Bayesian distributed lag models: estimating effects of particulate matter air pollution on daily mortality

Summary .  A distributed lag model (DLagM) is a regression model that includes lagged exposure variables as covariates; its corresponding distributed lag (DL) function describes the relationship between the lag and the coefficient of the lagged exposure variable. DLagMs have recently been used in environmental epidemiology for quantifying the cumulative effects of weather and air pollution on mortality and morbidity. Standard methods for formulating DLagMs include unconstrained, polynomial, and penalized spline DLagMs. These methods may fail to take full advantage of prior information about the shape of the DL function for environmental exposures, or for any other exposure with effects that are believed to smoothly approach zero as lag increases, and are therefore at risk of producing suboptimal estimates. In this article, we propose a Bayesian DLagM (BDLagM) that incorporates prior knowledge about the shape of the DL function and also allows the degree of smoothness of the DL function to be estimated from the data. We apply our BDLagM to its motivating data from the National Morbidity, Mortality, and Air Pollution Study to estimate the short-term health effects of particulate matter air pollution on mortality from 1987 to 2000 for Chicago, Illinois. In a simulation study, we compare our Bayesian approach with alternative methods that use unconstrained, polynomial, and penalized spline DLagMs. We also illustrate the connection between BDLagMs and penalized spline DLagMs. Software for fitting BDLagM models and the data used in this article are available online.     

Air pollution and the lung: epidemiological approach

Epidemiological evidence has concurred with clinical and experimental evidence to correlate current levels of ambient air pollution, both indoors and outdoors, with respiratory effects. In this respect, the use of specific epidemiological methods has been crucial. Common outdoor pollutants are particulate matter, nitrogen dioxide, carbon monoxide, volatile organic compounds and ozone. Short-term effects of outdoor air pollution include changes in lung function, respiratory symptoms and mortality due to respiratory causes. Increase in the use of health care resources has also been associated with short-term effects of air pollution. Long-term effects of cumulated exposure to urban air pollution include lung growth impairment, chronic obstructive pulmonary disease (COPD), lung cancer, and probably the development of asthma and allergies. Lung cancer and COPD have been related to a shorter life expectancy. Common indoor pollutants are environmental tobacco smoke, particulate matter, nitrogen dioxide, carbon monoxide, volatile organic compounds and biological allergens. Concentrations of these pollutants can be many times higher indoors than outdoors. Indoor air pollution may increase the risk of irritation phenomena, allergic sensitisation, acute and chronic respiratory disorders and lung function impairment. Recent conservative estimates have shown that 1.5-2 million deaths per year worldwide could be attributed to indoor air pollution. Further epidemiological research is necessary to better evaluate the respiratory health effects of air pollution and to implement protective programmes for public health.     

A comparison of health assessment approaches for evaluating the effects of contaminant-related stress on fish populations

Various methods are available for investigating the effects of environmental conditions or stress on fish ranging from assessments at the individual level to the population and community level. Each method, however, has limitations and advantages, and the type of method used influences the interpretation of stressor effects on fish health. We compared and evaluated three methods for investigating the effects of contaminants on fish health for populations in a stream receiving a variety of industrial effluents including PCBs, heavy metals (primarily mercury), and chlorine. These methods are: (1) individual bioindicator variables including measurements of biochemical, physiological, histopathological, and organismal-level responses, (2) statistically based integrative indices based on multivariate responses, and (3) integrative ecologically relevant measurements such as population and community-level responses. Many of the individual-level measurements reflect biochemical and physiological conditions and are therefore short-term response indicators of stress. Use of several individual variables in multivariate discriminant analysis allows a more integrative approach for evaluating response of fish to stressors. Community- and population-level measurements integrate the responses to a variety of environmental conditions and therefore may be less reflective of contaminant-induced stress. The three methods used together provide the best strategy for evaluating fish health in order that environmentally sound decisions can be made regarding the implications of contaminant-related stress on aquatic ecosystems.Managed by Martin Marietta Energy Systems, Inc., under contract DE-AC05-840R21400 with the U.S. Department of Energy.     

