Disruption of National Cancer Database Data Models in the First Year of the COVID-19 Pandemic

Author:

Lum Sharon S.1,Browner Amanda E.2,Palis Bryan2,Nelson Heidi2,Boffa Daniel3,Nogueira Leticia M.4,Hawhee Vicki5,McCabe Ryan M.2,Mullett Timothy6,Wick Elizabeth7

Affiliation:

1. Department of Surgery, Loma Linda University School of Medicine, Loma Linda, California

2. American College of Surgeons Cancer Programs, Chicago, Illinois

3. Division of Thoracic Surgery, Yale School of Medicine, New Haven, Connecticut

4. Surveillance and Health Equity Sciences, American Cancer Society, Kennesaw, Georgia

5. Baptist Health South Florida, Miami

6. Division of Cardiothoracic Surgery, University of Kentucky College of Medicine, Lexington

7. Division of Surgical Oncology, University of California, San Francisco School of Medicine, San Francisco

Abstract

ImportanceEach year, the National Cancer Database (NCDB) collects and analyzes data used in reports to support research, quality measures, and Commission on Cancer program accreditation. Because data models used to generate these reports have been historically stable, year-to-year variances have been attributed to changes within the cancer program rather than data modeling. Cancer submissions in 2020 were anticipated to be significantly different from prior years because of the COVID-19 pandemic. This study involved a validation analysis of the variances in observed to expected 2020 NCDB cancer data in comparison with 2019 and 2018.ObservationsThe NCDB captured a total of 1 223 221 overall cancer cases in 2020, a decrease of 14.4% (Δ = −206 099) compared with 2019. The early months of the COVID-19 pandemic (March-May 2020) coincided with a nadir of cancer cases in April 2020 that did not recover to overall prepandemic levels through the remainder of 2020. In the early months of the COVID-19 pandemic, the proportion of early-stage disease decreased sharply overall, while the proportion of late-stage disease increased. However, differences in observed to expected stage distribution in 2020 varied by primary disease site. Statistically significant differences in the overall observed to expected proportions of race and ethnicity, sex, insurance type, geographic location, education, and income were identified, but consistent patterns were not evident.Conclusions and RelevanceHistorically stable NCDB data models used for research, administrative, and quality improvement purposes were disrupted during the first year of the COVID-19 pandemic. NCDB data users will need to carefully interpret disease- and program-specific findings for years to come to account for pandemic year aberrations when running models that include 2020.

Publisher

American Medical Association (AMA)

Subject

Surgery

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