Measurement Error Correction for Predicted Spatiotemporal Air Pollution Exposures

Abstract

Background 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, i.e., 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 U.S. 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 non-parametric bootstrap. Results Third-trimester PM2.5 exposure was associated with lower birth weight in the uncorrected (−2.4g per 1 μg/m3 difference in exposure; 95% Confidence Interval [CI]: −3.9, −0.8) and bootstrap-corrected (−2.5g, 95% CI: −4.2, −0.8) analyses. Results for the unrestricted analysis were attenuated (−0.66g, 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.

Long-Term Coarse Particulate Matter Exposure Is Associated with Asthma among Children in Medicaid

“…However, for linear health models with a single pollutant, analytic measurement error corrections have identified relatively small amounts of bias (16,75). Differences in the spatial distribution of monitors and cohort subjects has been shown to introduce bias in some settings (73,76), but its overall impact is unclear (77,78). Finally, our analysis may not be generalizable to non-low-income children, but our focus on this population may be a strength, as there is evidence that low-income children are particularly vulnerable to the effects of pollution (2).…”

Error in estimating area‐level air pollution exposures for epidemiology

“…However, that approach, like most other measurement error methodology (Alexeeff, Carroll, & Coull, ; Bergen et al, ; Gryparis, Paciorek, Zeka, Schwartz, & Coull, ; Szpiro & Paciorek, ), was developed under a linear health model, and there currently is no analogue for a generalized linear model setting. However, a nonparametric bootstrap procedure can be used to correct the standard errors of (Bergen & Szpiro, ; Keller et al, ; Szpiro & Paciorek, ).…”

“…Szpiro and Paciorek () suggested that one method for achieving this compatibility is through restricting the cohort to subjects near (in some sense) a monitor. Keller, Chang, Strickland, and Szpiro () adopted this approach in a spatiotemporal setting by restricting to subjects residing within a county with a monitor in an analysis of infant birth weight and particulate matter (PM) exposure using census‐tract level outcomes.…”

Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies

“…Some cohort studies are also affected by spatial incompatibility, which refers to systematic differences in the distributions of locations of monitor sites in a monitoring network relative to locations of members of the population cohort. Spatial incompatibility can result in biased health effect estimates [ 7 , 8 ]. While there is some evidence that land use regression (LUR) models can be transferable to regions not included in the monitoring area [ 9 ], there is little guarantee that this will be the case in other, or even most, settings.…”