The COVID-19 vaccination acceptance/hesitancy rate and its determinants among healthcare workers of 91 Countries: A multicenter cross-sectional study.

The aim of this study was to investigate the COVID-19 vaccination acceptance rate and its determinants among healthcare workers in a multicenter study. This was a cross-sectional multi-center survey conducted from February 5 to April 29, 2021. The questionnaire consisted of 26 items in 6 subscales. The English version of the questionnaire was translated into seven languages and distributed through Google Forms using snowball sampling; a colleague in each country was responsible for the forward and backward translation, and also the distribution of the questionnaire. A forward stepwise logistic regression was utilized to explore the variables and questionnaire factors tied to the intention to COVID-19 vaccination. 4630 participants from 91 countries completed the questionnaire. According to the United Nations Development Program 2020, 43.6 % of participants were from low Human Development Index (HDI) regions, 48.3 % high and very high, and 8.1 % from medium. The overall vaccination hesitancy rate was 37 %. Three out of six factors of the questionnaire were significantly related to intention to the vaccination. While 'Perceived benefits of the COVID-19 vaccination' (OR: 3.82, p-value<0.001) and 'Prosocial norms' (OR: 5.18, p-value<0.001) were associated with vaccination acceptance, 'The vaccine safety/cost concerns' with OR: 3.52, p-value<0.001 was tied to vaccination hesitancy. Medical doctors and pharmacists were more willing to take the vaccine in comparison to others. Importantly, HDI with OR: 12.28, 95 % CI: 6.10-24.72 was a strong positive determinant of COVID-19 vaccination acceptance. This study highlighted the vaccination hesitancy rate of 37 % in our sample among HCWs. Increasing awareness regarding vaccination benefits, confronting the misinformation, and strengthening the prosocial norms would be the primary domains for maximizing the vaccination coverage. The study also showed that the HDI is strongly associated with the vaccination acceptance/hesitancy, in a way that those living in low HDI contexts are more hesitant to receive the vaccine.

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.

Antibiotic use in patients with COVID-19: a 'snapshot' Infectious Diseases International Research Initiative (ID-IRI) survey.

BACKGROUND: Antibiotics may be indicated in patients with COVID-19 due to suspected or confirmed bacterial superinfection. OBJECTIVES: To investigate antibiotic prescribing practices in patients with COVID-19. METHODS: We performed an international web-based survey and investigated the pattern of antibiotic use as reported by physicians involved in treatment of COVID-19. SPSS Statistics version 25 was used for data analysis. RESULTS: The survey was completed by 166 participants from 23 countries and 82 different hospitals. Local guidelines for antibiotic use in COVID-19 patients were reported by 61.8% (n = 102) of participants and for 82.9% (n = 136) they did not differ from local community-acquired pneumonia guidelines. Clinical presentation was recognized as the most important reason for the start of antibiotics (mean score = 4.07 and SD = 1.095 on grading scale from 1 to 5). When antibiotics were started, most respondents rated as the highest the need for coverage of atypical pathogens (mean score = 2.8 and SD = 0.99), followed by Staphylococcus aureus (mean score = 2.67 and SD = 1.05 on bi-modal scale, with values 1 and 2 for disagreement and values 3 and 4 for agreement). In the patients on the ward, 29.1% of respondents chose not to prescribe any antibiotic. Combination of ß-lactams and macrolides or fluoroquinolones was reported by 52.4% (n = 87) of respondents. In patients in the ICU, piperacillin/tazobactam was the most commonly prescribed antibiotic. The mean reported duration of antibiotic treatment was 7.12 (SD = 2.44) days. CONCLUSIONS: The study revealed widespread broad-spectrum antibiotic use in patients with COVID-19. Implementation of antimicrobial stewardship principles is warranted to mitigate the negative consequences of antibiotic therapy.

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.