Search Results (85)

This study aims to empirically analyze the patterns, intensity, and perceived barriers to physical activity among Polish university students during the COVID-19 pandemic. The research utilized a diagnostic survey method, employing a questionnaire. The online survey was conducted from December 2020 to May 2022 via the Webankieta.pl platform. The minimum sample size, calculated using the standard formula for estimating a proportion in a large population, was set at 1100 participants and was exceeded, with 1260 students providing valid responses. The results show that over half (55.8%, mainly women) of the respondents did not participate in regular physical activity during the pandemic. Participants cited lack of desire, fatigue, and low motivation—not pandemic restrictions—as primary reasons. Conversely, 44.2% of respondents, mostly men, reported engaging in regular physical activity. Most engaged in moderate-intensity activities two to five times a week, with vigorous activities performed slightly less often. Women were more likely to do both types, while men favored strength training. The most common activities included walking (61.6%), simple gymnastic exercises (43.1%), strength training with equipment (35.0%), cycling (34.5%), and calisthenics (30.2%). The majority (81.3%) exercised at home or nearby (33.4%). Reported barriers, especially among those who exercised regularly, were pandemic-related, such as limited or closed access to gyms, fitness centers, and pools (59.1%), along with time constraints (44.7%) and low motivation or determination (32.0%). The findings emphasize the importance of targeted interventions to boost physical activity among university students, particularly women and those with fewer financial resources. Universities should consider implementing programs that promote accessible, regular activity and initiatives to enhance motivation and foster long-term, health-promoting habits. Full article

Background/Objectives: Vaccine clinical trials face high costs, long timelines, and variable progression rates, yet systematic evidence linking trial design features to progression outcomes remains limited. This study aimed to identify trial design features associated with vaccine trial progression and to explore robust design configurations using machine learning approaches. Methods: We analyzed 1618 vaccine trials registered from 2012 to 2022. Progression was defined as phase advancement (phase I/II) or regulatory authorization (phase III). Logistic regression assessed associations with progression. Random forest classifiers with cross-validation were used to estimate predicted progression probabilities based on combinations of design features. Monte Carlo simulations compared model-identified robust configurations with randomly generated configurations. Results: Among 1618 trials, 579 achieved phase progressions, corresponding to an overall observed progression rate of 35.8%. Larger sample size, preventive vaccine purpose, COVID-19 indication, and enrollment across all age groups were consistently associated with higher observed odds of progression in both univariable and multivariable logistic regression analyses. In machine learning analyses, the pooled mean predicted progression probability of model-identified robust configurations was 48.93%, compared with 39.44% for historically observed design configurations, corresponding to a relative increase of 24.1%. Simulations further showed a lower projected cumulative development duration (106.87 vs. 128.25 months; −16.7%) and reduced projected cost (USD 100.67M vs. USD 108.33M; −7.1%) for robust configurations compared with historical strategies. Conclusions: This study provides a data-driven framework for characterizing historical vaccine trial design patterns. By integrating machine learning with observational registry data, it supports hypothesis generation and descriptive benchmarking of design features that may inform the design of future prospective or causal investigations. Full article

Background/Objectives: The COVID-19 pandemic substantially affected both the provision and demand for health services. There are few studies that analyzed factors associated with unmet healthcare needs during the COVID-19 pandemic relative to regular pre-pandemic period, mainly in high-income countries. This study examines the change in the likelihood of reporting unmet healthcare needs as well as individual demographic, socioeconomic, health-related, and geographical characteristics associated with unmet healthcare needs during the COVID-19 pandemic and during the pre-pandemic period in Serbia. Methods: We utilized data from the Survey on Income and Living Conditions in the Republic of Serbia for 2019 and 2021. Multivariable logistic regression analysis was conducted on the pooled sample comprising 21,422 respondents aged 16 years and older from both survey years. Results: Overall, respondents had 1.6 times higher odds of reporting unmet healthcare needs during the COVID-19 pandemic compared with the pre-pandemic period. In the pooled multivariable analysis, older adults (OR = 2.32), individuals reporting poor or very poor self-rated health (OR = 2.16), and those with chronic diseases (OR = 1.46) were more likely to report unmet healthcare needs. In contrast, individuals with higher levels of education (ORs = 0.72 for high school, 0.65 for college, and 0.51 for master’s degree), retired individuals (OR = 0.70), inactive persons (OR = 0.76), and students/pupils (OR = 0.22) had significantly lower odds of unmet needs, compared to their counterparts. Relative to the poorest income quintile, individuals in all higher income quintiles were less likely to report unmet healthcare needs, with the lowest odds observed in the 5th quintile (OR = 0.49). Residents of thinly populated areas had higher odds compared to those in densely populated areas (OR = 1.19). Conclusions: Identified associations with unmet healthcare needs should be used to develop targeted strategies for strengthening healthcare access, particularly in the context of future crises. Full article

