Abstract

Background:

Despite a vast air pollution epidemiology literature to date and the recognition that lower-socioeconomic status (SES) populations are often disproportionately exposed to pollution, there is little research identifying optimal means of adjusting for confounding by SES in air pollution epidemiology, nor is there a strong understanding of biases that may result from improper adjustment.

Objective:

We aim to provide a conceptualization of SES and a review of approaches to its measurement in the U.S. context and discuss pathways by which SES may influence health and confound effects of air pollution. We explore bias related to measurement and operationalization and identify statistical approaches to reduce bias and confounding.

Discussion:

Drawing on the social epidemiology, health geography, and economic literatures, we describe how SES, a multifaceted construct operating through myriad pathways, may be conceptualized and operationalized in air pollution epidemiology studies. SES varies across individuals within the contexts of place, time, and culture. Although no single variable or index can fully capture SES, many studies rely on only a single measure. We recommend examining multiple facets of SES appropriate to the study design. Furthermore, investigators should carefully consider the multiple mechanisms by which SES might be operating to identify those SES indicators that may be most appropriate for a given context or study design and assess the impact of improper adjustment on air pollution effect estimates. Last, exploring model contraction and expansion methods may enrich adjustment, whereas statistical approaches, such as quantitative bias analysis, may be used to evaluate residual confounding. https://doi.org/10.1289/EHP7980

中文翻译：

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空气污染与健康流行病学研究中社会经济状况的混淆：挑战与机遇

摘要

背景：

尽管迄今为止有大量的空气污染流行病学文献，并且认识到社会经济地位较低 (SES) 的人群往往不成比例地暴露在污染中，但很少有研究确定调整空气污染流行病学中 SES 混杂的最佳方法，也没有对不当调整可能导致的偏差的深刻理解。

客观的：

我们旨在提供 SES 的概念和对其在美国背景下的测量方法的回顾，并讨论 SES 可能影响健康和空气污染混杂效应的途径。我们探索与测量和操作相关的偏差，并确定减少偏差和混淆的统计方法。

讨论：

借鉴社会流行病学、健康地理学和经济文献，我们描述了 SES，一个通过无数途径运作的多方面结构，如何在空气污染流行病学研究中概念化和操作化。SES 在地点、时间和文化背景下因人而异。尽管没有单一的变量或指数可以完全捕捉 SES，但许多研究仅依赖于单一的衡量标准。我们建议检查适合研究设计的 SES 的多个方面。此外，研究人员应仔细考虑 SES 可能运行的多种机制，以确定最适合给定背景或研究设计的 SES 指标，并评估调整不当对空气污染影响估计的影响。最后的，探索模型收缩和扩展方法可以丰富调整，而统计方法，如定量偏差分析，可用于评估残留混杂。https://doi.org/10.1289/EHP7980