Ozone and childhood respiratory disease in three US cities: evaluation of effect measure modification by neighborhood socioeconomic status using a Bayesian hierarchical approach.

• Type of Material: Article
• Authors: Cassandra R. O' Lenick, Emory University; Howard Chang, Emory University; Michael Kramer, Emory University; Andrea Winquist, Emory University; James A. Mulholland, Georgia Institute of Technology; Mariel D. Friberg, Georgia Institute of Technology; Stefanie Sarnat, Emory University
• Language: English
• Date: 2017-04-05
• Publisher: Springer Verlag (Germany)
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s). 2017
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s12940-017-0244-2
• Title of Journal or Parent Work: Environmental Health and Preventive Medicine
• ISSN: 1342-078X
• Volume: 16
• Issue: 1
• Start Page: 36
• End Page: 36
• Grant/Funding Information: This publication was also made possible by grants to Emory University from the USEPA (Grant R82921301), the National Institute of Environmental Health Sciences (Grant R01ES11294), and the Electric Power Research Institute (Grants EP-P27723/C13172, EP-P4353/C2124, EP-P34975/C15892, EP-P45572/C19698, and EP-P25912/C12525).; This work was supported by a Clean Air Research Center grant to Emory University and the Georgia Institute of Technology from the US Environmental Protection Agency (USEPA; Grant, RD834799).
• Keywords: Bayesian; Air pollution; Children’s environmental health; Asthma; Environmental epidemiology; Meta-analysis; Socioeconomic status
• Research Categories: Environmental Sciences; Health Sciences, Public Health

Abstract

BACKGROUND: Ground-level ozone is a potent airway irritant and a determinant of respiratory morbidity. Susceptibility to the health effects of ambient ozone may be influenced by both intrinsic and extrinsic factors, such as neighborhood socioeconomic status (SES). Questions remain regarding the manner and extent that factors such as SES influence ozone-related health effects, particularly across different study areas. METHODS: Using a 2-stage modeling approach we evaluated neighborhood SES as a modifier of ozone-related pediatric respiratory morbidity in Atlanta, Dallas, & St. Louis. We acquired multi-year data on emergency department (ED) visits among 5-18 year olds with a primary diagnosis of respiratory disease in each city. Daily concentrations of 8-h maximum ambient ozone were estimated for all ZIP Code Tabulation Areas (ZCTA) in each city by fusing observed concentration data from available network monitors with simulations from an emissions-based chemical transport model. In the first stage, we used conditional logistic regression to estimate ZCTA-specific odds ratios (OR) between ozone and respiratory ED visits, controlling for temporal trends and meteorology. In the second stage, we combined ZCTA-level estimates in a Bayesian hierarchical model to assess overall associations and effect modification by neighborhood SES considering categorical and continuous SES indicators (e.g., ZCTA-specific levels of poverty). We estimated ORs and 95% posterior intervals (PI) for a 25 ppb increase in ozone. RESULTS: The hierarchical model combined effect estimates from 179 ZCTAs in Atlanta, 205 ZCTAs in Dallas, and 151 ZCTAs in St. Louis. The strongest overall association of ozone and pediatric respiratory disease was in Atlanta (OR = 1.08, 95% PI: 1.06, 1.11), followed by Dallas (OR = 1.04, 95% PI: 1.01, 1.07) and St. Louis (OR = 1.03, 95% PI: 0.99, 1.07). Patterns of association across levels of neighborhood SES in each city suggested stronger ORs in low compared to high SES areas, with some evidence of non-linear effect modification. CONCLUSIONS: Results suggest that ozone is associated with pediatric respiratory morbidity in multiple US cities; neighborhood SES may modify this association in a non-linear manner. In each city, children living in low SES environments appear to be especially vulnerable given positive ORs and high underlying rates of respiratory morbidity.