The aim of this study is to ascertain whether digitalization and sustainability engender higher revenues, by identifying the differences between IT firms vs. non-IT firms when confronted with the COVID-19 pandemic. We postulate that firms operating in fields related to information technology face different challenges and performance requirements than non-IT firms due to the disparity of technological absorption capacity, compatibility with the business model, necessary infrastructure, and also in terms of the marginal effect of growth. A quantitative analysis is conducted utilizing the Pooled Ordinary Least Squares method on a sample of companies retrieved from the Refinitiv Eikon database. The results reveal a significant positive relationship between digitalization and performance, and also sustainability and performance. Intersectoral analysis pointed out that digitalization had a significant negative impact for non-IT firms during the COVID-19 pandemic due to the critical function of digital technologies in empowering businesses to overcome adversity. Significant relationships are observed between sustainability and revenues, positive in the case of non-IT firms, and negative in the case of IT firms. The contributions of this study are relevant for companies, in the development of strategies, for scholars in deepening the research, and for policymakers in enabling the formulation of legal frameworks and the establishment of necessary infrastructure for sustainable development and digitalization. Full article

Objective: Although Las Vegas is a major tourist hub, it is not among the counties that are under CDC surveillance for Clostridioides difficile infection (CDI), a major nosocomial infection. To determine the distribution of C. difficile ribotypes in the Las Vegas area, we collected stool samples from CDI-positive patients at the University Medical Center (UMC). Methods: We included adult patients diagnosed with CDI and provided informed consent. C. difficile was isolated from the stool samples and ribotyped. Demographic information was also obtained and analyzed. All information was compared to the surveillance data from the CDC. Results: We identified more frequently in male patients than in the CDC data. Less than half of the patients used antibiotics prior to the infection. We observed several comorbidities in our patient sample pool, with cardiovascular disease and diabetes being the most prevalent comorbidities. Hypervirulent C. difficile strain 027 was the most prevalent ribotype. Except for two samples of ribotype 076, all other samples represented unique singlet ribotypes. Four of these ribotypes (160, 302, 363, and 813) have not been explicitly reported in humans. Conclusions: Due to the unique environment created by the tourism industry in Las Vegas, this population is exposed to national and international visitors. This study shows the pre-COVID landscape of C. difficile ribotypes in Las Vegas and offers valuable insights into the varieties of C. difficile that are currently infecting this community. Full article

Background: Validated instruments can be used to quantify vaccine hesitancy, but few provide transportable operational cut-offs or temporal stability metrics. We evaluated the Oxford Vaccine Hesitancy Scale (OVHS) to quantify discrimination for the self-reported historical COVID-19 vaccine refusal, derive and validate a single operational cut-off across populations, and assess the test–retest reliability. Methods: Five datasets (including one from a repeat administration) comprising 2451 assessments from 1989 demographically representative UK and US respondents were analyzed without pooling. Receiver operating characteristic (ROC) curves with 10,000 bootstrap replications (bias-corrected and accelerated and percentile Cis) were used to quantify discrimination. A Youden’s J near-optimal plateau algorithm constrained by a cross-dataset specificity floor (≥0.75) was used to select a transportable cut-off. A prevalence-agnostic aggregate Index of Union (Iuagg) provided secondary confirmation of this cut-off. Cut-off behaviour was visualized with a multi-sample two-graph ROC plot. The six-week test–retest reliability on a UK sample used a two-way mixed-effects, absolute-agreement ICC(A,1). Results: AUCs ranged 0.760–0.971 across datasets (derivation AUC: 0.960). The Youden plateau spanned scores 34–38; applying the specificity floor yielded an operational cut-off OVHS ≥ 35, which was confirmed by the Iuagg. At ≥35, sensitivity/specificity were 0.73–0.95/0.63–0.87 across samples; negative predictive value exceeded 0.90 in all cohorts. The test–retest reliability was good to excellent, with the OVHS total ICC(A,1) = 0.884 (95% CI 0.863–0.903); subscales ranged from 0.649 to 0.901 and the average-measure ICC(A,2) = 0.939. Conclusions: The OVHS demonstrates good-to-excellent discrimination for historical COVID-19 vaccine refusal and strong temporal stability. We found a single, transparent, and transportable operational cut-off (≥35). Our cut-off derivation framework is scale- and endpoint-agnostic and may generalize to other hesitancy instruments and decision cut-offs more broadly. Full article

