Including transgender populations in mathematical models for HIV treatment and prevention: current barriers and policy implications.


Journal

Journal of the International AIDS Society
ISSN: 1758-2652
Titre abrégé: J Int AIDS Soc
Pays: Switzerland
ID NLM: 101478566

Informations de publication

Date de publication:
Jun 2024
Historique:
received: 27 01 2024
accepted: 23 05 2024
medline: 13 6 2024
pubmed: 13 6 2024
entrez: 13 6 2024
Statut: ppublish

Résumé

Mathematical models of HIV have been uniquely important in directing and evaluating HIV policy. Transgender and nonbinary people are disproportionately impacted by HIV; however, few mathematical models of HIV transmission have been published that are inclusive of transgender and nonbinary populations. This commentary discusses current structural challenges to developing robust and accurate trans-inclusive models and identifies opportunities for future research and policy, with a focus on examples from the United States. As of April 2024, only seven published mathematical models of HIV transmission include transgender people. Existing models have several notable limitations and biases that limit their utility for informing public health intervention. Notably, no models include transgender men or nonbinary individuals, despite these populations being disproportionately impacted by HIV relative to cisgender populations. In addition, existing mathematical models of HIV transmission do not accurately represent the sexual network of transgender people. Data availability and quality remain a significant barrier to the development of accurate trans-inclusive mathematical models of HIV. Using a community-engaged approach, we developed a modelling framework that addresses the limitations of existing model and to highlight how data availability and quality limit the utility of mathematical models for transgender populations. Modelling is an important tool for HIV prevention planning and a key step towards informing public health interventions, programming and policies for transgender populations. Our modelling framework underscores the importance of accurate trans-inclusive data collection methodologies, since the relevance of these analyses for informing public health decision-making is strongly dependent on the validity of the model parameterization and calibration targets. Adopting gender-inclusive and gender-specific approaches starting from the development and data collection stages of research can provide insights into how interventions, programming and policies can distinguish unique health needs across all gender groups. Moreover, in light of the data structure limitations, designing longitudinal surveillance data systems and probability samples will be critical to fill key research gaps, highlight progress and provide additional rigour to the current evidence. Investments and initiatives like Ending the HIV Epidemic in the United States can be further expanded and are highly needed to prioritize and value transgender populations across funding structures, goals and outcome measures.

Identifiants

pubmed: 38867431
doi: 10.1002/jia2.26304
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e26304

Subventions

Organisme : National Institute of Allergy and Infectious Diseases
ID : F31AI152542
Organisme : American Sexually Transmitted Diseases Association
Organisme : University of Washington's School of Public Health
Organisme : National Institutes of Allergy and Infectious Disease
ID : UM1AI068617

Informations de copyright

© 2024 The Author(s). Journal of the International AIDS Society published by John Wiley & Sons Ltd on behalf of International AIDS Society.

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Auteurs

Diana M Tordoff (DM)

Department of Epidemiology, University of Washington, Seattle, Washington, USA.
School of Medicine, Stanford University, Palo Alto, California, USA.

Arjee Restar (A)

Department of Epidemiology, University of Washington, Seattle, Washington, USA.

Brian Minalga (B)

Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Atlas Fernandez (A)

Building Changes, Seattle, Washington, USA.

Dobromir Dimitrov (D)

Fred Hutchinson Cancer Center, Seattle, Washington, USA.
Department of Applied Mathematics, University of Washington, Seattle, Washington, USA.

Ann Duerr (A)

Department of Epidemiology, University of Washington, Seattle, Washington, USA.
Fred Hutchinson Cancer Center, Seattle, Washington, USA.
Department of Global Health, University of Washington, Seattle, Washington, USA.

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