Wei Yinghui, Kypraios Theodore, O'Neill Philip D, Huang Susan S, Rifas-Shiman Sheryl L, Cooper Ben S
1 Centre for Mathematical Sciences, School of Computing, Electronics and Mathematics, University of Plymouth, UK.
2 School of Mathematical Sciences, University of Nottingham, UK.
Stat Methods Med Res. 2018 Jan;27(1):269-285. doi: 10.1177/0962280215627299. Epub 2016 Mar 16.
Nosocomial pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant 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 vancomycin-resistant Enterococci 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 vancomycin-resistant Enterococci, and the proportion of patients colonized on admission.
耐甲氧西林金黄色葡萄球菌(MRSA)和耐万古霉素肠球菌(VRE)等医院病原体是导致住院患者出现严重发病和死亡的原因。利用患者预后数据评估控制措施的效果非常重要。在本文中,我们描述了使用患者层面的随机模型分析此类数据的方法,这些模型旨在描述潜在的未观察到的传播过程。这些方法应用于来自美国一家拥有八个重症监护病房(ICU)的医院的一项研究中关于耐万古霉素肠球菌的详细纵向患者层面数据。数据包括入院和出院日期、筛查测试的日期和结果,以及研究期间每位患者采取预防措施的日期。结果包括控制措施效果的估计值、未观察到的耐万古霉素肠球菌定植患者的比例,以及入院时定植患者的比例。