Racial and Socioeconomic Disparities in Breast Cancer Diagnostic Work Up and Outcomes

乳腺癌诊断工作和结果的种族和社会经济差异

基本信息

  • 批准号:
    10654528
  • 负责人:
  • 金额:
    $ 58.2万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-06-01 至 2026-05-31
  • 项目状态:
    未结题

项目摘要

Project Summary U.S. women of minority race/ethnicity, lower education, lower income, rural residence, and the underinsured experience higher breast cancer disease burden and lower survival rates than women without these characteristics, despite recent improvements in screening access and treatments. The majority of efforts to mitigate these disparities have focused on screening mammography access, but women must navigate multiple additional steps when cancer is suspected, including more imaging, biopsies, and specialist consultations. Each year, >12 million U.S. women enter this diagnostic care continuum. Failure to receive timely, quality evaluation leads to delayed diagnosis, more invasive procedures, advanced cancer stage at diagnosis, and greater mortality. Compared to screening, surprisingly little is known about disparities during this diagnostic period. It is estimated that up to 30% of women with abnormalities detected by mammography fail to obtain appropriate or timely follow-up, and up to 50% of racial/ethnic minorities and socioeconomically disadvantaged women experience such failures. A clearer understanding of disparities along the diagnostic continuum is hindered by the decentralized nature of breast cancer screening and diagnosis in the U.S., with disparities in care likely due to a complex combination of individual, residential, and healthcare delivery factors. We propose to conduct the largest U.S. observational study of disparities in diagnostic breast imaging to date. Specifically, we aim to 1) identify specific subpopulations of women with lower access to and use of key diagnostic imaging technologies; 2) determine differences in diagnostic outcomes that can serve as quality of care indicators based on race/ethnicity and socioeconomic status; and 3) identify differences in timeliness of diagnostic evaluation among disparities populations. We will use multi-level statistical modeling and mediation analyses to account for multifactorial interactions that likely influence inequitable diagnostic care. Our team, the Breast Cancer Surveillance Consortium, consists of national experts in breast cancer epidemiology, biostatistics, health services research, medicine, and radiology. The BCSC represents the largest longitudinal breast cancer imaging data resource linked to long-term outcomes that is representative of the general U.S. population by race/ethnicity. We systematically collect woman-, exam-, residential-, practice-, provider- and tumor-level data across seven regional registries and more than 200 individual practices. With data collected for 13 million breast imaging exams, 5.5 million of which were performed among traditional disparities populations, our team is well-positioned to carry out the proposed analyses. Our study will help shift the breast cancer disparities research paradigm from focusing on screening access to evaluating the entire diagnostic episode. By identifying novel quality of care metrics and “early warning” indicators of disparities, our results will inform both practice-level interventions aimed at closing local disparities gaps and national practice guidelines and policies directed towards more equitable breast cancer diagnostic evaluation.
项目摘要 美国少数民族/族裔、教育程度较低、收入较低、居住在农村和保险不足的妇女 乳腺癌的疾病负担更高,生存率更低, 尽管最近在筛查和治疗方面取得了进展,但这些疾病的特征仍然存在。大部分的努力, 减轻这些差距的重点是筛查乳房X光检查的机会,但妇女必须 当怀疑癌症时,需要采取多个额外步骤,包括更多的成像、活检和专家检查。 诊症计算每年,超过1200万美国妇女进入这一诊断护理连续体。未能接收 及时、高质量的评估会导致诊断延迟、更具侵入性的手术、晚期癌症分期, 诊断和更高的死亡率。与筛查相比,令人惊讶的是, 诊断期。据估计,高达30%的女性乳房X光检查发现异常, 未能获得适当或及时的后续行动,多达50%的种族/族裔少数群体和社会经济 处境不利的妇女经历了这种失败。更清楚地了解沿着诊断 连续性受到美国乳腺癌筛查和诊断的分散性的阻碍,与 护理差异可能是由于个人、住宅和医疗保健提供因素的复杂组合。 我们建议进行迄今为止美国最大的乳腺诊断成像差异观察研究。 具体而言,我们的目标是:1)确定获得和使用关键技术的妇女的特定亚群 诊断成像技术; 2)确定诊断结果的差异,这些差异可以作为 基于种族/民族和社会经济地位的护理指标;以及3)确定 在差异人群中进行诊断评价。我们将使用多级统计建模和调解 分析以解释可能影响不公平诊断护理的多因素相互作用。我们的团队, 乳腺癌监测联盟由全国乳腺癌流行病学专家组成, 生物统计学、卫生服务研究、医学和放射学。BCSC代表了最大的纵向 乳腺癌成像数据资源与长期结果相关,代表了美国的一般情况。 按种族/民族划分的人口。我们系统地收集妇女-,考试-,住宅-,实践-,供应商-和 7个区域登记中心和200多个个体诊所的肿瘤水平数据。采集数据的 1300万次乳腺成像检查,其中550万次是在传统的差距中进行的, 人口,我们的团队有能力进行拟议的分析。我们的研究将有助于转移乳房 癌症差异研究范式从关注筛查获得到评估整个诊断 插曲。通过确定新的护理质量指标和差异的“早期预警”指标,我们的结果将 为旨在缩小地方差距的实践层面干预措施和国家实践准则提供信息 以及旨在实现更公平的乳腺癌诊断评估的政策。

