Relative effectiveness of the second booster COVID-19 vaccines against laboratory confirmed SARS-CoV-2 infection in healthcare workers: VEBIS HCW VE cohort study (1 October 2022-2 May 2023).

Introduction: Repeated COVID-19 booster vaccination was recommended in healthcare workers (HCWs) to maintain protection. We measured the relative vaccine effectiveness (rVE) of the second booster dose of COVID-19 vaccine compared to the first booster, against laboratory-confirmed SARS-CoV-2 infection in HCWs.

Methods: In a prospective cohort study among HCWs from 12 European hospitals, we collected nasopharyngeal or saliva samples at enrolment and during weekly/fortnightly follow-up between October 2022 and May 2023.

Incidence of SARS-CoV-2 Infection Among European Healthcare Workers and Effectiveness of the First Booster COVID-19 Vaccine, VEBIS HCW Observational Cohort Study, May 2021-May 2023.

European countries have included healthcare workers (HCWs) among priority groups for COVID-19 vaccination. We established a multi-country hospital network to measure the SARS-CoV-2 incidence and effectiveness of COVID-19 vaccines among HCWs against laboratory-confirmed SARS-CoV-2 infection. HCWs from 19 hospitals in 10 countries participated in a dynamic prospective cohort study, providing samples for SARS-CoV-2 testing at enrolment and during weekly/fortnightly follow-up.

Effect of genetically predicted sclerostin on cardiovascular biomarkers, risk factors, and disease outcomes.

Sclerostin inhibitors protect against osteoporotic fractures, but their cardiovascular safety remains unclear. We conducted a cis-Mendelian randomisation analysis to estimate the causal effect of sclerostin levels on cardiovascular risk factors. We meta-analysed three GWAS of sclerostin levels including 49,568 Europeans and selected 2 SNPs to be used as instruments.

Standardised and Reproducible Phenotyping Using Distributed Analytics and Tools in the Data Analysis and Real World Interrogation Network (DARWIN EU).

Purpose: The generation of representative disease phenotypes is important for ensuring the reliability of the findings of observational studies. The aim of this manuscript is to outline a reproducible framework for reliable and traceable phenotype generation based on real world data for use in the Data Analysis and Real-World Interrogation Network (DARWIN EU). We illustrate the use of this framework by generating phenotypes for two diseases: pancreatic cancer and systemic lupus erythematosus (SLE).