Sexual Orientation Demographic Data in a Clinical Cohort of Transgender Patients

Sexual orientation in transgender adults in the United States

BACKGROUND

Sexual orientation refers to a person's enduring emotional, romantic, or sexual attractions to other people. Sexual orientation measures do not typically consider desires for, or sexual behavior with, transgender people. We describe measures inclusive of transgender people and characterize sexual orientation identity, behavior, and attraction in a representative sample of the U.S. transgender population.

METHODS

Between April 2016-December 2018, a U.S. national probability sample of transgender (n = 274) and cisgender (n = 1,162) adults were invited to complete a self-administered web or mailed paper survey. We assessed sexual identity with updated response options inclusive of recent identity terms (e.g., queer), and revised sexual behavior and attraction measures that included transgender people. Multiple response options were allowed for sexual behavior and attraction. Weighted descriptive statistics and sexual orientation differences by gender identity groups were estimated using age-adjusted comparisons.

RESULTS

Compared to the cisgender population, the transgender population was more likely to identify as a sexual minority and have heterogeneity in sexual orientation, behavior, and attraction. In the transgender population, the most frequently endorsed sexual orientation identities were "bisexual" (18.9%), "queer" (18.1%), and "straight" (17.6%). Sexually active transgender respondents reported diverse partners in the prior 5 years: 52.6% cisgender women (CW), 42.7% cisgender men (CM), 16.9% transgender women (TW), and 19.5% transgender men (TM); 27.7% did not have sex in the past 5 years. Overall, 73.6% were "somewhat"/ "very" attracted to CW, 58.3% CM, 56.8% TW, 52.4% TM, 59.9% genderqueer/nonbinary-females-at-birth, 51.9% genderqueer/nonbinary-males-at-birth. Sexual orientation identity, behavior, and attraction significantly differed by gender identity for TW, TM, and nonbinary participants (all p < 0.05).

CONCLUSIONS

Inclusive measures of sexual orientation captured diverse sexual identities, partner genders, and desires. Future research is needed to cognitively test and validate these measures, especially with cisgender respondents, and to assess the relation of sexual orientation and health for transgender people.

Patterns of gender identity data within electronic health record databases can be used as a tool for identifying and estimating the prevalence of gender-expansive people

Objective

Electronic health records (EHRs) within the United States increasingly include sexual orientation and gender identity (SOGI) fields. We assess how well SOGI fields, along with International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes and medication records, identify gender-expansive patients.

Materials and Methods

The study used a data set of all patients that had in-person inpatient or outpatient encounters at an academic medical center in a rural state between December 1, 2018 and February 17, 2022. Chart review was performed for all patients meeting at least one of the following criteria: differences between legal sex, sex assigned at birth, and gender identity (excluding blank fields) in the EHR SOGI fields; ICD-10 codes related to gender dysphoria or unspecified endocrine disorder; prescription for estradiol or testosterone suggesting use of gender-affirming hormones.

Results

Out of 123 441 total unique patients with in-person encounters, we identified a total of 2236 patients identifying as gender-expansive, with 1506 taking gender-affirming hormones. SOGI field differences or ICD-10 codes related to gender dysphoria or both were found in 2219 of 2236 (99.2%) patients who identify as gender-expansive, and 1500 of 1506 (99.6%) taking gender-affirming hormones. For the gender-expansive population, assigned female at birth was more common in the 12-29 year age range, while assigned male at birth was more common for those 40 years and older.

Conclusions

SOGI fields and ICD-10 codes identify a high percentage of gender-expansive patients at an academic medical center.

Ensuring equitable, inclusive and meaningful gender identity- and sexual orientation-related data collection in the healthcare sector: insights from a critical, pragmatic systematic review of the literature

In several countries, no gender identity- and sexual orientation-related data is routinely collected, if not for specific health or administrative/social purposes. Implementing and ensuring equitable and inclusive socio-demographic data collection is of paramount importance, given that the LGBTI community suffers from a disproportionate burden in terms of both communicable and non-communicable diseases. To the best of the authors' knowledge, there exists no systematic review addressing the methods that can be implemented in capturing gender identity- and sexual orientation-related data in the healthcare sector. A systematic literature review was conducted for filling in this gap of knowledge. Twenty-three articles were retained and analysed: two focussed on self-reported data, two on structured/semi-structured data, seven on text-mining, natural language processing, and other emerging artificial intelligence-based techniques, two on challenges in capturing sexual and gender-diverse populations, eight on the willingness to disclose gender identity and sexual orientation, and, finally, two on integrating structured and unstructured data. Our systematic literature review found that, despite the importance of collecting gender identity- and sexual orientation-related data and its increasing societal acceptance from the LGBTI community, several issues have to be addressed yet. Transgender, non-binary identities, and also intersex individuals remain often invisible and marginalized. In the last decades, there has been an increasing adoption of structured data. However, exploiting unstructured data seems to overperform in identifying LGBTI members, especially integrating structured and unstructured data. Self-declared/self-perceived/self-disclosed definitions, while being respectful of one's perception, may not completely be aligned with sexual behaviours and activities. Incorporating different levels of information (biological, socio-demographic, behavioural, and clinical) would enable overcoming this pitfall. A shift from a rigid/static nomenclature towards a more nuanced, dynamic, 'fuzzy' concept of a 'computable phenotype' has been proposed in the literature to capture the complexity of sexual identities and trajectories. On the other hand, excessive fragmentation has to be avoided considering that: (i) a full list of options including all gender identities and sexual orientations will never be available; (ii) these options should be easily understood by the general population, and (iii) these options should be consistent in such a way that can be compared among various studies and surveys. Only in this way, data collection can be clinically meaningful: that is to say, to impact clinical outcomes at the individual and population level, and to promote further research in the field.