Comparison of performance and anthropometric parameters in paediatric competitive athletes during COVID-19 pandemic in the Czech Republic.

OBJECTIVES: In 2020, measures against the spread of COVID-19 were adopted, including nationwide school closures, restrictions on the free movement of persons and leisure time sports activities. The aim was to assess the impact of COVID-19-associated restrictions on the performance of paediatric and adolescent competitive athletes by comparing basic anthropometric and performance parameters. METHODS: The sample comprised 389 participants (115 girls, 274 boys). All participants were examined during regular preventive sports health checks from September to November 2019 and a year later. At the initial examination, the mean age of the entire sample was 12.2 ± 2.7 years (median 12.0, minimum 7.0; maximum 17.0). The examination consisted of a complete medical history and physical examination including maximal exercise testing on a leg cycle ergometer. RESULTS: In the entire sample, as well as in the boy and girl subgroups, body height, weight, body mass index (BMI), BMI percentile, and power output significantly increased according to a percentile graph for boys and girls in 2020. A reduction in power output (W/kg) was found. By 2020, W/kg dropped in 56.4% of the youngest participants (7-13 years), 75% of those aged 14-16 years and 64.9% of the oldest individuals (16-17 years). The percentage of the youngest children with power output reductions was statistically significantly lower than the percentages of the other age subgroups (p = 0.007). There were no significant differences in results between genders. CONCLUSIONS: Performance and anthropometric parameters worsened especially among older children. This should be reflected when planning epidemic measures in case of any similar situation in the future.

Prevalence of Burnout in Healthcare Workers of Tertiary-Care Hospitals during the COVID-19 Pandemic: A Cross-Sectional Survey from Two Central European Countries.

COVID-19 has led to an unprecedented strain on healthcare workers (HCWs). This study aimed to determine the prevalence of burnout in hospital employees during a prolonged pandemic-induced burden on healthcare systems. An online survey among employees of a Czech and Slovak university hospital was conducted between November 2021 and January 2022, approximately when the incidence rates peaked in both countries. The Maslach Burnout Inventory-Human Services Survey was applied. We obtained 807 completed questionnaires (75.1% from Czech employees, 91.2% from HCWs, 76.2% from women; mean age of 42.1 ± 11 years). Burnout in emotional exhaustion (EE) was found in 53.2%, depersonalization (DP) in 33%, and personal accomplishment (PA) in 47.8% of respondents. In total, 148 (18.3%) participants showed burnout in all dimensions, 184 (22.8%) in two, and 269 (33.3%) in at least one dimension. Burnout in EE and DP (65% and 43.7%) prevailed in physicians compared to other HCWs (48.6% and 28.8%). Respondents from COVID-19-dedicated units achieved burnout in the EE and DP dimensions with higher rates than non-frontline HCWs (58.1% and 40.9% vs. 49.9% and 27.7%). Almost two years of the previous overloading of healthcare services, caused by the COVID-19 pandemic, resulted in the relatively high prevalence of burnout in HCWs, especially in physicians and frontline HCWs.

Predictors and characteristics of post-acute COVID-19 syndrome in healthcare workers.

BACKGROUND: Post-acute COVID-19 syndrome (PCS) is a multisystem disorder degrading the quality of life. The study determined characteristics and predictors of PCS in unvaccinated healthcare workers (HCWs) suffering from PCS based on a comparison with their fully recovered counterparts. METHODS: 305 HCWs were examined at least 12 weeks post COVID-19 symptom onset to obtain data about their acute phase of COVID-19 and current health status and tested for complete blood count, C-reactive protein (CRP), electrophoresis of plasma proteins and SARS-CoV-2-specific immunoglobulin (Ig) G and M. RESULTS: 181 (59.3%) HCWs reported persisting symptoms attributable to PCS during the examination and 124 (40.7%) HCWs stated no symptoms. In the entire sample, the mean CRP level slightly exceeded the normal range (6.63 mg/L, 95% confidence interval [CI] 5.96-7.3) while all other laboratory results were within the normal range. No statistically significant differences in laboratory results were revealed between both subgroups except for the mean Ig levels, which were higher in HCWs with PCS. The average number of symptoms of PCS was 1.9 (median 2). The most frequent symptoms of PCS were fatigue that interfered with daily life (47.5%), shortness of breath (38.1%), muscle or joint aches (16%), loss of smell (14.9%), headache (14.9%) and sleep disorders (11%). The only statistically significant predictors of PCS were female sex (odds ratio [OR] 1.48, 95% CI 1.059-2.067, p = .022) and increasing age (OR 1.04, 95% CI 1.01-1.07, p = .008). CONCLUSIONS: PCS appears to be a prevalent condition determined by female sex and increasing age.