Seasonal, daily and intradiurnal variation of PM10, NO2, NO and O3 in residential part of Zagreb, Croatia

Pollutants such as particulate matter, nitrogen oxides, carbon oxides, ground-level ozone, etc. are harmful to human health. Study of pollutant variation and its relationship with both dynamic and thermodynamic atmospheric boundary layer (ABL) structures is of importance not only for environmental protection but also for the public at large. The aim of this study was to analyze seasonal, daily and intradiurnal variation of PM10, NO2, NO and O3 in a residential part of an urban area, and the effect of some meteorological parameters. The study was conducted from January 1 till December 31, 2004 in the City of Zagreb using following methods: beta radiation absorption, chemiluminescence and UV photometry. The results presented in this article, show the dependence of air pollution levels upon traffic density, seasons and meteorological conditions. Considering the level of air pollution relative to the regulated limit and tolerated values, the measured 24-hour concentrations of all study pollutants exceeded the borderline values and/or tolerated values, however, the number of days with such pollutant concentrations did not exceed the allowed frequency. This is a preliminary study with the main objectives to point to the possible identification of the source of pollution and to assess the level of air contamination according to the new national legislation coordinated with European regulations. Future measurements and studies should evaluate in detail the causes of the concentration levels detected.     

On the equivalence of case-crossover and time series methods in environmental epidemiology

The case-crossover design was introduced in epidemiology 15 years ago as a method for studying the effects of a risk factor on a health event using only cases. The idea is to compare a case's exposure immediately prior to or during the case-defining event with that same person's exposure at otherwise similar "reference" times. An alternative approach to the analysis of daily exposure and case-only data is time series analysis. Here, log-linear regression models express the expected total number of events on each day as a function of the exposure level and potential confounding variables. In time series analyses of air pollution, smooth functions of time and weather are the main confounders. Time series and case-crossover methods are often viewed as competing methods. In this paper, we show that case-crossover using conditional logistic regression is a special case of time series analysis when there is a common exposure such as in air pollution studies. This equivalence provides computational convenience for case-crossover analyses and a better understanding of time series models. Time series log-linear regression accounts for overdispersion of the Poisson variance, while case-crossover analyses typically do not. This equivalence also permits model checking for case-crossover data using standard log-linear model diagnostics.     

Linking environmental and health care databases: Assessing the health effects of environmental pollutants

The assessment of pollutant effects on health status requires the mergence and analysis of two different databases: pollution measurements and health care information. This paper compares two subsets of these data: Ohio Environmental Protection Agency data on ambient air pollutants and Ohio Medicare data on respiratory diseases. Small area analysis was performed to assess statewide variations in hospital admission rates for respiratory diseases. The ambient air pollutant levels for each small area were compared to the variations in respiratory disease rates. Five groups of diseases correlated with pollutant levels. In addition, pollutant levels were significantly associated with medical complications. This study demonstrates the feasibility and benefit of linking environmental and health care databases and suggests the need for a more comprehensive, automated analysis of more pollutants and diseases.     

LCA study of air conditioners with an alternative refrigerant

In the refrigeration and air conditioning industry, technologies to reduce environmental impacts, such as global warming, ozone-layer depletion, and discharging industrial wastes, are getting much attention nowadays. This paper reports the Life Cycle Assessment conducted to comparatively analyze two air conditioner units for residential use. One is a traditional type with HCFC22 being used for its refrigerant and the other is with HFC410A. Because the main focus of this analysis is on the comparison of the refrigerants, data of the refrigerants used are taken from the actual measurements in their production and disposal stages. As a result, the ozone layer depleting effect can be eliminated completely by using HFC410A. On the other hand, the global warming effect doesn’t get reduced extensively by using HFC410A. However, it does so by treating used refrigerants with a proper waste management. Moreover, it can be proved that using HFC410A reduces environmental impacts in all the other impact categories assessed, which are acidification, air pollution, water pollution, and energy consumption. To conclude this case study, replacing HCFC22 with HFC410A for the refrigerant is certainly one of the effective methods for reducing environmental impacts given by air conditioners.     

