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

项目摘要 在美国，吸烟导致的可预防死亡占五分之一。性和性别少数群体 (SGM)吸烟和与烟草有关的死亡的风险更高。初步证据显示， 性少数群体有更高的资格率低剂量计算机断层扫描（LDCT），一个可靠的筛选 一种能降低吸烟引起的肺癌死亡风险的检测方法。最近，年龄和吸烟 在决定谁有资格进行LDCT筛查的资格指南中，包年数减少了。这 导致符合条件的人数增加，但没有研究性行为或性别的差异 对新旧标准进行了身份比较。SGM个体的一个可能原因 具有较高的合格率是SGM个体在个体处经历的风险因素的数量， 基于社会生态模型的人际、社区和政策层面。然而，个别国家 数据集不包含足够的识别为SGM的个体，无法推断可能的风险因素 以及SGM个体相应的高LDCT资格。改进估计和识别潜力 SGM个体中LDCT资格的差异和风险因素，一个有希望的选择是合并数据 来自多个国家的数据来源。此外，需要确定SGM个人是否 有资格接受LDCT的人吸烟率高于异性恋和顺性别者， 特别是随着时间的推移。总的来说，我有三个主要目标：1）合并四个具有国家代表性的数据集 并比较这些合并方法，以更好地估计吸烟和LDCT SGM个人的资格。2)比较新旧LDCT筛选合格性标准的趋势， 时间来了解有多少人是合格的，以及SGM个人如何比较他们的顺性别和 3）在社会生态模型的指导下，确定相关的风险因素 符合LDCT资格的SGM个人。为了实现这些目标，最先进的数据合并技术， 使用纵向和多年横截面数据的复杂调查分析，以及结构方程 模型将在West和McCabe博士的指导下使用，以了解多个层面的影响 社会生态模式对LDCT资格的影响。在目标完成后，论文， 演讲和报告将加强我的方法和实质性的培训，并推动多个 研究领域。在调查方法学领域，我将比较小规模调查的数据合并方法， 亚群，了解测量差异，并改善估计SGM个人。在 在公共卫生领域，我将扩大我们对SGM，异性恋和 顺性别个体在LDCT资格，肺癌和肺部疾病，我将致力于了解 社会生态模型的多个层次对SGM个体及其LDCT资格的影响。

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.