Tahilin Sanchez Karver, Carlos E. Rodriguez-Diaz, Tamara Taggart, and Deanna Kerrigan are with the Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington, DC. Kaitlyn Atkins, Ping Teresa Yeh, and Caitlin E. Kennedy are with the Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD. Virginia A. Fonner is with the Global Health, Population, and Nutrition Department, FHI 360, Durham, NC. Michael D. Sweat is with the Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston.
Am J Public Health. 2022 Jun;112(S4):S420-S432. doi: 10.2105/AJPH.2021.306639.
Across settings, individuals from populations that are multiply stigmatized are at increased risk of HIV and experience worse HIV treatment outcomes. As evidence expands on how intersecting stigmatized identities and conditions influence HIV outcomes, researchers have used diverse quantitative approaches to measure HIV-related intersectional stigma and discrimination. To date, no clear consensus exists regarding how to best quantitatively measure and analyze intersectional stigma and discrimination. To review and document existing quantitative measures of HIV-related intersectional stigma and discrimination to inform research, programmatic, and policy efforts. We searched 5 electronic databases for relevant studies. References of included articles were screened for possible inclusion. Additional articles were screened on the basis of consultations with experts in the field. We included peer-reviewed studies published between January 1, 2010, and May 12, 2021, that were HIV related and presented 1 or more quantitative measures of stigma and discrimination using an intersectional lens in measure design or analysis. Systematic methods were used to screen citations and abstract data via a standardized coding form. Data were analyzed by coding categories stratified according to 2 subgroups: (1) studies incorporating a single intersectional measure and (2) studies that examined intersectional stigma through analytical approaches combining multiple measures. Sixteen articles met the inclusion criteria, 7 of which explicitly referenced intersectionality. Ten studies were from the United States. All of the studies included participants living with HIV. Among the 4 studies incorporating a single intersectional stigma measure, 3 explored race and gender stigma and 1 explored gender and HIV stigma. Studies involving analytic approaches (n = 12) mostly examined intersectional stigma via interaction terms in multivariate regression models. Three studies employed structural equation modeling to examine interactive effects or latent constructs of intersectional stigma. Research on the measurement of HIV-related intersectional stigma and discrimination is currently concentrated in high-income settings and generally focuses on the intersection of 2 identities (e.g., race and gender). Efforts are needed to expand appropriate application of intersectionality in the development, adaptation, and use of measures of HIV-related intersectional stigma and discrimination. The use of context-, identity-, or condition-adaptable measures should be considered. Researchers should also carefully consider how to meaningfully engage communities in the process of measurement development. The measures and analytic approaches presented could significantly enhance public health efforts in assessing the impact of HIV-related intersectional stigma and discrimination on critical health outcomes. (. 2022;112(S4):S420-S432. https://doi.org/10.2105/AJPH.2021.306639).
在各种环境中，来自受多种污名化影响的人群的个体感染 HIV 的风险增加，并且 HIV 治疗结果更差。随着证据不断扩大，说明相互交织的污名化身份和状况如何影响 HIV 结果，研究人员已经使用各种定量方法来衡量与 HIV 相关的交叉污名化和歧视。迄今为止，对于如何最好地定量衡量和分析交叉污名化和歧视，尚无明确共识。为了审查和记录与 HIV 相关的交叉污名化和歧视的现有定量衡量标准，以为研究、规划和政策工作提供信息。我们在 5 个电子数据库中搜索了相关研究。对纳入文章的参考文献进行了筛选，以确定是否可能纳入。根据与该领域专家的磋商，还筛选了其他文章。我们纳入了 2010 年 1 月 1 日至 2021 年 5 月 12 日期间发表的同行评议研究，这些研究与 HIV 相关，并在测量设计或分析中使用交叉视角呈现了 1 种或多种定量的污名化和歧视衡量标准。通过标准化编码表格对引文和摘要数据进行了系统的筛选。根据 2 个子组对数据进行了分类编码：(1) 纳入了单一交叉衡量标准的研究，(2) 通过结合多种衡量标准的分析方法研究交叉污名化的研究。纳入了 16 篇符合纳入标准的文章，其中 7 篇明确提到了交叉性。10 项研究来自美国。所有研究都包括了 HIV 感染者。在纳入的 4 项单一交叉污名化衡量标准的研究中，有 3 项研究了种族和性别污名化，1 项研究了性别和 HIV 污名化。涉及分析方法的研究(n=12)主要通过多元回归模型中的交互项来研究交叉污名化。有 3 项研究采用结构方程模型来检验交叉污名化的交互效应或潜在结构。关于 HIV 相关交叉污名化和歧视衡量的研究目前集中在高收入环境中，并且通常侧重于两个身份的交叉（例如，种族和性别）。需要努力扩大交叉性在发展、调整和使用 HIV 相关交叉污名化和歧视衡量标准中的适当应用。应考虑使用适应情境、身份或状况的衡量标准。研究人员还应仔细考虑如何让社区有意义地参与到衡量制定过程中。所提出的措施和分析方法可以极大地加强公共卫生工作，评估与 HIV 相关的交叉污名化和歧视对关键健康结果的影响。（。2022；112(S4):S420-S432. https://doi.org/10.2105/AJPH.2021.306639)。
