Refining and embedding the Intersectional "MAIHDA" approach to intersectionality in quantitative social science research.

在定量社会科学研究中完善和嵌入交叉“MAIHDA”方法。

• 批准号: ES/X011313/1
• 负责人: Andrew Bell
• 金额: $ 90.67万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FX011313%2F1
• 关键词: Refining; embedding; Intersectional; MAIHDA; approach

There are big inequalities in our society, in a range of different things. Regarding health, we know that, in the UK, white people are generally more healthy than non-white people, and rich people are more healthy than poorer people. Beyond health, there are similar inequalities everywhere - in rates of unemployment, in income, in education levels, and so on.What is less well known is how these inequalities vary and interact. For instance, we know there is an ethnicity gap in health, but is that the same for men and women? Is it the same for rich women and poorer women? As we combine characteristics to produce smaller and smaller groups, it becomes clear that the inequalities in our society are complicated. And that complexity matters: it's important that we know who has worse health, so that we can target policies to the right people; it's important that we know who is being failed by our education system, so we can think about how the system could be changed to be more equitable. Fundamentally, in order to improve social justice, we need to first understand who is currently being let down by society. And that needs to be more than just a comparison of broad categories of people.The MAIHDA method is a statistical model that allows us to do this: it lets us to see which combinations of characteristics are associated with advantage, and which are associated with disadvantage. This innovative method has been incrementally developed between 2016 and 2022, and has already informed us about the nature of inequalities in a range of health outcomes.However, the method remains in its infancy, and this grant will refine the method to allow it to be used in situations that it has never been used before. This includes evaluating government policies to see which groups benefit from a particular policy, and which groups benefit less or even are harmed. The project will also allow us to consider how complex inequalities have changed over time, and how they vary from place to place. We will test our methodological refinements using real datasets which will act as exemplars in how these methods could be used going forward, relating to (among other things) obesity, covid-19, and environmental pollutants.We are also keen to expand the use of the method beyond health inequalities. As such, we will collaborate with academics and researchers in academic research centres and non-academic organisations, to implement the MAIHDA methods in other social science subject areas. This will include the analysis of carers, and how being a carer affects different groups of people in different ways. It will include an analysis of water quality in the United States, to see how different groups of people, in different places, are affected by poor-quality, polluted drinking water. And we will consider how educational inequalities vary for different groups of people, to see which groups are being let down by the education sector.Finally, a key part of this grant is training. We want to upskill researchers, in academia and beyond, to be able to use this method and our refinements to it in the future. As such we will produce a wide variety of online training materials, and run both online and in-person training courses, aimed at established academics, PhD students, and non-academic research organisations. These materials will be developed with other stakeholders to ensure that they meet the needs of those organisations and individuals.

我们的社会在许多不同的方面都存在巨大的不平等。关于健康，我们知道，在英国，白人普遍比非白人更健康，富人比穷人更健康。除了健康之外，各地都存在类似的不平等——失业率、收入、教育水平等等。但不太为人所知的是这些不平等如何变化和相互作用。例如，我们知道健康方面存在种族差异，但男性和女性也存在这种差异吗？富有的女人和贫穷的女人是一样的吗？当我们将不同的特征结合起来，产生越来越小的群体时，我们社会中的不平等现象显然变得很复杂。这种复杂性很重要：重要的是我们知道谁的健康状况更差，以便我们能够针对正确的人群制定政策；重要的是，我们知道我们的教育体系让谁失败了，这样我们就可以考虑如何改变该体系以使其更加公平。从根本上说，为了改善社会正义，我们首先需要了解谁正在被社会辜负。这需要的不仅仅是对大类人进行比较。MAIHDA 方法是一种统计模型，可以让我们做到这一点：它让我们了解哪些特征组合与优势相关，哪些特征组合与劣势相关。这种创新方法在 2016 年至 2022 年间逐步开发，已经让我们了解了一系列健康结果不平等的本质。然而，该方法仍处于起步阶段，这笔赠款将完善该方法，使其能够用于以前从未使用过的情况。这包括评估政府政策，看看哪些群体从特定政策中受益，哪些群体受益较少甚至受到伤害。该项目还将使我们能够考虑复杂的不平等是如何随着时间的推移而变化的，以及它们如何因地而异。我们将使用真实数据集测试我们的方法改进，这些数据集将作为未来如何使用这些方法的范例，涉及（除其他外）肥胖、covid-19 和环境污染物。我们还热衷于扩大超越健康不平等的方法。因此，我们将与学术研究中心和非学术组织的学者和研究人员合作，在其他社会科学学科领域实施 MAIHDA 方法。这将包括对护理人员的分析，以及护理人员如何以不同的方式影响不同的人群。它将包括对美国水质的分析，以了解不同地方的不同人群如何受到劣质、污染的饮用水的影响。我们将考虑不同群体的教育不平等有何不同，看看哪些群体对教育部门感到失望。最后，这笔赠款的一个关键部分是培训。我们希望提高学术界及其他领域的研究人员的技能，以便将来能够使用这种方法以及我们对其的改进。因此，我们将制作各种在线培训材料，并针对知名学者、博士生和非学术研究组织开设在线和现场培训课程。这些材料将与其他利益相关者一起开发，以确保它们满足这些组织和个人的需求。

项目成果

Extending intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) for longitudinal data, with application to mental health trajectories in the UK

扩展纵向数据的个体异质性和歧视准确性的交叉多层次分析（MAIHDA），并应用于英国的心理健康轨迹

• DOI: http://dx.10.31235/osf.io/jq57s
• 发表时间: 2023
• 作者: Bell A

Geographical and sociodemographic differences in statin dispensation after acute myocardial infarction in Sweden: a register-based prospective cohort study applying analysis of individual heterogeneity and discriminatory accuracy (AIHDA) for basic comparisons of healthcare quality

瑞典急性心肌梗死后他汀类药物分配的地理和社会人口学差异：一项基于登记的前瞻性队列研究，应用个体异质性和歧视准确性（AIHDA）分析来进行医疗质量的基本比较

• DOI: http://dx.10.1136/bmjopen-2022-063117
• 发表时间: 2023
• 期刊: BMJ Open
• 影响因子: 2.9
• 作者: Merlo J

A tutorial for conducting intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

对个体异质性和歧视准确性进行交叉多级分析的教程 (MAIHDA)

• DOI: http://dx.10.1016/j.ssmph.2024.101664
• 发表时间: 2024
• 期刊: SSM - Population Health
• 作者: Evans C