Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning

Journal article

Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.

Authors: Vekaria, B; Overton, C; Wiśniowski, A; Ahmad, S; Aparicio-Castro, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: BMC Infectious Diseases
• Volume: 21
• Issue: 1
• Pages: 700-700
• Article number: 700
• Publication date: 2021-07-22
• DOI: 10.1186/s12879-021-06371-6
• EISSN: 1471-2334
• ISSN: 1471-2334
• Language: English
• Keywords: Intensive care medicine; Internal medicine; Infectious disease (medical specialty); Medical emergency; Intensive care; Geography; Pandemic; Medicine; Coronavirus disease 2019 (COVID-19); Occupancy; Intensive care unit; Emergency medicine; Disease; Scale (ratio)