Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University, Montréal, Canada.
MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK.
BMC Med. 2024 Sep 19;22(1):404. doi: 10.1186/s12916-024-03580-z.
Including structural determinants (e.g. criminalisation, stigma, inequitable gender norms) in dynamic HIV transmission models is important to help quantify their population-level impacts and guide implementation of effective interventions that reduce the burden of HIV and inequalities thereof. However, evidence-based modelling of structural determinants is challenging partly due to a limited understanding of their causal pathways and few empirical estimates of their effects on HIV acquisition and transmission.
We conducted a scoping review of dynamic HIV transmission modelling studies that evaluated the impacts of structural determinants, published up to August 28, 2023, using Ovid Embase and Medline online databases. We appraised studies on how models represented exposure to structural determinants and causal pathways. Building on this, we developed a new methodological framework and recommendations to support the incorporation of structural determinants in transmission dynamics models and their analyses. We discuss the data and analyses that could strengthen the evidence used to inform these models.
We identified 17 HIV modelling studies that represented structural determinants and/or interventions, including incarceration of people who inject drugs (number of studies [n] = 5), violence against women (n = 3), HIV stigma (n = 1), and housing instability (n = 1), among others (n = 7). Most studies (n = 10) modelled exposures dynamically. Almost half (8/17 studies) represented multiple exposure histories (e.g. current, recent, non-recent exposure). Structural determinants were often assumed to influence HIV indirectly by influencing mediators such as contact patterns, condom use, and antiretroviral therapy use. However, causal pathways' assumptions were sometimes simple, with few mediators explicitly represented in the model, and largely based on cross-sectional associations. Although most studies calibrated models using HIV epidemiological data, less than half (7/17) also fitted or cross-validated to data on the prevalence, frequency, or effects of exposure to structural determinants.
Mathematical models can play a crucial role in elucidating the population-level impacts of structural determinants and interventions on HIV. We recommend the next generation of models reflect exposure to structural determinants dynamically and mechanistically, and reproduce the key causal pathways, based on longitudinal evidence of links between structural determinants, mediators, and HIV. This would improve the validity and usefulness of predictions of the impacts of structural determinants and interventions.
将结构决定因素（例如刑事定罪、污名化、不平等的性别规范）纳入动态 HIV 传播模型中对于量化其对人群的影响以及指导实施有效干预措施以减轻 HIV 负担和不平等现象非常重要。然而，由于对其因果途径的理解有限，以及对其对 HIV 获得和传播影响的实证估计很少，因此对结构决定因素进行基于证据的建模具有挑战性。
我们对截至 2023 年 8 月 28 日发表的动态 HIV 传播建模研究进行了范围界定审查，这些研究评估了结构决定因素的影响，使用了 Ovid Embase 和 Medline 在线数据库。我们评估了模型如何代表接触结构决定因素和因果途径。在此基础上，我们开发了一个新的方法学框架和建议，以支持在传播动力学模型及其分析中纳入结构决定因素。我们讨论了可以加强用于为这些模型提供信息的数据和分析。
我们确定了 17 项代表结构决定因素和/或干预措施的 HIV 建模研究，包括吸毒者被监禁（研究数量 [n] = 5）、暴力侵害妇女（n = 3）、HIV 污名（n = 1）和住房不稳定（n = 1）等（n = 7）。大多数研究（n = 10）动态地模拟暴露情况。几乎一半（8/17 项研究）代表了多种暴露史（例如当前、近期、非近期暴露）。结构决定因素通常被认为通过影响接触模式、避孕套使用和抗逆转录病毒治疗使用等中介因素间接影响 HIV。然而，因果途径的假设有时很简单，模型中很少有中介因素明确表示，并且主要基于横断面关联。尽管大多数研究使用 HIV 流行病学数据对模型进行校准，但只有不到一半（7/17）的研究还拟合或交叉验证了结构决定因素的流行率、频率或暴露效应的数据。
数学模型可以在阐明结构决定因素和干预措施对 HIV 的人群影响方面发挥关键作用。我们建议下一代模型动态地、机制性地反映对结构决定因素的暴露情况，并根据结构决定因素、中介因素和 HIV 之间的联系的纵向证据，再现关键的因果途径。这将提高结构决定因素和干预措施影响预测的有效性和有用性。
