Measurement Error Correction for Predicted Spatiotemporal Air Pollution Exposures
Air pollution cohort studies are frequently analyzed in two stages, first modeling exposure then using predicted exposures to estimate health effects in a second regression model. The difference between predicted and unobserved true exposures introduces a form of measurement error in the second stage health model. Recent methods for spatial data correct for measurement error with a bootstrap and by requiring the study design ensure spatial compatibility, that is, monitor and subject locations are drawn from the same spatial distribution. These methods have not previously been applied to spatiotemporal exposure data.Methods:
We analyzed the association between fine particulate matter (PM2.5) and birth weight in the US state of Georgia using records with estimated date of conception during 2002–2005 (n = 403,881). We predicted trimester-specific PM2.5 exposure using a complex spatiotemporal exposure model. To improve spatial compatibility, we restricted to mothers residing in counties with a PM2.5 monitor (n = 180,440). We accounted for additional measurement error via a nonparametric bootstrap.Results:
Third trimester PM2.5 exposure was associated with lower birth weight in the uncorrected (−2.4 g per 1 μg/m3 difference in exposure; 95% confidence interval [CI]: −3.9, −0.8) and bootstrap-corrected (−2.5 g, 95% CI: −4.2, −0.8) analyses. Results for the unrestricted analysis were attenuated (–0.66 g, 95% CI: −1.7, 0.35).Conclusions:
This study presents a novel application of measurement error correction for spatiotemporal air pollution exposures. Our results demonstrate the importance of spatial compatibility between monitor and subject locations and provide evidence of the association between air pollution exposure and birth weight.