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

使用来自大型临床数据研究网络的电子健康记录来了解跨性别者和性别不合格 (TGNC) 个体的癌症负担和癌症风险

• 批准号: 10056679
• 负责人: Jiang Bian
• 金额: $ 39.21万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10056679
• 关键词: Adoption; Age; Aging; Alcohol consumption; Alcohol or Other Drugs use; Alcohols; Behavioral; Cancer Burden; Caring; Cause of Death; Chronic; Clinical; Clinical Data; Clinical Research; Cohort Analysis; Cohort Studies; Coin; County; Data; Data Set; Data Sources; Development; Diagnosis; Discrimination; Disease; Economics; Electronic Health Record; Face; Florida; Funding; Future; Gender; Gender Identity; Gold; Health; Health behavior; Hormone use; Hospitals; Human Papilloma Virus-Related Malignant Neoplasm; Human Papillomavirus; Incidence; Individual; Information Retrieval; Knowledge; Literature; Logistic Models; Machine Learning; Malignant Neoplasms; Manuals; Medical; Mental Health; Methods; Minority Groups; Modeling; Monitor; Natural Language Processing; Outcome; Patients; Performance; Pharmaceutical Preparations; Physicians; Population; Procedures; Reporting; Research; Research Priority; Risk; Risk Behaviors; Risk Factors; Screening for cancer; Sex Behavior; Sexual and Gender Minorities; Sexually Transmitted Diseases; Source; Structure; Substance abuse problem; Surveys; System; Tobacco; Tobacco use; United States; Work; age related; aging population; base; cancer risk; cancer statistics; cancer type; clinical practice; cohort; comorbidity; computable phenotypes; data registry; deep learning; demographics; evidence base; gender nonconforming; health care service organization; health service use; high risk; informatics tool; malignant breast neoplasm; neoplasm registry; phenotyping algorithm; population based; programs; recruit; screening program; social; social exclusion; social stigma; stem; structured data; study population; transgender

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.

抽象的 跨性别者和性别不合格者 (TGNC) 面临着不成比例的不良健康负担 尽管关于 TGNC 独特健康问题的文献越来越多。 但由于 TGNC 健康状况的现有数据很少，他们的服务仍然严重不足。 由于与社会和经济边缘化、耻辱和歧视相关的问题，这种现象很常见，导致 由于 TGNC 个体往往不愿意自我认同，因此在获得基于人群的估计方面面临挑战 此外，过去的 TGNC 研究主要集中在传统调查上。 心理健康、药物使用和滥用以及性传播感染和疾病的数据有限。 适用于与年龄相关的慢性疾病，例如癌症，这是美国第二大死因 尽管如此，癌症仍是 TGNC 人群的首要研究重点之一。 随着 TGNC 人口老龄化的加剧，迫切需要描述这些人群的癌症负担 个人并了解癌症对他们的影响与非 TGNC 个人有何不同。 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 可用于研究的电子病历不仅包含重要的结构化数据，例如人口统计、诊断、 程序和药物，还有非结构化的临床叙述，例如医生的笔记 超过 80 条。 % 的临床信息记录在临床叙述中，其中包含更详细的患者信息 包括性别认同和癌症风险因素在内的信息受到这些观察的启发并建立在这些观察的基础上。 我们之前的研究涉及 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，使用以下方法检查 TGNC 人群的癌症负担和危险因素： 来自大型 EHR 网络的独特数据源 — OneFlorida，PCORI 资助的 13 个临床数据之一 为 PCORnet 做出贡献的研究网络 (CDRN) 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来识别 TGNC 个体，然后评估他们的 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。 我们的结果将支持定制的、证据性的开发。 2) 我们的研究将创建一群 TGNC 人员， 不仅可以在 EHR 中进行纵向跟踪，还可以招募用于未来的临床研究；3) 与 PCORnet CDRN 使我们的分析框架可推广到整个 PCORNet。 研究将增进我们对老龄化 TGNC 人群癌症的了解。