A key requirement in containing contagious diseases, like the COVID-19 pandemic, is the ability to efficiently carry out mass diagnosis over large populations, especially when testing resources are limited and rapid identification is essential for outbreak control. Some of the leading testing procedures, such as those utilizing qualitative polymerase chain reaction, involve using dedicated machinery which can simultaneously process a limited amount of samples. A candidate method to increase the test throughput is to examine pooled samples comprised of a mixture of samples from different patients. In this work, we study pooling-based tests which operate in a one-shot fashion, while providing an indication not solely on the presence of infection, but also on its level, without additional pool-tests, as often required in COVID-19 testing. As these requirements limit the application of traditional group-testing (GT) methods, we propose a multi-level GT scheme, which builds upon GT principles to enable accurate recovery using much fewer tests than patients, while operating in a one-shot manner and providing multi-level indications. We provide a theoretical analysis of the proposed scheme and characterize conditions under which the algorithm operates reliably and at affordable computational complexity. Our numerical results demonstrate that multi-level GT accurately and efficiently detects infection levels, while achieving improved performance and less pooled tests over previously proposed oneshot COVID-19 pooled-testing methods. Our simulations show that the efficient method proposed in this work can correctly identify the infected items and their infection levels with high probability at the known upper bound (for a maximum likelihood decoder in GT) on the number of tests. We also show that the method works well in practice when the number of infected items is not assumed to be known in advance. Full article

Background: COVID-19 vaccination has been a key tool in protecting healthcare workers (HCWs), but breakthrough infections have occurred. The durability of vaccine-induced immunity and its impact on HCWs remain critical for public health strategies. Methods: In this small cohort study (n = 32), we assessed antibody levels and breakthrough infection rates in HCWs over 12 months post-vaccination, providing insights for booster strategies and infection control. A cohort of 32 HCWs was screened for SARS-CoV-2 infection using weekly self-administered swabs and blood samples collected at baseline, 6 months, and 12 months. SARS-CoV-2 antibodies (IgG, IgM) targeting spike proteins and nucleocapsids were analyzed using a multi-antigen serology panel. Pooled nucleic acid testing was employed for infection detection. Results: Nine participants showed breakthrough infections, with nucleocapsid antibodies indicating prior infection. Eight of these cases occurred after the third vaccine dose during the Omicron-dominant period. Anti-spike antibody levels declined significantly in participants without prior infection, while those with breakthrough infections exhibited increased levels. The half-life of S1 and S1 receptor-binding domain (RDB) vaccine-induced antibodies was 144 and 166 days, respectively, which aligns with CDC data. These findings provide valuable insights for determining the optimal timing of booster doses. Conclusions: Our findings highlight the waning antibody levels over time and the occurrence of breakthrough infections. Although based on a small sample, these data support the need for ongoing monitoring and timely boosters. Full article

The COVID-19 pandemic presented many potentially traumatic circumstances. Research continues to investigate pandemic-related Post-traumatic Growth (PTG). However, most studies fail to fulfil the parameters of PTG whereby a triggering event must be of seismic intensity and have ceased before PTG can manifest, producing significant validity and reliability issues. The relationships between PTG, trait resilience and fear are also under-researched, particularly in circumstances where the parameters of PTG are met. This study examined the relationship between PTG, COVID-19-related fear and trait resilience. Participants (n = 229) completed an online questionnaire incorporating the Post-Traumatic Growth Inventory and the Connor–Davidson Resilience Scale. The sample participants were moderately traumatised with moderate–low PTG (M = 50.85). Participants reported greater levels of PTG compared to participants from pre-COVID studies, notably in relation to the constructs of Relating to Other (d = 0.29), New Possibilities (d = 0.47), Personal Strength (d = 0.39), and Spiritual Change (d = 0.29). Higher levels of resilience (B = 0.48) and COVID-19-related fear (B = 0.16) were associated with greater overall PTG. Younger participants also reported greater levels of PTG (B = −0.29). The findings advance current knowledge regarding the potential relationship between fear and PTG and demonstrate that trait resilience is a promotional factor, presenting opportunity for future intervention formulation. However, reform is required within the PTG literature pool. Future research investigating PTG must reach both parameters. In circumstances where this is impossible, research concerning newfound positive cognition during adverse circumstances should be re-explored as Post-Adversarial Appreciation (PAA) to maintain validity. Full article

