scholarly journals Interventions to increase seasonal influenza vaccine coverage in healthcare workers: A systematic review and meta-regression analysis

2015 ◽  
Vol 12 (3) ◽  
pp. 671-681 ◽  
Author(s):  
Theodore Lytras ◽  
Frixos Kopsachilis ◽  
Elisavet Mouratidou ◽  
Dimitris Papamichail ◽  
Stefanos Bonovas
Keyword(s):  
Systematic Review ◽  
Regression Analysis ◽  
Influenza Vaccine ◽  
Healthcare Workers ◽  
Seasonal Influenza ◽  
Vaccine Coverage ◽  
Seasonal Influenza Vaccine ◽  
Meta Regression ◽  
Meta Regression Analysis

scholarly journals Healthcare Workers’ Perspectives on the Upcoming COVID-19 Vaccine in Terms of Their Exposure to the Influenza Vaccine in Riyadh, Saudi Arabia: A Cross-Sectional Study

Vaccines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 465
Author(s):  
Leena R. Baghdadi ◽  
Shatha G. Alghaihb ◽  
Alanoud A. Abuhaimed ◽  
Dania M. Alkelabi ◽  
Rawan S. Alqahtani
Keyword(s):  
Saudi Arabia ◽  
Influenza Vaccine ◽  
Work Experience ◽  
Healthcare Workers ◽  
Seasonal Influenza ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Seasonal Influenza Vaccine ◽  
History Of

In 2019, a novel severe acute respiratory syndrome (SARS-CoV-2 (COVID-19)) caused a global pandemic. There was an urgent need to develop a vaccine against COVID-19 to reduce its spread and economic burden. The main objective of this study was to understand the attitudes and concerns of healthcare workers (HCWs) towards the upcoming COVID-19 vaccine, whether their decision was influenced by their history of taking the seasonal influenza vaccine, and factors that influence the acceptance of the upcoming COVID-19 vaccine. This was a cross-sectional study conducted in Riyadh, Saudi Arabia. We selected and surveyed 356 HCWs via an electronic self-administered questionnaire. A total of 61.16% of HCWs were willing to receive the COVID-19 vaccine, and 55.9% of them had received the seasonal influenza vaccine in the preceding year (2019–2020). The strongest predictors for taking the COVID-19 vaccine were the HCWs’ belief that the COVID-19 vaccine would be safe, needed even for healthy people, that all HCWs should be vaccinated against COVID-19, and that HCWs will have time to take the vaccine. Being female, being middle aged, having <5 years of work experience, having no fear of injections, and being a non-smoker were predictive factors for taking the upcoming COVID-19 vaccine. No associations were found between the intention to take the COVID-19 vaccine and a history of taking the seasonal influenza vaccine.

scholarly journals Temporal trends in sperm count: a systematic review and meta-regression analysis

2017 ◽  
Vol 23 (6) ◽  
pp. 646-659 ◽  
Author(s):  
Hagai Levine ◽  
Niels Jørgensen ◽  
Anderson Martino-Andrade ◽  
Jaime Mendiola ◽  
Dan Weksler-Derri ◽  
...  
Keyword(s):  
Systematic Review ◽  
Regression Analysis ◽  
Sperm Count ◽  
Temporal Trends ◽  
Meta Regression ◽  
Meta Regression Analysis

Seasonal Influenza Vaccine Compliance Among Hospital and Non-Hospital-Based Healthcare Workers

2012 ◽  
Vol 40 (5) ◽  
pp. e147-e148
Author(s):  
Terri Rebmann ◽  
Terri Rebmann ◽  
Kate Wright ◽  
John Anthony ◽  
Richard Knaup ◽  
...  
Keyword(s):  
Influenza Vaccine ◽  
Healthcare Workers ◽  
Seasonal Influenza ◽  
Seasonal Influenza Vaccine

scholarly journals Long-term manifestations and modifiers of prevalence estimates of the post-COVID-19 syndrome: A systematic review and meta-analysis.

