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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/摘要结直肠癌是确诊的第四大常见癌症，也是导致癌症死亡的第二大常见原因在美国。费城的年龄调整后的结直肠癌发病率--持续的高贫困率面积--比全国平均水平高出近25%。通过结肠镜或粪便对结直肠癌进行指南筛查免疫组织化学(FIT)可降低结直肠癌相关死亡率。尽管已被证明有规律的好处在费城，结肠镜检查、CRC筛查完成率仅为50%左右，居住在费城的非裔美国人的比例一直比白人低10-15%。不利的健康的社会决定因素，如高度贫困，导致不遵守CRC筛查对于非裔美国人和其他少数族裔社区来说，这是不成比例的。认识到这一点，2011年，宾夕法尼亚大学医学创建了一个导航计划，以增加对患者进行结肠镜检查的机会费城西部、南部和西南部服务不足的地区，通过提供减少障碍的服务癌症筛查，包括运输辅助和肠道准备的详细说明。尽管最初在增加结肠镜检查方面取得成功，推广这一导航计划的一个关键挑战是识别患者面临CRC风险增加的人群，他们可能从及时导航中受益最大。自动化机基于常规电子健康记录(EHR)数据的学习(ML)算法准确地估计了患者的结直肠癌的相对风险。高危人群可能会特别积极地遵守疾病筛查并以有效但资源有限的导航员计划为目标。本行政副刊的首要目标是支持艾布拉姆森癌症中心(ACC) 使命是提高生活在以下地区的高危个人的结直肠癌(CRC)筛查完成率持续贫困县通过设计、实施、传播和评估电子健康记录- 基于自动识别计划，以有效的、文化敏感的CRC筛查导航为目标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，本附录的目标是：1)使先前经过验证的基于eHR的机器学习算法适用于通过使用初级保健诊所服务ZIP的患者的数据重新训练模型来预测CRC检测代码中有很高比例的种族和少数民族居住在费城，一个持续贫困的县 2)实现和评估基于算法的CRC导航的可行性和有效性在7个初级保健诊所之一就诊的344名患者中增加结直肠癌筛查的计划在费城，结直肠癌高危人群，结肠镜检查未完成。加在一起，这些这些项目旨在增加循证筛查，以减轻高危人群中结直肠癌的负担。通过使用循证、有针对性、对文化敏感的CRC，生活在持续贫困县的个人筛查导航在一定程度上解决了阻止结直肠癌筛查的社会因素。