Bayesian modeling of air pollution health effects with missing exposure data.
The authors propose a new statistical procedure that utilizes measurement error models to estimate missing exposure data in health effects assessment. The method detailed in this paper follows a Bayesian framework that allows estimation of various parameters of the model in the presence of missing covariates in an informative way. The authors apply this methodology to study the effect of household-level long-term air pollution exposures on lung function for subjects from the Southern California Children's Health Study pilot project, conducted in the year 2000. Specifically, they propose techniques to examine the long-term effects of nitrogen dioxide (NO2) exposure on children's lung function for persons living in 11 southern California communities. The effect of nitrogen dioxide exposure on various measures of lung function was examined, but, similar to many air pollution studies, no completely accurate measure of household-level long-term nitrogen dioxide exposure was available. Rather, community-level nitrogen dioxide was measured continuously over many years, but household-level nitrogen dioxide exposure was measured only during two 2-week periods, one period in the summer and one period in the winter. From these incomplete measures, long-term nitrogen dioxide exposure and its effect on health must be inferred. Results show that the method improves estimates when compared with standard frequentist approaches.
Molitor
John
J
Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90089-9011, USA. jmolitor@usc.edu
Molitor
Nuoo-Ting
NT
Jerrett
Michael
M
McConnell
Rob
R
Gauderman
Jim
J
Berhane
Kiros
K
Thomas
Duncan
D
D000393:Air Pollutants / Q000009:adverse effects* / Q000032:analysis; D000397:Air Pollution / Q000009:adverse effects* / Q000032:analysis; D001499:Bayes Theorem; D015982:Bias; D002140:California / Q000453:epidemiology; D002648:Child; D003625:Data Collection; D004784:Environmental Monitoring / Q000379:methods; D062665:Epidemiological Monitoring; D005541:Forced Expiratory Volume; D006801:Humans; D015233:Models, Statistical; D009585:Nitrogen Dioxide / Q000633:toxicity*; D012043:Regression, Psychology; D012129:Respiratory Function Tests; D012140:Respiratory Tract Diseases / Q000453:epidemiology* / Q000209:etiology; D018570:Risk Assessment; D012307:Risk Factors; D014797:Vital Capacity
D000393:Air Pollutants; D009585:Nitrogen Dioxide