A new method for near real-time, nationwide surveillance of nosocomial COVID-19 in Norway: providing data at all levels of the healthcare system, March 2020 to March 2022

Background: Great efforts have been made to mini-mise spread and prevent outbreaks of COVID-19 in hospitals. However, there is uncertainty in identify-ing nosocomial vs community-acquired infections. To minimise risks and evaluate measures, timely data on infection risk in healthcare institutions are required.Aims: To design an automated nationwide surveillance system for nosocomial COVID-19 using existing data to analyse the impact of community infection rates on nosocomial infections, to explore how changes in case definitions influence incidence and to identify patients and wards at highest risk and effects of SARS-CoV-2 variants. Methods: We used data from the Norwegian real-time emergency preparedness register (Beredt C19), which includes all patients nationwide admit-ted to Norwegian hospitals between March 2020 and March 2022 with a positive SARS-CoV-2 PCR test dur-ing their hospital stay or within 7 days post-discharge. COVID-19 cases were assigned to categories depending on the time between admission and testing. Results: Infection rates for definite/probable nosocomial COVID-19 increased from 0.081% in year 1 to 0.50% in year 2 in hospital admissions 7 days or longer. Varying the definitions resulted in large changes in registered nosocomial infections. Infection rates were similar across different ward types. By 2022, 58% of patients with a definite/probable nosocomial infection had received three vaccine doses.Conclusion: Automated national surveillance for nosocomial COVID-19 is pos-sible based on existing data sources. Beredt C19 pro-vided detailed information with only 5% missing data on hospitals/wards. Epidemiological definitions are possible to standardise, enabling easier comparison between regions and countries.

Automated summary

An automated nationwide surveillance system was designed to analyze the impact of community infection rates on nosocomial infections, evaluate the effects of case definition changes, and identify high-risk patients and wards as well as the impact of SARS-CoV-2 variants. Existing data sources were used for this purpose.