Note: The views expressed in this discussion paper are those of the author and are not necessarily those of the members or staff of the Puget Sound Regional Council. 2 Informational Paper on "Compact Growth and Adverse Health Impacts" This paper was developed by University of Washington Doctoral Student Gail Sandlin under a contract with the Puget Sound Regional Council. The scope of the contra...

Sharon Abel, PhD Jane Baran, PhD Anthony Cacace, PhD Gail Chermak, PhD$ Susan Dalebout, PhD Jay Hall 111, PhD Linda Hood, PhD Lisa Hunter, PhD James Jerger, PhD Susan Jerger, PhD Robert Keith, PhD Frank Musiek, PhD Ross Roeser, PhD Christine Sloan, PhD University of Toronto, Toronto, ON University of Massachusetts, Amherst, MA Albany Medical College, Albany, NY Washington State University, Pull...

INTRODUCTION Accurate individualised breast cancer risk assessment is essential to provide risk-benefit analysis prior to initiating interventions designed to lower breast cancer risk. Several mathematical models for the estimation of individual breast cancer risk have been proposed. However, no single model integrates family history, hormonal factors, and benign breast disease in a comprehensi...

A multivariate model to assess breast cancer risk was developed by Gail et aL (M. H. Gail, 1. A. Brinton, D. B. Byar, D. K. Corle, S. B. Green, C. Schairer, and J. J. Mulvihill, J. NatI. Cancer Inst., 81: 1879-1886, 1989) based on data analysis of the Breast Cancer Detection and Demonstration Project. We evaluated the model’s usefulness for assigning women to risk groups for counseling and foll...

BACKGROUND The objective was to compare women's personal estimates of their risk with objective breast cancer risk estimates and to describe the risk factors for breast cancer identified by women. METHODS Telephone survey of a random sample of 761 rural and urban women with no history of breast cancer. Survey instrument included measures of perceptions of lifetime risk for breast cancer for t...

A multivariate model to assess breast cancer risk was developed by Gail et al. (M. H. Gail, L. A. Brinton, D. B. Byar, D. K. Corle, S. B. Green, C. Schairer, and J. J. Mulvihill, J. Natl. Cancer Inst., 81: 1879-1886, 1989) based on data analysis of the Breast Cancer Detection and Demonstration Project. We evaluated the model's usefulness for assigning women to risk groups for counseling and fol...

Risk prediction algorithms are an important tool for identifying individuals at high risk of developing the disease who can then be offered individually tailored clinical management. Several algorithms that predict the probability of breast cancer incidence are currently used in clinical practice. It is uncertain, though, as to which of the breast cancer risk prediction models performs best for...

BACKGROUND Women and their clinicians are increasingly encouraged to use risk estimates derived from statistical models, primarily that of Gail et al., to aid decision making regarding potential prevention options for breast cancer, including chemoprevention with tamoxifen. METHODS We evaluated both the goodness of fit of the Gail et al. model 2 that predicts the risk of developing invasive b...

We used data from a multicentre case-control study conducted in Italy between 1991 and 1994 on over 2500 cases of breast cancer and a comparable number of controls, and estimates of breast cancer incidence in Italy to compute individual breast cancer risk for Italian women. The estimated probabilities between age 50 and 80 ranged from approximately 5% (for a woman with no family history and low...