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Cancer Causes & Control

, Volume 18, Issue 4, pp 439–447 | Cite as

Estimating age-specific breast cancer risks: a descriptive tool to identify age interactions

  • William F. Anderson
  • Rayna K. Matsuno
  • Mark E. Sherman
  • Jolanta Lissowska
  • Mitchell H. Gail
  • Louise A. Brinton
  • Xiaohong (Rose) Yang
  • Beata Peplonska
  • Bingshu E. Chen
  • Philip S. Rosenberg
  • Nilanjan Chatterjee
  • Neonila Szeszenia-Dąbrowska
  • Alicja Bardin-Mikolajczak
  • Witold Zatonski
  • Susan S. Devesa
  • Montserrat García-Closas
Original Paper

Abstract

Objective

Clarifying age-specific female breast cancer risks and interactions may provide important etiologic clues.

Method

Using a population-based case–control study in Poland (2000–2003) of 2,386 incident breast cancer cases and 2,502 control subjects aged 25–74 years, we estimated age-specific breast cancer incidence rates according to risk factors.

Results

Breast cancer risks were elevated among women with positive family history (FH), younger age at menarche, older age at first full-term birth, nulliparity, exogenous hormonal usage, and reduced physical activity (PA). Notwithstanding overall risks, we observed statistically significant quantitative (non-crossover) and qualitative (crossover) age interactions for all risk factors except for FH and PA. For example, nulliparity compared to parity reduced breast cancer risk among women ages 25–39 years then rates crossed or reversed, after which nulliparity increased relative risks among women ages 40–74 years.

Conclusion

Though quantitative age interactions could be expected, qualitative interactions were somewhat counterintuitive. If confirmed in other populations, qualitative interactions for a continuous covariate such as age will be difficult to reconcile in a sequential (multistep or monolithic) ‘stochastic’ breast cancer model. Alternatively, the reversal of relative risks among younger and older women suggests subgroup heterogeneity with different etiologic mechanisms for early-onset and late-onset breast cancer types.

Keywords

Population-based case–control study Absolute risks Relative risks Odds ratio Rate ratios 

Notes

Acknowledgments

This research was supported in part by the Intramural Research Program of the NIH/National Cancer Institute

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Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • William F. Anderson
    • 1
    • 4
  • Rayna K. Matsuno
    • 1
  • Mark E. Sherman
    • 1
  • Jolanta Lissowska
    • 1
    • 2
  • Mitchell H. Gail
    • 1
  • Louise A. Brinton
    • 1
  • Xiaohong (Rose) Yang
    • 1
  • Beata Peplonska
    • 3
  • Bingshu E. Chen
    • 1
  • Philip S. Rosenberg
    • 1
  • Nilanjan Chatterjee
    • 1
  • Neonila Szeszenia-Dąbrowska
    • 3
  • Alicja Bardin-Mikolajczak
    • 2
  • Witold Zatonski
    • 2
  • Susan S. Devesa
    • 1
  • Montserrat García-Closas
    • 1
  1. 1.Division of Cancer Epidemiology and GeneticsNational Cancer Institute, National Institute of HealthRockvilleUSA
  2. 2.Department of Cancer Epidemiology and PreventionCancer Center and Marie Sklodowska-Curie Institute of OncologyWarsawPoland
  3. 3.Nofer Institute of Occupational MedicineŁódźPoland
  4. 4.Biostatistics BranchDHHS/NIH/NCI/DCEGBethesdaUSA

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