title: A novel method for estimating transgender status using electronic medical records.
abstract: OBJECTIVE
  We describe a novel algorithm for identifying transgender people and determining their male-to-female (MTF) or female-to-male (FTM) identity in electronic medical records of an integrated health system.
  
  
  METHODS
  A computer program scanned Kaiser Permanente Georgia electronic medical records from January 2006 through December 2014 for relevant diagnostic codes, and presence of specific keywords (e.g., "transgender" or "transsexual") in clinical notes. Eligibility was verified by review of de-identified text strings containing targeted keywords, and if needed, by an additional in-depth review of records. Once transgender status was confirmed, FTM or MTF identity was assessed using a second program and another round of text string reviews.
  
  
  RESULTS
  Of 813,737 members, 271 were identified as possibly transgender: 137 through keywords only, 25 through diagnostic codes only, and 109 through both codes and keywords. Of these individuals, 185 (68%, 95% confidence interval [CI]: 62%-74%) were confirmed as definitely transgender. The proportions (95% CIs) of definite transgender status among persons identified via keywords, diagnostic codes, and both were 45% (37%-54%), 56% (35%-75%), and 100% (96%-100%). Of the 185 definitely transgender people, 99 (54%, 95% CI: 46%-61%) were MTF, 84 (45%, 95% CI: 38%-53%) were FTM. For two persons, gender identity remained unknown. Prevalence of transgender people (per 100,000 members) was 4.4 (95% CI: 2.6-7.4) in 2006 and 38.7 (95% CI: 32.4-46.2) in 2014.
  
  
  CONCLUSIONS
  The proposed method of identifying candidates for transgender health studies is low cost and relatively efficient. It can be applied in other similar health care systems.
authors.affiliation: School of Public Health, Georgia State University, Atlanta; Center for Clinical and Outcomes Research, Kaiser Permanente Georgia, Atlanta.
authors.forename: Douglas
authors.initials: D
authors.lastname: Roblin
authors.affiliation: Center for Clinical and Outcomes Research, Kaiser Permanente Georgia, Atlanta.
authors.forename: Joshua
authors.initials: J
authors.lastname: Barzilay
authors.affiliation: Center for Clinical and Outcomes Research, Kaiser Permanente Georgia, Atlanta.
authors.forename: Dennis
authors.initials: D
authors.lastname: Tolsma
authors.affiliation: Center for Clinical and Outcomes Research, Kaiser Permanente Georgia, Atlanta.
authors.forename: Brandi
authors.initials: B
authors.lastname: Robinson
authors.affiliation: Center for Clinical and Outcomes Research, Kaiser Permanente Georgia, Atlanta.
authors.forename: Laura
authors.initials: L
authors.lastname: Schild
authors.affiliation: Center for Clinical and Outcomes Research, Kaiser Permanente Georgia, Atlanta.
authors.forename: Lee
authors.initials: L
authors.lastname: Cromwell
authors.affiliation: Rollins School of Public Health, Emory University, Atlanta, GA.
authors.forename: Hayley
authors.initials: H
authors.lastname: Braun
authors.affiliation: Rollins School of Public Health, Emory University, Atlanta, GA.
authors.forename: Rebecca
authors.initials: R
authors.lastname: Nash
authors.affiliation: Emory College of Arts and Sciences, Atlanta, GA.
authors.forename: Joseph
authors.initials: J
authors.lastname: Gerth
authors.affiliation: Division of Research, Kaiser Permanente, Oakland, CA.
authors.forename: Enid
authors.initials: E
authors.lastname: Hunkeler
authors.affiliation: Kaiser Permanente Southern California, Pasadena.
authors.forename: Virginia P
authors.initials: VP
authors.lastname: Quinn
authors.affiliation: Emory University, School of Medicine, Atlanta, GA; The Atlanta VA Medical Center, Atlanta, GA.
authors.forename: Vin
authors.initials: V
authors.lastname: Tangpricha
authors.affiliation: Rollins School of Public Health, Emory University, Atlanta, GA. Electronic address: mgoodm2@emory.edu.
authors.forename: Michael
authors.initials: M
authors.lastname: Goodman
mesh_terms: D000465:Algorithms; D003661:Decision Support Techniques; D057286:Electronic Health Records; D005260:Female; D005783:Gender Identity; D005845:Georgia; D006801:Humans; D008297:Male; D063106:Transgender Persons / Q000523:psychology*; D014189:Transsexualism / Q000175:diagnosis* / Q000523:psychology
keywords: Algorithm; Electronic medical records; Prevalence; Transgender