Heterogeneity in COVID-19 patient volume, characteristics and outcomes across US Department of Veterans Affairs facilities: an observational cohort study
- PMID: 34006034
- PMCID: PMC7941674
- DOI: 10.1136/bmjopen-2020-044646
Heterogeneity in COVID-19 patient volume, characteristics and outcomes across US Department of Veterans Affairs facilities: an observational cohort study
Abstract
Objective: Studies describe COVID-19 patient characteristics and outcomes across populations, but reports of variation across healthcare facilities are lacking. The objectives were to examine differences in COVID-19 patient volume and mortality across facilities, and understand whether facility variation in mortality was due primarily to differences in patient versus facility characteristics.
Design: Observational cohort study with multilevel mixed effects logistic regression modelling.
Setting: The Veterans Health Administration (VA) is the largest healthcare system in the USA.
Participants: Patients with COVID-19.
Main outcome: All-cause mortality within 45 days after COVID-19 testing (March-May, follow-up through 16 July 2020).
Results: Among 13 510 patients with COVID-19, 3942 (29.2%) were admitted (2266/3942 (57.5%) ward; 1676/3942 (42.5%) intensive care unit (ICU)) and 679/3942 (17.2%) received mechanical ventilation. Marked heterogeneity was observed across facilities in median age (range: 34.3-83.9 years; facility mean: 64.7, SD 7.2 years); patient volume (range: 1-737 at 160 facilities; facility median: 48.5, IQR 14-105.5); hospital admissions (range: 1-286 at 133 facilities; facility median: 11, IQR 1-26.5); ICU caseload (range: 1-85 at 115 facilities; facility median: 4, IQR 0-12); and mechanical ventilation (range: 1-53 at 90 facilities; facility median: 1, IQR 0-5). Heterogeneity was also observed in facility mortality for all patients with COVID-19 (range: 0%-29.7%; facility median: 8.9%, IQR 2.4%-13.7%); inpatients (range: 0%-100%; facility median: 18.0%, IQR 5.6%-28.6%); ICU patients (range: 0%-100%; facility median: 28.6%, IQR 14.3%-50.0%); and mechanical ventilator patients (range: 0%-100%; facility median: 52.7%, IQR 33.3%-80.6%). The majority of variation in facility mortality was attributable to differences in patient characteristics (eg, age).
Conclusions: Marked heterogeneity in COVID-19 patient volume, characteristics and mortality were observed across VA facilities nationwide. Differences in patient characteristics accounted for the majority of explained variation in mortality across sites. Variation in unadjusted COVID-19 mortality across facilities or nations should be considered with caution.
Keywords: COVID-19; adult intensive & critical care; general medicine (see internal medicine).
© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
Conflict of interest statement
Competing interests: None declared.
Figures


Similar articles
-
Association of Intensive Care Unit Patient Load and Demand With Mortality Rates in US Department of Veterans Affairs Hospitals During the COVID-19 Pandemic.JAMA Netw Open. 2021 Jan 4;4(1):e2034266. doi: 10.1001/jamanetworkopen.2020.34266. JAMA Netw Open. 2021. PMID: 33464319 Free PMC article.
-
Facility-Level Variation in Dialysis Use and Mortality Among Older Veterans With Incident Kidney Failure.JAMA Netw Open. 2021 Jan 4;4(1):e2034084. doi: 10.1001/jamanetworkopen.2020.34084. JAMA Netw Open. 2021. PMID: 33449098 Free PMC article.
-
Risk Factors Associated With In-Hospital Mortality in a US National Sample of Patients With COVID-19.JAMA Netw Open. 2020 Dec 1;3(12):e2029058. doi: 10.1001/jamanetworkopen.2020.29058. JAMA Netw Open. 2020. PMID: 33301018 Free PMC article.
-
Protection of staff and families during COVID-19 pandemic: experience from a research institute in Bangladesh.Lancet Reg Health Southeast Asia. 2023 Dec 29;22:100344. doi: 10.1016/j.lansea.2023.100344. eCollection 2024 Mar. Lancet Reg Health Southeast Asia. 2023. PMID: 38482157 Free PMC article. Review.
-
Pitfalls when comparing COVID-19-related outcomes across studies-lessons learnt from the ERACODA collaboration.Clin Kidney J. 2021 Feb 2;14(Suppl 1):i14-i20. doi: 10.1093/ckj/sfab027. eCollection 2021 Mar. Clin Kidney J. 2021. PMID: 33796283 Free PMC article. Review.
Cited by
-
Factors associated with, and variations in, COVID-19 hospital death rates in England's first two waves: observational study.BMJ Open. 2022 Jun 30;12(6):e060251. doi: 10.1136/bmjopen-2021-060251. BMJ Open. 2022. PMID: 35772812 Free PMC article.
-
Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.Korean J Intern Med. 2024 Nov;39(6):882-897. doi: 10.3904/kjim.2024.098. Epub 2024 Oct 29. Korean J Intern Med. 2024. PMID: 39468926 Free PMC article. Review.
-
COVID-19 Mortality Differences: Patient-related Data and Intensive Care Unit Load Are Prerequisites.Ann Am Thorac Soc. 2022 Sep;19(9):1622-1623. doi: 10.1513/AnnalsATS.202203-230LE. Ann Am Thorac Soc. 2022. PMID: 35522444 Free PMC article. No abstract available.
References
-
- Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China:summary of a report of 72 314 cases from the Chinese center for disease control and prevention. JAMA 2020. - PubMed
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical