Respiratory Syncytial Virus-Associated Hospital Admissions in Children Younger Than 5 Years in 7 European Countries Using Routinely Collected Datasets. (20th August 2020)

• Record Type: Journal Article
• Authors: Reeves, Rachel M; van Wijhe, Maarten; Tong, Sabine; Lehtonen, Toni; Stona, Luca; Teirlinck, Anne C; Fernandez, Liliana Vazquez; Li, You; Giaquinto, Carlo; Fischer, Thea Kølsen; Demont, Clarisse; Heikkinen, Terho; Speltra, Irene; van Boven, Michiel; Bøås, Håkon; Campbell, Harry
• Is Part Of: Journal of infectious diseases. Volume 222(2020)Supplement 7
• Page Start: 599
• Page End: 605
• Publication Date: 2020-08-20
• Subjects: respiratory syncytial virus -- RSV -- hospital admissions -- national registry data -- Europe; Communicable diseases -- Periodicals; Diseases -- Causes and theories of causation -- Periodicals; Medicine -- Periodicals; Communicable Diseases -- Periodicals; Electronic journals; 616.9
• DOI: 10.1093/infdis/jiaa360
• Languages: English
• ISSNs: 0022-1899

Abstract

Abstract: Background: Respiratory syncytial virus (RSV) is a leading cause of respiratory tract infection (RTI) in young children. Registries provide opportunities to explore RSV epidemiology and burden. Methods: We explored routinely collected hospital data on RSV in children aged < 5 years in 7 European countries. We compare RSV-associated admission rates, age, seasonality, and time trends between countries. Results: We found similar age distributions of RSV-associated hospital admissions in each country, with the highest burden in children < 1 years old and peak at age 1 month. Average annual rates of RTI admission were 41.3–112.0 per 1000 children aged < 1 year and 8.6–22.3 per 1000 children aged < 1 year. In children aged < 5 years, 57%–72% of RTI admissions with specified causal pathogen were coded as RSV, with 62%–87% of pathogen-coded admissions in children < 1 year coded as RSV. Conclusions: Our results demonstrate the benefits and limitations of using linked routinely collected data to explore epidemiology and burden of RSV. Our future work will use these data to generate estimates of RSV burden using time-series modelling methodology, to inform policymaking and regulatory decisions regarding RSV immunization strategy and monitor the impact of future vaccines.