Advancing novel methods to measure and analyze multiple types of discrimination for population health research

项目摘要 我们的研究旨在改进测量和分析人口中多种类型歧视的方法 健康研究。我们将比较新的内隐与传统的外显（自我报告）暴露措施， 研究不同的方法来模拟暴露于多种类型的歧视，并测试新的 使用因果中介分析的假设。我们的动机是深刻的关切，目前广泛- 所采用的方法低估了歧视对人口健康的影响。 我们的基于人群的研究直接解决了这些问题，并重点关注与以下两个方面相关的健康结果： 当前和终身的歧视，损害生活质量，增加患慢性病和 药物滥用：心理困扰和睡眠障碍。我们将使用第一个的改良版本- 沿着我们先前验证过的内隐联想测试（IAT）， 自我报告措施，这是最广泛使用的措施，在卫生研究的歧视。 为了确保每个对照组有350名参与者，我们将招募1092名成年患者成员和工作人员 从两个不同的社区卫生中心随机挑选，以实现以下具体目标： 目标1.使用隐性和显性（自我报告）措施评估遭受歧视的情况， 经验证的简要IAT格式，以便能够及时有效地评估美国的终生暴露， 基于种族/民族、性别认同、年龄、性取向和体重的歧视，并评估 以不同方式将个人遭受多种类型歧视的情况联合收割机结合起来。 目标2.测试关于遭受歧视的健康影响的假设， 跨社会比较组的组合测量（即，有色人种与白色人;性别少数 [GM/TGNC（跨性别/不符合）]与顺性别女性与顺性别男性与;老年人vs. 年轻人;性少数[LGBQ（女同性恋/男同性恋/双性恋/酷儿）/SM]与异性恋;肥胖与非肥胖） 目标2.1。检验内隐和外显歧视措施将独立的假设 与心理困扰和睡眠障碍有关（睡眠不足;睡眠呼吸障碍）。 目标2.2。使用因果中介技术，量化歧视作为健康中介的影响 委员会还将审查不公平现象的影响，并确定哪种类型的歧视措施及其组合产生最大的影响。 影响：如果我们的假设得到支持，结果将表明，主要措施和 报告指出，一些方法低估了歧视对健康的影响，更好的方法需要使用 多种类型歧视的隐性和显性措施以及反事实因果调解 技术.我们希望我们的研究，设计高内部效度，将显着推进方法， 在基于人群的大型研究中，对多种类型的暴露进行可行的测量和严格的分析， 歧视，知识至关重要的证据，以促进健康公平，一个优先事项，美国卫生机构。

Project Summary Our study seeks to advance methods to measure and analyze multiple types of discrimination for population health research. We will compare novel implicit vs. conventional explicit (self-report) measures of exposure, investigate different approaches to modeling exposure to multiple types of discrimination, and test novel hypotheses using causal mediation analyses. We are motivated by profound concerns that current widely- used approaches are underestimating the impact of discrimination on population health. Our population-based study directly tackles these issues, and focuses on two health outcomes linked to current and lifetime discrimination that harm quality of life and increase risk of both chronic disease and substance abuse: psychological distress and sleep disorders. We will employ a refined version of the first- ever implicit association test (IAT) for discrimination we recently developed, along with our prior validated self-report measure that is among the most widely-used measures in health research on discrimination. To ensure 350 participants per comparison group, we will recruit 1092 adult patient members and staff randomly selected from two diverse community health centers to address the following specific aims: Aim 1. Assess exposure to discrimination using implicit and explicit (self-report) measures, using the validated brief IAT format, to enable time-efficient assessment of lifetime exposure in the US to discrimination based on: race/ethnicity, gender identity, age, sexual orientation, and weight, and assess different ways to combine individuals’ exposure to multiple types of discrimination. Aim 2. Test hypotheses about the health impact of exposure to discrimination, for single and combined measures across the social comparison groups (i.e., people of color vs. white; gender minority [GM/TGNC (transgender/non-conforming)] vs. cis-gender women vs. cis-gender men vs.; older vs. younger; sexual minority [LGBQ (lesbian/gay/bisexual/queer)/SM] vs. heterosexual; obese vs. non-obese) Aim 2.1. Test the hypotheses that the implicit and explicit discrimination measures will be independently associated with psychological distress and sleep disorders (insufficient sleep; sleep disordered breathing). Aim 2.2. Using causal mediation techniques, quantify the effects for discrimination as mediator of health inequities and determine which type and combination of discrimination measures yields the largest effects. Impact: If our hypotheses are supported, results would demonstrate that predominant measures and methods underestimate the health impacts of discrimination, and that better methods would entail use of implicit and explicit measures of multiple types of discrimination and counterfactual causal mediation techniques. We expect our study, designed for high internal validity, will significantly advance methods for feasibly measuring and rigorously analyzing, in large population-based studies, exposure to multiple types of discrimination, knowledge crucial for evidence to promote health equity, a priority for US health agencies.