Department of Sociology, University of Oregon, Eugene, OR, United States.
Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
Soc Sci Med. 2018 Apr;203:64-73. doi: 10.1016/j.socscimed.2017.11.011. Epub 2017 Nov 30.
Examining interactions between numerous interlocking social identities and the systems of oppression and privilege that shape them is central to health inequalities research. Multilevel models are an alternative and novel approach to examining health inequalities at the intersection of multiple social identities. This approach draws attention to the heterogeneity within and between intersectional social strata by partitioning the total variance across two levels.
Utilizing a familiar empirical example from social epidemiology-body mass index among U.S. adults (N = 32,788)-we compare the application of multilevel models to the conventional fixed effects approach to studying high-dimension interactions. Researchers are often confronted with the need to explore numerous interactions of identities and social processes. We explore the interactions of five dimensions of social identity and position-gender, race/ethnicity, income, education, and age-for a total of 384 unique intersectional social strata.
We find that the multilevel approach provides advantages over conventional models, including scalability for higher dimensions, adjustment for sample size of social strata, model parsimony, and ease of interpretation.
Considerable variation is attributable to the within-strata level, indicating the low discriminatory accuracy of these intersectional identities and the high within-strata heterogeneity of risk that remains unexplained. Multilevel modeling is an innovative and valuable tool for evaluating the intersectionality of health inequalities.
研究健康不平等问题的核心是检验众多相互关联的社会身份以及塑造这些身份的压迫和特权体系之间的相互作用。多层次模型是一种检验多种社会身份交叉点健康不平等的替代和新颖方法。这种方法通过将总方差划分为两个层次，关注交叉社会阶层内部和之间的异质性。
利用社会流行病学中一个熟悉的实证例子——美国成年人的体重指数（N=32788），我们比较了多层次模型在研究高维交互作用时的应用与传统固定效应方法的应用。研究人员经常需要探索身份和社会过程的众多交互作用。我们探讨了社会身份和地位的五个维度——性别、种族/民族、收入、教育和年龄——的交互作用，总共涉及 384 个独特的交叉社会阶层。
我们发现，多层次方法优于传统模型，包括更高维度的可扩展性、社会阶层样本量的调整、模型简约性和易于解释。
大量的变异归因于阶层内水平，表明这些交叉身份的区分准确性较低，阶层内风险的异质性较高，仍有待解释。多层次建模是评估健康不平等交叉性的一种创新和有价值的工具。
