The moving-window Bayesian maximum entropy framework: estimation of PM2.5 yearly average concentration across the contiguous United States

Geostatistical methods are widely used in estimating long-term exposures for air pollution epidemiological studies, despite their limited capabilities to handle spatial non-stationarity over large geographic domains and uncertainty associated with missing monitoring data. We developed a moving-window (MW) Bayesian Maximum Entropy (BME) method and applied this framework to estimate fine particulate matter (PM2.5) yearly average concentrations over the contiguous U.S. The MW approach accounts for the spatial non-stationarity, while the BME method rigorously processes the uncertainty associated with data missingnees in the air monitoring system. In the cross-validation analyses conducted on a set of randomly selected complete PM2.5 data in 2003 and on simulated data with different degrees of missing data, we demonstrate that the MW approach alone leads to at least 17.8% reduction in mean square error (MSE) in estimating the yearly PM2.5. Moreover, the MWBME method further reduces the MSE by 8.4% to 43.7% with the proportion of incomplete data increased from 18.3% to 82.0%. The MWBME approach leads to significant reductions in estimation error and thus is recommended for epidemiological studies investigating the effect of long-term exposure to PM2.5 across large geographical domains with expected spatial non-stationarity

Obesity Is A Modifier of Autonomic Cardiac Responses to Fine Metal Particulates

Background: Increasing evidence suggests that obesity may impart greater susceptibility to adverse effects of air pollution. Particulate matter, especially PM$_{2.5}$ (particulate matter with aero-dynamic diameter ≤2.5 μm), is associated with increased cardiac events and reduction of heart rate variability (HRV).Objectives Our goal was to investigate whether particle-mediated autonomic modulation is aggravated in obese individuals.Methods We examined PM$_{2.5}$-mediated acute effects on HRV and heart rate (HR) using 10 24-hr and 13 48-hr ambulatory electrocardiogram recordings collected from 18 boilermakers (39.5 ± 9.1 years of age) exposed to high levels of metal particulates. Average HR and 5-min HRV [SDNN: standard deviation of normal-to-normal intervals (NN); rMSSD: square-root of mean squared-differences of successive NN intervals; HF: high-frequency power 0.15–0.4 Hz] and personal PM$_{2.5}$ exposures were continuously monitored. Subjects with body mass index ≥ 30 kg/m$^2$ were classified as obese. Mixed-effect models were used for statistical analyses. Results: Half (50%) of the study subjects were obese. After adjustment for confounders, each 1-mg/m$^3$ increase in 4-hr moving average PM$_{2.5}$ was associated with HR increase of 5.9 bpm [95% confidence interval (CI), 4.2 to 7.7] and with 5-min HRV reduction by 6.5% (95% CI, 1.9 to 11.3%) for SDNN, 1.7% (95% CI, –4.9 to 8.4%) for rMSSD, and 8.8% (95% CI, –3.8 to 21.3%) for HF. Obese individuals had greater PM$_{2.5}$-mediated HRV reductions (2- to 3-fold differences) than nonobese individuals, and had more PM$_{2.5}$-mediated HR increases (9-bpm vs. 4-bpm increase in HR for each 1-mg/m$^3$ increase in PM$_{2.5}$; p < 0.001). Conclusions: Our study revealed greater autonomic cardiac responses to metal particulates in obese workers, supporting the hypothesis that obesity may impart greater susceptibility to acute cardiovascular effects of fine particles

BME Estimation of Residential Exposure to Ambient PM10 and Ozone at Multiple Time Scales

BackgroundLong-term human exposure to ambient pollutants can be an important contributing or etiologic factor of many chronic diseases. Spatiotemporal estimation (mapping) of long-term exposure at residential areas based on field observations recorded in the U.S. Environmental Protection Agency’s Air Quality System often suffer from missing data issues due to the scarce monitoring network across space and the inconsistent recording periods at different monitors.ObjectiveWe developed and compared two upscaling methods: UM1 (data aggregation followed by exposure estimation) and UM2 (exposure estimation followed by data aggregation) for the long-term PM10 (particulate matter with aerodynamic diameter ≤ 10 μm) and ozone exposure estimations and applied them in multiple time scales to estimate PM and ozone exposures for the residential areas of the Health Effects of Air Pollution on Lupus (HEAPL) study.MethodWe used Bayesian maximum entropy (BME) analysis for the two upscaling methods. We performed spatiotemporal cross-validations at multiple time scales by UM1 and UM2 to assess the estimation accuracy across space and time.ResultsCompared with the kriging method, the integration of soft information by the BME method can effectively increase the estimation accuracy for both pollutants. The spatiotemporal distributions of estimation errors from UM1 and UM2 were similar. The cross-validation results indicated that UM2 is generally better than UM1 in exposure estimations at multiple time scales in terms of predictive accuracy and lack of bias. For yearly PM10 estimations, both approaches have comparable performance, but the implementation of UM1 is associated with much lower computation burden.ConclusionBME-based upscaling methods UM1 and UM2 can assimilate core and site-specific knowledge bases of different formats for long-term exposure estimation. This study shows that UM1 can perform reasonably well when the aggregation process does not alter the spatiotemporal structure of the original data set; otherwise, UM2 is preferable

