McBryde E S, Pettitt A N, Cooper B S, McElwain D L S
School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland 4001, Australia.
J R Soc Interface. 2007 Aug 22;4(15):745-54. doi: 10.1098/rsif.2007.0224.
Antibiotic-resistant nosocomial pathogens can arise in epidemic clusters or sporadically. Genotyping is commonly used to distinguish epidemic from sporadic vancomycin-resistant enterococci (VRE). We compare this to a statistical method to determine the transmission characteristics of VRE.
A structured continuous-time hidden Markov model (HMM) was developed. The hidden states were the number of VRE-colonized patients (both detected and undetected). The input for this study was weekly point-prevalence data; 157 weeks of VRE prevalence. We estimated two parameters: one to quantify the cross-transmission of VRE and the other to quantify the level of VRE colonization from sporadic sources. We compared the results to those obtained by concomitant genotyping and phenotyping. We estimated that 89% of transmissions were due to ward cross-transmission while 11% were sporadic. Genotyping found that 90% had identical glycopeptide resistance genes and 84% were identical or nearly identical on pulsed-field gel electrophoresis (PFGE). There was some evidence, based on model selection criteria, that the cross-transmission parameter changed throughout the study period. The model that allowed for a change in transmission just prior to the outbreak and again at the peak of the outbreak was superior to other models. This model estimated that cross-transmission increased at week 120 and declined after week 135, coinciding with environmental decontamination.
We found that HMMs can be applied to serial prevalence data to estimate the characteristics of acquisition of nosocomial pathogens and distinguish between epidemic and sporadic acquisition. This model was able to estimate transmission parameters despite imperfect detection of the organism. The results of this model were validated against PFGE and glycopeptide resistance genotype data and produced very similar results. Additionally, HMMs can provide information about unobserved events such as undetected colonization.
耐抗生素的医院病原体可呈流行集群形式出现或散在发生。基因分型常用于区分万古霉素耐药肠球菌（VRE）的流行株和散在株。我们将其与一种统计方法进行比较，以确定VRE的传播特征。
构建了一个结构化连续时间隐马尔可夫模型（HMM）。隐藏状态为VRE定植患者数量（包括已检测到和未检测到的）。本研究的输入数据为每周现患率数据；共157周的VRE现患情况。我们估计了两个参数：一个用于量化VRE的交叉传播，另一个用于量化散在来源的VRE定植水平。我们将结果与通过同时进行基因分型和表型分析获得的结果进行了比较。我们估计89%的传播是由于病房内交叉传播，而11%是散在发生。基因分型发现90%具有相同的糖肽耐药基因，84%在脉冲场凝胶电泳（PFGE）上相同或几乎相同。基于模型选择标准，有证据表明交叉传播参数在整个研究期间发生了变化。允许在疫情爆发前和爆发高峰期传播发生变化的模型优于其他模型。该模型估计交叉传播在第120周增加，在第135周后下降，这与环境去污时间一致。
我们发现HMM可应用于系列现患率数据，以估计医院病原体获得的特征，并区分流行获得和散在获得。尽管对病原体的检测不完善，但该模型仍能够估计传播参数。该模型的结果通过PFGE和糖肽耐药基因分型数据进行了验证，结果非常相似。此外，HMM可提供关于未观察到的事件（如未检测到的定植）的信息。
