Prediction of fine particulate matter chemical components for the Multi-Ethnic Study of Atherosclerosis cohort: A comparison of two modeling approaches

Recent epidemiological cohort studies of the health effects of PM2.5 have developed exposure estimates from advanced exposure prediction models. Such models represent spatial variability across participant residential locations. However, few cohort studies have developed exposure predictions for PM2.5 components. We used two exposure modeling approaches to obtain long-term average predicted concentrations for four PM2.5 components: sulfur, silicon, and elemental and organic carbon (EC and OC). The models were specifically developed for the Multi-Ethnic Study of Atherosclerosis (MESA) cohort as a part of the National Particle Component and Toxicity (NPACT) study. The spatio-temporal model used 2-week average measurements from a monitoring campaign focusing on MESA participants, whereas the national spatial model relied on long-term means of daily measurements from the existing federally directed monitoring network. The spatio-temporal modeling framework consisted of long-term means, temporal trends, and spatio-temporal residuals. Spatial fields for long-term means and temporal trends were characterized in universal kriging with a land use regression component based on selected geographic covariates. The national spatial model was also constructed in a universal kriging approach with the mean model characterized by partial least squares scores instead of selected covariates. The cross-validation statistics of the two exposure models were 0.59 to 0.94 for sulfur, EC, and OC but 0.38 to 0.45 for silicon across the six study areas. Predicted long-term concentrations of PM2.5 components from the two models were fairly or highly correlated across cities within each of all four components except for OC, largely dominated by the between-city contrast. However, predictions were less correlated within each city than across cities. The national spatial model gave lower magnitude and less variable predictions than the spatio-temporal model. Different sources of monitoring data and modeling approaches between the two models contributed to these results. Predictions of long-term average concentrations for PM2.5 components for study subjects will allow us to investigate health effects of PM2.5 components and identify PM2.5 components responsible for the PM2.5 association

Spatial measurement error methods in air pollution epidemiology

Thesis (Ph.D.)--University of Washington, 2014Air pollution epidemiology cohort studies often implement a two-stage approach to estimating associations of continuous health outcomes with one or more exposures. An inherent problem in these studies is that the exposures of interest are usually unobserved. Instead observations are available at misaligned monitoring locations. The first stage entails building exposure models with the monitoring data and predicting at subject locations; the second stage uses the predictions to estimate health effects. This induces measurement error that can induce bias and affect the standard error of resulting estimates. Berkson-like error arises from smoothing the exposure surface, while classical-like error comes from estimating the exposure model parameters. Accurately characterizing and correcting for both types of measurement error depends on assumptions made about the spatial surface and exposure model used to derive predictions. This dissertation addresses spatial measurement error in air pollution epidemiology. We first describe and apply parametric measurement error methodology when assuming the exposure surface is a stochastic Gaussian process. We extend these parametric approaches by deriving P-SIMEX, which yields more flexible bias correction. We then motivate a semi-parametric framework wherein the exposure surface is viewed as fixed and modeled with penalized regression splines. We discuss the resulting measurement error, describe how the exposure model penalty regulates measurement error, and derive an analytic bias correction. Finally we extend the semi-parametric methodology to the multi-pollutant setting. We show the direction of the biases are unpredictable, and the magnitude of the biases are much larger than those in single-pollutant studies. We derive a multi-pollutant bias correction that can be combined with a simple non-parametric bootstrap to achieve accurate 95% confidence interval coverage. Throughout we apply our methods to analyzing associations of continuous health outcomes with predicted exposures in the Multi-Ethnic Study of Atherosclerosis and the Sister Study of the National Institute of Environmental Health Sciences

A Regionalized National Universal Kriging Model Using Partial Least Squares Regression for Estimating Annual PM2.5 Concentrations in Epidemiology