Metabolic Syndrome and Inflammatory Responses to Long-Term Particulate Air Pollutants

BackgroundHuman data linking inflammation with long-term particulate matter (PM) exposure are still lacking. Emerging evidence suggests that people with metabolic syndrome (MS) may be a more susceptible population.ObjectivesOur goal was to examine potential inflammatory responses associated with long-term PM exposure and MS-dependent susceptibility.MethodsWe conducted secondary analyses of white blood cell (WBC) count and MS data from The Third National Health and Nutrition Examination Survey and PM10 (PM with aerodynamic diameter 1 year (n = 2,978; age 48.5 ± 17.8 years). Mixed-effects models were constructed to account for autocorrelation and potential confounders.ResultsAfter adjustment for demographics, socioeconomic factors, lifestyles, residential characteristics, and MS, we observed a statistically significant association between WBC count and estimated local PM10 levels (p = 0.035). Participants from the least polluted areas (1-year PM10 < 1st quartile cutoff: 27.8 μg/m3) had lower WBC counts than the others (difference = 145 × 106/L; 95% confidence interval, 10–281). We also noted a graded association between PM10 and WBC across subpopulations with increasing MS components, with 91 × 106/L difference in WBC for those with no MS versus 214, 338, and 461 × 106/L for those with 3, 4, and 5 metabolic abnormalities (trend-test p = 0.15).ConclusionsOur study revealed a positive association between long-term PM exposure and hematological markers of inflammation and supported the hypothesized MS-dependent susceptibility

Seat inclination, use of lumbar support and low-back pain of taxi drivers

Objectives Epidemiologic evidence supporting optimal seating is limited and inconsistent. This cross-sectional study was conducted to examine the association between seat inclination, use of lumbar support, and the prevalence of clinically significant low-back pain among taxi drivers. Methods A digital inclinometer was used to measure inclinations of seat surfaces (θseat) and backrests (θback), and calculate the back-to-thigh angle (θback-thigh). Structured interviews were conducted to gather information on the use of lumbar support and the prevalence of low-back pain that had led to medical attention or absence from driving in the past month. A multiple logistic regression analysis was used to estimate the prevalence odds ratio (OR) with adjustment for age, body mass index, professional seniority, monthly driving hours, and the intensity of exposure to whole-body vibration. Results Among 224 drivers, the mean θseat and θbackrest were 14.5 (SD 9.6) and 95.1 (SD 2.7) degrees, respectively, resulting in θback-thigh of 80.6 (SD 9.3) degrees. Fifty-five percent used a lumbar support regularly, but 25% reportedly had significant low-back pain. The prevalence of low-back pain was 23% among those with θback-thigh 91 degrees. The adjusted OR comparing those with a θback-thigh of ≤91 degrees to those with a θback-thigh of >91 degrees was 5.11 [95% confidence interval (95% CI) 1.07~24.4]. For regularly using drivers versus those not using lumbar support, the prevalence of low-back pain was 18% versus 34%, with an adjusted OR of 0.33 (95% CI 0.16~0.68). Neither θseat nor θbackrest alone was significantly associated with low-back pain. Conclusions The epidemiologic observation of this study was consistent with the results of prior biomechanical studies on appropriate seat inclinations and the use of lumbar support. Prospective studies are needed to confirm the true beneficial effects of these seating parameters

