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Estimating the prevalence of breast cancer using a disease model: data problems and trends

By Michelle E Kruijshaar, Jan J Barendregt and Lonneke V van de Poll-Franse

Abstract

BACKGROUND: Health policy and planning depend on quantitative data of disease epidemiology. However, empirical data are often incomplete or are of questionable validity. Disease models describing the relationship between incidence, prevalence and mortality are used to detect data problems or supplement missing data. Because time trends in the data affect their outcome, we compared the extent to which trends and known data problems affected model outcome for breast cancer. METHODS: We calculated breast cancer prevalence from Dutch incidence and mortality data (the Netherlands Cancer Registry and Statistics Netherlands) and compared this to regionally available prevalence data (Eindhoven Cancer Registry, IKZ). Subsequently, we recalculated the model adjusting for 1) limitations of the prevalence data, 2) a trend in incidence, 3) secondary primaries, and 4) excess mortality due to non-breast cancer deaths. RESULTS: There was a large discrepancy between calculated and IKZ prevalence, which could be explained for 60% by the limitations of the prevalence data plus the trend in incidence. Secondary primaries and excess mortality had relatively small effects only (explaining 17% and 6%, respectively), leaving a smaller part of the difference unexplained. CONCLUSION: IPM models can be useful both for checking data inconsistencies and for supplementing incomplete data, but their results should be interpreted with caution. Unknown data problems and trends may affect the outcome and in the absence of additional data, expert opinion is the only available judge

Topics: Research
Publisher: BioMed Central
Year: 2003
DOI identifier: 10.1186/1478-7954-1-5
OAI identifier: oai:pubmedcentral.nih.gov:156644
Provided by: PubMed Central

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Citations

  1. (2000). advanced risk analysis for spreadsheets
  2. (2000). Baan CA and Bonneux L An indirect estimate of the incidence of non-insulin-dependent diabetes mellitus Epidemiology
  3. (2002). Barendregt JJ and Hoeymans N The use of models in the estimation of disease epidemiology Bull World Health Organ
  4. (1975). Breast cancer in southeast North Brabant and in North Limburg; trends in incidence and earlier diagnosis in an unscreened female population,
  5. (1955). Cancer Incidence and Survival in the Southeast of the Netherlands
  6. (1987). Certification and coding of two underlying causes of death in The Netherlands and other countries of the
  7. (1955). Incidence, care and survival in the Southeast of the Netherlands
  8. (2001). Klokman WJ and Coebergh JW Excess mortality from breast cancer 20 years after diagnosis when life expectancy is normal
  9. (1997). Lobbezo IE and Poos MJJC Volksgezondheid Toekomst Verkenning
  10. (1993). Noncancer deaths in white adult cancer patients
  11. (1985). On the utility of the lognormal model for analysis of breast cancer survival
  12. (1994). Quantifying disability: data, methods and results Bull World Health Organ
  13. (2000). The Australian Burden of Disease Study: measuring the loss of health from diseases, injuries and risk factors Med J Aust
  14. (1990). The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries and risk factors in
  15. (1997). Utrecht: Vereniging van Integrale Kankercentra
  16. (1998). van den and Bonneux L Coping with multiple morbidity in a life table Mathematical Population Studies

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