A measurement error model for time-series studies of air pollution and mortality

One barrier to interpreting the observational evidence concerning the adverse health effects of air pollution for public policy purposes is the measurement error inherent in estimates of exposure based on ambient pollutant monitors. Exposure assessment studies have shown that data from monitors at central sites may not adequately represent personal exposure. Thus, the exposure error resulting from using centrally measured data as a surrogate for personal exposure can potentially lead to a bias in estimates of the health effects of air pollution. This paper develops a multi-stage Poisson regression model for evaluating the effects of exposure measurement error on estimates of effects of particulate air pollution on mortality in time-series studies. To implement the model, we have used five validation data sets on personal exposure to PM10. Our goal is to combine data on the associations between ambient concentrations of particulate matter and mortality for a specific location, with the validation data on the association between ambient and personal concentrations of particulate matter at the locations where data have been collected. We use these data in a model to estimate the relative risk of mortality associated with estimated personal-exposure concentrations and make a comparison with the risk of mortality estimated with measurements of ambient concentration alone. We apply this method to data comprising daily mortality counts, ambient concentrations of PM10measured at a central site, and temperature for Baltimore, Maryland from 1987 to 1994. We have selected our home city of Baltimore to illustrate the method; the measurement error correction model is general and can be applied to other appropriate locations.Our approach uses a combination of: (1) a generalized additive model with log link and Poisson error for the mortality-personal-exposure association; (2) a multi-stage linear model to estimate the variability across the five validation data sets in the personal-ambient-exposure association; (3) data augmentation methods to address the uncertainty resulting from the missing personal exposure time series in Baltimore. In the Poisson regression model, we account for smooth seasonal and annual trends in mortality using smoothing splines. Taking into account the heterogeneity across locations in the personal-ambient-exposure relationship, we quantify the degree to which the exposure measurement error biases the results toward the null hypothesis of no effect, and estimate the loss of precision in the estimated health effects due to indirectly estimating personal exposures from ambient measurements.     

Deaths and Medical Visits Attributable to Environmental Pollution in the United Arab Emirates

Background

This study estimates the potential health gains achievable in the United Arab Emirates (UAE) with improved controls on environmental pollution. The UAE is an emerging economy in which population health risks have shifted rapidly from infectious diseases to chronic conditions observed in developed nations. The UAE government commissioned this work as part of an environmental health strategic planning project intended to address this shift in the nature of the country’s disease burden.

Methods and Findings

We assessed the burden of disease attributable to six environmental exposure routes outdoor air, indoor air, drinking water, coastal water, occupational environments, and climate change. For every exposure route, we integrated UAE environmental monitoring and public health data in a spatially resolved Monte Carlo simulation model to estimate the annual disease burden attributable to selected pollutants. The assessment included the entire UAE population (4.5 million for the year of analysis). The study found that outdoor air pollution was the leading contributor to mortality, with 651 attributable deaths (95% confidence interval [CI] 143–1,440), or 7.3% of all deaths. Indoor air pollution and occupational exposures were the second and third leading contributors to mortality, with 153 (95% CI 85–216) and 46 attributable deaths (95% CI 26–72), respectively. The leading contributor to health-care facility visits was drinking water pollution, to which 46,600 (95% CI 15,300–61,400) health-care facility visits were attributed (about 15% of the visits for all the diseases considered in this study). Major study limitations included (1) a lack of information needed to translate health-care facility visits to quality-adjusted-life-year estimates and (2) insufficient spatial coverage of environmental data.

Conclusions

Based on international comparisons, the UAE’s environmental disease burden is low for all factors except outdoor air pollution. From a public health perspective, reducing pollutant emissions to outdoor air should be a high priority for the UAE’s environmental agencies.     

