TY - JOUR
T1 - Algorithm to identify transgender and gender nonbinary individuals among people living with HIV performs differently by age and ethnicity
AU - Chyten-Brennan, Jules
AU - Patel, Viraj V.
AU - Ginsberg, Mindy S.
AU - Hanna, David B.
N1 - Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2021/2
Y1 - 2021/2
N2 - 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 =.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.
AB - 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 =.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.
KW - Algorithms
KW - Electronic health records
KW - HIV
KW - Transgender persons
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U2 - 10.1016/j.annepidem.2020.09.013
DO - 10.1016/j.annepidem.2020.09.013
M3 - Article
C2 - 33010416
AN - SCOPUS:85097452914
SN - 1047-2797
VL - 54
SP - 73
EP - 78
JO - Annals of Epidemiology
JF - Annals of Epidemiology
ER -