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Addressing racial disparities in lung cancer screening

解决肺癌筛查中的种族差异

基本信息

批准号：
10210043
负责人：
Melinda Aldrich
金额：
$ 65.86万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
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：
2021-04-05 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10210043
关键词：
Accounting Address Advisory Committees African American Age Algorithms Biometry Calibration Cessation of life Clinical Clinical Data Cohort Studies Communities Data Decision Aid Development Diagnostic Eligibility Determination Etiology Failure Focus Groups Gold Guidelines Individual Knowledge Lead Life Lung Machine Learning Malignant Neoplasms Malignant neoplasm of lung Measures Mission Modeling National Cancer Institute National Health Interview Survey Natural History Online Systems Patients Population Precision Health Preventive service Property Prospective cohort Provider Public Health Informatics Publishing Race Research Risk Risk Factors Rural Screening for cancer Smoking Smoking Behavior Smoking History Statistical Methods Statistical Models Surveys Techniques Testing Translating United States Validation Variant Visit Woman Women&apos s Health Work age group base black women cancer epidemiology cancer risk clinical implementation cohort community engagement comorbidity data harmonization design experience health disparity high risk improved innovation low socioeconomic status lung cancer screening machine learning algorithm mortality multi-ethnic multidisciplinary novel patient oriented personalized predictions personalized risk prediction population based predictive modeling prospective racial difference racial disparity racial diversity real world application risk prediction model rural area screening screening guidelines screening program shared decision making simulation statistical and machine learning study population tool urban area web-based tool

项目摘要

PROJECT SUMMARY/ABSTRACT Screening promotes early detection of cancer to decrease mortality. Unfortunately, significant racial disparities exist in lung cancer screening. Recently published findings by our team show that under current national screening guidelines African Americans have reduced eligibility for lung cancer screening compared to whites. These screening guidelines are based on a combination of age and smoking pack-year criteria derived from a national lung screening trial that was primarily (91%) white. Importantly, smoking behaviors and baseline risks for lung cancer differ greatly between African Americans and whites. Because of this, a risk-based screening strategy may provide a more equitable assessment of eligibility than current screening guidelines. However, the development of personalized risk prediction models for lung cancer in African Americans has been limited. To address this gap and to improve equity in screening eligibility, we propose building a personalized prediction tool using the combined data from three large-scale population-based prospective cohorts with substantial African American representation. The combined cohorts have over 336,000 individuals (44% African American) and 9,132 incident lung cancer cases from across the United States. We propose the following three aims: 1) construct a well-calibrated natural-history model of lung cancer risk for screening in African Americans, 2) evaluate lung cancer screening strategies by simulation and identify sub-populations who would benefit from screening, accounting for comorbidities and false-positives, and 3) develop a web-based decision aid for screening that is culturally appropriate. We will employ innovative machine learning techniques and state-of-the- art statistical methods to build a well-calibrated lung cancer prediction model for African Americans. Careful examination will identify sub-populations (such as women, low socioeconomic status, rural, age groups, etc.) that will benefit from screening. A key innovative aspect of this proposal is its community-engaged approach and partnership with a Community Advisory Board, both of which will help translate our empirical findings into the design of a patient-oriented decision aid. This project is relevant to the mission of the National Cancer Institute since it focuses on establishing equity in lung cancer screening eligibility. Our findings will have sustained impact on precision health and motivate improved clinical strategies for the early detection of lung cancer for African Americans.

项目总结/文摘