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计划的工作人员开发的： 1）开发基于模拟模型的新型计算引擎，以支持由于年龄，乳房密度，家族病史和先前的乳房活检史等个体危险因素而患乳腺癌的风险高于平均水平的女性：当前的临床指南建议对高危女性进行各种乳腺癌预防和早期检测选择，包括降低风险的药物，例如他莫昔芬和芳香酶抑制剂，以及除了年度乳腺摄影外，还具有磁共振成像MRI的补充筛查。这些选择中的每一个都有不同的福利和危害概况，这将取决于个人风险因素。年度乳房X线摄影可以早期检测到肿瘤，导致诊断后的早期阶段并提高生存率，但与与乳房密度相关的假阳性造成了损害。 MRI可以检测到比乳房X线摄影更多的肿瘤，但它还检测到需要进一步随访程序的非癌性病变。降低风险的药物降低了将乳腺癌培养的可能性降低了近一半，帮助女性避免乳腺癌诊断和治疗的长期后果，但是这些药物可以根据年龄以及一小部分女性诱导绝经症状，增加子宫内膜癌或其他疾病的风险。最终，妇女选择干预可能取决于她将如何权衡鉴于个人风险的这些不同选择和结果的损害。为了解决这些复杂性，过去的研究集中在单一风险因素，具有选定因素的风险预测工具或仅筛选策略上。在我们的研究中，我们对既定的数学模型进行了调整，以合成有关临床风险因素的信息，以及通过筛查和初级预防和降低风险药物的筛查和初级预防的影响，以提供个性化数据，以帮助识别更有可能从各种干预措施或最小损害的干预措施中受益的女性。由于消费者的意识和参与乳腺癌护理的参与，该数据现在将特别有用。在未来的研究中，基于仿真模型的计算引擎将被发展为可用于临床环境的临床决策工具。 2）确定模拟结构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：我们使用仿真建模来告知基于Web的个性化临床决策工具的开发。尽管在我们的模型中，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的衡量标准或对癌症过程和结果的直接影响。对结构种族主义的影响进行建模可能会提供有用的数据，以告知政策和实践，这些政策和实践可以消除结构性种族主义并促进癌症护理递送中的公平性。但是，这种努力的先决条件将需要有关将结构种族主义与包括癌症死亡率在内的癌症过程联系起来的测量和现实数据的信息。 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.