Health Equity and Decision Sciences

健康公平与决策科学

基本信息

批准号：
10907357
负责人：
Jinani Jayasekera
金额：
$ 9.41万
依托单位：
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10907357
关键词：
Address Age Aromatase Inhibitors Awareness Behavioral Big Data Breast Cancer Prevention Breast biopsy Caring Characteristics Clinical Collection Complex Conduct Clinical Trials Data Data Sources Development Early Diagnosis Endometrial Carcinoma Equity Family Future Genomics Guidelines Health Personnel Health Services High Risk Woman Individual Intervention Laboratory Research Lesion Link Magnetic Resonance Imaging Malignant Neoplasms Mammography Manuscripts Mathematical Model Simulation Measurement Measures Menopausal Symptom Modeling Online Systems Outcome Patient-Focused Outcomes Patients Peer Review Pharmaceutical Preparations Policies Primary Prevention Procedures Process Recommendation Recording of previous events Research Risk Risk Factors Risk Reduction Science Stage at Diagnosis Structural Racism Tamoxifen Testing Uncertainty Validation Woman breast cancer diagnosis breast density cancer care cancer therapy care delivery clinical practice clinical risk efficacy evaluation ethnic disparity follow-up health equity health goals health literacy improved knowledge base malignant breast neoplasm mathematical ability mathematical model models and simulation mortality novel personalized decision personalized health care predictive tools programs racial disparity risk prediction screening shared decision making supplemental screening tool tumor usability web based interface

项目摘要

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.

以下手稿是由该研究项目的工作人员编写的： 1) 开发一种基于模拟模型的新型计算引擎，为因年龄、乳腺密度、家族史和既往乳腺活检史等个人风险因素而患乳腺癌风险高于平均水平的女性提供支持：目前的临床指南向高危女性推荐各种乳腺癌预防和早期检测方案，包括他莫昔芬和芳香酶抑制剂等降低风险的药物，以及除了每年一次的乳房X光检查之外，还使用磁共振成像MRI进行补充筛查。这些选择中的每一个都有不同的好处和坏处，这取决于个人的风险因素。每年一次的乳房X光检查可以及早发现肿瘤，从而实现早期诊断并提高生存率，但也存在与乳腺密度相关的假阳性带来的危害。 MRI 比乳房 X 光检查可以检测到更多的肿瘤，但它也可以检测到需要进一步随访的非癌性病变。降低风险的药物可将患乳腺癌的可能性降低近一半，帮助女性避免乳腺癌诊断和治疗带来的终生后果，但这些药物可能会根据年龄诱发更年期症状，并且在一小部分女性中，这些症状会增加子宫内膜癌或其他疾病的风险。最终，女性对干预措施的选择可能取决于她在考虑个人风险的情况下如何权衡这些不同选择和结果的利弊。为了解决这些复杂性，过去的研究主要集中在单一风险因素、具有选定因素的风险预测工具或单独的筛查策略。在我们的研究中，我们采用了既定的数学模型来综合有关临床风险因素的信息以及通过筛查进行早期检测和通过降低风险药物进行一级预防的影响，以提供个性化数据，帮助识别更有可能从各种风险中受益的女性。危害最小的干预措施或干预措施组合。由于消费者对乳腺癌护理的认识和参与不断增强，这些数据现在将特别有用。在未来的研究中，基于模拟模型的计算引擎将被开发成可用于临床环境的临床决策工具。 2) 确定模拟结构性种族主义对美国癌症死亡率影响的机会、挑战和未来方向：我们使用仿真建模来为基于网络的个性化临床决策工具的开发提供信息。虽然个人和结构性种族主义在我们的模型中被概念化为种族差异的根本原因，但现有模型尚未纳入结构性种族主义或其对癌症过程和结果的直接影响的衡量标准。对结构性种族主义的影响进行建模可能会提供有用的数据，为消除结构性种族主义和促进癌症护理提供公平的政策和实践提供信息。然而，这些努力的先决条件是，这些模型需要有关测量的信息和将结构性种族主义与包括癌症死亡率在内的癌症过程联系起来的现实世界数据。我们通过对同行评审的文章进行范围审查来解决这一差距，这些文章评估了结构性种族主义对美国癌症死亡率相关的种族和民族差异的影响。该研究强调了新型模拟模型开发的机遇、挑战和未来方向这将为美国提供公平的癌症护理服务提供信息。重要的是，总体研究结果用于为最佳实践提供建议，将结构性种族主义的影响纳入模拟模型。