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.

摘要