2021 ◽  
Author(s):  
Daniel De-la-Rosa-Martínez ◽  
Marco Antonio Delaye-Martínez ◽  
Omar Yaxmehen Bello-Chavolla ◽  
Alejandro Sicilia-Andrade ◽  
Isaac David Juárez-Cruz ◽  
...  
Keyword(s):  
Systematic Review ◽  
Regression Analysis ◽  
Meta Analysis ◽  
Population Characteristics ◽  
Published Data ◽  
System Disease ◽  
Prevalence Estimates ◽  
Meta Regression ◽  
Meta Regression Analysis

Background: Post-acute COVID-19 syndrome (PACS) is a multi-system disease comprising persistent symptomatology after the acute phase of infection. Long-term PACS effects significantly impact patient outcomes, but their incidence remains uncharacterized due to high heterogeneity between studies. Therefore, we aimed to summarize published data on PACS, characterizing the clinical presentation, prevalence, and modifiers of prevalence estimates. Method: In this systematic review and meta-analysis, we research MEDLINE for original studies published from January 1st, 2020, to January 31st, 2021, that reported proportions of PACS manifestations. Studies were eligible for inclusion if they included patients aged ≥18 years with confirmed COVID-19 by RT-PCR or antigen testing and a minimum follow-up of 21 days. The prevalence of individual manifestations across studies was pooled using random-effects meta-analysis. For evaluating determinants of heterogeneity, meta-regression analysis was performed. This study was registered in PROSPERO (CRD42019125025). Results: After screening 1,235 studies, we included 29 reports for analysis. Twenty-seven meta-analyses were performed, and 61 long-term manifestations were described. The pooled prevalence of PACS was 56% (95%CI 45-66%), with the most common manifestations being diminished health status, fatigue, asthenia, dyspnea, myalgias, hyposmia and dysgeusia. Most of the included studies presented high heterogeneity. After conducting the meta-regression analysis, we identified that age, gender, number of comorbidities, and reported symptoms significantly modify the prevalence estimation of PACS long-term manifestations. Conclusion: PACS is inconsistently reported between studies, and population characteristics influence the prevalence estimates due to high heterogeneity. A systematized approach for the study of PACS is needed to characterize its impact adequately.

Systematic review and meta-regression analysis of technical efficiency in dairy farms

2016 ◽  
Vol 65 (3) ◽  
pp. 279-301 ◽  
Author(s):  
Taciana Mareth ◽  
Antonio Marcio Tavares Thomé ◽  
Fernando Luiz Cyrino Oliveira ◽  
Luiz Felipe Scavarda
Keyword(s):  
Systematic Review ◽  
Regression Analysis ◽  
Technical Efficiency ◽  
Geographical Location ◽  
Dairy Farms ◽  
Two Dimensions ◽  
Farm Size ◽  
Content Type ◽  
Meta Regression ◽  
Meta Regression Analysis

Purpose – The purpose of this paper is to complement and extend previous literature reviews on Technical Efficiency (TE) in dairy farms, analysing the effects of different methodologies and study-specific characteristics on Mean TE (MTE). Design/methodology/approach – The researchers independently conducted a systematic review of more than 400 abstracts and 85 full-text papers. Original keywords were applied to seven key electronic databases. Results from a meta-regression analysis of 85 published papers totalling 443 TE distributions in dairy farms worldwide are discussed. Findings – The variation in the MTE indexes reported in the literature can be explained by the methodology of estimations (method of estimation, functional form of frontier models, model dimensionality), the farms geographical location and farm size. Additionally, the results suggest that, given the state of technology prevailing in each country at the time that the studies on TE were conducted, dairy farmers in the sample could increase milk output by 20.9 per cent (level of inefficiency), on average, if they produce on their frontiers. Originality/value – This study makes two important contributions: first, it updates and compares previous works on frontier estimation of TE in dairy farms; and second, it adds two dimensions of dairy farms, size (herd and land area) and economic development, to the known differentials of TE measurement.

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