Objectives: To assess whether exposure to an infant safe sleep initiative was associated with maternal report of infant safe sleep practice at home and to identify other predictive factors.
Methods: After linking Pennsylvania data on infant safe sleep initiative implementation at 27 hospitals to birth certificate and Pregnancy Risk Assessment Monitoring System (PRAMS) data from 2017 to 2021, we generated descriptive statistics to compare infant safe sleep practice and other characteristics between respondents exposed to the initiative and all other PRAMS respondents with a hospital birth. Using multivariable logistic regression, we modeled the association between exposure to the initiative and maternal self-report of placing their infant to sleep on their back, on a separate surface, without soft objects, or room sharing without bed sharing.
Background: Data quality is fundamental to maintaining the trust and reliability of health data for both primary and secondary purposes. However, before the secondary use of health data, it is essential to assess the quality at the source and to develop systematic methods for the assessment of important data quality dimensions.
Objective: This case study aims to offer a dual aim-to assess the data quality of height and weight measurements across 7 Belgian hospitals, focusing on the dimensions of completeness and consistency, and to outline the obstacles these hospitals face in sharing and improving data quality standards.
Background: HEV is a positive-sense, single-stranded RNA virus of the Hepeviridae family. Although HEV accounts for more than 3 million symptomatic cases of viral hepatitis per year, specific anti-HEV therapy and knowledge about HEV pathogenesis are scarce.
Methods: To gain a deeper understanding of the HEV infectious cycle and guide the development of novel antiviral strategies, we here used an RNAi mini screen targeting a selection of kinases, including mitogen-activated protein kinases, receptor tyrosine kinases, and Src-family kinases.
Genomic epidemiology offers important insight into the transmission and evolution of respiratory viruses. We used metagenomic sequencing from negative SARS-CoV-2 antigen tests to identify a wide range of respiratory viruses and generate full genome sequences, offering a streamlined mechanism for broad respiratory virus genomic surveillance.
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