The long term objective of this proposal is to enhance the New Hampshire Mammography Network's research capacity by expanding data collection to include determining outcomes in women who experienced mammographic abnormalities and whose subsequent breast outcomes are not recorded in our registry, determining what motivated women who experienced a false negative mammogram to obtain follow-up care, and collecting or merging supplemental data to allow us to conduct five special research projects. These special studies will include: 1) comparing risk and behavioral characteristics of screened and unscreened NH women; 2) determining the relationships between benign breast pathology characteristics and mammography performance; 3) exploring the influence of menstrual cycle phase on breast density and breast cancer screening performance; 4) developing risk prediction models for invasive and non-invasive breast cancer (including using breast density as a marker for breast cancer risk); and 5) using regression modeling techniques to develop a longitudinal model of mammography/health states defined by screening compliance, mammography outcomes, follow-up and disease outcomes in individual subjects and to determine predictors for transitions between these states, including an evaluation of mammography-related predictors for all cause mortality and breast-cancer mortality. Our proposed projects will benefit the work of the Breast Cancer Surveillance Consortium in two ways. First, determining sources of inadequate follow-up (whether it is a patient factor or data collection issue) will identify the capture rate of population-based mammography registries and may identify possible intervention points for those with abnormal findings who are resistant to seeking care. Second, we plan to merge data from three registries (New Hampshire, Vermont, and North Carolina) to conduct three of our study aims. By pooling data from these three registries, the number of cancer cases for analysis will be increased and the characteristics of the population represented in the data will be more diverse.
Our specific aims can easily be addressed with data from the three proposed sites; however, any validation studies could apply well established methods from this proposed work to a larger dataset accessed from the Consortium in the future.
Our aims will allow us to employ local analytic expertise while contributing new knowledge to the Surveillance Consortium.
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