Effects of particulate air pollution on cardiovascular health: a population health risk assessment

Particulate matter (PM) air pollution is increasingly recognized as an important and modifiable risk factor for adverse health outcomes including cardiovascular disease (CVD). However, there are still gaps regarding large population risk assessment. Results from the nationwide Behavioral Risk Factor Surveillance System (BRFSS) were used along with air quality monitoring measurements to implement a systematic evaluation of PM-related CVD risks at the national and regional scales. CVD status and individual-level risk factors were collected from more than 500,000 BRFSS respondents across 2,231 contiguous U.S. counties for 2007 and 2009. Chronic exposures to PM pollutants were estimated with spatial modeling from measurement data. CVD outcomes attributable to PM pollutants were assessed by mixed-effects logistic regression and latent class regression (LCR), with adjustment for multicausality. There were positive associations between CVD and PM after accounting for competing risk factors: the multivariable-adjusted odds for the multiplicity of CVD outcomes increased by 1.32 (95% confidence interval: 1.23-1.43) and 1.15 (1.07-1.22) times per 10 μg/m(3) increase in PM(2.5) and PM(10) respectively in the LCR analyses. After controlling for spatial confounding, there were moderate estimated effects of PM exposure on multiple cardiovascular manifestations. These results suggest that chronic exposures to ambient particulates are important environmental risk factors for cardiovascular morbidity.     

Impact of indoor air pollution on health,comfort and productivity of the occupants

In this special report, the possible causes of indoor air pollution and its impact on the health, comfort and productivity of the building occupant are discussed. The causes and symptoms of sick building syndrome, allergy and environmental illnesses and building related illnesses are discussed in the context of building environments. The remediation and prevention measures examine the solution to the problems caused by indoor air pollution in buildings.     

Long-run health consequences of air pollution: Evidence from Indonesia's forest fires of 1997

While many studies in the medical literature documented causal relationships between air pollution and negative health outcomes immediately following exposure, much less is known about the long run health consequences of pollution exposure. Using the 1997 Indonesian forest fires as a natural experiment, we estimate the long term effects of air pollution on health outcomes. We take advantage of the longitudinal nature of the Indonesia Family Life Survey (IFLS), which collects detailed individual data on a multitude of health outcomes, in both 1997 and 2007. We find significant negative effects of pollution, which persist in the long run. Men and the elderly are impacted the most, while children seem to recover almost completely from these early shocks. For the entire population, an extra standard deviation in the pollution level increases the likelihood of a poor general health status by almost 3%.     

The Sibyla model and development of beech forests affected by air pollution

The paper deals with the health status, production and structure of autochthonous beech stands in the eastern part of the Krkonoše Mts. (Czech Republic). The region was affected by increased air pollution load due to long-range SO₂ transmission from the late 1970s to the early 1990s. In 1980, five research plots were established in long unmanaged beech stands at an optimum stage. Tree coordinates and initial biometrical measurements were made in 1980 and repeated in 2005. The defoliation of individual trees was monitored every year (ICP Forests principles). The actual stand development is compared with the model development worked out by the Sibyla growth simulator in variants with a model of natural mortality and with entering a real mortality of trees. Differences between the stand characteristics of actual and simulated development of forest stands suggest some production losses. The reason to the losses can be seen in the long-term impact of air pollution in the first decade of monitoring and its after-effects on soil quality and worsening health condition of stands. The anticipated acceleration in the onset of the phase of stand disintegration under the influence of air pollution load was not demonstrated.     

Quantifying the health impacts of air pollution under a changing climate—a review of approaches and methodology

Climate change has been predicted to affect future air quality, with inevitable consequences for health. Quantifying the health effects of air pollution under a changing climate is crucial to provide evidence for actions to safeguard future populations. In this paper, we review published methods for quantifying health impacts to identify optimal approaches and ways in which existing challenges facing this line of research can be addressed. Most studies have employed a simplified methodology, while only a few have reported sensitivity analyses to assess sources of uncertainty. The limited investigations that do exist suggest that examining the health risk estimates should particularly take into account the uncertainty associated with future air pollution emissions scenarios, concentration-response functions, and future population growth and age structures. Knowledge gaps identified for future research include future health impacts from extreme air pollution events, interactions between temperature and air pollution effects on public health under a changing climate, and how population adaptation and behavioural changes in a warmer climate may modify exposure to air pollution and health consequences.     

