Applying Computational Phenotypes To Assess Mental Health Disorders Among Transgender Patients in the United States

应用计算表型评估美国跨性别患者的心理健康障碍

• 批准号: 10604723
• 负责人: Theo Beltran
• 金额: $ 3.97万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10604723
• 关键词: Address; Adherence; Adopted; Algorithms; American; Anxiety; Attention Deficit Disorder; Caring; Chronic Disease; Code; Communication; Computerized Medical Record; Data; Data Sources; Databases; Decision Making; Depressive disorder; Diagnosis; Diagnostic; Discrimination; Disparity population; Drug Prescriptions; Effectiveness; Epidemiology; Evaluation; Faculty; Future; Gender Identity; General Population; Goals; Guidelines; HIV; Health Policy; Health trends; Healthcare; High Prevalence; Individual; Inpatients; Intervention; Knowledge; Managed Care; Measures; Medical; Mental Depression; Mental Health; Mental Health Services; Mental disorders; Methods; Modeling; Monitor; Outcome; Outpatients; Patients; Pattern; Performance; Persons; Pharmaceutical Preparations; Pharmacoepidemiology; Pharmacologic Substance; Phase; Phenotype; Policies; Population; Prevalence; Privatization; Probability; Procedures; Psychiatric Diagnosis; Psychiatric therapeutic procedure; Psychologist; Psychotherapy; Public Health; Recommendation; Records; Reporting; Research; Research Personnel; Risk; Role; Sample Size; Self-Injurious Behavior; Services; Suicide attempt; Time; Time trend; Training; Translating; United States; Victimization; Violence; Visit; Vulnerable Populations; Weight; Work; antiretroviral therapy; billing data; career; cisgender; clinical epidemiology; clinically actionable; eHealth; electronic medical record system; epidemiology study; experience; gender affirming care; health assessment; health care service utilization; health disparity; health equity; health inequalities; high risk; improved; insurance claims; large datasets; longitudinal analysis; medication compliance; phenotyping algorithm; sex assigned at birth; skills; social; social group; study population; transgender; transgender women; treatment duration; trend

PROJECT SUMMARY Transgender (TG) individuals have high prevalence of depression (64%) and are nearly nine times as likely to attempt suicide compared to the general population. Studies have not consistently collected data related to TG identity nor have they used the recommended two-step method of asking for assigned sex at birth and current gender identity. This population is largely overlooked in epidemiologic studies due to small sample size. With inconsistent and inaccurate ascertainment of TG patients in real-world data sources, TG people are missed and their health trends over time are understudied. Researchers are unable to identify and meaningfully address mental health inequities for this population, which can further exacerbate and perpetuate psychiatric conditions. It remains unclear what the health care utilization trends are for TG patients with psychiatric disorders and their adherence and persistence to psychiatric medications over time. Understanding this population’s mental health using large datasets is imperative in order to optimize the care management of psychiatric conditions experienced by TG people. The objectives of this proposal are to: (1) apply and evaluate the performance of existing computational phenotypes (CPs) to identify TG patients with depressive disorders (DD), anxiety, and attention deficit disorders (ADD), (2) assess adherence and prevalence of psychiatric medications among TG patients diagnosed with psychiatric disorders compared to cisgender patients with these diagnoses, and (3) examine the risk of non-fatal self-harm among TG patients with psychiatric disorders receiving psychotherapy compared to TG patients not receiving psychotherapy. For all aims, IBM MarketScan from years 2008 to 2020 will be used, which is a large, longitudinal medical claims database that includes inpatient and outpatient visits and prescription medication use. The completion of the proposed aims will provide real-world evidence on mental health care for TG individuals in the United States. The training plan outlined in this proposal will equip the applicant with critical knowledge and necessary skills in social and pharmacoepidemiology and transgender mental health. This plan will prepare him to successfully complete the proposed aims and to progress into a role as an independent, interdisciplinary researcher studying the intersection of pharmacoepidemiology and transgender health equity in the US. The applicant is extremely well supported by an interdisciplinary group of social, psychiatric, and pharmacoepidemiology faculty, health disparities researchers, and pharmaceutical researchers with the requisite expertise to support his doctoral research and prepare him for the next phase of his career.

项目摘要 跨性别者（TG）个体的抑郁症患病率很高（64％），几乎是九倍的抑郁症 与普通人群相比尝试自杀。研究尚未始终收集与TG相关的数据 身份也没有使用推荐的两步方法来出生时要求分配的性别 性别认同。由于样本量较小，在流行病学研究中，该人群在很大程度上被忽略了。和 在现实世界中，TG患者的TG患者不一致和不准确确定，TG人员被错过 随着时间的流逝，他们的健康趋势被理解。研究人员无法识别和有意义 解决该人群的心理健康不平等，这可能会进一步加剧和延续精神病学 状况。目前尚不清楚TG患者的医疗保健利用趋势是什么 随着时间的流逝，疾病及其对精神药物的坚持和持久性。了解这一点 必须使用大型数据集的人口健康，以优化 TG人经历的精神病。 该提案的目标是：（1）应用和评估现有计算的性能 表型（CPS）鉴定患有抑郁症的TG患者（DD），动画和注意力不足 TG患者的疾病（添加），（2）评估遵守和精神药物的患病率 与有这些诊断的官员患者相比，被诊断患有精神疾病，（3） 检查接受精神疾病的TG患者中非致命自我伤害的风险 与未接受心理治疗的TG患者相比，心理治疗。对于所有目标，IBM MarketScan来自 将使用2008年至2020年的年度，这是一个大型的纵向医学索赔数据库 住院和门诊就诊和处方药的使用。拟议的目标的完成将 为美国TG个人提供有关心理保健的现实证据。 该提案中概述的培训计划将使申请人获得关键知识和必要的知识 社交和药物ePidemiology and Transgender心理健康的技能。这个计划将使他做好准备 成功完成拟议的目标，并成为独立的跨学科角色 研究人员研究了美国药物ePIDEMIOGIOG和跨性别健康公平的交集。 申请人得到了一个社会，精神病学跨学科的群体，并 药物ePidemiology教师，健康差异研究人员和药物研究人员 必要的专业知识，以支持他的博士研究，并为他的职业生涯下一个阶段做好准备。