Health Equity and Decision Sciences

点击翻译按钮获取中文摘要

The following manuscripts were developed by the staff of this research program: 1) Development of a novel simulation model-based calculation engine to support women who are at higher-than-average risk of developing breast cancer due to individual risk factors such as age, breast density, family history, and prior history of breast biopsy: Current clinical guidelines recommend various breast cancer prevention and early detection options to high-risk women, including risk-reducing medication such as tamoxifen and aromatase inhibitors, and supplemental screening with magnetic resonance imaging MRI, in addition to annual mammography. Each of these choices has a different profile of benefits and harms that will depend on individual risk factors. Annual mammography can detect tumors early, leading to early stage at diagnosis and improved survival, but has harms related to false positives linked to breast density. MRI can detect more tumors than mammography, but it also detects non-cancerous lesions that require further follow-up procedures. Risk-reducing drugs lower the likelihood of developing breast cancer by nearly half, helping women avoid the life-long consequences of breast cancer diagnosis and treatment, but these medications can induce menopausal symptoms based on age, and in a small percent of women, increase the risk of endometrial cancer or other conditions. Ultimately, a womans choice of intervention may depend on how she will weigh harms against benefits for these different options and outcomes given individual risk. To address these complexities, past studies have focused on either single risk factors, risk prediction tools with selected factors, or screening strategies alone. In our study, we adapted an established mathematical model to synthesize information on clinical risk factors and the impact of early detection with screening and primary prevention with risk-reducing medication to provide personalized data that to help identify women who are more likely to benefit from various interventions or combinations of interventions with the least harms. This data will be especially useful now due to growing consumer awareness and involvement in breast cancer care. In a future study, the simulation model-based calculation engine will be developed into a clinical decision tool that can be used in clinical settings. 2) Identifying the opportunities, challenges, and future directions for simulation modeling the effects of structural racism on cancer mortality in the U.S: We use simulation modeling to inform the development of web-based personalized clinical decision tools. While individual and structural racism are conceptualized as the root cause of racial disparities in our models, the existing models have not yet incorporated measures of structural racism or its direct impact on cancer processes and outcomes. Modeling the effects of structural racism could potentially provide useful data to inform policies and practices that could eliminate structural racism and promote equity in cancer care delivery. However, prerequisite to such efforts, the models would require information on measurement and real-world data linking structural racism to cancer processes including cancer mortality. We addressed this gap by conducting a scoping review of peer-reviewed articles evaluating the impact of structural racism on cancer mortality-related racial and ethnic disparities in the U.S. The study highlighted the opportunities, challenges, and future directions for the development of novel simulation models that will inform equitable cancer care delivery in the U.S. Importantly, the overall findings were used to provide recommendations for best practices to incorporate the effects of structural racism into simulation models.