Advertisement

Cancer Causes & Control

, Volume 13, Issue 2, pp 101–111 | Cite as

Projecting SEER cancer survival rates to the US: an ecological regression approach

  • Angela Mariotto
  • Riccardo Capocaccia
  • Arduino Verdecchia
  • Andrea Micheli
  • Eric J. Feuer
  • Linda Pickle
  • Limin X. Clegg
Article

Abstract

Objectives: Cancer survival information is available only in areas covered by cancer registration. The objective of this study is to project cancer survival for the entire US as well as states from survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program. Methods: Five-year breast, prostate, and colorectal cancer relative survival rates from SEER are regressed on socioeconomic, demographic, and health variables at the county level. These models are first validated by comparing the observed rates with projected rates for counties not used in the estimation process. Results: Education was the best indicator of longer cancer survival. Other important predictors of the geographical variability of survival varied by cancer site. Better survival was predicted for breast and prostate than for colorectal cancer. Conclusions: Data from cancer registries can be used in ecological models to provide national and state estimates of patients' survival rates. These estimates are useful in targeting areas in which to promote earlier diagnosis or improved access to care, and may also aid in monitoring the quality of survival data collected by individual cancer registries.

cancer survival ecological models SEER socioeconomic and demographic factors 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Surveillance, Epidemiology, and End Results (SEER) Program Public-Use CD-ROM (1973–1997) National Cancer Institute, DCCPS, Cancer Surveillance Research Program, Cancer Statistics Branch, releasedApril 2000, based on the August 1999 submission.Google Scholar
  2. 2.
    Frey CM, McMillen MM, Cowan CD, Horm JW, Kessler LG (1992) Representativeness of Surveillance, Epidemiology and End Results Program data: recent trends in cancer mortality rates. J Natl Cancer Inst 84: 872–877.Google Scholar
  3. 3.
    Frey CM, Feuer EJ, Timmel MJ (1994) Projection of incidence rates to a larger population using ecologic variables. Stat Med 13: 1755–1770.Google Scholar
  4. 4.
    Verdecchia A, Capocaccia R, Egidi V, Golini A (1989) A method for the estimation of chronic disease morbidity and trends from mortality data. Stat Med 8: 201–216.Google Scholar
  5. 5.
    Swan J, Wingo P, Clive R, et al. (1998) Cancer surveillance in the US: can we have a national system? Cancer 83: 1282–1291.Google Scholar
  6. 6.
    Chen VW, Howe HL, Xu XC, Hotes JL, Correa CN (eds). (2000) Cancer in NorthAmerica, 1993–1997, vol. 1: Incidence. Springfield, IL: North American Association of Central Cancer Registries, April.Google Scholar
  7. 7.
    Area Resource File (1997) Office of Research and Planning, Bureau of Health Professionals.Google Scholar
  8. 8.
    Cella DF, Orav EJ, Kornbith AB, et al. (1991) Socioeconomic status and cancer survival. J Clin Oncol 9: 1500–1509.Google Scholar
  9. 9.
    Wells BL, Horm JW (1998) Targeting the underserved for breast and cervical cancer screening: the utility of ecological analysis using the National Health Interview Survey. Am J Public Health 88: 1484–1489.Google Scholar
  10. 10.
    Breen N, Figueroa JB (1996) Stage of breast and cervical cancer diagnosis in disadvantage neighborhoods: a prevention policy perspective. Am J Prev Med 12: 319–326.Google Scholar
  11. 11.
    Bassett MT, Krieger N (1986) Social class and black-white differences in breast cancer survival. Am J Public Health 76: 1440–1403.Google Scholar
  12. 12.
    Ederer F, Axtell LM, Cutler SJ (1961) The relative survival rate: a statistical methodology. Natl Cancer Inst Monogr 6: 101–121.Google Scholar
  13. 13.
    Hakulinen T, Tenkanen L (1987) Regression analysis of relative survival rates. Roy Stat Soc 36: 309–317.Google Scholar
  14. 14.
    McCullagh P, Nelder JA (1989) Generalized Linear Models. London: Chapman & Hall, pp. 124–128.Google Scholar
  15. 15.
    Kendall M, Stuart A (1977) The Advanced Theory of Statistics, vol. I, 4th ed. London: Charles Griffin, pp. 246–248.Google Scholar
  16. 16.
    Gatta G, Capocaccia R, et al. (2001) Comparison of the survival in American and European cancer patients diagnosed in the late eighties. (In preparation).Google Scholar
  17. 17.
    Berrino F, Sant M, Verdecchia A, et al., eds. (1995) Survival of Cancer Patients in Europe: the EUROCARE Study. Lyon: IARC Scientific Publications no. 132.Google Scholar
  18. 18.
    Sant M and the EUROCARE Working Group (1999) Overview of the EUROCARE-2 results on survival of cancer patients diagnosed in 1985-89. In: Berrino F, Capocaccia R, Estève J, Gatta G, Hakulinen T, Micheli A, Sant M, Verdecchia A, eds. Survival of Cancer Patients in Europe: the EUROCARE-2 Study. Lyon: IARC Scientific Publications no. 151, pp. 525–541.Google Scholar
  19. 19.
    Coleman MP, Babb P, Damiecki P, et al. (1999) Cancer survival trends in England and Wales, 1971–1995: deprivation and NHS region. Studies in Medical and Population Subjects, 60: London: HMSO.Google Scholar
  20. 20.
    Gatta G, Buiatti E, Conti E, et al. (1997) Variations in the survival of adult cancer patients in Italy. In: Verdecchia A, Micheli A, Gatta G (1997) Survival in cancer patients in Italy: the ITACARE study. Tumori 83: 497–507.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Angela Mariotto
    • 1
    • 2
    • 3
    • 4
  • Riccardo Capocaccia
    • 1
  • Arduino Verdecchia
    • 1
  • Andrea Micheli
    • 5
  • Eric J. Feuer
    • 2
  • Linda Pickle
    • 2
  • Limin X. Clegg
    • 2
  1. 1.Department of EpidemiologyIstituto Superiore di SanitàRomeItaly
  2. 2.Division of Cancer Control and Population SciencesNational Cancer Institute, NIHBethesdaUSA
  3. 3.National Cancer InstituteBethesda
  4. 4.FedEX: RockvilleUSA
  5. 5.Divisione di EpidemiologiaIstituto Nazionale TumoriMilanItaly

Personalised recommendations