scholarly journals Is COVID-19 a Real Incentive for Flu Vaccination? Let the Numbers Speak for Themselves

Vaccines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 276
Author(s):  
Marcello Di Pumpo ◽  
Giuseppe Vetrugno ◽  
Domenico Pascucci ◽  
Elettra Carini ◽  
Viria Beccia ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Teaching Hospital ◽  
Vaccination Coverage ◽  
Linear Regression Model ◽  
Healthcare Workers ◽  
Surveys And Questionnaires ◽  
Flu Vaccination ◽  
Significant Difference ◽  
Seasonal Flu

Seasonal flu vaccination is one of the most important strategies for preventing influenza. The attitude towards flu vaccination in light of the COVID-19 pandemic has so far been studied in the literature mostly with the help of surveys and questionnaires. Whether a person chooses to be vaccinated or not during the COVID-19 pandemic, however, speaks louder than any declaration of intention. In our teaching hospital, we registered a statistically significant increase in flu vaccination coverage across all professional categories between the 2019/2020 and the 2020/2021 campaign (24.19% vs. 54.56%, p < 0.0001). A linear regression model, based on data from four previous campaigns, predicted for the 2020/2021 campaign a total flu vaccination coverage of 30.35%. A coverage of 54.46% was, instead, observed, with a statistically significant difference from the predicted value (p < 0.0001). The COVID-19 pandemic can, therefore, be considered as an incentive that significantly and dramatically increased adherence to flu vaccination among our healthcare workers.

scholarly journals Flu vaccination coverage in a large Italian teaching hospital: the example of the leaders

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
M Di Pumpo ◽  
A Barbara ◽  
D I La Milia ◽  
A Tamburrano ◽  
D Vallone ◽  
...  
Keyword(s):  
Teaching Hospital ◽  
Vaccination Coverage ◽  
Healthcare Workers ◽  
Cross Sectional Study ◽  
Vaccination Rate ◽  
Coverage Rate ◽  
Cross Sectional ◽  
Vaccination Coverage Rate ◽  
Flu Vaccination ◽  
Frail Patients

Abstract Annual flu vaccination among healthcare workers (HCWs) is recommended to prevent influenza and to avoid its transmission especially to frail patients. In our teaching hospital, flu vaccination rate among HCWs has been growing during last 3 years. The aim of this study was therefore to describe the flu vaccination coverage across the past 3 years and to analyze which factors lead to such increase. We performed a cross-sectional study on all HCWs of Fondazione Policlinico Universitario “A. Gemelli” (FPG) hospital of Rome (Italy) to determine the flu vaccination coverage. Socio-demographic and occupational data were collected from hospital personnel records and included age, gender, previous flu vaccination, profession and workplace unit. On site vaccination plus academic detailing involving leaders have been the main strategies adopted in this last 3 years that have already proved to be effective in increasing vaccination coverage among HCWs. During the 2018-2019 season, we analyzed how the flu vaccination coverage among leaders (nurse coordinators and head physicians) could affect all HCWs coverage rate. Flu vaccination rate increased from 9.57% in the 2016-17 to 14.24% in the 2017-18 and to 22.38% in 2018-2019. A total of 4035 HCWs employed in the FPG were included in 2018-19. Concerning the role played by vaccination of leaders in increasing general vaccination coverage during the 2018-2019, the group of HCWs with a vaccinated leader showed a higher coverage rate (28.65%) than the group with a non-vaccinated leader (16.22%) (p &lt; 0.0001). The results are preliminary. Flu vaccination coverage of HCWs in our hospital during the last 3 years has been increasingly higher. Vaccination of the leaders, in addition to previously implemented effective strategies, resulted to be a key factor in increasing flu vaccination coverage among all HCWs. Socio-demographic and occupational variables can significantly influence the coverage rate as well. Key messages Annual flu vaccination among healthcare workers (HCWs) is recommended to prevent influenza and to avoid its transmission especially to frail patients. This study shows the growing flu vaccination coverage rate in our teaching hospital and the effectiveness of the example given by the vaccinated leaders in increasing the coverage among all HCWs.

scholarly journals Reactogenicity Correlates Only Weakly with Humoral Immunogenicity after COVID-19 Vaccination with BNT162b2 mRNA (Comirnaty®)

