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Time between Symptom Onset, Hospitalisation and Recovery or Death: a Statistical Analysis of Different Time-Delay Distributions in Belgian COVID-19 Patients
Christel Faes; Steven Abrams; Dominique Van Beckhoven; Geert Meyfroidt; Erika Vlieghe; Niel Hens.
Afiliación
  • Christel Faes; Data Science Institute (DSI), I-BioStat, Hasselt University
  • Steven Abrams; Data Science Institute (DSI), I-BioStat, Hasselt University & Global Health Institute (GHI), University of Antwerp
  • Dominique Van Beckhoven; Department of Epidemiology and public health, Sciensano
  • Geert Meyfroidt; Department and Laboratory of Intensive Care Medicine, UniversityHospitals Leuven and KU Leuven
  • Erika Vlieghe; Department of General Internal Medicine, Infectious and TropicalDiseases, University Hospital Antwerp
  • Niel Hens; Data Science Institute (DSI), I-BioStat, Universiteit Hasselt & Centre for Health Economics Research and Modelling of InfectiousDiseases (CHERMID
Preprint en En | PREPRINT-MEDRXIV | ID: ppmedrxiv-20156307
ABSTRACT
BackgroundThere are different patterns in the COVID-19 outbreak in the general population and amongst nursing home patients. Different age-groups are also impacted differently. However, it remains unclear whether the time from symptom onset to diagnosis and hospitalization or the length of stay in the hospital is different for different age groups, gender, residence place or whether it is time dependent. MethodsSciensano, the Belgian Scientific Institute of Public Health, collected information on hospitalized patients with COVID-19 hospital admissions from 114 participating hospitals in Belgium. Between March 14, 2020 and June 12, 2020, a total of 14,618 COVID-19 patients were registered. The time of symptom onset, time of COVID-19 diagnosis, time of hospitalization, time of recovery or death, and length of stay in intensive care are recorded. The distributions of these different event times for different age groups are estimated accounting for interval censoring and right truncation in the observed data. ResultsThe truncated and interval-censored Weibull regression model is the best model for the time between symptom onset and diagnosis/hospitalization best, whereas the length of stay in hospital is best described by a truncated and interval-censored lognormal regression model. ConclusionsThe time between symptom onset and hospitalization and between symptom onset and diagnosis are very similar, with median length between symptom onset and hospitalization ranging between 3 and 10.4 days, depending on the age of the patient and whether or not the patient lives in a nursing home. Patients coming from a nursing home facility have a slightly prolonged time between symptom onset and hospitalization (i.e., 2 days). The longest delay time is observed in the age group 20-60 years old. The time from symptom onset to diagnosis follows the same trend, but on average is one day longer as compared to the time to hospitalization. The median length of stay in hospital varies between 3 and 10.4 days, with the length of stay increasing with age. However, a difference is observed between patients that recover and patients that die. While the hospital length of stay for patients that recover increases with age, we observe the longest time between hospitalization and death in the age group 20-60. And, while the hospital length of stay for patients that recover is shorter for patients living in a nursing home, the time from hospitalization to death is longer for these patients. But, over the course of the first wave, the length of stay has decreased, with a decrease in median length of stay of around 2 days.
Licencia
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Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Experimental_studies / Prognostic_studies / Rct Idioma: En Año: 2020 Tipo del documento: Preprint
Texto completo: 1 Colección: 09-preprints Base de datos: PREPRINT-MEDRXIV Tipo de estudio: Experimental_studies / Prognostic_studies / Rct Idioma: En Año: 2020 Tipo del documento: Preprint