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Confounding by Socioeconomic Status in Epidemiological Studies of Air Pollution and Health: Challenges and Opportunities.
Hajat, Anjum; MacLehose, Richard F; Rosofsky, Anna; Walker, Katherine D; Clougherty, Jane E.
Affiliation
  • Hajat A; Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington, USA.
  • MacLehose RF; Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
  • Rosofsky A; Health Effects Institute, Boston, Massachusetts, USA.
  • Walker KD; Health Effects Institute, Boston, Massachusetts, USA.
  • Clougherty JE; Department of Environmental and Occupational Health, Dornsife School of Public Health, Drexel University, Philadelphia, Pennsylvania, USA.
Environ Health Perspect ; 129(6): 65001, 2021 06.
Article in En | MEDLINE | ID: mdl-34124937
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Air Pollutants / Air Pollution Type of study: Observational_studies / Prognostic_studies Aspects: Determinantes_sociais_saude Limits: Humans Language: En Journal: Environ Health Perspect Year: 2021 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Air Pollutants / Air Pollution Type of study: Observational_studies / Prognostic_studies Aspects: Determinantes_sociais_saude Limits: Humans Language: En Journal: Environ Health Perspect Year: 2021 Document type: Article Affiliation country: United States