tetano
Editor, Senior Moderator
Infect Control Hosp Epidemiol
. 2022 Dec 16;1-7.
doi: 10.1017/ice.2022.297. Online ahead of print.
A new automated national register-based surveillance system for outbreaks in long-term care facilities in Norway detected three times more severe acute respiratory coronavirus virus 2 (SARS-CoV-2) clusters than traditional methods
Kirsten Gravningen[SUP] 1 2 3 [/SUP], Petter Nymark[SUP] 1 [/SUP], Torgeir B Wyller[SUP] 3 4 [/SUP], Oliver Kacelnik[SUP] 1 [/SUP]
Affiliations
Abstract
Objective: To develop and test a new automated surveillance system that can detect, define and characterize infection clusters, including coronavirus disease 2019 (COVID-19), in long-term care facilities (LTCFs) in Norway by combining existing national register data.
Background: The numerous outbreaks in LTCFs during the COVID-19 pandemic highlighted the need for accurate and timely outbreak surveillance. As traditional methods were inadequate, we used severe acute respiratory coronavirus virus 2 (SARS-CoV-2) as a model to test automated surveillance.
Methods: We conducted a nationwide study using data from the Norwegian preparedness register (Beredt C19) and defined the study population as an open cohort from January 2020 to December 2021. We analyzed clusters (≥3 individuals with positive SARS-CoV-2 test ≤14 days) by 4-month periods including cluster size, duration and composition, and residents' mortality associated with clusters.
Results: The study population included 173,907 individuals; 78% employees and 22% residents. Clusters were detected in 427 (43%) of 993 LTCFs. The median cluster size was 4-8 individuals (maximum, 50) by 4-month periods, with a median duration of 9-17 days. Employees represented 60%-82% of cases in clusters and were index cases in 60%-90%. In the last 4-month period of 2020, we detected 107 clusters (915 cases) versus 428 clusters (2,998 cases) in the last period of 2021. The 14-day all-cause mortality rate was higher in resident cases from the clusters. Varying the cluster definitions changed the number of clusters.
Conclusion: Automated national surveillance for SARS-CoV-2 clusters in LTCFs is possible based on existing data sources and provides near real-time detailed information on size, duration, and composition of clusters. Thus, this system can assist in early outbreak detection and improve surveillance.
. 2022 Dec 16;1-7.
doi: 10.1017/ice.2022.297. Online ahead of print.
A new automated national register-based surveillance system for outbreaks in long-term care facilities in Norway detected three times more severe acute respiratory coronavirus virus 2 (SARS-CoV-2) clusters than traditional methods
Kirsten Gravningen[SUP] 1 2 3 [/SUP], Petter Nymark[SUP] 1 [/SUP], Torgeir B Wyller[SUP] 3 4 [/SUP], Oliver Kacelnik[SUP] 1 [/SUP]
Affiliations
- PMID: 36524319
- DOI: 10.1017/ice.2022.297
Abstract
Objective: To develop and test a new automated surveillance system that can detect, define and characterize infection clusters, including coronavirus disease 2019 (COVID-19), in long-term care facilities (LTCFs) in Norway by combining existing national register data.
Background: The numerous outbreaks in LTCFs during the COVID-19 pandemic highlighted the need for accurate and timely outbreak surveillance. As traditional methods were inadequate, we used severe acute respiratory coronavirus virus 2 (SARS-CoV-2) as a model to test automated surveillance.
Methods: We conducted a nationwide study using data from the Norwegian preparedness register (Beredt C19) and defined the study population as an open cohort from January 2020 to December 2021. We analyzed clusters (≥3 individuals with positive SARS-CoV-2 test ≤14 days) by 4-month periods including cluster size, duration and composition, and residents' mortality associated with clusters.
Results: The study population included 173,907 individuals; 78% employees and 22% residents. Clusters were detected in 427 (43%) of 993 LTCFs. The median cluster size was 4-8 individuals (maximum, 50) by 4-month periods, with a median duration of 9-17 days. Employees represented 60%-82% of cases in clusters and were index cases in 60%-90%. In the last 4-month period of 2020, we detected 107 clusters (915 cases) versus 428 clusters (2,998 cases) in the last period of 2021. The 14-day all-cause mortality rate was higher in resident cases from the clusters. Varying the cluster definitions changed the number of clusters.
Conclusion: Automated national surveillance for SARS-CoV-2 clusters in LTCFs is possible based on existing data sources and provides near real-time detailed information on size, duration, and composition of clusters. Thus, this system can assist in early outbreak detection and improve surveillance.