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

Szczegóły
Abstrakt

Tytuł:: Algorithm to identify transgender and gender nonbinary individuals among people living with HIV performs differently by age and ethnicity.
Autorzy:: Chyten-Brennan J; Division of General Internal Medicine, Department of Medicine, Montefiore Medical Center-Albert Einstein College of Medicine, Bronx, NY. Electronic address: .
Patel VV; Division of General Internal Medicine, Department of Medicine, Montefiore Medical Center-Albert Einstein College of Medicine, Bronx, NY.
Ginsberg MS; Department of Epidemiology and Population Health, Montefiore Medical Center-Albert Einstein College of Medicine, Bronx, NY.
Hanna DB; Department of Epidemiology and Population Health, Montefiore Medical Center-Albert Einstein College of Medicine, Bronx, NY.
Źródło:: Annals of epidemiology [Ann Epidemiol] 2021 Feb; Vol. 54, pp. 73-78. Date of Electronic Publication: 2020 Oct 01.
Typ publikacji:: Journal Article; Research Support, N.I.H., Extramural
Język:: English
Imprint Name(s):: Original Publication: New York, NY : Elsevier, c1990-
MeSH Terms:: Algorithms*
Gender Identity*
HIV Infections*/diagnosis
HIV Infections*/epidemiology
HIV Infections*/ethnology
Transgender Persons*/statistics & numerical data
Adult ; Age Distribution ; Electronic Health Records ; Ethnicity/statistics & numerical data ; Female ; Humans ; Male ; Middle Aged ; Prospective Studies ; Reproducibility of Results ; Retrospective Studies
References:: Lancet HIV. 2019 Oct;6(10):e696-e704. (PMID: 31285182)
J Am Med Inform Assoc. 2018 Jul 1;25(7):905-908. (PMID: 29635362)
Sex Transm Dis. 2019 Apr;46(4):e38-e41. (PMID: 30383620)
Ann Epidemiol. 2016 Mar;26(3):198-203. (PMID: 26907539)
Ethn Dis. 2019 Jun 13;29(Suppl 2):441-450. (PMID: 31308617)
J Sex Res. 2011 Mar;48(2-3):285-96. (PMID: 20336575)
AIDS Care. 2017 Dec;29(12):1491-1498. (PMID: 28343404)
AIDS Educ Prev. 2011 Dec;23(6):508-20. (PMID: 22201235)
Appl Clin Inform. 2014 Jun 18;5(2):557-70. (PMID: 25024769)
J Assoc Nurses AIDS Care. 2014 Nov-Dec;25(6):657-63. (PMID: 24880490)
Glob Public Health. 2016 Aug-Sep;11(7-8):866-87. (PMID: 26785800)
LGBT Health. 2015 Sep;2(3):228-34. (PMID: 26788671)
Glob Public Health. 2016 Aug-Sep;11(7-8):835-48. (PMID: 26785751)
Am J Public Health. 2018 Nov;108(S4):S274-S276. (PMID: 30383426)
AIDS Patient Care STDS. 2013 Nov;27(11):621-7. (PMID: 24138486)
J Acquir Immune Defic Syndr. 2016 Aug 15;72 Suppl 3:S243-7. (PMID: 27429190)
Grant Information:: K23 MH102118 United States MH NIMH NIH HHS; L30 MH117751 United States MH NIMH NIH HHS; K01 HL137557 United States HL NHLBI NIH HHS; P30 AI124414 United States AI NIAID NIH HHS; UL1 TR002556 United States TR NCATS NIH HHS
Contributed Indexing:: Keywords: Algorithms; Electronic health records; HIV; Transgender persons
Entry Date(s):: Date Created: 20201003 Date Completed: 20210301 Latest Revision: 20220202
Update Code:: 20240105
PubMed Central ID:: PMC7883669
DOI:: 10.1016/j.annepidem.2020.09.013
PMID:: 33010416
: Czasopismo naukowe

Full Text Finder

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.
(Copyright © 2020 Elsevier Inc. All rights reserved.)

Informacja