Many cohort studies in environmental epidemiology require accurate modeling and prediction of fine scale spatial variation in ambient air quality across the U.S. This modeling requires the use of small spatial scale geographic or “land use” regression covariates and some degree of spatial smoothing. Furthermore, the details of the prediction of air quality by land use regression and the spatial variation in ambient air quality not explained by this regression should be allowed to vary across the continent due to the large scale heterogeneity in topography, climate, and sources of air pollution. This paper introduces a regionalized national universal kriging model for annual average fine particulate matter (PM2.5) monitoring data across the U.S. To take full advantage of an extensive database of land use covariates we chose to use the method of Partial Least Squares, rather than variable selection, for the regression component of the model (the “universal” in “universal kriging”) with regression coefficients and residual variogram models allowed to vary across three regions defined as West Coast, Mountain West, and East. We demonstrate a very high level of cross-validated accuracy of prediction with an overall R2 of 0.88 and well-calibrated predictive intervals. In accord with the spatially varying characteristics of PM2.5 on a national scale and differing kriging smoothness parameters, the accuracy of the prediction varies by region with predictive intervals being notably wider in the West Coast and Mountain West in contrast to the East

A National Model Built with Partial Least Squares and Universal Kriging and Bootstrap-based Measurement Error Correction Techniques: An Application to the Multi-Ethnic Study of Atherosclerosis

Studies estimating health effects of long-term air pollution exposure often use a two-stage approach, building exposure models to assign individual-level exposures which are then used in regression analyses. This requires accurate exposure modeling and careful treatment of exposure measurement error. To illustrate the importance of carefully accounting for exposure model characteristics in two-stage air pollution studies, we consider a case study based on data from the Multi-Ethnic Study of Atherosclerosis (MESA). We present national spatial exposure models that use partial least squares and universal kriging to estimate annual average concentrations of four PM2.5 components: elemental carbon (EC), organic carbon (OC), sulfur (S), and silicon (Si). Our models perform well, with cross-validated R2s ranging from 0.62 to 0.95. We predict PM2.5 component exposures for the MESA cohort and estimate cross-sectional associations with carotid intima-media thickness (CIMT), adjusting for subject-specific covariates. In naïve analyses that do not account for measurement error, we find statistically significant associations between CIMT and increased exposure to OC, S, and Si. We correct for measurement error using recently developed methods that account for the spatial structure of predicted exposures. OC exhibits little spatial correlation, and the corrected inference is unchanged from the naïve analysis. The S and Si exposure surfaces display notable spatial correlation, resulting in corrected confidence intervals (CIs) that are 50% wider than the naïve CIs, but that are still statistically significant. The impact on health effect inference is concordant with the degree of spatial correlation in the exposure surfaces

Physical and Biological Stream Health in an Agricultural Watershed after 30+ Years of Targeted Conservation Practices

Agricultural activities within watersheds can have negative effects on river ecosystems, but numerous conservation practices can be implemented that reduce soil erosion, increase water infiltration, slow runoff, and improve soil quality. Our study focused on analyzing overall stream health (instream and riparian physical, instream biological) at 56 stream sites within an agricultural watershed (83,000 hectares, 70% croplands, and rangelands) in southeastern Minnesota, USA, with a 30+-year history of targeted conservation practices to protect local water resources of importance for tourism and recreation. After implementation of >900 best management practices (BMPs) over the last 20 years in the study subwatersheds, only 20% of the stream sites examined exhibited good stream health, and 40% were in poor condition, based on a combination of instream and riparian factors and aquatic community integrity. Time since implementation, location, and total coverage of BMPs within the relatively large subwatersheds all may have contributed to the apparently limited effectiveness of these conservation management practices toward producing observable improvements in stream health to date. Many indicators of stream health (e.g., fine sediments, sediment embeddedness, fish biotic integrity) differed significantly among subwatersheds, but those differences could not be explained by differences in numbers or coverages of BMPs in those subwatersheds. Longitudinal stream health patterns were similar among subwatersheds (moderate health in headwaters, poor in mid-reaches, good in lower reaches), likely due, in part, to similarities in locations of spring discharges and channel instability. New rules protecting stream riparia, maintenance of existing BMPs, and future BMPs targeting remaining problem areas should lead to improving stream health in this large watershed