ABSTRACT
BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.
Subject(s)
COVID-19 , Cross Infection , Humans , COVID-19/epidemiology , SARS-CoV-2 , Cross Infection/epidemiology , Hospitals , Norway/epidemiologyABSTRACT
BACKGROUND: The number of elderly people is constantly increasing in the western world. Many of these elderly spend their last years in a nursing home. Long-term care residents frequently have infections. However, there is only limited knowledge with regard to the spectrum of infections and the usage of antibiotics in nursing homes, in Norway and also in other European countries. MATERIAL AND METHODS: Prevalence of infections, risk factors related to infections and antibiotic usage were studied in four nursing homes in Baerum county. RESULTS: Of all 262 nursing home residents, 8.4% had an infection; 3.4% received antibiotic treatment. 66% of residents were more than 80 years old, 98% had a private room. Of all residents 3.4% had a urinary tract infections, 1.9% a skin infection, 1.1% a respiratory tract infection, and 1.9% an eye infection. 42% of all residents were treated with psychopharmacological drugs. 3.9% had an urinary catheter, and 11% skin ulcers. INTERPRETATION: Our study did not discover any extraordinary problems with infections or antibiotic overuse in the nursing homes investigated. However, further studies are warranted in order to learn more about this issue in these institutions, which may represent an important but frequently underestimated source of resistant bacteria in a community.