Background: Next-generation sequencing (NGS) has emerged as a transformative tool in precision medicine, offering insights into actionable genomic alterations and informing clinical decision-making in childhood and adolescent/young adult (AYA) solid tumors. Methods: We conducted a systematic review and meta-analysis to assess the utility of NGS in identifying actionable genomic alterations and its impact on clinical decision-making. Studies involving patients aged 0–40 years with solid tumors were included. Data were extracted using Covidence, and pooled estimates were calculated using a random-effects model. Bias was assessed using Begg–Mazumdar, Egger, and Harbord tests. Results: Out of 13,624 references screened, 24 studies met eligibility criteria, comprising 5278 patients and 5359 samples, of which 5207 provided usable data. The pooled proportion of actionable alterations was 57.9% (95% CI: 49.0–66.5%), with minimal evidence of publication bias. Clinical decision-making outcomes were reported in 21 studies, with a pooled proportion of 22.8% (95% CI: 16.4–29.9%). Germline mutation rates, reported in 11 studies, yielded a pooled proportion of 11.2% (95% CI: 8.4–14.3%), consistent with rates typically observed in childhood cancers. Significant heterogeneity was observed across studies due to differences in sequencing methodologies, tumor types, and sampling strategies. Conclusions: NGS demonstrates considerable potential in identifying actionable genomic targets and guiding clinical decision-making in childhood and AYA solid tumors. However, the variability in methodologies underscores the need for standardized protocols and reporting practices to enhance comparability and generalizability. This meta-analysis highlights the promise of genomic medicine while acknowledging challenges posed by heterogeneity in study designs. Full article

The COVID-19 pandemic has increased the interest in the use of wastewater-based surveillance (WBS) strategy for infectious disease monitoring, especially when clinical cases are underreported. The excretion of monkey virus (MPXV) in the feces of both symptomatic and preclinical individuals has further driven the interest in WBS applicability to MPXV monitoring in wastewater to support its mitigation efforts. We performed a systematic review with meta-analysis, using six databases to assess MPXV detection in wastewater. We performed a random-effects model meta-analysis to calculate the pooled prevalence at a 95% confidence interval (95% CI). Also, we carried out a subgroup analysis according to the country regions and a sensitivity analysis excluding studies classified as having a high risk of bias. The overall MPXV positivity rate in wastewater was estimated at 22% (95% CI: 14−30%; I² = 94.8%), with more detection rate in North America (26%, 95% CI: 8–43%) compared to Europe and Asia (22%, 95% CI: 12–31%). The MPXV detection rate was significantly higher in 2022 studies (22%, 95% CI: 13–31%) compared to 2023 (19%, 95% CI: 14–25%). The real-time PCR platform significantly detected more MPXV (24%, 95% CI: 14–34%) than the digital droplet PCR-based studies (17%, 95% CI: 4–31%), which was used less frequently. Viral concentration with centrifugation procedure indicated higher detection rates (21%, 95% CI: 10–33%) than other known sample concentration protocols. Generally, MPXV detection rates in wastewater samples strongly correlate with incidence cases of mpox (range of R = 0.78–0.94; p < 0.05). Findings from this study suggest that WBS of MPXV could be employed as an epidemiological early warning tool for disease monitoring and mpox outbreak prediction similar to the clinical case-based surveillance strategies. Full article

The coronavirus disease (COVID-19) pandemic has created an urgent need for rapid, accurate, and cost-effective diagnostic tools. In this study, an economical electrochemical immunosensor for the rapid diagnosis of COVID-19 was developed and optimized based on charge transfer resistance (Rct) values obtained by electrochemical impedance spectroscopy (EIS) from the interaction between antibodies (anti-SARS-CoV-2) immobilized as a bioreceptor and the virus (SARS-CoV-2). The sensor uses modified pencil graphite electrodes (PGE) coated with poly(4-hydroxybenzoic acid), anti-SARS-CoV-2, and silver nanoparticles. The immobilization of anti-SARS-CoV-2 antibodies was optimized at a concentration of 1:250 for 30 min, followed by blocking the surface with 0.01% bovine serum albumin for 10 min. The optimal conditions for virus detection in clinical samples were a 1:10 dilution with a response time of 20 min. The immunosensor responded linearly in the range of 0.2–2.5 × 10⁶ particles/μL. From the relationship between the obtained signal and the concentration of the analyzed sample, the limit of detection (LOD) and limit of quantification (LOQ) obtained were 1.21 × 10⁶ and 4.04 × 10⁶ particles/μL, respectively. The device did not cross-react with other viruses, including Influenza A and B, HIV, and Vaccinia virus. The relative standard deviation (RSD) of the six immunosensors prepared using the shared-pool sample was 3.87. Decreases of 22.3% and 12.4% were observed in the response values of the ten immunosensors stored at 25 °C and 4.0 °C, respectively. The sensor provides timely and accurate results with high sensitivity and specificity, offering a cost-effective alternative to the existing diagnostic methods. Full article

