McGuire F Hunter, Beccia Ariel L, Peoples JaNiene E, Williams Matthew R, Schuler Megan S, Duncan Alexis E
The Brown School, Washington University in St. Louis, St. Louis, MO, United States.
Division of Adolescent/Young Adult Medicine, Boston Children's Hospital, Boston, MA, United States.
Am J Epidemiol. 2024 Dec 2;193(12):1662-1674. doi: 10.1093/aje/kwae121.
This study examined how race/ethnicity, sex/gender, and sexual orientation intersect under interlocking systems of oppression to socially pattern depression among US adults. With cross-sectional data from the 2015-2020 National Survey on Drug Use and Health (n = 234 722), we conducted a design-weighted, multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) under an intersectional framework to predict past-year and lifetime major depressive episodes (MDEs). With 42 intersectional groups constructed from 7 race/ethnicity, 2 sex/gender, and 3 sexual orientation categories, we estimated age-standardized prevalence and excess or reduced prevalence attributable to 2-way or higher interaction effects. Models revealed heterogeneity across groups, with prevalence ranging from 1.9% to 19.7% (past-year) and 4.5% to 36.5% (lifetime). Approximately 12.7% (past year) and 12.5% (lifetime) of total individual variance was attributable to between-group differences, indicating key relevance of intersectional groups in describing the population distribution of depression. Main effects indicated, on average, that people who were White, women, gay/lesbian, or bisexual had greater odds of MDE. Main effects explained most between-group variance. Interaction effects (past year: 10.1%; lifetime: 16.5%) indicated another source of heterogeneity around main effects average values, with some groups experiencing excess or reduced prevalence compared with main effects expectations. We extend the MAIHDA framework to calculate nationally representative estimates from complex sample survey data using design-weighted, Bayesian methods. This article is part of a Special Collection on Mental Health.
本研究探讨了在美国成年人中，种族/族裔、性别以及性取向如何在相互关联的压迫系统下相互交织，从而在社会层面上形成抑郁症的分布模式。利用2015 - 2020年全国药物使用和健康调查的横断面数据（n = 234722），我们在交叉性框架下进行了设计加权的多层次个体异质性和歧视准确性分析（MAIHDA），以预测过去一年和一生中的重度抑郁发作（MDE）。我们从7个种族/族裔、2种性别和3种性取向类别构建了42个交叉性群体，估计了年龄标准化患病率以及归因于双向或更高阶交互效应的患病率过高或降低的情况。模型显示各群体之间存在异质性，过去一年的患病率范围为1.9%至19.7%，一生的患病率范围为4.5%至36.5%。总个体方差的约12.7%（过去一年）和12.5%（一生）可归因于组间差异，这表明交叉性群体在描述抑郁症的人群分布方面具有关键相关性。主效应平均表明，白人、女性、男同性恋/女同性恋或双性恋者患MDE的几率更高。主效应解释了大部分组间方差。交互效应（过去一年：10.1%；一生：16.5%）表明围绕主效应平均值存在另一个异质性来源，一些群体的患病率与主效应预期相比过高或降低。我们扩展了MAIHDA框架，使用设计加权的贝叶斯方法从复杂样本调查数据中计算具有全国代表性的估计值。本文是心理健康专题文集的一部分。
