Misattribution bias of COVID-19 hospitalizations in Alberta using an admission algorithm
There was misattribution bias in COVID-19 hospitalization determination using the admission algorithm. The findings from this analysis led to improvements in the algorithm to improve precision. Public health jurisdictions should review their COVID-19 hospitalization reporting approaches to ensure validity and consideration of incidental cases.
Authors
- David R. Strong (ORCID: https://orcid.org/0000-0002-5383-9032)
- Jordan Ross (ORCID: https://orcid.org/0000-0001-7624-5723)
- Kristin Klein
- Oscar E. Larios (ORCID: https://orcid.org/0000-0001-6716-7938)
- Samantha James (ORCID: https://orcid.org/0000-0002-0322-9025)
- John Maynard Conly (ORCID: https://orcid.org/0000-0002-3348-0157)
- A. Uma Chandran
- Tri Dinh
Institutions
- Alberta Health Services (CA)
- University of Alberta (CA)
- University of Calgary (CA)
Publication Details
- Journal
- Journal of the Association of Medical Microbiology and Infectious Disease Canada
- Published
- 2024-12-19
- DOI
- https://doi.org/10.3138/jammi-2024-0011
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Centers for Disease Control and Prevention