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

Abstract

BackgroundGround-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.MethodsUsing 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.ResultsThe 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.ConclusionsResults 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-017-0244-2) contains supplementary material, which is available to authorized users.

Association between air pollution and sleep disordered breathing in children

“…In children, research has focused so far in the direct relationship between different air pollutants and asthma symptoms, acute wheezing episodes, and hospitalizations . There is also strong evidence that both indoor pollution as well as outdoor pollution increase the risk of several respiratory diseases in children. As far as SDB is concerned, associations between SDB, second‐hand smoking, and indoor pollution have been described.…”

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

“…Additional file 2: Offers example R code that requires the use of the simulated data in Additional file 3 (log odds ratios, standard errors, and area-level indicators of poverty) to demonstrate how to fit combined Bayesian hierarchical meta-regressions similar to the ones specified in Stage 2 models in O'Lenick et al, 2017 [1].The R code also demonstrates how to make graphs similar to the ones presented in Figure 3 …”

“…This article [1] has been updated to include an R code and example dataset as Additional files 2 and 3. The R code and example dataset are also shown below.…”

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ErratumThis article [1] has been updated to include an R code and example dataset as Additional files 2 and 3. The R code and example dataset are also shown below.

In addition, the following text has been removed from page 4 of the article:

"To demonstrate the methods we used to fit the combined meta-regression, the online supplement includes example R code with an example dataset (example data are not real but are similar in magnitude and structure to the output from the case-crossover analyses in Stage 1".)

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Internações de crianças por doenças respiratórias em São Paulo e suas relações com as condições climáticas e o contexto socioeconômico

“…De acordo com a literatura, a poluição do ar e as mudanças de temperatura (e seus extremos) são uma das principais razões para o aumento dos riscos de hospitalizações e das mortes causadas por doenças respiratórias no mundo (BRAGA et al, 2001;MUGGEO;HAJAT, 2009;XU et al, 2013;O' LENICK et al, 2017a;SHERBAKOV et al, 2018).…”