Vaccines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1063
Author(s):  
Jürgen Held ◽  
Jan Esse ◽  
Koray Tascilar ◽  
Philipp Steininger ◽  
Kilian Schober ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Healthcare Workers ◽  
Model Performance ◽  
Neutralization Assay ◽  
Spike Protein ◽  
Igg Antibody ◽  
Boost Vaccination ◽  
Antibody Levels

mRNA vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), such as BNT162b2 (Comirnaty®), have proven to be highly immunogenic and efficient but also show marked reactogenicity, leading to adverse effects (AEs). Here, we analyzed whether the severity of AEs predicts the antibody response against the SARS-CoV-2 spike protein. Healthcare workers without prior SARS-CoV-2 infection, who received a prime-boost vaccination with BNT162b2, completed a standardized electronic questionnaire on the duration and severity of AEs. Serum specimens were collected two to four weeks after the boost vaccination and tested with the COVID-19 ELISA IgG (Vircell-IgG), the LIAISON® SARS-CoV-2 S1/S2 IgG CLIA (DiaSorin-IgG) and the iFlash-2019-nCoV NAb surrogate neutralization assay (Yhlo-NAb). A penalized linear regression model fitted by machine learning was used to correlate AEs with antibody levels. Eighty subjects were enrolled in the study. Systemic, but not local, AEs occurred more frequently after the boost vaccination. Elevated SARS-CoV-2 IgG antibody levels were measured in 92.5% of subjects with Vircell-IgG and in all subjects with DiaSorin-IgG and Yhlo-NAb. Gender, age and BMI showed no association with the antibody levels or with the AEs. The linear regression model identified headache, malaise and nausea as AEs with the greatest variable importance for higher antibody levels (Vircell-IgG and DiaSorin-IgG). However, the model performance for predicting antibody levels from AEs was very low for Vircell-IgG (squared correlation coefficient r2 = 0.04) and DiaSorin-IgG (r2 = 0.06). AEs did not predict the surrogate neutralization (Yhlo-NAb) results. In conclusion, AEs correlate only weakly with the SARS-CoV-2 spike protein antibody levels after COVID-19 vaccination with BNT162b2 mRNA.

Different Patterns of Pulse Spectrum Between Survivors and Non-survivors During Progressive Hemorrhage in Rats

2006 ◽  
Vol 34 (04) ◽  
pp. 575-589 ◽  
Author(s):  
Yu Hsin Chang ◽  
Jui Shan Lin ◽  
Jaung Geng Lin ◽  
Yue Der Lin ◽  
Tsai Chung Li ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Blood Volume ◽  
Linear Regression Model ◽  
Pulse Spectrum ◽  
Acute Hemorrhage ◽  
Arterial Blood ◽  
Study Results ◽  
Significant Difference ◽  
Harmonic Proportion

Previous work from our laboratory has demonstrated that the percentage differences of 2nd (C2) and 3rd (C3) pulse harmonics related to Kidney and Spleen were both increased toward another steady state in rats after acute hemorrhage. Therefore, it is suggested that changes in pulse spectra might represent the ability of animals to survive a model of progressive hemorrhage. In this study, the difference of the pulse spectra patterns between survivors and non-survivors after progressive hemorrhage (by loss of 5%, 10% or 20% of the estimated blood volume) in anesthetized rats is determined. Seven rats, dead within 2 hours after a loss of 20% of the estimated blood volume hemorrhage, were defined as 'non-survivors'. The other eleven rats, more than 2 hours after hemorrhage, were defined as 'survivors'. Pulse waves of arterial blood pressure before and after the hemorrhage were measured in parallel to the pulse spectrum analysis. Data among different phases were analyzed using one-way analysis of variance (ANOVA) with Duncan's test for pairwise comparisons. Differences between survivor and non-survivor groups at each phase were analyzed using Student's t-test. A mixed-effects linear regression model was applied to evaluate the relationship in harmonics, which significantly differed between the two groups. The study results showed that in rats, during progressive hemorrhage, the percentage differences of 2nd harmonic proportion increased significantly; however, the result failed to show any significant difference between survivors and non-survivors. After the third blood withdrawal process, the percentage differences of 3rd harmonic proportion increased more significantly in the survivors. In addition, the percentage differences of 1st harmonic proportion related to the Liver for the survivor group was significantly lower than that of the non-survivors. After analysis with the mixed linear regression model, C3 and C1 demonstrated a linear regression relationship, and there existed significant differences between survivors and non-survivors. These results suggest that C3 might play an important role in physiology regarding surviving capability after progressive hemorrhage.

