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.

项目总结-项目2