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，吸烟占可预防死亡人数的五分之一。性和性别少数群体 (SGM)吸烟和烟草相关死亡的风险更高。初步证据表明， 性少数群体接受低剂量计算机断层扫描(LDCT)的合格率更高，这是一种可靠的筛查方法 降低吸烟引起的肺癌死亡风险的测试。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，减少了携带年限。这 导致符合条件的人数增加，但没有对性取向或性别差异进行研究 对新旧标准进行了同一性比较。SGM患者的一个可能原因 具有更高的合格率是指SGM个人在个人中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，个人国民 数据集不包含足够多的被识别为SGM的个体，无法对可能的风险因素做出推断 以及SGM个人相应的高LDCT资格。改善估计和身份识别潜力 在SGM个体中获得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，还需要确定特别工作组的个人是否 与异性恋者和顺性恋者相比，有资格接受LDCT的吸烟率更高， 尤其是随着时间的推移。总的来说，我有三个主要目标：1)合并四个具有全国代表性的数据集 并对这些合并方法进行比较，以生成更好的吸烟和LDCT估计 SGM个人的资格；2)比较新旧LDCT筛查资格标准的趋势 时间来了解有多少人符合条件，以及SGM个人与他们的顺性别和 3)在社会生态模型的指导下，确定相关的危险因素 具有LDCT资格的SGM个人。为了达到这些目标，最先进的数据合并技术， 利用纵向和多年横断面数据和结构方程进行复杂调查分析 建模将在韦斯特博士和麦凯布博士的指导下使用，以了解多个层面的影响 社会生态模式对最不发达国家的资格有影响。在完成目标后，文件， 演讲和报告将加强我的方法和实质性培训，并推动 研究领域。在调查方法领域，我将比较小型和小型企业的数据合并方法 亚群，了解测量差异，并改进对SGM个体的估计。在 在公共卫生领域，我将扩大我们对SGM、异性恋和 顺式性别个体在LDCT资格、肺癌和肺部疾病方面，我将致力于了解 社会生态模式的多层次对SGM个体及其最低限度感染资格的影响。

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.