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Disparities in health condition diagnoses among aging transgender and cisgender medicare beneficiaries, 2008-2017

INTRODUCTION: The objective of this research is to provide national estimates of the prevalence of health condition diagnoses among age-entitled transgender and cisgender Medicare beneficiaries. Quantification of the health burden across sex assigned at birth and gender can inform prevention, resear...

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Autores principales: Hughto, Jaclyn M. W., Varma, Hiren, Babbs, Gray, Yee, Kim, Alpert, Ash, Hughes, Landon, Ellison, Jacqueline, Downing, Jae, Shireman, Theresa I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040837/
https://www.ncbi.nlm.nih.gov/pubmed/36992801
http://dx.doi.org/10.3389/fendo.2023.1102348
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author Hughto, Jaclyn M. W.
Varma, Hiren
Babbs, Gray
Yee, Kim
Alpert, Ash
Hughes, Landon
Ellison, Jacqueline
Downing, Jae
Shireman, Theresa I.
author_facet Hughto, Jaclyn M. W.
Varma, Hiren
Babbs, Gray
Yee, Kim
Alpert, Ash
Hughes, Landon
Ellison, Jacqueline
Downing, Jae
Shireman, Theresa I.
author_sort Hughto, Jaclyn M. W.
collection PubMed
description INTRODUCTION: The objective of this research is to provide national estimates of the prevalence of health condition diagnoses among age-entitled transgender and cisgender Medicare beneficiaries. Quantification of the health burden across sex assigned at birth and gender can inform prevention, research, and allocation of funding for modifiable risk factors. METHODS: Using 2009–2017 Medicare fee-for-service data, we implemented an algorithm that leverages diagnosis, procedure, and pharmacy claims to identify age-entitled transgender Medicare beneficiaries and stratify the sample by inferred gender: trans feminine and nonbinary (TFN), trans masculine and nonbinary (TMN), and unclassified. We selected a 5% random sample of cisgender individuals for comparison. We descriptively analyzed (means and frequencies) demographic characteristics (age, race/ethnicity, US census region, months of enrollment) and used chi-square and t-tests to determine between- (transgender vs. cisgender) and within-group gender differences (e.g., TMN, TFN, unclassified) difference in demographics (p<0.05). We then used logistic regression to estimate and examine within- and between-group gender differences in the predicted probability of 25 health conditions, controlling for age, race/ethnicity, enrollment length, and census region. RESULTS: The analytic sample included 9,975 transgender (TFN n=4,198; TMN n=2,762; unclassified n=3,015) and 2,961,636 cisgender (male n=1,294,690, female n=1,666,946) beneficiaries. The majority of the transgender and cisgender samples were between the ages of 65 and 69 and White, non-Hispanic. The largest proportion of transgender and cisgender beneficiaries were from the South. On average, transgender individuals had more months of enrollment than cisgender individuals. In adjusted models, aging TFN or TMN Medicare beneficiaries had the highest probability of each of the 25 health diagnoses studied relative to cisgender males or females. TFN beneficiaries had the highest burden of health diagnoses relative to all other groups. DISCUSSION: These findings document disparities in key health condition diagnoses among transgender Medicare beneficiaries relative to cisgender individuals. Future application of these methods will enable the study of rare and anatomy-specific conditions among hard-to-reach aging transgender populations and inform interventions and policies to address documented disparities.
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spelling pubmed-100408372023-03-28 Disparities in health condition diagnoses among aging transgender and cisgender medicare beneficiaries, 2008-2017 Hughto, Jaclyn M. W. Varma, Hiren Babbs, Gray Yee, Kim Alpert, Ash Hughes, Landon Ellison, Jacqueline Downing, Jae Shireman, Theresa I. Front Endocrinol (Lausanne) Endocrinology INTRODUCTION: The objective of this research is to provide national estimates of the prevalence of health condition diagnoses among age-entitled transgender and cisgender Medicare beneficiaries. Quantification of the health burden across sex assigned at birth and gender can inform prevention, research, and allocation of funding for modifiable risk factors. METHODS: Using 2009–2017 Medicare fee-for-service data, we implemented an algorithm that leverages diagnosis, procedure, and pharmacy claims to identify age-entitled transgender Medicare beneficiaries and stratify the sample by inferred gender: trans feminine and nonbinary (TFN), trans masculine and nonbinary (TMN), and unclassified. We selected a 5% random sample of cisgender individuals for comparison. We descriptively analyzed (means and frequencies) demographic characteristics (age, race/ethnicity, US census region, months of enrollment) and used chi-square and t-tests to determine between- (transgender vs. cisgender) and within-group gender differences (e.g., TMN, TFN, unclassified) difference in demographics (p<0.05). We then used logistic regression to estimate and examine within- and between-group gender differences in the predicted probability of 25 health conditions, controlling for age, race/ethnicity, enrollment length, and census region. RESULTS: The analytic sample included 9,975 transgender (TFN n=4,198; TMN n=2,762; unclassified n=3,015) and 2,961,636 cisgender (male n=1,294,690, female n=1,666,946) beneficiaries. The majority of the transgender and cisgender samples were between the ages of 65 and 69 and White, non-Hispanic. The largest proportion of transgender and cisgender beneficiaries were from the South. On average, transgender individuals had more months of enrollment than cisgender individuals. In adjusted models, aging TFN or TMN Medicare beneficiaries had the highest probability of each of the 25 health diagnoses studied relative to cisgender males or females. TFN beneficiaries had the highest burden of health diagnoses relative to all other groups. DISCUSSION: These findings document disparities in key health condition diagnoses among transgender Medicare beneficiaries relative to cisgender individuals. Future application of these methods will enable the study of rare and anatomy-specific conditions among hard-to-reach aging transgender populations and inform interventions and policies to address documented disparities. Frontiers Media S.A. 2023-03-13 /pmc/articles/PMC10040837/ /pubmed/36992801 http://dx.doi.org/10.3389/fendo.2023.1102348 Text en Copyright © 2023 Hughto, Varma, Babbs, Yee, Alpert, Hughes, Ellison, Downing and Shireman https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Hughto, Jaclyn M. W.
Varma, Hiren
Babbs, Gray
Yee, Kim
Alpert, Ash
Hughes, Landon
Ellison, Jacqueline
Downing, Jae
Shireman, Theresa I.
Disparities in health condition diagnoses among aging transgender and cisgender medicare beneficiaries, 2008-2017
title Disparities in health condition diagnoses among aging transgender and cisgender medicare beneficiaries, 2008-2017
title_full Disparities in health condition diagnoses among aging transgender and cisgender medicare beneficiaries, 2008-2017
title_fullStr Disparities in health condition diagnoses among aging transgender and cisgender medicare beneficiaries, 2008-2017
title_full_unstemmed Disparities in health condition diagnoses among aging transgender and cisgender medicare beneficiaries, 2008-2017
title_short Disparities in health condition diagnoses among aging transgender and cisgender medicare beneficiaries, 2008-2017
title_sort disparities in health condition diagnoses among aging transgender and cisgender medicare beneficiaries, 2008-2017
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040837/
https://www.ncbi.nlm.nih.gov/pubmed/36992801
http://dx.doi.org/10.3389/fendo.2023.1102348
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