Abramson Cancer Center Support Grant

艾布拉姆森癌症中心支持补助金

基本信息

批准号：
10408409
负责人：
ROBERT H VONDERHEIDE
金额：
$ 20万
依托单位：
UNIVERSITY OF PENNSYLVANIA
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：
2021-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10408409
关键词：
Address Administrative Supplement African American Age Algorithms Area Cancer Center Cancer Center Support Grant Cancer Etiology Cessation of life Clinic Clinical Code Colonoscopy Colorectal Adenoma Colorectal Cancer Communities Consumption County Data Detection Diagnosis Disease Effectiveness Electronic Health Record Family Practice Frequencies Goals Guidelines Hispanics Immunochemistry Immunohistochemistry Incidence Income Individual Instruction Internal Medicine Intervention Intestines Knowledge Machine Learning Malignant Neoplasms Medicine Methods Minority Mission Modeling Outcome Patient Care Patients Pennsylvania Performance Philadelphia Poverty Poverty Areas Preparation Primary Health Care Process Program Effectiveness Public Health Recording of previous events Relative Risks Research Research Personnel Research Proposals Resources Retrospective cohort Risk Risk Factors Screening for cancer Services Telephone Testing Time Training Transportation United States Universities adenoma adherence rate base cancer diagnosis cancer risk cohort colon cancer screening colorectal cancer prevention colorectal cancer risk colorectal cancer screening community living design effectiveness evaluation ethnic diversity ethnic minority population evidence base feasibility testing high risk high risk population improved machine learning algorithm minority communities mortality novel patient population programs racial diversity racial minority risk stratification screening screening guidelines social social factors social health determinants socioeconomics success transportation access underserved area usability

项目摘要

PROJECT SUMMARY/ABSTRACT Colorectal cancer is the 4th most common cancer diagnosed and the 2nd most common cause of cancer death in the United States. The age-adjusted incidence of CRC in Philadelphia County – a persistently high poverty area– is nearly 25% above the national average. Guideline-based screening for CRC via colonoscopy or fecal immunohistochemistry (FIT) reduces CRC-associated mortality. Despite the proven benefit of regular colonoscopy, CRC screening completion rates remain only around 50% in Philadelphia County and are consistently 10-15% lower for African-Americans than White individuals living in Philadelphia County. Adverse social determinants of health such as high poverty contribute to CRC screening nonadherence disproportionately for African-American and other minority communities. Recognizing this, in 2011, Penn Medicine created a navigation program to increase access to screening colonoscopies for patients in underserved areas of West, South, and Southwest Philadelphia by providing services that reduce barriers to cancer screening, including transportation assistance and detailed instructions on bowel prep. Despite initial success in increasing colonoscopies, a key challenge in scaling this navigation program is identifying patient populations at increased risk of CRC, who may benefit most from timely navigation. Automated machine learning (ML) algorithms based on routine electronic health record (EHR) data accurately estimate a patient’s relative risk of CRC. High-risk individuals may be particularly motivated to comply with disease screening recommendations and be targeted with an effective but resource-constrained navigator program. The overarching goals of this Administrative Supplement is to support the Abramson Cancer Center (ACC) mission to increase colorectal cancer (CRC) screening completion among high-risk individuals living in a persistent poverty county by designing, conducting, disseminating and evaluating an electronic health record- based automated identification program to target effective, culturally-sensitive CRC screening navigation to individuals who have not completed an ordered colonoscopy or fecal immunochemical test (FIT). Specifically, the goals of this supplement are to: 1) Adapt a previously validated EHR-based machine learning algorithm to predict CRC detection by retraining the model using data from patients seen in primary care clinics serving zip codes with a high proportion of racial and ethnic minorities living in Philadelphia County, a persistent poverty county; and 2) Implement and evaluate the feasibility and effectiveness of an algorithm-based CRC navigation program to increase colorectal cancer screening among 344 patients seen at one of 7 primary care practices within Philadelphia county who are at high risk of CRC, have uncompleted colonoscopies. Together, these projects aim to increase evidence-based screening in order to reduce the burden of CRC among high-risk individuals living in a persistent poverty county by utilizing evidence-based, targeted, culturally-sensitive CRC screening navigation that in part addresses social factors that prevent colorectal cancer screening.

项目摘要/摘要结直肠癌是被诊断出的第四个最常见的癌症，也是癌症死亡的第二个最常见原因在美国。费城县CRC的年龄调整后的事件 - 持续的高贫困面积 - 几乎比全国平均水平高25％。通过结肠镜检查或粪便基于指南的CRC筛查免疫组织化学（FIT）降低了与CRC相关的死亡率。尽管经常受到公认的好处在费城县，结肠镜检查，CRC筛查完成率仅约50％，并且是与居住在费城县的白人相比，非裔美国人的始终低10-15％。对手健康的社会决定者，例如高贫困，有助于CRC筛查不遵守对于非裔美国人和其他少数民族社区而言，不成比例。认识到这一点，2011年宾夕法尼亚医学创建了一项导航计划，以增加对患者进行筛查结肠镜检查的访问西部，南部和西南费城服务欠佳的地区通过提供减少障碍的服务癌症筛查，包括运输援助和有关肠准备的详细说明。尽管最初在增加结肠镜检查方面的成功，扩展该导航程序的关键挑战是确定患者 CRC风险增加的人群可能会从及时导航中受益。自动化机器基于常规电子健康记录（EHR）数据的学习（ML）算法准确估计患者的 CRC的相对风险。高风险的人可能特别有动力遵守疾病筛查建议，并通过有效但资源受限的导航器计划进行针对性。这种行政补充的总体目标是支持艾布拉姆森癌症中心（ACC）居住在A 通过设计，进行，传播和评估电子健康记录 - 基于自动识别计划，以针对有效的，对文化敏感的CRC筛查导航尚未完成有序结肠镜检查或粪便免疫化学测试（FIT）的个人。具体来说，该补充的目标是：1）将先前验证的基于EHR的机器学习算法调整为通过使用来自初级保健诊所中的患者的数据来预测CRC检测居住在费城县的种族和少数民族比例很高的代码，这是一种持续的贫困县; 2）实施和评估基于算法的CRC导航的可行性和有效性在7种初级保健实践之一中看到的344名患者中结直肠癌筛查的计划在费城县，有CRC的高风险，有未完成的结肠镜检查。在一起，这些项目旨在增加基于证据的筛查，以减少高风险中CRC的燃烧通过使用循证，有针对性的文化敏感的CRC，生活在持续贫困县的个人筛查导航，部分解决了预防大肠癌筛查的社会因素。