This study was aimed at identifying the prevalence of concurrent experience, poor mental health and problematic substance use, and its associated factors, among Canadian adults during the COVID-19 pandemic. A nationwide repeated cross-sectional sample of 14,897 Canadian adults (quota-sampled, weighted) were recruited on ten occasions between October 2020 and March 2022 using online panels. Concurrent experience was defined as mild to severe symptoms of depression (Patient Health Questionnaire-9) and/or anxiety (Generalized Anxiety Disorder-7) AND meeting screening criteria for problematic cannabis (Cannabis Use Disorder Identification Test-Revised) and/or problematic alcohol use (Alcohol Use Disorder Identification Test). Multivariable binary logistic regression models were fitted to identify the associated factors of concurrent experience using Stata v14.2 SE software. The pooled prevalence of concurrent experience was 17.12%, and 45.54% of the participants reported at least one experience (mental health symptoms or problematic substance use). The highest prevalence of concurrent experience per province was reported in Saskatchewan (19.4%) and the lowest in Quebec (13.6%). Younger adults, male respondents, those identifying as 2SLGBTQ+, self-reporting ethnocultural minority status, diagnostic history of mental health and substance use disorder, suicidal ideation, and lower ability to handle unexpected/difficult situations were significantly associated with concurrent experience during the COVID-19 pandemic in Canada. This analysis showed that the COVID-19 pandemic significantly impacted mental health and substance use in interrelated ways. Data-driven province-specific interventions might be helpful toward a client-centered and integrated mental health and substance use care system in Canada. Full article

The recreational waters of hotels are widely used by locals and travelers all around the world, and this impacts public health. In this regard, the WHO frequently offers recommendations—especially during the COVID-19 era—that operators and managers of public pools, hot tubs, or splash pads should continue to follow. This study aims to evaluate the microbiological results of COVID-19 sanitation protocols in recreational waters in hotels during the summer periods post-COVID-19 (2020–2022) and compare them with those pre-COVID-19 (2019). Of the samples tested during the pre-pandemic period, less than 1/5 were positive for total coliforms, common aerobic bacteria, and P. aeruginosa, while less than 3% of the samples tested were positive for E. coli. During the pandemic years, the percentages dropped by half for total coliforms, common aerobic bacteria, and E. coli, while only the percentage for P. aeruginosa remained the same. The relative risk (RR) for the presence of P. aeruginosa in recreational waters was greatly affected by chlorine levels, pH values, and water temperature. Our results elucidate the impact of sanitation protocols on the safety of recreational waters in hotels during public health crises. Moreover, the challenges operators face in maintaining long-term implementation of these protocols are highlighted, which in turn dictates the appropriate choice of preventive and applicable measures to ensure health and safety. Full article

In recent years, fiscal policy in the Eurozone (EZ) has faced challenges posed by the strong and rapid increase in inflation as a consequence of the COVID-19 pandemic and other geo-political crises. Due to the fear of “fiscal inflation” present during episodes of fiscal stimulus during the pandemic crisis, this paper assesses the relationship between discretionary fiscal policy and inflation in developed EZ economies, taking into consideration the rise in energy prices as a control variable. This study considers the econometric framework of heterogeneous, non-stationary panels (Pooled Mean Group (PMG) and Common Correlated Effects Mean Group (CCEMG) estimators). Using quarterly panel data for the period 2015q1–2024q1, the results show that, in the long run, the effects of fiscal policy on inflation are insignificant. However, covering only the pandemic and other geo-political crises (2020q1–2024q1), research shows a significant negative long-run relationship between fiscal expenditure and inflation and heterogeneous short-run fiscal adjustments due to the lack of a fiscal union in the EU economies. Hence, accompanied by monetary policy, the discretionary response of fiscal policy to inflationary shock was oriented in the same direction—the reduction in inflationary pressures during a geo-political crisis. Fiscal policy mitigated inflationary pressures in these recent crises, while in the long run, it did not affect nominal variables, indicating that there is no evidence of fiscal inflation in the sample of EZ economies during a stabilization period or under crisis conditions. Full article