Obesity Is A Modifier of Autonomic Cardiac Responses to Fine Metal Particulates

Background: Increasing evidence suggests that obesity may impart greater susceptibility to adverse effects of air pollution. Particulate matter, especially PM$_{2.5}$ (particulate matter with aero-dynamic diameter ≤2.5 μm), is associated with increased cardiac events and reduction of heart rate variability (HRV).Objectives Our goal was to investigate whether particle-mediated autonomic modulation is aggravated in obese individuals.Methods We examined PM$_{2.5}$-mediated acute effects on HRV and heart rate (HR) using 10 24-hr and 13 48-hr ambulatory electrocardiogram recordings collected from 18 boilermakers (39.5 ± 9.1 years of age) exposed to high levels of metal particulates. Average HR and 5-min HRV [SDNN: standard deviation of normal-to-normal intervals (NN); rMSSD: square-root of mean squared-differences of successive NN intervals; HF: high-frequency power 0.15–0.4 Hz] and personal PM$_{2.5}$ exposures were continuously monitored. Subjects with body mass index ≥ 30 kg/m$^2$ were classified as obese. Mixed-effect models were used for statistical analyses. Results: Half (50%) of the study subjects were obese. After adjustment for confounders, each 1-mg/m$^3$ increase in 4-hr moving average PM$_{2.5}$ was associated with HR increase of 5.9 bpm [95% confidence interval (CI), 4.2 to 7.7] and with 5-min HRV reduction by 6.5% (95% CI, 1.9 to 11.3%) for SDNN, 1.7% (95% CI, –4.9 to 8.4%) for rMSSD, and 8.8% (95% CI, –3.8 to 21.3%) for HF. Obese individuals had greater PM$_{2.5}$-mediated HRV reductions (2- to 3-fold differences) than nonobese individuals, and had more PM$_{2.5}$-mediated HR increases (9-bpm vs. 4-bpm increase in HR for each 1-mg/m$^3$ increase in PM$_{2.5}$; p < 0.001). Conclusions: Our study revealed greater autonomic cardiac responses to metal particulates in obese workers, supporting the hypothesis that obesity may impart greater susceptibility to acute cardiovascular effects of fine particles

Prospective Study of Metal Fume-Induced Responses of Global Gene Expression Profiling in Whole Blood

Metal particulate inhalation causes pulmonary and cardiovascular diseases. Our previous results showed that systemic responses to short-term occupational welding-fume exposure could be assessed by microarray analyses in whole-blood total RNA sampled before and after exposure. To expand our understanding of the duration of particulate-induced gene expression changes, we conducted a study using a similar population 1 yr after the original study and extended our observations in the postexposure period. We recruited 15 individuals with welding fume exposure and 7 nonexposed individuals. Thirteen of the 22 individuals (9 in exposed group and 4 in nonexposed group) had been monitored in the previous study. Whole-blood total RNA was analyzed at 3 time points, including baseline, immediately following exposure (approximately 5 h after baseline), and 24 h after baseline, using cDNA microarray technology. We replicated the patterns of Gene Ontology (GO) terms associated with response to stimulus, cell death, phosphorus metabolism, localization, and regulation of biological processes significantly enriched with altered genes in the nonsmoking exposed group. Most of the identified genes had opposite expression changes between the exposure and postexposure periods in nonsmoking welders. In addition, we found dose-dependent patterns that were affected by smoking status. In conclusion, short-term occupational exposure to metal particulates causes systemic responses in the peripheral blood. Furthermore, the acute particulate-induced effects on gene expression profiling were transient in nonsmoking welders, with most effects diminishing within 19 h following exposure

Perinatal Exposure to Hazardous Air Pollutants and Autism Spectrum Disorders at Age 8

Hazardous air pollutants are plausible candidate exposures for autism spectrum disorders. They have been explored in recent studies for their role in the development of these disorders

Geographic Access to Health Services and Diagnosis with an Autism Spectrum Disorder

To assess the impact of geographic health services factors on the timely diagnosis of autism

Global Gene Expression Profiling in Whole-Blood Samples from Individuals Exposed to Metal Fumes

Accumulating evidence demonstrates that particulate air pollutants can cause both pulmonary and airway inflammation. However, few data show that particulates can induce systemic inflammatory responses. We conducted an exploratory study using microarray techniques to analyze whole-blood total RNA in boilermakers before and after occupational exposure to metal fumes. A self-controlled study design was used to overcome the problems of larger between-individual variation interferences with observations of relatively smaller changes caused by environmental exposure. Moreover, we incorporated the dichotomous data of absolute gene expression status in the microarray analyses. Compared with nonexposed controls, we observed that genes with altered expression in response to particulate exposure were clustered in biologic processes related to inflammatory response, oxidative stress, intracellular signal transduction, cell cycle, and programmed cell death. In particular, the preinflammatory cytokine interleukin 8 and one of its receptors, chemokine receptor 4, seemed to play important roles in early-stage response to heavy metal exposure and were down-regulated. Furthermore, most observed expression variations were from nonsmoking exposed individuals, suggesting that smoking profoundly affects whole-blood expression profiles. Our study is the first to demonstrate that with a paired sampling study design of pre- and postexposed individuals, small changes in gene expression profiling can be measured in whole-blood total RNA from a population-based study. This technique can be applied to evaluate the host response to other forms of environmental exposures