项目成果

期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Improving Health Equity in Adherence to Diagnostic Breast Imaging Workup Recommendations.
Addressing Racial Inequities in Access to State-of-the-Art Breast Imaging.
解决获得最先进的乳腺成像方面的种族不平等问题。
  • DOI:
    10.1148/radiol.222405
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    19.7
  • 作者:
    Lee,ChristophI;Lawson,MarissaB
  • 通讯作者:
    Lawson,MarissaB
Tackling Health Disparities in Radiology: A Practical Conceptual Framework.
  • DOI:
    10.1016/j.jacr.2021.09.032
  • 发表时间:
    2022-03
  • 期刊:
  • 影响因子:
    4.5
  • 作者:
    Lawson, Marissa B.;Scheel, John R.;Onega, Tracy;Carlos, Ruth C.;Lee, Christoph, I
  • 通讯作者:
    Lee, Christoph, I
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CHRISTOPH I LEE其他文献

CHRISTOPH I LEE的其他文献

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{{ truncateString('CHRISTOPH I LEE', 18)}}的其他基金

Population-Based Evaluation of Artificial Intelligence for Mammography Prior to Widespread Clinical Translation
在广泛临床转化之前对乳腺 X 线摄影人工智能进行基于人群的评估
  • 批准号:
    10651842
  • 财政年份:
    2022
  • 资助金额:
    $ 58.2万
  • 项目类别:
Population-Based Evaluation of Artificial Intelligence for Mammography Prior to Widespread Clinical Translation
在广泛临床转化之前对乳腺 X 线摄影人工智能进行基于人群的评估
  • 批准号:
    10445206
  • 财政年份:
    2022
  • 资助金额:
    $ 58.2万
  • 项目类别:
Racial and Socioeconomic Disparities in Breast Cancer Diagnostic Work Up and Outcomes
乳腺癌诊断工作和结果的种族和社会经济差异
  • 批准号:
    10394189
  • 财政年份:
    2021
  • 资助金额:
    $ 58.2万
  • 项目类别:
Racial and Socioeconomic Disparities in Breast Cancer Diagnostic Work Up and Outcomes
乳腺癌诊断工作和结果的种族和社会经济差异
  • 批准号:
    10094564
  • 财政年份:
    2021
  • 资助金额:
    $ 58.2万
  • 项目类别:
Artificial Intelligence for Improved Breast Cancer Screening Accuracy: External Validation, Refinement, and Clinical Translation
人工智能提高乳腺癌筛查准确性:外部验证、细化和临床转化
  • 批准号:
    10544496
  • 财政年份:
    2020
  • 资助金额:
    $ 58.2万
  • 项目类别:
Artificial Intelligence for Improved Breast Cancer Screening Accuracy: External Validation, Refinement, and Clinical Translation
人工智能提高乳腺癌筛查准确性:外部验证、细化和临床转化
  • 批准号:
    10320906
  • 财政年份:
    2020
  • 资助金额:
    $ 58.2万
  • 项目类别:
Artificial Intelligence for Improved Breast Cancer Screening Accuracy: External Validation, Refinement, and Clinical Translation
人工智能提高乳腺癌筛查准确性:外部验证、细化和临床转化
  • 批准号:
    9912472
  • 财政年份:
    2020
  • 资助金额:
    $ 58.2万
  • 项目类别:
Project 2
项目2
  • 批准号:
    10705584
  • 财政年份:
    2011
  • 资助金额:
    $ 58.2万
  • 项目类别:
Project 2
项目2
  • 批准号:
    10411222
  • 财政年份:
    2011
  • 资助金额:
    $ 58.2万
  • 项目类别:

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