Abstract Number:
3614
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Diana Govier (1), Meike Niederhausen (2), Yumie Takata (3), Alex Hickok (4), Mazhgan Rowneki (4), Holly McCready (4), Valerie Smith (5), Thomas Osborne (6), Edward Boyko (7), George Ioannou (8), Matthew Maciejewski (9), Elizabeth Viglianti (10), Amy Bohnert (10), Ann O'Hare (11), Theodore Iwashyna (12), Denise Hynes (4)
Institutions:
(1) Center to Improve Veteran Involvement in Care, VA Portland Health Care System,, Portland, OR, (2) Oregon Health & Science University University – Portland State University School of Public Health, Portland, OR, (3) College of Health, Oregon State University, Corvallis, OR, (4) Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland, OR, (5) Department of Medicine, Duke University, Durham, NC, (6) VA Palo Alto Health Care System, Palo Alto, CA, (7) Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System, Seattle, WA, (8) Center of Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound Health Care System, Seattle, WA, (9) Center of Innovation to Accelerate Discovery & Practice Transformation, VA Durham Health Care System, Durham, NC, (10) VA Center for Clinical Management Research, VA Ann Arbor Health Care System, Ann Arbor, MI, (11) Center of Innovation for Veteran-Centered and Value-Driven Care,VA Puget Sound Health Care System, Seattle, WA, (12) School of Medicine, Johns Hopkins University, Baltimore, MD
Co-Author(s):
MEIKE NIEDERHAUSEN
Oregon Health & Science University University – Portland State University School of Public Health
Alex Hickok
Center to Improve Veteran Involvement in Care, VA Portland Health Care System
Mazhgan Rowneki
Center to Improve Veteran Involvement in Care, VA Portland Health Care System
Holly McCready
Center to Improve Veteran Involvement in Care, VA Portland Health Care System
Edward Boyko
Seattle Epidemiologic Research and Information Center, VA Puget Sound Health Care System
George Ioannou
Center of Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound Health Care System
Matthew Maciejewski
Center of Innovation to Accelerate Discovery & Practice Transformation, VA Durham Health Care System
Elizabeth Viglianti
VA Center for Clinical Management Research, VA Ann Arbor Health Care System
Amy Bohnert
VA Center for Clinical Management Research, VA Ann Arbor Health Care System
Ann O'Hare
Center of Innovation for Veteran-Centered and Value-Driven Care,VA Puget Sound Health Care System
Denise Hynes
Center to Improve Veteran Involvement in Care, VA Portland Health Care System
First Author:
Diana Govier
Center to Improve Veteran Involvement in Care, VA Portland Health Care System,
Presenting Author:
Abstract Text:
Potentially preventable hospitalizations (PPH) are used to measure health system performance. During the COVID-19 pandemic, disruptions to healthcare may have hindered disease treatment and increased the risk of PPH. Messaging to stay home when sick, as well as post-infection sequelae may have compounded risks among those with COVID-19. Using an emulated target trial design with monthly sequential trials, we compared risk of a PPH among 189,136 Veterans with COVID-19 between March 1, 2020, and April 30, 2021 and 943,084 matched uninfected comparators. The primary outcome was a first PPH in Veterans Health Administration (VHA) hospitals, or in community hospitals either paid by VHA or Medicare fee-for-service. Extended Cox models were used to examine adjusted hazard ratios (aHRs) of PPH among Veterans with COVID-19 and comparators during varying follow-up periods: 0-30, 0-90, 0-180, and 0-365 days. In total, 3.1% (3.8% of infected and 3.0% of comparators) of Veterans had a PPH during one-year follow-up. The risk of a PPH was greater among Veterans with COVID-19 than comparators in four follow-up periods: 0-30-day aHR=3.26; 0-90-day aHR=2.12; 0-180-day aHR=1.69; 0-395-day aHR=1.44.
Keywords:
emulated target trial|preventable hospitalization|extended Cox modelling|SARS-CoV-2|COVID-19|Veterans
Sponsors:
Section on Statistics in Epidemiology
Tracks:
Causal Inference
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