing COVID-19, there are critical concerns about their adverse effects on human health. This study aims to assess the health effects of sanitizers and disinfectants on a global scale. A total of 91,056 participants from 154 countries participated in this cross-sectional study. Information on the use of sanitizers and disinfectants and health was collected using an electronic questionnaire, which was translated into 26 languages via web-based platforms. The findings of this study suggest that detergents, alcohol-based substances, and chlorinated compounds emerged as the most prevalent chemical agents compared to other sanitizers and disinfectants examined. Most frequently reported health issues include skin effects and respiratory effects. The Chi-square test showed a significant association between chlorinated compounds (sodium hypochlorite and per-chlorine) with all possible health effects under investigation (p-value <0.001). Examination of risk factors based on multivariate logistic regression analysis showed that alcohols and alcohols-based materials were associated with skin effects (OR, 1.98; 95%CI, 1.87-2.09), per-chlorine was associated with eye effects (OR, 1.83; 95%CI, 1.74-1.93), and highly likely with itching and throat irritation (OR, 2.00; 95%CI, 1.90-2.11). Furthermore, formaldehyde was associated with a higher prevalence of neurological effects (OR, 2.17; 95%CI, 1.92-2.44). Furthermore, formaldehyde was associated with a higher prevalence of neurological effects (OR, 2.17; 95%CI, 1.92-2.44). The use of sodium hypochlorite and per-chlorine also had a high chance of having respiratory effects. The findings of the current study suggest that health authorities need to implement more awareness programs about the side effects of using sanitizers and disinfectants during viral epidemics especially when they are used or overused.

Traditional Media at the Beginning of the Unknown: Managing Health Uncertainty through TV News Programs in Turkey

The purpose of this research is to find how TV news programs, as part of traditional media source, supply the needs of public within the uncertainty management theory and information-seeking scope during the initial phase of COVID-19 pandemic. A thematic analysis of first four streaming of each TV news programs with the first four highest ratings between March 18th and May 1th 2020 in Turkey was chosen for this study. This period was chosen because it was the first peak of the pandemic in Turkey when people felt uncertain and needed more information about their health. The results have shown that the uncertainty and information gaps have also been reflected in TV programs including the information provided by the experts. This study also reveals that in cases which concern public health, such as the COVID-19 pandemic, the traditional media continues to play a significant role in meeting the information needs of the society, and that the traditional media also uses social media's speed in conveying information. We suggest that future studies may focus on the interaction of traditional media and social media in meeting information-seeking behaviours and managing uncertainty to acquire more data.

A Comprehensive Health Effects Assessment of the Use of Sanitizers and Disinfectants during COVID-19 Pandemic: A Global Survey (preprint)

COVID-19 has affected all aspects of human life so far. From the outset of the pandemic, preventing the spread of COVID-19 through the observance of health protocols, especially the use of sanitizers and disinfectants was given more attention. Despite the effectiveness of disinfection chemicals in controlling and preventing COVID-19, there are critical concerns about their adverse effects on human health. This study aims to assess the health effects of sanitizers and disinfectants on a global scale. A total of 91056 participants from 154 countries participated in this cross-sectional study through an electronic questionnaire. Results implied that detergents (67%), alcohol-based materials (56%), and chlorinated compounds (32%) were the most commonly used types of sanitizers and disinfectants. Most frequently reported health issues include skin complications 48.8% and respiratory complications 29.8%. The Chi-square test showed a significant association between chlorinated compounds with all possible health complications under investigation (p-value < 0.001). Examination of risk factors based on multivariate regression analysis showed that alcohols-based materials were associated with skin complications (OR, 1.98; 95%CI, 1.87–2.09), per-chlorine was associated with eye complications (OR, 1.83; 95%CI, 1.74–1.93), and highly likely with itching and throat irritation (OR, 2.00; 95%CI, 1.90–2.11). Furthermore, formaldehyde was associated with a higher prevalence of neurological complications (OR, 2.17; 95%CI, 1.92–2.44). The findings of the current study suggest that health authorities need to implement more awareness programs about the side effects of using sanitizers and disinfectants during viral epidemics.

CONSEQUENCES OF SOCIAL ISOLATION AND LONELINESS IN PEOPLE OVER THE AGE OF 65 DURING THE COVID-19 PANDEMIC: A MIXED METHODS STUDY

Introduction: The purpose of this study was to examine the social and emotional loneliness of people (n = 216) over the age of 65 during the COVID-19 pandemic and then to understand in-depth the consequences of social isolation and loneliness among participants selected from the survey participants. Materials and Method: We used a sequential explanatory mixed methods design consisting of two phases. In the quantitative phase, the 11-item Loneliness Scale for the Elderly (LSE) was used for determining the level of loneliness. This scale was adapted by Akgül and Yeşilyaprak (2015). In the qualitative phase, an open-ended question survey and semi-structured in-depth interviews were conducted with 14 participants chosen through purposeful sampling. Results: The quantitative data showed that the mean score for emotional loneliness (5.74) was higher than the mean score for social loneliness (2.14). There were no significant differences among the age groups. However, the mean score for overall loneliness increased (6.52) with age. There was a significant difference between overall loneliness and marital status (p = 0.025) and living alone (p = 0.046). Three major themes were identified in the qualitative phase: Emotional consequences, social consequences, and physical consequences. Conclusion: The results suggest that emotional loneliness and limited interaction with loved ones are the main consequences of the social isolation imposed during the pandemic. These results should be taken into account when developing strategies to facilitate the daily routines, well-being, social interaction-based activities, and social support systems for older people during times of social isolation.

