PDF(Pubmed)
Abstract：
BACKGROUND: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
摘要：
背景：HIV的数学模型在指导和评估HIV政策方面具有独特的重要性。变性人和非二元性人不成比例地受到艾滋病毒的影响；然而，关于HIV传播的数学模型很少发表,这些模型包括跨性别人群和非二元人群.本评论讨论了当前发展稳健和准确的跨包容性模型的结构性挑战，并确定了未来研究和政策的机会。重点是美国的例子。
结论：截至2024年4月，只有七个已发表的艾滋病毒传播数学模型包括跨性别者。现有模型有几个显著的局限性和偏见，限制了它们在告知公共卫生干预方面的效用。值得注意的是,没有模特包括变性男性或非二元个体，尽管这些人群相对于顺性人群受到艾滋病毒的影响不成比例。此外,现有的HIV传播数学模型不能准确代表跨性别者的性网络。数据的可用性和质量仍然是开发准确的跨包容性艾滋病毒数学模型的重大障碍。使用社区参与的方法，我们开发了一个建模框架,解决了现有模型的局限性,并强调了数据的可用性和质量如何限制了跨性别人群数学模型的效用.
结论：建模是艾滋病毒预防计划的重要工具，也是为公共卫生干预措施提供信息的关键步骤。变性人人口的规划和政策。我们的建模框架强调了准确的跨包容性数据收集方法的重要性，因为这些分析对于为公共卫生决策提供信息的相关性在很大程度上取决于模型参数化和校准目标的有效性。从研究的开发和数据收集阶段开始，采用包容性别和针对性别的方法，可以提供有关干预措施如何，规划和政策可以区分所有性别群体的独特健康需求。此外，鉴于数据结构的局限性，设计纵向监测数据系统和概率样本对于填补关键研究空白至关重要，突出进展，并为当前证据提供额外的严谨性。可以进一步扩大投资和倡议，如结束美国的艾滋病毒流行，这是非常需要的，以优先考虑和重视跨资金结构的跨性别人口，目标和结果度量。
结论：截至2024年4月，只有七个已发表的艾滋病毒传播数学模型包括跨性别者。现有模型有几个显著的局限性和偏见，限制了它们在告知公共卫生干预方面的效用。值得注意的是,没有模特包括变性男性或非二元个体，尽管这些人群相对于顺性人群受到艾滋病毒的影响不成比例。此外,现有的HIV传播数学模型不能准确代表跨性别者的性网络。数据的可用性和质量仍然是开发准确的跨包容性艾滋病毒数学模型的重大障碍。使用社区参与的方法，我们开发了一个建模框架,解决了现有模型的局限性,并强调了数据的可用性和质量如何限制了跨性别人群数学模型的效用.
结论：建模是艾滋病毒预防计划的重要工具，也是为公共卫生干预措施提供信息的关键步骤。变性人人口的规划和政策。我们的建模框架强调了准确的跨包容性数据收集方法的重要性，因为这些分析对于为公共卫生决策提供信息的相关性在很大程度上取决于模型参数化和校准目标的有效性。从研究的开发和数据收集阶段开始，采用包容性别和针对性别的方法，可以提供有关干预措施如何，规划和政策可以区分所有性别群体的独特健康需求。此外，鉴于数据结构的局限性，设计纵向监测数据系统和概率样本对于填补关键研究空白至关重要，突出进展，并为当前证据提供额外的严谨性。可以进一步扩大投资和倡议，如结束美国的艾滋病毒流行，这是非常需要的，以优先考虑和重视跨资金结构的跨性别人口，目标和结果度量。
