Leveraging Big Data for Transgender and Gender Minority Health Equity Research

项目摘要 性别少数群体的健康差距和保健障碍（即，变性人，性别不一致， 非二元，双性个体和其他少数性别身份）已经确定，并针对 包括美国国立卫生研究院在内的许多组织和机构都在消除这种疾病。越来越 一系列研究表明，性别少数群体在各种领域普遍受到歧视和羞辱， 上下文（例如，医疗保健、就业、住房、公共住宿和教育）， 影响他们的身体、心理、行为和社会健康。更令人担忧的是缺乏可靠的数据 收集工作需要监测的健康需求的性别少数（GM）人口在美国。 该项目的目标是开发世界上最大的少数性别（GM）患者队列， 健康保险索赔数据，包含超过330亿条索赔记录，覆盖范围超过1.5亿 住在美国使用来自FAIR Health的数据（一个私人保险索赔库， 超过60个国家和地区的付款人和第三方管理员），该项目将确定，描述， 比较健康结果，卫生服务利用，以及健康的社会决定因素之间的匹配 GM和非GM患者的队列。我们还将评估国家政策环境之间的相关性， FAIR健康数据中转基因和非转基因患者之间的健康差异。最后，我们将确定一个纵向 转基因和非转基因个体的队列随时间推移进行跟踪。该项目将导致新的发现， 了解健康结果，卫生服务利用率，以及GM健康的社会决定因素 在美国的人。

PROJECT SUMMARY Health disparities and barriers to health care for gender minorities (i.e., transgender, gender non-conforming, non-binary, intersex individuals, and other minority gender identities) have been identified and targeted for elimination by numerous organizations and agencies, including the National Institutes of Health. A growing body of research indicates that gender minorities experience widespread discrimination and stigma in various contexts (e.g., health care, employment, housing, public accommodations, and education) — which negatively affects their physical, mental, behavioral, and social health. More concerning is the absence of robust data collection efforts needed to monitor the health needs of gender minority (GM) populations in the United States. The goal of this project is to develop the world’s largest cohort of gender minority (GM) patients using private health insurance claims data that contains more than 33 billion claim records and over 150 million covered lives in the United States. Using data from FAIR Health (a repository of private insurance claims submitted by over 60 national and regional payors and third-party administrators), this project will identify, describe, and compare health outcomes, health services utilization, and the social determinants of health between a matched cohort of GM and non-GM patients. We will also evaluate correlations between state policy environments and health disparities between GM and non-GM patients in FAIR Health data. Finally, we will identify a longitudinal cohort of GM and non-GM individuals to follow over time. This project will lead to new discoveries in understanding health outcomes, health services utilization, and the social determinants of health for GM people in the United States.