Drivers of Variation in Contact Networks in Long Term Care Facilities

项目总结 我们的首要目标是通过表征耐药细菌的特性来提高对其传播的理解 在约25个长期护理机构(LTCFs)的全国样本中建立联系网络。每个参赛者 在本研究过程中，LTCF将被访问两次，以进行初步数据收集。医疗保健专业人员 (HCP)-将通过几种不同的方式确定居民联系网络。我们将联系网络定义为 有可能作为传播媒介的个体之间的相互作用。我们的主要技术是 通过参与的医疗保健专业人员的自我报告来估计谁联系了谁 LTCFs。这一方法将与使用有关工作人员分配、单位分区和 工作人员-居民比率，以推断接触模式。此外，我们还将通过直接观察来 收集有关入房率和行为的信息，例如手部卫生，这些信息会影响 相互作用导致了收购。设施和驻地评估也将通过图表审查进行 和调查。居民级别的数据将包括设备的存在、感染状态、抗生素使用、伤口护理、 以及合并症。在一到两个参与的设施中，我们将使用两项新技术来捕获更多 LTCFs内联系人的详细数据。首先，我们将使用基于BAGE的传感器网络来构建网络 在个人接近的基础上。这种方法将使我们能够检查医疗保健专业人员和 居民在LTCFs的单元内活动，并分析密切接触的序列。然而，这项技术 可能不足以确定医疗保健专业人员是否触摸了患者。因此，在较小的数量中 对于房间，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 使收集有关使用长袍、手套和口罩等行为的高精度数据成为可能。我们的 分析模型将侧重于联系网络结构、设施特征和 个人居民和HCP属性。我们将确定每个人的唯一联系人数量 对于不同类型的居民和HCP，在统计上是不同的。派生联系人网络的其他方式 将在重叠和完整性方面进行比较。还将使用时态模型来检查 网络随时间的动态变化。我们的假设是联系网络的结构会有所不同 根据LTCFs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.