Why do people consent to receiving SARS-CoV-2 vaccinations? A representative survey in Germany.

OBJECTIVE: To answer the question: Why do people consent to being vaccinated with novel vaccines against SARS-CoV-2? DESIGN: Representative survey. SETTING: Online panel. PARTICIPANTS: 1032 respondents of the general German population. METHOD: A representative survey among German citizens in November/December 2021 that resulted in 1032 complete responses on vaccination status, sociodemographic parameters and opinions about the COVID-19 situation. RESULTS: Almost 83% of the respondents were vaccinated. The major motivation was fear of medical consequences of an infection and the wish to lead a normal life again. The major motivation to be not vaccinated was the fear of side effects and scepticism about long-term effectiveness and safety. Sixteen per cent of vaccinated respondents reported some serious side effect, while more than 30% reported health improvements, mostly due to the relief of psychological stress and social reintegration. We also validated a 'Corona Orthodoxy Score-COS' consisting of seven items reflecting opinions on COVID-19. The scale is reliable (alpha=0.76) and unidimensional. The COS was a highly significant predictor of vaccination status and readiness to be vaccinated in a multivariable logistic regression model. Those who were vaccinated were more likely to live in smaller households (OR=0.82, p=0.024), had a higher income (OR=1.27, p<0.001), a higher COS score (OR 1.4, p<0.0001) and used less alternative media (OR=0.44, p=0.0024) and scientific publications (OR=0.42, p=0.011) as information sources. CONCLUSIONS: The major motives for being vaccinated are fear of medical symptoms and the wish to lead a normal life. Those not wanting to be vaccinated cite a lack of knowledge regarding long-term safety and side effects as reasons. This can likely only be overcome by careful and active long-term efficacy and safety monitoring.

Identifying factors associated with COVID-19 related deaths during the first wave of the pandemic in Europe

Aim To clarify the high variability in COVID-19-related deaths during the first wave of the pandemic, we conducted a modeling study using publicly available data. Materials and methods We used 13 population- and country-specific variables to predict the number of population-standardized COVID-19-related deaths in 43 European countries using generalized linear models: the test-standardized number of SARS-CoV-2-cases, population density, life expectancy, severity of governmental responses, influenza-vaccination coverage in the elderly, vitamin D status, smoking and diabetes prevalence, cardiovascular disease death rate, number of hospital beds, gross domestic product, human development index and percentage of people older than 65 years. Results We found that test-standardized number of SARS-CoV-2-cases and flu vaccination coverage in the elderly were the most important predictors, together with vitamin D status, gross domestic product, population density and government response severity explaining roughly two-thirds of the variation in COVID-19 related deaths. The latter variable was positively, but only weakly associated with the outcome, i.e., deaths were higher in countries with more severe government response. Higher flu vaccination coverage and low vitamin D status were associated with more COVID-19 related deaths. Most other predictors appeared to be negligible. Conclusion Adequate vitamin D levels are important, while flu-vaccination in the elderly and stronger government response were putative aggravating factors of COVID-19 related deaths. These results may inform protection strategies against future infectious disease outbreaks.

Low Vitamin D Status in a Cancer Patient Population from Franconia, Germany.

BACKGROUND: Vitamin D has been shown to be associated with reduced risk and severity of COVID-19 and exerts regulating effects on all hallmarks of cancer. The goal of this study was to analyze the vitamin D status of a cancer patient cohort from our clinic in the Franconian region, Germany. METHODS: 25-hydroxyvitamin D concentrations were available for 116 patients included in prospective trials in our clinic. Associations of vitamin D with anthropometric and blood parameters were investigated using Kendall's τ correlation coefficients and linear regression. RESULTS: A total of 57 patients (49.1%) were vitamin D deficient (<20 ng/mL), and 92.2% did not meet the recommended vitamin D level of 40 ng/mL. There was a strong negative association between vitamin D and leukocyte count (τ = -0.173, p = 0.007) and C-reactive protein concentration (τ = -0.172, p = 0.007). In linear regression, the most important variables for predicting vitamin D levels were (in order of decreasing importance) season, fat mass index, platelet, and leukocyte count. CONCLUSIONS: Despite appeals towards medical societies to target widespread vitamin D deficiency in Germany more than 10 years ago, our data indicate that these have been without avail. Low vitamin D levels in cancer patients should be corrected using reasonable sun exposure and supplements.

The Epistemology of a Positive SARS-CoV-2 Test.

We investigate the epistemological consequences of a positive polymerase chain reaction SARS-CoV test for two relevant hypotheses: (i) V is the hypothesis that an individual has been infected with SARS-CoV-2; (ii) C is the hypothesis that SARS-CoV-2 is the cause of flu-like symptoms in a given patient. We ask two fundamental epistemological questions regarding each hypothesis: First, how much confirmation does a positive test lend to each hypothesis? Second, how much evidence does a positive test provide for each hypothesis against its negation? We respond to each question within a formal Bayesian framework. We construe degree of confirmation as the difference between the posterior probability of the hypothesis and its prior, and the strength of evidence for a hypothesis against its alternative in terms of their likelihood ratio. We find that test specificity-and coinfection probabilities when making inferences about C-were key determinants of confirmation and evidence. Tests with < 87% specificity could not provide strong evidence (likelihood ratio > 8) for V against ¬V regardless of sensitivity. Accordingly, low specificity tests could not provide strong evidence in favor of C in all plausible scenarios modeled. We also show how a positive influenza A test disconfirms C and provides weak evidence against C in dependence on the probability that the patient is influenza A infected given that his/her symptoms are not caused by SARS-CoV-2. Our analysis points out some caveats that should be considered when attributing symptoms or death of a positively tested patient to SARS-CoV-2.