The Impact of the COVID-19 Pandemic On Cancer Care in Radiation Oncology
S Utkarsh1,2*, S Andrea3,4,5, M Gray6, D Wazer1,2, K Leonard1,2, R Munbodh1,2, (1) Department of Radiation Oncology, Warren Alpert Medical School Of Brown University, Providence, RI, (2) Department of Radiation Oncology, Rhode Island Hospital, Providence, RI, (3) Lifespan Biostatistics Core, Rhode Island Hospital, Providence, RI, (4) VA Portland Health Care System, Portland, OR, (5) OHSU-PSU School of Public Health, Portland, OR, (6) Department of Molecular Pharmacology, Physiology, and Biotechnology, Brown University, Providence, RI
Presentations
PO-GePV-M-49 (Sunday, 7/25/2021) [Eastern Time (GMT-4)]
Purpose: Current studies evaluating the impact of COVID-19 on Radiation Oncology (RO) departments describe alterations in practice patterns, assess acute effects, and offer management strategies. However, there is a lack of data documenting practice alterations. We aimed to assess the early impact of the first two waves of COVID-19 on RO practice.
Methods: Interrupted time series analyses were conducted using electronic health record data from a major RO department from January 2018 through February 2021 (164-weeks). Segmented regression models were used to estimate the immediate impact of our state’s COVID-19 Wave1 and Wave2 on weekly number of CT simulations (CT) and physics initial chart reviews (PCR), and percentage of patients receiving treatments hypofractionated at javascript:void(0);4 Gy per fraction (g4Gy). State-level COVID-19 data were used to define the start and end of Wave1 as well as the start of Wave2.
Results: Both CT and PCR presented a sudden drop at the start of Wave1 relative to the same time period in previous years. CT dropped by 22% (95% CI: -49.7%,-3.4%) and PCR dropped by 17.9% (95% CI:-36.0%,-3.8%). In contrast, there was a sudden 11.6% increase in patients receiving treatments g4Gy. Immediately after Wave1, CT and PCR increased by 23% and 27%, respectively. Weekly rates of CT, PCR, and g4Gy at the start of Wave2 did not deviate significantly from expected pre-pandemic rates. Wave2 is ongoing but preliminary data suggest decreasing CT and PC. Short post-pandemic within and between wave follow-up limit statistical power to detect a slope change among these points.
Conclusion: Despite RO facilities remaining fully operational during the pandemic, large fluctuations in patient volumes and workload were observed. Contributing factors included reduced cancer screenings and elective surgery, internal policies to alter treatment regimens, and patients declining treatment. Our findings may help inform preparations for future novel aberrations in workflow.
ePosters
Keywords
Treatment Techniques, Statistical Analysis, Modeling
Taxonomy
IM- Dataset Analysis/Biomathematics: Machine learning
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