Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway.
Department of Infection Control and Preparedness, Norwegian Institute of Public Health, Oslo, Norway; Department of Microbiology, Oslo University Hospital, Oslo, Norway.
J Hosp Infect. 2023 May;135:50-54. doi: 10.1016/j.jhin.2023.02.014. Epub 2023 Mar 11.
Notifications to the Norwegian Institute of Public Health of outbreaks in Norwegian healthcare institutions are mandatory by law, but under-reporting is suspected due to failure to identify clusters, or because of human or system-based factors. This study aimed to establish and describe a fully automatic, register-based surveillance system to identify clusters of healthcare-associated infections (HAIs) of SARS-CoV-2 in hospitals and compare these with outbreaks notified through the mandated outbreak system Vesuv.
We used linked data from the emergency preparedness register Beredt C19, based on the Norwegian Patient Registry and the Norwegian Surveillance System for Communicable Diseases. We tested two different algorithms for HAI clusters, described their size and compared them with outbreaks notified through Vesuv.
A total of 5033 patients were registered with an indeterminate, probable, or definite HAI. Depending on the algorithm, our system detected 44 or 36 of the 56 officially notified outbreaks. Both algorithms detected more clusters then officially reported (301 and 206, respectively).
It was possible to use existing data sources to establish a fully automatic surveillance system identifying clusters of SARS-CoV-2. Automatic surveillance can improve preparedness through earlier identification of clusters of HAIs, and by lowering the workloads of infection control specialists in hospitals.
根据法律规定，向挪威公共卫生研究所报告挪威医疗机构的疫情暴发是强制性的，但由于未能识别出聚集性病例，或者由于人为或系统因素，存在漏报的情况。本研究旨在建立和描述一个完全自动的、基于登记的监测系统，以识别医院中与 SARS-CoV-2 相关的医院获得性感染（HAI）聚集性，并将这些聚集性与通过强制性暴发系统 Vesuv 报告的暴发进行比较。
我们使用了来自紧急准备登记处 Beredt C19 的关联数据，该数据基于挪威患者登记处和挪威传染病监测系统。我们测试了两种用于 HAI 聚集的不同算法，描述了它们的规模，并将其与通过 Vesuv 报告的暴发进行了比较。
共有 5033 名患者被登记为不确定、可能或明确的 HAI。根据算法的不同，我们的系统检测到了 56 起官方通报暴发中的 44 起或 36 起。两种算法都检测到了比官方报告更多的聚集性病例（分别为 301 例和 206 例）。
利用现有的数据源建立一个完全自动的监测系统来识别 SARS-CoV-2 的聚集性是可行的。自动监测可以通过更早地识别 HAI 聚集性，以及降低医院感染控制专家的工作量，来提高准备工作的水平。
