Health Equity and Decision Sciences

健康公平与决策科学

• 批准号: 10907357
• 负责人: Jinani Jayasekera
• 金额: $ 9.41万
• 依托单位国家: 美国
• 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

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.

本研究计划的工作人员编写了以下手稿： 1)开发一种新的基于模拟模型的计算引擎，以支持因年龄、乳房密度、家族史和乳房活检病史等个人风险因素而患乳腺癌风险高于平均水平的女性： 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，包括降低风险的药物，如他莫昔芬和芳香酶抑制剂，以及除了每年的乳房X光检查外，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益处和危害，这将取决于个人的风险因素。每年一次的乳房X光检查可以早期发现肿瘤，从而在早期诊断并提高存活率，但与乳房密度有关的假阳性也有危害。与乳房X光照相相比，核磁共振可以发现更多的肿瘤，但它也可以检测到需要进一步后续程序的非癌症病变。降低风险的药物将患乳腺癌的可能性降低了近一半，帮助女性避免了乳腺癌诊断和治疗的终生后果，但这些药物可能会引发基于年龄的更年期症状，在一小部分女性中，会增加患子宫内膜癌或其他疾病的风险。归根结底，女性对干预措施的选择可能取决于她将如何权衡这些不同选择和结果的利弊，考虑到个人风险。为了解决这些复杂性，过去的研究要么专注于单一的风险因素，要么关注带有选定因素的风险预测工具，要么只关注筛查策略。 在我们的研究中，我们采用了一个已建立的数学模型来综合有关临床风险因素和早期发现与筛查的影响以及初级预防与降低风险药物的影响的信息，以提供个性化数据，帮助识别更有可能从各种干预措施或干预措施组合中获益且危害最小的女性。由于消费者意识的增强和对乳腺癌护理的参与，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设置的临床决策工具。 2)确定模拟模拟结构性种族主义对美国癌症死亡率的影响的机会、挑战和未来方向： 我们使用模拟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。虽然在我们的模型中，个人和结构性种族主义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措施。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服务公平的政策和做法提供参考。然而，作为这些努力的先决条件，这些模型将需要有关测量和将结构性种族主义与癌症过程(包括癌症死亡率)联系起来的真实世界数据的信息。我们通过对评估结构性种族主义对美国癌症死亡率相关种族和民族差异的影响的同行评议文章进行范围审查来解决这一差距。这项研究强调了开发新的模拟模型的机会、挑战和未来方向，这些模型将为美国公平的癌症护理提供信息。重要的是，总体研究结果被用来提供将结构性种族主义的影响纳入模拟模型的最佳实践的建议。