Using Sensor Networks to Study the Effect of Peripatetic Healthcare Workers on the Spread of Hospital-Associated Infections
Using Sensor Networks to Study the Effect of Peripatetic Healthcare Workers on the Spread of Hospital-Associated Infections
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DOI：
10.1093/infdis/jis542
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发表时间：
2012-11-15
影响因子：
6.4
通讯作者：
Polgreen, Philip M.
中科院分区：
文献类型：
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作者：
Hornbeck, Thomas;Naylor, David;Polgreen, Philip M.
背景已经观察到超级传播事件，其中具有可测量的高连接性的个人负责感染大量人。我们的目标是确定流动（例如，高度移动的或高度连接）的医护人员与较少连接的工作人员相比，手部卫生不遵守的影响。我们使用了一个基于微粒的传感器网络来记录20张病床的重症监护病房中医护人员和患者之间的接触。从该网络收集的数据形成了基于代理的模拟的基础，以模拟具有各种传播概率的医院病原体的传播。我们确定了联系最多和最少的医疗工作者。然后我们比较了手部卫生不依从性作为连通性的函数的影响。这些数据证实了巡回医疗工作者的存在。此外，使用我们的真实的接触网络数据进行的基于代理的模拟证实，当连接最多的医护人员不进行手部卫生时，感染患者的平均数量显著较高，而当连接最少的医护人员不遵守时，感染患者的平均数量显著较低。卫生保健工作者接触模式中的异质性极大地影响疾病扩散。我们的研究结果应该为未来的感染控制干预提供信息，并鼓励应用社会网络分析来研究医疗环境中的疾病传播。
Background. Super-spreading events, in which an individual with measurably high connectivity is responsible for infecting a large number of people, have been observed. Our goal is to determine the impact of hand hygiene noncompliance among peripatetic (eg, highly mobile or highly connected) healthcare workers compared with less-connected workers.Methods. We used a mote-based sensor network to record contacts among healthcare workers and patients in a 20-bed intensive care unit. The data collected from this network form the basis for an agent-based simulation to model the spread of nosocomial pathogens with various transmission probabilities. We identified the most-and least-connected healthcare workers. We then compared the effects of hand hygiene noncompliance as a function of connectedness.Results. The data confirm the presence of peripatetic healthcare workers. Also, agent-based simulations using our real contact network data confirm that the average number of infected patients was significantly higher when the most connected healthcare worker did not practice hand hygiene and significantly lower when the least connected healthcare workers were noncompliant.Conclusions. Heterogeneity in healthcare worker contact patterns dramatically affects disease diffusion. Our findings should inform future infection control interventions and encourage the application of social network analysis to study disease transmission in healthcare settings.