Spatial Modeling of Air Pollution in Studies of Its Short‐Term Health Effects

Summary In studies that estimate the short‐term effects of air pollution on health, daily measurements of pollution concentrations are often available from a number of monitoring locations within the study area. However, the health data are typically only available in the form of daily counts for the entire area, meaning that a corresponding single daily measure of pollution is required. The standard approach is to average the observed measurements at the monitoring locations, and use this in a log‐linear health model. However, as the pollution surface is spatially variable this simple summary is unlikely to be an accurate estimate of the average pollution concentration across the region, which may lead to bias in the resulting health effects. In this article, we propose an alternative approach that jointly models the pollution concentrations and their relationship with the health data using a Bayesian spatio‐temporal model. We compare this approach with the simple spatial average using a simulation study, by investigating the impact of spatial variation, monitor placement, and measurement error in the pollution data. An epidemiological study from Greater London is then presented, which estimates the relationship between respiratory mortality and four different pollutants.     

Ecosystem health as measured from the molecular to the community level of organization,with reference to sediment bioassessment

The current recognition that chemical measurements are uncertain indicators of biological consequences of pollution has shifted the emphasis away from assessing environmental chemistry alone toward the inclusion of measurements of the health of organisms. Effects of pollutants begin with the individual, have subsequent repercussions on population level processes, and ramifications for community structure and functions. Pollutants act at a molecular level and the biochemical lesions is the first step in the manifestation of effects. Technologies that operate at the cellular level assist in elucidating toxicity. Higher levels of integration include an organism's capacity for growth. Laboratory bioassays andin situ research can monitor physiological incapacities and assist in predicting population level effects. A yet higher level of organization is that of the ecological community.     

Molecular epidemiology studies of carcinogenic environmental pollutants. Effects of polycyclic aromatic hydrocarbons (PAHs) in environmental pollution on exogenous and oxidative DNA damage

Exposure to high levels of environmental air pollution is known to be associated with an increased carcinogenic risk. The individual contribution to this risk derived from specific carcinogenic chemicals within the complex mixture of air pollution is less certain, but may be explored by the use of molecular epidemiological techniques. Measurements of biomarkers of exposure, of effect and of susceptibility provide information of potential benefit for epidemiological and cancer risk assessment. The application of such techniques has been mostly concerned in the past with the carcinogenic polycyclic aromatic hydrocarbons (c-PAHs) that are associated with particulate matter in air pollution, and has showed clear evidence of genotoxic effects, such as DNA adducts, chromosome aberrations (CA) and ras oncogene overexpression, in environmentally exposed Czech and Polish populations. We are currently extending these studies by an investigation of populations exposed to environmental pollution in three European countries, Czech Republic, Slovak Republic and Bulgaria. This pays particular attention to PAHs, but also investigates the extent of radically induced (oxidative) DNA damage in the exposed populations. Policemen, bus drivers and controls, who carried personal monitors to determine their exposures to PAHs have been studied, and blood and urine were collected. Antioxidant and dietary status were assessed in these populations. Stationary monitors were also used for ambient air monitoring. Amongst the parameters studied in the biological samples were: (a) exposure biomarkers, such as PAH adducts with DNA, p53 and p21(WAF1) protein levels, (b) oxidative DNA damage, (c) the biological effect of the exposure by measurement of chromosome damage by fluorescence in situ hybridisation (FISH) or conventional methods, and (d) polymorphisms in carcinogen metabolising and DNA repair enzymes. Repair ability was also measured by the Comet assay. In vitro systems are being evaluated to characterise the genotoxicity of the organic compounds adsorbed to air particles.