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

Importance: Each 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. Observations: The 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 Relevance: Historically 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.

Evaluating information loss in the National Cancer Database from cases lost to follow-up.

BACKGROUND AND OBJECTIVES: Cancer registries must focus on data capture which returns value while reducing resource burden with minimal loss of data. Identifying the optimum length of follow-up data collection for patients with cancer achieves this goal. METHODS: A two-step analysis using entropy calculations to assess information gain for each follow-up year, and second-order differences to compare survival outcomes between the defined follow-up periods and lifetime follow-up. A total of 391 567 adult cases, deidentified in the National Cancer Database and diagnosed in 1989. Comparisons examined a subset of 61 908 lung cancer cases, 48 387 colon and rectal cancer cases, and 64 134 breast cancer cases in adults. A total of 4133 pediatric cases were diagnosed in 1989 examining 1065 leukemia cases and 494 lymphoma cases. RESULTS: Annual increases in information gain fell below 1% after 16 years of follow-up for adult cases and 9 years for pediatric cases. Comparison of second-order differences showed 62% of the comparisons were similar between 15 years and lifetime follow-up when examining restricted mean survival time. In addition, 90% of the comparisons were statistically similar when comparing hazard ratios. CONCLUSIONS: Survival analysis using 15 years postdiagnosis follow-up showed minimal differences in information gain compared to lifetime follow-up.

Twenty-Five Years of Cancer Follow-Up; Is the Data Worth the Effort?

BACKGROUND: Substantial resources are dedicated to long-term follow-up within cancer registries; however, the completeness of these data is poorly characterized. Our objectives were to quantify long-term cancer follow-up data completeness and the effort required to collect these data using the National Cancer Database (NCDB). METHODS: To quantify data completeness, patients diagnosed with cancer in 1989 were identified in the NCDB and loss to follow-up rates were assessed for 25 years after diagnosis. To quantify data collection effort, patients diagnosed from 1989 to 2014 who were alive and eligible for follow-up in 2014 were identified and the effort to perform patient follow-up was obtained via a survey of tumor registrars. The effort to perform follow-up beyond various intervals after diagnosis was calculated. RESULTS: In total, 484,201 patients at 958 hospitals were diagnosed with cancer in 1989. After 5 years, 6.5% of patients were lost to follow-up (13.1% of living patients), 50.3% were deceased, and 43.2% had ongoing follow-up. After 15 years, 22.9% were lost to follow-up (68.7% of living patients), 66.7% were deceased, and 10.5% had ongoing follow-up. By 25 years, loss to follow-up increased to 28.6% (93.7% of living patients), 69.5% were deceased, and 1.9% had ongoing follow-up. In 2014, 522,838 h were spent performing follow-up for 2,091,353 patients at 1456 hospitals who were >15 years from their initial cancer diagnosis. CONCLUSIONS: While 5-year follow-up is excellent in the NCDB, loss to follow-up increases over time. The impact of curtailing data collection is under investigation and follow-up duration requirements will be re-evaluated.

Quality Assurance and Continuing Education: A Cyclic Approach for Maintaining High Quality Data in a High Volume Cancer Registry.

Quality Assurance and Education are 2 areas of the Cancer Registry that go hand in hand. High-quality data can only be maintained through routine surveillance of data quality coupled with tailored continuing education of certified tumor registrars (CTRs). However, the magnitude of information a CTR is required to know, the rapid frequency with which standards change, and growing demands on the time of the CTRs can be roadblocks to maintaining quality in the Cancer Registry. Here we describe a robust approach to quality assurance in a high-volume hospital-based Cancer Registry, leveraging a repeated cycle of quality assessment and educational activities targeting identified opportunities for improvement. Establishing such an approach encourages the professional development of CTRs while simultaneously ensuring the highest quality data for use in population-based cancer surveillance, cancer research, and patient care.

How to Build an Abstract.

Creating an abstract from the electronic medical record (EMR) can seem overwhelming at first. There is so much information, it is difficult to know where to start and how to work your way through all the documents in a short period of time. This article provides one roadmap to build an abstract that can provide a template for a new abstractor or an alternative way of doing things for an experienced abstractor.

Registry Resources: A Summary Resource Guide for Education, Training, and Online Help for New and Current Cancer Registrars: Part II.

The Registry Resources guide useful and beneficial education resources to new and current cancer registrars. Further development of this resource guide can incorporate more state standards along with any other resources that you use on a regular basis that were not included in this resource guide.