Machine Learning at the Service of Survival Analysis: Predictions Using Time-to-Event Decomposition and Classification Applied to a Decrease of Blood Antibodies against COVID-19

The Cox proportional hazard model may predict whether an individual belonging to a given group would likely register an event of interest at a given time. However, the Cox model is limited by relatively strict statistical assumptions. In this study, we propose decomposing the time-to-event variable into 'time';and 'event';components and using the latter as a target variable for various machine-learning classification algorithms, which are almost assumption-free, unlike the Cox model. While the time component is continuous and is used as one of the covariates, i.e., input variables for various classification algorithms such as logistic regression, naïve Bayes classifiers, decision trees, random forests, and artificial neural networks, the event component is binary and thus may be modeled using these classification algorithms. Moreover, we apply the proposed method to predict a decrease or non-decrease of IgG and IgM blood antibodies against COVID-19 (SARS-CoV-2), respectively, below a laboratory cut-off, for a given individual at a given time point. Using train-test splitting of the COVID-19 dataset (n=663 individuals), models for the mentioned algorithms, including the Cox proportional hazard model, are learned and built on the train subsets while tested on the test ones. To increase robustness of the model performance evaluation, models' predictive accuracies are estimated using 10-fold cross-validation on the split dataset. Even though the time-to-event variable decomposition might ignore the effect of individual data censoring, many algorithms show similar or even higher predictive accuracy compared to the traditional Cox proportional hazard model. In COVID-19 IgG decrease prediction, multivariate logistic regression (of accuracy 0.811), support vector machines (of accuracy 0.845), random forests (of accuracy 0.836), artificial neural networks (of accuracy 0.806) outperform the Cox proportional hazard model (of accuracy 0.796), while in COVID-19 IgM antibody decrease prediction, neither Cox regression nor other algorithms perform well (best accuracy is 0.627 for Cox regression). An accurate prediction of mainly COVID-19 IgG antibody decrease can help the healthcare system manage, with no need for extensive blood testing, to identify individuals, for instance, who could postpone boosting vaccination if new COVID-19 variant incomes or should be flagged as high risk due to low COVID-19 antibodies.

Network Analysis for Uncovering the Relationship between Host Response and Clinical Factors to Virus Pathogen: Lessons from SARS-CoV-2.

Analysing complex datasets while maintaining the interpretability and explainability of outcomes for clinicians and patients is challenging, not only in viral infections. These datasets often include a variety of heterogeneous clinical, demographic, laboratory, and personal data, and it is not a single factor but a combination of multiple factors that contribute to patient characterisation and host response. Therefore, multivariate approaches are needed to analyse these complex patient datasets, which are impossible to analyse with univariate comparisons (e.g., one immune cell subset versus one clinical factor). Using a SARS-CoV-2 infection as an example, we employed a patient similarity network (PSN) approach to assess the relationship between host immune factors and the clinical course of infection and performed visualisation and data interpretation. A PSN analysis of ~85 immunological (cellular and humoral) and ~70 clinical factors in 250 recruited patients with coronavirus disease (COVID-19) who were sampled four to eight weeks after a PCR-confirmed SARS-CoV-2 infection identified a minimal immune signature, as well as clinical and laboratory factors strongly associated with disease severity. Our study demonstrates the benefits of implementing multivariate network approaches to identify relevant factors and visualise their relationships in a SARS-CoV-2 infection, but the model is generally applicable to any complex dataset.

Vaccination against COVID-19 as prevention of occupational disease in University of Defence members in the Czech Republic - motivation for vaccination and reasons for hesitancy.

