Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY 10027;
Department of Sociology, Stockholm University, 114 19 Stockholm, Sweden.
Proc Natl Acad Sci U S A. 2021 Sep 14;118(37). doi: 10.1073/pnas.2111190118.
Antimicrobial-resistant organisms (AMROs) can colonize people without symptoms for long periods of time, during which these agents can spread unnoticed to other patients in healthcare systems. The accurate identification of asymptomatic spreaders of AMRO in hospital settings is essential for supporting the design of interventions against healthcare-associated infections (HAIs). However, this task remains challenging because of limited observations of colonization and the complicated transmission dynamics occurring within hospitals and the broader community. Here, we study the transmission of methicillin-resistant (MRSA), a prevalent AMRO, in 66 Swedish hospitals and healthcare facilities with inpatients using a data-driven, agent-based model informed by deidentified real-world hospitalization records. Combining the transmission model, patient-to-patient contact networks, and sparse observations of colonization, we develop and validate an individual-level inference approach that estimates the colonization probability of individual hospitalized patients. For both model-simulated and historical outbreaks, the proposed method supports the more accurate identification of asymptomatic MRSA carriers than other traditional approaches. In addition, in silica control experiments indicate that interventions targeted to inpatients with a high-colonization probability outperform heuristic strategies informed by hospitalization history and contact tracing.
抗微生物药物耐药生物体（AMRO）可在无症状的情况下在人体内长期定植，在此期间，这些生物体可能在医疗机构中未被察觉地传播给其他患者。准确识别医疗机构中 AMRO 的无症状传播者对于支持针对医疗保健相关性感染（HAIs）的干预措施的设计至关重要。然而，由于对定植的观察有限，以及医院内部和更广泛的社区中复杂的传播动态，这项任务仍然具有挑战性。在这里，我们使用基于数据的基于代理的模型，该模型通过去识别的真实住院记录提供信息，研究了 66 家瑞典医院和住院患者使用的耐甲氧西林金黄色葡萄球菌（MRSA）等流行的 AMRO 在医院内的传播。将传输模型、患者对患者的接触网络以及定植的稀疏观察相结合，我们开发并验证了一种个体水平的推断方法，该方法可估计住院患者个体的定植概率。对于模型模拟和历史暴发，与其他传统方法相比，所提出的方法支持更准确地识别无症状 MRSA 携带者。此外，在硅基控制实验中表明，针对高定植概率住院患者的干预措施优于基于住院史和接触追踪的启发式策略。
