Simon Dellicour1,2, Catherine Linard3,4, Nina Van Goethem5, Daniele Da Re6, Jean Artois7, Jérémie Bihin3, Pierre Schaus8, François Massonnet6, Herman Van Oyen5,9, Sophie O Vanwambeke6, Niko Speybroeck10, Marius Gilbert7. 1. Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, 50 av. FD Roosevelt, 1050, CP160/12, Bruxelles, Belgium. simon.dellicour@ulb.ac.be. 2. Department of Microbiology, Immunology and Transplantation, Laboratory for Clinical and Epidemiological Virology, Rega Institute, KU Leuven - University of Leuven, Leuven, Belgium. simon.dellicour@ulb.ac.be. 3. Institute of Life-Earth-Environment (ILEE), Université de Namur, Rue de Bruxelles 61, 5000, Namur, Belgium. 4. NAmur Research Institute for LIfe Sciences (NARILIS), Université de Namur, Rue de Bruxelles 61, 5000, Namur, Belgium. 5. Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium. 6. Earth & Life Institute, Georges Lemaître Centre for Earth and Climate Research, UCLouvain, Place Louis Pasteur 3, 1348, Louvain-la-Neuve, Belgium. 7. Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, 50 av. FD Roosevelt, 1050, CP160/12, Bruxelles, Belgium. 8. ICTEAM, UCLouvain, 1348, Louvain-la-Neuve, Belgium. 9. Public Health and Primary Care, Gent University, Gent, Belgium. 10. Institute of Health and Society (IRSS), Université Catholique de Louvain, Clos Chapelle-aux-champs 30, 1200, Brussels, Belgium.
Abstract
BACKGROUND: The COVID-19 pandemic is affecting nations globally, but with an impact exhibiting significant spatial and temporal variation at the sub-national level. Identifying and disentangling the drivers of resulting hospitalisation incidence at the local scale is key to predict, mitigate and manage epidemic surges, but also to develop targeted measures. However, this type of analysis is often not possible because of the lack of spatially-explicit health data and spatial uncertainties associated with infection. METHODS: To overcome these limitations, we propose an analytical framework to investigate potential drivers of the spatio-temporal heterogeneity in COVID-19 hospitalisation incidence when data are only available at the hospital level. Specifically, the approach is based on the delimitation of hospital catchment areas, which allows analysing associations between hospitalisation incidence and spatial or temporal covariates. We illustrate and apply our analytical framework to Belgium, a country heavily impacted by two COVID-19 epidemic waves in 2020, both in terms of mortality and hospitalisation incidence. RESULTS: Our spatial analyses reveal an association between the hospitalisation incidence and the local density of nursing home residents, which confirms the important impact of COVID-19 in elderly communities of Belgium. Our temporal analyses further indicate a pronounced seasonality in hospitalisation incidence associated with the seasonality of weather variables. Taking advantage of these associations, we discuss the feasibility of predictive models based on machine learning to predict future hospitalisation incidence. CONCLUSION: Our reproducible analytical workflow allows performing spatially-explicit analyses of data aggregated at the hospital level and can be used to explore potential drivers and dynamic of COVID-19 hospitalisation incidence at regional or national scales.
BACKGROUND: The COVID-19 pandemic is affecting nations globally, but with an impact exhibiting significant spatial and temporal variation at the sub-national level. Identifying and disentangling the drivers of resulting hospitalisation incidence at the local scale is key to predict, mitigate and manage epidemic surges, but also to develop targeted measures. However, this type of analysis is often not possible because of the lack of spatially-explicit health data and spatial uncertainties associated with infection. METHODS: To overcome these limitations, we propose an analytical framework to investigate potential drivers of the spatio-temporal heterogeneity in COVID-19 hospitalisation incidence when data are only available at the hospital level. Specifically, the approach is based on the delimitation of hospital catchment areas, which allows analysing associations between hospitalisation incidence and spatial or temporal covariates. We illustrate and apply our analytical framework to Belgium, a country heavily impacted by two COVID-19 epidemic waves in 2020, both in terms of mortality and hospitalisation incidence. RESULTS: Our spatial analyses reveal an association between the hospitalisation incidence and the local density of nursing home residents, which confirms the important impact of COVID-19 in elderly communities of Belgium. Our temporal analyses further indicate a pronounced seasonality in hospitalisation incidence associated with the seasonality of weather variables. Taking advantage of these associations, we discuss the feasibility of predictive models based on machine learning to predict future hospitalisation incidence. CONCLUSION: Our reproducible analytical workflow allows performing spatially-explicit analyses of data aggregated at the hospital level and can be used to explore potential drivers and dynamic of COVID-19 hospitalisation incidence at regional or national scales.
Authors: Mohammad Sarmadi; Vahid Kazemi Moghanddam; Aisha S Dickerson; Luigi Martelletti Journal: Air Qual Atmos Health Date: 2020-10-14 Impact factor: 3.763
Authors: S J Dancer; K Cormack; M Loh; C Coulombe; L Thomas; S J Pravinkumar; K Kasengele; M-F King; J Keaney Journal: J Hosp Infect Date: 2021-12-01 Impact factor: 3.926