Detection of new coronavirus disease from chest x-ray images using pre-trained convolutional neural networks

The COVID-19 is a virus that spreads quickly with a high mortality rate. Rapid and accurate early diagnosis has a key role to reduce the mortality and to decrease the economic cost of this pandemic. For this purpose, diagnostic kits and diagnosis using medical imaging methods have been investigated. Among the medical imaging tools, diagnosis with the help of Computed Tomography and X-ray images is very important. Three different ResNet models (ResNet 50, ResNet 101, and ResNet 152) were investigated (a) to discriminate patients with COVID-19 from normal subjects, (b) to discriminate patients with COVID-19 from patients with Pneumonia, and (c) to discriminate patients with COVID-19, patients with Pneumonia, and normal subjects. ResNet 50 model gave the highest performances among these three models. As a result, we achieved the accuracy of 99.3% to discriminate COVID-19 and Normal, the accuracy of 99.2% to discriminate COVID-19 and Pneumonia, and the accuracy of 97.3% to discriminate COVID-19, Normal, and Pneumonia. In conclusion, the pre- trained ResNet 50 model has a big potential to detect the patients with COVID-19 quickly and accurately using chest X-Ray images only. We believe that this study will help to defeat the epidemic.

Detection of Covid-19 Patients with Convolutional Neural Network Based Features on Multi-class X-ray Chest Images

Covid-19 is a very serious deadly disease that has been announced as a pandemic by the world health organization (WHO). The whole world is working with all its might to end Covid-19 pandemic, which puts countries in serious health and economic problems, as soon as possible. The most important of these is to correctly identify those who get the Covid-19. Methods and approaches to support the reverse transcription polymerase chain reaction (RT-PCR) test have begun to take place in the literature. In this study, chest X-ray images, which can be accessed easily and quickly, were used because the covid-19 attacked the respiratory systems. Classification performances with support vector machines have been obtained by using the features extracted with residual networks (ResNet-50), one of the convolutional neural network models, from these images. While Covid-19 detection is obtained with support vector machines (SVM)-quadratic with the highest sensitivity value of 9635% with the 5 -fold cross-validation method, the highest overall performance value has been detected with both SVM-quadratic and SVM-cubic above 99%. According to these high results, it is thought that this method, which has been studied, will help radiology specialists and reduce the rate of false detection.

COVID-19 antibody seroprevalence in Duhok, Kurdistan Region, Iraq: A population-based study (preprint)

Abstract Objective This population-based study aimed to evaluate the seroprevalence of antibodies to SARS-CoV-2 in Duhok City, Kurdistan Region of Iraq. Methods We analyzed the national COVID-19 database that contains data regarding COVID-19 testing, management, and clinical outcomes in Duhok. For this study, different subdistricts within each district of Duhok were considered distinct clusters. Blood samples were collected from and questionnaires were administered to eligible and consenting participants who were members of different families from the subdistricts. Immunoassays were conducted to detect antibodies against SARS-CoV-2, and the associations between certain variables were investigated. Results The average number cases of COVID-19 before November 2020 was 23141 {+/-} 4364, which was significantly higher than the average number of cases between November 2020 and February 2021 (3737 {+/-} 2634; P = 0.001). A total of 743 individuals agreed to participate and were enrolled in the study. Among the participants, 465/743 (62.58%) were found to have antibodies against severe acute respiratory syndrome coronavirus 2. Among the participants with antibodies, 262/465 (56.34%) denied having any history of COVID-19-related symptoms. The most common symptom was fever (81.77%), followed by myalgia (81.28%). We found that antibody levels increased steadily with age (Pearson correlation coefficient = 0.117; P = 0.012). A significant association was found between antibody levels and the presence of symptoms (P = 0.023; odds ratio = 1.0023; 95% confidence interval = 1.0002-1.0061). Conclusions A significant reduction in the number of COVID-19 cases was observed. This might be due to the high prevalence of SARS-CoV-2 antibodies in Duhok. However, infection-prevention measures should be followed as it remains unclear whether acquired immunity is protective against reinfection. It expected that the infection rates during the next wave will not be as high as the first wave due to the high infection rate in the society.

Detection of Covid-19 Patients with Convolutional Neural Network Based Features on Multi-class X-ray Chest Images

Covid-19 is a very serious deadly disease that has been announced as a pandemic by the world health organization (WHO). The whole world is working with all its might to end Covid-19 pandemic, which puts countries in serious health and economic problems, as soon as possible. The most important of these is to correctly identify those who get the Covid-19. Methods and approaches to support the reverse transcription polymerase chain reaction (RT-PCR) test have begun to take place in the literature. In this study, chest X-ray images, which can be accessed easily and quickly, were used because the covid19 attacked the respiratory systems. Classification performances with support vector machines have been obtained by using the features extracted with residual networks (ResNet-50), one of the convolutional neural network models, from these images. While Covid-19 detection is obtained with support vector machines (SVM)-quadratic with the highest sensitivity value of 96.35% with the 5-fold cross-validation method, the highest overall performance value has been detected with both SVM-quadratic and SVM-cubic above 99%. According to these high results, it is thought that this method, which has been studied, will help radiology specialists and reduce the rate of false detection. © 2020 IEEE.