Statistical Meta-Analysis: Air Pollution & Children’s Health

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https://csa.ru.ac.bd/stat-ijss/journals/v-11s-iss-15-pdf/

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http://hdl.handle.net/11603/30596

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UMBC Mathematics and Statistics Department
UMBC Center for Interdisciplinary Research and Consulting (CIRC)
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Date

2011

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journal articles
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Citation of Original Publication

Stanwyck, Elizabeth, and Rong Wei. “Statistical Meta-Analysis: Air Pollution & Children’s Health.” International Journal of Statistical Science 11 (2011): 223–244. https://csa.ru.ac.bd/stat-ijss/journals/v-11s-iss-15-pdf/.

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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract

There have been numerous studies seeking to establish an association between air pollution and children’s adverse health outcomes, and the ultimate findings are often varied. A few studies found a statistically significant association between an increase in a specific pollutant and an adverse health effect among children, while others find a non-significant association between the same pair of variables. These conflicting results undermine confidence in the final conclusions, and this leads naturally to a novel application of the so-called statistical meta-analysis whose primary objective is to integrate or synthesize the findings from independent and comparable studies. In this paper we first review a recent statistical meta-analysis paper by Weinmayr et al. (2010) dealing with studies on the effects of NO₂ and PM₁₀ on some aspects of children’s health. In the second part of this paper, we conduct our own meta-analysis focusing on the association between children’s (binary) health outcomes (such as cough and respiratory symptoms) and four pollutants: PM₁₀, NO₂, SO₂, and O₃. While we find a statistically significant association with every pollutant, it turns out that for PM₁₀, NO₂, and SO₂, there is significant heterogeneity among the estimated effect sizes (odds ratios). Finally, we explore the techniques of meta-regression by incorporating distinct study features to meaningfully explain the heterogeneity.