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Published, Version of Record (VoR)access removed by US government, 1 Dec 2025Open Access (License Unspecified), Restricted
Journal article
Evaluating the Impact of the Clean Heat Program on Air Pollution Levels in New York City
Environmental health perspectives, v 129(12), pp 127701-127701
Dec 2021
PMID: 34878319
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Evaluating the Impact of the Clean Heat Program on Air Pollution Levels in New York City
- Creators
- Lyuou Zhang - Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, USA.Mike Z He - Icahn School of Medicine at Mount SinaiElizabeth A Gibson - Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, USA.Frederica Perera - Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, USA.Gina S Lovasi - Drexel UniversityJane E Clougherty - Drexel UniversityDaniel Carrión - Icahn School of Medicine at Mount SinaiKimberly Burke - Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, USA.Dustin Fry - Drexel UniversityMarianthi-Anna Kioumourtzoglou - Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, USA.
- Publication Details
- Environmental health perspectives, v 129(12), pp 127701-127701
- Grant note
- F31 ES029372 / NIEHS NIH HHS T32 HD049311 / NICHD NIH HHS R01 ES030616 / NIEHS NIH HHS P50 ES009600 / NIEHS NIH HHS P30 ES009089 / NIEHS NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative; Environmental and Occupational Health
- Web of Science ID
- WOS:000968891300014
- Scopus ID
- 2-s2.0-85122520677
- Other Identifier
- 991019173722804721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Environmental Sciences
- Public, Environmental & Occupational Health
- Toxicology