Current Methods and Challenges for Epidemiological Studies of the Associations Between Chemical Constituents of Particulate Matter and Health.

• Type of Material: Article
• Authors: Jenna Krall, Emory University; Howard Chang, Emory University; Stefanie Sarnat, Emory University; Roger D. Peng, Johns Hopkins University; Lance Waller, Emory University
• Language: English
• Date: 2015-12
• Publisher: Springer Verlag (Germany)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © Springer International Publishing AG 2015
• Final Published Version (URL): http://dx.doi.org/10.1007/s40572-015-0071-y
• Title of Journal or Parent Work: Current Environmental Health Reports
• ISSN: 2196-5412
• Volume: 2
• Issue: 4
• Start Page: 388
• End Page: 398
• Grant/Funding Information: This publication was made possible by US EPA grant R834799 (HHC, LAW, and SES), NIEHS grants T32 ES012160 (LAW and JRK), 5R21ES022795-02 (HHC and SES), R01ES019560 (RDP), and Electric Power Research Institute grants EP-P45572/C19698 (SES) and 10002467 (HHC and SES).
• Keywords: Particle Size; Humans; Air Pollutants; Health effects; Environmental epidemiology; Air Pollution; Chemical speciation; Particulate matter constituents; Particulate Matter; Environmental Exposure; Environmental Monitoring; Statistical methods; Epidemiologic Methods
• Research Categories: Health Sciences, Epidemiology; Environmental Sciences

Abstract

Epidemiological studies have been critical for estimating associations between exposure to ambient particulate matter (PM) air pollution and adverse health outcomes. Because total PM mass is a temporally and spatially varying mixture of constituents with different physical and chemical properties, recent epidemiological studies have focused on PM constituents. Most studies have estimated associations between PM constituents and health using the same statistical methods as in studies of PM mass. However, these approaches may not be sufficient to address challenges specific to studies of PM constituents, namely assigning exposure, disentangling health effects, and handling measurement error. We reviewed large, population-based epidemiological studies of PM constituents and health and describe the statistical methods typically applied to address these challenges. Development of statistical methods that simultaneously address multiple challenges, for example, both disentangling health effects and handling measurement error, could improve estimation of associations between PM constituents and adverse health outcomes.