Background A broad literature base provides evidence of association between air pollution and paediatric asthma. Socioeconomic status (SES) may modify these associations; however, previous studies have found inconsistent evidence regarding the role of SES.
Methods Effect modification of air pollution–paediatric asthma morbidity by multiple indicators of neighbourhood SES was examined in Atlanta, Georgia. Emergency department (ED) visit data were obtained for 5–18 years old with a diagnosis of asthma in 20-county Atlanta during 2002–2008. Daily ZIP Code Tabulation Area (ZCTA)-level concentrations of ozone, nitrogen dioxide, fine particulate matter and elemental carbon were estimated using ambient monitoring data and emissions-based chemical transport model simulations. Pollutant–asthma associations were estimated using a case-crossover approach, controlling for temporal trends and meteorology. Effect modification by ZCTA-level (neighbourhood) SES was examined via stratification.
Results We observed stronger air pollution–paediatric asthma associations in ‘deprivation areas’ (eg, ≥20% of the ZCTA population living in poverty) compared with ‘non-deprivation areas’. When stratifying analyses by quartiles of neighbourhood SES, ORs indicated stronger associations in the highest and lowest SES quartiles and weaker associations among the middle quartiles.
Conclusions Our results suggest that neighbourhood-level SES is a factor contributing vulnerability to air pollution-related paediatric asthma morbidity in Atlanta. Children living in low SES environments appear to be especially vulnerable given positive ORs and high underlying asthma ED rates. Inconsistent findings of effect modification among previous studies may be partially explained by choice of SES stratification criteria, and the use of multiplicative models combined with differing baseline risk across SES populations.
- AIR POLLUTION
- CHILD HEALTH
- Environmental epidemiology
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Contributors CRO, AW, JAM, HHC, MRK and SES designed the study and directed its implementation. JAM and MDF provided air pollution exposure data, analytical design and modelling assistance. CRO, AW, HHC, MRK, LAD and SES analysed the data. CRO, AW, JAM, HHC, MRK, LAD and SES interpreted the results. CRO, AW, JAM, MDF, HHC, MRK, LAD and SES wrote the manuscript.
Funding 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 (Grant, RD834799). This publication was also made possible by grants to Emory University from the US Environmental Protection Agency (Grant R82921301), the National Institute of Environmental Health Sciences (Grant R01ES11294) and the Electric Power Research Institute (Grants EP-P27723/C13172 and EP-P4353/C2124).
Disclaimer The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the USEPA.
Competing interests None declared.
Ethics approval Ethics approval was obtained by the Emory University Institutional Review Board (IRB: IRB00046509).
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Data used in this study include data on emergency department visits, air pollution concentrations and socioeconomic data at the ZIP code level in Atlanta. Data use agreements with participating hospitals and the Georgia Hospital Association prevent sharing of the emergency department visit data outside the research team. Air pollution data were generated by the Georgia Institute of Technology research team using a fusion of publicly available air monitoring data and modelled air pollution estimates; the outputs are not currently publicly available. Finally, we used socioeconomic data from Census 2000 and the American Community Survey, which are already publicly available through various forums.
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