NYU Langone Health, New York University School of Medicine, New York, New York, United States.
Callen Lorde Community Health Center, NYU Grossman School of Medicine, New York City, New York, United States.
Appl Clin Inform. 2021 Mar;12(2):222-228. doi: 10.1055/s-0041-1725184. Epub 2021 Mar 17.
There are specific issues regarding sexual orientation (SO) collection and analysis among transgender and nonbinary patients. A limitation to meaningful SO and gender identity (GI) data collection is their consideration as a fixed trait or demographic data point.
A de-identified patient database from a single electronic health record (EHR) that allows for searching any discrete data point in the EHR was used to query demographic data (sex assigned at birth and current GI) for transgender individuals from January 2011 to March 2020 at a large urban tertiary care academic health center.
A cohort of transgender individuals were identified by using EHR data from a two-step demographic question. Almost half of male identified (46.70%, = 85) and female identified (47.51%, = 86) individuals had "heterosexual/straight" input for SO. Overall, male and female identified (i.e., binary) GI aggregate categories had similar SO responses. Assigned male at birth (AMAB) nonbinary individuals ( = 6) had "homosexual/gay" SO data input. Assigned female at birth (AFAB) nonbinary individuals ( = 56) had almost half "something else" SO data input (41.67%, = 15). Individuals with "choose not to disclose" for GI ( = 249) almost all had "choose not to disclose" SO data (96.27%, = 232).
Current SO categories do not fully capture transgender individuals' identities and experiences, and limit the clinical and epidemiological utility of collecting this data in the current form. Anatomical assumptions based on SO should be seen as a potential shortcoming in over-reliance on SO as an indicator of screening needs and risk factors.
跨性别和非二元性别患者的性取向 (SO) 收集和分析存在特定问题。将 SO 和性别认同 (GI) 数据收集视为固定特征或人口统计学数据点是限制其具有实际意义的一个因素。
从一个允许在电子健康记录 (EHR) 中搜索任何离散数据点的单一电子健康记录 (EHR) 中使用匿名患者数据库，对 2011 年 1 月至 2020 年 3 月期间，一家大型城市三级学术医疗中心的跨性别个体进行了 EHR 中出生时性别和当前 GI 的人口统计学数据查询。
使用两步式人口统计学问题的 EHR 数据，确定了跨性别个体的队列。几乎一半的男性（46.70%，85 人）和女性（47.51%，86 人）个体的 SO 输入为“异性恋/直”。总体而言，男性和女性（即二元）GI 综合类别具有相似的 SO 反应。出生时被指定为男性（AMAB）的非二元性别个体（6 人）的 SO 数据输入为“同性恋/男同性恋”。出生时被指定为女性（AFAB）的非二元性别个体（56 人）的 SO 数据输入几乎有一半为“其他”（41.67%，15 人）。GI 选择“不披露”的个体（249 人）的 SO 数据几乎全部选择“不披露”（96.27%，232 人）。
当前的 SO 类别不能完全捕捉跨性别个体的身份和经历，并且以当前形式收集这些数据限制了其临床和流行病学的应用。基于 SO 的解剖学假设应被视为过度依赖 SO 作为筛查需求和风险因素指标的潜在缺陷。
