Project 2

项目2

• 批准号: 10411222
• 负责人: CHRISTOPH I LEE
• 金额: $ 27.9万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-27 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10411222
• 关键词: Academy; Address; Adherence; Adopted; Adoption; Advanced Malignant Neoplasm; Artificial Intelligence; Attention; Automobile Driving; Benchmarking; Breast; Breast Cancer Detection; Breast Cancer Surveillance Consortium; Cancer Burden; Cancer Detection; Cancer Intervention and Surveillance Modeling Network; Caring; Cohort Studies; Communities; Community Practice; Data; Diagnosis; Diagnostic; Diffuse; Digital Breast Tomosynthesis; Digital Mammography; Disease; Education; Enabling Factors; Equilibrium; Ethnic Origin; Evaluation; General Population; Health care facility; Image Analysis; Imaging technology; Income; Individual; Institutes; Insurance; Intervention; Low income; Malignant Neoplasms; Mammography; Medicine; Minority Women; Modeling; Morbidity - disease rate; Neighborhoods; Outcome; Patients; Performance; Population; Quality of Care; Race; Registries; Reporting; Research; Research Design; Resources; Risk; Rural Population; Screening for cancer; Sensitivity and Specificity; Services; Site; Societies; Structural Racism; Technology; Time; Training; Underserved Population; United States National Institutes of Health; Woman; Work; artificial intelligence algorithm; base; black women; breast imaging; cancer health disparity; cancer invasiveness; case control; clinical practice; cohort; cost; design; ethnic minority; experience; follow-up; health care delivery; health disparity; health equity; health equity promotion; image guided; imager; imaging facilities; improved; innovation; malignant breast neoplasm; minority health disparity; models and simulation; mortality; multilevel analysis; new technology; racial and ethnic; radiologist; routine screening; rural residence; screening; screening disparities; social health determinants; tool; tv watching; underserved community

PROJECT SUMMARY – Project 2 Underserved breast cancer screening populations, including those that are predominantly composed of racial/ethnic minorities, lower income, lower educated, and rural populations, continue to have a higher breast cancer morbidity and mortality burden than their counterparts. These populations tend to have lower follow-up rates after abnormal screening, more missed cancers, and more advanced stage disease at the time of diagnosis. Drivers of inequities are likely multi-factorial and include not only woman-level enabling factors but also neighborhood-level social determinants of health and facility-level factors that influence access to and use of high quality screening, timely diagnostic evaluation, and treatment. The National Institute of Minority Health and Health Disparities, on behalf of the NIH, reports that a major barrier in achieving health equity is that prior disparities research efforts have focused on individual enabling factors rather than neighborhood or healthcare delivery factors. Understanding the impact of breast imaging facility-level drivers of inequities is particularly important as new screening technologies, including artificial intelligence (AI), are rapidly adopted in clinical practice. If newer technologies do not diffuse equitably across communities, persistent breast cancer disparities may be further exacerbated. Our overall project objective is to identify modifiable breast imaging facility-level factors that drive breast cancer screening disparities. Using an observational cohort study design and simulation modeling, we will explore how targeted facility-level changes that aim to increase access to and use of routine screening and targeted use of AI for improved imaging interpretation accuracy can promote greater equity in screening outcomes. We will leverage the robust, longitudinal, multi-level Breast Cancer Surveillance Consortium data across eight regional U.S. breast imaging registries to pursue the following specific aims: Aim 1) Perform multi-level analyses to identify facility-level factors (e.g., on-site technologies) that drive disparities in screening performance and outcomes. Aim 2) Using a retrospective matched case control design and five commercially available AI technologies, evaluate whether commercially available AI tools for automated mammography interpretation can aid low-performing facilities to meet or exceed national mammography performance benchmarks. Aim 3) Using three established microsimulation models and results from Aims 1 and 2, estimate the long-term, population-level benefits, harms, and costs of enacting facility-level quality-of-care interventions (e.g., AI for higher performance) for the overall U.S. screening population and for underserved subpopulations. Elevating the quality-of-care at low-performing facilities has the potential to tip the balance towards greater screening benefits and less harms at the population-level, while also promoting health equity.

项目摘要 – 项目 2 乳腺癌筛查服务不足的人群，包括主要由少数种族/族裔、收入较低、受教育程度较低和农村人口组成的人群，其乳腺癌发病率和死亡率负担仍然高于其他人群。这些人群在异常筛查后的随访率往往较低，漏诊的癌症较多，诊断时疾病处于晚期。不平等的驱动因素可能是多因素的，不仅包括妇女层面的促成因素，还包括社区层面的健康社会决定因素以及影响高质量筛查、及时诊断评估和治疗的获得和使用的设施层面的因素。国家少数民族健康和健康差异研究所代表美国国立卫生研究院报告说，实现健康公平的一个主要障碍是，之前的差异研究工作主要集中在个人促成因素，而不是社区或医疗保健提供因素。随着包括人工智能 (AI) 在内的新筛查技术在临床实践中迅速采用，了解乳腺成像设施层面的不平等驱动因素的影响尤为重要。如果新技术不能在社区之间公平传播，持续存在的乳腺癌差异可能会进一步加剧。我们的总体项目目标是确定导致乳腺癌筛查差异的可修改乳腺成像设施层面的因素。通过观察性队列研究设计和模拟模型，我们将探讨如何有针对性地进行设施层面的改变，以增加常规筛查的获取和使用，以及有针对性地使用人工智能来提高影像判读的准确性，从而促进筛查结果的更大公平性。我们将利用美国八个地区乳腺影像登记处强大、纵向、多层次的乳腺癌监测联盟数据来实现以下具体目标： 目标 1) 进行多层次分析，以确定导致筛查表现和结果差异的设施层面因素（例如现场技术）。目标 2) 使用回顾性匹配病例对照设计和五种商用人工智能技术，评估用于自动化乳房 X 线摄影解读的商用人工智能工具是否可以帮助性能低下的设施达到或超过国家乳房 X 线摄影性能基准。目标 3) 使用三个已建立的微观模拟模型以及目标 1 和 2 的结果，估计针对美国整体筛查人群和服务不足的亚人群实施设施级护理质量干预措施（例如，提高绩效的人工智能）的长期人口水平效益、危害和成本。提高低绩效设施的护理质量有可能在人口层面上实现更大的筛查效益和更少的伤害，同时促进健康公平。