Boston College, William F. Connell School of Nursing, Chestnut Hill, MA, USA.
Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA.
Epidemics. 2023 Jun;43:100679. doi: 10.1016/j.epidem.2023.100679. Epub 2023 Mar 11.
Differences in infectious disease risk, acquisition, and severity arise from intersectional systems of oppression and resulting historical injustices that shape individual behavior and circumstance. We define historical injustices as distinct events and policies that arise out of intersectional systems of oppression. We view historical injustices as a medium through which structural forces affect health both directly and indirectly, and are thus important to study in the context of infectious disease disparities. In this critical analysis we aim to highlight the importance of incorporating historical injustices into mathematical models of infectious disease transmission and provide context on the methodologies to do so. We offer two illustrations of elements of model building (i.e., parameterization, validation and calibration) that can allow for a better understanding of health disparities in infectious disease outcomes. Mathematical models that do not recognize the historical forces that underlie infectious disease dynamics inevitably lead to the individualization of our focus and the recommendation of untenable individual-behavioral prescriptions to address the burden of infectious disease.
传染病风险、感染和严重程度的差异源于交叉压迫系统和由此产生的历史不公正，这些因素塑造了个人的行为和环境。我们将历史不公正定义为源于交叉压迫系统的独特事件和政策。我们认为，历史不公正事件是结构性力量直接和间接影响健康的媒介，因此在传染病差异的背景下，研究历史不公正事件非常重要。在这项批判性分析中，我们旨在强调将历史不公正事件纳入传染病传播的数学模型中的重要性，并提供相关方法的背景信息。我们提供了两个模型构建元素（即参数化、验证和校准）的说明，这可以帮助我们更好地理解传染病结果中的健康差异。不承认构成传染病动态基础的历史力量的数学模型不可避免地导致我们将重点个体化，并建议采取不可行的个体行为处方来解决传染病负担。
