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A multilevel model of postmenopausal breast cancer incidence.
- Author(s): Hiatt, Robert A
- Porco, Travis C
- Liu, Fengchen
- Balke, Kaya
- Balmain, Allan
- Barlow, Janice
- Braithwaite, Dejana
- Diez-Roux, Ana V
- Kushi, Lawrence H
- Moasser, Mark M
- Werb, Zena
- Windham, Gayle C
- Rehkopf, David H
- et al.
Published Web Locationhttp://cebp.aacrjournals.org/content/23/10/2078.long
No data is associated with this publication.
BackgroundBreast cancer has a complex etiology that includes genetic, biologic, behavioral, environmental, and social factors. Etiologic factors are frequently studied in isolation with adjustment for confounding, mediating, and moderating effects of other factors. A complex systems model approach may present a more comprehensive picture of the multifactorial etiology of breast cancer.
MethodsWe took a transdisciplinary approach with experts from relevant fields to develop a conceptual model of the etiology of postmenopausal breast cancer. The model incorporated evidence of both the strength of association and the quality of the evidence. We operationalized this conceptual model through a mathematical simulation model with a subset of variables, namely, age, race/ethnicity, age at menarche, age at first birth, age at menopause, obesity, alcohol consumption, income, tobacco use, use of hormone therapy (HT), and BRCA1/2 genotype.
ResultsIn simulating incidence for California in 2000, the separate impact of individual variables was modest, but reduction in HT, increase in the age at menarche, and to a lesser extent reduction in excess BMI >30 kg/m(2) were more substantial.
ConclusionsComplex systems models can yield new insights on the etiologic factors involved in postmenopausal breast cancer. Modification of factors at a population level may only modestly affect risk estimates, while still having an important impact on the absolute number of women affected.
ImpactThis novel effort highlighted the complexity of breast cancer etiology, revealed areas of challenge in the methodology of developing complex systems models, and suggested additional areas for further study.
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