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

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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.

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

No MeSH data available.


Related in: MedlinePlus

Spatial representation of estimated mean ORs accounting for ZCTA-specific NDI values in each city. In Fig. 4, average ORs between ozone and respiratory disease accounting for ZCTA-specific NDI values were estimated for each ZCTA in Atlanta (a), Dallas (b), and St. Louis (c) using a combined model that included a cubic function of the NDI. Abbreviations: NDI, Neighborhood Deprivation Index; OR, odds ratio; SES, socioeconomic status; ZCTA, ZIP Code Tabulation Area
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Fig4: Spatial representation of estimated mean ORs accounting for ZCTA-specific NDI values in each city. In Fig. 4, average ORs between ozone and respiratory disease accounting for ZCTA-specific NDI values were estimated for each ZCTA in Atlanta (a), Dallas (b), and St. Louis (c) using a combined model that included a cubic function of the NDI. Abbreviations: NDI, Neighborhood Deprivation Index; OR, odds ratio; SES, socioeconomic status; ZCTA, ZIP Code Tabulation Area

Mentions: Spatial mapping, in the context of this study, was used to generate hypotheses about spatial influences and assess potential confounding of apparent effect modification by neighborhood SES. To visually and qualitatively explore spatial patterning, we transferred estimated mean ZCTA-specific ORs from combined models that included cubic functions of the NDI (Fig. 3c) onto spatial maps (Fig. 4). The spatial maps presented in Fig. 4 reveal possible spatial patterning of the ORs and this mapping exercise allowed us to qualitatively assess commonalities among cities and consider possible alternative modifiers of ozone-related respiratory morbidity. For example, ORs appear stronger in areas clustered near urban centers and along major roadways, suggesting common areas of concern in each city. Based on these observations, in secondary analyses we tested whether ZCTAs that included an interstate highway had significantly stronger associations between ozone and respiratory disease; however, we did not find evidence of effect modification by ZCTAs that included an interstate highway (results not shown). Given how we estimated the spatial distribution of ambient ozone (using a regional transport model to interpolate between observations at regulatory ambient monitor sites) we were limited in our ability to detect effect modification associated with nearness to major roadways.Fig. 4


Ozone and childhood respiratory disease in three US cities: evaluation of effect measure modification by neighborhood socioeconomic status using a Bayesian hierarchical approach
Spatial representation of estimated mean ORs accounting for ZCTA-specific NDI values in each city. In Fig. 4, average ORs between ozone and respiratory disease accounting for ZCTA-specific NDI values were estimated for each ZCTA in Atlanta (a), Dallas (b), and St. Louis (c) using a combined model that included a cubic function of the NDI. Abbreviations: NDI, Neighborhood Deprivation Index; OR, odds ratio; SES, socioeconomic status; ZCTA, ZIP Code Tabulation Area
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5382444&req=5

Fig4: Spatial representation of estimated mean ORs accounting for ZCTA-specific NDI values in each city. In Fig. 4, average ORs between ozone and respiratory disease accounting for ZCTA-specific NDI values were estimated for each ZCTA in Atlanta (a), Dallas (b), and St. Louis (c) using a combined model that included a cubic function of the NDI. Abbreviations: NDI, Neighborhood Deprivation Index; OR, odds ratio; SES, socioeconomic status; ZCTA, ZIP Code Tabulation Area
Mentions: Spatial mapping, in the context of this study, was used to generate hypotheses about spatial influences and assess potential confounding of apparent effect modification by neighborhood SES. To visually and qualitatively explore spatial patterning, we transferred estimated mean ZCTA-specific ORs from combined models that included cubic functions of the NDI (Fig. 3c) onto spatial maps (Fig. 4). The spatial maps presented in Fig. 4 reveal possible spatial patterning of the ORs and this mapping exercise allowed us to qualitatively assess commonalities among cities and consider possible alternative modifiers of ozone-related respiratory morbidity. For example, ORs appear stronger in areas clustered near urban centers and along major roadways, suggesting common areas of concern in each city. Based on these observations, in secondary analyses we tested whether ZCTAs that included an interstate highway had significantly stronger associations between ozone and respiratory disease; however, we did not find evidence of effect modification by ZCTAs that included an interstate highway (results not shown). Given how we estimated the spatial distribution of ambient ozone (using a regional transport model to interpolate between observations at regulatory ambient monitor sites) we were limited in our ability to detect effect modification associated with nearness to major roadways.Fig. 4

View Article: PubMed Central - PubMed

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.

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

No MeSH data available.


Related in: MedlinePlus