Low Dose Computed Tomography (LDCT) Eligibility and Outcome differences between Sexual and Gender Minorities and their Sexual and Gender Majority Counterparts

性和性别少数群体与性和性别多数群体之间的低剂量计算机断层扫描 (LDCT) 资格和结果差异

• 批准号: 10605428
• 负责人: Curtiss Wayne Engstrom
• 金额: $ 4.74万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-28 至 2026-08-27
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10605428
• 关键词: Accounting; Address; Adult; Age; Aging; Attitude; Behavior; Behavioral Risk Factor Surveillance System; Cessation of life; Communities; Complex; Cross-Sectional Studies; Data; Data Collection; Data Set; Data Sources; Disparity; Drug usage; Eligibility Determination; Equation; Fellowship; Gender; Gender Identity; Guidelines; Health; Health Surveys; Health behavior and outcomes; Heterosexuals; Incidence; Individual; Individual National Research Service Award; Knowledge; Lesbian Gay Bisexual Transgender; Longitudinal cohort; Lung; Malignant neoplasm of lung; Measurement; Measures; Mentors; Mentorship; Methodology; Methods; Modeling; Morbidity - disease rate; National Health Interview Survey; Nicotine; Outcome; Paper; Persons; Policies; Population; Population Assessment of Tobacco and Health; Prevalence; Probability; Procedures; Psychological Stress; Public Health; Qualifying; Recommendation; Reporting; Research; Risk; Risk Factors; Risk Reduction; Sample Size; Sampling; Sexual Health; Sexual and Gender Minorities; Sexuality; Smoke; Smoker; Smoking; Smoking Behavior; Smoking History; Subgroup; Survey Methodology; Surveys; Techniques; Testing; Time; Tobacco; Tobacco smoking behavior; Tobacco use; Training; United States; United States Preventative Services Task Force; Urbanicity; Weight; Work; aged; cancer diagnosis; cigarette smoking; cisgender; combustible cigarette; computed tomography screening; experience; gender minority group; health disparity; high risk; improved; interest; knowledge base; low dose computed tomography; mortality; multiple data sources; multiple datasets; novel; peer; pre-doctoral; preventable death; primary outcome; psychological distress; screening; screening disparities; sexual identity; sexual minority; sexual minority group; skill acquisition; social; transgender; trend

Project Summary Cigarette smoking accounts for 1 in 5 preventable deaths in the United States. Sexual and gender minorities (SGM) are at higher risk of cigarette smoking and tobacco-related deaths. Preliminary evidence indicates that sexual minorities have higher eligibility rates for low dose computed tomography (LDCT), a reliable screening test that reduces the risk of lung cancer mortality caused by tobacco smoking. Recently, the age and smoking pack years were reduced in the eligibility guidelines, which determine who qualifies for LDCT screening. This led to an increase in the number of people who were eligible, but no work on differences by sexuality or gender identity comparing the new and old criteria has been conducted. One possible cause of SGM individuals having higher eligibility rates is the number of risk factors SGM individuals experience at the individual, interpersonal, community, and policy levels based on the social ecological model. However, individual national data sets do not contain enough individuals that identify as SGM to make inferences about possible risk factors and the corresponding high LDCT eligibility for SGM individuals. To improve estimates and identity potential disparities and risk factors among SGM individuals for LDCT eligibility, one promising option is to merge data from multiple national data sources. Additionally, there is a need to determine whether SGM individuals who are eligible for LDCT are smoking at a higher rate compared to heterosexual and cisgender individuals, especially over time. Overall, I have three main aims: 1) Merge four nationally representative data sets together, and compare these methods of merging to generate better estimates of cigarette smoking and LDCT eligibility for SGM individuals.; 2) Compare trends in the new and old LDCT screening eligibility criteria over time to understand how many people are eligible and how SGM individuals compare to their cisgender and heterosexual counterparts; and 3) Guided by the social ecological model, identify the risk factors associated with LDCT eligibility for SGM individuals. To address these aims, state-of-the-art techniques for merging data, complex survey analysis using longitudinal and multi-year cross-sectional data, and structural equation modeling will be used under the mentorship of Drs. West and McCabe to understand the effects multiple levels of the social ecological model have on LDCT eligibility. Upon completion of the aims, the papers, presentations, and reports will enhance my methodological and substantive training and advance multiple fields of research. In the survey methodology field, I will compare methods of data merging for small subpopulations, understand measurement differences, and improve estimates for SGM individuals. In the public health realm, I will expand our understanding of possible differences between SGM, heterosexual, and cis-gender individuals in LDCT eligibility, lung cancer, and lung conditions, and I will aim to understand the effects of multiple levels of the social ecological model on SGM individuals and their LDCT eligibility.

项目摘要 吸烟在美国占5分之一的可预防死亡。性别和性别少数群体 （SGM）吸烟和与烟草有关的死亡的风险更高。初步证据表明 性少数族裔具有较高的低剂量计算机断层扫描（LDCT）的资格率，这是可靠的筛查 测试可以降低吸烟引起的肺癌死亡率的风险。最近，年龄和吸烟 资格指南中的包装年减少了，这确定了谁有资格进行LDCT筛选。这 导致有资格的人数增加，但没有因性或性别而差异的工作 进行了比较新标准的身份。 SGM个人的一个可能原因 较高的资格率是SGM个人在个人经历的风险因素的数量， 基于社会生态模型的人际关系，社区和政策水平。但是，个人民族 数据集没有足够的个人来识别为SGM来推断可能的风险因素 以及SGM个体的相应高LDCT资格。提高估计和身份潜力 SGM个人在LDCT资格中的差异和风险因素，一种有希望的选择是合并数据 来自多个国家数据源。此外，有必要确定SGM个人是否 与异性恋者和奇异人相比 特别是随着时间的流逝。总体而言，我有三个主要目的：1）合并四个国家代表性数据集 一起，并比较这些合并的方法，以产生更好的香烟吸烟和LDCT的估计 SGM个人的资格。 2）比较新的和旧LDCT筛选资格标准的趋势 是时候了解有多少人有资格，以及SGM个人如何与他们的Cisgender相提并论 异性恋对应物； 3）在社会生态模型的指导下，确定相关的风险因素 具有SGM个体的LDCT资格。为了解决这些目标，用于合并数据的最新技术， 使用纵向和多年横截面数据以及结构方程的复杂调查分析 建模将在DR的指导下使用。西方和麦凯布了解效果多个层次 社会生态模型具有LDCT资格。目标完成后，论文， 演示文稿和报告将增强我的方法论和实质性培训，并提高多个 研究领域。在调查方法领域，我将比较小的数据合并方法 亚群，了解测量差异并改善SGM个体的估计。在 公共卫生领域，我将扩展我们对SGM，异性恋和 在LDCT资格，肺癌和肺部疾病中的顺式性别个人，我将旨在了解 社会生态模型的多个级别对SGM个体及其LDCT资格的影响。