RESUMO
BACKGROUND The average lifetime risk of breast cancer for an American woman is 12.5%, but individual risks vary significantly. Risk modeling is a standard of care for breast cancer screening and prevention with recommended tools to stratify individual risks based on age, family history, breast density, and a host of other known risk factors. Because of a lack of resources rurally, we have not consistently met this standard of care within all of North Carolina.METHODS We implemented a quality improvement project to assess the risk for breast cancer by gathering data on community risks. We implemented an evidence-based tool (Tyrer-Cuzick) for quantifying risk within a mostly rural population of Eastern North Carolina and developed customized services for women meeting elevated-risk definition. These services included additional imaging for elevated-risk women and a risk-reduction program. We also assessed genetic risks for hereditary breast and ovarian cancer in our at-risk population using National Comprehensive Cancer Network (NCCN) guidelines based on family history and added local genetics extenders to help test more women. We analyzed data regularly using Plan-Do-Study-Act methods to improve outcomes over 1 year.RESULTS We screened a population of 4500 women at a community hospital over a 1-year period for their individual lifetime cancer risk and genetic risk. Breast cancer risk was quantitated at the time of mammography, and women were stratified into 3 groups for risk management. Within our screening population, 6.3% of women were at high risk (defined by a lifetime breast cancer risk greater than or equal to 20%) and another 8.1% were above-average risk (defined by a lifetime breast cancer risk of 15%-20%). These women (14.4%) could potentially benefit from additional risk-management strategies. Additionally, 20% of all unaffected women within a typical screening population of Eastern North Carolina met NCCN guidelines for hereditary breast cancer and ovarian cancer testing independent of their cancer risk score. Using a model of targeted intervention within a population with elevated risks can be helpful in improving outcomes.LIMITATIONS This population within Eastern North Carolina is mostly rural and represents a potentially biased population, as it involves older women undergoing annual mammography. It may not be broadly applicable to the entire population based on age, geography, and other risks.CONCLUSIONS This model for improving cancer risk assessment and testing at a small community hospital in Eastern North Carolina was successful and addressed a community need. We discovered a high rate of increased-risk women who can benefit from individualized risk management, and a higher percentage of women who potentially benefit from genetic testing. These higher cumulative risks may in part explain some of the disparities seen for breast-cancer-specific outcomes in some parts of the state.
