Drivers of Variation in Contact Networks in Long Term Care Facilities

项目摘要 我们的首要目标是通过表征耐药菌的特性， 接触网络在全国样本约25个长期护理设施（LTCF）。每个参与 在本研究过程中，将对LTCF进行两次访视，以收集主要数据。医疗保健专业人员 (HCP)将以几种不同的方式查明居民联系网络。我们将联系网络定义为 有可能介导传播的个体之间的相互作用。我们的主要技术 估计谁接触谁将通过参与的卫生保健专业人员的自我报告， LTCF。这种方法将与使用有关工作人员分配、单位细分和 工作人员与居民的比例来推断接触模式。此外，我们将使用直接观察， 收集有关房间进入率和行为的信息，如手部卫生，这些信息会影响 互动导致收购。还将通过图表审查进行设施和驻地评估 和调查。居民级数据将包括器械的存在、感染状态、抗生素使用、伤口护理， 和合并症。在其中一到两个参与的设施中，我们将使用两种新技术来捕获更多的 关于LTCF内联系人的详细数据。首先，我们将使用一个基于标记的传感器网络来构建网络 是基于个人的亲近这种方法将使我们能够检查医疗保健专业人员和 居民在LTCF单元内移动，并分析密切接触者的序列。但是这项技术 可能不足以确定医疗保健专业人员是否接触了患者。因此，在一个较小的数量 我们将使用第二种技术策略，计算机视觉和深度传感。这种方法 使得收集关于行为的高粒度数据成为可能，例如使用长袍、手套和面具。我们 分析模型将侧重于联系网络结构、设施特性和 个人居民和HCP属性。我们将确定每个人的独特联系人数量 不同类型的居民和HCP的统计学差异。获取联系人网络的替代方法 将在重叠和完整性方面进行比较。时间模型也将用于检查 网络随时间的动态变化。我们的假设是接触网络的结构会有所不同 根据LTCF提供的临床服务，这些结构特性将具有显著的 对传播和控制的影响。

PROJECT SUMMARY Our overarching goal is to improve understanding of transmission of resistant bacteria by characterizing contact networks in a national sample of approximately 25 long-term care facilities (LTCFs). Each participating LTCF will be visited twice during the course of this study for primary data collection. Healthcare professional (HCP) – resident contact networks will be ascertained in several different ways. We define contact networks as interactions between individuals that have the potential to mediate transmission. Our primary technique for estimating who contacted whom will be through self-report by health care professionals in the participating LTCFs. This approach will be compared to use of information about staff assignments, unit sub-divisions, and staff-resident ratios to make inferences about contact patterns. In addition, we will use direct observation to collect information about room entry rates and behaviors, such as hand hygiene, that influence whether an interaction results in acquisition. Facility and resident assessments will also be performed through chart review and survey. Resident level data will include presence of devices, infection status, antibiotic use, wound care, and comorbidities. In one to two of the participating facilities, we will use two new technologies to capture more detailed data on contacts within LTCFs. First, we will use a badge-based-sensor network to construct networks on the basis of personal proximity. This approach will allow us to examine how healthcare professionals and residents move within units of LTCFs and to analyze sequences of close contacts. However, this technology may not be sufficient to determine if the healthcare professional touched the patient. Thus, in a smaller number of rooms, we will use a second technological strategy, computer vision and depth sensing. This approach makes it feasible to collect highly granular data on behaviors such as use of gowns, gloves, and masks. Our analytical models will focus on dependencies between contact network structure, facility characteristics, and individual resident and HCP attributes. We will determine whether the number of unique contacts per individual are statistically dissimilar for different types of residents and HCPs. Alternative ways to derive contact networks will be compared with respect to overlap and completeness. Temporal models will also be used to examine dynamic changes in networks over time. Our hypothesis is that the structure of contact networks will vary according to the clinical services offered by LTCFs and that these structural properties will have significant implications for transmission and control.