The potential association between ambient PM2.5 exposure and the risk of infant bronchiolitis and otitis media is a significant public health concern. This concern is supported by growing toxicologic and epidemiologic evidence that ambient PM2.5 exposure increases potentiation of the disease process. Infant bronchiolitis and otitis media may have serious long-term consequences for the affected infants and the healthcare related costs are substantial. However, available studies are sparse and have important limitations. This study will address many of these weaknesses and make significant contributions to understanding the relationship between ambient PM2.5 exposure and infant bronchiolitis and otitis media.
The first aim i s to improve PM2.5 exposure assessment compared to earlier studies by supplementing data from ground monitors with remote sensing data from satellites. Instead of linking infant ZIP code centroids to data from the closest monitor, advanced statistical modeling will be used to predict the PM2.5 concentration at the infants'geocoded addresses. This will reduce exposure misclassification compared to existing studies.
The second aim i s to integrate the PM2.5 exposure with data from a large longitudinal Massachusetts (MA) birth cohort linked to hospital data and the MA Birth Defects Registry. With more than 40,000 cases of inpatient and outpatient infant bronchiolitis and 125,000 cases of inpatient and outpatient otitis media, analyses will have sufficient power to detect small effects as well as examine vulnerable subpopulations including infants born premature or with pre- existing respiratory or cardiac conditions and/or congenital anomalies. The linked PELL dataset allows for case-crossover analyses for short-term exposures and analyses of long-term exposures using sibling controls to minimize confounding bias. The combination of traditional epidemiologic study designs with innovative methods will reduce confounding bias often associated with registry-based analyses. To examine the possibility of residual confounding, the third aim will use generalized additive models to examine the space- time pattern of residual risk after including ambient PM2.5 exposure and adjusting for known risk factors. The results of the proposed study will provide new information on infant bronchiolitis and otitis media, two outcomes for which there is a strong indication of an environmental component and for which there is increasing prevalence in the vulnerable infant population. For an exposure as widespread as ambient PM2.5, even small associations can have a significant public health impact.

Public Health Relevance

The proposed work will study the relationship between exposure to ambient PM2.5 (particulate matter with a diameter of 2.5 5m or less) and infant bronchiolitis, the leading cause of infant morbidity, and otitis media (ear infection), one of the most common childhood infections, using data from a population based longitudinal birth cohort in Massachusetts (MA). While associations have been observed between these outcomes and environmental tobacco smoke and wood burning, few studies have focused on PM2.5. Not only is PM2.5 a serious public health concern, there is increasing prevalence of infant bronchiolitis and otitis media in the vulnerable infant population.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Research Project (R01)
Project #
7R01ES019897-02
Application #
8306911
Study Section
Infectious Diseases, Reproductive Health, Asthma and Pulmonary Conditions Study Section (IRAP)
Program Officer
Gray, Kimberly A
Project Start
2011-07-25
Project End
2016-04-30
Budget Start
2012-06-12
Budget End
2013-04-30
Support Year
2
Fiscal Year
2012
Total Cost
$346,144
Indirect Cost
$87,931
Name
University of California Irvine
Department
None
Type
Schools of Public Health
DUNS #
046705849
City
Irvine
State
CA
Country
United States
Zip Code
92697
Chang, Howard H; Hu, Xuefei; Liu, Yang (2014) Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling. J Expo Sci Environ Epidemiol 24:398-404