将健康的社会决定因素纳入基于主体的艾滋病毒传播模型:方法挑战和未来方向。

Frontiers in epidemiology Pub Date : 2025-02-27 eCollection Date: 2025-01-01 DOI:10.3389/fepid.2025.1533119
Anna L Hotton, Pedro Nascimento de Lima, Arindam Fadikar, Nicholson T Collier, Aditya S Khanna, Darnell N Motley, Eric Tatara, Sara Rimer, Ellen Almirol, Harold A Pollack, John A Schneider, Robert J Lempert, Jonathan Ozik
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引用次数: 0

摘要

艾滋病毒预防研究领域的重点是了解健康的社会决定因素(例如住房、就业、监禁)对艾滋病毒传播的影响,并制定干预措施,以解决艾滋病毒风险的潜在结构性驱动因素。然而,这种干预措施是资源密集型的,在后勤上具有挑战性,而且它们的评估往往受到样本量小和随访时间短的限制。由于基于主体的模型允许对反事实实验进行详细和大规模的模拟,因此可以展示干预措施组合的潜在影响,否则这些干预措施在经验环境中可能无法进行评估,并有助于规划有效利用公共卫生资源。需要有足够现实的计算模型,以便对解决艾滋病毒传播的社会结构驱动因素的干预措施进行评估,尽管迄今为止大多数艾滋病毒模型都侧重于对传播动态的更近距离影响。对传染病的复杂社会原因进行建模特别具有挑战性,因为关系十分复杂,而且在衡量和量化将健康的社会决定因素与艾滋病毒风险联系起来的因果关系方面存在局限性。不确定性存在于用于参数化模型的变量、性传播网络的表示和模型结构(即代表艾滋病毒传播系统的因果途径)本身之间的关联的大小和方向。本文将回顾有关将健康的社会决定因素纳入艾滋病毒传播的流行病学模型的文献状况。利用我们正在进行的工作中的例子,我们将讨论不确定性量化和稳健决策方法,以解决上述一些挑战,并为该领域未来的方法学工作提出方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incorporating social determinants of health into agent-based models of HIV transmission: methodological challenges and future directions.

There is much focus in the field of HIV prevention research on understanding the impact of social determinants of health (e.g., housing, employment, incarceration) on HIV transmission and developing interventions to address underlying structural drivers of HIV risk. However, such interventions are resource-intensive and logistically challenging, and their evaluation is often limited by small sample sizes and short duration of follow-up. Because they allow for both detailed and large-scale simulations of counterfactual experiments, agent-based models (ABMs) can demonstrate the potential impact of combinations of interventions that may otherwise be infeasible to evaluate in empirical settings and help plan for efficient use of public health resources. There is a need for computational models that are sufficiently realistic to allow for evaluation of interventions that address socio-structural drivers of HIV transmission, though most HIV models to date have focused on more proximal influences on transmission dynamics. Modeling the complex social causes of infectious diseases is particularly challenging due to the complexity of the relationships and limitations in the measurement and quantification of causal relationships linking social determinants of health to HIV risk. Uncertainty exists in the magnitude and direction of associations among the variables used to parameterize the models, the representation of sexual transmission networks, and the model structure (i.e. the causal pathways representing the system of HIV transmission) itself. This paper will review the state of the literature on incorporating social determinants of health into epidemiological models of HIV transmission. Using examples from our ongoing work, we will discuss Uncertainty Quantification and Robust Decision Making methods to address some of the above-mentioned challenges and suggest directions for future methodological work in this area.

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