2016/02/15 by Yinghui Wei, Theodore Kypraios, Wei, Yinghui +11 · 1 citation
Biochemistry, Genetics and Molecular Biology · Decision Sciences · Mathematics · Medicine · #Advanced Statistical Process Monitoring #Antibiotics #Antimicrobial Resistance in Staphylococcus #Applications (stat.AP) #Bacterial Identification and Susceptibility Testing #Biology #Computer science #Emergency medicine #Enterococcus #FOS: Biological sciences #FOS: Computer and information sciences #Infection control #Intensive care #Intensive care medicine #Medicine #Microbiology #Populations and Evolution (q-bio.PE) #Staphylococcus aureus #Transmission (telecommunications) #Vancomycin #Vancomycin-Resistant Enterococci #Vancomycin-resistant Enterococcus #q-bio.PE #stat.AP
paper · pdf · doi:10.48550/arxiv.1602.04721
published in arXiv (Cornell University) (Cornell University)
arxiv created 2016/02/15 · openalex publication_date 2016/02/15 · arxiv updated 2016/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Nosocomial pathogens such as Methicillin-Resistant \em Staphylococcus aureus (MRSA) and Vancomycin-resistant \em Enterococci (VRE) are the cause of significant morbidity and mortality among hospital patients. It is important to be able to assess the efficacy of control measures using data on patient outcomes. In this paper we describe methods for analysing such data using patient-level stochastic models which seek to describe the underlying unobserved process of transmission. The methods are applied to detailed longitudinal patient-level data on VRE from a study in a US hospital with eight intensive care units (ICUs). The data comprise admission and discharge dates, dates and results of screening tests, and dates during which precautionary measures were in place for each patient during the study period. Results include estimates of the efficacy of the control measures, the proportion of unobserved patients colonized with VRE and the proportion of patients colonized on admission.