Identifying Gender Minority Patients' Health And Health Care Needs In Administrative Claims Data.

Ana M Progovac, Benjamin Lê Cook, Brian O Mullin, Alex McDowell, Maria Jose Sanchez R, Ye Wang, Timothy B Creedon, Mark A Schuster
Author Information
  1. Ana M Progovac: Ana M. Progovac ( aprogovac@cha.harvard.edu ) is a senior scientist in the Health Equity Research Lab in the Department of Psychiatry, Cambridge Health Alliance, in Cambridge, and an instructor in the Department of Psychiatry, Harvard Medical School, in Boston, both in Massachusetts.
  2. Benjamin Lê Cook: Benjamin Lê Cook is director of the Health Equity Research Lab and director of research in the Department of Psychiatry, Cambridge Health Alliance, and an assistant professor in the Department of Psychiatry, Harvard Medical School.
  3. Brian O Mullin: Brian O. Mullin is a senior analyst in Health Equity Research Lab and the Children's Health Initiative in the Department of Psychiatry, Cambridge Health Alliance.
  4. Alex McDowell: Alex McDowell is a PhD student in the Department of Health Care Policy, Harvard Medical School, and a research assistant at the Health Equity Research Lab in the Department of Psychiatry, Cambridge Health Alliance.
  5. Maria Jose Sanchez R: Maria Jose Sanchez R. is a research assistant in the Health Equity Research Lab in the Department of Psychiatry, Cambridge Health Alliance.
  6. Ye Wang: Ye Wang is a research scientist in the Disparities Research Unit, Massachusetts General Hospital, in Boston.
  7. Timothy B Creedon: Timothy B. Creedon is a researcher at IBM Watson Health in Cambridge, Massachusetts. At the time this work was conducted, he was a research associate in the Health Equity Research Lab in the Department of Psychiatry, Cambridge Health Alliance.
  8. Mark A Schuster: Mark A. Schuster is founding dean and CEO of the Kaiser Permanente School of Medicine, in Pasadena, California. At the time this work was conducted, he was chief of the Division of General Pediatrics, Boston Children's Hospital, and the William Berenberg Professor of Pediatrics in the Department of Pediatrics, Harvard Medical School.

Abstract

Health care utilization patterns for gender minority Medicare beneficiaries (those who are transgender or gender nonbinary people) are largely unknown. We identified gender minority beneficiaries using a diagnosis-code algorithm and compared them to a 5 percent random sample of non-gender minority beneficiaries from the period 2009-14 in terms of mental health and chronic diseases, use of preventive and mental health care, hospitalizations, and emergency department (ED) visits. Gender minority beneficiaries experienced more disability and mental illness. When we adjusted for age and mental health, we found that they used more mental health care. And when we adjusted for age and chronic conditions, we found that they were more likely to be hospitalized and to visit the ED. There were several small but significant differences in preventive care use. Findings were similar for disabled and older cohorts. These findings underscore the need to capture gender identity in health data to better address this population's health needs.

Keywords

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Grants

  1. T32 MH019733/NIMH NIH HHS
  2. UL1 TR001102/NCATS NIH HHS

MeSH Term

Administrative Claims, Healthcare
Adult
Aged
Aged, 80 and over
Chronic Disease
Persons with Disabilities
Emergency Service, Hospital
Hospitalization
Humans
Medicare
Mental Disorders
Middle Aged
Needs Assessment
Patient Acceptance of Health Care
Sexual and Gender Minorities
United States

Word Cloud

Created with Highcharts 10.0.0healthmentalHealthcaregenderminoritybeneficiariesGenderMinorityMedicarechronicusepreventiveEDadjustedagefoundutilizationpatternstransgendernonbinarypeoplelargelyunknownidentifiedusingdiagnosis-codealgorithmcompared5 percentrandomsamplenon-genderperiod2009-14termsdiseaseshospitalizationsemergencydepartmentvisitsexperienceddisabilityillnessusedconditionslikelyhospitalizedvisitseveralsmallsignificantdifferencesFindingssimilardisabledoldercohortsfindingsunderscoreneedcaptureidentitydatabetteraddresspopulation'sneedsIdentifyingPatients'CareNeedsAdministrativeClaimsDataDisparitiesMentalHealth/SubstanceAbuse

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