1,094 research outputs found

    Spatial Misalignment in time series studies of air pollution and health data

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    Time series studies of environmental exposures often involve comparing daily changes in a toxicant measured at a point in space with daily changes in an aggregate measure of health. Spatial misalignment of the exposure and response variables can bias the estimation of health risk and the magnitude of this bias depends on the spatial variation of the exposure of interest. In air pollution epidemiology, there is an increasing focus on estimating the health effects of the chemical components of particulate matter. One issue that is raised by this new focus is the spatial misalignment error introduced by the lack of spatial homogeneity in many of the particulate matter components. Current approaches to estimating short-term health risks via time series modeling do not take into account the spatial properties of the chemical components and therefore could result in biased estimation of those risks. We present a spatial-temporal statistical model for quantifying spatial misalignment error and show how adjusted heath risk estimates can be obtained using a regression calibration approach and a two-stage Bayesian model. We apply our methods to a database containing information on hospital admissions, air pollution, and weather for 20 large urban counties in the United States

    Ozone and Mortality: A Meta-Analysis of Time-Series Studies and Comparison to a Multi-City Study (The National Morbidity, Mortality, and Air Pollution Study)

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    While many time-series studies of ozone and daily mortality identified positive associations,others yielded null or inconclusive results. We performed a meta-analysis of 144 effect estimates from 39 time-series studies, and estimated pooled effects by lags, age groups,cause-specific mortality, and concentration metrics. We compared results to estimates from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), a time-series study of 95 large U.S. cities from 1987 to 2000. Both meta-analysis and NMMAPS results provided strong evidence of a short-term association between ozone and mortality, with larger effects for cardiovascular and respiratory mortality, the elderly, and current day ozone exposure as compared to other single day lags. In both analyses, results were not sensitive to adjustment for particulate matter and model specifications. In the meta-analysis we found that a 10 ppb increase in daily ozone is associated with a 0.83 (95% confidence interval: 0.53, 1.12%) increase in total mortality, whereas the corresponding NMMAPS estimate is 0.25%(0.12, 0.39%). Meta-analysis results were consistently larger than those from NMMAPS,indicating publication bias. Additional publication bias is evident regarding the choice of lags in time-series studies, and the larger heterogeneity in posterior city-specific estimates in the meta-analysis, as compared with NMAMPS

    Time-Series Studies of Particulate Matter

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    Studies of air pollution and human health have evolved from descriptive studies of the early phenomena of large increases in adverse health effects following extreme air pollution episodes, to time-series analyses and the development of sophisticated regression models. In fact, advanced statistical methods are necessary to address the many challenges inherent in the detection of a small pollution risk in the presence of many confounders. This paper reviews the history, methods, and findings of the time-series studies estimating health risks associated with short-term exposure to particulate matter, though much of the discussion is applicable to epidemiological studies of air pollution in general. We review the critical role of epidemiological studies in setting regulatory standards and the history of PM epidemiology and time-series analysis. We also summarize recent time-series results and conclude with a discussion of current and future directions of time-series analysis of particulates, including research on mortality displacement and the resolution of results from cohort and time-series studies

    A study on modeling nitrogen dioxide concentrations using land-use regression and conventionally used exposure assessment methods

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    The land-use regression (LUR) approach to estimate the levels of ambient air pollutants is becoming popular due to its high validity in predicting small-area variations. However, only a few studies have been conducted in Asian countries, and much less research has been conducted on comparing the performances and applied estimates of different exposure assessments including LUR. The main objectives of the current study were to conduct nitrogen dioxide (NO2) exposure assessment with four methods including LUR in the Republic of Korea, to compare the model performances, and to estimate the empirical NO2 exposures of a cohort. The study population was defined as the year 2010 participants of a government-supported cohort established for bio-monitoring in Ulsan, Republic of Korea. The annual ambient NO2 exposures of the 969 study participants were estimated with LUR, nearest station, inverse distance weighting, and ordinary kriging. Modeling was based on the annual NO2 average, traffic-related data, land-use data, and altitude of the 13 regularly monitored stations. The final LUR model indicated that area of transportation, distance to residential area, and area of wetland were important predictors of NO2. The LUR model explained 85.8% of the variation observed in the 13 monitoring stations of the year 2009. The LUR model outperformed the others based on leave-one out cross-validation comparing the correlations and root-mean square error. All NO2 estimates ranged from 11.3–18.0 ppb, with that of LUR having the widest range. The NO2 exposure levels of the residents differed by demographics. However, the average was below the national annual guidelines of the Republic of Korea (30 ppb). The LUR models showed high performances in an industrial city in the Republic of Korea, despite the small sample size and limited data. Our findings suggest that the LUR method may be useful in similar settings in Asian countries where the target region is small and availability of data is low

    Identification of senescence and death in Emiliania huxleyi and Thalassiosira pseudonana: Cell staining, chlorophyll alterations, and dimethylsulfoniopropionate (DMSP) metabolism

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    We measured membrane permeability, hydrolytic enzyme, and caspase-like activities using fluorescent cell stains to document changes caused by nutrient exhaustion in the coccolithophore Emiliania huxleyi and the diatom Thalassiosira pseudonana, during batch-culture nutrient limitation. We related these changes to cell death, pigment alteration, and concentrations of dimethylsulfide (DMS) and dimethylsulfoniopropionate (DMSP) to assess the transformation of these compounds as cell physiological condition changes. E. huxleyi persisted for 1 month in stationary phase; in contrast, T. pseudonana cells rapidly declined within 10 d of nutrient depletion. T. pseudonana progressively lost membrane integrity and the ability to metabolize 5-chloromethylfluorescein diacetate (CMFDA; hydrolytic activity), whereas E. huxleyi developed two distinct CMFDA populations and retained membrane integrity (SYTOX Green). Caspase-like activity appeared higher in E. huxleyi than in T. pseudonana during the post-growth phase, despite a lack of apparent mortality and cell lysis. Photosynthetic pigment degradation and transformation occurred in both species after growth; chlorophyll a (Chl a) degradation was characterized by an increase in the ratio of methoxy Chl a : Chl a in T. pseudonana but not in E. huxleyi, and the increase in this ratio preceded loss of membrane integrity. Total DMSP declined in T. pseudonana during cell death and DMS increased. In contrast, and in the absence of cell death, total DMSP and DMS increased in E. huxleyi. Our data show a novel chlorophyll alteration product associated with T. pseudonana death, suggesting a promising approach to discriminate nonviable cells in nature

    The Exposure–Response Curve for Ozone and Risk of Mortality and the Adequacy of Current Ozone Regulations

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    Time-series analyses have shown that ozone is associated with increased risk of premature mortality, but little is known about how O(3) affects health at low concentrations. A critical scientific and policy question is whether a threshold level exists below which O(3) does not adversely affect mortality. We developed and applied several statistical models to data on air pollution, weather, and mortality for 98 U.S. urban communities for the period 1987–2000 to estimate the exposure–response curve for tropospheric O(3) and risk of mortality and to evaluate whether a “safe” threshold level exists. Methods included a linear approach and subset, threshold, and spline models. All results indicate that any threshold would exist at very low concentrations, far below current U.S. and international regulations and nearing background levels. For example, under a scenario in which the U.S. Environmental Protection Agency’s 8-hr regulation is met every day in each community, there was still a 0.30% increase in mortality per 10-ppb increase in the average of the same and previous days’ O(3) levels (95% posterior interval, 0.15–0.45%). Our findings indicate that even low levels of tropospheric O(3) are associated with increased risk of premature mortality. Interventions to further reduce O(3) pollution would benefit public health, even in regions that meet current regulatory standards and guidelines