OBJECTIVES: The aim of this cross-sectional study was to investigate the determinants of COVID-19 vaccine acceptance in University of Defence members. BACKGROUND: Vaccination is the most important method of prevention against COVID-19 and achieving sufficient vaccination rate is thus essential to maintain the military capability. METHODOLOGY: An online questionnaire was distributed electronically to 2,408 respondents in autumn 2021. The survey was designed to collect demographic predictors of vaccination, data on motivation and reasons for refusing vaccination. RESULTS: A total of 626 completed questionnaires were analyzed, of which 557 (89 %) were vaccinated and 69 (11 %) were unvaccinated respondents. The most significant predictors of vaccine acceptance were: concern about COVID-19 (OR 2.44, p < 0.001), history of COVID-19 (OR 0.39, p = 0.001). The most frequently cited motives for vaccination were health protection (74.7 %) and an easier social life (69.1 %), while concerns about vaccine safety and vaccine adverse effects (79.1 %) followed by lack of confidence in vaccine efficacy (68.7 %) were the main reasons for vaccine refusal. CONCLUSION: To increase the vaccination rate it is necessary to target the younger population and increase awareness of vaccine safety and efficacy. If these measures are not sufficient to encourage voluntary vaccine acceptance, consideration should be given to making vaccination mandatory for selected professional groups (Tab. 5, Fig. 1, Ref. 25).

The kinetics and predictors of anti-SARS-CoV-2 antibodies up to 8 months after symptomatic COVID-19: A Czech cross-sectional study.

The presence of neutralizing SARS-CoV-2-specific antibodies indicates protection against (re)infection, however, the knowledge of their long-term kinetics is limited. This study analyzed the presence of COVID-19-induced antibodies in unvaccinated healthcare workers (HCWs) over the period of 1-8 months post symptom onset (SO) and explored the determinants of persisting immunoglobulin (Ig) seropositivity. Six hundred sixty-two HCWs were interviewed for anamnestic data and tested for IgG targeting the spike protein (S1 and S2) and IgM targeting the receptor-binding domain. A Cox regression model was used to explore potential predictors of seropositivity with respect to the time lapse between SO and serology testing. 82.9% and 44.7% of HCWs demonstrated IgG and IgM seropositivity, respectively, with a mean interval of 83 days between SARS-CoV-2 detection and serology testing. On average, HCWs reported seven symptoms in the acute phase lasting 20 days. IgG seropositivity rates among HCWs decreased gradually to 80%, 50%, and 35% at 3, 6, and 8 months after SO, while IgM seropositivity fell rapidly to 60%, 15%, and 0% over the same time intervals. The number of symptoms was the only predictor of persisting IgG seropositivity (odds ratio [OR] 1.096, 95% confidence interval [CI] 1.003-1.199, p = 0.043) and symptom duration a predictor of IgM seropositivity (OR 1.011, 95% CI 1.004-1.017, p = 0.002). Infection-induced anti-SARS-CoV-2 IgG rates drop to a third in seropositive participants over the course of 8 months. Symptom count and duration in the acute phase of COVID-19 are both relevant to the subsequent kinetics of antibody responses.

Relationship between Acute-Phase Symptoms and Immunoglobulin G Seropositivity up to Eight Months after COVID-19.

Background and Objectives: Given the limited knowledge of antibody responses to COVID-19 and their determinants, we analyzed the relationship between the occurrence of acute-phase symptoms and infection-induced immunoglobulin (Ig) G seropositivity up to 8 months post-symptom onset. Materials and Methods: In this cross-sectional study, 661 middle-aged unvaccinated healthcare workers (HCWs) were interviewed about the presence of symptoms during the acute phase of their previously confirmed COVID-19 and were tested for specific IgG, targeting the spike protein (S1 and S2). The dependence of seropositivity on the symptom occurrence was explored through multiple logistic regression, adjusted for the interval between symptom onset and serology testing, and through classification and regression trees. Results: A total of 551 (83.4%) HCWs showed seropositivity and, inversely, 110 (16.6%) HCWs were seronegative. The chance of IgG seropositivity was increased by dyspnea (odds ratio (OR) 1.48, p < 0.001) and anosmia (OR 1.52, p = 0.021). Fever in HCWs with dyspnea resulted in the highest detected seropositivity rate, and anosmia in HCWs without dyspnea significantly increased the proportion of seropositivity. Conclusion: Clinical manifestation of the acute phase of COVID-19 predisposes to the development of infection-induced antibody responses. The findings can be applied for assessing the long-term protection by IgG, and thus, for creating effective surveillance strategies.

