Large Scale Air Pollution Estimation Method Combining Land Use Regression and Chemical Transport Modeling in a Geostatistical Framework

In recognition that intraurban exposure gradients may be as large as between-city variations, recent air pollution epidemiologic studies have become increasingly interested in capturing within-city exposure gradients. In addition, because of the rapidly accumulating health data, recent studies also need to handle large study populations distributed over large geographic domains. Even though several modeling approaches have been introduced, a consistent modeling framework capturing within-city exposure variability and applicable to large geographic domains is still missing. To address these needs, we proposed a modeling framework based on the Bayesian Maximum Entropy method that integrates monitoring data and outputs from existing air quality models based on Land Use Regression (LUR) and Chemical Transport Models (CTM). The framework was applied to estimate the yearly average NO₂ concentrations over the region of Catalunya in Spain. By jointly accounting for the global scale variability in the concentration from the output of CTM and the intraurban scale variability through LUR model output, the proposed framework outperformed more conventional approaches

Additional file 3: Table S1. of Road traffic noise and children’s inattention

Main results of ipw analyses, showing effect estimates for road traffic noise with 95% CIs. Table S2. Results of weighted analysis including both postnatal and pregnancy road traffic noisea. (DOCX 56 kb

Additional file 1: of Road traffic noise and children’s inattention

Directed acyclic graphs for pregnancy sample. (PDF 304 kb

Editorial: human-nature interactions: perspectives on conceptual and methodological issues

Background: Three large trials of fluoxetine for stroke recovery (FOCUS (fluoxetine or control under supervision), AFFINITY (the Assessment oF FluoxetINe In sTroke recovery) and EFFECTS (Efficacy oF Fluoxetine—a randomisEd Controlled Trial in Stroke)) have been collaboratively designed with the same basic protocol to facilitate an individual patient data analysis (IPDM). The statistical analysis plan for the three individual trials has already been reported in Trials, including a brief description of the IPDM. In this protocol, we describe in detail how we will perform the IPDM. Methods/design: Data from EFFECTS and AFFINITY will be transferred securely to the FOCUS statistician, who will perform a one-stage IPDM and a two-stage IPDM. For the one-stage IPDM, data will be combined into a single data set and the same analyses performed as described for the individual trials. For the two-stage IPDM, the results for the three individual trials will be combined using fixed effects meta-analyses. The primary and secondary outcome domains for the IPDM are the same as for individual trials. We will also perform analyses according to several subgroups including country of recruitment, ethnicity and trial. We will also explore the effects of fluoxetine on our primary and secondary outcomes in subgroups defined by combinations of characteristics. We also describe additional research questions that will be addressed using the combined data set, and published subsequently, including predictors of important post-stroke problems such as seizures, low mood and bone fracture

Agreement of Land Use Regression Models with Personal Exposure Measurements of Particulate Matter and Nitrogen Oxides Air Pollution

Land use regression (LUR) models are often used to predict long-term average concentrations of air pollutants. Little is known how well LUR models predict personal exposure. In this study, the agreement of LUR models with measured personal exposure was assessed. The measured components were particulate matter with a diameter smaller than 2.5 μm (PM_2.5), soot (reflectance of PM_2.5), nitrogen oxides (NO_<i>x</i>), and nitrogen dioxide (NO₂). In Helsinki, Utrecht, and Barcelona, 15 volunteers (from semiurban, urban background, and traffic sites) followed prescribed time activity patterns. Per participant, six 96 h outdoor, indoor, and personal measurements spread over three seasons were conducted. Soot LUR models were significantly correlated with measured average outdoor and personal soot concentrations. Soot LUR models explained 39%, 44%, and 20% of personal exposure variability (<i>R</i>²) in Helsinki, Utrecht, and Barcelona. NO₂ LUR models significantly predicted outdoor concentrations and personal exposure in Utrecht and Helsinki, whereas NO_<i>x</i> and PM_2.5 LUR models did not predict personal exposure. PM_2.5, NO₂, and NO_<i>x</i> models were correlated with personal soot, the component least affected by indoor sources. LUR modeled and measured outdoor, indoor, and personal concentrations were highly correlated for all pollutants when data from the three cities were combined. This study supports the use of intraurban LUR models for especially soot in air pollution epidemiology

The Pregnancy Exposome: Multiple Environmental Exposures in the INMA-Sabadell Birth Cohort

The “exposome” is defined as “the totality of human environmental exposures from conception onward, complementing the genome” and its holistic approach may advance understanding of disease etiology. We aimed to describe the correlation structure of the exposome during pregnancy to better understand the relationships between and within families of exposure and to develop analytical tools appropriate to exposome data. Estimates on 81 environmental exposures of current health concern were obtained for 728 women enrolled in The INMA (INfancia y Medio Ambiente) birth cohort, in Sabadell, Spain, using biomonitoring, geospatial modeling, remote sensors, and questionnaires. Pair-wise Pearson’s and polychoric correlations were calculated and principal components were derived. The median absolute correlation across all exposures was 0.06 (5th–95th centiles, 0.01–0.54). There were strong levels of correlation within families of exposure (median = 0.45, 5th–95th centiles, 0.07–0.85). Nine exposures (11%) had a correlation higher than 0.5 with at least one exposure outside their exposure family. Effectively all the variance in the data set (99.5%) was explained by 40 principal components. Future exposome studies should interpret exposure effects in light of their correlations to other exposures. The weak to moderate correlation observed between exposure families will permit adjustment for confounding in future exposome studies

The Pregnancy Exposome: Multiple Environmental Exposures in the INMA-Sabadell Birth Cohort

The “exposome” is defined as “the totality of human environmental exposures from conception onward, complementing the genome” and its holistic approach may advance understanding of disease etiology. We aimed to describe the correlation structure of the exposome during pregnancy to better understand the relationships between and within families of exposure and to develop analytical tools appropriate to exposome data. Estimates on 81 environmental exposures of current health concern were obtained for 728 women enrolled in The INMA (INfancia y Medio Ambiente) birth cohort, in Sabadell, Spain, using biomonitoring, geospatial modeling, remote sensors, and questionnaires. Pair-wise Pearson’s and polychoric correlations were calculated and principal components were derived. The median absolute correlation across all exposures was 0.06 (5th–95th centiles, 0.01–0.54). There were strong levels of correlation within families of exposure (median = 0.45, 5th–95th centiles, 0.07–0.85). Nine exposures (11%) had a correlation higher than 0.5 with at least one exposure outside their exposure family. Effectively all the variance in the data set (99.5%) was explained by 40 principal components. Future exposome studies should interpret exposure effects in light of their correlations to other exposures. The weak to moderate correlation observed between exposure families will permit adjustment for confounding in future exposome studies

Additional file 1 of A systematic comparison of statistical methods to detect interactions in exposome-health associations

Supplementary results (tables and figures). (PDF 1220 kb

Mixed effect regression models used to analyse (1) the change in difference between GPAQ and SenseWear measurements; (2) the effect of personal attributes on the difference between both methods.

<p>Mixed effect regression models used to analyse (1) the change in difference between GPAQ and SenseWear measurements; (2) the effect of personal attributes on the difference between both methods.</p