Assaf P. Oron

Blood Pressure Response to Controlled Diesel Exhaust Exposure in Human Subjects

Exposure to traffic-related air pollution is associated with risk of cardiovascular disease and mortality. We examined whether exposure to diesel exhaust increased blood pressure (BP) in human subjects. We analyzed data from 45 nonsmoking subjects, 18 to 49 years of age in double-blinded, crossover exposure studies, randomized to order. Each subject was exposed to diesel exhaust, maintained at 200 &mgr;g/m3 of fine particulate matter, and filtered air for 120 minutes on days separated by ≥2 weeks. We measured BP pre-exposure, at 30-minute intervals during exposure, and 3, 5, 7, and 24 hours from exposure initiation and analyzed changes from pre-exposure values. Compared with filtered air, systolic BP increased at all of the points measured during and after diesel exhaust exposure; the mean effect peaked between 30 and 60 minutes after exposure initiation (3.8 mm Hg [95% CI: −0.4 to 8.0 mm Hg] and 5.1 mm Hg [95% CI: 0.7–9.5 mm Hg], respectively). Sex and metabolic syndrome did not modify this effect. Combining readings between 30 and 90 minutes, diesel exhaust exposure resulted in a 4.4-mm Hg increase in systolic BP, adjusted for participant characteristics and exposure perception (95% CI: 1.1–7.7 mm Hg; P=0.0009). There was no significant effect on heart rate or diastolic pressure. Diesel exhaust inhalation was associated with a rapid, measurable increase in systolic but not diastolic BP in young nonsmokers, independent of perception of exposure. This controlled trial in humans confirms findings from observational studies. The effect may be important on a population basis given the worldwide prevalence of exposure to traffic-related air pollution.

A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the multi-ethnic study of atherosclerosis and air pollution.

Background: Cohort studies of the relationship between air pollution exposure and chronic health effects require predictions of exposure over long periods of time. Objectives: We developed a unified modeling approach for predicting fine particulate matter, nitrogen dioxide, oxides of nitrogen, and black carbon (as measured by light absorption coefficient) in six U.S. metropolitan regions from 1999 through early 2012 as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Methods: We obtained monitoring data from regulatory networks and supplemented those data with study-specific measurements collected from MESA Air community locations and participants’ homes. In each region, we applied a spatiotemporal model that included a long-term spatial mean, time trends with spatially varying coefficients, and a spatiotemporal residual. The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression. We estimated time trends from observed time series and used spatial smoothing methods to borrow strength between observations. Results: Prediction accuracy was high for most models, with cross-validation R2 (R2CV) > 0.80 at regulatory and fixed sites for most regions and pollutants. At home sites, overall R2CV ranged from 0.45 to 0.92, and temporally adjusted R2CV ranged from 0.23 to 0.92. Conclusions: This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants. We have generated participant-specific predictions for MESA Air to investigate health effects of long-term air pollution exposures. These successes highlight modeling advances that can be adopted more widely in modern cohort studies. Citation: Keller JP, Olives C, Kim SY, Sheppard L, Sampson PD, Szpiro AA, Oron AP, Lindström J, Vedal S, Kaufman JD. 2015. A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution. Environ Health Perspect 123:301–309; http://dx.doi.org/10.1289/ehp.1408145

A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates.

The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of

Pediatric Infection and Intestinal Carriage Due to Extended-Spectrum-Cephalosporin-Resistant Enterobacteriaceae

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Extended- Versus Narrower-Spectrum Antibiotics for Appendicitis

NOx in the Los Angeles area during a 10 year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated

Intestinal Neuronal Dysplasia-Like Submucosal Ganglion Cell Hyperplasia at the Proximal Margins of Hirschsprung Disease Resections.

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