[Social participation in nursing homes with Covid-19 protection measures in the second pandemic wave? Linkage of prescriptions and survey]. / Soziale Teilhabe in Pflegeheimen mit Covid-19-Schutzmaßnahmen in der zweiten Pandemiewelle?

Social participation in nursing homes with Covid-19 protection measures in the second pandemic wave? Linkage of prescriptions and survey Abstract: Background: The implementation of protective measures in nursing homes during the Covid-19 pandemic was accompanied with the loss of social participation opportunities in the first Covid-19 wave; this has been inadequately studied for the second Covid wave. Aim: To evaluate the association of protective measures and social participation restrictions for the second Covid-19 wave to develop recommendations for action. Methods: Mixed-methods online surve y of nursing home managers (n = 873) in the second wave. Binary logistic generalized estimating equations were used to evaluate the likelihood of the elimination of social participation services in nursing homes as a function of legal protections. Open-ended responses about maintaining social participation were content analyzed (n = 1042). Results: More than one in two nursing home managers reported prohibited group events (66,6%), and more than one in three prohibited creative activities (42,3%). Visitation restrictions (85,2%) were common in the second wave. The loss of participation services and the protective measures were significantly associated. Qualitative data showed that, for example, the acquisition of mobile shopping stores were opportunities to counteract the social exclusion of the residents. Discussion: The association between the loss of social participation services with the implementation of protective measures was clearly pronounced. Nursing practitioners should focus on enabling social participation whilst guaranteeing protection against infection best possible.

A Multi-Criteria Decision Support and Application to the Evaluation of the Fourth Wave of COVID-19 Pandemic.

The COVID-19 pandemic caused important health and societal damage across the world in 2020-2022. Its study represents a tremendous challenge for the scientific community. The correct evaluation and analysis of the situation can lead to the elaboration of the most efficient strategies and policies to control and mitigate its propagation. The paper proposes a Multi-Criteria Decision Support (MCDS) based on the combination of three methods: the Group Analytic Hierarchy Process (GAHP), which is a subjective group weighting method; Extended Entropy Weighting Method (EEWM), which is an objective weighting method; and the COmplex PRoportional ASsessment (COPRAS), which is a multi-criteria method. The COPRAS uses the combined weights calculated by the GAHP and EEWM. The sum normalization (SN) is considered for COPRAS and EEWM. An extended entropy is proposed in EEWM. The MCDS is implemented for the development of a complex COVID-19 indicator called COVIND, which includes several countries' COVID-19 indicators, over a fourth COVID-19 wave, for a group of European countries. Based on these indicators, a ranking of the countries is obtained. An analysis of the obtained rankings is realized by the variation of two parameters: a parameter that describes the combination of weights obtained with EEWM and GAHP and the parameter of extended entropy function. A correlation analysis between the new indicator and the general country indicators is performed. The MCDS provides policy makers with a decision support able to synthesize the available information on the fourth wave of the COVID-19 pandemic.

Forecasting the Endemic/Epidemic Transition in COVID-19 in Some Countries: Influence of the Vaccination.

OBJECTIVE: The objective of this article is to develop a robust method for forecasting the transition from endemic to epidemic phases in contagious diseases using COVID-19 as a case study. METHODS: Seven indicators are proposed for detecting the endemic/epidemic transition: variation coefficient, entropy, dominant/subdominant spectral ratio, skewness, kurtosis, dispersion index and normality index. Then, principal component analysis (PCA) offers a score built from the seven proposed indicators as the first PCA component, and its forecasting performance is estimated from its ability to predict the entrance in the epidemic exponential growth phase. RESULTS: This score is applied to the retro-prediction of endemic/epidemic transitions of COVID-19 outbreak in seven various countries for which the first PCA component has a good predicting power. CONCLUSION: This research offers a valuable tool for early epidemic detection, aiding in effective public health responses.

Evolving Clinical Manifestations and Outcomes in COVID-19 Patients: A Comparative Analysis of SARS-CoV-2 Variant Waves in a Romanian Hospital Setting.

The aim of this study was to evaluate differences in the clinical manifestations and outcomes in hospitalized patients with COVID-19 in a single Romanian center during four pandemic waves determined by different SARS-CoV-2 variants of concern (VOCs). A retrospective study on 9049 consecutive hospitalized adult patients was performed between 27 February 2020 and 31 March 2023. The study interval was divided into waves based on national data on SARS-CoV-2 VOCs' circulation. Multivariate logistic regression models were built, predicting death and complications as functions of comorbidities, therapy, wave, severity form, and vaccination status, and adjusted for ages ≥65 years. Pulmonary (pneumothorax/pneumomediastinum, pulmonary embolism) and extrapulmonary complications (liver injury, acute kidney injury, ischemic/hemorrhagic stroke, myocardial infarction, and gastrointestinal bleeding) were present, more frequently in ICU hospitalized patients and with differences between waves. The highest in-hospital mortality was found in patients presenting pneumothorax/pneumomediastinum. All of the evaluated risk factors were significantly associated with death, except for obesity and the Omicron wave. Our study highlights the changing nature of COVID-19 and acknowledges the impacts of viral mutations on disease outcomes. For all four waves, COVID-19 was a severe disease with a high risk of poor outcomes.

Respiratory viral infections other than SARS CoV-2 among the North Indian patients presenting with acute respiratory illness during the first COVID-19 wave.

Acute respiratory infections due to viral or bacterial etiology can cause 60 deaths per one lakh population. Viral etiology is more common as compared to bacterial, but lack of definite diagnosis leads to increased usage of empirical antibiotics. During the first wave of the COVID-19 pandemic, there was a need to identify co-infections especially in severe acute respiratory illness (SARI) patients to identify it as one of the cofactors for increased severity of illness and to identify the causative agents in COVID-19 negative individuals. The SARS CoV-2 real time PCR was carried out using ICMR approved kits and the other respiratory viruses were detected using the multiplex commercially available real time  kit. A total of 186 patients presenting with either SARI (89.8%) or influenza like illness (10.2%) were included in the study. Out of these, 43 (23.1%) were positive for SARS CoV-2 RNA and 2 (4.6%) patients with SARI showed concomitant infection with either human rhinovirus or human respiratory syncytial virus . Out of 143 patients negative for SARS CoV-2, 35 (24.5%) were positive for one or more microbial infections and 28 (19.6%) infected with other respiratory viral infection most common being human rhinovirus. The results suggest that viral coinfections are significantly higher among COVID-19 negative individuals (24.5% vs 4.6%) presenting with respiratory illness as compared to COVID-19 positive individuals possibly due to viral interference and competitive advantage of SARS-CoV-2 in modulating the host immunity. Further detailed research is required for the understanding of mechanisms of viral co-infection.

Mapping of SARS-CoV-2 spike protein evolution during first and second waves of COVID-19 infections in India.

Aim: The aim of this study was to investigate the SARS-CoV-2 spike protein evolution during the first and second wave of COVID-19 infections in India. Materials & Methods: Detailed mutation analysis was done in 763 samples taken from GISAID for the ten most affected Indian states between March 2020 to August 2021. Results: The study revealed 242 mutations corresponding to 207 sites. Fifty one novel mutations emerged during the assessment period, including many with higher transmissibility and immune evasion functions. Highest number of mutations per spike protein also rose from 5 (first wave) to 13 (second wave). Conclusion: The study identified mutation-rich and no mutation regions in the spike protein. The conserved spike regions can be useful for designing future diagnostics, vaccines and therapeutics.