Motivation for COVID-19 Vaccination in Priority Occupational Groups: A Cross-Sectional Survey.

Due to the limited availability of COVID-19 vaccines, occupational groups with priority access were identified prior to vaccination. The study aimed to analyze motives for vaccination in these occupational groups. METHODS: Members of occupational groups, who were vaccinated at the vaccination center of University Hospital Olomouc before 30 April 2021, were asked to fill in an online questionnaire. RESULTS: A total of 3224 completed questionnaires were obtained from 1332 healthcare workers, 1257 school employees, 363 social service workers, 210 security force members, and 62 critical infrastructure workers. The most frequent motive for vaccination was the effort to protect family members (76.2%), the effort to prevent the spread of COVID-19 in one's profession (72.3%), followed by concerns about COVID-19 itself (49.1%) and exemptions from anti-epidemic measures (36.8%). Only for social services, the motive focused on one's profession was mentioned more often (75.2%) than the motive focused on the family (71.1%). At the level of detailed profession-oriented motives, a collegial effort of security force members to protect co-workers and not to endanger the workplace was dominant. CONCLUSIONS: The effort to prevent the spread of COVID-19 in the professional environment is a strong motive for vaccination, and strongest among social service workers.

Motivation to COVID-19 Vaccination and Reasons for Hesitancy in Employees of a Czech Tertiary Care Hospital: A Cross-Sectional Survey.

High vaccination coverage among healthcare workers (HCWs) is crucial for managing the COVID-19 pandemic. The aim was to determine the demand for vaccination among all employees (n = 4553) of a tertiary care hospital after several weeks of the vaccine's availability, and to analyze motives for acceptance and reasons for hesitancy through an anonymous online questionnaire. Upon the completion of data collection, the hospital's vaccination coverage was at 69.8%. A total of 3550 completed questionnaires were obtained (2657 from vaccinated, 893 from unvaccinated employees). Significant predictors of vaccine acceptance were: age (odds ratio (OR) 1.01, 95% confidence interval (CI) 1.01-1.02), sex (OR (females) 0.58, 95% CI 0.45-0.75), job type (OR (non-physician HCWs) 0.54, 95% CI 0.41-0.72; OR (non-HCWs) 0.51, 95% CI 0.37-0.71), fear of COVID-19 (OR 1.4, 95% CI 1.34-1.46), history of COVID-19 (OR 0.41, 95% CI 0.34-0.49) and of influenza vaccination (OR 2.74, 95% CI 2.12-3.57). The most frequent motive for acceptance was the effort to protect family members (84%), while concerns about vaccine safety and side effects (49.4%), followed by distrust in the vaccine's efficacy (41.1%) were the top reasons for hesitancy. To increase vaccination coverage among HCWs, it is necessary to raise awareness of vaccine safety and efficacy.

Demand and motivation for influenza vaccination among healthcare workers before and during the COVID-19 era: a cross-sectional survey.

Protection of healthcare workers (HCWs) against influenza is essential for patient health and a functional health system. The study aimed to analyze the demand for seasonal influenza vaccination (SIV) among various groups of HCWs in a tertiary care hospital before and during the COVID-19 pandemic and to identify their motives for this season's SIV. Before this influenza season (2020/21), the hospital management offered free SIV to all HCWs and promoted it on the internal network. Out of 4,167 HCWs, 630 HCWs expressed interest in SIV and were vaccinated in the hospital. They filled in a total of 603 self-administered pen-and-paper questionnaires. The mean age of the respondents (374 females and 229 males) was 45 ± 12 years. Physicians accounted for 48% of the vaccinated persons but for only 24% (p < .001) of all HCWs to whom SIV was offered. Only 16% of respondents vaccinated this year also received SIV before the last season (2019/20), with the proportion of physicians (19%) being statistically significantly higher than that of non-physicians (13%, p = .045) and the proportion of chronically ill HCWs (22%) being higher than that of healthy individuals (13%, p = .004). Most frequently, respondents' motivation to get vaccinated this year was self-protection (61%), that is concerns about contracting influenza together with COVID-19 or alone, followed by family protection (58%) and patient protection (53%). In conclusion, COVID-19 contributed to an increased demand for SIV among HCWs and the threat of contracting it together with influenza was the most frequent motive.