Breast cancer is the most common cancer and the second leading cause of cancer death among women. The incidence of breast cancer peaks between ages 75-79 and women >75 years are the fastest growing segment of the US population. However, none of the randomized controlled trials evaluating screening mammography included women >75 years and it is not known whether mammography screening helps older women live longer. Meanwhile, overdiagnosis (diagnosis of tumors that are of no threat) and harms from breast cancer treatment increase with age. Screening decisions need to be informed by a woman's life expectancy and her risk of developing breast cancer. We previously developed a model to estimate older women's life expectancy. Our current proposal will provide the complementary essential information on older women's breast cancer risk. The Gail model, the most widely available breast cancer prediction model, was developed among few women >75 years (none >80) and includes factors (e.g., early menarche) that may not be important for late-life breast cancer due the time that has passed since these factors influenced estrogen levels. Meanwhile, emerging data suggest that obesity and increased bone mass (markers of life-long elevations in estrogen) may have greater impact on older women's breast cancer risk and these factors are not included in the Gail model. We, an interdisciplinary team of internists, geriatricians, epidemiologists, biostatisticians, health servces researchers, and an oncologist, aim to develop and validate a novel breast cancer risk prediction model for women >75 years that outperforms the Gail model. We will use data from: 1) Nurses'Health Study (NHS) and 2) Women's Health Initiative (WHI). The NHS is a longitudinal study of 121,700 nurses aged 30-55 in 1976 who have been interviewed biennially. Participants now range in age from 66-91. The WHI enrolled ~162,000 women ages 50-79 in at least one of three clinical trials or an observational study between 1993 and 1998. In 2005, ~110,000 of these women without a history of breast cancer agreed to extended follow-up;we will include these women in our analyses. In both NHS and WHI, data are repeatedly collected on participants'health behaviors and medical history, including incident breast cancer. We propose the following Aims: 1) We will compare the Gail model's performance between women >75 years and younger postmenopausal women in the NHS and WHI;2) We will examine whether breast cancer risk factors change with age and we will use competing risks regression to develop a prediction model for breast cancer among women >75 years;and 3) We will validate the performance of this model in the WHI and will compare the performance of our model with the Gail model. Impact: A valid tool to assess late-life breast cancer risk is greatly needed to inform mammography screening decisions. As more women are living to advanced age, our work is crucial to ensure that resources around breast cancer screening are allocated optimally. Our work has great potential to improve quality of care and quality of life for older women.
Because the population of women aged 75 and older is growing rapidly in the US and worldwide, and the incidence of breast cancer increases with age, a worldwide epidemic of breast cancer is expected in the coming years;despite this, older women's decisions whether or not to undergo mammography screening are complex since none of the randomized screening trials included women aged 75 and older. As a Beeson Scholar, Dr. Schonberg's research focused on informing mammography screening decisions by collecting data on older women's life expectancy and screening preferences;however, information on older women's individualized risk of breast cancer is needed to further improve their screening decisions. In this application we will identify risk factors for late-life breast cncer using these factors to develop and validate a clinical prediction tool for women aged 75 and older to be used by older women and their doctors to make more informed decisions about breast cancer screening, thereby improving both their quality of care and quality of life.
