Annals of Surgical Oncology

, Volume 20, Issue 6, pp 1759–1765 | Cite as

Comparison of Cases Captured in the National Cancer Data Base with Those in Population-based Central Cancer Registries

  • Catherine C. Lerro
  • Anthony S. RobbinsEmail author
  • Jerri Linn Phillips
  • Andrew K. Stewart
Healthcare Policy and Outcomes



The National Cancer Data Base (NCDB) is a large, geographically diverse hospital-based cancer registry that has been used to study factors related to cancer diagnosis, treatment, and survival. The primary purpose of this study was to compare the case counts and characteristics of patients in NCDB with population-based registries reported in the United States Cancer Statistics (USCS).


Cancer case counts from NCDB were compared to case counts from USCS to measure NCDB’s case coverage, or the percentage of cases captured. Case coverage was examined by a variety of characteristics, including state of residence, race/ethnicity, age, and primary cancer site.


The overall NCDB case coverage was 67.4 %, ranging from a high of 88.7 % for Delaware to a low of 27.1 % for Arizona. Case coverage for white, black, and Asian/Pacific Islander cases was high (64.7 % to 67.4 %), but it was much lower for American Indians/Alaskan Natives (32.8 %) and those of Hispanic ethnicity (51.1 %). Among the elderly (aged 65 + years), case coverage is much lower compared to persons younger than 65 (63.0 % and 73.0 %, respectively). Case coverage also varied widely by site, with the highest being cervix (77.9 %) and the lowest being melanoma (50.6 %).


This study highlights the geographic- and site-specific variation in NCDB case coverage, primarily as a result of NCDB facility presence and data collection and processing protocols. These findings illustrate the strengths and limitations of NCDB as a resource for nationwide data on cancer diagnosis, treatment, and survival.


Cancer Site National Cancer Data Base Case Count Case Coverage American Hospital Association Annual Survey 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Elizabeth Ward, Stacey Fedewa, and Katherine Virgo from the ACS’s Intramural Research Department for their help and guidance with this project. Supported in part by ACS Intramural Research funds.


The authors declare no conflict of interest.


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Copyright information

© Society of Surgical Oncology 2013

Authors and Affiliations

  • Catherine C. Lerro
    • 1
  • Anthony S. Robbins
    • 2
    Email author
  • Jerri Linn Phillips
    • 3
  • Andrew K. Stewart
    • 3
  1. 1.Department of Environmental Health SciencesYale University School of Public HealthNew HavenUSA
  2. 2.Health Services Research ProgramAmerican Cancer SocietyAtlantaUSA
  3. 3.Commission on CancerAmerican College of SurgeonsChicagoUSA

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