Using Modifiable Risk Factors to Predict Inferior Care and Survival after Breast Cancer Diagnosis: A Novel Approach to Addressing Health Disparities

使用可改变的风险因素来预测乳腺癌诊断后的不良护理和生存：解决健康差异的新方法

• 批准号: 10462957
• 负责人: Oluwadamilola M. Fayanju
• 金额: $ 19.44万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-06 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10462957
• 关键词: Address; Adherence; Age; American Joint Committee on Cancer; Award; Biological; Biology; Breast; Breast Cancer Patient; Breast Cancer Risk Factor; Breast Cancer Treatment; Breast Cancer survivor; Caring; Cellular Phone; Cessation of life; Characteristics; Clinic; Cost Savings; Data; Data Collection; Data Set; Databases; Development; Diagnosis; Dimensions; Disease; Ethnic Origin; Female Breast Carcinoma; Focus Groups; Future; Genomics; Geographic Locations; Guidelines; Health system; Healthcare; Healthcare Systems; High Risk Woman; Individual; Inferior; Interdisciplinary Study; Intervention; Interview; Life; Malignant Neoplasms; Measures; Modeling; Modification; National Comprehensive Cancer Network; Odds Ratio; Oncologist; Online Systems; Outcome; Patient Care; Patient Outcomes Assessments; Patients; Positioning Attribute; Primary Health Care; Prognosis; Provider; Quality of Care; Race; Recurrence; Research; Research Personnel; Resources; Risk; Risk Factors; Secure; Socioeconomic Status; Staging; System; Tablets; Testing; Time; Tobacco use; Treatment outcome; United States; United States Department of Veterans Affairs; Validation; Veterans Health Administration; Woman; base; breast cancer diagnosis; cancer care; clinical decision-making; cohort; cost; design; glycemic control; health disparity; improved; improved outcome; indexing; instrument; malignant breast neoplasm; modifiable risk; mortality; neoplasm registry; novel strategies; patient home care; precision medicine; predictive modeling; prognostic; programs; prospective; psychosocial; response; risk prediction model; simulation; tool; treatment disparity

RESEARCH SUMMARY / ABSTRACT Despite overall declines in breast cancer mortality in the United States, significant disparities in breast cancer treatment and outcomes persist. Access-related, biological, psychosocial, and provider-specific factors previously demonstrated to be associated with disparities after breast cancer diagnosis are, on the whole, static systemic or individual features that are difficult or impossible to change. As an alternative approach to addressing disparities in breast cancer, we propose creating a risk prediction model for Breast cancer Risk of Inferior Survival and Care (BRISC) that incorporates dynamic, modifiable risk factors to identify women at greatest risk for compromised care and worse survival after diagnosis with breast cancer. After development and validation, we will use the BRISC model to conduct statistical simulations and costing activities to estimate the improvement in value (i.e., outcomes achieved per health-care dollar spent) achieved via implementation of high-impact, risk-modifying interventions to facilitate receipt of guideline-concordant breast cancer treatment. Finally, we will develop a parsimonious, clinic-based data collection tool to be completed by patients and providers for the purpose of operationalizing the BRISC model as a component of routine oncologic care.

研究总结/摘要 尽管美国乳腺癌死亡率总体下降，但乳腺癌之间的显著差异 治疗和结果持续存在。与获取相关的、生物学的、社会心理的和特定于提供者的因素 以前被证明与乳腺癌诊断后的差异有关的是，总的来说， 难以或不可能改变的静态、系统性或个体性特征。作为一种替代方法 针对乳腺癌的差异，我们建议建立乳腺癌风险预测模型。 较低的生存和护理(BRISC)，结合动态的、可修改的风险因素来识别女性 在被诊断为乳腺癌后，最大的风险是损害护理和更差的存活率。开发后 和验证，我们将使用BRISC模型进行统计模拟和成本计算活动，以估计 价值的改善(即每花费一美元医疗保健所取得的成果)通过实施 高影响力、降低风险的干预措施，以促进接受符合指南的乳腺癌治疗。 最后，我们将开发一个节俭的、基于临床的数据收集工具，由患者和 将BRISC模式作为常规肿瘤学护理的一个组成部分。