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

项目摘要 跨性别者（TG）抑郁症的患病率很高（64%）， 与一般人群相比，自杀未遂。研究没有一致地收集与TG相关的数据 他们也没有使用建议的两步方法，要求在出生时指定性别， 性别认同由于样本量小，这一人群在流行病学研究中很大程度上被忽视。与 真实世界数据源中TG患者的确定不一致和不准确，TG患者被遗漏 他们的健康趋势也没有得到充分的研究。研究人员无法识别和有意义的 解决这一人群的心理健康不平等问题，这可能进一步加剧和延续精神疾病， 条件目前尚不清楚TG精神病患者的卫生保健利用趋势是什么， 疾病及其随时间对精神药物的依从性和持久性。理解这一 人口的心理健康使用大型数据集是必要的，以优化护理管理， TG患者的精神状况。 该建议的目标是：（1）应用和评估现有的计算性能 表型（CP），以确定TG患者的抑郁障碍（DD），焦虑和注意力缺陷 （2）评估TG患者中精神药物的依从性和患病率 与具有这些诊断的顺性别患者相比，被诊断为精神障碍，以及（3） 检查接受精神疾病治疗的TG患者非致命性自我伤害的风险， 与未接受心理治疗的TG患者相比。为了实现所有目标，来自 将使用2008年至2020年，这是一个大型纵向医疗索赔数据库，包括 住院和门诊就诊以及处方药的使用。完成拟议目标将 提供真实世界的证据，在美国的TG个人的精神卫生保健。 本建议书中概述的培训计划将使申请人具备关键知识和必要的 社会和药物流行病学以及跨性别心理健康方面的技能。这个计划将使他准备好 成功地完成拟议的目标，并逐步成为一个独立的，跨学科的作用， 研究药物流行病学和跨性别健康公平的交叉在美国的研究员。 申请人得到了社会，精神病学和心理学跨学科小组的大力支持。 药物流行病学教师，健康差异研究人员和制药研究人员与 他的博士研究和准备他的职业生涯的下一个阶段所需的专业知识。

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.