Using Electronic Health Records from a Large Clinical Data Research Network to Understand Cancer Burden and Cancer Risks Among Transgender and Gender Nonconforming (TGNC) Individuals

摘要 跨性别和性别歧视（TGNC）人群面临着不成比例的不利健康负担 结果。虽然有越来越多的文献对独特的健康问题之间的TGNC 然而，由于现有的关于TGNC健康的数据很少，他们仍然严重缺乏服务。报告不足 由于与社会和经济边缘化、耻辱和歧视有关的问题， 由于TGNC个人通常不愿意自我识别，因此在获得基于人口的估计方面存在挑战 不愿意参与传统的调查。此外，过去的TGNC研究主要集中在 精神健康、药物使用和滥用以及性传播感染和疾病。数据有限 可用于与年龄相关的慢性疾病，如癌症，美国第二大死亡原因 States.尽管如此，癌症是TGNC人群中的首要研究重点之一。的快速 随着TGNC人口的老龄化，迫切需要描述这些人群中的癌症负担 了解癌症如何影响他们与非TGNC个体相比的差异。上 另一方面，电子健康记录（EHR）系统的快速采用使得纵向临床数据 可供研究。EHR不仅包含重要的结构化数据，如人口统计学，诊断， 程序和药物，以及非结构化的临床叙述，如医生的笔记。80多 %的临床信息记录在临床叙述中，其中包含更详细的患者 包括性别认同和癌症风险因素的信息。受这些观察的启发， 我们以前的研究1）TGNC性别认同术语的充分性，2）临床自然语言 信息提取的处理方法，以及3）基于EHR的队列研究，我们建议进行一项 基于人群的队列分析，使用 来自大型EHRs-OneFlorida网络的独特数据源，这是PCORI资助的13个临床数据之一 研究网络（CDRN）为PCORnet做出贡献。使用结构化和非结构化OneFlorida 数据，我们将首先开发可计算的表型，以确定TGNC个体，随后评估其 癌症风险。我们的研究意义重大，因为：1）没有基于人群的癌症风险队列研究， 在TGNC人群中进行。我们的研究结果将支持开发量身定制的证据- 基于癌症筛查计划的TGNC人群; 2）我们的研究将创建一个TGNC人群队列， 不仅可以在EHR中纵向跟踪，还可以为未来的临床研究招募; 3）与 PCORnet CDRN使我们的分析框架可推广到整个PCORNet。总体而言，拟议 这项研究将提高我们对老年TGNC人群癌症的认识。

ABSTRACT Transgender and gender nonconforming (TGNC) people face a disproportionate burden of adverse health outcomes. Although there is a growing body of literature on the unique health issues among TGNC populations, they remain severely underserved as existing data on TGNC health are scarce. Under-reporting is common due to issues related to social and economic marginalization, stigma, and discrimination, leading to challenges in obtaining population-based estimates since TGNC individuals are often unwilling to self-identify and reluctant to participate in traditional surveys. Further, past TGNC research has primarily focused on mental health, substance use and abuse, and sexual transmitted infections and diseases. There is limited data available on age-related chronic conditions such as cancer, the second leading cause of death in the United States. Nonetheless, cancer is one of the top research priorities among the TGNC population. With a rapidly growing aging TGNC population, there is an urgent need to characterize the cancer burden among these individuals and understand how cancer impact them differentially compared to non-TGNC individuals. On the other hand, rapid adoption of electronic health record (EHR) systems has made longitudinal clinical data available for research. EHRs contain not only important structured data, such as demographics, diagnoses, procedures, and medications, but also unstructured clinical narratives such as physician’s notes. More than 80 percent of the clinical information is documented in clinical narratives, which contain more detailed patient information including gender identity and cancer risk factors. Motivated by these observations and built upon our previous studies on 1) the adequacy of TGNC gender identity terms, 2) clinical natural language processing methods for information extraction, and 3) EHR-based cohort studies, we propose to conduct a population-based cohort analysis to examine the cancer burden and risk factors among TGNC people using a unique data source from a large network of EHRs—OneFlorida, one of the 13 PCORI-funded clinical data research networks (CDRNs) contributing to the PCORnet. Using both structured and unstructured OneFlorida data, we will first develop computable phenotypes to identify TGNC individuals and subsequently evaluate their cancer risk. Our research is significant because: 1) no population-based cohort studies on cancer risk have been conducted among the TGNC population. Our results will support the development of tailored, evidence- based cancer screening programs for TGNC people; 2) our research will create a cohort of TGNC people that can be not only tracked longitudinally in EHR but also recruited for future clinical studies; and 3) working with a PCORnet CDRN makes our analysis framework generalizable to the overall PCORNet. Overall, the proposed research will advance our knowledge in cancer among the aging TGNC population.