Algorithm to identify transgender and gender nonbinary individuals among people living with HIV performs differently by age and ethnicity

Purpose: HIV research among transgender and gender nonbinary (TGNB) people is limited by lack of gender identity data collection. We designed an EHR-based algorithm to identify TGNB people among people living with HIV (PLWH) when gender identity was not systematically collected. Methods: We applied EHR-based search criteria to all PLWH receiving care at a large urban health system between 1997 and 2017, then confirmed gender identity by chart review. We compared patient characteristics by gender identity and screening criteria, then calculated positive predictive values for each criterion. Results: Among 18,086 PLWH, 213 (1.2%) met criteria as potential TGNB patients and 178/213 were confirmed. Positive predictive values were highest for free-text keywords (91.7%) and diagnosis codes (77.4%). Confirmed TGNB patients were younger (median 32.5 vs. 42.5 years, P < .001) and less likely to be Hispanic (37.1% vs. 62.9%, P 1/4 .03) than unconfirmed patients. Among confirmed patients, 15% met criteria only for prospective gender identity data collection and were significantly older. Conclusion: EHR-based criteria can identify TGNB PLWH, but success may differ by ethnicity and age. Retrospective versus intentional, prospective gender identity data collection may capture different patients. To reduce misclassification in epidemiologic studies, gender identity data collection should address these potential differences and be systematic and prospective. (C) 2020 Elsevier Inc. All rights reserved.