A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models.
BACKGROUND
Exposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health.


METHODS
ZIP-code level estimates of exposure for six pollutants (CO, NOx, EC, PM2.5, SO4, O3) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error. Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs.


RESULTS
Substantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3-85% for population error, and 31-85% for total error. When CO, NOx or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copollutants based on the estimated type I error rate.


CONCLUSIONS
The impact of exposure error must be considered when interpreting results of copollutant epidemiologic models, due to the possibility of attenuation of main pollutant RRs and the increased probability of false positives when measurement error is present.
['Dionisio|Kathie L|KL|http://orcid.org/0000-0002-3120-0633', 'Chang|Howard H|HH|', 'Baxter|Lisa K|LK|']
D000393:Air Pollutants D000397:Air Pollution D015982:Bias D002248:Carbon Monoxide D002947:Cities D003198:Computer Simulation D004636:Emergency Service, Hospital D004781:Environmental Exposure D005845:Georgia D006801:Humans D008962:Models, Theoretical D009589:Nitrogen Oxides D010126:Ozone D052638:Particulate Matter D012140:Respiratory Tract Diseases D012306:Risk D013431:Sulfates
Exposure measurement error Copollutant Bias Exposure assessment Exposure modeling