scholarly journals Cognitive processes affect the gait of subjects with Parkinson’s and Alzheimer’s disease in dual tasks

2015 ◽  
Vol 64 (2) ◽  
pp. 154-159
Author(s):  
Gustavo Christofoletti ◽  
Lílian Assunção Felippe ◽  
Paulo de Tarso Müller ◽  
Fernanda Beinotti ◽  
Guilherme Borges
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Linear Regression ◽  
Regression Model ◽  
Cognitive Processes ◽  
Dual Task ◽  
Linear Regression Model ◽  
Cognitive Functions ◽  
Dual Tasks ◽  
Significant Difference

Objective To investigate the relation between gait parameters and cognitive impairments in subjects with Parkinson’s disease (PD) and Alzheimer’s disease (AD) during the performance of dual tasks. Methods This was a cross-sectional study involving 126 subjects divided into three groups: Parkinson group (n = 43), Alzheimer group (n = 38), and control group (n = 45). The subjects were evaluated using the Timed Up and Go test administered with motor and cognitive distracters. Gait analyses consisted of cadence and speed measurements, with cognitive functions being assessed by the Brief Cognitive Screening Battery and the Clock Drawing Test. Statistical procedures included mixed-design analyses of variance to observe the gait patterns between groups and tasks and the linear regression model to investigate the influence of cognitive functions in this process. A 5% significant level was adopted. Results Regarding the subjects’ speed, the data show a significant difference between group vs task interaction (p = 0.009), with worse performance of subjects with PD in motor dual task and of subjects with AD in cognitive dual task. With respect to cadence, no statistical differences was seen between group vs task interaction (p = 0.105), showing low interference of the clinical conditions on such parameter. The linear regression model showed that up to 45.79%, of the variance in gait can be explained by the interference of cognitive processes. Conclusion Dual task activities affect gait pattern in subjects with PD and AD. Differences between groups reflect peculiarities of each disease and show a direct interference of cognitive processes on complex tasks.

scholarly journals Pulmonary nodules volumetry. Variability in Results After Manual Correction of Contours

2019 ◽  
Vol 100 (2) ◽  
pp. 74-81
Author(s):  
E. V. Rozengauz ◽  
D. V. Nesterov ◽  
Z. A. Al’derov ◽  
N. M. Korablin
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Pulmonary Nodules ◽  
Manual Segmentation ◽  
Adjacent Structures ◽  
Significant Difference ◽  
Vascular Structures ◽  
The Impact ◽  
Manual Correction

Objective. To study variability of volumetric of the pulmonary nodules volumetry after manual correction of their contours.Material and methods. Twenty-seven nodules uncircumscribed from the vascular structures and pleura were selected. A linear regression model was used to investigate the impact of the size of a nodule, the area of its contact with the adjacent structures on variability in results.Results. The linear regression model based on contact area and nodule size can correctly predict volumetry variability.Conclusion. Even after manual segmentation volumetry remain suitable method for size assessment of lung nodules. Segmentation should be made with the same person because of significant difference of interobserver and intraobserver variabilities.

scholarly journals Antibody responses to BNT162b2 mRNA COVID-19 vaccine in 2,015 healthcare workers in a single tertiary referral hospital in Japan

2021 ◽  
Author(s):  
Takahiro Kageyama ◽  
Kei Ikeda ◽  
Shigeru Tanaka ◽  
Toshibumi Taniguchi ◽  
Hidetoshi Igari ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Predictive Factors ◽  
Linear Regression Model ◽  
Healthcare Workers ◽  
Antibody Responses ◽  
Clinical Implications ◽  
Multivariate Linear Regression Model ◽  
Tertiary Referral ◽  
Tertiary Referral Hospital

We measured antibody responses in 2,015 healthcare workers who were receiving 2 doses of BNT162b2 mRNA vaccine against SARS-CoV-2. The vast majority (99.9%) had either seroconversion or a substantial increase in antibody titer. A multivariate linear regression model identified predictive factors for antibody responses which may have clinical implications.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

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