Project 2

项目概要-项目2 服务不足的乳腺癌筛查人群，包括那些主要由少数种族/族裔、低收入、低教育和农村人口组成的人群，仍然比他们的同行有更高的乳腺癌发病率和死亡率负担。这些人群在异常筛查后的随访率往往较低，漏诊的癌症更多，诊断时疾病更晚期。不平等的驱动因素可能是多因素的，不仅包括妇女层面的有利因素，还包括社区层面的健康社会决定因素和影响获得和使用高质量筛查、及时诊断评估和治疗的设施层面因素。国家少数民族健康和健康差异研究所代表NIH报告说，实现健康公平的一个主要障碍是，以前的差异研究工作集中在个人的有利因素上，而不是社区或医疗保健提供因素。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，因为包括人工智能（AI）在内的新筛查技术正在临床实践中迅速采用。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可修改的乳腺成像设施水平的因素，推动乳腺癌筛查的差异。使用观察性队列研究设计和模拟建模，我们将探讨旨在增加常规筛查的获得和使用以及有针对性地使用AI以提高成像解释准确性的有针对性的设施级别变化如何促进筛查结果的公平性。我们将利用美国八个地区乳腺成像登记处的强大的、纵向的、多层次的乳腺癌监测联盟数据，以实现以下具体目标：目标1）进行多层次分析，以确定机构层面的因素（例如，现场技术），导致筛查性能和结果的差异。目的2）使用回顾性匹配病例对照设计和五种市售AI技术，评估用于自动乳腺X射线摄影判读的市售AI工具是否可以帮助低性能设施达到或超过国家乳腺X射线摄影性能基准。目标3）使用三个已建立的微观模拟模型和目标1和2的结果，估计制定机构级护理质量干预措施（例如，AI用于更高的性能），用于整个美国筛查人群和服务不足的亚群。提高低绩效设施的护理质量有可能在人口水平上实现更大的筛查益处和更少的危害，同时促进健康公平。

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.