Pelupessy Inti, Bonten Marc J M, Diekmann Odo
Department of Mathematics, University of Utrecht, Budapestlaan 6, 3584 CD Utrecht, The Netherlands.
Proc Natl Acad Sci U S A. 2002 Apr 16;99(8):5601-5. doi: 10.1073/pnas.082412899. Epub 2002 Apr 9.
The emergence of antibiotic resistance among nosocomial pathogens has reemphasized the need for effective infection control strategies. The spread of resistant pathogens within hospital settings proceeds along various routes of transmission and is characterized by large fluctuations in prevalence, which are typical for small populations. Identification of the most important route of colonization (exogenous by cross-transmission or endogenous caused by the selective pressure of antibiotics) is important for the design of optimal infection control strategies. Such identification can be based on a combination of epidemiological surveillance and costly and laborious as well as time-consuming methods of genotyping. Furthermore, analysis of the effects of interventions is hampered by the natural fluctuations in prevalence. To overcome these problems, we introduce a mathematical algorithm based on a Markov chain description. The input is longitudinal prevalence data only. The output is estimates of the key parameters characterizing the two colonization routes. The algorithm is tested on two longitudinal surveillance data sets of intensive care patients. The quality of the estimates is determined by comparing them to accurate estimates based on additional information obtained by genotyping. The results warrant optimism that this algorithm may help to quantify transmission dynamics and can be used to evaluate the effects of infection control interventions more carefully.
医院病原体中抗生素耐药性的出现再次凸显了有效感染控制策略的必要性。耐药病原体在医院环境中的传播通过多种传播途径进行，其特点是患病率大幅波动，这在小群体中很典型。确定最重要的定植途径（通过交叉传播的外源性途径或由抗生素选择性压力引起的内源性途径）对于设计最佳感染控制策略很重要。这种确定可以基于流行病学监测以及昂贵、费力且耗时的基因分型方法的结合。此外，患病率的自然波动阻碍了对干预措施效果的分析。为克服这些问题，我们引入了一种基于马尔可夫链描述的数学算法。输入仅为纵向患病率数据。输出是表征两种定植途径的关键参数的估计值。该算法在两个重症监护患者的纵向监测数据集上进行了测试。通过将估计值与基于基因分型获得的额外信息得出的准确估计值进行比较来确定估计质量。结果让人乐观地认为，该算法可能有助于量化传播动态，并可用于更仔细地评估感染控制干预措施的效果。
