Soobader M, Cubbin C, Gee G C, Rosenbaum A, Laurenson J
STATWORKS, 800 Matthew Court, Suite 102, Braintree, MA 02184, USA.
Environ Res. 2006 Oct;102(2):172-80. doi: 10.1016/j.envres.2006.05.001. Epub 2006 Jun 15.
Reducing racial/ethnic and socioeconomic environmental health disparities requires a comprehensive multilevel conceptual and quantitative approach that recognizes the various levels through which environmental health disparities are produced and perpetuated. We propose a conceptual framework that incorporates the micro level, contained within the local level, which in turn is contained within the macro level. We discuss the utility of multilevel techniques to examine environmental level (both physical and social) and individual-level factors to appropriately quantify and improve our understanding of environmental health disparities. We discuss the reasoning and the methodological approach behind multilevel modeling, including differentiating between individual and contextual influences on individual outcomes. Next we address the questions and principles that guide the choice of levels or geographic units in multilevel studies. Finally, we address the ways in which different data sources can be combined to produce suitable data for multilevel analyses. We provide some examples of how such data sources can be linked to create multilevel data structures, and offer suggestions to facilitate the integration of multilevel techniques in environmental health disparities research and monitoring.
减少种族/族裔和社会经济方面的环境卫生差异需要一种全面的多层次概念和定量方法,该方法要认识到环境卫生差异产生和长期存在的各个层面。我们提出一个概念框架,它纳入了微观层面,微观层面包含在地方层面之内,而地方层面又包含在宏观层面之内。我们讨论多层次技术在研究环境层面(包括物理和社会层面)和个体层面因素方面的效用,以便适当地量化并增进我们对环境卫生差异的理解。我们讨论多层次建模背后的推理和方法路径,包括区分个体和背景对个体结果的影响。接下来,我们阐述指导多层次研究中层面或地理单元选择的问题和原则。最后,我们论述如何将不同数据源结合起来以生成适合多层次分析的数据。我们提供一些示例,说明此类数据源如何能够链接起来以创建多层次数据结构,并提出建议以促进多层次技术在环境卫生差异研究和监测中的整合。